Data collection and methods - Ufireg · Data collection and methods Report This project is implemented through the CENTRAL EUROPE Programme co-financed by the ERDF UFIREG Ultrafine

Data collection and methods

Report

This project is implemented through the CENTRAL EUROPE Programme co-financed by

the ERDF

UFIREG

Ultrafine Particles – an evidence based contribution to

the development of regional and European

environmental and health policy

Ultrafine Particles – an evidence based contribution to the development of regional and European

environmental and health policy (UFIREG).

This document was prepared by the consortium of the UFIREG project. For more information,

please visit the project website: www.ufireg-central.eu

Report: Data collection and methods

December 2014

© UFIREG Project 2014

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CONTENTS

Summary ................................................................................................................................................ 6

1 Introduction ...................................................................................................................................... 7

1.1 Cities ........................................................................................................................................ 7

1.2 Measurement sites ................................................................................................................... 8

2 Status of data collection ................................................................................................................... 9

2.1 Air pollution and meteorological data ..................................................................................... 9

2.1.1 Instrumentation and data availability .................................................................................. 9

2.1.2 Quality of data and traceability of quality assurance measures ......................................... 17

2.1.3 Imputation of missing data ................................................................................................ 20

2.2 Epidemiological data ............................................................................................................. 20

2.2.1 Daily deaths and hospital admissions ................................................................................ 20

2.2.2 Descriptive analyses .......................................................................................................... 22

2.2.3 Confounder variables ........................................................................................................ 23

2.3 Sociodemographic data ......................................................................................................... 23

3 Statistical methods and models ..................................................................................................... 24

3.1 Analysis of air quality data .................................................................................................... 24

3.1.1 Temporal and spatial variation .......................................................................................... 24

3.1.2 Correlation analysis between UFP and basic pollutants and meteorological parameters .. 25

3.1.3 Meteorological cluster analyses based on backward trajectories ...................................... 25

3.1.4 Nucleation analysis ............................................................................................................ 25

3.1.5 Source apportionment of particle size distribution ............................................................ 27

3.2 Epidemiological analysis ....................................................................................................... 28

3.2.1 General modelling strategy................................................................................................ 28

3.2.2 Confounder adjustment ..................................................................................................... 28

3.2.3 Lag structure of exposure .................................................................................................. 28

3.2.4 Effect modification for age and sex ................................................................................... 29

3.2.5 Meta-analytical techniques ................................................................................................ 29

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3.2.6 Sensitivity analyses ........................................................................................................... 29

3.2.7 Additional analyses ........................................................................................................... 29

3.2.8 Software............................................................................................................................. 30

4 Conclusions ..................................................................................................................................... 30

5 Abbreviations .................................................................................................................................. 31

6 References ....................................................................................................................................... 32

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FIGURES

Figure 1. Results of quality assurance experiments in Dresden ............................................................ 18

Figure 2. Results of quality assurance experiments in Dresden with corrected data ............................ 18

Figure 3. Results of quality assurance experiments in Prague .............................................................. 19

Figure 4. Results of quality assurance experiments in Ljubljana .......................................................... 19

Figure 5. Examples of daily plots used for the nucleation analysis ...................................................... 26

Figure 6. The decision path used in NPF event characterization .......................................................... 26

TABLES

Table 1. Characterization of traffic impact at the different UFIREG stations......................................... 8

Table 2. Start of UFP measurements and instrumentation type at UFIREG sites. ................................ 10

Table 3. Overview of available parameters at the different UFIREG sites. .......................................... 10

Table 4. Sources of air pollutant data. ................................................................................................... 11

Table 5. Data availability in % for Chernivtsi ....................................................................................... 12

Table 6. Data availability in % forAugsburg. ........................................................................................ 14

Table 7. Data availability in % for Dresden-Winckelmannstraße. ........................................................ 15

Table 8. Data availability in % for Ljubljana-Bezigrad. ....................................................................... 16

Table 9. Data availability in % for Prague-Suchdol .............................................................................. 12

Table 10. Data availability for the time period of the epidemiological analysis. .................................. 17

Table 11. Primary and secondary outcomes including ICD-10 codes. ................................................. 21

Table 12. Description of city-specific (cause-specific) mortality outcomes. ........................................ 22

Table 13. Description of cause-specific hospital admissions by city. ................................................... 23

Table 14. Sociodemographic information of the five UFIREG cities. ................................................. 24

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SUMMARY

The project Ultrafine particles – an evidence based contribution to the development of regional and

European environmental and health policy (UFIREG) investigated short-term effects of ultrafine

particles on mortality and morbidity, including their influence on cardiovascular and respiratory

diseases in order to provide an evidence based contribution to environmental and health policy.

Within UFIREG, ambient number size distributions of (ultra)fine particles were determined using

custom-made mobility particle size spectrometers in five European cities: Dresden, Augsburg

(Germany), Prague (Czech Republic), Ljubljana (Slovenia) and Chernivtsi (Ukraine). Additional

information on other air pollutants and meteorological parameters were obtained from local or

regional monitoring authorities. On the basis of the measured air quality data, epidemiological studies

were carried out in all participating cities on a daily basis. Epidemiological data, especially

information on (cause-specific) mortality and morbidity, were collected using official deaths and

hospital admission statistics.

Besides the epidemiological analysis, another objective of the UFIREG project was to assess the

spatial and temporal variability of ultrafine particles (UFP) in the five participating cities and to

identify factors influencing air quality at the respective sites to increase the understanding of sources,

behaviour, and variation of (ultra)fine particles in urban areas in Europe.

For comparable measurements as basis for the UFIREG studies, a profound quality assurance of

measurements is essential. Therefore, a comprehensive quality assurance programme was part of

UFIREG which comprised staff training, an initial comparison of the UFP measuring instruments,

frequent on-site comparisons against reference instruments at all sites and automatic control units at

two sites. Furthermore, instrument maintenance as well as data validation was optimised and

harmonised within the project.

Data collection in the UFIREG project provided a dataset for air quality analyses which covered a time

period from May 2012 to April 2014 (Chernivtsi January 2013 to December 2014). Due to varying

starting points of the measurements and differences in availability of epidemiological data, the dataset

for epidemiological analyses covered the time period 2012 to 2013 for Prague and Ljubljana, 2011 to

2012 for Dresden and Augsburg and January 2013 to March 2014 for Chernivtsi.

Different statistical methods and models were applied to the UFIREG dataset. The association

between UFP, other air pollutants, and mortality or hospital admissions was investigated using Poisson

regression models allowing for overdispersion following a two-step modelling strategy. First,

confounder models were established and tested in order to set up a basic confounder model a priori for

all cities. In a second step air pollution effects were estimated by city. Finally, city-specific estimates

were pooled using meta-analyses methods.

Moreover, a wide variety of analysis was carried out with the UFIREG air quality dataset. To assess

the impact of the air mass origin a meteorological cluster analysis based on back trajectories was

performed. For source apportionment, positive matrix factorisation was applied. Further analysis

included a correlation analysis between UFP and basic air pollutants as well as meteorological

parameters and identifying the impact of nucleation on the UFP number concentrations.

The report describes the status of data collection of the UFIREG dataset and explains all methods and

statistical models in detail.

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1 INTRODUCTION

Thousands of premature deaths, many cases of illness and costs of hundreds of billion euros are

caused by air pollution in Europe every year. While the effects of PM10 and PM2.5 on human health are

intensively studied, information on health impacts of ultrafine particles (ambient particles smaller than

100 nm) is still limited although a lot of measurements indicate high levels of air pollution in cities

with ultrafine particles (UFP) originating from traffic exhausts and other combustion processes. The

few studies on UFP that have been conducted so far could not fill the gap of epidemiological

knowledge on the effects of UFP on health.

Therefore, the UFIREG project (July 2011 to December 2014) aimed at investigating short-term

effects of different size classes of (ultra)fine particles on (cause-specific) mortality and morbidity in

order to provide an evidence based contribution to environmental and health policy and to identify

further needs of research.

Within UFIREG, ambient number size distributions of (ultra)fine particles are determined in five

European cities: Dresden, Augsburg (Germany), Prague (Czech Republic), Ljubljana (Slovenia) and

Chernivtsi (Ukraine). In addition to the particle number concentrations (PNC), data on other air

pollutants and meteorological parameters were provided by local or regional monitoring authorities.

On the basis of these measured air quality data, epidemiological studies were carried out in all

participating cities. Epidemiological data, especially information on (cause-specific) mortality and

morbidity, were collected on a daily basis using official deaths and hospital admission statistics.

Moreover, project partners have gathered socio-demographical data of their cities.

