Report on Footprint of Passive Control Systems · 2018. 10. 25. · Prashant Kumar and Guillem Camprodon The first draft. V0.2 30/8/2017 Salem S. Gharbia Task leader’s edits. Draft

Report on Footprint of Passive Control Systems ––––––––– –––– D3.3 October 2018

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689954.

Ref. Ares(2018)5269851 - 14/10/2018

D3.3 Report on footprint of PCSs

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Project Acronym and Name

iSCAPE - Improving the Smart Control of Air Pollution in Europe

Grant Agreement Number

689954

Document Type Report

Document version & WP No.

V. 09 WP3

Document Title Report on Footprint of Passive Control Systems

Main authors Salem S. Gharbia, Abhijith K. V., Andreas N. Skouloudis, Erika Brattich, Athanasios Votsis, Thor-Bjørn Ottosen, Antti.Makela, Väinö Nurmi, Carl Fortelius, Kirsti Jylhä, Achim Drebs, Gaia Papini, Francesco Matacchiera, Arianna Valmassoi, Alessio Brunetti, Francesco Barbano, Francesco Pilla, Silvana Di Sabatino, Beatrice Pulvirenti, Prashant Kumar and Guillem Camprodon

Partner in charge University College Dublin (UCD)

Contributing partners University College Dublin (UCD), University of Surrey (UoS), European Commission Joint Research Centre (JRC), University of Bologna (UNIBO), Finnish Meteorological Institute (FMI), Emilia-Romana Protection and Environmental Regional Agency (ARPA-ER), Institute for Advanced Architecture of Catalonia (IAAC)

Release date

The publication reflects the author’s views. The European Commission is not liable for any use that may be made of the information contained therein.


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Document Control Page

Short Description This report is the output of the work Task 3.2 of the iSCAPE project which addresses the footprint of PCSs and their benefits on each iSCAPE city of intervention. The report aims to summarize the iSCAPE intervention evaluation methods, sites description, instruments setup and experimental protocols for the potential of using physical passive controls (low boundary walls), green infrastructure (trees, hedges, green walls and/or roofs) and the utilization of photo-catalytic coatings (in road tiles or walls).

Review status Action Person Date

Quality Check Coordination Team

Internal Review John Gallagher (TCD) Beatrice Pulvirenti (UNIBU)

31/8/2017

Internal Review Giorgio Bagordo (T6)

Marisa Fuchs (TUDO)

10/10/2018

Distribution Public

Statement of originality: This deliverable contains original unpublished work except where clearly indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation or both.


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Revision history

Version Date Modified by Comments

V0.1

20/8/2017

Salem S. Gharbia, Abhijith K. V., Andreas N. Skouloudis, Erika Brattich, Athanasios Votsis, Väinö Nurmi, Carl Fortelius, Kirsti Jylhä, Achim Drebs, Gaia Papini, Francesco Matacchiera, Arianna Valmassoi, Alessio Brunetti, Francesco Barbano, Francesco Pilla, Silvana Di Sabatino, Beatrice Pulvirenti, Prashant Kumar and Guillem Camprodon

The first draft.

V0.2 30/8/2017 Salem S. Gharbia

Task leader’s edits.

Draft for internal review process.

V0.3 31/8/2017 John Gallagher Received the internal reviewers’ comments.

V0.4 31/8/2017 Salem S. Gharbia Draft after addressing the reviewers’ comments.

V0.5 1/9/2017 Beatrice Pulvirenti Received the internal reviewers’ comments.

V0.6 4/9/2017 Salem S. Gharbia Draft after addressing the reviewers’ comments.

V0.7 29/9/2018 Salem S. Gharbia, Abhijith K. V., Erika Brattich, Thor-Bjørn Ottosen, Antti.Makela

Addressing the reviewers’ comments.

V0.8 3/10/2018 Salem S. Gharbia

Task leader’s edits.

Draft for internal review process.


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V0.9 12/10/2018 Gharbia et. al.

Addressing the iSCAPE internal reviewers’ comments.

Table of Contents Table of Contents ....................................................................................................................... - 4 - List of Tables .............................................................................................................................. - 6 - List of Figures ............................................................................................................................ - 7 - List of Abbreviations ................................................................................................................. - 9 -

1 Executive Summary .................................................................................................. - 11 - 2 Introduction................................................................................................................ - 12 - 3 Low Boundary Wall ................................................................................................... - 13 -

Recap of WP1 recommendations on LBW ................................................................... - 13 - Methodology of LBW evaluation (Dublin) .................................................................... - 14 -

LBW intervention assessment and evaluation .......................................................... - 14 - Site selection criteria for the LBW intervention .......................................................... - 15 - LBW Site description ................................................................................................ - 18 - Instrumentation, data setup and collection ................................................................ - 20 - Experimental protocol ............................................................................................... - 21 -

SWOT analysis of LBW intervention ............................................................................ - 21 -

4 Photocatalytic coatings ............................................................................................ - 24 - Expected efficiency from tests with individual pollutants .......................................... - 24 - Identification of street locations for real applications ................................................ - 25 - Annual climatic characterizations at street level ........................................................ - 26 - Details of local monitoring campaigns ........................................................................ - 28 - Expected efficiency at neighborhood and street level ................................................ - 29 -

5 Green infrastructure design ..................................................................................... - 31 - State of the art for green infrastructure (GI) evaluation at city-scale......................... - 31 - GI evaluation at city-scale - Guildford (UK) ................................................................. - 33 -

Modelling approach .................................................................................................. - 33 - Modelled domain ...................................................................................................... - 33 - Model inputs and validation ...................................................................................... - 34 - Modelled scenarios for "what if" analysis .................................................................. - 35 -

SWOT analysis for Guildford ........................................................................................ - 36 - GI evaluation at city-scale - Bologna (IT) ..................................................................... - 38 -

Methodology for GI evaluation at city-scale assessment for Bologna (IT) ................. - 42 - SWOT analysis for Bologna .......................................................................................... - 45 - GI evaluation at city-scale - Vantaa (FI)........................................................................ - 47 -

Methodology for GI evaluation at city-scale assessment for Vantaa ......................... - 52 -


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SWOT analysis for Vantaa ............................................................................................ - 54 -

6 Green infrastructure evaluation at neighborhood scale ........................................ - 55 - State of the art for GI evaluation at neighborhood-scale ............................................ - 55 -

Near-road environment ............................................................................................. - 55 - Methodology for GI evaluation for neighborhood-scale assessment for Guildford . - 57 -

Site description ......................................................................................................... - 57 - Instrument setup ....................................................................................................... - 59 - Experimental protocol ............................................................................................... - 60 -

Methodology for GI evaluation for neighbourhood-scale assessment for Bologna . - 60 - Site description ......................................................................................................... - 61 - Instrument setup ....................................................................................................... - 63 - Experimental protocol ............................................................................................... - 73 -

Methodology for GI evaluation at neighborhood-scale assessment for Vantaa ....... - 76 - SWOT analysis............................................................................................................... - 77 -

7 A summary table of measures .................................................................................. - 80 - 8 References / Bibliography ........................................................................................ - 82 - Appendix (A) Low Boundary Walls Location Selection ................................................ - 91 - Appendix (B) Technical specifications for the instruments ....................................... - 103 - Appendix (c) The chemistry of the atmospheric pollutants ....................................... - 120 -


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List of Tables TABLE 1: KEY STUDIES RELATED TO THE USE OF LBWS AS A PASSIVE CONTROL SYSTEM........................................ - 17 - TABLE 2: DETAILS OF TWO MONITORING LOCATIONS. .......................................................................................... - 18 - TABLE 3: SWOT ANALYSIS OF THE LBW INTERVENTIONS FOR OBTAINING LOCAL-SCALE AIR QUALITY BENEFITS. ....... - 22 - TABLE 4: SWOT ANALYSIS OF GREEN INFRASTRUCTURAL (GI) INTERVENTIONS FOR OBTAINING CITY-SCALE AIR QUALITY

BENEFITS. .............................................................................................................................................. - 37 - TABLE 5: AREAS OCCUPIED BY GI IN THE AREA OF BOLOGNA METROPOLITAN CITY. ................................................ - 41 - TABLE 6: SWOT ANALYSIS OF THE BOLOGNA INTERVENTION. ............................................................................. - 45 - TABLE 7. STATISTICS OF GREEN INFRASTRUCTURE IN FINLAND 2014-2015. ......................................................... - 48 - TABLE 8: SWOT ANALYSIS OF THE VANTAA INTERVENTION. ................................................................................ - 54 - TABLE 9: DETAILS OF SIX MONITORING LOCATIONS. ............................................................................................ - 58 - TABLE 10: DESCRIPTION OF VARIABLES IN CEPTOMETER ACCUPAR LP-80. ......................................................... - 72 - TABLE 14. A SUMMARY TABLE OF MEASURES. .................................................................................................... - 81 - TABLE 15: TECHNICAL SPECIFICATION OF GILL R3-50 SONIC ANEMOMETER. ...................................................... - 105 - TABLE 16: TECHNICAL SPECIFICATIONS OF HCS2S3 THERMOHYGROMETER. ...................................................... - 106 - TABLE 17: TECHNICAL SPECIFICATIONS OF NET RADIOMETER CNR4. ................................................................. - 109 - TABLE 18: TECHNICAL SPECIFICATIONS OF VAISALA BAROMETER PTB110. ........................................................ - 111 - TABLE 19: TECHNICAL SPECIFICATION OF LI-COR LI-7500A CO2/H2O ANALYSER. ............................................ - 112 - TABLE 20: TECHNICAL SPECIFICATIONS OF THE T200 NO/NO2/NOX ANALYSER. ................................................ - 113 - TABLE 21: TECHNICAL SPECIFICATIONS OF THE T300 CO ANALYSER. ................................................................ - 114 - TABLE 22: TECHNICAL SPECIFICATIONS OF THE T100 SO2 ANALYSER. .............................................................. - 115 - TABLE 23: TECHNICAL SPECIFICATIONS OF THE THERMO SCIENTIFIC MODEL 49I O3 ANALYSER. ........................... - 116 - TABLE 24: TECHNICAL SPECIFICATIONS OF THE AIRTOXIC CHROMATOTECH BTEX ANALYSER. ............................ - 117 - TABLE 25: TECHNICAL SPECIFICATIONS OF THE FAI SWAM 5A PM10 AND PM2.5 SAMPLER.................................. - 119 - TABLE 26: TECHNICAL SPECIFICATIONS OF CEPTOMETER MODEL ACCUPAR LP-80. ............................................ - 119 -


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List of Figures FIGURE 1:SCHEMATIC REPRESENTATIONS OF THE TWO MONITORING LOCATIONS WITH THE TWO LBW CONFIGURATIONS.

THE ORANGE CIRCLE AND BLACK RING DENOTE MEASUREMENT POINT BEHIND AND IN FRONT OF THE LBW, RESPECTIVELY. ...................................................................................................................................... - 19 -

FIGURE 2: DETAILS OF THE NASSAU STREET LBW INTERVENTION LOCATION. ....................................................... - 19 - FIGURE 3:DETAILS OF THE PEARSE STREET LBW INTERVENTION LOCATION.......................................................... - 20 - FIGURE 4: SCHEMATIC DIAGRAM OF THE HETEROGENEOUS PHOTOCATALYSIS PRODUCED BY AN ANATASE TIO2 LAYER.

(BOONEN & BEELDENS, 2014). ................................................................................................................ - 24 - FIGURE 5: EXPERIMENT TO COMPARE THE PERFORMANCES OF ALL CERAMIC SAMPLES WITH PURETI COATINGS. ..... - 25 - FIGURE 6: AERIAL VIEW OF THE LAZZARETTO CAMPUS SITE (CREDIT: BING). ......................................................... - 26 - FIGURE 7: THE METEOROLOGICAL SITES IN EMILIA ROMAGNA AND THE LOCATION OF THE PORTA CASTIGLIONE WEATHER

STATION COMPARED TO THE LAZZARETTO UNIVERSITY CAMPUS WHERE EXPERIMENTS WILL BE CARRIED OUT. .. - 27 - FIGURE 8: THREE NEARLY COMPLETE CYCLES OF SOLAR RADIATION NEAR THE LAZZARETTO UNIVERSITY CAMPUS. THE

BLACK LINE REPRESENTS THE ROLLING DAILY TREND LINE. .......................................................................... - 27 - FIGURE 9: THREE NEARLY COMPLETE CYCLES OF ACCUMULATED RAIN. THE BLACK LINE REPRESENTS THE ROLLING DAILY

TREND LINE. ........................................................................................................................................... - 28 - FIGURE 10: THE CORRESPONDING WIND SPEED AND DIRECTION. THE BLACK LINE REPRESENTS THE ROLLING DAILY TREND

LINE. ..................................................................................................................................................... - 28 - FIGURE 11: GRAPHICAL REPRESENTATION OF THE EXPERIMENTAL AREA WHERE THE PHOTOCATALYTIC PAINTS WILL BE

UTILISED. ............................................................................................................................................... - 29 - FIGURE 12: THE ATTRIBUTION OF CORINAIR EMISSIONS SOURCES FOR THE PROVINCE BOLOGNA FROM 1990 TO 2010

FOR NOX (IN MG). ROAD TRANSPORT IS THE MAIN EMISSION SOURCE FOR THIS POLLUTANT............................ - 30 - FIGURE 13: THE ATTRIBUTION OF CORINAIR EMISSIONS SOURCES FOR THE PROVINCE BOLOGNA FROM 1990 TO 2010

FOR NON-METHANE VOCS (IN MG). ROAD TRANSPORT IS NOT THE MAIN SOURCE OF POLLUTION FOR THIS POLLUTANT. ........................................................................................................................................... - 31 -

FIGURE 14: MODELLED DOMAIN OF GUILDFORD BOROUGH ALONG WITH THE MAJOR ROADS AND BUILDINGS. ............ - 34 - FIGURE 15: (A) COMPARISON WITH THE NATIONAL AVERAGE FOR URBAN GREEN AREAS PER CAPITA (M2) IN MAJOR ITALIAN

CITIES; (B) DENSITY OF URBAN GREEN AREAS IN THE MACRO-ZONES OF ITALY; (C) AVAILABILITY (GREEN) AND DENSITY OF URBAN GREEN AREAS (GREY) IN CITIES WITH MORE 200.000 INHABITANT OR METROPOLITAN AREAS (ISTAT, 2011). ...................................................................................................................................... - 38 -

FIGURE 16: BOLOGNA URBAN ECOSYSTEM. MAP OBTAINED BY QUICKBIRD SATELLITE ............................................ - 41 - FIGURE 17: BOLOGNA URBAN ECOSYSTEM. RESULTS OBTAINED BY THE GI CLASSIFICATION. .................................. - 42 - FIGURE 18. PERCENTAGE OF GREEN AREAS IN SOME EUROPEAN CITIES. ADAPTED FROM

HTTPS://SUOMIFINLAND100.FI/SATISFACTION-WITH-GREEN-AREA-IS-THE-HIGHEST-IN-FINLAND/?LANG=EN. ....... - 47 - FIGURE 19. THE PERCENTAGE OF GREEN IN THE CITY DISTRICTS OF VANTAA. FROM MÄKYNEN (2017). ................... - 49 - FIGURE 20. THE RESPONDENTS VIEW ON THE INCREASE OF GREEN INFRASTRUCTURE IN THE FUTURE VANTAA. ........ - 50 - FIGURE 21. THE RESPONDENTS VIEW ON THE DECREASE OF QUIET AND NATURAL ENVIRONMENTS IN THE FUTURE

VANTAA. ................................................................................................................................................ - 50 - FIGURE 22. RESPONDENTS VIEW ON THE TYPE OF DESIRED NATURE AND RECREATIONAL AREAS IN VANTAA IN A TEN-YEAR

TIME FRAME. .......................................................................................................................................... - 51 - FIGURE 23. RESPONDENTS VIEW ON WHAT THEY APPRECIATE IN NATURE AND RECREATIONAL AREAS. ..................... - 51 - FIGURE 24: SCHEMATIC REPRESENTATIONS OF SIX MONITORING LOCATIONS WITH THE TYPE OF VEGETATION AND ROAD

DETAILS. THE ORANGE CIRCLE AND BLACK RING DENOTE MEASUREMENT POINT BEHIND AND IN FRONT OF THE VEGETATION BARRIER, RESPECTIVELY. ...................................................................................................... - 59 -

FIGURE 25: INSTRUMENTS ARE MOUNTED ON TRIPOD AND KEPT CLOSE TO EACH OTHER DURING INTER-CALIBRATION. IN THE FIGURE, 1) GRIMM AEROSOL SPECTROMETER, 2) PTRAK 8525, 3) QTRAK 7575, 4) MICROAETH AE51, 5) WEATHER STATION KESTREL 4500. .......................................................................................................... - 60 -


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FIGURE 26: A) POSITION OF BOLOGNA (YELLOW DOT; 44°29’37’’N, 11°20’19’’E) IN NORTH-EAST ITALY; B) POSITION OF THE TWO STREET CANYONS IN BOLOGNA; C) STREET VIEW OF LAURA BASSI VERATTI STREET WITH TREES; D) STREET VIEW OF MARCONI STREET WITHOUT TREES. .................................................................................. - 62 -

FIGURE 27: A) LEAF SPECIMEN OF PLATANUS A. B) TRUNK DETAIL OF PLATANUS A................................................ - 63 - FIGURE 28: THE THERMALCAM FLIR T620 (A) REAR SECTION (B) FRONT SECTION ............................................... - 64 - FIGURE 29: SCHEMATIC ILLUSTRATION OF THE STEPS USED TO MEASURE A COMPONENT OF WIND VELOCITY THROUGH AN

ULTRASONIC ANEMOMETER. ..................................................................................................................... - 65 - FIGURE 30: GILL R3-50 SONIC ANEMOMETER. .................................................................................................. - 66 - FIGURE 31: HCS2S3 THERMOHYGROMETER..................................................................................................... - 67 - FIGURE 32: CNR4 NET RADIOMETER. ............................................................................................................... - 67 - FIGURE 33: VAISALA BAROMETER PTB110. ...................................................................................................... - 68 - FIGURE 34: LI-COR LI-7500A CO2/H2O ANALYSER. ......................................................................................... - 69 - FIGURE 35: EXAMPLE OF ARPA-ER MOBILE LABORATORIES. .............................................................................. - 70 - FIGURE 36: (A) PARTS OF THE CEPTOMETER; (B) FRONT VIEW OF THE CEPTOMETER. ............................................. - 71 - FIGURE 37: ARPA-ER MOBILE LABORATORIES IN LAURA BASSI VERATTI ST. (44°29’00.52’’N, 11°22’03.11’’E) AND

MARCONI ST. (44°29’56.21’’N, 11°20’18.56’’E). ...................................................................................... - 73 - FIGURE 38: POSITIONING OF THE SONIC ANEMOMETERS IN LAURA BASSI VERATTI ST. ........................................... - 74 - FIGURE 39: SECOND ANEMOMETER POSITIONED ON THE BANISTER OF A BALCONY AT THE SECOND FLOOR OF A 15M

