Intraurban and Longitudinal Variability of Classical Pollutants in Kraków,...

[Full title: Intraurban and Longitudinal Variability of Classical Pollutants in Kraków, Poland, 2000–2010]

Int. J. Environ. Res. Public Health 2015 , 12 , 4967-4991; doi:10.3390/ijerph 120504967 International Journal of Environmental Research and Public Health ISSN 1660-4601 www.mdpi.com/journal/ijerph Article Intraurban and Longitudinal Variability of Classical Pollutants in Kraków, Poland, 2000–2010 Hyunok Choi 1, *, Steven Melly 2 and John Spengler 3 1 Department of Environmental Health Sciences, Epidemiology, and Biostatistics, School of Public Health, State University of New York at Albany, One University Place, Rm 153, Rensselaer, NY 12144, USA 2 Department of Epidemiology and Biostatistics, Drexel University School of Public Health, 3215 Market St., Philadelphia, PA 19104, USA; E-Mail: sjm 389@drexel.edu 3 Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, P.O. Box 15677, Landmark 406 West, 401 Park Drive, Boston, MA 02215, USA; E-Mail: spengler@hsph.harvard.edu * Author to whom correspondence should be addressed; E-Mail: hchoi@albany.edu; Tel.: +1-518-402-0401. Academic Editor: Paul B. Tchounwou Received: 5 January 2015 / Accepted: 30 March 2015 / Published: 6 May 2015 Abstract: In spite of a dramatic decrease in anthropogenic emissions, ambient concentrations of major pollutants have not changed within many urban locations. To clarify the relationship between ambient air quality trend and the population exposures, we compared the intraurban versus temporal variability of the collocated measurements of five major air pollutants including particulate matter (PM) with an aerodynamic diameter <10 µm (PM 10 ), < 2.5 µm (PM 2.5 ), tropospheric ozone (O 3 ), sulfur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ), in Kraków, Poland, during the 2000  2010 period. Strong seasonal trends and overall absence of spatial heterogeneity in PM 10 and PM 2.5 , except in the traffic monitoring site, were observed across the monitoring network. The range of median PM 2.5 concentrations during winter (54–64 µg/m 3 ) was 3- to 4-times higher than the summer medians (15–26 µg/m 3 ) across the sites during 2009  2010. Furthermore, large proportion of PM 10 appears to be comprised of PM 2.5 (PM 2.5 /PM 10 concentration ratios range, 0.5–0.7). At each monitoring site, the Pearson’s correlation coefficients between PM 2.5 and PM 10 OPEN ACCESS

Int. J. Environ. Res. Public Health 2015 , 12 4968 ranged between 0.944 and 0.963, suggesting a health-relevance of PM 10 monitoring. One ln-unit increase in PM 10 was associated with 92%–100% increase in PM 2.5 concentrations in the same location. While PM 10 did not demonstrate a clear temporal trend, SO 2 concentrations steadily declined by 40% during the 2000–2010 period. Summertime median NO 2 concentration was acutely elevated (70  g/m 3 vs. 22  g/m 3 ) at the traffic oriented site compared to the city’s central monitoring site. The traffic and the industrial sites were associated with highest number of days during which 24-hour mean PM 10 and PM 2.5 concentrations exceeded the European Union standard. Steadily growing contributions by vehicular emissions appear to be associated with the absence of clear trend in PM 10 . Current practices of air quality control within Kraków may not be adequate for the protection of the public’s health Keywords: air pollution; Krakow; coal combustion; exposure misclassification; exposure assessment 1. Introduction In spite of reduction in anthropogenic emission of major air pollutants within Europe during the last several decades, such a trend has not been matched by corresponding declines in childhood asthma and allergy prevalence [1,2]. Poland represents an example of such a contradiction. Staring around 1954 under the Communist regime [3], the country has emerged as one of the highest producers and consumers of coal within Europe [4–7]. For example, total annual emission of particulate matter within Kraków, a city with one of the highest historic levels of air pollution within eastern Europe, is estimated at 150,000 tons during the 1970 s [3]. The associated mean ambient PM 10 concentrations during the same period range between 180 μ g/m 3 (the city center) and 109 μ g/m 3 (the suburbs) [3]. Starting in 1980 s, a number of semi-ecologic investigations in Krakow have shown an association between chronic exposures to airborne PM with cause-specific mortality [8,9]. In particular, exposures to particulate matter (PM) have demonstrated robust associations with wide number of health end-points [10–18]. At same time, concerns over the deterioration of the natural environment as well as the city’s cultural heritage sites have also grown [19]. Around 1989, Poland’s political transition to democracy following the collapse of communism has led to a substantial decrease in airborne concentrations of SO 2 , black carbon, PM, and airborne heavy metals [20]. Beginning around 1995, both regional and national government bodies have made concerted efforts to improve the air quality [3]. To deepen our understanding of the early-life environmental contributions on childhood asthma and neurocognitive impairments, we have been following prospective birth cohort in Krakow since 2000. Our exposure assessment analyses have shown that individual pregnant woman’s personal exposure to particle-bound large PAHs is predominantly influenced by corresponding ambient concentrations [21–23]. In addition, there is an extremely high correlation between total sum of eight pro-carcinogenic PAHs and simultaneously monitored PM 2.5 concentration [10,24,25]. Furthermore, between-person variability in personal exposure to PAHs at given 48-hour window are much smaller than within-person variability [23] or that of the mean ambient concentration [21]. We reported that time-activity pattern