Such measurements and data collections resulted in a considerable quantity of data. Therefore, a

central database was developed, in which all air pollution data are stored. For reasons of data

protection especially in Germany, epidemiological data are not part of the database. Besides the

storage of data, the use of the data as well as the analysis methods had to be coordinated. That was the

task of two different transnational workgroups within the project (workgroup on UFP measurements,

so-called Exposure workgroup, and on epidemiological analysis, so-called Epidemiological

workgroup) which met regularly at least twice a year. Partly in collaboration, both groups developed

codebooks for exposure variables, potential confounders as well as health outcomes and a statistical

analysis plan as guidance for the analysis of the epidemiological studies. Besides the epidemiological

analyses, the different air pollution situations at the five UFIREG sites were investigated with various

analysis methods.

This report summarises the status of data collection and gives an overview about the methods of

analysis and statistical models used in the UFIREG studies.

1.1 Cities

Prague is the largest of the five UFIREG cities with about 1.2 million inhabitants and an area of

almost 500 km2. Therewith, Prague has also the highest density of population (app. 2,500

inhabitants/km²) among the UFIREG cities. Dresden, the second largest city in the UFIREG project,

has about 500,000 inhabitants in an area of more than 300 km2. The number of inhabitants in

Augsburg, Ljubljana and Chernivtsi is comparable and ranged from about 260,000 (Chernivtsi) to

280,000 (Augsburg, Ljubljana) inhabitants during the study period. Ljubljana, however, is larger than

Augsburg and Chernivtsi with an area of 275 km2 and is therefore characterized by a lower population

density (app. 1,000 inhabitants/km²). Regarding socio-demographical, socio-economic and legislative

aspects such as environmental regulations, there are some substantial differences between the cities.

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1.2 Measurement sites

All UFIREG measurement sites are located in urban or suburban residential zones with local and/or

district heating and are classified as urban background stations according to EU directive 2008/50/EC

(EC, 2008).

For every station a detailed description similar to the form used in the EU-project ESCAPE (European

Study of Cohorts for Air Pollution Effects) is available. It contains information about the exact

location, cityscape around the station, traffic and other impacts such as plants and parking lots. Table 1

gives an overview about traffic impacts. The sites were representative for a large part of the urban

population and had no roads with heavy traffic in immediate vicinity.

Table 1. Characterization of traffic impact at the different UFIREG stations for PNC measurements

(approximate distances).

Augsburg Dresden Prague Ljubljana Chernivtsi

Measurement height 4 m 4 m 4 m 4 m 11 m

Distance to nearest

street 120 m 15 m 50 m 25 m 10 m

Distance to nearest

major street 120 m 100 m 270 m 60 m 200 m

Distance to nearest

intersection 190 m 30 m 450 m 110 m 300 m

Distance to nearest

traffic light 160 m 200 m 850 m 210 m 300 m

Width of nearest

street 15 m

5 m (10 m

with parking) 4 m

6 m (15 m

with parking)

5 m (10 m

with parking)

Street configuration Interrupted

rows of houses

Interrupted

rows of houses

Few buildings

around

Largely

uninterrupted

rows of houses

on one side

Interrupted

rows of houses

Traffic intensity of

nearest major road

(cars per day)

19,680 (2010) 23,400 (2013) 15,200 (2012) 13,300 (2008) 2,000-10,000

(2012)

Parking lot within

100 m Yes (large) Yes (small) No Yes (small) No

Registered vehicles in

the city1

50,863

gasoline cars

& 149 gasoline

HDV and

19,333 diesel

cars & 4972

diesel HDV

(1.4.2010)

51,533 diesel

vehicles and

161,508

gasoline

vehicles (only

passenger cars,

1.1.2013)

No data

available

80,000 diesel

vehicles and

97,000

gasoline

vehicles (all

vehicles, 2013)

No data

available

1 Not only locally registered cars have to be considered, but also transit traffic. Ljubljana, for instance, is part of

one of the most important transalpine crossings from west to east.

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The UFIREG measurement stations in Dresden and Prague were integrated in local air quality

monitoring networks. At these sites, other air pollution parameters such as PM10, PM2.5, NOx, SO2, O3,

and partially black carbon as well as meteorological parameters were determined. In Augsburg and in

Ljubljana, air pollutant data collected in addition to PNC data were obtained from other locations as

the PNC data because the local monitoring authorities were not project partners and therewith not the

owner of the UFP measuring instruments. Due to a construction work in Ljubljana, the location of the

site of the Slovenian Environment Agency (ARSO) which delivered PM, gas and meteorological data

was changed in November 2013 to the immediate proximity of the PNC station. The providers of the

pollutant and meteorological data are shown in Table 4.

2 STATUS OF DATA COLLECTION

To provide reasonable and useful datasets for air quality and epidemiological analyses the status of

data collection within UFIREG differs between air pollution, epidemiological and socio-

demographical data. Depending on the start of the measurements, the time period May 2012 to April

2014 (except Chernivtsi: January 2013 to December 2014) was chosen for air quality analyses. For

epidemiological analyses the dataset covered the period 2012 to 2013 for Prague and Ljubljana, 2011

to 2012 for Augsburg and Dresden and January 2013 to March 2014 for Chernivtsi.

2.1 Air pollution and meteorological data

2.1.1 Instrumentation and data availability

All air pollution and meteorological data collected within the project were transferred in different

ways (via email, ftp-server or cloud) to the Saxon State Office for Environment, Agriculture and

Geology and are currently stored in the central UFIREG database. Air pollutant and meteorological

data are available from 2011 (Augsburg, Dresden), 2012 (Prague, Ljubljana) or 2013 (Chernivtsi) until

2014. For sustainability reasons, the database will be handed over to all measuring project partners at

the end of the project in order to continue the use for each station. Therewith, a simple way of

analysing different temporal averages, variations and data availability is enabled for future

measurements.

In the scope of UFIREG, PNC measurements were performed using basic closed-loop mobility

particle-size spectrometer, either Differential or Scanning Mobility Particle Sizer (DMPS/SMPS).

They enable highly size-resolved PNC measurements in the range from 10 to 800 nm (except in

Prague: 10 to 500 nm) with particle number concentrations between 100 to 100,000 particles per cm³.

For more sophisticated analyses of highly size-resolved PNSD data in Prague, data was only used

beneath a particle size of 200 nm because of the incomplete multiple charge correction concerning

larger particles after the instrumental update in March 2012.

Regular maintenance activities of the instruments and data processing as well as data validation were

harmonised within the project. Data processing (so-called inversion) of the electrical mobility

distribution (measured by the spectrometer) into the true particle number size distribution included the

multiple charge correction according to Pfeifer et al. (2014), coincidence correction of the

condensation particle counter (CPC) and the correction of the counting efficiency of the CPC. Particle

losses due to diffusion in the inlet system and the spectrometer were also quantified using theoretical

functions in the data evaluation software (Wiedensohler et al., 2012). In addition to the DMPS/SMPS,

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two Ultrafine Particle Monitors, recently developed within the former EU project UFIPOLNET, were

operated in Prague and Augsburg.

Different installation times of these PNC measurement devices (see Table 2) and different

measurement programmes of the monitoring authorities (see Table 3) led to different availability of air

pollutant and meteorological data. Therefore, the Exposure workgroup agreed to consider as final data

set a two-year period from May 2012 to April 2014 except Chernivtsi (January 2013 to December

2014) to cover two full years of measurements for the studies on air quality. For technical reasons the

instrument in Augsburg had to be changed twice, in July and November 2013. However, this was not

relevant for the epidemiological analysis of the Augsburg data since the analysed time period covered

2011 and 2012.

Table 2. Start of UFP measurements and instrumentation type at UFIREG sites.


SMPS/TDMPS 2004 December, 2010 April 11th

, 2012 April 5th

, 2012 January 19

th,

2013

Type of

SMPS/TDMPS

TDMPS

TROPOS-

design / since

Nov. 2013

TSMPS

TROPOS-

design (with

Kr85

-bipolar

diffusion

charger,

74 MBq), PM2.5

inlet

SMPS

TROPOS-

design (with

Kr85

-bipolar

diffusion

charger,

370 MBq)

equipped with

automated

function

control, PM1

inlet

TSI SMPS

retrofitted

according to

TROPOS

standard (with

Kr85

-bipolar

diffusion

charger,

370 MBq), PM1

inlet

SMPS

TROPOS-

design (with

Ni63

-bipolar

diffusion

charger,

95 MBq2), PM1

inlet

SMPS

TROPOS-

design (with

Ni63

-bipolar

diffusion

charger,

95 MBq)

equipped with

automated

function

control, PM1

inlet

UFP 330 May, 2012 May, 2012

PM10 and PM2.5 mass concentrations were either determined with a Tapered Element Oscillating

Microbalance/Filter Dynamics Measurement System (TEOM/FDMS in Dresden and Ljubljana), high

volume samplers (HVS in Dresden and Ljubljana) or via ß-Absorption (in Augsburg and Prague).

