Analysis of station classification and network design

Analysis of station classification and network design

INERIS (Laure Malherbe, Anthony Ung), NILU (Philipp Schneider), RIVM (Frank de Leeuw, Benno Jimmink)

18th EIONET meeting -Dublin- 25th October 2013

Context

An increasing amount of available data on air quality in Europe

Extension of data coverage for all pollutants both in time and space Much effort dedicated to data QA/QC (e.g. JRC-AQUILA quality

programmes) Information on siting requested in current and future reporting

obligations

18th EIONET meeting, Dublin, 25th October 2013

O3 and PM10 monitoring stations for which data have been reported to AirBase

AirBase v7, www.eea.europa.eu

(JRC- AQUILA Position Paper on siting criteria and station classification)

Context

...but still limited information on the monitoring strategies underlying site selection; on the fitness for purpose of the selected measurement

locations.

An encouragement to refine existing station classification schemes or develop

supplementary ones, to develop meta-information describing the station surroundings

(land use, population density,...)


(JRC- AQUILA Position Paper on siting criteria and station classification)

Objectives of the study

First part : evaluation of the network design from several angles: evolution from 1996 fulfilment of the EUROAIRNET criteria compliance with the AQD

Second part: a supplementary classification scheme (presented by L. Rouïl at the17th EIONET meeting, 2012): update the classification according to Joly and Peuch (2012)

methodology and check its robustness analyse the results on the European scale investigate specific situations

NB: study mainly focused on NO2, O3 and PM


Evaluation of the network


Evolution of the network

Selected years: 1996: state before the implementation of the Framework AQ

Directive (information for the majority of EU15 Members) 2004: from EU15 to EU 25 2007: from EU25 to EU27 2011: the most recent year available in AirBase


class type of area type of station

(sub)urban background (U)

urban backgroundsuburban background

traffic (T)

urban trafficsuburban trafficrural trafficunknown traffic

regional background (R)

rural background

industrial (I)

urban industrialsuburban industrialrural industrialunknown industrial

Considered station categories:


1996 2007

2004 2011

PM10

Monitoring criteria


EURO-AIRNET (Larssen et al., 1999): number of cities to be included in a European

representative network:• all large cities (>500,000)• 25% of medium cities (250,000-500,000)• 10% of small cities (50,000-250,000)

Agg/zone >UAT L-UAT

250 2 1

500 3 2

750 3 2

1000 4 2

1500 6 3

2000 7 3

2750 8 4

3750 10 4

4750 11 6

6000 13 6

more 15 7

Minimum requirements for PM (PM10 + PM2.5)

AQ Directive The number of stations in an

agglomeration/zone depends on population and current AQ status

NB: Countries may also use modelling as a supplementary assessment tool ; in that case these numbers may be reduced by up to 50% under the conditions set in Dir. 2008/50/CE, Art. 7

Monitoring criteria


Application of EUROAIRNET criteria:

Minimum monitoring coverageActual PM10

monitoring coverage

Actual NO2

monitoring coverage

all large cities (>500,000) 72 of 73 (99%) 59 of 73 (81%)

25% of medium cities (250,000-500,000)

93 of 116 (80%)

92 of 116 (79%)

10% of small cities (50,000-250,000)

555 of 750 (74%)

528 of 750 (70%)

Monitoring criteria


Application of AQD criteria:Ex: PM monitoring, 702 zones/agglomerations common to 1996, 2004, 2007 and 2011. Compliance with AQD considering the assessment regimes applicable in 2011:

Results for NO2 monitoring:

Number of zones for which:

1996 2004 2007 2011

Nstations = Nmin 32 108 102 113

Nstations > Nmin 6 193 306 423

Nstations < Nmin 664 401 294 166

Number of zones for which:

1996 2004 2007 2011

Nstations = Nmin 211 174 132 105

Nstations > Nmin 15 337 462 516

Nstations < Nmin 476 191 108 81

Supplementary classification according to Joly & Peuch (2012)

methodology


Brief recall of the methodology

Methodology developed in the framework of GMES/MACC program

Objective: establishing a pollutant-specific objective classification of stations based on the temporal variability of the observation data

For each considered pollutant, time series are summarized by eight indicators characterizing the diurnal cycle, the weekend effect and the high frequency variations.

The classification is performed in two stages:1) Definition of the classes, from class1 to 10, with a selected set of

stations. A linear discriminant analysis is performed so as to best discriminate between rural stations and stations most influenced by human activities (“urban + traffic” sites).

2) Classification a posteriori of the other stations.

ETC/ACM Technical Paper 2012/17 (2013)


Update and analysis with AirBase v7


Spatial distribution of the classes

O3

PM10

NO2

In some countries, all station classes are present. For some pollutants, other countries only have low or high station classes. Classification is missing for some stations (criteria not filled, missing data, only daily values reported)

More stations have been classified:


Number of stations classified in ETC/ACM (2013) study(AirBase v6, 2002-2010)

Number of stations classified in this study(AirBase v7, 2002-2011)

NO2 2697 3136

PM10 1822 2248

O3 2098 2349

Monitoring stations not classified last year but classified in this study


PM10

O3

NO2

As in 2012, the classification has been analysed in relation with EoI classification and auxiliary variables (population density, land use) This analysis confirms the robustness of the methodology.


Distribution of PM10 classes as a function of EoI classification

Mean and median population density around PM10 measurement

stations for each class


Different types of specific situations have been identified : stations for which the classification (according to Joly & Peuch, 2012)

does not well match the types of area and site provided in AirBase; stations in specific environments (e.g.: high population density); stations displaying very different classes according to the measured

pollutant.


Interesting cases


Distribution of population density in each PM10 class

Three stations have a population density higher than 20000 inhab./km2

They are located in the same area: Paris

They are all urban background but do not have the same class.

Interesting cases: example


PM10 classes in Berlin

Berlin agglomeration contains all EoI types of area/station.It also contains almost all the class numbers, with growing numbers from rural background to traffic sites.As in Paris, two urban background stations are interesting for study.

Interesting cases: example

General conclusions

Station classification and detailed description of the station surroundings provide helpful support: to interpret air quality data to have a better idea of the station representativeness to select the most relevant sets of stations for trend analysis,

model evaluation, data assimilation, air quality mapping, impact studies...

This can be achieved by the joint use of different classification schemes such as EoI (type of area/type of

site) and Joly & Peuch (2012) methodology auxiliary data such as population density, land cover, emissions...

It is proposed to compile and make such information available to data users.


Proposed content of the spreadsheet

Station code Coordinates Ozone classification (if applicable) EoI Classification:

Type of area Type of station Characteristics of zone (if available)

Classification according to Joly & Peuch methodology (2012): Class number for O3, NO2 and PM10

Dominant emission sector(s) (if available) LAU and NUTS codes (EBM) Population density within 1 km radius (JRC database and ORNL) Proportion of each main land cover class within 1 km radius

(CORINE) Other remarks


A premilinary Excel file is available => link

Documents

Analysis of station classification and network design