Int. J. Environ. Res. Public Health 2015 , 12 4969 of the individual women was not a significant predictor of the personal exposure to particle-bound PAHs [21]. Contrary to our expectation, prenatal exposures to PM 2.5 and PAHs pose significantly increased risks of intrauterine growth restriction, wheezing symptoms, and asthma during childhood, respectively, in spite of reduction in coal-burning related pollutant emissions [10–15,21–23,26–29]. Considering the impact of the ambient sources on the personal exposure, the overarching aim of this investigation is to characterize the intraurban trend of five major pollutants across the years 2000  2010. The time-period of our interest corresponds to prenatal and first seven to ten years of the cohort children’s life. This analysis is expected to lay the groundwork for the clarification of the relationship between long-term intraurban trend and chronic exposure profile of each child in the cohort. Furthermore, we posit a priori that reduction in coal-burning related emissions is associated with temporally corresponding decline in PM 10 concentration during the 11-year period of interest. In order to answer this postulate, we: (1) describe the overall trend in the five pollutant concentrations at the six monitors over an 11-year period; (2) compare the size of the season-dependent variability in the five pollutant concentrations according to the site; and (3) explore the influence of the known emission sources and the meteorological factors on PM 10 and PM 2.5 concentrations. Pollutants of interest include particulate matter (PM) with aerodynamic diameter < 10 µm (PM 10 ), PM < 2.5 µm (PM 2.5 ), tropospheric ozone (O 3 ), sulfur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ). 2. Methods 2.1. Study Site Characterization Kraków, Poland, holds a unique position within Polish cultural and academic heritage. Located in southeastern Poland (see Figure 1), it encompasses 327 km 2 and supports 757,400 inhabitants as of 2005. The city has at least three well-recognized air pollution sources: industrial and coal-fired power plants [20], coal-burning domestic stoves with no or outdated abatement technologies [4,5], and automobile traffic [20]. Following Poland’s annexation to Soviet Union around 1954, a coal-burning steel mill ( i.e . Lenin Steelworks) and a power plant ( i.e . Kraków- Łę g plant) were built [3]. To date, these industrial plants continue to provide electricity and heat for new sections of the city. However, collapse of communism in 1989 has reduced the heavy industrial activities within and around the city [3]. Such shift also introduced a gas-operated heating system within the city (covering approximately 30% of the homes) [3]. Accordingly, sulfur dioxide and particulate matter concentrations have steadily decreased. Kraków is located in the Vistula river valley surrounded by Carpathian Foothills to the south and The Kraków-Cz ę stochowa Upland to the north (Figure 1). This geographic location has been associated with atmospheric inversions approximately 27% of the entire year, particularly during wintertime [3]. 2.2. The Air Pollutant Sampling and Analysis The ambient air quality monitoring network in Kraków is operated by Voivodship Inspectorate for Environmental Protection in Krakow (VIEP). Krakow air monitoring network was launched in 1991 in collaboration between the US Environmental Protection Agency and Voivodship Sanitary-Epidemiological