Information on devices and methods of gas and meteorological measurements can be found elsewhere:

German stations: http://www.env-it.de/stationen/public/open.do

Czech station: http://www.chmi.cz or

http://portal.chmi.cz/portal/dt?action=content&provider=JSPTabContainer&menu=JS

PTabContainer/P1_0_Home&nc=1&portal_lang=cs#PP_TabbedWeather

Slovenian station: http://www.arso.gov.si/zrak/kakovost%20zraka/

For harmonisation, hourly averages of measured gas data under the detection limit of the respective

devices were replaced with the half of the detection limit. PM10 data obtained from TEOM lower than

-5 µg/m³ were set as invalid.

2 The finally installed instruments in Ljubljana and Chernivtsi contain bipolar diffusion charger with Ni

63 instead

of Kr85

. That meant less bureaucratic effort for the handling as well as the import. The comparability of

measurements was tested during the initial intercomparison workshop in March 2012.

http://www.env-it.de/stationen/public/open.do



http://portal.chmi.cz/portal/dt?action=content&provider=JSPTabContainer&menu=JSPTabContainer/P1_0_Home&nc=1&portal_lang=cs#PP_TabbedWeather

http://portal.chmi.cz/portal/dt?action=content&provider=JSPTabContainer&menu=JSPTabContainer/P1_0_Home&nc=1&portal_lang=cs#PP_TabbedWeather

http://www.arso.gov.si/zrak/kakovost%20zraka/

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Table 3. Overview of available parameters at the different UFIREG sites (status as of June 2014); PNC:

particle number concentrations, BC: black carbon, p: air pressure, T: air temperature, RH: relative

humidity, GLRD: global radiation, WD: wind direction, WS: wind speed; *parameters were measured

at different urban background sites in the city).

PNC PM10 PM2.5 SO2 O3 CO NO NO2 BC p T RH GLRD Wind Rain

Augsburg* x x x x x x x x x x x x x x

Dresden x x x x x x x x x x x x x x

Prague x x x x x x x x x x x x x (x)*

Ljubljana* x x x x x x x x x x x x

Chernivtsi x x x x

Table 4. Sources of air pollutant data.

Augsburg3 Chernivtsi Dresden Ljubljana

4 Prague

PNC

Helmholtz Zentrum

München/Augsburg

University

L.I.Medved’s Research

Center of Preventive

Toxicology, Food and

Chemical Safety, Ministry of

Health, Ukraine (State

enterprise)

Saxon State

Office for

Environment,

Agriculture and

Geology

The National

Laboratory of

Health,

Environment

and Food

Institute of Chemical

Process Fundamentals/

Czech

Hydrometeorological

Institute

PM/gas data

Bavarian

Environment

Agency

-

Saxon State

Office for

Environment,

Agriculture and

Geology

Slovenian

Environment

Agency

Czech

Hydrometeorological

Institute

Meteorology

Bavarian

Environment

Agency

www.hmn.ru/index.php

?index=8&value=33658

Saxon State

Office for

Environment,

Agriculture and

Geology

Slovenian

Environment

Agency

Czech

Hydrometeorological

Institute

EU station

code for the

official

monitoring site

DEBY007/

DEBY099 DESN092 SI0003A CZ0ASUC

In Chernivtsi, there are no data on other air pollutants than PNC due to financial, bureaucratic and

technical issues: Since UFIREG could not afford to buy the air quality (TSP, gases) and

3 Air pollutants were measured at different urban background stations within the city (3-4 km distance).

4 Air pollutants were measured at different urban background stations within the city (600 m distance) until

November 2013.

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meteorological data of the official network (offer in 2012: app. 300 € per month) in Chernivtsi,

meteorological data had to be downloaded from the respective Ukrainian weather website (Table 4).

The mobility particle size spectrometers operated within UFIREG deliver data in a 5 to 20 minute

time-resolution. In general, hourly and daily averages were calculated with a threshold of 75 % data

availability. The overall data availability for the air pollution analysis dataset is higher than 75 %.

Missing days during this time period were caused by retrofitting or modification (especially in

Dresden) and malfunction as well as repair of the instruments. Even stealing of equipment led to data

failure. Tables 5 to 10 give an overview about the data availability of the different parameters at each

station.

Daily averages for the epidemiological analyses were calculated for the day of the event (lag 0) up to

five days prior to the event (lag 5) for all air pollution variables. For meteorological parameters daily

averages of the same day up to 13 days prior to the event (lag 13) were calculated.

Table 5. Data availability in % for Chernivtsi from January 2013 to April 2014 based on hourly data

(orange: data availability below 75 %).

Month PNC Wind T RH p

01.2013 41 0 0 0 0

02.2013 97 48 48 48 48

03.2013 98 78 78 78 78

04.2013 98 74 74 74 74

05.2013 97 79 79 79 79

06.2013 97 63 63 63 63

07.2013 88 79 79 79 79

08.2013 97 79 79 79 79

09.2013 98 80 80 80 80

10.2013 98 78 78 78 78

11.2013 89 77 77 77 77

12.2013 98 41 41 41 41

01.2014 97 66 66 66 66

02.2014 64 79 79 79 79

03.2014 17 59 59 59 59

04.2014 97 67 67 67 67

05.2014 78 66 66 66 66

06.2014 86 67 67 67 67

07.2014 90 62 62 62 62

08.2014 91 66 66 66 66

09.2014 82 67 67 67 67

10.2014 94 68 68 68 68

11.2014 94 68 68 68 68

12.2014 85 67 67 67 67

PNC T Rain RH p

Total 86 66 66 66 66

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Table 6. Data availability in % for Augsburg (Bourges Platz [NO, NO2], Friedberger Straße [PNC, PNC-

UFP 330], BLfU [PM10, PM2.5, SO2, O3, CO & meteorology) from May 2012 to April 2014 based on hourly

data (orange: data availability below 75 %). Meteorological data are also available from the station

Friedberger Straße in Augsburg.

Month PNC PNC-UFP 330

PM10 PM2.5 SO2 O3 CO NOx Wind T Rain RH p GLRD

05.2012 97 73 100 92 100 100 91 100 100 100 0 100 100 100

06.2012 98 45 99 100 100 96 100 100 100 100 0 100 100 100

07.2012 97 99 100 100 99 100 100 100 100 100 0 100 100 100

08.2012 95 99 99 100 100 100 100 100 100 100 0 100 100 100

09.2012 88 100 100 100 100 100 100 100 100 100 0 100 100 100

10.2012 92 100 99 100 100 100 99 100 100 100 0 100 100 100

11.2012 92 100 100 98 99 100 95 87 100 100 0 100 100 100

12.2012 91 99 100 93 100 100 100 100 100 100 0 100 100 100

01.2013 85 86 100 100 100 100 100 100 100 100 100 100 100 100

02.2013 95 100 100 100 100 100 100 99 99 100 100 100 100 100

03.2013 92 78 100 100 100 100 100 100 100 100 100 100 100 100

04.2013 95 98 100 99 100 100 100 100 100 100 99 100 100 100

05.2013 87 96 99 100 99 100 100 100 100 100 100 100 100 100

06.2013 94 100 100 100 100 100 100 100 99 100 100 100 100 100

07.2013 96 97 100 100 99 100 100 100 98 100 100 100 100 100

08.2013 100 100 100 100 100 99 100 100 97 100 100 100 100 100

09.2013 100 100 100 100 99 94 100 100 98 100 100 100 100 100

10.2013 10 99 100 99 96 100 100 100 99 100 100 100 100 100

11.2013 59 86 100 79 100 99 100 100 100 100 100 100 100 100

12.2013 100 87 100 100 99 100 100 100 99 100 100 100 100 100

01.2014 100 96 100 93 99 100 100 99 99 100 100 100 100 100

02.2014 100 99 100 100 100 99 100 100 99 100 100 100 100 100

03.2014 100 100 100 100 99 92 99 98 98 100 100 100 100 100

04.2014 100 99 100 100 99 100 100 100 99 100 100 100 100 100

PNC PNC-UFP 330

PM10 PM2.5 SO2 O3 CO NOx Wind T Rain RH p GLRD

Total 90 93 100 98 99 99 99 99 99 100 67 100 100 100

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Table 7. Data availability in % for Dresden-Winckelmannstraße from May 2012 to April 2014 based on

hourly data except of PM2.5 (orange: data availability below 75 %).