BUILDING IN LAURA BASSI VERATTI ST. AND MARCONI ST. .......................................................................... - 75 - FIGURE 40: THIRD ANEMOMETER POSITIONED ON THE ROOF OF A 15M BUILDING ABOVE THE STREET CANYON. ......... - 75 -


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List of Abbreviations ADMS Atmospheric Dispersion Modeling System AROME Applications de la Recherche à l’Opérationnel à Méso-Echelle =

mesoscale applications of research for operational use BTEX benzene, toluene, ethylbenzene, and xylenes CFD Computational Fluid Dynamics CO carbon monoxide CO2 carbon dioxide EC elemental carbon ES Ecosystem Services FIR Far Infrared Radiation GI Green Infrastructure

GIS Geographic Information System H2SO4 sulphuric acid HARMONIE Hirlam Aladin Regional/Meso-scale Operational NWP In Europe HIRLAM High Resolution Limited Area Modelling HNO3 nitric acid HO2 peroxy radical ISTAT National Institute of Statistics LAD Leaf Area Density LAI Leaf Area Index LBW Low Boundary Wall NDIR Non-Dispersive InfraRed NO nitric oxide NO2 nitrogen dioxide NOx nitrogen oxides NWP Numerical Weather Prediction O3 ozone OH hydroxyl radical


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ONA Optimised Noise-reduction Averaging algorithm PAN peroxyacyl nitrate PAR Photosynthetically Active Radiation PBL Planetary Boundary Layer PCS Passive Control Systems PM particulate matter PMx all particles with aerodynamic diameter less or equal to x μm PNC Particle number concentration RAD Root Area Density RCP Representative Carbon Pathway SAU Utilized Agricultural Surface SO2 sulphur dioxide SURFEX Surface Externalisée SWOT strengths, weaknesses, opportunities, threats TCI Thermal Comfort Index

UFP ultra-fine particles UHI Urban Heat Island VOCs Volatile Organic Compounds WP Work Package WTP Willingness to Pay


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1 Executive Summary Due to the serious impacts on public health, it is essential to control air pollution, especially in and around cities where a majority of the world’s population lives and pollution concentrations are typically much higher than in rural areas. Passive control systems (PCSs) are interventions for reducing air pollution, which include low boundary walls, green infrastructure (GI), and photocatalytic coating. In this report, we analyse the footprint and the benefits of implementing PCSs as interventions to reduce personal exposure to air pollution in the built environment, with a specific focus on their application in iSCAPE cities. In addition to discussing the available literature, this report provides the methodologies for the assessment and evaluation of PCSs interventions. This report summarises the iSCAPE intervention evaluation methods, sites description, instruments setup and experimental protocols for the potential of using physical passive controls (low boundary walls) and green infrastructure (trees, hedges, green walls and/or roofs), and the utilisation of photo-catalytic coatings (in road tiles or walls). This report considers a SWOT (strengths – weaknesses – opportunities – threats) analysis for each type of PCS intervention.


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2 Introduction The majority of the population lives in urban areas in both the European Union (72%; European Environment Agency 2015) and indeed globally (54%; United Nations 2014). Air pollution levels in many European cities are above permissible limits (Guerreiro et al. 2016; European Environment Agency 2013) and thus air pollution is considered one of the primary environmental health risks ( European Environment Agency 2015). It is therefore important to control air pollution, especially in and around cities where a majority of the world’s population lives (UN, 2015) and where the pollution levels are also typically much higher than those in rural areas. Road traffic is the dominant source of air pollutants including particulate matter (PM), nitrogen oxides (NOx) carbon monoxide (CO) and volatile organic compounds (VOCs) (HEI 2010). Air pollutants from traffic are emitted close to a ground level causing elevated pollutant concentrations near highways compared to urban background concentrations (Karner et al. 2010). These traffic generated emissions contribute to increased air pollution exposure in “on the road”, “near field/road” and “far field” micro environments (Batterman et al. 2014; Batterman 2013). In on-road micro environments, drivers, commuters, pedestrians, and cyclists are exposed to a higher level of air pollution. A significant amount of population lives in near-road environments. In the EU25 Member States around 29% of the population live within 500 metres of a major road (Entec, U.K., 2006). However, 45 million people live or work within 100 m from massive traffic ways in the US alone (EPA 2016) while about 40% of Toronto’s population lives within 500 m of an expressway or 100 m from a major road (HEI 2010). The majority of these people are low-income or minority residents (Tian et al. 2013; Carrier et al. 2014b). Exposure to traffic-related air pollutants by vulnerable children at school escalates concerns over air quality in the near-road region (Carrier et al. 2014a; Kim et al. 2004). Numerous studies have demonstrated the association of adverse health impacts on people living near-road conditions with proximity to highways. The range of health implications includes an exacerbation of asthma (Volk et al. 2011; Evans et al. 2014; Clark et al. 2010), impaired lung function (Laumbach & Kipen 2012), cardiovascular morbidity and mortality (Cahill et al. 2011; Brook et al. 2010; Wilker et al. 2013), adverse birth outcomes (Michelle et al. 2012), and cognitive declines (Volk et al. 2011; HEI 2010). Passive control systems (PCSs) are interventions for reducing air pollution, which include low boundary walls (LBW), green infrastructure (GI), and photocatalytic coating (Gallagher et al., 2015; Mo et al., 2009). With GI interventions, several solutions exist such as trees and hedges (Abhijith et al., 2017). This report is the output of task 3.2 of the iSCAPE project which addresses the footprint of PCSs that are discussed in WP1 and their benefits on each iSCAPE city of intervention.


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3 Low Boundary Wall This section presents the Low Boundary Wall (LBW) intervention to reduce personal exposure from air pollution on a local scale (street scale). In iSCAPE, Dublin city in Ireland has been chosen as the location for examining LBWs in the built environment. LBWs are a type of physical Passive Control Systems (PCS) that has been shown to effectively impact on air flow and pollutant dispersion in low-rise street canyons. Dublin provides multiple locations to examine the implications of the LBW as a type of PCS. This report presents the LBW site location options and selection criteria.

Recap of WP1 recommendations on LBW The LBW intervention has been discussed in detail in deliverable report 1.2 as part of iSCAPE Work Package (WP) 1. The report can be accessed here (https://www.iscapeproject.eu/wp-content/uploads/2017/09/iSCAPE_D1.2_Guidelines-to-Promote-Passive-Methods-for-Improving-Urban-Air-Quality-in-Climate-Change-Scenarios.pdf). The recommendations and conclusions drawn from this report are summarised as follows. LBWs can provide a solution to enhance localized dispersion and improve air quality in distinct street canyon settings. LBWs have many potential drawbacks, as the concentration of pollutants can increase in front of the LBWs (similar to noise barriers). Wind direction, street geometry and position of the LBW, may cause an increase of air pollutant concentrations behind the barrier, therefore having an opposite effect to its intended use. Since wind direction is variable, an LBW may have a positive effect today and an adverse effect tomorrow, which makes the design process very hard in terms of city planning, as a result of this we must be cautious as to where these are placed. The important points, recommendations and some guidelines regarding the use of LBWs as physical control systems can be summarised as follows:

• LBWs act as a baffle at street level and increase the distance between the pollutant source and human receptor;

• Both measurements and modelling studies show LBWs as an active physical passive control method;

• Reductions in pollutant concentrations have been reported on the footpaths in most wind conditions when LBWs exist;

• Low wind speeds, wall and canyon geometry, impact the effectiveness of the LBWs to promote dispersion and the development of vortices in street canyons, which transport pollutants to roof level and escape the street canyon;


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• Adverse effects on air quality were measured on the leeward footpath from model simulations for perpendicular wind conditions, where the LBWs exist;

• More research is needed to develop guidelines to provide practical instructions for implementing LBWs in a street canyon environment;

• An increase in pollutants concentrations on the road may occur when LBWs are located in a street.

Methodology of LBW evaluation (Dublin)

LBW intervention assessment and evaluation The assessment and evaluation plan of the Dublin LBW intervention is built on the following evaluation methods, which will be implemented as part of iSCAPE WP6:

• Measuring study for the real-world LBW application in Dublin,

• CFD (Computational Fluid Dynamics) modelling study of the street canyon before and after the LWB intervention supported by a wind tunnel experiment, mainly to calibrate the CFD models.

Five studies have been implemented, to date, in Dublin to study the effectiveness of using LBWs as an intervention in the built-in environment to reduce the personal exposure to air pollution, these are summarised in Table 1 (Gallagher et al., 2012, Gallagher et al., 2013, King et al., 2009, McNabola et al., 2008, McNabola et al., 2009). Table 1 shows that two evaluation methods have been mainly used to assess the LBW intervention, the real-world measuring studies and the CFD modelling. In general, benzene, CO, PM2.5 or NOx are used by the mentioned studies as single pollutants to quantify the impact of LBWs on air quality in urban street canyons. The use of LBWs has been first investigated through some initial studies, implemented along a boardwalk in Dublin, Ireland (McNabola et al., 2008, King et al., 2009). Those studies investigated the influence of a boundary wall constructed between a boardwalk and an adjacent road with three lanes of one-directional traffic in Dublin city centre. The McNabola et al. (2008) real-world measuring study concluded that a LBW acted as a baffle, that when located on the outer edge of footpaths or in the centre of the street canyon, altered pollutant dispersion within thestreet canyon. A follow-on study by King et al. (2009), a CFD modelling study based on the McNabola et al. (2008) study, reported that the effect of the boardwalk on air and noise pollution was that the segregation of human and vehicular traffic increased the distance between the source and the receptor and led to a reduction in pollutant exposure. McNabola et al. (2008) performed an air quality sampling study which measured reductions of between 35% and 57% in personal pollutant exposure for pedestrians walking along the boardwalk as opposed to the adjacent footpath. Following the field sampling study McNabola et al. (2009) performed a general computational modelling study to model the case and again


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calculated reductions in personal pollutant exposure of up to 40% and 75% in perpendicular and parallel wind conditions, respectively. Gallagher et al. (2012) in a later study found that footpath LBWs models ranged from a 19% to a 30% reduction on the leeward footpath and reductions of 26% to 50% on the windward footpath. Gallagher et al. (2013) took the same case forward and assessed LBWs in a real study and reported reductions in pollutant concentrations of up to 35% to a maximum increase of 25% on the footpaths in varying wind conditions. Regarding the effects of the LBW physical characteristics, King et al. (2009) concluded that the height of the LBW impacted the effectiveness of the barrier on air flow and pollutant concentrations on the footpath based on the McNabola et al. (2009) CFD modeling study. Also, McNabola et al. (2009) reported that the location of the LBWs impacted the results for pollutant concentrations. The street layout, limited wind conditions and omission of vehicular turbulence are noted to provide inaccuracies in the results compared to real case conditions (McNabola et al., 2009). The simplification in the emission models generated errors, which were accounted to be more influential in model results for low wind speeds in the street canyon (Gallagher et al., 2012, Gallagher et al., 2015). A study by Gallagher et al. (2013) adopted a semi-empirical equation for a real LBW case study to calibrate the models and account for factors such as vehicular turbulence, in addition to the fleet composition in the street canyon. The study reported that the omission of vehicular turbulence decreases the street level dispersion. The turbulence effects of LBWs is dependent on site-specific characteristics such as: street geometry, wind conditions and vehicular turbulence (Gallagher et al., 2013, McNabola et al., 2008).

Site selection criteria for the LBW intervention University College Dublin (UCD), Dublin City Council (DCC) and Trinity College Dublin (TCD) setup the site selection criteria for implementing the LBW intervention. The selection criteria are as follows:

• A minimum of one location should be a typical street canyon; • The location should be in a street with several traffic lanes i.e. localised source of

pollution; • The road should have heavy traffic patterns and with a footpath; • The location should have the potential to reduce personal exposure to air pollution for

a vulnerable population group; • The already existing barriers (LBWs) should enable the building of new LBWs; • The intervention campaign should be implemented with minimal or no traffic

disruptions; • The location should be safe to set-up the instruments;


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• The existing LBW can be a small barrier, continuous street furniture or continues road steel railing;

• The intervention location should be in Dublin city centre; • The intervention location should have minimum large vegetation (trees), which may

affect the airflow; • The selected street canyon locations should have narrow (1:1) or regular (2:1) aspect

ratios; • The existing LBW should have enough length (at least 8 m), to allow for an effective

campaign; • The location should have easy access to power supply; • The location should have a safe and easy access to implement the experiments.

After undertaking the Dublin LBW location selection campaign based on the above criteria, a number of locations around the city of Dublin were shortlisted, these have been summarised in Appendix (A). Regarding the specific locations available, only a few solid barriers can be considered as barriers for improving localized air quality for pedestrians (no place to walk in most of the existing barriers). However, only three options of urban furniture are continuous enough in this category to be examined as LBWs. Although all railings seem feasible, some of them are not on a busy street and therefore considered not worth exploring. Therefore, two LBW intervention locations were found to follow the setup criteria. These locations are described in section 3.2.3.


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Reference Evaluation method Location & methods description Findings (McNabola et al., 2008)

Real-world measuring study

Air quality samples were taken along the length of a boardwalk in Dublin city to study whether pedestrians using the boardwalk would have a lower air pollution exposure than those using the adjoining footpath along the road. The same case has been modelled using CFD to provide more understanding.

The results of the study show significant reductions in pedestrian exposure to both traffic derived particulates and hydrocarbons along the boardwalk as opposed to the footpath.

(King et al., 2009)

CFD modelling This study offers a combined analysis of pedestrian exposure to noise and air pollution within a specific urban setting in Dublin, Ireland.

The results show that the boardwalk has reduced pedestrian exposure to air and noise pollution and that further reductions may be achieved by more strict segregation of pedestrian and road traffic in urban areas.

(McNabola et al., 2009)

CFD modelling The impact of low boundary walls on the dispersion of air pollutants in street canyons has been brought forward in this investigation study using CFD modelling, again in Dublin, Ireland.

The results of this study show that a low boundary wall located in the central median of the street canyon creates a significant reduction in pedestrian exposure on the footpath. Reductions of up to 40% were found for perpendicular wind directions and up to 75% for parallel wind directions, relative to the same canyon with no wall.

(Gallagher et al., 2012)

CFD modelling This numerical modelling study assessed the spatial distribution of concentrations of a tracer pollutant in a street canyon as a result of introducing of passive controls in asymmetrical street canyons to reduce personal exposure to air pollutants on footpaths.

The percentage difference in concentrations induced by the presence of footpath LBWs ranged from an increase of up to 19% to a reduction of 30% on the leeward footpath, with reductions between 26% and 50% on the windward footpath with varying H1/H2 ratios.

(Gallagher et al., 2013)

Real-world measuring study & CFD modelling

This study investigates the potential real-world application of passive control systems to reduce personal pollutant exposure in an urban street canyon in Dublin, using both modelling and measurement approaches.

The results indicate that lane distribution, fleet composition and vehicular turbulence all affect pollutant dispersion, in addition to the canyon geometry and local meteorological conditions. The paper suggests that the use of passive controls displayed mixed results for improvements in air quality on the footpaths for different wind and traffic conditions.

Table 1: Key studies related to the use of LBWs as a passive control system.


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LBW Site description For the iSCAPE LBW intervention in Dublin, two locations have been considered, with two different configurations. The study will evaluate air pollution reduction potentials of these two settings and the monitoring sites selected based on the local conditions of the two locations. Both locations are near major roads of Dublin city centre and have been detailed in Table 2 and Figures 2 & 3. The two sites have been selected to assess the impact of LBW distance from the road. One of them is close to the traffic (≤1m) and other is away from the road (≥2m). Figure 1 shows a schematic diagram of the monitoring sites. The sites are situated in a busy area with more than two 3-story buildings on either side of a two lane road. The two sites are located in the heart of the Dublin city center around Trinity College Dublin and can also be used to reduce the personal exposure to air pollution of vulnerable school children who pass the city center on their way to the school near the dockland. As the two locations are located around Trinity College Dublin (TCD), each one located near one of the main entrances for TCD, the intervention can also help reduce personal exposure to air pollution for pedestrians in and around TCD. Full traffic volume history and direction of roads at each site were secured and available to be used in the simulation study. Dimensions of LBWs, the distance from the edge of the road to monitoring locations, and width of lanes are illustrated in Figure 1. The plan is to quantify the pollutant reduction potentials of the two different LBW configurations by comparing pollutant concentrations in the clear area and behind the LBW. The statistical analysis of the data collected during a campaign can also give some insight on the impact of meteorology and LBW characteristics on pollutant removal.

Site Name with type of vegetation Name of the road, number of lanes, width of the road and direction

Distance from road

LBW attributes L: Length W: Width H: Height

A. Pearse St-LBW Pearse St 4 lanes- One direction ~ 16m N-S

0.7m L: ~7m W:0.1m H:1.2m

B. Nassau St-LBW Nassau St 4 lanes – One direction ~16m N-S

2m L:~36m W:~0.2m H:~2m

Table 2: Details of two monitoring locations.


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Figure 1:Schematic representations of the two monitoring locations with the two LBW configurations. The orange

circle and black ring denote measurement point behind and in front of the LBW, respectively.

Figure 2: Details of the Nassau street LBW intervention location.

Symmetrical


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Figure 3:Details of the Pearse street LBW intervention location.

Instrumentation, data setup and collection As part of the measurement study on the LBW interventions in Dublin, the plan is to monitor PM2.5, PM10, NOx, CO2 and CO. The instrument should provide particulate matter concentrations on 1-min time resolution.


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CO and CO2 are monitored in ppm having time base of 1-min. Local meteorological conditions (air temperature, relative humidity and wind speed and direction) are logged by the portable weather station at 1-min resolution. All instrument data is averaged to 1 minute to match with the wind data. Traffic counting to be used in this study is collected from Dublin City Council (DCC).

Experimental protocol A set of instruments mounted on a tripod stand at 1.5m height. The Pearse street monitoring site, namely A, has a bright area (without any disturbance to air flow) adjacent to the LBW. However, B (Nassau street) the LBW has a steel fence on top of the LBW which might affect the airflow to the monitoring instruments. The CFD evaluation study for the intervention will therefore investigate and quantify the consequences of the steel fence on top of the LBW in addition to studying the effect of having a perforated LBW. The portable weather stations will be always attached to the tripod in the clear area or front of the LBW. The campaign is designed to conduct 15 days of monitoring per site, making a total of 30 days. The field measurement will start at 08.00 h and end at around 18.00 h (local time). This will enable to collect 8 to 10 hours of data every day. Inter calibration between each set of instruments is achieved by running instruments side by side for 20 to 30 min prior and finishing the measurements.