Int. J. Environ. Res. Public Health 2015 , 12 4970 Station in Kraków (1968  2001) and the Voivodship Inspectorate for Environmental Protection in Kraków (1992  present) [30]. It has been providing automatic continuous measurement of air pollutants such as SO 2 , NO, NO 2 , NO x , CO, O 3 , particulate matter with an aerodynamic diameter less than 10 μ m (PM 10 ), and from 2009 also PM 2.5 . Laboratory of VIEP got accreditation (contract no AB 176) of Polish Centre for Accreditation (PCA) in 1998 for air quality monitoring testing as a first air monitoring network laboratory in Poland. Primary automatic analytic methods for the pollutants during our investigation period include: UV fluorescence for SO 2 and chemiluminescent method for NO and NO 2 with gas analyzers produced by Thermo Environmental Instruments Inc, (Franklin, MA, USA, model 43 A for SO 2 and 42 for NO and NO 2 ), Environment S.A. (Poissy Cedex, France, model AF 22 M for SO 2 and AC 32 M for NO and NO 2 ) and Teledyne Advanced Pollution Instrumentario (San Diego, CA, USA, model API 100 A and API 200 A). (Thermo Environmental Instruments, Inc Franklin, MA, USA, absorption of UV radiation for O 3 with ozone analyzers produced by Thermo Environmental Instruments Inc, (Franklin, MA, USA, model 49 i) and Environment S.A. (Poissy Cedex, France, model O 3 42 M); beta attenuation, oscillating microbalance and optical method for PM 10 and PM 2.5 with instruments produced by Andersen Instruments, Inc. (Smyrna, GA, USA, model RAAS 10), Rupprecht & Patashnick, Co. (Albany, NY, USA, model 1400 TEOM), Met One Instruments, Inc. (Grants Pass, OR, USA, model BAM-1020), Environment S.A. (Poissy Cedex, France, model MP 101 M) and GRIMM Aerosol Technik GmbH & Co (Ainring, Germany). As a laboratory with accreditation (PCA no AB 176) it has implemented system of quality control and assessment in monitoring network according to PN-EN ISO/IEC 17025 norm. It is focused on internal quality control based on qualified staff, instruments calibrations, and completeness of measurements series. To guarantee accuracy and reliability of derived measurements, Krakow VIEP laboratory participate in inter-laboratories comparisons (both in Poland and other EU countries, e.g. Joint Research Center, Ispra, Italy) as well as in national and international proficiency tests. It is a member of AQUILA network. It is also responsible for setting up inter-calibration meeting on the national as well as EU level as a National Calibration Reference Laboratory. PM 10 , SO 2 , and NO 2 were monitored in all six stations year round as 24-hr mean concentrations. O 3 was monitored in four stations ( i.e. , URBAN, CENTRAL, SUB 1, and SUB 2) during 2000  2010 period. In contrast, PM 2.5 were monitored in four stations ( i.e ., URBAN, TRAFFIC, INDU, and SUB 2) during 2009  2010 period only. Meteorological data were monitored in INDU, SUB 1, and SUB 2 sites for temperature and wind speed during January 2000  December 2010. Figure 1 shows the approximate location of the six monitoring stations providing data for this analysis. The Rynek G ł ówny (CENTRAL) station sits atop a bell tower in Old Town Square of Kraków of approximately 0.04 km 2 in size. Since construction during 13 th century, this largest central plaza in Europe has been a pedestrian square. In contrast, the Aleja Krasi ń skiego (TRAFFIC) station is located on a busy road in the commercial hub near the CENTRAL site. The Krowodrza (URBAN) station sits on the northern mixed residential and commercial zone as the urban background site. Nowa Huta (INDU) station represents a mixed suburban and industrial zone. Prokocim (SUB 1) station represents the newly expanded southern district. Kurdwanów (SUB 2), located in southern edge of Krakow, represents urban background site.

Int. J. Environ. Res. Public Health 2015 , 12 4971 Figure 1. Six monitoring stations cover four districts. The city center is shown in gray. Considering Rynek G ł ówny (CENTRAL) (2000  2004) as the reference point, Krowodrza (URBAN) (2000  2010), Aleja Krasi ń skiego (TRAFFIC) (2000  2010), Nowa Huta (INDU) (2000  2010), Prokocim (SUB 1) (2000-2003), and Kurdwanów (SUB 2)(2010) are 3.9 km, 1 km, 9.8 km, 7.5 km, and 7 km away, respectively 2.3. Statistical Analysis 2.3.1. Descriptive Analysis Present analysis includes the data from four to six monitoring station during January 2000 and December 2010 period for PM 10 , SO 2 , O 3 , and NO 2 and 2009  2010 period for PM 2.5 . Seasons were defined as summer (June  August), transitional (April, May, September, and October), and winter (November  March). Considering large variability in sample size by site, year and season, extensive non-parametric analyses were conducted for each pollutant. The relevance of predictor variables were examined using Mann–Whitney U-test or the Kruskal-Wallis test depending on the number of categories for the independent variables at α = 0.05 level of significance. There were no pollutant concentrations below the detection limit. All extreme and outlying values were double-checked for accuracy in measurement. Upon positive verification, they were retained in the data. Descriptive analysis was conducted to identify monitoring sites, season, and year, which demonstrate significantly elevated concentrations.

Int. J. Environ. Res. Public Health 2015 , 12 4972 2.3.2. Linear Regression Model Pollution variables were natural-log (ln) transformed in order to achieve normal distributions (Komolgorov-Smirnov tests > 0.05) and homoscedasticity. To better understand the relative contribution of PM 10 on PM 2.5 , a linear regression model of PM 2.5 was run at four respective monitoring sites which simultaneously monitored PM 10 on PM 2.5 in 2009  2010 combined data. The outcome, PM 2.5 , was modeled as a linear function of PM 10 as the main predictor variable, controlling for temperature, and wind speed, at the four collocated sites. Consistent with earlier investigations, the model regression coefficient was defined as a marker for model accuracy, and adjusted-R 2 as a marker for model precision [31]. 2.3.3. Generalized Linear Mixed Effects Model A linear mixed effects model was fit by entering year, month, and sites as indicators variables shown in equation [1] in order to detect a trend without imposing a structure on the relationship. The reference categories were set as Saturday, December, CENTRAL and Year 2010 for the variables, weekday, month, site, and year, respectively:       ) ( ) ( ) ( ) ln( 5 1 6 1 11 1 10 1 WS Temp Site Weekday Month Year X p i o i o m i m n i n i                       (1) Where α represent the y-intercept; β n , γ m , θ o , δ p , ζ , and η , respectively, represent the slope of the independent variables. All independent variables were forward selected if the probability of given variable in the model showed F ≤ 0.05, and removed it if the probability of the model had F ≥ 0.10. The pollutant, X, concentration was predicted by rewriting equation [1] as follows:       )] ( ) ( ) ( [ ] [ ] [ 5 1 6 1 11 1 10 1 WS Temp Site Weekday Month Year EXP EXP X p i o i o m i m n i n                       (2) In order to compare the relative importance of each predictor (e.g., year), we calculated concentration impact factor of given predictor variable as IF i = exp[ ∑β i × (variable) i ], holding all other variables constant [32]. That is, the impact factor refers to concentration change associated with a given predictor variable apart from the baseline level ( i.e . y-intercept). Accordingly, the intercept term of the equation [2] reflects the mean concentration at the baseline level for all predictors (e.g., Saturday, July, CENTRAL site, Year 2010, wind speed decrease by ≥ 1 m/s, and one °C reduction in ambient temperature from 14.60 °C). The impact factor at the reference level for a given variable equals 1, given that exp[0] = 1. Accordingly, impact factor > 1 indicates predicted concentration, which is greater than the baseline concentration. On the other hand, impact factor < 1 indicates a lower predicted concentration for a given predictor variable relative to the baseline level. We conducted all statistical analyses in SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). All figures were generated using IBM ® SPSS © version 22.0 (SPSS Inc., Chicago, IL, USA).