Month PNC PM10 PM2.5 SO2 O3 CO NO NO2 Wind T Rain RH p GLRD

05.2012 88 100 100 100 100 100 100 100 100 100 100 100 100

06.2012 72 100 100 97 100 100 100 100 100 100 100 100 100

07.2012 94 100 100 99 100 100 100 100 100 100 100 100 100

08.2012 85 100 100 99 99 99 99 100 100 100 100 100 100

09.2012 89 97 97 99 100 100 100 100 100 100 100 100 100

10.2012 90 100 97 100 100 100 100 100 100 100 100 100 100

11.2012 97 90 97 98 95 99 99 100 100 100 100 100 100

12.2012 98 100 94 100 100 100 100 100 100 100 100 100 100

01.2013 98 97 100 99 99 99 99 100 100 100 100 100 100

02.2013 94 100 100 100 100 100 100 100 100 100 100 100 100

03.2013 95 100 100 100 100 100 100 100 100 100 100 100 100

04.2013 96 100 100 99 95 99 99 100 100 100 100 100 100

05.2013 93 100 100 100 93 100 100 100 100 100 100 100 100

06.2013 73 100 100 81 100 100 100 100 100 100 100 100 100

07.2013 94 100 100 86 98 98 98 98 98 98 98 98 98

08.2013 52 100 100 98 99 99 99 100 100 100 100 100 100

09.2013 79 100 100 100 99 100 100 100 100 100 100 100 100

10.2013 98 100 100 100 96 100 100 100 100 100 100 100 100

11.2013 94 100 100 100 100 99 99 100 100 100 100 100 100

12.2013 97 100 100 100 100 100 100 100 100 100 100 100 100

01.2014 71 100 100 100 100 100 100 100 100 100 100 100 100

02.2014 82 100 100 99 99 99 99 100 100 100 100 100 100

03.2014 94 100 100 100 100 100 100 100 99 100 99 99 100

04.2014 97 100 100 100 100 100 100 100 100 100 100 100 100

PNC PM10 PM2.5 SO2 O3 CO NO NO2 Wind T Rain RH p GLRD

Total 88 98 99 98 99 100 100 100 100 100 100 100 100

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Table 8. Data availability in % for Ljubljana-Bezigrad from May 2012 to April 2014 based on hourly data

except of PM2.5 (orange: data availability below 75 %).

Month PNC PM10 PM2.5 SO2 O3 CO NO NO2 Wind T Rain RH p GLRD

05.2012 65 96 81 96 96 96 84 94 100 100 100 100 100 100

06.2012 59 99 100 96 96 96 85 93 100 100 100 100 100 100

07.2012 28 100 100 96 96 96 83 91 100 100 100 100 100 100

08.2012 71 98 100 96 96 96 85 94 100 100 100 100 100 100

09.2012 55 100 97 95 95 96 96 96 100 100 100 100 100 100

10.2012 75 99 100 96 95 95 84 96 100 99 100 100 100 100

11.2012 50 98 100 96 96 93 71 90 100 100 100 100 100 100

12.2012 75 95 100 94 96 96 89 89 100 100 100 100 100 100

01.2013 37 100 100 83 95 91 91 78 100 100 100 100 100 100

02.2013 9 100 100 80 89 39 91 91 100 100 100 100 100 100

03.2013 94 99 84 60 60 0 53 53 100 100 100 100 100 100

04.2013 97 99 97 88 95 77 70 81 100 100 100 100 100 100

05.2013 76 100 100 96 96 96 95 95 100 100 100 100 100 100

06.2013 69 100 100 85 92 93 91 93 100 100 100 100 100 100

07.2013 98 98 100 93 96 95 96 96 100 100 100 100 100 100

08.2013 100 83 97 96 96 93 93 95 98 98 93 98 97 92

09.2013 100 100 93 95 95 88 93 94 100 100 100 100 100 100

10.2013 99 94 100 95 96 76 95 95 100 100 100 100 99 99

11.2013 99 99 100 84 84 90 71 90 97 97 97 99 98 96

12.20135 100 93 100 89 96 96 88 96 100 100 100 100 99 100

01.2014 100 95 0 87 93 95 83 83 100 100 100 100 100 100

02.2014 100 100 0 91 92 93 88 94 100 100 100 100 100 100

03.2014 100 99 0 96 95 95 80 95 54 54 54 54 54 53

04.2014 93 99 0 95 95 95 70 94 100 100 100 100 100 100

PNC PM10 PM2.5 SO2 O3 CO NO NO2 Wind T Rain RH p GLRD

Total 77 94 81 91 93 86 84 90 90 98 98 98 98 98

5 Change of site location for all pollutants except PNC

- 16 -


Table 9. Data availability in % for Prague-Suchdol from May 2012 to April 2014 based on hourly data

(orange: data availability below 75 %). To complete pressure and rain data, a second station was used in

Prague: Prague Ruzyne at the airport.

Month PNC PNC-UFP 330

PM10 PM2.5 SO2 O3 CO NO NO2 Wind T RH p GLRD

05.2012 93 92 94 99 98 99 100 98 98 100 100 100 0 100

06.2012 88 88 86 87 86 88 77 87 87 88 88 88 0 88

07.2012 82 83 98 98 97 98 98 97 97 98 98 98 0 98

08.2012 80 99 97 99 97 97 99 95 95 99 100 96 0 100

09.2012 71 100 61 99 98 100 100 98 98 100 100 100 0 100

10.2012 88 100 99 99 99 100 99 99 99 100 100 77 0 100

11.2012 100 100 100 100 98 100 98 98 98 100 100 100 0 100

12.2012 90 100 100 99 99 100 99 99 99 100 100 90 45 100

01.2013 100 100 99 98 98 100 98 98 98 100 100 77 38 100

02.2013 100 98 100 99 99 100 98 99 99 100 100 100 48 100

03.2013 100 99 98 98 99 100 99 98 98 95 100 100 100 100

04.2013 90 100 99 99 100 100 98 98 98 100 100 100 100 100

05.2013 88 100 94 95 99 99 98 98 98 100 99 99 99 99

06.2013 93 99 98 95 99 99 98 98 98 99 99 99 99 99

07.2013 48 96 93 94 96 96 96 96 96 96 96 96 96 96

08.2013 0 100 99 94 100 100 100 100 100 100 100 100 100 100

09.2013 12 100 68 83 93 100 99 95 95 100 100 100 100 100

10.2013 94 100 86 99 100 100 100 100 100 100 100 100 100 100

11.2013 30 100 97 98 100 100 100 100 100 100 100 100 100 100

12.2013 64 100 100 99 100 100 100 100 100 100 100 100 100 100

01.2014 66 100 99 99 99 100 99 99 99 100 100 100 100 100

02.2014 88 90 97 98 99 99 98 98 98 99 99 99 99 99

03.2014 64 100 99 97 99 100 98 99 99 100 100 100 100 100

04.2014 93 66 100 100 98 100 100 98 98 100 100 100 100 100

PNC PNC-UFP 330

PM10 PM2.5 SO2 O3 CO NO NO2 Wind T RH p GLRD

Total 76 96 94 97 98 99 98 98 98 99 99 97 63 99

- 17 -


The PNC data availability for the epidemiological analyses varied greatly between the cities (see Table

10), which of course has an influence on the city-specific results of the epidemiological analyses.

Table 10. Data availability of particle number concentration measurements for the time period of the

epidemiological analysis.


Time period January 2011 -

December 2012

January 2011 -

December 2012

April 12th 2012 -

December 2013

April 4th, 2012 –

December 2013

January 19th, 2013 –

May 2014

Missing days 19 (3 %) 93 (13 %) 165 (26 %) 202 (32 %) 48 (10 %)

Data availability 97 % 87 % 74 % 68 % 90 %

2.1.2 Quality of data and traceability of quality assurance measures

For comparable measurements as basis for the epidemiological studies, a profound quality assurance

(QA) of measurements is essential. Too many fluctuations in data quality and too high uncertainties

can influence the outcomes of the epidemiological statistics especially in the case of short-term

studies. Therefore, an extensive quality assurance programme was an essential part of UFIREG aiming

at a high data quality. First of all, all mobility particle size spectrometers used in UFIREG were based

on the same measurement principle and fulfilled the ACTRIS recommendations (Wiedensohler et al.,

2012). Two of the instruments (in Dresden and Chernivtsi) were even equipped with the automated

function control (Schladitz et al., 2014). Secondly, there was a continuous evaluation of the

performance of instruments. The first intercomparison of the four UFIREG spectrometers took place

in a central laboratory in March 2012 before the measurements started in Prague, Ljubljana and

Chernivtsi. During the initial intercomparison at the World Calibration Center for Aerosol Physics

(WCCAP) hosted at the Leibniz Institute for Tropospheric Research (TROPOS), none of the tested

spectrometer deviated more than 20 % in the size range between 15 nm and 50 nm and more than

10 % in the size range between 50 nm and 200 nm against the reference instrument. The deviation for

particles smaller than 15 nm was found to be between 20 % and 60 %. This was one of the reasons

why the size class from 10 to 20 nm was excluded from the epidemiological analysis.