SWOT analysis of LBW intervention SWOT (strengths – weaknesses – opportunities – threats) analysis is a powerful tool for planning the future directions of a business/non-profit venture by assessing its strengths and weaknesses together with the foreseeable opportunities and treats (Helms and Nixon, 2010). This tool has also been used by a few researchers to study various city planning activities such as environmental planning (Marcucci and Jordan, 2013) and storm-water management (Mguni et al., 2016). By using this technique, the LBW intervention in Dublin has been analysed, and the impact on air quality summarised in Table 3. The SWOT analysis shows that LBWs can act as a baffle and alter air flow patterns at street level. Limited research projects have, to date, addressed LBWs as a passive control system, so the iSCAPE deployment of LBWs is essential to improve this knowledge. The review process from the available literature shows that LBWs have the potential of enhancing local dispersion in the built environment. The height of the LBW, its location in the street and whether spaces exist in the barrier have found to influence air flow in street canyons. The confined street canyon study needs to be expanded to a city-scale, as the frequency and variation of road characteristics and intersections are not considered in the LBW studies to date. There is some evidence that LBWs could cause deteriorations in air quality for vehicular users and, in particular, pedestrians and cyclists.


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Strength Weakness • LBWs act as a baffle and alter air

flow patterns at street level. • Both measurements and modelling

studies show LBWs as an active physical passive control method.

• LBWs have the potential of enhancing local dispersion in the built environment.

• Reductions in pollutant concentrations have been reported on the footpaths in most wind conditions where LBWs are present.

• Modeling studies show that LBWs can decrease the overall pollutants levels from 26% up to 50% on windward footpaths.

• The concentration of pollutants increases in front of the LBWs.

• Based on the wind direction, there is some evidence that LBWs could cause deteriorations in air quality for vehicular users and, in particular, pedestrians and cyclists. In general, LBW influence the dispersion pattern of the pollution, but it does not reduce the total concentrations in the street canyon, which would lead to have a negative effect on the road users under some circumstances.

• Depending on wind direction, street geometry and position of the LBW, it may cause air pollutant concentration to increase behind it, having the opposite effect to its intention.

• Adverse effects on air quality were measured on the leeward footpath from model simulations for perpendicular wind conditions, where the LBWs exist.

• More research is needed to develop guidelines to provide practical instructions for implementing LBWs in a street canyon environment.

Opportunities Threats • iSCAPE project can provide

detailed studies which can provide practical implementation recommendations regarding the size, height, length and direction of the LBWs.

• iSCAPE project can provide Dublin City Council with guidelines recommendations on the implementation of LBWs in the built environment.

• The results of the experiments may be used by Dublin City Council to correctly plan the deployment of new interventions.

• Since wind direction is variable, an LBW may have a positive effect today and an adverse effect tomorrow. This complicates the city planning design process, with the placing of LBWs needing to take into careful consideration wind variability.

• LBWs can act as obstacles on the footpath which prevent the easy access to shops, especially for loading purposes.

• LBWs are hard to be integrated and accepted in city planning practices. That is mainly because the lack of knowledge on their use and the movement restriction that LBWs might cause to pedestrian and shops owners in the cities.

• LBWs are very hard to introduce to the public community as important urban planning items.

• The simulations undertaken may not take into consideration all aspects required by city plans

Table 3: SWOT analysis of the LBW interventions for obtaining local-scale air quality benefits.


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4 Photocatalytic coatings Although photocatalysis is a well-known process, its use in real weather conditions is still at the experimental stage, and the scientific literature on the subject has not yet reached a full consensus. The process mimics photosynthesis, and as outlined in Figure 4, air purification through heterogeneous photocatalysis consists of a number of different steps. Under the influence of UV-light, the photoactive TiO2 at the surface of the material is activated; subsequently, the pollutants are oxidized due to the presence of the photocatalyst and precipitated on the surface of the material. Finally, the products of the reaction can be removed from the surface by the rain or by cleaning/washing with water. The photocatalysis reaction has been described in detail by the work carried out in WP1 for deliverable D1.2.

Figure 4: Schematic diagram of the heterogeneous photocatalysis produced by an anatase TiO2 layer. (Boonen

& Beeldens, 2014).

Expected efficiency from tests with individual pollutants

The photocatalytic oxidation of NO is usually assumed to be a surface reaction between NO and an oxidizing species formed upon the adsorption of a photon by the photocatalyst, e.g., a hydroxyl radical, both adsorbed at the surface of the photocatalyst. It has been shown that the final product of the photocatalytic oxidation of NO in the presence of TiO2 is nitric acid (HNO3) while HNO2 and NO2 have been identified as intermediate products in the gas phase over the photocatalyst. The resulting reaction pathway of the photocatalytic oxidation of NO has been proposed as a photocatalytic conversion of NO via HNO2 to yield NO2, which is subsequently oxidized by the attack of a hydroxyl radical to the final product HNO3:

NOads + OHads → HNO2ads


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HNO2ads + OHads → NO2ads + H2Oads

NO2ads + OHads → HNO3ads

In this study, the approach has been to monitor NO2 directly after the ceramic sample was illuminated by a source of UV light. The gas carrier was wet air (RH ~ 10%), while 1 ppm of NO2 was mixed to the gas carrier to measure its abatement. In the following figure 5, examples of the performed measurements are shown. What can be observed is the very wide reaction underwent by the sensor to 1 ppm of NO2 in wet air. Moreover, for each experiment, a clear difference between the behaviour of various samples is evident.

Figure 5: Experiment to compare the performances of all ceramic samples with PURETI coatings.

Identification of street locations for real applications The objective of iSCAPE is to verify the effectiveness of photocatalytic paints applied in real weather conditions. This latter aspect will be studied in the area of the new University Campus of the Engineering Faculty of the University of Bologna, located in via Terracini. It is an area of 3500 square meters, located in the north-western suburbs of the city (figure 3), consisting of about ten buildings, among which some exterior walls will be chosen, forming a canyon. The paints will be supplied by PURETI (www.pureti.com), partner of the project. The Lazzaretto Campus is about 3 km north-west from the city centre. It is located north of a train station railroad junction and the area is surrounded by deeply urbanized areas to the north and to the east. The Lazzaretto area is placed within the Navile district, an area subject to the Municipality’s urbanistic plan “University buildings in the Navile neighbourhood” that will be taken forward until


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the end of 2018. The aim of the plan is the construction of a residential area with more than 2000 new buildings and an extension of the Engineering Faculty with new classrooms and student accommodations that will take up an area of 25000 m2. The area is currently made up of several buildings, some commercial depots and the Engineering Faculty.

Figure 6: Aerial view of the Lazzaretto Campus site (credit: Bing).

It is hard to identify the total population of this area due to the high presence of commuters, both students and workers. In 2016, the Municipality census reported about 60000 people living in the entire Navile district, of which the Lazzaretto area is part of.

Annual climatic characterizations at street level The climatological conditions that should be taken into account during the study are: the solar radiation at the location where the photocatalytic tests will be carried out, the amount of accumulated rain in the site, because this will help in revisiting the catalyst, and the wind direction and speed because this will indicate the upstream origins of the anthropogenic pollutants. Although within the area of Emilia-Romagna a lot of public and private meteorological stations are operating as shown in Figure 7, the site with the closest proximity to the Lazzaretto campus is the weather station of Porta Castiglione (Castiglione Gate). Porta Castiglione is one of the ten gates of the old city and is located in the south-western side of the city. Since it is only 3.8 km away from Lazzaretto, it can be assumed that its measurements can be representative also for the University site. Figure 8 shows the amount of solar radiation for the area since 1 Jan 2015. The figure shows the seasonal distribution of the irradiation: it oscillates from 400 W/m2 during winter to 800-900


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W/m2 during summer. Peaks are present as well, especially in the period from April to August, when differences between the maximum and the minimum values can reach up to 200 W/m2. These spikes can be associated to the summer heatwaves that occurred during August 2015 and 2016, when temperatures in the same station reached 35 °C.

Figure 7: The meteorological sites in Emilia Romagna and the location of the Porta Castiglione weather station compared to the Lazzaretto University Campus where experiments will be carried out.

Figure 8: Three nearly complete cycles of solar radiation near the Lazzaretto University Campus. The black line

represents the rolling daily trend line.

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Figure 9 and 10 show the corresponding accumulated rain in mm and the wind speed and direction from 1 Jan 2015 till the 20 July 2017.

Figure 9: Three nearly complete cycles of accumulated rain. The black line represents the rolling daily trend line.

Figure 10: The corresponding wind speed and direction. The black line represents the rolling daily trend line.

Details of local monitoring campaigns During the summer of 2018, the Lazzaretto area will host some open-air experiments to investigate the role of photocatalytic coats in pollution removal. An approach similar to those followed for the experimental campaign in the city centre will be adopted. In particular, two street canyons will be identified between the university buildings. One area will be painted by PURETI and the other will be left untouched. The graphical representation area of this domain is indicated in Figure 11.


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Two levels of monitoring will be set up to monitor the situation and to find differences between them. In order to have a complete picture of the environmental situation, at the lower point an ultrasonic anemometer, a barometer and a thermo-hygrometer will be placed, designed to measure wind horizontal and vertical components at high frequency, atmospheric pressure, relative humidity and temperature. At the top of the street of each canyon an anemometer, a radiometer to measure the solar radiation and a LICOR, which inspects CO2 and water vapour concentration, will be installed. This last instrument allows to identify fluxes, if coupled with an anemometer. Measurements will start before the painting of the walls. This first period of measurement will enable to understand if there are differences among the two selected street canyons and to take them into account in the comparison campaign that will follow. At both canyons, measurements of air quality gaseous pollutants (NOx, O3, CO concentrations) and meteorological parameters (temperature, relative humidity, pressure and wind speed/direction) will be performed. The comparison between the measurements at the two sites will give information about the capacity of the Pureti photocatalytic coatings to capture pollutants in a real open field campaign. The campaigns will be conducted with 5 local sites for measuring local air-quality and with one local meteorological station providing measurements with at least four values registered per hour. The corresponding reference data from at least two conventional stations located nearby will be also utilised.

Figure 11: Graphical representation of the experimental area where the photocatalytic paints will be utilised.

Expected efficiency at neighborhood and street level The expected efficiency at a neighbourhood and street level was estimated through the rate of NO2 abatement of the photocatalytic tests with PURETI coatings. Although we still need to test different RH percentages and different UV irradiation to define the best abatement conditions, we already know that the ratio between the signal in NO2 and the signal in air has given


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promising comparisons even only for NO2. The following percentages of NO2 were obtained: Sample A, 17%; Sample B, 29%; Sample C, 39%; and Sample D, 26%. Finally, it could be important to test other gases (particularly VOCs) with the aim to better define the performances of PURETI photocatalytic coatings. Taking into account that the area of Bologna is characterised by the emissions shown in Figures 12 and 13, we estimate that the application of photocatalysis at Lazzaretto will improve the annual mean concentrations by between 20 and 30% at least. Of course, this is a positive abatement result that will be able to remain active for several months due to the regular rain frequencies of the area.

Figure 12: The attribution of CORINAIR emissions sources for the province Bologna from 1990 to 2010 for NOx

(in Mg). Road transport is the main emission source for this pollutant.

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Figure 13: The attribution of CORINAIR emissions sources for the province Bologna from 1990 to 2010 for non-

methane VOCs (in Mg). Road transport is not the main source of pollution for this pollutant.

5 Green infrastructure design State of the art for green infrastructure (GI) evaluation at city-scale

Vegetation surfaces such as leaves, stems, and bark serve as effective deposition sites for particulate matter (PM), and uptake gaseous air pollutants through their leaf stomata, leading to an overall reduction of pollutant concentration in the air, and improvement in the air quality. To quantify this air quality benefit due to vegetation, pollutant deposition models (such as UFORE and iTree) can be used, which calculate the pollutant deposition/uptake by vegetation based on the airflow conditions, vegetation characteristics, and pollutant concentration (Hirabayashi et al., 2011, Hirabayashi et al., 2012, Hirabayashi et al., 2015). Such models have been extensively used for assessing the air quality and health benefits of urban vegetation in the US. Based on 11 different cities it has been reported that urban trees and shrubs can help to reduce CO by 0.009%, NO2 by 2.7%, O3 by 4.4%, PM10 by 3.5%, and SO2 by 4.3% (Nowak et al., 2006); and PM2.5 by 0.24% in 10 different US cities (Nowak et al., 2013). For the whole US, Nowak et al. (2014) estimated that trees help to reduce NO2 by 0.296%, O3 by 0.514%, PM2.5 by 0.199%, and SO2 by 0.483%; thereby providing health benefits valued between $1.5–13.0 billion annually. A similar pollutant deposition modelling study was performed by Tallis et al. (2011) for assessing the effect of urban tree canopy on the removal of PM10 for the Greater London Authority (GLA). It was estimated that the current urban tree canopy (20% area of the domain) led to PM10 reduction by 1.4%, and an increase in the tree canopy (from 20% area to 30% area) would lead to a reduction of PM10 by 2.6 % in GLA. It was also suggested

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that tree plantation, with a large proportion of coniferous to broad-leaved, should be targeted in high pollution zones such as busy streets to maximize their PM10 reduction potential (Tallis et al., 2011). In addition to pollutant uptake/deposition on vegetation that provides air quality benefits, vegetation can also change the airflow and pollutant dispersion characteristics in the urban environment, which can lead to either an improvement or deterioration in the air quality. To determine the effect of vegetation on local-scale air quality, sophisticated techniques such as wind-tunnel and CFD modelling of pollutant dispersion have been undertaken. Overall, it has been found that under trafficked street canyons, the dispersion effect due to trees can have a negative impact on air quality, whereas low-vegetation such as hedges lead to local-scale air quality benefits (Janhäll, 2015, Abhijith et al., 2017). However, it is extremely difficult to conduct such studies at larger scales (such as at city and regional scale) due to the high amount of resources required to build large scale wind-tunnel or CFD models. Therefore, there exist only a handful of such studies. Jeanjean and co-workers have studied the effectiveness of trees to disperse traffic emissions by using a wind-tunnel validated CFD model to simulate a 4 km2 area in Leicester city centre in the UK (Jeanjean et al., 2015, Jeanjean et al., 2016). They reported that the concentrations of traffic-generated air pollutants reduced by 7–9% at the pedestrian height owing to enhanced pollutant dilution due to an increase in the air turbulence levels caused by trees. Barnes et al. (2014) studied the effect of varying the surface roughness of the urban environment on the pollutant dispersion characteristics, which is an indirect method to simulate the effect of vegetation (increasing the vegetation cover leads to an increase in the surface roughness). They simulated pollution dispersion in a 6.5 km2 area of central Birmingham, UK by using a Gaussian plume dispersion model ADMS-Urban. Their model results showed that an increase in the surface roughness (or increasing the vegetation cover) would lead to a reduction in the ground-level pollutant concentrations, both locally in the area of increased roughness and downwind of that area. As evident from the above discussion, urban vegetation can provide air quality benefit through a combination of the deposition and dispersion effects on air pollutants. To maximize this benefit, it has been proposed that vegetation must be (i) near the air pollution source (e.g. roadsides) since high concentration of air pollutants would lead to higher deposition rates, and (ii) close to the ground-level since it enhances the pollutant dispersion while allowing air from aloft to dilute the ground-level pollutants (Janhäll, 2015). However, there is hardly any study that has quantified the potential air quality benefits that can be obtained from such a targeted vegetation planting at the city-scale, which forms the motivation for this study. The present study is primarily targeted at accessing the potential of roadside vegetation in helping cities and boroughs in the UK to comply with the air quality standard for nitrogen dioxide (NO2) concentration around roads - the only standard that UK is currently failing to meet (DEFRA, 2017). For example, in the borough of Guildford, an estimated 52 roads will exceed the annual mean limit (40 µg/m3) for NO2 in 2017. In order to comply with the current and future air quality standards, the UK government is undertaking several nationwide programs, which


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include fleet modernisation and promoting public transportation. Despite those efforts, it is estimated that 45 and 27 roads in Guildford will exceed the annual mean limit for NO2 in 2020 and 2030 respectively (DEFRA, 2017). Since high NO2 concentrations occur in certain places due to highly localised reasons, the onus has been put on local authorities to tackle this issue through a host of innovative approaches and technologies. As discussed above, deploying roadside vegetation can be an effective way of improving air quality, and can assist the local city planners in meeting their air quality targets. Therefore, through this investigation, we will evaluate the potential of roadside vegetation in controlling the above-mentioned exceedances of NO2 beyond the regulatory limit for Guildford; this can serve as a case study for future replication amongst other cities and boroughs across the UK and Europe.

GI evaluation at city-scale - Guildford (UK)

Modelling approach To assess the city-scale benefits of new vegetation planting, we will use the integrated modelling approach suggested by Tiwary et al. (2009) that combines (i) pollutant dispersion modelling by using a Gaussian plume model (ADMS-Urban) and (ii) deposition modelling of air pollutants on vegetation by using an appropriate deposition model (e.g., UFORE or i-Tree). In this approach, we will use the following steps:

1. Develop spatio-temporal maps of the deposition velocities on the vegetation surfaces in Guildford by using the vegetation characteristics and the meteorological conditions as inputs in the UFORE/i-Tree model. These maps would be developed for the different “what-if” scenarios described in later in this report;

2. Develop high-resolution air pollution maps for the different scenarios by running ADMS-Urban, while accounting for the pollutant deposition on the vegetation surfaces through the spatio-temporal maps of deposition velocities developed in Step (1);

3. Compare the pollutant concentrations in Guildford with and without roadside vegetation under the different scenarios to assess the effects of a proposed vegetation planting strategy on air quality.

Modelled domain The integrated modelling approach described above will be used to study the air quality benefits of the existing and proposed vegetation cover for a 19 km × 26 km area that encompasses the complete Guildford borough in the UK (borough area = 270.9 km2) as shown in Figure 14. The land use in Guildford is predominantly residential, and about half of the city’s population (estimated at around 130,000) lives within the urban area of Guildford town, located in the centre of Guildford borough (GBC, 2016).


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Figure 14: Modelled domain of Guildford borough along with the major roads and buildings.

Model inputs and validation

Model inputs Road traffic is the major source of air pollution in Guildford (GBC, 2016), and the major roads (M roads, A roads, and B roads) in Guildford are shown in Figure 14. In the modelled domain, there are 16.6 km of M roads (motorways), 232.9 km of A roads, and 99.6 km of B roads; henceforth referred to as “major roads”, and 1379.0 km of local/minor roads; henceforth referred to as “minor roads”. A majority of the traffic volume passes through the major roads; whereas minor roads have relatively lower traffic volumes. In order to estimate the pollutant emissions from the roads, ADMS-Urban utilizes the EFT v7.0 developed by DEFRA (2016), which requires (i) vehicle counts, fleet composition, and traffic speed as inputs. We obtained the data for the traffic counts and fleet composition in Guildford from the Department for Transport (DfT), UK which operates ~130 traffic counters for “major roads”, and ~30 traffic counters for “minor roads”. The traffic speed on the roads was assumed to be constant, and taken to be the average traffic speed in UK: 59.2 miles/hour for M roads (DfT, 2016), 37 miles/hour for urban A roads (DfT, 2017), and 18.9 miles/hour for rural A roads (DfT, 2017). The traffic speed at B roads was assumed to be equal to that for the A roads, and for the “minor roads”, the traffic speed was assumed to be 20 miles/hour.