Int. J. Environ. Res. Public Health 2015 , 12 4973 3. Results and Discussion 3.1. Descriptive Analyses 3.1.1. PM 10 Table 1 and Figure 2 show site-specific central tendencies and the exceedance days (>25 μ g/m 3 for PM 2.5 and >50 μ g/m 3 for PM 10, based on the current EU standard), and PM 2.5 /PM 10 ratios. During summer, TRAFFIC (21%) and INDU (16%) sites were respectively associated with the highest number of days during which 24-hour mean PM 10 concentration exceeded 50 µg/m 3 (the current EU standard) compared to the CENTRAL site (3%). Two suburban sites (SUB 1 and SUB 2) were associated with even fewer number of exceedance days during the transition season (5 and 2%, respectively). Similar trend was seen in the number of exceedance days for PM 10 during the transition season for TRAFFIC (31%) and INDU (37%) site, compared to the CENTRAL site (9%). Such spatial variability was particularly acute during winter, in which TRAFFIC and INDU had highest proportion of exceedance days (39% and 51%, respectively, vs. 13% in CENTRAL site). INDU was associated with a widest range for daily PM 10 concentration (6.6  592  g/m 3 ) during winter (Figure 2). Due to the high mean summer PM 10 concentration at TRAFFIC site, the mean winter/summer ratio for PM 10 concentrations were lower for TRAFFIC (1.5), compared to the INDU (1.9) as well as URBAN (2.0). Figure 2. Distributions of daily concentrations of PM 10 during summer (June, July, and August); transition (April, May, September, and October); winter (November through March). The dotted line shows the EU standard of 50 µg/m 3 for PM 10 . Boxes show 25 th, 50 th and 75 th percentile; the whiskers show 5 th and the 95 th percentile values. The symbols, ○ and * , represent measurements that are >1.5- and >3-fold of the interquartile range. Examining the city-wide average PM 10 annual levels of the 11-year period, we observe year to year variability but little evidence regarding improvement of PM 10 air quality (43.7 ± 26.5; 35.9 ± 24.4; 68.1 ± 46.5; 60.5 ± 45.4; 58.7 ± 39.6; 57.4 ± 44.6; 70.6 ± 64.3; 57.4 ± 42.4; 54.8 ± 39.3; 64.0 ± 40.9; and 57.1 ± 39.5 μ g/m 3 ). Such annual mean is considerably higher than those reported in other urban background sites in European countries during the 1998  2002 period (Germany 28–38 μ g/m 3 ;

Int. J. Environ. Res. Public Health 2015 , 12 4974 Spain 31  42 μ g/m 3 ; Sweden 17  23 μ g/m 3 ; the Netherlands 25 μ g/m 3 ; United Kingdom 25 μ g/m 3 ; Switzerland 24 μ g/m 3 ) [33]. 3.1.2. PM 2.5 As indicated by Table 1 and Figure 3, site-specific daily mean PM 2.5 concentrations showed a distinct seasonal trend. The median PM 2.5 concentration during summer were highest at the TRAFFIC site (26  g/m 3 ) and uniform overall at other sites (18  g/m 3 for URBAN; 15  g/m 3 for INDU; and 16  g/m 3 for SUB 2). In addition, the same site was also associated with higher number of exceedance days (6%) as well as highest mean PM 2.5 concentration (25.2 ± 7.0  g/m 3 ), compared to all other sites during the summer. During the transition season, TRAFFIC site was associated with the highest median (40  g/m 3 ) as well as a highest number of exceedance days (8%), whereas other sites demonstrated overall uniform median concentration (25  g/m 3 at URBAN; 27  g/m 3 at INDU; 26  g/m 3 at SUB 2). In contrast, similar numbers of exceedance days as well as the mean were observed during winter for the URBAN, TRAFFIC, and INDU sites (11%, 12%, and 13%, respectively) compared to the SUB 2 site. Accordingly, such seasonal pattern was associated with highest median winter/summer ratios for the INDU and SUB 2 (3.7 and 3.4, respectively) and the lowest winter/summer ratio for the TRAFFIC (2.5). Taking all four sites together, the combined annual mean concentrations of PM 2.5 were 43.6 ± 31.6  g/m 3 during 2009 and 46.7 ± 43.1  g/m 3 during 2010. Such concentrations far exceeded the annual mean EU standard of 10  g/m 3 [34]. Figure 3. Distributions of daily concentrations of PM 2.5 during summer (June, July, and August); transition (April, May, September, and October); winter (November through March) of PM 2.5 , The dotted line shows the EU standard of 25 µg/m 3 . 3.2. PM 2.5 and PM 10 Relationship 3.2.1. PM 2.5 /PM 10 Concentration Ratio The relative abundance of fine fraction to PM 10 is shown according to site and season in Table 1. The PM 2.5 /PM 10 ratio exhibited a distinct seasonality. However, there were no clear differences across