On the other hand, high voltage fluctuations led to high day-to-day instrumental variability in the

smallest size range which is impractical for short-term health studies. The performance of the

instruments on-site was regularly checked and quality assured by short-term (48 to 72 hours)

instrumental comparisons against the WCCAP reference mobility particle size spectrometer at the

respective stations, so-called Round-Robin-Tests (Table 11). As of October 2012, a uniform reporting

about on-site QA experiments was introduced to compare the data resulting from QA from different

stations and time points. The deviation of the instruments was mostly around 10 %. In the case of

greater deviations, the instruments were checked for failures and the data were corrected if necessary.

QA results and data corrections are exemplarily shown for Dresden, Prague and Ljubljana (Figure 1-

4). For traceability reasons, the three existing WCCAP reference mobility particle size spectrometers

were compared to each other whenever more than one of them was in the laboratory but at least once a

year all three reference instruments were compared to each other. Moreover, all Condensation Particle

Counters (CPC) of the WCCAP were regularly calibrated and their counting efficiency was compared

to the WCCAP reference electrometer which, in turn, was frequently checked at the German

metrology institute (Braunschweig). As an additional measure in the Saxon air quality monitoring

network where the station in Dresden belongs to, all CPCs (parts of the spectrometer and the

automated function control) were also calibrated once per year against an electrometer at the WCCAP

laboratory.

- 18 -


Correction of data was necessary when the instrument was out of the 10 % range over all size classes

for a longer time period. This was the case for Dresden (Figure 1-2), Augsburg and Prague in 2013.

The correction factors were determined by comparing to the WCCAP reference instrument.

Figure 1. Results of quality assurance experiments against WCCAP reference mobility particle size

spectrometer in Dresden, N2-N8: particle size classes (see page 24, chapter 3), y-axis: slope of the

correlation plot between the tested instrument and the reference instrument, dotted line: 10 % range

(network criterion: allowed temporal drift within three months), solid line: 25 % range (network

criterion: allowed expanded measurement uncertainty for daily averages), red squares indicate the need

of corrections.


spectrometer in Dresden with corrected data set from May 2013 to January 2014, dotted line: 10 % range,

solid line: 25 % range.

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

0 1 2 3 4 5 6 7 8

Slo

pe

SM

PS

Dre

sde

n Dresden Okt 12

Dresden Feb 13

Dresden Mai 13

Dresden Sep 13

Dresden Jan 14

Dresden Mai 14

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

0 1 2 3 4 5 6 7 8

Slo

pe

SM

PS

Dre

sde

n

Dresden Okt 12

Dresden Feb 13

Dresden Mai 13

Dresden Sep 13

Dresden Jan 14

Dresden Mai 14

N2 N3 N4 N5 N6 N7 N8

N2 N3 N4 N5 N6 N7 N8

- 19 -


After correction of the data of the instrument in Dresden, the deviation between 20 and 200 nm was

mostly within + 20 % against the reference instrument data. The correction was necessary because of

an instrument failure and the change of the classifier (DMA) within the mobility particle size

spectrometer.


spectrometer in Prague, since a calibration workshop at TROPOS in September 2013 (where this

instrument took part due to another project) a scalar correction factor of +5 % was applied to the whole

size range, dotted line: 10 % range, solid line: 25 % range.


spectrometer in Ljubljana, dotted line: 10 % range, solid line: 25 % range. During the comparisons very

low concentrations in the smallest particle fractions were observed due to the use of diffusions screens for

better comparability with CPC data.

In Ljubljana, the mobility particle size spectrometer measured very constantly, but overall, the criteria

for measurement devices in official monitoring networks (temporal drift within three months less than

10 % and expanded measurement uncertainty for daily averages less than 25 %) could not be reached

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

0 1 2 3 4 5 6 7 8

Prague Okt 12

Prague Mai 13

Prague Mai 14

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

0 1 2 3 4 5 6 7 8

Ljubljana Jun 13

Ljubljana Nov 13

Ljubljana Jun 14

N2 N3 N4 N5 N6 N7 N8

N2 N3 N4 N5 N6 N7 (N8)

- 20 -


for all size classes and all UFIREG instruments.

Table 11: Overview of all QA experiments within UFIREG

Augsburg Chernivtsi Dresden6 Ljubljana Prague

December 2012 July 2013

(CPC comparison) July 2012 July 2012 October 2012

July 2013 November 2013 October 2012 June 2013 May 2013

March 2014 February 2013 November 2013 May 2014

May 2013 June 2014 November 2014

September 2013

January 2014

May 2014

October 2014

The regular maintenance activities of the instruments and data processing as well as data validation

were harmonised within the project. Regular maintenance included, for instances, the check of the

correct sizing, the flow rates and the high voltage of the instruments once per month by the stations

personnel. With regard to the status parameters such as temperature, flow rates of the instruments and

relative humidity, the entire data set was classified automatically into valid and invalid data.

Furthermore, periods for service maintenance and automated function control measurements were

excluded automatically during the data validation procedure.

2.1.3 Imputation of missing data

Imputation of missing data was only possible for Augsburg and Prague where an additional urban

background measurement station was available. Imputation was performed using a modified APHEA

(Air Pollution and Health: A European approach) procedure (Katsouyanni et al., 1996; Berglind et al.,

2009). Missing hours of one monitor were imputed by a weighted average of the other monitor. If the

respective hour was not available at both monitors, the average of the preceding and the following

hour was used. Daily 24-hour averages were only calculated if 75 % of the hourly values were

available. As a sensitivity analysis effect estimates for Augsburg and Prague were recalculated using

the data set with imputed missing data.

2.2 Epidemiological data

2.2.1 Daily deaths and hospital admissions

Data on daily deaths and hospital admissions were collected for each of the five cities. Data on daily

deaths were collected for the residents of the city who died in the city. Data on daily hospital

admissions for each city were collected for the residents of the city who were hospitalized within the

city. Infants younger than 1 year were excluded from the analyses. Only ordinary (no day-hospital)

and acute (no scheduled) hospitalizations were considered, since the aim of the study was to

investigate the association between daily pollutants concentrations and acute adverse health outcomes.

Moreover, only the primary diagnosis was considered for the identification of the outcomes. Finally,

repeated hospitalizations were allowed for each subject; however, repeated events within 28 days with

6 Due to the proximity of Dresden to TROPOS in Leipzig, more QA experiments could be realised.

- 21 -


the same primary diagnosis were eliminated, under the assumption that the two events represent the

same “episode”.

The main diagnosis and cause of death, respectively, are based on the International Statistical

Classification of Diseases and Related Health Problems (ICD-10). Deaths due to natural causes (ICD-

10: A00-R99), deaths and hospital admissions due to cardiovascular (ICD-10: I00-I99) and respiratory

diseases (ICD-10: J00-J99) as well as hospital admissions due to diabetes (ICD-10: E10-E14) were

considered as primary outcomes on which all analyses were conducted. Moreover, secondary

outcomes were taken into account on which only selected analyses were performed (Table 12).

Mortality and hospital admission data for Augsburg and Dresden were obtained from the Research

Data Centres of the Federal Statistical Office and the Statistical Offices of the Länder. For Ljubljana,

mortality data derived from the National Institute of Public Health, hospital admission data were

obtained from the Statistical Office of the Republic of Slovenia. All data for Prague were provided by

the Institute of Health Information and Statistics of the Czech Republic. For Chernivtsi, the source of

mortality data is the Main Statistic Department in Chernivtsi Region, data on hospital admissions were

collected directly from the hospitals.

Depending on the start of the measurements and the availability of epidemiological data, the following

study periods were chosen for the epidemiological analyses: Augsburg and Dresden: 2011 to 2012;

Ljubljana and Prague: 2012 to 2013; Chernivtsi: 2013 until March 2014. The time period 2011 to 2012

for German cities was chosen due to German data protection rules; data on cause-specific mortality

and hospital admissions of 2013 could not be obtained during project duration.

Table 12. Primary and secondary outcomes including ICD-10 codes.

