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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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Report: Data collection and methods
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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Report: Data collection and methods
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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Report: Data collection and methods
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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Report: Data collection and methods
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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Report: Data collection and methods
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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Report: Data collection and methods
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.
Augsburg Dresden Prague Ljubljana Chernivtsi
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.
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Report: Data collection and methods
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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Report: Data collection and methods
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
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Report: Data collection and methods
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
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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.
Augsburg Dresden Prague Ljubljana Chernivtsi
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.
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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.
Figure 2. Results of quality assurance experiments against WCCAP reference mobility particle size
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
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Report: Data collection and methods
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.
Figure 3. Results of quality assurance experiments against WCCAP reference mobility particle size
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.
Figure 4. Results of quality assurance experiments against WCCAP reference mobility particle size
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)
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Report: Data collection and methods
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.
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Report: Data collection and methods
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
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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.
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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).
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Report: Data collection and methods
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
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Report: Data collection and methods
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.
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Report: Data collection and methods
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
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Report: Data collection and methods
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.
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Report: Data collection and methods
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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Report: Data collection and methods
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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Report: Data collection and methods
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.
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Report: Data collection and methods
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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Report: Data collection and methods
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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
[email protected] | [email protected]
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
[email protected] | [email protected]
Institute of Experimental Medicine, Academy of Sciences of the Czech Republic
Vídeňská 1083, CZ-142 20 Prague
Dr. Miroslav Dostál
Czech Hydrometeorological Institute
Na Šabatce 2050/7, CZ-143 06 Prague
Jiří Novák
The National Laboratory of Health, Environment and Food
Prvomajska ulica 1, SI-2000 Maribor
Matevz Gobec
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
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