Int. J. Environ. Res. Public Health 2015 , 12 4975 the sites in the ratios. During the summer, the mean ratio ranged between 0.5 and 0.7 among the four sites. During the winter, the same ratio ranged between 0.7 and 0.8. Overall, these ratios are consistent with ratios observed in other urban and semi-urban locations within Europe, including Netherlands, Germany, and Spain at 0.8 [33]. However, considerably lower ratios have been observed more frequently in U.S. locations (range, 0.3–0.7) [35], in Sweden and in the Canary Islands (0.4), Santiago, Chile (range, 0.4  0.6) [32], or Athens, Greece (range, 0.4  0.6) [32]. 3.2.2. Pearson’s Correlation Coefficients The ln-transformed PM 2.5 and PM 10 across the sites were associated with coefficients between 0.826 and 0.963 based on the collocated measurements on given day (Table 2). In particular, at each site ( i.e ., URBAN, TRAFFIC, INDU, and SUB 2, respectively), the correlations between PM 2.5 and PM 10 ranged between 0.944 and 0.963. Within URBAN, INDU, and SUB 2 sites, one ln-unit increase in PM 10 was able to explain 91%, 93%, and 91% of total variability in PM 2.5 , respectively (Table 3). In addition, one ln-unit increase in PM 10 concentration predicted 92%, 100%, and 99% increase in PM 2.5 in the same sites. In contrast, the same model for the TRAFFIC site was associated with lower accuracy ( β = 0.74) and precision (adjusted R 2 = 0.60) in predicting PM 2.5 concentration per same unit increase in PM 10 . 3.2.3. SO 2 Clear, yet, overall consistent seasonal variability in the median SO 2 concentration was observed across the sites in the 2000  2010 combined data (Figure 4). As shown in Table 4, the mean winter SO 2 concentration was approximately 3-times higher than that during the summer, except in SUB 2. Spatial variability in SO 2 concentration was examined by standardizing the concentration at given site by that at the CENTRAL site, collocated measurement on given date (Table 4). During summer, SO 2 concentration was highest at the TRAFFIC site relative to the CENTRAL site (1.8 vs. 1.1 in all remaining sites). A same pattern for SO 2 was again observed during the transition season with highest ratio for the TRAFFIC site (1.8) compared to the remaining sites (range, 0.8  1.2). However, during the winter, little differences were observed among the URBAN, TRAFFIC, INDU, and SUB 1 sites (range, 0.9  1.3). During the years 1968  1973, mean daily SO 2 concentration in Krakow was 119 µg/m 3 with various sub-sections of the city reporting even higher mean annual concentration of SO 2 (80  120 µg/m 3 ) [3]. In contrast, the median concentration during the winter over the years 2000  2010 period (Table 4 and Figure 4) at the CENTRAL (20 µg/m 3 ) and the INDU (14 µg/m 3 ) sites reflect a reduction in SO 2 contribution to ambient air pollution in Kraków. Spatiotemporal variability in relative abundance of SO 2 against PM 10 was compared as annual mean SO 2 /PM 10 ratio based on collocated 24-hour measurements (Figure 5). Wide variability in SO 2 /PM 10 ratio was observed between the sites at the onset of the study period. The three sites in the northeastern portion of the city (CENTRAL, URBAN, and TRAFFIC) are associated with the highest ratios (range, 0.5  0.6), while SUB 1 and INDU site have ratios < 0.4. However, the rates at all sites, except URBAN, were associated with a uniform decline to 0.3 in 2002. Subsequently, the annual mean SO 2 /PM 10 ratios decreased steadily in URBAN, INDU, and TRAFFIC sites between 2004 and 2009.