ICD-10 code

Mortality Hospital admissions

Primary

outcome

Secondary

outcome

Primary

outcome

Secondary

outcome

Natural causes of death A00 – R99 X

Diabetes E10 – E14 X

Diseases of the circulatory system I00 – I99 X X

Cardiac diseases I00 – I52 X X

Ischaemic heart diseases I00 – I25 X X

Acute coronary events I21 – I23 X X

Arrhythmias I46 – I49 X X

Heart failure I50 X X

Cerebrovascular diseases I60 – I69 X X

Haemorrhagic stroke I60, I61 X

Ischemic stroke I63, I65, I66 X

Diseases of the respiratory system J00 – J99 X X

Lower respiratory tract infections

(LRTI)

J09 – J18, J20 –

J22 X X

Pneumonia J12 – J18 X

Chronic obstructive pulmonary disease

(COPD) J40 - J44, J47 X X

Asthma J45 – J46 X

- 22 -


2.2.2 Descriptive analyses

Table 13 shows the description of mortality outcomes. There were about 2,500 deaths due to natural

causes in Augsburg, 4,500 in Dresden, 2,000 in Ljubljana, 10,000 in Prague and 2,300 in Chernivtsi

per year. In Augsburg, Dresden and Ljubljana 40 % to 45 % of deaths were due to cardiovascular

diseases in the respective study periods. In Prague, roughly 50 % of deaths occurred because of

cardiovascular causes in 2012. With 67 %, most of the natural death cases in the year 2013 were due

to cardiovascular diseases in Chernivtsi. In all cities, except Chernivtsi, 5 % to 7 % of natural

mortality was attributable to respiratory diseases. In Chernivtsi, the proportion of deaths due to

respiratory diseases was only 2 % in 2013.

Table 13. Description of (cause-specific) mortality outcomes by city.

City Year Population Mean daily natural

death counts (SD)

Mean daily

cardiovascular death

counts (SD)

Mean daily

respiratory death

counts (SD)

Augsburg 2011 266 647 6.9 (2.5) 3.1 (1.7) 0.5 (0.8)

2012 272 699 7.2 (2.8) 3.1 (1.7) 0.4 (0.6)

Chernivtsi 2013 258 371 6.3 (2.7) 4.3 (2.1) 0.1 (0.4)

Dresden 2011 517 765 12.5 (3.6) 5.7 (2.4) 0.7 (0.9)

2012 525 105 13.1 (3.8) 5.8 (2.5) 0.7 (0.9)

Ljubljana 2012 280 607 5.8 (2.5) 2.3 (1.5) 0.4 (0.6)

2013 282 994 5.7 (2.4) 2.3 (1.5) 0.3 (0.5)

Prague 2012 1 246 780 27.1 (5.7) 13.7 (4.1) 1.5 (1.3)

2013 1 243 201 26.5 (5.9) 12.8 (3.8) 1.7 (1.4)

Outcome definitions: natural causes ICD-10 A00-R99, cardiovascular diseases ICD-10 I00-I99,

respiratory diseases ICD-10 J00-J99.

A description of hospital admissions by city for each year is shown in Table 14. So far, hospital

admission data is available for Augsburg (2011-2012), Chernivtsi (2013), Dresden (2011-2012),

Prague (2012-2013) and Ljubljana (2012). In all cities mean daily counts of cardiovascular hospital

admissions were higher than the mean daily counts of respiratory hospital admissions.

- 23 -


Table 14. Description of cause-specific hospital admissions by city.

City Year Population

Mean daily

cardiovascular hospital

admissions (SD)

Mean daily respiratory

hospital admissions

(SD)

Augsburg 2011 266 647 19.5 (8.5) 8.3 (3.9)

2012 272 699 19.7 (8.8) 8.6 (4.4)

Chernivtsi 2013 258 371 12.1 (5.7) 5.2 (3.3)

Dresden 2011 517 765 34.0 (12.6) 11.7 (4.7)

2012 525 105 34.3 (13.3) 11.5 (4.9)

Ljubljana 2012 280 607 14.4 (7.2) 8.2 (4.6)

Prague 2012 1 246 780 22.3 (8.7) 7.9 (4.0)

2013 1 243 201 24.3 (8.1) 9.8 (4.8)

Outcome definitions: cardiovascular diseases ICD-10 I00-I99, respiratory diseases ICD-10 J00-J99

(categories J33, J34 and J35 were excluded by hand for Augsburg and Dresden).

2.2.3 Confounder variables

We also collected information on other variables, used for confounding adjustment. They include

long-term time-trend (date order), indicator variables for weekdays and holidays, meteorological

parameters (air temperature, relative humidity, barometric pressure), and – if available - influenza

epidemics.

Information on influenza epidemics in Augsburg and Dresden were provided by the German Influenza

Working Group of the Robert Koch Institute. Data on influenza epidemics in Prague were obtained

from The National Institute of Public Health in Prague and from the Hygiene Station of the City of

Prague. Information on influenza epidemics in Ljubljana were provided by the National Institute of

Public Health in Slovenia. No information on influenza epidemics was available in Chernivtsi.

2.3 Sociodemographic data

Sociodemographic data such as number of inhabitants (per age-group and per sex), estimated

percentage of smokers, population density or number of newborns and deceased persons was used to

describe the population in the cities involved in the project.

Data for Augsburg derived from the statistical yearbook of Augsburg. For Dresden data were obtained

from the census in 2011 and the Statistical Office of the Free State of Saxony. The Statistical Office of

the Republic of Slovenia provided sociodemographic data for Ljubljana. Data for Prague were

obtained from the Institute of Health Information and Statistics of the Czech Republic and the Czech

statistical office. For Chernivtsi data derived from the Main Statistic Department in Chernivtsi Region.

In all cities, except Augsburg, the number of newborns was higher than the number of deceased

persons during the respective study periods (Table 15).

- 24 -


Table 15. Sociodemographic information of the five UFIREG cities.

City Year Population City area

(km2)

Density of

population* Newborns

Deceased

persons

Augsburg 2011 266,647 146.9 1815.8 2,253 2,820

2012 272,699 146.9 1857.0 2,465 2,950

Dresden 2011 517,765 328.3 1577.1 5,907 4,772

2012 525,105 328.3 1599.4 6,001 5,040

Ljubljana 2012 280,607 275.0 1020.4 3,084 2,272

2013 282,994 275.0 1029.1 2,982 2,242

Prague 2012 1,246,780 496.2 2512.7 14,176 12,411

2013 1,243,201 496.2 2505.4 13,867 12,149

Chernivtsi 2013 258,371 153.0 1688.7 2,751 2,447

* Inhabitants/km2

3 STATISTICAL METHODS AND MODELS

Different statistical methods and models were applied to the UFIREG dataset depending on the

purpose of the varying analyses. In general, averages were calculated with a threshold of 75 % data

availability for all analysis. For most of the analysis the particle number size distribution (PNSD) data

were reduced to 7 size classes (N2=10-20 nm, N3=20-30 nm, N4=30-50 nm, N5=50-70 nm, N6=70-

100 nm, N7=100-200 nm and N8=200-800 nm).

3.1 Analysis of air quality data

A wide variety of analysis was carried out with the UFIREG air quality dataset using different

calculation and statistical software including Excel, the statistical open source project software R

(Version 3.0.17, R Core Team, 2014) and IGOR Pro (Version 6.22) depending on the respective

method.

3.1.1 Temporal and spatial variation

One aim of the UFIREG project was to analyse the temporal and spatial variation of particle number

concentrations (PNC) among the five participating cities. The openair package (Carslaw and Ropkins,

2012a; Carslaw and Ropkins, 2012b) of R was used for visualisation of the temporal variation of each

site, especially for the daily, weekly and seasonal variability. Daily variation analysis was separated

into weekdays and weekends to detect the traffic influence on PNC.

In order to analyse similarities and differences between the stations, Pearson and Spearman regression

analyses were carried out either with hourly and daily averages of non-logarithmically transformed

PNC or logarithmically transformed PNC of the seven size classes. Furthermore, the coefficient of

divergence (COD) was calculated with hourly and daily averages as follows (reported previously by

Birmili et al., 2013, for different sites in Dresden):

CODa,b = √1

𝑘∑ (

𝑁i,a−𝑁i,b

𝑁i,a+𝑁i,b)²𝑘

𝑖=1 (1)

7 R version 3.0.1 (2013-05-16) -- "Good Sport", Copyright (C) 2013 The R Foundation for Statistical Computing

- 25 -


where a and b are two measurement sites and Ni is the observed particle number concentration for the

ith sample, and k is the total number of samples.

3.1.2 Correlation analysis between UFP and basic pollutants and meteorological parameters

To be representative for correct estimation of urban population, monitoring stations selected for the

UFIREG project should fulfil criteria for urban background sites. However, the selected location of the

stations is always a compromise between the requirements on the type of station and real possibilities

as requirements for operation, connection to electricity etc. The aim of the correlation analyses carried

out for UFP, PM and gaseous pollutant concentrations (NO2, NO, SO2, O3, etc.) as well as

meteorological parameters was also to indicate whether and how the selected monitoring stations were

influenced by local sources by indication of the processes and sources involved in the creation and

growth of individual modes of particles in the air.