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Meteorological variables such as wind velocity and direction, temperature, relative humidity, and cloud cover are required to solve the transport equations in the ADMS-Urban model. The hourly data for those variables were obtained from a weather station located in South Farnborough (latitude = 51:28N longitude = 00:77W, and altitude = 65 metres), which is at a distance of 14.5 km from the centre of the modelled domain. The land-cover data for the modelled domain was obtained from the 2015 land cover map, which is produced by the Centre for Ecology and Hydrology (CEH), UK based on satellite imagery and digital cartography at a resolution of 25 m (Rowland et al., 2017).

Model validation Model validation will be performed by comparing the model results for the annual mean NO2 concentration in Guildford in 2015 (2015-BASE scenario described) with the corresponding concentrations at 17 different sites in Guildford, as measured by the Guildford borough council (GBC, 2016). Those measurements include roadside, urban background, and rural background concentrations of NO2.

Modelled scenarios for "what if" analysis In order to evaluate the benefits of planting roadside vegetation in Guildford vis-à-vis reducing the roadside NO2 concentration and complying with the relevant standards, as discussed in Section 5.1, we will investigate different scenarios with and without roadside vegetation for the years 2015 and 2039 as described below. The year 2015 has been chosen to represent the current situation in Guildford since data for the model inputs is freely available for this year. The year 2039 has been chosen since 2040 is the year when the strategic road network (SRN) of UK aspires to have zero breaches of road-side air quality (DfT, 2015) and the UK government will end the sale of new conventional petrol and diesel cars and vans (DEFRA, 2017). This means that the end of year 2039 would mark a radical shift towards zero-emission vehicles, and therefore year 2039 is an ideal year for studying the impact of planting road-side vegetation on avoiding the ongoing breaches in air quality standards near roads. 2015-BASE: This is the baseline case for the year 2015 with the currently estimated vegetation cover on the major roads. 2015-BASE-NoRV: This is a hypothetical scenario for the year 2015, which assumes that there does not exist any roadside vegetation. By comparing this scenario with the 2015-BASE, we will be able to estimate the air quality benefits provided by the existing road-side vegetation in Guildford. 2039-BAU: This is the business as usual scenario for the year 2039, which assumes that the traffic and fleet composition have changed, while the roadside vegetation remains at the same level as a 2015-BASE scenario. By comparing this scenario with the 2015-BASE, we will be


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able to estimate the air quality benefits provided by the existing road-side vegetation in Guildford for the year 2039. 2039-MaxRT-Con: This is an alternative scenario for the year 2039 with the maximum possible coniferous tree cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting coniferous trees along all major roads. 2039-MaxRT-Dec: This is an alternative scenario for the year 2039 with the maximum possible deciduous tree cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting deciduous trees along all major roads. 2039-MaxRH-Con: This is an alternative scenario for the year 2039 with the maximum possible coniferous hedge cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting coniferous hedges along all major roads. 2039-MaxRH-Dec: This is an alternative scenario for the year 2039 with the maximum possible deciduous hedge cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting deciduous hedges along all major roads. Thus, through systematically studying the seven scenarios outlined above, we will estimate the air quality benefits of planting trees or hedges along major roads in Guildford, and estimate the potential for reductions in the exceedances of the NO2 limit value in the year 2039.

SWOT analysis for Guildford From our SWOT analysis, Table 4, it becomes clear that GI interventions have a positive effect on the city-scale air quality and provide other benefits as well including energy savings in buildings, avoiding a storm-water runoff, urban heat island mitigation, and carbon sequestration. However, integrating GI practices into city planning is a complex task, and can often compete with other high-priority development activities such as housing and road construction. There also seems to be a lack of understanding about the benefits of GI interventions in the public, which forms the main weakness and poses considerable threats to the widespread adoption of GI practices. Despite this lack of understanding, GI practices are generally perceived positively by the public, and opportunities exist for city planners to retrofit existing built areas or design newly built areas while adopting GI design practices.


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Strengths Weaknesses

• Sufficient evidence exists showing that GI interventions have a positive effect on air quality.

• Air quality benefits obtained are for a long-term and sustainable.

• GI interventions are aesthetically pleasing and are perceived positively by the public.

• GI interventions provide other benefits such as energy savings in buildings, avoiding storm-water runoff, urban heat island mitigation, and carbon sequestration.

• Design guidelines for deploying GI interventions are complex or unavailable.

• Implementation of GI in existing built areas can be challenging due to technical and space requirements.

• Short-term quantification of Air quality benefits obtained from deploying GI interventions are difficult compared to obtaining them for long time durations.

• Lack of widespread public knowledge about the benefits.

• Green roofs and walls have high deployment and maintenance cost.

Opportunities Threats

• Tree plantation is being increasingly recognised as an important measure to combat air pollution and climate change.

• Retrofitting GI interventions in existing built areas is possible and can be promoted by raising awareness or various incentives.

• Increased adoption of green roofs and walls in building design will likely reduce their running energy cost.

• The iSCAPE project can provide general recommendations for Guildford Borough Council on the implementation of GI interventions in the built environment.

• The results of the iSCAPE experiments may be used by Guildford Borough Council to correctly plan the deployment of new GI interventions.

• Poor design of GI interventions can lead to air quality deterioration in certain situations.

• Not well integrated into city planning practices.

• GI interventions often compete with other developmental activities such as housing and transportation.

• Often viewed as less important than other development activities.

Table 4: SWOT analysis of green infrastructural (GI) interventions for obtaining city-scale air quality benefits.

As evident from the SWOT analysis, deploying GI practices have a multitude of benefits for a city; however, quantifying them poses a challenge. Through this investigation, we plan to


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quantify the air quality benefits of deploying road-side GI interventions by simulating the different model scenarios discussed in Section 5.2.4 by using Guildford as a case study. From the modelling results, we will demonstrate the potential of deploying roadside vegetation in curtailing NO2 exceedances for the year 2039, thus providing the necessary impetus for city planners in Guildford towards adopting such GI practices.

GI evaluation at city-scale - Bologna (IT) The frequency and types of urban green areas in major Italian cities can be inferred by data provided from ISTAT (National Institute of Statistics). In particular, for the year 2011, urban green areas represent 2.7% of territory of the provincial capitals (more than 550 million m2). Protected areas represent 14.8% of municipal areas, while the Utilized Agricultural Surface (SAU) is equal to 45.5% of the territory. According to the National Institute of Statistics, every resident has an average of 30.3m2 of urban green spaces (Figure 15).

Figure 15: (a) Comparison with the national average for urban green areas per capita (m2) in major Italian cities; (b) density of urban green areas in the macro-zones of Italy; (c) Availability (green) and density of urban green

areas (grey) in cities with more 200.000 inhabitant or metropolitan areas (ISTAT, 2011).

ISTAT defines 43 provincial capitals with a "green profile" which are characterized by a: significant endowment of urban green spaces (19 cities), natural protected areas (11 cities), or agricultural use areas (11 cities), while only two cities have all the three above characteristics. Only in 15% of the provincial capitals, urban green areas are equal to or greater than 50 m2 per capita, while in 17.7% they do not exceed the threshold of 9 m2 per capita. Focusing on the relation with the national average, about a fifth of cities have higher density and availability


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values than the national average (such as th cities of Sondrio, Trento, Potenza and Matera); while half of major Italian cities are characterized by having both indicators lower the national average (with more than 70% of these located in the South). Bologna, together with 24 other Italian cities, is characterized by having both values below the national average values of green areas (ISTAT, 2011). Over 60% of the cities has a lower average value of urban green area density, while in 25.8% of cities urban green areas show an impact on the overall surface of the town of more than 4%, and in nine cities green areas cover more than 10% of the territory. Figure 15.b shows the square meters of green areas per capita in Italian cities divided by macro-regions. The lowest value is detected for cities in the Centre of Italy (23 m2 per inhabitant) and in North-West (24.3 m2). In North-east cities, average values are almost double compared to those just mentioned (45.4 m2 per inhabitant). Finally, values are comparatively high in the South (37.1 m2) and slightly higher in the Islands (26.7 m2). Continuing to consider the division by macro-areas, more than half of the main cities of the North-west are characterized by higher ‘green areas density’ value of more than 4%, while in the North-east 36% have a higher value. In the Centre and in the Islands, more than 80% of cities have lower ‘green areas density’, with only 13.6% and 9.5% of cities having a value greater than 4%. Finally, main cities in the South are placed in an intermediate position, with an allocation of 15.4%. Examining the different types of urban green areas shows specific characterizations in different urban contexts. Specifically, historical green areas, such as parks, villas and private gardens, represent a third of all urban green areas, ‘public service green areas’ represent 15.9%, lastly street furniture account for 9.4%. The types listed above account for 60% of all the green areas in Italians capitals. Other types affect so much less significant, accounting for only 10% of the urban green detected and specifically: sports areas (3.8%), school gardens/courtyards (3.4%), ‘urban forestry areas’ (2.4%) and urban gardens (0.2%). The remaining “other” urban green spaces (about 30% of the total) include botanical gardens, zoos, cemeteries, green fallows and other green surfaces that do not coincide with the classes mentioned above. A type of green growth spreading in cities are the "urban gardens", set up in around 44 administrations. While in 58 municipalities urban green areas include "botanical gardens" (Verde Urbano, ISTAT, 2011). For a more in-depth analysis of green areas in the city of Bologna, focusing on the types and amount of green areas in urban and suburban areas, one needs to take into account that the city of Bologna, as its neighboring cities, is a metropolitan city. This analysis is carried out within the area covered by Bologna, Sasso Marconi, Casalecchio di Reno, Zola Predosa, Castelmaggiore, Calderara di Reno, Granarolo dell'Emilia, Castenaso, San Lazzaro di Savena, Ozzano dell'Emilia and Pianoro, considering continuous urban residential and commercial patterns. The boundaries of the urban ecosystem are shown in Figure 16. The area runs along the cardinal directions (Emilia, San Donato, San Vitale, Futa and Porrettana), and by including


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neighboring municipalities, has a total surface of 279 km2. Within this urbanized area there are some agricultural areas cultivated with arable crops and fruit trees. To these, are added, to the south, forest areas dominated by downy oak (Quercus pubescens) and plants of coniferous. In the urban area, the green areas are represented by parks, trees, bushes, private gardens. The urban ecosystem of Bologna has an area of approximately 279 km2, for a total of 521,840 inhabitants and includes the areas of Bologna city, Sasso Marconi, Casalecchio di Reno, Zola Predosa, Castelmaggiore, Calderara di Reno, Granarolo dell'Emilia, Castenaso, San Lazzaro di Savena, Ozzano dell'Emilia and Pianoro. The district of Bologna is considered a "metropolitan city" through the 142/1990 Law, which sets out the principles of ordinance of municipalities and provinces and determines its functions. From data elaborated the net green surface, excluding green road areas, is 185 km2, or 66% of the totalarea. Nearly two-thirds of these are agricultural lands, situates mostly in the plain, and therefore cannot be placed at the same level as public parks, which by definition are accessible to all. Eliminating from the count the agricultural areas shows that greenery covers 24% of the territory of the urban ecosystem and on average every inhabitant has 131 m2 of green (Table 5).


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Figure 16: Bologna urban ecosystem. Map obtained by Quickbird satellite

Type of GI Surface (m2)

Tree crowns and bushes 38,624

Meadows, flowerbeds and courtyards 29,691

Agricultural green 116,233

Total 184,548

Table 5: Areas occupied by GI in the area of Bologna metropolitan city.


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Figure 17: Bologna urban ecosystem. Results obtained by the GI classification.

Methodology for GI evaluation at city-scale assessment for Bologna (IT)

Two methodologies are described here, one experimental, based on satellite imaging, and one theoretical, based on city-scale numerical codes. A geographic information system was created containing all vector and raster information, such as road mapping, buildings, public green cards, hydrography, the map of Bologna with fake colors and the green map in three classes, with the DEM used to calculate slopes. The multispectral image data obtained by the Quickbird satellite, used for the green classification,


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has been compared with the data of the pancromatic Emilia-Romagna image obtained in 2002-2003 by the Emilia-Romagna Regional Authority. The satellite collected panchromatic (black and white) imagery at 61 centimeter resolution and multispectral imagery at blue range (450-520 nm), green (520-600 nm), red (630-690 nm), near-IR (760-890 nm). Given the high resolution of the images, at a methodological level, by green area we mean any surface on which vegetation is present, both herbaceous or woody, public or private. This kind of generalization makes it possible to take full advantage of the potential of the classification algorithms, even on highly fragmented surfaces of small sizes. As agricultural areas are heterogeneous, due to the presence of various classes of land (from arable land / cultivated meadows to plowed land) and given the objective difficulty of discriminating plowed soil or turpentine at the multispectral level, it was decided to mask these variables with the vector coverage of regional soil use in 2003 and re-insert them into the classification. The use of soil in 2003 was achieved with the photo-interpretation of pan-chromatic Quickbird images, acquired at the same time as the multispectral data. This expectation, with a minimum cartographic unit of 1.56 ha and a working scale of 1: 25,000, does not detail the smaller sized green areas that are embedded in the urban fabric. Two classes of GI have been considered:

• tree crowns and bushes, forest areas and sports areas;

• meadows, flowerbeds and courtyards. These two classes represent quite well the reality of greenery in Bologna, which is characterized by the presence of a large tree-lined network and by parks and yards with woody vegetation pools and meadows on compacted clay soils, heavily cracked in the summer season. It was not possible to discriminate more classes, such as hardwood conifers, because with the four bands available, it is not possible to obtain a separability between the various species. For the purposes of classification, the SpectralAngleMapper algorithm was chosen. The first result obtained with 0.1 rad thresholds has undergone the high ability to discriminate against green. It was interesting to note that in some areas of the mosaic image, it was possible to classify the green roof. At the same time, however, there are false classifications on the roofs due to very hybrid spectral responses. Finally, it was decided to mask the areas affected by buildings, using the vector coverage provided by the SIT Office of the City of Bologna. With this operation, about 54 hectares of greenery have been eliminated, consisting of hanging gardens, building blocks and false classifications in proportion not to be reckoned but with a prevalence of false classification in the historic center and building blocks in suburban areas.


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Subsequently, to improve the preliminary result obtained, they two different thresholds were applied:

1. 0.2 rad threshold for GI class 1. 2. 0.1 rad threshold for GI class 2.

In the two classes identified, information on previously masked agricultural areas was added. The classification was subsequently vectorized and uploaded in a GIS environment for overlapping with the area boundaries and population data. The second methodology is based on the simulations by means of CFD model ENVI-met, together with a non-dimensional model ADMS-Urban. ENVI-met is a prognostic non-hydrostatic model composed by a three-dimensional main model and one-dimensional Atmospheric Boundary Layer model. It uses Reynolds-Averaged Navier-Stokes equations combined with the advection-diffusion equation using the standard k-εmodel. The model solves those basic equations forward in time by simulating wind field modification due to buildings, roads and vegetation. The inlet profile and top model conditions are obtained from ADMS-Urban one-dimensional model and zero-gradient condition is used for the output profile. Vegetation model is treated as a 1D column and each plant is distinguished for its LAD (Leaf Area Density) and RAD (Root Area Density). Vegetation is an active element of the model in terms of evapo-transpiration processes, shadow and drag effects. Temperature of the ground surfaces is calculated from an energy balance of the net radiative energy fluxes, turbulent fluxes of heat and vapour and soil heat flux, while the temperature of building facades is computed by taking into account the heat transmission through walls and roofs. The differential equations are solved on a staggered grid system using the finite difference method. It is worth noticing that ENVI-met cannot capture meteorological variations of wind temperature induced by large scale variation of meteorological conditions. This is because the diurnal evolution of temperature and wind field is based on initial conditions only. This feature implicitly limits the comparison with field measurements to those days characterized by weak synoptic forcing and stationary weather conditions.


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SWOT analysis for Bologna The following table 6 presents the SWOT analysis for the Bologna intervention.


• The methodology employs both experimental data as well as model simulations

• Simulations through models of the ADMS modelling family (ADMS-Urban and ADMS-TH) gives results for urban pollutant concentrations and temperature distribution with a good accuracy

• From ADMS results it is possible to obtain distributions that can be input values for numerical codes working at neighborhood scale (ENVI-met, Fluent)

• Urban air temperature is sensitive to urban morphology, whose influence is not captured by models.

• Misinterpreted buildings albedo may interfere with regular energy propagation


• It will be possible to test the reliability of this approach by comparing numerical results with data from local measurements

• The results of the simulations may be used by the Municipality of Bologna to correctly plan the deployment of new interventions

• The results of the simulations may be used to provide general guidelines about the implementation of new interventions concerning green infrastructure

• Measured local values of energy balance terms are needed to apply ADMS

• The actual general urban city plan will definitely depend on other aspects not considered in the simulations

Table 6: SWOT analysis of the Bologna intervention.

Despite the weaknesses highlighted in this SWOT analysis, this methodology involving both experimental data and simulations was chosen to have a comprehensive method for modelling air quality and temperature distribution at city scale but considering also complex urban morphologies including street canyons. In addition, the ADMS-Urban model is being used across the world for air quality management and assessment studies of complex situations in


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urban areas, cities, towns and close to motorways, roads and large industrial areas, in particular also by local authorities, which can be useful to present the results to stakeholders.


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GI evaluation at city-scale - Vantaa (FI) About 78% of the surface area of Finland is forest; this makes Finland the most forested country in Europe and the number 10 in the world. Furthermore, inland water areas comprise about 10% of the surface area. These numbers suggest that green is present practically everywhere in Finland, from small towns to the largest cities. According to the Finnish Environmental Institute (SYKE), the Finnish cities typically have more green areas than other European cities (Fig. 18). As is the case in other Nordic countries, also in Finland anyone can enjoy the nature no matter the ownership of the land (so called, “every man’s right”).

Figure 18. Percentage of green areas in some European cities. Adapted from https://suomifinland100.fi/satisfaction-with-green-area-is-the-highest-in-finland/?lang=en.

Based on the statistics of 2014-2015 (Tajakka, 2016), recreational land and water areas and private estates consist of nearly 10 million hectares in total (Table 7). Finnish municipalities have on average of about 630 hectares of green infrastructure; this figure includes parks and traffic (road side) infrastructure (statistics of 2014, Tajakka 2016). The city of Vantaa, located in the capital region of Finland, is growing rapidly in population especially due to its excellent logistical position; for example, the main Finnish airport is located in Vantaa and all the main highways and railways pass through it. The surface area of the city is about 240 km2, and the population is about 225 000 (status in 2018); with respect to the population, Vantaa is the fourth largest city in Finland. In Vantaa, the green infrastructure equals to about 3000 hectares (including road side green infrastructure), and about 97% of the population lives within 300 m from any green infrastructure (Vantaa, 2018).


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Green infrastructure Surface area (hectares) Note

Traffic (road sides) 15

Recreational (land) 3,9 million

Recreational (water) 3,2 million

Historical/traditional environments

479723 The sites taken care of by Metsähallitus Forestry Ltd and agricultural sites that have received EU-funding.

Public estates 4080 Estates owned by the government.