Int. J. Environ. Res. Public Health 2015 , 12 4976 Table 1. Concentration distributions for PM 10 (years 2000  2010) and PM 2.5 (years 2009–2010). a refers to the number of days that exceeded the current EU standard. PM 2.5 ( μ g/m 3 ) PM 10 ( μ g/m 3 ) PM 2.5 /PM 10 N Mean ± SD Min Max >25 a (%) N Mean ± SD Min Max >50 a (%) Mean ± SD CENTRAL summer 305 33.7 ± 14.6 9.4 93.9 33(3%) transition 459 39.8 ± 18.8 9.8 112.6 123 (9%) winter 507 55.3 ± 43.0 9.2 334.3 210 (13%) winter/summer 1.4 URBAN summer 90 18.7 ± 6.5 7.0 42.0 12 (1%) 495 29.2 ± 11.0 10.0 75.0 18 (2%) 0.7 ± 0.1 transition 120 28.8 ± 13.7 7.0 68.0 57 (4%) 751 47.5 ± 24.3 8.0 147.0 281 (21%) 0.6 ± 0.1 winter 202 62.8 ± 37.0 7.0 207.0 180 (11%) 1069 69.5 ± 49.6 7.7 354.0 598 (36%) 0.8 ± 0.1 winter/summer 3.0 2.0 TRAFFIC summer 116 25.2 ± 7.0 11.0 42.0 58 (6%) 548 49.1 ± 22.8 12.8 169.1 214 (21%) 0.6 ± 0.1 transition 132 46.1 ± 24.8 10.0 138.0 113 (8%) 700 59.2 ± 29.4 11.0 222.4 414 (31%) 0.7 ± 0.1 winter 209 76.6 ± 52.6 8.0 350.0 191 (12%) 947 85.7 ± 58.9 6.8 424.8 644 (39%) 0.7 ± 0.1 winter/summer 2.5 1.5 INDU summer 109 14.9 ± 6.6 3.0 36.0 7 (1%) 890 35.8 ± 18.7 7.0 126.0 158 (16%) 0.5 ± 0.1 transition 167 30.9 ± 16.3 4.0 83.0 90 (7%) 1147 53.0 ± 32.1 5.0 191.0 502 (37%) 0.6 ± 0.1 winter 242 65.4 ± 42.1 6.0 246.0 211 (13%) 1491 73.2 ± 57.1 6.6 592.0 856 (51%) 0.8 ± 0.1 winter/summer 3.7 1.9 SUB 1 summer 202 31.4 ± 11.5 12.4 70.5 14 (1%) transition 297 38.6 ± 16.8 10.5 102.6 72 (5%) winter 385 44.8 ± 33.0 8.6 206.0 110 (7%) winter/summer 1.2

Int. J. Environ. Res. Public Health 2015 , 12 4977 Table 1. Cont. PM 2.5 ( μ g/m 3 ) PM 10 ( μ g/m 3 ) PM 2.5 /PM 10 N Mean ± SD Min Max >25 a (%) N Mean ± SD Min Max >50 a (%) Mean ± SD SUB 2 summer 89 16.8 ± 6.3 5.0 30.0 9 (1%) 84 30.2 ± 10.0 8.0 50.0 0 (0%) 0.6 ± 0.1 transition 118 31.6 ± 21.1 5.0 106.0 60 (5%) 119 42.4 ± 26.8 6.0 133.0 30 (2%) 0.7 ± 0.1 winter 61 71.8 ± 56.6 14.0 234.0 49 (3%) 30 55.0 ± 27.1 17.0 103.0 14 (1%) 0.8 ± 0.1 winter/summer 3.4 1.6 Table 2. Pearson’s correlation coefficients between PM 2.5 and PM 10 among the sites . ** denotes correlation coefficient which are significant at a < 0.01. PM 10 PM 2.5 CENTRAL URBAN SUB 1 TRAFFIC INDU SUB 2 URBAN TRAFFIC INDU SUB 2 PM 10 CENTRAL 1 0.829 ** 0.875 ** 0.835 ** 0.833 ** URBAN 1 0.841 ** 0.751 ** 0.898 ** 0.961 ** 0.951 ** 0.928 ** SUB 1 1 0.737 ** 0.825 ** TRAFFIC 1 0.836 ** 0.903 ** 0.880 ** 0.944 ** 0.911 ** 0.904 ** INDU 1 0.888 ** 0.886 ** 0.918 ** 0.963 ** 0.896 ** SUB 2 1 0.871 ** 0.826 ** 0.947 ** PM 2.5 URBAN 1 0.967 ** 0.957 ** TRAFFIC 1 0.951 ** 0.953 ** INDU 1 0.947 ** SUB 2 1