Statistical analyses and result visualisation were carried out again with R and R package openair

(Carslaw and Ropkins, 2012a; Carslaw and Ropkins, 2012b). A non-parametric Spearman rank

correlation analysis has been applied as a procedure less sensitive to outliers. For comparison, also

Pearson correlation coefficients for logarithmically transformed pollutant and UFP concentrations

were calculated.

3.1.3 Meteorological cluster analyses based on backward trajectories

To investigate the influence of air mass origin on PNSD patterns, a meteorological cluster analysis

based on back trajectories was performed8 according to the trajectory-clustering algorithm used before

in Heintzenberg et al. (2011) which was principally based on the approach of Dorling et al. (1992).

Sensitivity analyses revealed that the best clustering results are based on trajectories reaching 48 h

back in time instead of 72 h or 96 h. Therefore, 48 h back trajectories were computed at the starting

level of 500 m above the ground at 12:00 UTC (consistency with the radio sounding ascents) for every

day using HYSPLIT, a trajectory model provided by the National Oceanic and Atmospheric

Administration (NOAA) Air Resources Laboratory (Draxler and Hess, 2014) As basis for the

trajectory calculation served the data of the Global Data Assimilation System (GDAS) of the NOAA.

Radio soundings were obtained from national meteorological stations in Lindenberg (for Dresden),

Zagreb (for Ljubljana), Oberschleißheim (for Augsburg) and Prague. Highly size resolved particle

number size distribution data and other air pollutants were averaged for the period between 10:00–

18:00 local winter time. Since the dataset of Chernivtsi did not cover two full years when the analysis

was performed, the cluster analysis was not performed for this site.

3.1.4 Nucleation analysis

Frequency of the new particle formation (NPF) events was assessed at every UFIREG station, using

the method described in Dal Maso et al. (2005). The daily plots of PNSD (Figure 5 left), and the daily

plots of the positions of the modes of the multimodal distributions were considered (Figure 5 right);

the method of the mode position determination using mathematical gnostics is described in Ždímal et

al. (2008). Every day was classified into one of the category “event” (nucleation in other words),

“non-event”, “undefined”, or “missing”. The decision path is shown in Figure 6.

8 In cooperation with Leibniz Institute for Tropospheric Research.

- 26 -


Figure 5. Examples of daily plots used for the nucleation analysis. Left side: Daily plot of PNSD. Right

side: Daily plot of PNSD mode positions. Top: event day. Middle: non-event day. Bottom: undefined day.

Figure 6. The decision path used in NPF event characterization, derived from Dal Maso et al. (2005), thick

lines denote “yes”, dashed lines “no“.

The NPF analysis was performed on PNSD data measured between May 2012 and April 2014 at all

UFIREG stations. The only exception was the Chernivtsi station that started the measurements in

January 2013. From the number of event, non-event, undefined, and missing days in each month,

annual cycles were computed. If there were more than 50 % of days in a month classified as missing at

- 27 -


any station, the month was not included in the analysis.

To define the impact of nucleation on the ultrafine particle number concentration, the nucleation

strength factor (NSF) was determined which was first introduced by Salma et al. (2014). It was

calculated as the ratio of UFP and particles larger than 100 nm on nucleation days divided by the ratio

of UFP and particles larger than 100 nm on non-event days (formula 2). Undefined days were

disregarded for this calculation.

NSF =(𝑁10-100nm 𝑁>100nm)NPF days⁄

(𝑁10-100nm 𝑁>100nm)Non-NPF days⁄ (2)

Density plots of five-minute PNSD data averaged for all nucleation and non-nucleation days separated

by seasons including the growth rates for nucleation days were visualised and determined using the

PARticle Growth And Nucleation (PARGAN) model for IGOR software

(http://www.personal.kent.edu/~slee19/softwares.htm) (Kanawade et al., 2012; Erupe et al., 2010;

Verheggen, 2004; Verheggen and Mozurkewich, 2006).

3.1.5 Source apportionment of particle size distribution

For source apportionment, highly size-resolved PNSD data covering a size range of 10 nm to 800 nm

in 40 size bins was used for Augsburg, Dresden, Ljubljana and Chernivtsi. Except for Chernivtsi,

gaseous pollutants (SO2, CO, O3, NO, NO2, NOx) and PM10 were used in correlation analysis with

factors. Meteorological parameters including temperature, relative humidity, wind speed, wind

direction and global radiation were available. All data were used in 1-hour time resolution.

Source apportionment analyses were carried out using US EPA PMF 3.0

(http://www.epa.gov/heasd/research/pmf.html). PMF is a multivariate tool that decomposes a matrix

of data sample into two sub-matrices, the factor profiles and factor contributions (Paatero, 1999). PMF

requires a matrix of data uncertainty, which is an important feature of PMF aiming to scale the data

individually. In this study, uncertainties were estimated using following methods (Ogulei et al., 2006;

Paatero, 2007). Missing values were replaced with 5/6 of the mean values of that size bin. To reduce

their importance in PMF, the uncertainties were set to two times of the mean values. For other data,

the uncertainty is estimated as

𝑆𝑖𝑗 = 0.02(𝑁𝑖𝑗 + �̅�𝑗) + 𝐶3 𝑁𝑖𝑗, (3)

where Nij is the observed number concentration for jth size bin and i

th sample, and �̅�𝑗 is mean value of

jth size bin. C3 is the error fraction and was estimated to be 0.05. An extra uncertainty of 5% was added

to all the size bins in the model.

The original PNSD profiles generated by PMF only provide information on number concentration. To

obtain the factor volume profiles, volume profiles are calculated assuming spherical shape of the

particles. A range of factor numbers (4 - 8) has been tested. The 5-factor method, for instance, was

chosen for source apportionment in Ljubljana. Factor profiles (number and volume size distribution),

contributions to total number and volume concentrations, factor diurnal variations and correlations

between factors and gaseous pollutants were used to assist in determining source types.

http://www.personal.kent.edu/~slee19/softwares.htm

http://www.epa.gov/heasd/research/pmf.html

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3.2 Epidemiological analysis

Epidemiological analyses were conducted to investigate the short-term effects of UFP and other air

pollutants on (cause-specific) mortality and hospital admissions in all the five cities.

3.2.1 General modelling strategy

The association between UFP, other air pollutants, and mortality or hospital admissions was

investigated using Poisson regression models allowing for overdispersion. A two-step modelling

strategy was followed with first establishing the confounder models and then testing the air pollution

effects, to ensure meaningful statistical tests for air pollution effects later on. A basic confounder

model was set up a priori for all cities. In a second step air pollution effects were estimated by city.

The city-specific estimates were then pooled using meta-analyses methods. The exposure-response

function for the air pollutants was assessed using natural cubic regression splines. Finally, the

robustness of the air pollution effects was assessed with respect to confounder selection in sensitivity

analyses.

3.2.2 Confounder adjustment

The basic model included date order (representing time-trend), dummy variables for day of the week

(Monday to Sunday), a dummy variable for holidays (holidays vs. non-holidays), a dummy variable

for the decrease of the populations present in the city during vacation periods (Christmas, Easter,

summer vacation), a dummy variable for influenza epidemics (if available), air temperature (average

of lags 0-1 [lag 0: same-day; lag 1: one day before the event] to represent effects of high temperatures

and average of lags 2-13 [lag 2: two days prior to the event; lag 13: 13 days prior to the event] to

represent effects of low temperatures), and relative humidity (average of lags 0-1 and average of lags

2-13).

Natural cubic regression splines were used to allow for non-linear confounder adjustment. The spline

for date order was fixed to have four degrees of freedom per year to sufficiently represent long-term

trend and seasonality. Splines for meteorological variables were fixed to three degrees of freedom.

3.2.3 Lag structure of exposure

For each primary outcome the following steps were implemented:

(1) Single-lag models were performed from lag 0 (same day of the event) up to lag 5 (five days prior

to the event) to visually examine the lag structure of the association between particle exposures and

health outcomes.

(2) Cumulative effect models (Armstrong, 2006) were used to represent immediate effects (lag 0-1),

delayed effects (lag 2-5) and prolonged effects (lag 0-5). These three cumulative lags were selected a

priori. For each combination of exposure/outcome, the cumulative lag with the strongest effect was

chosen as reference lag. This choice was relevant to identify the lagged exposure to be used for

additional analyses as two-pollutant models, sensitivity analyses on time-trend and temperature

adjustment, and analysis of the secondary outcomes. The choice was based on the meta-analytic

estimates of the a priori cumulative lags, and the heterogeneity among city-specific estimates. In

particular, for each secondary outcome the referent lag from the primary outcome of the same group

was applied.