Municipality parks 149610 Parks belonging to the A- and B-class circle.

Municipality forests 430117

Cemetaries 3993 Those of the Lutherin Church.

Golf courses 8580

Private estates 2,34 million Small residential buildings, row houses and apartment buildings.

Table 7. Statistics of green infrastructure in Finland 2014-2015.


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Figure 19. The percentage of green in the city districts of Vantaa. From Mäkynen (2017).

Recently, Vantaa has made together with its citizens a “Future visions of Vantaa city planning” survey (see a short cartoon video at https://vantaakanava.fi/tulevaisuuden-vantaata-tehdaan-nyt/, in Finnish). A complete report in Finnish is also available (Vantaa, 2018). In the project, city stakeholders activated the citizens to give feedback of how they see their city in 10, 50- and 100-time frames (i.e., in 2027, 2067 and 2117). One part of the survey was related to the environmental aspects and green infrastructure. There were a total of 850 respondents in the survey. In the following, we summarize some of the main findings from the survey based on all responses (the original statistics include also information on gender- and age-based results). Overall, citizens of Vantaa clearly appreciate the amount of green infrastructure. For example, 88% of the respondents consider the increase of green infrastructure as desired (Fig. 20), while the decrease of quiet and natural areas in a more compact city is not desired (79% of the respondents), (Fig. 21).


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Figure 20. The respondents view on the increase of green infrastructure in the future Vantaa.

Figure 21. The respondents view on the decrease of quiet and natural environments in the future Vantaa.

88%

10%

2%

The amount of green is increased in the urban areas. For example, the amount of green roofs and walls as well as

urban cultivation increases.

DesiredNeutralNon-desired

6%

15%

79%

When the city gets more compact, the amount of quiet and natural areas decreases.

DesiredNeutralNon-desired


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Figure 22. Respondents view on the type of desired nature and recreational areas in Vantaa in a ten-year time frame.

Figure 23. Respondents view on what they appreciate in nature and recreational areas.

In a 10-year time frame, forests and parks are enjoyed the most (Fig. 22). Also, in the nature the peace and quiet as well as clean air are well-appreciated (Fig. 23).

0% 20% 40% 60% 80% 100%

Close by forests

Parks

Sport and activity parks

Nature conservation areas

Open fields and meadows

Playgrounds

Other

What kind of nature and recreational areas you would like to exploit in Vantaa ten years from now?

0% 20% 40% 60% 80% 100%

Peace and quiet

Clean air

Easy accessibility

Natural diversity

Possibility for physical exercising

Beauty

Mushrooms, berries etc

Other

In nature and recreatioanal areas I appreciate the most…


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Methodology for GI evaluation at city-scale assessment for Vantaa

In the socioeconomic impact assessment conducted in WP5, we will assess the benefits of green infrastructure (GI) based on the modelling results from SURFEX simulations and the thermal comfort index (TCI) calculations. The instruments used as part of the methodology in Vantaa give exact information regarding the local climatic conditions as well as air quality in the specific regions (see Table 14), which is then used, for example, for the verification of the SURFEX-modeling. The instruments represent different urban environments (urban natural and urban built). Our approach in Vantaa for the socioeconomic assessment is to study the total value that people attach to all ecosystem services (ES) provided by the simulated GI. In this case, the benefits are not categorized based on ES but rather on the value of the total package being analysed, also including other spatial characteristics of the urban economy and the built environment. Our chosen method for this is the hedonic pricing method, in which the observed prices in residential real estate markets are used to analyse the willingness to pay for the proximity of properties to a given ecosystem (see Votsis 2017 for an application to Helsinki’s municipality). The willingness to pay for different GI types is an indicator of the economic impacts of different GI interventions. We have an existing database of real estate transactions in Vantaa for the years 1970-2011 which provides a detailed picture of the housing market in Vantaa and the Finnish Capital Region, and the years 2000-2011 are coupled with a GIS-database of the existing GI and other urban characteristics. We will analyse the value of the current ecosystems and simulate the changes resulting from the simulated GI. The hedonic pricing method will be complemented with 1) the impact pathway approach to assess the air quality benefits of GI, 2) energy saving calculations to assess the energy saving potential of GI, 3) aesthetic value calculations to assess the aesthetic value of GI (already partly contained in the hedonic calculations), and 4) earlier green roof benefit simulations to assess the value of proposed green roof implementation (see Nurmi et al. 2016 for an application to Helsinki’s centre). The urban climate of Vantaa and the effect of interventions under current and altered conditions are to be simulated using the SURFEX atmosphere surface interaction model (Masson et al. 2013) at scales ranging from the neighborhood to the city scale, yielding estimates of the urban climate itself, as well as the anthropogenic usage of energy for heating and cooling of building space. While the most straightforward and reliable method of simulation would be to conduct extended integrations of a high-resolution climate model, representing the present and altered climates, the computational cost involved would be far too high for the current project. Instead, an alternative procedure, based on the concept of climatically representative periods, will be followed. The starting point will be a set of 12 monthly periods, chosen so as to represent a typical year for the period 1980-2009 in terms of observed temperature, humidity, insolation and wind speed (Jylhä et al., 2011). High-resolution hind casts will be produced for these


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months using the Harmonie-AROME NWP system (Bengtsson et al., 2017), and data necessary to apply SURFEX will be extracted from the hind casts. By using this data, the effects of various interventions on the urban climate will be established by configuring SUREFX accordingly. Altered climatic conditions will be taken into account by modifying the atmospheric forcing data extracted from Harmonie-AROME according to chosen representative carbon pathways (RCPs).


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SWOT analysis for Vantaa The following table 8 presents the SWOT analysis for the Vantaa intervention.


• Able to estimate evidence-based real-world changes (in urban economic behavior and socioeconomic impacts) resulting from GI rather than hypothetical changes (stated, for instance, in interviews)

• As people see GI as a bundle of goods rather than a list of ecosystem services, the hedonic pricing method is the right way to approach the socioeconomic value of GI, especially in a spatially explicit urban planning context.

• Complementary analysis of the value of thermal comfort index, air quality changes, aesthetic value and energy saving is able to break down the value of GI into components.

• Some market imperfections, relating notably to imperfect and asymmetrical information in the housing market, may result in the estimated value being lower than the sum of all the benefit components.

• We are not able to distinguish between which specific ecosystem service the value is based upon.

• In cases where the GI intervention (or its impact) is extensive, the equilibrium is different after the simulation, so in principle the marginal values could change as well.

Opportunities Threats • The method can offer comparison values

of alternative GI options or interventions and provide interested stakeholders such as the local government, with recommendations on how the GI should be implemented optimally;

• The method can highlight what kind, type and amount of GI is optimal at a given location, as well as what kind of fiscal policies are needed to make a GI investment economically feasible and sustainable.

• Simulation results may not describe the real changes after such GI implementation. For this reason, we will perform the aforementioned complement approaches as well, and compare the results obtained with each approach.

Table 8: SWOT analysis of the Vantaa intervention.


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6 Green infrastructure evaluation at neighborhood scale State of the art for GI evaluation at neighborhood-scale

Near-road environment Numerous exposure assessment investigations analysed pollutant concentration distribution in the near-road environment and findings from these studies are summarised in a number of reviews (Karner et al. 2010; Pasquier and André 2017). The near-road pollutant concentration levels are affected by distance to the road, road configuration, meteorology, and adjacent infrastructure geometries such as noise barriers and vegetation. In general, the concentration of pollutants in near road environments decrease with the distance from the road. Decay in concentration of pollutants with distance from highway can be rapid, gradual or no trend irrespective of their reactiveness. For example, inert pollutant CO and reactive ultrafine particles (UFP) show rapid decline in their concentration. Whereas gradual decrease with distance is observed in elemental carbon (EC, inert) and PM10 (reactive) while no trend is noticed in PM2.5 concentration (Karner et al. 2010; Pasquier & André 2017). Depending up on the type of pollutant, concentration reaches close to background levels by about 80m to 600m from the road (Karner et al. 2010; Pasquier & André 2017; Patton et al. 2017). Apart from distance to the road, specific road ways characteristics such as at elevated, at-grade, depressed roads can also influence the pollutant concentration distribution near highways (Patton et al. 2014; Baldauf et al. 2013; Steffens et al. 2014). Moreover, meteorological conditions affects near-road pollutant concentrations (Pasquier and André 2017). When wind direction is perpendicular to the road, i.e. the wind flows from the road to the nearby areas, pollutants reach longer distance in downwind compared to winds parallel or inclined to the road (Karner et al. 2010). Lower concentrations are observed with high wind speeds and the opposite with low wind speeds (He and Dhaniyala 2012; Zhang et al. 2015). In addition, stable atmospheric conditions in winter seasons induce higher pollutant concentrations as opposed to a decrease under relatively unstable summer periods (Padró-Martínez et al. 2012; Barros et al. 2013; Pasquier and André 2017). Apart from the above-discussed elements modifying near-road air quality, also noise barriers and vegetation along the highways, strongly alter the pollutant concentration profile in the immediate region (<50m) from the traffic emissions. Noise barriers reduced pollutant concentration behind the barrier by 50% compared to without a barrier (Hagler, Tang, et al. 2011; Hagler et al. 2012; Finn et al. 2010). Whereas, vegetation near highways reduced the concentration of pollutants in the adjacent downwind region by 15% to 60% compared to a clear area without vegetation (Abhijith et al. 2017). These infrastructural solutions are effective in


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reducing pollutant concentration in the most important exposure zone, which is 50m from the road. In this zone, the concentrations of pollutants remains more than 50% of the on-road levels (Pasquier and André 2017; Cahill et al. 2016; Karner et al. 2010; Baldauf et al. 2013; Patton et al. 2017). Vegetation along the highways consisting in trees as well as other vegetation types such as hedges, shrubs and bushes form a barrier to the pollutants reaching nearby areas and these are referred to in studies as ‘vegetation barriers’ or green belts (Brantley et al. 2014; Islam et al. 2012; Morakinyo & Lam 2016). Green infrastructure acts as a barrier for pollutants released from the roads to reach adjacent areas. This creates a high pollutant concentration zone between road and vegetation, forcing pollutants to loft over vegetation. Thus, vegetation barriers help in vertical dispersion and dilution of pollutants before reaching downwind region. The barrier effect of green belts depends upon meteorological parameters, and physical characteristics of the green belt such as position, thickness, height and porosity (Abhijith et al. 2017; Baldauf 2017). Several studies have investigated pollutant reduction by green infrastructures along highways; they adopted modelling (Morakinyo & Lam 2016; Neft et al. 2016), experimental (Fantozzi et al. 2015; Al-Dabbous & Kumar 2014; Brantley et al. 2014; Hagler et al. 2012) or combined modelling and experimental (Morakinyo et al. 2016) methodologies for assessing pollutant reduction potential of various types of vegetation. Overall, most of the studies reported a positive effect of vegetation on reducing air pollution, however some investigations observed mixed and negative effects in pollution abatement (Abhijith et al. 2017). Among the physical characteristics of the green belt, thickness and density are the main factors determining in lowering near-road pollution exposure. The increase in thickness as well as the density of vegetation results in decrease in pollutant concentration (Tong et al. 2016; Chen et al. 2016; Shan et al. 2007). Studies identified a thickness of 10m for 50% reduction of pollutant concentration (Neft et al. 2016; Shan et al. 2007). However, other vegetation parameters such as height and spacing of vegetation, leaf thickness, and the presence of hairs or wax on the leaf surface also influence pollutant reduction by green infrastructure. Wind speed and direction, humidity and temperature affect neighbourhood air quality in near road environment. For example, the highest reduction is observed in perpendicular wind direction (Brantley et al. 2014). Evergreen trees are preferred to provide pollutant reduction feature in all seasons along roads (Baldauf et al. 2013). Baldauf (2017) listed general recommendation for planting vegetation barriers along highways for improving air quality in near road environments. Although previous investigations have quantified pollutant reduction by various green infrastructures, they have considered either one or two pollutants or vegetation types. Similarly, the recommendations by Baldauf (2017) requires further refining for better practical implementation. For example, the 10m thick vegetation barrier may not be feasible in many near-road situations. In addition, earlier studies are inadequate in understanding the dilution/dispersion and deposition components of pollution reduction by vegetation. Overall,


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detailed quantifications of individual green interventions are required to enrich existing knowledge to overcome the above-discussed limitations. This report proposes a methodology to: (i) quantify air pollution reduction potential of different vegetation barriers such as hedges, trees, and mixed vegetation, (ii) study effects of vegetation characteristics on air pollution removal, (iii) evaluate and compare the effect of vegetation on reducing various regulated (PM2.5, PM10, CO) and unregulated pollutants (UFP, black carbon, BC), (iv) study the impact of meteorology on near road environment, and (v) provide insight on dilution and dispersion component of vegetation induced pollutant reduction. We focus on the following pollutants as a part of this work: UFP, PM1, PM2.5, PM10, CO, and BC. These pollutants have different decay characteristics with distance from the road. CO and UFPs have rapid concentration decay with distance, whileBC and PM10 have gradual decay. PM2.5 has no trend in concentration variation with distance from the road. These pollutants represent each classification based on pollutant concentration decay with distance, and reactiveness in near road environment (Pasquier and André 2017; Karner et al. 2010).

Methodology for GI evaluation for neighborhood-scale assessment for Guildford

Site description In this study, we have considered three vegetation configurations: (i) trees, (ii) hedges and (iii) mixed vegetation barrier with trees and shrubs. The study evaluates air pollution reduction potentials of these three types of vegetation with monitoring locations selected based on type of green infrastructure present near highways. Six sites have been identified near major roads of Guildford town and detailed in Table 9. Guildford town is a highly populated area in Guildford borough, which is a part of Surrey County (Surrey-i 2015). Guildford Borough has a population of 137,183 (Surrey-i 2015). The most popular mode of transportation is the car that represents about 72% of trips to work and 42% of commutes to school (Al-Dabbous & Kumar 2014). For assessing the impact of green infrastructure distance from the road, we have selected two sites for each vegetation type. One of them is close to the traffic (≤1m) while the other is away from the road (≥2m). Figure 24 the shows schematic diagram of the monitoring sites. In particular, these include Aldershot-Hedge and Aldershot-Tree sites that are along the same road; they are approximately 200m away from each other (Figs. 24 a, c). The green infrastructure on Aldershot sites is close to the traffic emissions. The sites are situated in a residential area with double story houses on either side of a two-lane road. Similarly, the Sutherland-Tree site and the Sutherland-vegetation barrier site are 100m apart from each other and are next to a recreational park near a four-lane road (Figs. 24 d, e). The vegetation in Sutherland sites is away from traffic emissions. The Stoke Road-Hedge site is next to a children’s play area adjacent a two-lane street and the hedge is away from traffic emissions (Fig 24b). The vegetation barrier site at


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Shalford is next to a public park and a busy two-lane traffic road is close to the barrier. Average traffic volume and direction of roads at each site were counted and are provided in Table 9. Dimensions of green infrastructure, distance from edge of road to monitoring point, and width of lanes are depicted in Figure 24. We are aiming to quantify the pollutant reduction potentials of different vegetation by comparing the concentration levels of clear areas and behind vegetation. Moreover, the statistical analysis of data collected during campaign can give some insight on the impact of meteorology and vegetation characteristics on pollutant removal.

Site Name with type of vegetation

Name of the road, number of lanes, width of the road and direction

Average hourly Traffic volume per hour

Vegetation type

Distance from road

Vegetation barrier attributes L: Length W: Width H: Height

A. Aldershot-Hedge A323 2 lanes ~ 7m E-W

750 Hedge 1m L:36m W:1m H:1.2m

B. Stoke park-hedge A320 2 lanes ~7m

1200 Hedge 2m L:~36m W:~1.2m H:~2m

C. Aldershot-Tree A323 2 lanes ~ 7m E-W

750 Tree 1m L: ~40m W:~6m H:~ 10m

D. Sutherland-Tree A3100 4 lanes ~13m NW-SE

1650 Tree

3m L: ~50m W: ~9m H: ~7m

E. Sutherland- vegetation barrier

A3100 4 lanes ~13m NW-SE

1650 Trees and hedge

3m L: ~40m W:~7m H: ~5m

F. Shalford-vegetation barrier

A281 2 lanes ~ 7m N-S

1200 Trees and hedge

1m L: ~66 m W:~3.5 m H: ~4 m

Table 9: Details of six monitoring locations.


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Figure 24: Schematic representations of six monitoring locations with the type of vegetation and road details.

The orange circle and black ring denote measurement point behind and in front of the vegetation barrier, respectively.

Instrument setup In this work, we have monitored PM1, PM2.5, PM10, PNC, BC and CO. Two GRIMM aerosol monitors, model EDM 107 and 11-C measured PM1, PM2.5, and PM10. The instrument provides particulate matter concentrations on 31 different channels at 6 second time resolution. In addition, particles collected on a PTFE filter in the GRIMM allows for chemical and morphological exploration. Two PTRAK 8525 (TSI Inc.) are employed to measure PNC in the size range of 0.2 to 1 μm. In this study, we set PTRAK to record PNC values at every 6 seconds. BC concentrations are collected using a couple of MicroAeth AE51 (Aeth Labs) with time averaging period of 10 seconds. Attenuation generated due to instrumental optical and electronic noise is rectified by post processing the data with Optimised Noise-reduction Averaging algorithm (ONA; Hagler, et al. 2011). CO and CO2 are monitored in ppm with two QTRAK 7575 (TSI Inc.) having a time base of 6 seconds. Local meteorological conditions (air temperature, relative humidity and wind speed and direction) are logged by a portable weather station Kestrel 4500 at 1-min resolution. All instrument data is averaged to 1 minute to match with the wind data. Traffic counting is performed by using the SMART Traffic Counter App developed by University of Wollongong, Australia.


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Experimental protocol One set of instruments (includes GRIMM, PTRAK, QTRAK, and MicroAeth) are mounted on a tripod stand at 1.5m height. Three monitoring sites, namely A, C, and F, have a clear area (without any disturbance to air flow) adjacent to the green infrastructure. On other sites, B, D, and E the green intervention is continued leaving no clear area (without any vegetation) along the same road. Due to this reason, one of the instruments set up is placed in the clear area and the remaining one is positioned behind the green infrastructure on sites A, C, and F. Both tripods are located equidistant from the road. Whereas, on sites B, D, and E tripods are held in front and behind the vegetation. The portable weather station is always attached to the tripod in the clear area or in front of the vegetation. The campaign is designed to conduct 5 days of monitoring per site, making a total of 30 days. The field measurement is started and ended around 08.00 h and 18.00 h (local time), respectively. This enables to collect 8 to 10 hours of data every day. Inter calibration between each set of instruments is achieved by running instruments side by side for 20 to 30 min prior and finishing the measurements as shown in Figure 25. No field campaigns are carried out on rainy days. Traffic is counted in the first 20 minutes of an hour.

Figure 25: Instruments are mounted on tripod and kept close to each other during inter-calibration. In the figure,

1) GRIMM aerosol spectrometer, 2) PTRAK 8525, 3) QTRAK 7575, 4) MicroAeth AE51, 5) weather station Kestrel 4500.