Int. J. Environ. Res. Public Health 2015 , 12 4978 Table 3. Site-specific model of PM 2.5 (outcome) as a linear function of PM 10 (predictor), adjusting for temperature and wind speed. Site Name Predictor β (95% CI) Adjusted-R 2 URBAN y-intercept − 0.14 ( − 0.32 0.05) (Ln) PM 10 0.92 (0.87 0.97) 0.914 TRAFFIC y-intercept 0.62 ( − 0.31 1.54) (Ln) PM 10 0.74 (0.51 0.97) 0.602 INDU y-intercept − 0.53 ( − 0.65 − 0.40) (Ln) PM 10 1.00 (0.97 1.03) 0.931 SUB 2 y-intercept − 0.38 ( − 0.57 − 0.18) (Ln) PM 10 0.99 (0.94 1.04) 0.909 Table 4. Concentration distributions for SO 2 , O 3 , and NO 2 by site and season, 2000  2010. SO 2 O 3 NO 2 N Mean ± SD MIN MAX N Mean ± SD MIN MAX N Mean ± SD MIN MAX CENTRAL summer 360 7.7 ± 3.4 1.3 25.1 29 38.0 ± 12.8 23.5 72.0 246 23.6 ± 6.7 9.7 43.5 transition 545 10.6 ± 5.1 1.9 37.0 399 29.2 ± 9.4 8.9 60.1 winter 672 24.2 ± 17.1 4.7 193.9 621 35.2 ± 13.1 11.3 93.6 winter/summer 2.8 1.5 URBAN summer 711 6.1 ± 3.3 1.0 25.8 649 48.3 ± 16.7 14.0 130.6 611 29.7 ± 8.7 8.5 59.0 transition 1051 9.7 ± 6.2 1.0 41.1 872 33.8 ± 16.6 3.0 89.6 981 33.9 ± 10.7 8.6 68.5 winter 1397 25.3 ± 21.9 1.0 214.1 1131 24.5 ± 16.0 2.0 85.2 1192 37.7 ± 16.5 7.0 130.0 winter/summer 3.3 0.5 1.2

Int. J. Environ. Res. Public Health 2015 , 12 4979 Table 4. Cont. SO 2 O 3 NO 2 N Mean ± SD MIN MAX N Mean ± SD MIN MAX N Mean ± SD MIN MAX TRAFFIC summer 915 8.6 ± 5.9 1.0 41.9 874 70.5 ± 15.5 25.7 125.7 transition 1236 12.0 ± 7.7 1.0 55.8 1226 69.0 ± 16.4 21.7 123.5 winter 1582 25.0 ± 19.2 2.0 204.1 1526 62.3 ± 19.3 20.8 152.6 winter/summer 2.9 0.9 INDU summer 849 6.5 ± 3.7 1.0 27.3 884 25.0 ± 7.1 7.0 53.9 transition 1116 8.3 ± 4.8 1.0 37.5 1152 28.7 ± 9.1 2.7 61.0 winter 1509 18.3 ± 14.9 2.7 183.7 1586 35.2 ± 14.3 7.0 130.0 winter/summer 2.5 1.3 SUB 1 summer 182 7.6 ± 2.7 1.3 14.7 179 49.3 ± 13.9 15.9 109.4 169 25.1 ± 7.9 7.3 49.7 transition 266 8.2 ± 4.4 1.7 24.4 204 41.2 ± 17.4 5.7 78.8 261 28.8 ± 9.2 6.7 57.1 winter 350 21.6 ± 18.1 2.8 162.6 287 30.6 ± 15.9 5.2 73.6 302 32.6 ± 13.2 6.7 80.8 winter/summer 2.2 0.6 1.3 SUB 2 summer 87 2.7 ± 1.4 1.0 7.0 86 44.5 ± 14.1 18.0 78.0 86 31.5 ± 9.2 14.0 56.0 transition 120 4.8 ± 2.9 1.0 13.0 117 32.5 ± 14.7 4.0 70.0 112 31.3 ± 11.2 12.0 68.0 winter 58 17.6 ± 18.2 2.0 75.0 69 21.7 ± 19.0 1.0 62.0 69 40.2 ± 16.0 17.0 87.0 winter/summer 4.8 0.3 1.3

Int. J. Environ. Res. Public Health 2015 , 12 4980 Figure 4. Distributions of daily concentrations SO 2 by season. The dotted line shows the EU standard of 125 µg/m 3 for SO 2 . Figure 5. SO 2 /PM 10 concentration ratio according to site and year. 3.2.4. NO 2 In contrast to other pollutants, NO 2 exhibited larger spatial heterogeneity in their median concentrations. Such heterogeneity was particularly apparent during summer (Tables 4 and 5, Figures 6 and 7). During the summer, the median NO 2 at the TRAFFIC site was 3-times higher than that at the CENTRAL site (70 vs. 22  g/m 3 ). During all seasons, the median NO 2 was lowest at the CENTRAL site compared to all other sites. This reflects the fact that the CENTRAL station sits within a square, protected from automobile traffic. In contrast, TRAFFIC was also the site in which the inverse trend was observed against the season. While all other sites were associated with an elevated median NO 2 concentration during winter, the median NO 2 concentration was highest during summer at the TRAFFIC site.