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3.2.4 Effect modification for age and sex

For the primary mortality outcomes, the epidemiological analysis was conducted for all ages

(increasing the statistical power of the analysis) as well as stratified for deaths among those below 65

years of age, between 65 and 74 years, between 74 and 84 years, and above 85 years for deaths from

natural causes and cardiovascular diseases and for respiratory deaths among those above 75 years. For

all secondary mortality outcomes the analyses were done only for all ages together except for COPD

the analyses were conducted only for the age-group above 75 years of age. For the investigation of

effect modification by sex only deaths from natural causes were analysed for the age-groups outlined

above.

For each primary hospital admission outcome, models were implemented for all ages as well as by

age-group (1-17, 18-44, 45-64, 65-74, 75-84, 85+) and sex. For all secondary hospital admission

outcomes the analyses were done only for cases among those below 75 years of age, between 75 and

84 years and above 85 years.

3.2.5 Meta-analytical techniques

City-specific effect estimates were combined with fixed- and random-effects models. For each meta-

analytical estimate, a ²-test for heterogeneity was performed and the corresponding p-value reported,

together with the I2-statistic, which represents the proportion of total variation in effect estimates that

is due to between-cities heterogeneity.

3.2.6 Sensitivity analyses

The sensitivity of the air pollution effects were assessed by re-running the above described analyses

with the following differences:

(1) Different values of smoothness for the non-linear components were specified, especially for the

time trend.

(2) The functional forms of the smooth terms were compared with polynomials of up to the fourth

order. Models were estimated with polynomials replacing the splines and the sensitivity of the linear

parameter estimates were assessed.

(3) Air temperature and relative humidity were replaced by apparent temperature, a combination of

both. Apparent temperature was calculated using the following formula (Kalkstein and Valimont,

1986; Steadman, 1979): at = -2.653 + (0.994 x temp) + (0.0153 x dp x dp) with at= apparent

temperature, temp=air temperature and dp=dew point temperature.

(4) It was adjusted for air temperature by using temperature above the median for heat effects and

below the median for cold effects (see Samoli et al., 2013).

(5) Additionally, barometric pressure was included in the models.

(6) Effect estimates for Augsburg and Prague were recalculated using the data set with imputed

missing data.

3.2.7 Additional analyses

For each primary outcome, we used two-pollutant models to assess interdependencies of pollutant

effects, including PM and gaseous pollutants in turn. Two-pollutant-models were run only if the

correlation between two pollutants did not exceed 0.6.

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3.2.8 Software

Data management was conducted using SAS statistical package (version 9.3; SAS Institute Inc, Cary,

NC) and IBM SPSS Statistics version 18. Statistical analyses were performed using R project for statis

tical computing (version 2.15.3, http://www.r-project.org/) using the “mgcv” package.

4 CONCLUSIONS

Within UFIREG, a unique data set of quality assured ambient particle number size distribution

measurements in Central European cities was created. Therewith, a comprehensive analysis regarding

air quality and short-term health effects of ultrafine particles was enabled including the application of

a variety of methods. Moreover, it has to be emphasised that for the first time an assessment of a

Ukrainian site was part of such a study.

Difficulties encountered in data collection were caused by the different start points of measurements at

the UFIREG sites and by the delayed start of measurements in Chernivtsi due to bureaucratic and

customs requirements. In addition, limited availability of epidemiological data complicated the

completion of the final data set. As a solution, different time periods were chosen regarding the dataset

for air quality analyses (May 2012 – April 2014; Chernivtsi: January 2013 – April 2014) and the

dataset for epidemiological analyses. As 2013 epidemiological data for German cities will not be

available before spring/summer 2015, the dataset for epidemiological analyses covers the time period

2011 – 2012 for Augsburg and Dresden, the time period 2012- 2013 for Ljubljana and Prague, and the

time period January 2013 – March 2014 for Chernivtsi.

In addition to the analysis, important experiences and knowledge about the PNC measuring routine as

well as about quality assurance measures could be gained although the uncertainty of highly size-

resolved PNC measurements is still higher than for other air pollutants. However, in-depth analysis

can only be performed with size-related PNC data, especially with regard to source apportionment.

In summary, the detailed description of the data collection, statistical methods and models within this

report mentions the limitations of the UFIREG study, but shows also the strengths and the extent of

the analyses carried out within this project.

http://www.r-project.org/

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5 ABBREVIATIONS

ARSO Slovenian Environment Agency

COD Coefficient of divergence

CPC Condensation particle counter

DMPS Differential Mobility Particle Sizer

GDAS Global Data Assimilation System

GLRD Global radiation

HDV Heavy duty vehicles

HYSPLIT Hybrid Single Particle Lagrangian Integrated Trajectory Model

BLfU Bavarian Environment Agency

NOAA National Oceanic and Atmospheric Administration

NPF New particle formation

p Pressure

PM Particulate matter

PMF Positive matrix factorisation

PNC Particle number concentration

PNSD Particle number size distribution

QA Quality assurance

RH Relative humidity

SMPS Scanning Mobility Particle Sizer

T Temperature

TDMPS Twin Differential Mobility Particle Sizer

TROPOS Leibniz Institute for Tropospheric Research

TSMPS Twin Scanning Mobility Particle Sizer

UFP Ultrafine particles

US EPA United States Environmental Protection Agency

WCCAP World Calibration Center for Aerosol Physics

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http://www.sciencedirect.com/science/journal/13522310

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Ultrafine Particles – an evidence based contribution to the development of regional and

European environmental and health policy (UFIREG)

INTERREG IV B CENTRAL EUROPE

Project number: 3CE288P3

Duration: 7/2011 – 12/2014

Website: www.ufireg-central.eu

Project Partners

Technische Universität Dresden, Research Association Public Health Saxony

(Lead Partner)

Fetscherstr. 74, D-01307 Dresden

Dr. Anja Zscheppang | Dr. Monika Senghaas

[email protected] | [email protected]

Saxon State Office for Environment, Agriculture and Geology

Pillnitzer Platz 3, D-01326 Dresden

Dr. Gunter Löschau | Dr. Susanne Bastian


Helmholtz-Zentrum München – German Research Center for Environmental Health

Institute of Epidemiology II

Ingolstädter Landstr. 1, D-85764 Neuherberg

Dr. Josef Cyrys | Dr. Alexandra Schneider


Institute of Experimental Medicine, Academy of Sciences of the Czech Republic

Vídeňská 1083, CZ-142 20 Prague

Dr. Miroslav Dostál

[email protected]

Czech Hydrometeorological Institute

Na Šabatce 2050/7, CZ-143 06 Prague

Jiří Novák

[email protected]

The National Laboratory of Health, Environment and Food

Prvomajska ulica 1, SI-2000 Maribor

Matevz Gobec

[email protected]

L.I. Medved’s Research Center of Preventive Toxicology, Food and Chemical Safety, Ministry of Health,

Ukraine (State enterprise)

Heroiv oborony str. 6, UA-03680 Kiev

Prof. Dr. Dr. Leonid Vlasyk

[email protected]

http://www.ufireg-central.eu/

mailto:[email protected]

Technische Universität Dresden

Research Association Public Health Saxony

Dresden, Germany

www.tu-dresden.de

Saxon State Office for Environment,

Agriculture and Geology

Dresden, Germany

www.smul.sachsen.de/lfulg

Helmholtz Zentrum München – German

Research Center for Environmental Health

(GmbH)

Neuherberg, Germany

www.helmholtz-muenchen.de

Institute of Experimental Medicine,

Academy of Sciences of the Czech Republic

Prague, Czech Republic

www.iem.cas.cz

Czech Hydrometeorological Institute

Prague, Czech Republic

www.chmi.cz

National Laboratory of Health, Environment

and Food

Maribor, Slovenia

www.nlzoh.si

L.I. Medved´s Research Center of Preventive

Toxicology, Food and Chemical Safety,

Ministry of Health, Ukraine (State enterprise)

Kiev, Ukraine

www.medved.kiev.ua

UFIREG is implemented through the CENTRAL EUROPE Programme co-financed by the ERDF

http://www.tu-dresden.de/

http://www.smul.sachsen.de/lfulg

http://www.helmholtz-muenchen.de/

http://www.chmi.cz/

Documents

Data collection and methods - Ufireg · Data collection and methods Report This project is implemented through the CENTRAL EUROPE Programme co-financed by the ERDF UFIREG Ultrafine