Methodology for GI evaluation for neighbourhood-scale assessment for Bologna

The characterization of Green Infrastructure (GI) in Bologna takes place with two intensive field campaigns, one in the summer and one in the winter. In this work, influence of tree-lanes is


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studied inside two different street canyons of Bologna. Terms like "street canyon", or "urban street canyon", refer to the urban structure where a street is flanked by two opposite rows of buildings. In cities, between buildings there is usually a lane, travelled by cars and pedestrians. Inside an urban street canyon, traffic jams are a source of gases and aerosols that negatively affect human health. Green infrastructures influence the environment inside the canyon both by removing pollution and by trapping it under their crowns. Pollution removal is carried out firstly by photosynthesis, by which the plants can absorb sunlight and carbon dioxide for their metabolism. Another important removing factor is the leaf capture of particulate matter, which is removed from the proximity of the tree crown and can be disposed of by removing the fallen leaves. On the other hand, trees act also as obstacles to the circulation of air. Due to the limited porosity of their crowns, air mass velocity decreases and so does pollutant efficiency removal. The two intensive field campaigns are planned with the precise purpose of correctly evaluating the efficiency of GI in urban street canyons. This was undertaken by identifying two urban street canyons inside the city, sharing similar exposition of vulnerable population (e.g. elderly people and children), similar traffic conditions and emitting sources, but very different in terms of vegetation, i.e. one area (Laura Bassi Veratti St.) includes intensive green spaces, while the other one (Marconi St.) is almost free from vegetation. Following the acquisition and analysis of experimental data collected during the field campaigns, the prognostic three-dimensional and non-hydrostatic CFD-based model ENVI-met 4.2 (M. Bruse and team, http://www.envi-met.com/), one of the few available models capable of simulating airflow interactions with buildings and vegetation (Bruse and Fleer, 1998) will be applied for the evaluation of green structures. This approach was previously validated by Maggiotto et al. (2014), where the model simulations from the temperature perturbation-type model ADMS-TH and the CFD-based ENVI-met were directly compared with field observations: the comparison highlighted that although both models showed good agreement with experimental observations, ENVI-met is more appropriate to take into account the effect of buildings geometry and vegetation on flow pattern and temperature distribution in different scenarios. In the following, we present firstly the two sites, then the instrumental setup and finally the adopted experimental protocol adopted for the two field campaigns, of which the summer campaign is currently under way.

Site description The study area covers both the historical city centre as well as the residential part outside the walls of Bologna, in the south-eastern part of the city. The study area comprises two main parallel street canyons (Laura Bassi Veratti St. and Marconi St.) running north-south with building heights from 5m to 25m. Laura Bassi Veratti St. is characterized by the presence of a tree lane of deciduous trees at both sides of the street (Figure 26c), while Marconi St. is a tree-


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free street canyon (Figure 26d). Laura Bassi Veratti St. is a 700m long lane located in a residential area south-east of the city centre. The street is thus surrounded by both small private houses (2-3 floors) and higher buildings (4-5 floors), so the height is very variable. Marconi St., about 600m long, is surrounded by buildings with at least 4-5 floors, which means 15m on average. The street is composed of 4 lanes, two for private and two reserved for public transport. Along the carriageway, there are porticos, covered pathways with sideways. No vegetative element is planted along this road, except for the last 50 m approaching one street end, where there is a single lane of deciduous trees placed on one side of the road. Besides having the same north-south orientation, the two streets have the same orientation also with regards to the prevalent impinging wind. (iSCAPE D1.4).

Figure 26: a) Position of Bologna (yellow dot; 44°29’37’’N, 11°20’19’’E) in north-east Italy; b) Position of the two street canyons in Bologna; c) street view of Laura Bassi Veratti street with trees; d) street view of Marconi street

without trees.

More than 100 trees (Platanus Acerifolia Mill) are located along both sides of the Laura Bassi Veratti St. The spacing between tree trunks is approximately 8m so there is a leaf crown interference. Platanus Acerifolia (Mill.) is a hybrid of Platanus Orientalis and Platanus Occidentalis, widespread in European urban habitats, from planes up to 700-800m a.s.l. The


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average height of the branch-free trunk is about 5m and the crown extends up to 15m in height (Fig. 26a). Platanus A. is a large deciduous tree growing 20-30m in most landscapes and exceptionally over 40m, with a trunk up to 3m or more in circumference. Numerous lateral branches, often with a disorganized structure arise from the trunk. The leaves are thick and stiff-textured, broad, palmately lobed, superficially maple-like, leaves are 15-20cm wide, with 3-5 lobes (Figure 27a). The bark is usually pale grey-green, smooth and exfoliating, or buff-brown and not exfoliating (Figure 27b). The young leaves in spring are coated with minute, fine, stiff hairs at first, but these wear off and by late summer the leaves are hairless or nearly so. Platanus A. is a prolific bloomer, featuring flowers borne in clusters of one to three spheres on a pendulus stem, with male and female flowers on separate stems; its round flowers appear in April-May. The fruit matures in about 6 months, to 2–3 cm diameter, and comprises a dense spherical cluster of achenes with numerous stiff hairs; the cluster breaks up slowly over the winter to release the numerous 2–3 mm seeds.

Figure 27: a) Leaf specimen of Platanus A. b) trunk detail of Platanus A.

The tree grows best in full sun but also thrives in partial shade, grows in almost any soils, acidic or alkaline, loamy, sandy or clay. It grows best in moist, well-drained soil, but tolerate dry soil as well. The plant tolerates poor conditions, including heat, drought, and poor soils. Due to its well-known resilience to urban conditions including air pollution, it has been commonly used in urban areas.

Instrument setup In this section, we describe the equipment used for the experimental field campaign in Bologna.


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Thermal imaging camera Thermal imaging cameras can be employed in the analysis of environmental temperatures at microclimate scales to estimate the impact of urbanization across the metropolitan area, providing input data for mesoscale models, and enhance the prediction of UHI (Urban Heat Island) expansion through assessing the microclimate impact of growth and neighborhood design. Many studies previously highlighted temperature variations in urban areas using high-resolution remote sensing imagery with the addition of ground thermography techniques for specific measurements of situ surface (e.g., Di Sabatino et al., 2009; Maggiotto, 2014). The aim of this methodology is to quantify thermal variation in relation to urban land use, further processing remote sensing data, through Geographic Information Systems (GIS). These techniques can be easily used by non-modeling researchers to describe urban thermal environments, getting results readily available to urban planners and policy makers. With this aim, two high performance FLIR T620 ThermalCAM (Figure 28), with uncooled microbolometer, 640 x 480 pixels resolution and an image acquisition frequency of 50-603 Hz, will be used in Bologna campaign.

Figure 28: The ThermalCAM FLIR T620 (a) rear section (b) front section

The large bright 4.3-inch LCD screen, presents sharp and bright images also in outdoor environments. ThermalCAM has an integrated high-quality visual camera with 5-megapixel resolution that generates crisp visual images in all conditions. The range for temperature collection ranges from -40°C to + 650 °C with a precision ±2°C, or ± 2% of the range. Measurements are acquired by means of a movable pointer and include automatic identification of the minimum or maximum temperature within an area (round or square), isotherms (and visible alarm acoustic), delta T and automatic indication of the deviation. Atmospheric attenuation correction is automatic and is based on input distance, ambient temperature and relative humidity. ThermalCAM displays simultaneously on the LCD screen these parameters, frizzing with thermal image. Thermal images will be accompanied by measurements of temperature of the surface via a digital USB Thermo-Hygrometer.


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Ultrasonic anemometers Sonic anemometers are devices using ultrasonic sound waves to measure the three components of wind velocity (u, v, w) and air temperature. Wind velocity is calculated according to the time of flight of sonic pulses between pair of transducers as schematically shown in Figure 29.

Figure 29: Schematic illustration of the steps used to measure a component of wind velocity through an

ultrasonic anemometer.

The spatial resolution is given by the path length between transducers, typically 10 and 20cm. Sonic anemometers allow high resolution measurements until 50Hz, which makes them well suited for atmospheric turbulence measurements. Due to the absence of moving parts, they are also suitable for long-term use in exposed automated weather stations and weather buoys. As reported in Figure 29, sonic anemometers use the time-of-flight measurements to calculate the speed of sound C along each axis; deriving the mean speed sound from that calculated along each axis, and taking into account the effect of the cross-wind normal to the measurement axes, the sonic temperature (equivalent to a virtual temperature) of the air is derived as, which can be converted into a true temperature assuming atmospheric pressure and humidity as known. It is worth to note, however, that due to the high sensitivity of sonic anemometers to very small errors in the speed of sound measurement, the accuracy of the derived sonic temperature is


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not generally good enough to be relied on as a true temperature measurement. This is not particularly important, though, since the primary purpose of the sonic temperature is a fast response measurement to be combined with the w vertical wind component in order to calculate heat fluxes. A separate sensor for absolute temperature measurements is used in this field campaign. High frequency measurements of wind velocity components and air temperature at high frequency can be used to derive useful quantities to characterize the atmospheric boundary layer, such as the turbulence and thermal stratification of the atmosphere, from which information on the potential of spreading of pollutants from the measurement site can be inferred. In fact, the use of fast and precise anemological and temperature data has always been considered particularly useful in the field of Planetary Boundary Layer (PBL) dynamics, air pollution monitoring and agrometeorology experimental studies (Sozzi et al., 2002). Since 1957, year of the first documented use of a sonic anemometer in micrometeorology, the sonic anemometer continues to be the main instrument for the direct measurement of momentum and sensible heat turbulent fluxes (eddy correlation method). Consequently, several studies in recent years have been devoted to the analysis flow and turbulence intensity, within and above street canyons (Rotach., 1994; Louka et al., 1998), or yielded airflow patterns, stability conditions, and turbulence properties as a function of the incoming wind direction (Dobre et al., 2005; Zajic et al., 2011). The instrumentation employed in the Bologna field campaign comprises 6 ultrasonic anemometers GILL R3-50 (Figure 30), installed at three different heights at the two sites as described in section 6.3.4.

Figure 30: GILL R3-50 sonic anemometer.


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Technical specifications of GILL R3-50 and other instrumentation used in the field campaigns is reported in Appendix B.

Thermo-hygrometer The HC2S3 Rotronic temperature and relative humidity probe (Figure 31) are installed with anti-radiation screen together with sonic anemometers for accurate temperature and humidity measurements; the probe includes a polyethylene filter that protects its sensor from fine dust and particles and minimizes water absorption and retention.

Figure 31: HCS2S3 thermohygrometer.

Net radiometer The CNR4 net radiometer (Figure 32), manufactured by Kipp & Zonen, measures the energy balance between incoming short-wave and Long Wave Far Infrared Radiation (FIR) versus surface-reflected short-wave and outgoing long-wave radiation.

Figure 32: CNR4 net radiometer.

The CNR4 net radiometer consists of a pyranometer pair, one facing upwards, the other facing downwards, and a pyrgeometer pair in a similar configuration. The pyranometer pair measures the short-wave radiation and the pyrgeometer pair measures long-wave radiation. The upper long-wave detector of CNR4 has a meniscus dome, which ensures that water droplets roll off


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easily, and improves the field of view to nearly 180°, compared with the 150° for a flat window. All 4 sensors are integrated directly into the instrument body, instead of separate modules mounted onto the housing, but are each calibrated individually for optimal accuracy. Two temperature sensors, a Pt-100 and Thermistor, are integrated for compatibility with every data logger. The temperature sensor is used to provide information to correct the infrared readings for the temperature of the instrument housing. Care has been taken to place the long-wave sensors close to each other and close to the temperature sensors. This assures that the temperatures of the measurement surfaces are the same and accurately known, which improves the quality of the long-wave measurements. The design is very light in weight and has an integrated sun shield that reduces thermal effects on both long-wave and short-wave measurements. The cables are yellow with waterproof connectors as used with all our new radiometers. The mounting rod can be unscrewed for transport.

Barometer The Vaisala Barometer PTB110 (Figure 33) is designed for accurate barometric pressure measurements at room temperature and for general environmental pressure monitoring over a wide temperature range. It uses the Vaisala BAROCAP® sensor, a silicon capacitive absolute pressure sensor developed by Vaisala for barometric pressure measurement applications.

Figure 33: Vaisala Barometer PTB110.


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Open path CO2/H2O Gas Analyzer The LI-COR LI-7500 DS (Figure 34) is a high speed, high precision, non-dispersive infrared (NDIR) gas analyzer that accurately measures densities of carbon dioxide and water vapor in turbulent air structures. With the eddy covariance technique, this data is used in conjunction with sonic anemometer air turbulence data to determine the fluxes of CO2 and water vapor.

Figure 34: LI-COR LI-7500A CO2/H2O analyser.

ARPA-ER Mobile laboratories ARPA-ER mobile laboratories (Figure 35) are vans equipped for air quality and meteorological measurements.


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Figure 35: Example of ARPA-ER mobile laboratories.

The mobile laboratories are equipped for continuous measurements of atmospheric pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O3), BTEX (benzene, toluene, ethylbenzene, and xylenes), and particulate matter (PM10 and PM2.5). The instrumental setup on the mobile laboratories consists of:

• a chemiluminescence NO/NO2/NOx API 200E analyser; • a gas filter correlation CO API 300E analyser; • an UV fluorescence SO2 API 100E analyser; • a Thermo Scientific UV photometric Model 49i Ozone Analyser; • a GC/PID Chromatotec Air Toxic for automatic monitoring of BTEX; • a FAI SWAM 5a Dual Channel monitor for sampling PM10 and PM2.5.

The meteorology measurements in mobile laboratories include: wind speed measured by cup anemometer and wind direction measured by wind vane; atmospheric pressure measured by a barometer; temperature and relative humidity measured by a thermohygrometer; rain as measured by a rain gauge.

Ceptometer The AccuPAR model LP-80 (Figure 36) is a menu-driven, battery-operated linear PAR ceptometer, used to measure light interception in plant canopies, and to calculate Leaf Area Index (LAI). It consists of an integrated microprocessor-driven datalogger and one probe. The probe contains 80 independent sensors, spaced 1cm apart. The photosensors measure PAR (Photosynthetically Active Radiation) in the 400-700nm waveband. The AccuPAR displays PAR in units of micromols per meter squared per second (mol m-2s-1). The instrument is capable of


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hand-held or unattended measurement. The AccuPAR can be operated in environments with temperatures from 0 to 50 °C, and in relative humidity of up to 100%.

Figure 36: (a) Parts of the ceptometer; (b) Front view of the ceptometer.

Within a plant canopy, Accu-PAR measures a PAR which is a combination of radiation transmitted though the canopy and radiation scattered by leaves within the canopy. Since the complete model of transmission and scattering (Norman and Jarvis, 1974) is very complex and not suitable for inversion, LP-80 determines LAI from a formula suggested by Norman (1979) suggested a simple light scattering model giving the fraction of transmitted PAR , τ (ratio of PAR measured below the canopy to PAR above the canopy), below a canopy of LAI, L

! = exp &'()*+.-./0)2

3)*4567/0*)

8 (2)

where fb is the fraction of incident PAR which is beam, a is the leaf absorptivity in the PAR band (AccuPAR assumes 0.9 in LAI sampling routines), and K is the extinction coefficient for the canopy. Inverting equation 2 gives the following

9 = ;3)*

4567/0*)< => ?

'()*+.-./0) (3)

whose result is within a few percent of values derived from the complete Norman-Jarvis model. The Ceptometer was used by several authors for LAI estimation in several types of discontinuous canopies (e.g., Brenner et al. 1995; Peper and McPherson 1998; Hyer and Goetz 2004; Serrano and Peñuelas 2005). Experiments conducted in Florida's Gainesville Sun described that thicker tree canopies in Gainesville led to lower consumer power usage per capita than in Ocala (Jensen, 2000). AccuPAR LP-80 measurements can be used to quantify effect of riparian vegetation, removal on stream energy balance that have occurred through management and can provide inputs to models of stream temperature.


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Variable Description

PAR (µm-2s-2)

PAR (Photosynthetically Active Radiation) is defined as the radiation in the 400 to 700 nanometer waveband. It represents the portion of the spectrum which plants use for photosynthesis. Under a plant canopy, radiation levels can vary from full sun to almost zero over the space of a few centimetres. Therefore, reliable measurement of PAR requires many samples at different locations under the canopy. Intercepted PAR data can be used for determining important parameters of canopy structure and for the calculation of LAI (Leaf Area Index).

Tau (τ) It is defined as the ratio of below canopy PAR measurements to the most recent above canopy PAR value. It is measured automatically by the instrument, based upon the PAR readings you make.

LAI (m2m-2)

LAI (Leaf Area Index) is defined as the area of leaves per unit area of soil surface. The AccuPAR calculates LAI based on the above and below-canopy PAR measurements along with other variables that relate to the canopy architecture and position of the sun. These variables are the zenith angle, a fractional beam measurement value (automatically calculates), and a leaf area distribution parameter (also known as x) for particular canopy analyzed.

Zenith Angle (z)

Zenith angle can be defined as the angle that sun makes with respect to the zenith, or the point in the sky directly overhead, vertical to where you stand. The zenith is defined as being 0° and the horizon is 90°. The zenith angle of the sun is necessary for calculation of certain canopy structure parameters, such as LAI.

Fraction of Beam Radiation

(Fb)

Fractional beam radiation is the ratio of direct beam radiation coming from the sun to radiation coming from all ambient sources like the atmosphere or reflected from other surfaces. A fractional beam radiation value is necessary for calculation of LAI using PAR data. The AccuPAR obtains this value by comparing the above canopy PAR measurement to the calculated value of potential incoming solar radiation at your location and zenith angle.

Leaf Distributio

n Parameter

(x)

Leaf Distribution Parameter (also known as Chi or x) refers to the distribution of leaf angles within a canopy. The parameter x is the ratio of the length of the horizontal to the vertical axis of the spheroid described by the leaf angle distribution of a canopy. It can also be measured as the ratio of the projected area of an average canopy element (a leaf, for example) on a horizontal plane to its projection on a vertical plane. The default value for x is 1.0, which assumes the canopy angle distribution to be spherical. For onions, (vertical crop) x would be about 0.7, while on the other extreme, strawberries, would have a x value of about 3 (horizontal crop).

Table 10: Description of variables in Ceptometer AccuPAR LP-80.


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Experimental protocol In the following, we describe the intensive field campaign in Bologna.

Thermographic campaign The two thermal cameras will be used to analyse temperature distribution of building façades and ground surfaces in Laura Bassi Veratti and Marconi Streets. Images at the two sites will be simultaneously taken during a 24-hour acquisition with regular intervals of 1 hour (a total of 12 acquisitions in 24 hours). The day will be selected according to the weather forecast conditions, as a clear-sky, calm wind, day during the week of 20-25th August. This choice will allow to collect images at 12:00 (close to the maximum surface temperature), 14:00, 16:00 (close to the maximum air temperature), 18:00, 20:00, 22:00 (close to maximum UHI intensity). This will be possible by using the two hand-held IR cameras by an operator on foot, allowing us to move quickly through normal working day traffic and narrow spaces. To be able to maintain a similar resolution for all images, several shots of portions of the same building façade at several heights will be taken. Images will be taken using a standard camera set-up, and then the images will be analysed using the software FLIR quick-report 1.2 where all analysis parameters will be settled. The building analysed will be selected on the basis of the homogeneity of construction material and the absence of obstacles (balconies, eave, etc.), metal or glass. Ground measurements in the centre of street crossings will be also carried out.

Air quality measurements At both sites, ARPA-ER mobile laboratories are located along the streets (Figure 37) and are collecting high time resolution (1 minute) measurements of air quality gaseous pollutants (NOx, O3, CO concentrations) and meteorological (temperature, relative humidity) parameters; hourly measurements of BTEX (benzene, toluene and xylene), SO2 concentrations and other meteorological parameters (pressure and wind speed/direction); as well as daily measurements of particulate matter (PM10 and PM2.5) concentrations.

Figure 37: ARPA-ER mobile laboratories in Laura Bassi Veratti St. (44°29’00.52’’N, 11°22’03.11’’E) and Marconi

St. (44°29’56.21’’N, 11°20’18.56’’E).


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Flow measurement campaign At both sites, three GILL R3-50 ultrasonic anemometers are installed at three different heights (Figure 38). In particular, two sonic anemometers are positioned inside the street canyons: the first (Anemometer 1) is just above the roof of the ARPA-ER mobile laboratories, just below the tree crown in Laura Bassi Veratti St. at z = 4.5 m agl; and the second anemometer (Anemometer 2) is positioned on the banister of a balcony at the second floor of a 15m high building (Figure 39), just above the tree crowns in Laura Bassi Veratti St. Thermo-hygrometers HC2S3 and Vaisala PTB110 are also installed at the first level to complement measurements with high time resolution temperature, relative humidity and atmospheric pressure data.

Figure 38: Positioning of the sonic anemometers in Laura Bassi Veratti St.


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Figure 39: Second anemometer positioned on the banister of a balcony at the second floor of a 15m building in

Laura Bassi Veratti St. and Marconi St.

The third anemometer (Figure 40) is positioned together with a CNR4 net radiometer, and a LI-840A LICOR for fast reliable measurements of solar radiation, atmospheric water vapor and CO2 concentrations, on the roof of a 15m building, at 3 m from the floor of the roof building, at a height of 18 m agl. All anemometers are set to collect measurements every 50 ms. The LI-7500A LICOR, when coupled to sonic anemometers, allows to identify CO2 and H2O fluxes.

Figure 40: Third anemometer positioned on the roof of a 15m building above the street canyon.

Simultaneous measurements at the two sites enables for a direct comparison between two urban street canyons, one with trees and one without. The installation is already functioning and will remain active until the first half of September. The idea is also to compare


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measurements taken during the month of August, when traffic decreases in Bologna due to the summer holidays, with those of September, when traffic will be back to normal levels. A further experimental campaign is also planned during winter 2017-2018 in order to study the winter environmental conditions in Bologna, with the same experimental setup. Besides the reduction of the vegetative element, in that period several phenomena typical of the Po Valley occur, such as thermal inversions, frequent stagnant conditions due to reduced ventilation, fog and reduced incoming solar radiation which deeply affect pollution levels inside and outside the city.

LAI/LAD estimation In situ measurements of the PAR photosynthetically active radiation absorbed by the radiation will be carried out. Three intensive measurements will be carried out capturing the evolution of leaf fall (October, November, December). Due to the large number of trees in Laura Bassi Veratti Street, we will select a tree based on two features: tree height and canopy density. All measurements are going to be taken parallel to the ground and perpendicularly to the orientation of Laura Bassi Veratti Street. Five replicas will be done at the same measurement point just near the crown (where the sensor measures unobstructed PAR) and at its base (where the LAI is supposed to be maximum). The two points will be chosen at different heights so as to have a qualitative and quantitative analysis of the radiation reaching the lower levels of the vegetation.

Methodology for GI evaluation at neighborhood-scale assessment for Vantaa

Previous analysis (Votsis 2017) has shown that when housing markets are used as indicators, what can be considered optimal GI interventions in Helsinki change from one neighborhood to the other, notably due to factors relating to density, scarcity of natural land uses, and urban development drivers. With this assessment we will be able to focus this knowledge on the benefits of GI-induced air quality. The hedonic pricing method described in section 5.4 will be applied in Vantaa in a spatially disaggregate fashion to derive results at a neighborhood scale. We will therefore apply the same methodology described in section 5.4. It is also possible to derive a spatially variable “willingness to pay” (described in 5.4) at a neighborhood resolution, whereas the estimated neighborhood impacts are controlled by other property-level or neighborhood-level marginal benefits and costs that go beyond the GI interventions. Although the aggregation of neighborhood-scale impacts with city-wide impacts uses additional economic information (see section 5.7), the basic blocks are the costs-benefits at a property and neighborhood scale, since they are derived from property-level microeconomic behaviors.


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In terms of data, this is possible as the available real estate transaction data is based on point observations with precise geographical locations and that the GI and other built-environmental information overlaid on the economic data comes at a 10-meter spatial resolution. The high spatial resolutions of the SURFEX and TCI simulations in this case enable the neighborhood-scale economic analysis, resulting in a spatially resolved social, economic, ecological, and climate dataset at a neighborhood-level resolution of economic impacts.

SWOT analysis Overall, vegetation has a positive effect on air quality in near road environment. Green infrastructure is identified as a sustainable approach to improve air quality and it has potential to combine with other interventions. The main weakness emerged is a lack of space requirement near highways to accommodate thick vegetation barriers. In addition, absence of regulatory recommendations on planting and maintaining green infrastructure in near road environment. Opportunities revealed further research can form generic recommendations. These recommendations can be combined with existing urban design regulations. The proposed field campaigns are aiming to quantify the air pollutant reduction by different vegetation and their comparison expects to reveal suitable configuration in near road conditions. The studies are also trying to generate a better understanding of pollutant reduction mechanisms of vegetation i.e. dispersion and deposition. The impact of meteorological conditions such as wind speed and direction, temperature and humidity on air quality improvement by vegetation will be clarified. In neighbourhood scale, we found relatively similar strengths, weaknesses, opportunities and threats in SWOT analysis for different cities. In order to minimise repetition, we compiled a common SWOT analysis for all studied cities and city specific points are tabulated as subsections in each column of SWOT table.


General

• Green infrastructures (GI) are effective in reducing pollutant concentration up to 50% in near road conditions (< 50m from highways).

• GI enhance air pollutant dispersion and captures through deposition

• Sustainable approach to improve air quality and mitigate climate change

• Ease in integrating with other infrastructural interventions

• Large thickness is required to reach certain levels of pollutant reduction.

• Considerable space is required to implement vegetation barrier near major roads.

• Frequent maintenance such as pruning, weeding, manuring and watering are needed

• No guidelines for planting and maintaining vegetation are available.

• Suitability of green infrastructure and dimension requirements are site specific

Methodology


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• Other benefits like storm water management, biodiversity and aesthetics improvement.

Methodology

• It is an experimental analysis, so it is scientifically focused on the target cities;

• Involves collaboration between several authorities (e.g., municipality, environmental agency, University)

Socioeconomic aspects

• Able to show that a good GI intervention at one neighborhood will not necessarily work at another neighborhood, because a key driver for the economic impacts of GI at a certain area is its location in the network of urban economic activities.

• Able to estimate evidence-based real-world changes (in urban economic behavior and socioeconomic impacts) resulting from GI rather than hypothetical changes (stated, for instance, in interviews)

• As people see GI as a bundle of goods rather than a list of ecosystem services, the hedonic pricing method is the right way to approach the socioeconomic value of GI, especially in a spatially explicit urban planning context.

• Complementary analysis of the value of thermal comfort index, air quality changes, aesthetic value and energy saving is able to break down the value of GI into components.

Bologna

• The correct planning of two intensive experimental field campaigns involving

• The correct planning of experimental campaigns also involves the identification of the measurement site well in advance

• The measuring site has to be easily reachable;

• If it is public place, probably permissions documents are needed;

• Different approaches are needed in presence or absence of electricity

• Weather and time period definitely affect the observations because of their effect on pollutant levels;

• Weather can be also a challenge for measurement security;

• Many instruments work in different ways and several operations are needed to make the measurements comparable;

• Some market imperfections, relating notably to imperfect and asymmetrical information in the housing market, may result in the estimated value being lower than the sum of all the benefit components.

• We are not able to distinguish between which specific ecosystem service the value is based upon.

• In cases where the GI intervention (or its impact) is extensive, the equilibrium is different after the simulation, so in principle the marginal values could change as well.

Bologna

• The campaign involves an elaborated set of different instruments at various levels, involving the availability of personnel dedicating time to the calibration even prior to the campaign, their correct installation and control/maintenance during the campaign


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both the University and the Regional Environmental Protection Agency and collecting high-resolution meteorological and turbulence variables together with air quality data enables the collection of a large and robust dataset giving the possibility to analyze a big quantity of physical and micro-meteorological processes at local scale

• The collaboration with the Environmental Protection Agency gives the possibility to obtain other air quality and meteorological variables to validate the simulations


• Detailed studies can deliver generic recommendations for green infrastructure implementation.

• Further investigations can provide better understanding of dispersion and deposition as part of pollutant reduction as well as the optimum dimensions of vegetation in different built environment conditions

• Development of policy recommendations on green infrastructures and integration of these to the existing city design regulations

• Scientific community and general public are searching for greener alternatives for solving air pollution and climate change

• Data obtained can be useful also beyond the purpose of the project;

• We will be able to better understand Green Infrastructure reliability:

• Results may suggest guidelines on the correct placing of GI at local scale;

• Results may indicate the circumstances under which GI is effective in reducing pollution levels;

• The results retrieved from the project can be an instrument for administrators for green areas managing and for planning

• Difficult to produce a generalised solution for all roads due to varying meteorological conditions and seasonal wind directions

• Different guidelines are needed for different urban environments such as open road and street canyons.

• Improper design can deteriorate air quality • Considered less important during highway

design • Site specific recommendations on green

infrastructure increase complexity in implementation

•

• Comprehensive researches are required to develop implementation level recommendations on GI planting.

• Site specific recommendations on green infrastructure increase complexity in implementation

• Aggregation: uncertainty for urban policy can be generated when results of a bottom-up neighborhood impact analysis, in which location decisions matter, diverge from result of a city-wide analysis in which only the overall welfare impacts matter.

• Simulation results may not describe the real changes after such GI


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the deployment of new interventions in future city plans;

• It can lead to future collaborations between University and local authorities (municipality, environmental agency…)

• Results can be used to compare the values of alternative GI options or interventions and provide recommendations on how the GI should be implemented optimally; for instance, what kind of GI is optimal at a given location and how much of it, as well as what kind of fiscal policies are needed to make a GI investment economically feasible and sustainable.

implementation. For this reason, we will perform the aforementioned complement approaches as well, and compare the results obtained with each approach.

Bologna

• It may be difficult to implement new GI interventions in the historical city center

• Difficulties in generalizing the results obtained through an experimental campaign to local scale, and more so to city scale

• The findings cannot be integrated in urban city plans by urban city planners if the project does not reach and convince local authorities and stakeholders

• The differences between the tree-lined streets and the streets without trees could depend on other factors than the presence of vegetation, which make difficult to understand and disentangle the effect of the intervention

7 A summary table of measures This section provides a summary table of measures (Table 14) which allows an overview of the approaches and work to be done in each of the locations. Location The

intervention Period List of the used

equipment Monitored pollutants

Street Canyon (Yes/No)

Bologna Green infrastructure (trees)

Summer 2017 Winter 2017/2018

- 2 ARPAE vans (mobile laboratories)

- 3 sonic anemometers

- 3 thermohygrometers

- 1-2 barometers

NO, NO2, NOx, CO, O3, SO2, BTEX, PM10, PM2.5

YES


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- 2 net radiometers - 2 LIcor - 2 FLIR IR cameras

Dublin (Pearse Street)

Low-Boundary Wall (LBW)

March 2018 to March 2019

- 2 Teledyne Chemiluminescent NO/NO2/NOx Analyzers (Model 200E and 200EU)

- A wind vane installed on the rooftop of the adjacent building to monitor the wind speed and wind direction.

- 2 PM analyzer

NO, NO2, NOx, PM2.5 and PM10

YES

Guildford Green infrastructure (hedges, trees and trees with hedges)

May 2017 to May 2019

- 2 GRIMM spectrometers- PM10, PM2.5, PM1

- 2 QTRACK from TSI –CO

- 2- PTRAK- ultrafine particle

- 2 MicroAETH from AETH labs- AE51 and MA200 – BC

- NO/NO2/NOX analyzer –2B technologies

- A LP80 ceptometer-LAI

PM10, PM2.5, PM1, PNC, NO, NO2, NOx, BC, CO

No, open-road conditions

Vantaa Green infrastructure

May 2017 to May 2019

- 3 anemometers - 3 thermo- and

hygrometers - 3 net radiometers

NO, NO2, NOx, BC, CO, PM10, PM2.5

One station is in a street canyon; however, the remaining two stations are in build-up area, and in a green park area, respectively.

Table 11. A summary table of measures.


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Appendix (A) Low Boundary Walls Location Selection


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In the following, the Dublin LBW location selection campaign results are reported.


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Appendix (B) Technical specifications for the instruments


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In the following, we report Tables containing the main technical specifications for instruments adopted in the experimental field campaigns in Bologna

GILL R3-50 Anemometer Wind Speed Specifications

Range 0-45 m/s

Accuracy <1.0% RMS

Resolution 0.01 m/s

Wind Direction Specifications

Range 0-359°

Accuracy ≤ ±1.0°RMS

Resolution 1°

Speed of sound

Range 300-370 m/s

Accuracy 0.01 m/s

Resolution ≤ ±0.5% @20°C

Measurement

Ultrasonic output rate 50Hz

Output formats UVW, Speed of sound

Digital output

Communication RS422 full duplex, 8 data bits, 1 stop bit, no parity

Baud rates 2400-115200

Output rate Selectable 0.4 to 50s

Power requirement

Anemometer 9-30V DC (<150mA @24V DC or 300 mA @12V DC)


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Mechanical

Material Aluminium/Carbon fibre

Weight 1.0Kg

Size 750mm x 240mm

Environmental

Protection class IP65

Operating temperature -40ºC to +60ºC

Precipitation 300mm/hr

Table 12: Technical specification of GILL R3-50 sonic anemometer.

HCS2S3 thermohygrometer Specifications

Electronics Operating Limits

-40° to +100°C

Storage Temperature Range

-50° to +100°C

Filter Description Polyethylene (standard) or Teflon (optional, ordered separately)

Current Consumption < 4.3 mA (@ 5 Vdc)

< 2.0 mA (@ 12 Vdc)

Supply Voltage 5 to 24 Vdc

Startup Time 1.5 s (typical)

Maximum Startup Current < 50 mA (for 2 μs)

Analog Outputs ±3 mV (maximum) offset at 0 V.

< ±1 mV (0.1°C, 0.1% R. H.) deviation for digital signal


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Diameter 15 mm (0.6 in.)

Length 85 mm (3.3 in.) without connector

183 mm (7.25 in.) with connector

Weight 10 g

Air temperature

Sensor PT100 RTD, IEC 751 1/3 Class B

Measurement Range -40° to +60°C (default)

Output Signal Range 0 to 1 V

Accuracy ±0.1°C with standard configuration settings (at 23°C)

Long-Term Stability < 0.1°C/year

Sensor Time Constant - Standard PE Filter

≤ 22 s (63% step change [1 m/s air flow at sensor])

Sensor Time Constant - Optional Teflon Filter

≤ 30 s (Typical 4 s, 63% of a step change [1 m/s air flow at sensor])

Relative humidity

Sensor ROTRONIC® Hygromer IN-1

Measurement Range 0 to 100% RH (non-condensing)

Output Signal Range 0 to 1 Vdc

Long-Term Stability < 1% RH per year

Accuracy ±0.8% RH with standard configuration settings (at 23°C)

Sensor Time Constant - Standard PE Filter

≤ 22 s (63% of a 35 to 80% RH step change [1 m/s air flow at sensor])

Sensor Time Constant - Optional Teflon Filter

≤ 30 s (Typical 10 s, 63% of a 35 to 80% RH step change [1 m/s air flow at sensor])

Table 13: Technical specifications of HCS2S3 thermohygrometer.


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Net radiometer CNR4 Specifications

Pyranometer Spectral Response

305 to 2800 nm

Pyrgeometer Spectral Response

4.5 to 42 μm

Response Time < 18 s

Temperature Dependence of Sensitivity

< 4% (-10° to +40ºC)

Sensitivity Range 5 to 20 μV W-1 m2

Pyranometer Output Range 0 to 15 mV

Pyrgeometer Output Range ±5 mV

Non-Linearity < 1%

Tilt Error < 1%

Pyranometer Uncertainty in Daily Total

< 5% (The uncertainty values are for a 95% confidence level.)

Pyrgeometer Uncertainty in Daily Total

< 10% (The uncertainty values are for a 95% confidence level.)

Directional Error < 20 W m-2 (pyranometer)

Angles up to 80° with 1000 W/m2 beam radiation

Operating Temperature Range -40° to +80°C

Compliance Conforms to the CE guideline 89/336/EEC 73/23/EEC.

Height 6.6 cm dome-to-dome

Width 11.1 cm

Length 23.5 cm


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Weight 850 g without cable

Table 14: Technical specifications of net radiometer CNR4. VAISALA PTB110 Operating range

Pressure ranges 500 ... 1100 hPa

600 ... 1100 hPa

800 ... 1100 hPa

800 ... 1060 hPa

600 ... 1060 hPa

Temperature range -40 ... +60 °C

Humidity range non-condensing

General

Supply voltage 10 … 30VDC

Supply voltage control with TTL level trigger

Supply voltage sensitivity negligible

Current consumption < 4mA

in shutown mode < 1μA

Output voltage 0 … 2.5V DC

0 … 5V DC

Output frequency 500 … 1100Hz

Resolution 0.1hPa

Load resistance minimum 10kΩ

Load capacitance maximum 47nF


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Settling time 1s to reach full accuracy after power-up

Response time 500ms to reach full accuracy

Acceleration sensitivity negligible

Pressure connector M5 (10-32) internal thread

Pressure fitting barbed fitting for 1/8''

Minimum pressure limit 0hPa abs

Maximum pressure limits 2000hPa abs

Electrical connector removable connector for 5 wires (AWG 28 … 16)

Terminals Pin 1: external triggering

Pin 2: signal ground

Pin 3: supply ground

Pin 4: supply voltage

Pin 5: signal output

Housing material, plastic cover ABS/PC blend

Housing classification IP32

Metal mounting plate Al

Weight 90g

Accuracy

Linearity ±0.25 hPa

Hysteresis ±0.03 hPa

Repeatability ±0.03 hPa

Pressure calibration uncertainty ±0.15 hPa

Voltage calibration uncertainty ± 0.7 mV


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Frequency calibration uncertainty

± 0.3 Hz

Accuracy at +20 °C ±0.3 hPa

Total accuracy at

+15 ... +25 °C ±0.3 hPa

0 ... +40 °C ±0.6 hPa

-20 ... +45 °C ±1.0 hPa

40 ... +60 °C ±1.5 hPa

Long-term stability ±0.1 hPa/year

Table 15: Technical specifications of Vaisala Barometer PTB110.

LI-7500A specifications

CO2 H2O

Calibration range 0-3000 ppm 0 - 60 ppt

Accuracy Within 1% of reading

Within 2% of reading

Zero drift (per °C) ±0.1 ppm typical ±0.03 ppt typical

±0.3 ppm max. ±0.05 ppt max.

RMS noise (typical @ 370 ppm CO2 and 10 mmol mol-1 H2O)

5 Hz 0.08 ppm 0.0034 ppt

10 Hz 0.11 ppm 0.0047 ppt

20 Hz 0.16 ppm 0.0067 ppt

Gain drift (% of reading per °C) ±0.02% typical ±0.15% typical

±0.1% max. ±0.30% max.


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@ 370 ppm @ 20 ppt

Direct sensitivity to H2O (mol CO2/mol H2O)

±2.00E-05 typical ---

±4.00E-05 max. ---

Direct sensitivity to CO2 (mol H2O/mol CO2)

--- ±0.02 typical

--- ±0.05 max

Table 16: Technical specification of LI-COR LI-7500A CO2/H2O analyser.

T200 Specifications

Ranges Min: 0 - 50 ppb full scale

Max: 0 - 20,000 ppb full scale (selectable, dual-range supported)

Measurement Units ppb, ppm, μg/m3, mg/m3 (selectable)

Zero Noise < 0.2 ppb (RMS)

Span Noise < 0.5% of reading (RMS) above 50 ppb

Lower Detectable Limit 0.4 ppb

Zero Drift < 0.5 ppb/24 hours

Span Drift < 0.5% of full scale/24 hours

Lag Time 20 seconds

Rise/Fall Time < 60 seconds to 95%

Linearity 1% of full scale

Precision 0.5% of reading above 50 ppb

Sample Flow Rate 500 cc/min ±10%

Power Requirements 100V-120V, 220V-240V, 50/60 Hz


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Analog Output Ranges 10V, 5V, 1V, 0.1V (selectable)

Recorder Offset ±10%

Operating Temperature Range

5 - 40ºC

Dimensions (HxWxD) 178 x 432 x 597 mm

Weight Analyzer: 18 kg

External pump: 7 kg

Table 17: Technical specifications of the T200 NO/NO2/NOx analyser.

T300 Specifications

Ranges Min: 0 - 1 ppm full scale

Max: 0 - 1,000 ppm full scale (selectable, dual-range supported)


Zero Noise < 0.2 ppm (RMS)

Span Noise < 0.5% of reading (RMS) above 5 ppm

Lower Detectable Limit 0.04 ppm

Zero Drift < 0.1 ppm/24 hours


Lag Time 10 seconds



Precision 0.5% of reading above 5 ppm




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Analog Output Ranges 10V, 5V, 1V, 0.1V (selectable)



5 - 40°C operating


Weight 18 kg

Table 18: Technical specifications of the T300 CO analyser.

T100 Specifications

Ranges Min: 0 - 50 ppb full scale

Max: 0 - 20,000 ppb full scale (selectable, dual-range supported)


Zero Noise < 0.2 ppb (RMS)

Span Noise < 0.5% of reading (RMS) above 50 ppb

Lower Detectable Limit

0.4 ppb

Zero Drift < 0.5 ppb/24 hours


Lag Time 20 seconds



Precision 0.5% of reading above 50 ppb




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Analog Output Ranges

10V, 5V, 1V, 0.1V (selectable)



5 - 40°C operating


Weight 16.2 kg

Table 19: Technical specifications of the T100 SO2 analyser.

Thermo Scientific 49i

Custom Ranges 0 to 0.05 to 200ppm, 0 to 0.1 to 400mg/m3

Flow Rate 1 to 3L/min.

Height 219mm

Depth 584mm

Linearity ±1% full scale

Precision 1ppb

Inputs 16 Digital Inputs (standard), 8 0 to 10VDC Analog

Preset Measurement Ranges

0 to 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50,100

Response Time 20 seconds (10 second lag time)

Span Drift <1% full scale per month

Temperature 20° to 30°C (Performance) or 0° to 45°C (Operating)

Operating voltage 100 to 115VAC; 220 to 240VAC

Weight 16kg

Width 425mm


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Zero drift <1 ppb

Zero noise 0.25 ppb RMS

Table 20: Technical specifications of the Thermo Scientific Model 49i O3 analyser.

airTOXIC Chromatotech

Lower detection limit ≤ 0.01 ppb = 0.0325 μg/m3

Detection range and linearity: BENZENE

3.25 to 3250 μg/m3 = 0-1000 ppb

0.32 to 325 μg/m3 = 0-100 ppb

0.032 to 32.5 μg/m3 =0-10 ppb

Relative standard variation: PRECISION

Better than 0.3 % over 48h (Retention Time)

Better than 2 % over 48 h on 1 ppb (Concentration)

Cycle time 15 or 20 or 30 min

Gas supply Nitrogen: 4 ml/min (inlet 3 bars; 1/8’’ swagelok)

Air or nitrogen for CALIB: 50 ml/min continuously

and 180 ml/min in CALIB method

Detector cleaning : 3 ml/min

15ml/min , Sample inlet (vacuum pump) ¼’’swagelok,

Sample volume 20 to 400 ml or more (programmable)

Operation Temperature Room with air conditioning: 10 to 35°c

Power supply main 230V 50Hz or 115V 60 Hz

Electrical consumption: Average 150 VA, Peak 360 VA


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Dimensions Rack 482 mm (19")Height 222 mm (5U), depth 600 mm

Weight 18 kg =analyser ( 37 kg with packaging )

Table 21: Technical specifications of the airTOXIC Chromatotech BTEX analyser.

FAI Swam 5a

Mass measurement operative interval

Mass thickness till 5 mg/cm²

Mass thickness measurement reproducibility

±2 μg/cm²

Mass measurement reproducibility

± 10 μg; ± 15 μg; ± 23 μg respectively with sampling ß spot area 5.20; 7.07;

11.95 cm²

β source 14C with 3.7MBeq (100 μCi) nominal activity

Operating flow rate Programmable in the range 0.8 – 2.5 m³/h

Flow rate measurement reproducibility

1% of the measured value

Flow rate measurement relative uncertainty

2% of the measured value

Flow rate control Automatic, with regulation valve moved by a step motor. Stability in flow rate

control better than the 1% of the required nominal value

Max allowed pressure drop 40 kPa at 2.3 m³/h

Filters Loader/Unloader capacity

No. 36 filter cartridges (72 on demand)

Filter cartridges Standard supply: for Æ 47 mm filter membranes


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Service compressed air Operating pressure 200÷300 kPa (supplied by an auxiliary air compressor supplied

with the instrument)

Power supply 230 V (± 10%) 50 Hz single-phase

Absorbed electric power 1200 W (max)

Power supply continuity in direct current

2 12 V 3.5 Ah floating batteries - Autonomy to complete mass measurements and

filters movements

Air compressor unit 12 l/min at 300 kPa

Operating conditions inside the installation

Relative Humidity lower then 85% (with no condensate)

cabinet

Storage conditions Temperature within - 10 and + 55 °C

Relative Humidity lower then 85% (with no condensate)

Dimensions (W x D x H)

Sampling unit 430 x 540 x 370 mm

Vacuum pump unit 200 x 320 x 200 mm

Air compressor unit 180 x 320 x 200 mm

Weights

Sampling unit 36 kg

Vacuum pump unit 10 kg

Air compressor unit 18 kg

Sampling inlets manufactured by FAI

− PM10 sampling inlet (LVS-PM10 model, in compliance with EN 1234-1


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Instruments (on customer demand)

standard, working at 2.3 m3/h)

− PM10 sampling inlet LVS-PM10 with 1 m3/h nominal flow rate (equivalent

to the LVS-PM10 EN 1234-1 model)

− PM2.5 sampling inlet (LVS-PM2.5 model, nominal flow rate 2.3 m3/h)

− PM2.5 sampling inlet (LVS-PM2.5 model, nominal flow rate 1 m3/h)

− PM1 sampling inlet (LVS-PM1 model, nominal flow rate 2.3 m3/h)

Table 22: Technical specifications of the FAI Swam 5a PM10 and PM2.5 sampler.

Accupar LP-80

Operating Environment 0° to 50° C (32°-122°)

100% relative humidity

Probe Length 86.5cm

Overall Length 102cm

PAR Range 0 to >2500µmol m-2 s-1

Resolution 1µmol m-2 s-1

Minimum Spatial resolution 1cm

Unattended logging interval User selectable, between 1 and 60 minutes

External PAR sensor connector Locking 5-pin sealed circular connector

Table 23: Technical specifications of ceptometer model AccuPAR LP-80.


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Appendix (c) The chemistry of the atmospheric pollutants


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In atmospheric chemistry, NOx is a generic term for the nitrogen oxides mostly relevant for air pollution, namely nitric oxide (NO) and nitrogen dioxide (NO2). These gases contribute to the formation of smog, particulate matter and acid rains, as well as tropospheric ozone. NOx gases are usually produced from the reaction between atmospheric nitrogen and oxygen during combustion of fuels in air at high temperatures, such as that occurring in car engines. In fact, oxygen and nitrogen do not react at ambient temperatures, but at high temperatures they undergo an endothermic reaction producing various oxides of nitrogen. Such high temperatures are normally encountered inside an internal combustion engine or a power station boiler, during the combustion of a mixture of air and a fuel, and naturally in lightning. Thermal NOx refers to NOx formed through high temperature oxidation of the diatomic nitrogen found in combustion air. The formation rate is primarily a function of temperature and the residence time of nitrogen at that temperature. At high temperatures (> 1600°C) molecular nitrogen and oxygen in the combustion air disassociate into their atomic states and participate in a series of reactions. Below the three main reactions mechanism producing thermal NOx:

N2+O→NO+N N+O2→NO+O N+OH→NO+H

All three reactions are reversible. In atmospheric chemistry, NOx stands for the total concentration of NO and NO2; the ratio NO/NO2 is determined by the intensity of sunshine (which converts NO2 to NO) and the concentration of ozone (which reacts with NO to form again NO2). In the presence of excess oxygen, NO reacts with it to form NO2, with a time depending on the concentration in air. Carbon monoxide is a colorless, odorless, tasteless gas that is slightly denser than air. It is toxic to hemoglobic animals when encountered in concentrations higher than 35 ppm. In the atmosphere, it is spatially variable and short lived, with a role in the formation of tropospheric ozone. It is produced from the partial oxidation of carbon-containing compounds, when there is not enough oxygen to produce CO2, such as when operating a stove or an internal combustion engine in an enclosed space. Carbon monoxide is present in small amounts in the atmosphere, mainly produced by volcanic activity but also from natural and man-made fires. Fossil-fuel combustion also contributes to its production. Besides from its high biological toxicity due to its combination with hemoglobin to produce carboxyhemoglobin, which usurps the space in hemoglobin that normally carries oxygen but is ineffective for delivering oxygen to tissues, it is also a short-lived greenhouse gas and has an indirect radiative forcing effect by elevating concentrations of methane and tropospheric ozone through chemical reactions with other


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atmospheric constituents (e.g., the hydroxyl radical OH) that would otherwise destroy them. Also, it can be eventually oxidized to CO2 through natural reactions in the atmosphere. Together with aldehydes, it can take part to the series of chemical reactions forming photochemical smog. In particular, it reacts with OH to produce a radical intermediate HOCO, which rapidly transfers its radical hydrogen to O2 to form peroxy radical (HO2) and carbon dioxide. HO2 subsequently reacts with NO to form NO2 and OH. Since OH is formed during the formation of NO2, the balance of the sequence of reactions starting with CO and leading to the formation of O3 is

CO+O2+hH → CO2+O3

where “hv” refers to the photon of light absorbed by NO2. In urban areas, it is a temporary atmospheric pollutant from the exhaust of internal combustion engines but also from incomplete combustion of various other fuels (wood, coal, charcoal, oil, natural gas, and trash). Sulphur dioxide SO2 is a toxic gas with a pungent, irritating smell. It is the product of burning sulfur or burning materials containing sulfur

S+O2→SO2 It is a noticeable component in the atmosphere, especially produced by volcanic eruptions. It is a major air pollutant and has significant impacts on human health. In addition, the atmospheric concentration of SO2 can affect the habitat suitability for plant communities, as well as animal life. It is a precursor to acid rain and atmospheric particulate matter. Ozone is a gas naturally present in the atmosphere. Most ozone (91%) is found in the stratosphere (from 10-16 km above Earth’s surface up to about 50 km altitude). The stratospheric region with the highest ozone concentration is commonly known as “ozone layer”, extending all over the globe with some variations in altitude and thickness. The remaining O3, about 10%, is found in the troposphere. Although being chemically identical, stratospheric and tropospheric ozone have very different roles in the atmosphere and very different effects on living beings. Stratospheric ozone, in fact, plays a beneficial role by absorbing most of the biologically damaging ultraviolet sunlight (UV-B). The absorption of UV radiation by ozone creates a source of heat, which actually forms the stratosphere itself. Without O3 layer filtering action, more of the Sun’s UV-B radiation would penetrate the atmosphere and would reach the Earth’s surface. On the contrary, at the Earth’s surface, O3 comes into direct contact with life-forms and displays its destructive side, with harmful effects on crop production, forest growth, and human health. Near-surface ozone is a key component of photochemical “smog”. Ozone also acts as a greenhouse gas, absorbing some of the infrared energy emitted by the Earth. The majority of tropospheric ozone formation occurs when NOx, CO and Volatile Organic Compounds (VOCs) react in the atmosphere in the presence of sunlight. Motor vehicle exhaust, industrial emissions, and chemical solvents are the major anthropogenic sources of these chemicals. Although these precursors often originate in urban areas, winds can carry NOx hundreds of kilometers far apart, causing ozone formation to occur in less populated regions


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as well. The chemical reactions involved in tropospheric ozone formation are a series of complex cycles in which CO and VOCs are oxidized to form water vapor and CO2.

∙OH+CO→∙HOCO ∙HOCO+O2→HO2+CO2

HOCO then react with NO to give NO2 which is photolyzed to give atomic oxygen and, through reaction with oxygen, O3.

HO2∙+NO→∙OH+NO2 NO2+hν→NO+O(

3P) N(OP) + NQ → NO

The balance of this sequence of chemical reactions is CO+2O2+hν→CO2+O3

This cycle involving HOx and NOx is terminated by the reaction of OH with NO2 to form nitric acid or by the reaction of peroxy radicals with each other to form peroxides. BTEX form an important group of aromatic Volatile Organic Compounds (VOCs) because of their role in tropospheric chemistry and the risk posed to human health. Together with PAHs, they are grouped in the list of potential carcinogens. In addition, they undergo complex photochemical reactions giving rise to a number of highly toxic and carcinogenic secondary pollutants, such as tropospheric ozone and peroxyacyl nitrate (PAN), which are injurious not only to human health but also to vegetation (Saxena and Ghosh, 2012). In urban atmosphere, BTEX constitute up to 60% of non-methane VOCs, and are considered an efficient indicator of pollution arising from road traffic (because of increased global consumption of gasoline). In particular, among BTEX, benzene has been chosen as a prime target for assessment of pollution levels in the urban atmosphere as it is considered to be a genotoxic carcinogen with fatal and mutagenic effects. According to Brocco et al. (1997), “the reaction of the BTEX with OH and or nitrate (NO3) radicals serves as the dominant degradation processes for aromatic VOCs in the atmosphere and the resulting products contribute to secondary organic aerosol (SOA) formation by nucleation and condensation”. Odum et al. (1997) reported that “the reaction of toluene with NOx in the presence of a light source formed SOA with a significant aerosol yield and therefore, aromatic VOCs influence gas phase pollutants directly and particle-phase pollutants indirectly”. In the presence of NOx, BTEX react with OH radicals to form ozone thus modifying the oxidizing capacity of the atmosphere (Atkinson et al., 2000). Particulate matter, also known as particle pollution, is a complex mixture of extremely small particles and liquid droplets that get into the air. This complex mixture includes both organic and inorganic particles, such as dust, pollen, soot, smoke, and liquid droplets. These particles very greatly in size, composition, and origin. Particles can be directly emitted (primary particles), for instance when dust is carried by wind, or indirectly formed (secondary particles), when gaseous precursors previously emitted into the air turn into particulate matter. It is convenient to classify particles by their aerodynamic diameter properties because: 1) these properties


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govern the transport and removal of particles from the air; 2) they also govern their deposition within the respiratory system; 3) they are associated with the chemical composition and sources of particles. All these properties are conveniently summarized by the aerodynamic diameter, i.e., the size of a unit-density sphere with the same aerodynamic characteristics. Particles are usually sampled and described on the basis of their aerodynamic diameter, usually called simply the particle size. Mass and composition in urban environments tend to be divided into two main groups: coarse and fine particles. The barrier between these two fractions of particles lies between 1 and 2.5 μm, but is generally fixed by convention at 2.5 μm in aerodynamic diameter for measurement purposes. PM stands for particulate matter suspended in air, while PM followed by a number refers to all particles with a certain maximum size (aerodynamic diameter), including all smaller particles. So, PM10(2.5) is particulate matter with an aerodynamic diameter of up to 10(2.5) μm; so, PM10 includes PM2.5 which includes ultrafine particles (PM0.1). The largest particles (coarse) are mechanically produced by the break-up of larger solid particles: for instance, wind-blown dust from agriculture, uncovered soil, unpaved roads, or mining operations. Traffic produces road dust and air turbulence that can stir up road dust. Near coasts, evaporation of sea spray can produce large particles. Pollen grains, spores, plant and insect parts are also in this size range. Fine particles are largely formed from reaction of gaseous precursors. The smallest particles, less than 0.1 μm, are formed by nucleation, i.e. condensation of low-vapor-pressure substances formed by high-temperature vaporization or by chemical reactions in the atmosphere to form new particles. Sub-micrometre sized particles can result from condensation of metals or organic compounds that are vaporized in high-temperature combustion processes or by condensation of gases that have been converted in atmospheric reactions to low-vapor pressure substances. For instance, SO2 is oxidized in the atmosphere to form sulphuric acid (H2SO4) which can be neutralized by NH3 to form ammonium sulfate. NO2 is oxidized to nitric acid (HNO3) which in turn can react to form ammonium nitrate. Secondary sulphate and nitrate particles are usually the dominant components of fine particles.

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Report on Footprint of Passive Control Systems · 2018. 10. 25. · Prashant Kumar and Guillem Camprodon The first draft. V0.2 30/8/2017 Salem S. Gharbia Task leader’s edits. Draft