Int. J. Environ. Res. Public Health 2015 , 12 4981 As shown in Figure 6, exceedance of the EU annual standard of 40 μ g/m 3 was observed most frequently at the TRAFFIC site. Largest seasonal fluctuation for the median NO 2 was observed at CENTRAL (Winter/summer = 1.5). During the winter, the median NO 2 concentration in TRAFFIC was approximately 1.8-times higher than that at the CENTRAL (60  g/m 3 vs. 33  g/m 3 ) (Table 4). Relative abundance of NO 2 against PM 10 was estimated as annual mean NO 2 /PM 10 ratio based on collocated 24-hour measurements (Figure 6). TRAFFIC was the only site for which NO 2 /PM 10 ratio consistently remained greater than unity. Figure 6. Distributions of daily NO 2 concentration (µg/m 3 ) by season. The dotted line shows the EU annual mean standard of 40 µg/m 3 . Figure 7. NO 2 /PM 10 ratio by site and year. Furthermore, Table 5 shows the spatial variability in concentration ratios of PM 10 /PM 10, considering the CENTRAL site in the denominator. Overall, there was little difference in PM 10 concentration at URBAN, INDU and SUB 1 sites, considering the PM 10 concentration CENTRAL site as the reference. Regardless of season, the ratios of PM 10 concentrations of given site, relative to CENTRAL site did not markedly differ from unity (range, 0.9  1.3). On the other hand, the median PM 10 concentration at the TRAFFIC site was 50% (during summer and transition season) and 60% higher (during winter) than those at the CENTRAL site (range, 1.5  1.6). As shown in Table 5, spatial concentration ratios were highest at TRAFFIC for PM 10 , SO 2 , and NO 2 regardless of season.

Int. J. Environ. Res. Public Health 2015 , 12 4982 Table 5. Spatial concentration variability ratios using collocated monitors in 2000  2010 combined data. Denominator (reference) is set as the concentration of given pollutant at CENTRAL. Numerator Denominator URBAN CENTRAL TRAFFIC CENTRAL INDU CENTRAL SUB 1 CENTRAL N Mean ± SD N Mean ± SD N Mean ± SD N Mean ± SD PM 10 Summer 70 1.2 ± 0.4 279 1.5 ± 0.5 263 1.1 ± 0.4 161 1.2 ± 0.3 Transition 152 1.3 ± 0.4 419 1.5 ± 0.5 420 1.3 ± 0.6 271 1.1 ± 0.3 Winter 135 1.3 ± 0.4 496 1.9 ± 0.7 483 1.3 ± 0.4 263 1.2 ± 0.4 Overall 357 1.3 ± 0.4 1194 1.7 ± 0.6 1166 1.3 ± 0.5 695 1.1 ± 0.3 SO 2 Summer 253 1.1 ± 0.5 321 1.8 ± 0.6 281 1.1 ± 0.5 132 1.1 ± 0.4 Transition 464 1.2 ± 0.5 522 1.8 ± 0.6 411 0.9 ± 0.4 265 0.8 ± 0.5 Winter 604 1.3 ± 0.4 650 1.3 ± 0.3 575 0.9 ± 0.3 333 0.9 ± 0.3 Overall 1321 1.2 ± 0.4 1493 1.6 ± 0.6 1267 0.9 ± 0.4 730 0.9 ± 0.4 NO 2 Summer 92 1.3 ± 0.3 197 3.3 ± 0.7 213 1.2 ± 0.3 128 1.1 ± 0.2 Transition 262 1.1 ± 0.3 376 2.5 ± 0.7 297 1.1 ± 0.3 200 1.0 ± 0.2 Winter 347 1.1 ± 0.3 584 1.8 ± 0.4 580 1.0 ± 0.2 269 1.0 ± 0.3 Overall 701 1.1 ± 0.3 1157 2.3 ± 0.8 1090 1.0 ± 0.2 597 1.0 ± 0.3 3.2.5. O 3 Compared to the summer O 3 concentration, the median level during transition and winter were 20% and 50%, respectively, of the summer level at the URBAN station. In SUB 1 station, the median O 3 decreased by 20% during transition, and by 40% during winter compared to the median during summer. Within SUB 2 station, the median O 3 decreased by 26% during the transition season, and by 67% during winter compared to the median during summer (Table 4 and Figure 8). Such levels remained well under the EU standard, 120 µg/m 3 , based on the daily 8-hour mean. Figure 8. Distributions of daily mean tropospheric ozone concentration by season. The dotted line on (a) shows the EU standard of 120 µg/m 3 for maximum daily 8-hour mean. 3.3. Regression Model Results Figures 9–11 and Table A 1 show the effects of the site, year, month, season, day of the week, temperature, and wind speed on the pollutants. The mean predicted concentration of PM 10 , PM 2.5 , SO 2 , NO 2 , and O 3 were 5.38 μ g/m 3 , 5.61 μ g/m 3 , 2.55 μ g/m 3 , 4.31 μ g/m 3 , and 3.19 μ g/m 3 , respectively at the

Int. J. Environ. Res. Public Health 2015 , 12 4983 reference points ( i.e ., site CENTRAL, year 2010, summer, Saturday, wind speed ≥ 0.90 m/s, and temperature ≥ 14.60 °C). Figure 9. Yearly concentration impact factors. 3.3.1. Site Effect Mean concentration difference at each site relative to CENTRAL is shown for the pollutants based on regression coefficients and standard error in Table A 1. The mean concentrations of PM 10 were 19% higher in TRAFFIC and 10% higher at INDU site, compared to the CENTRAL site. For PM 2.5 , CENTRAL, SUB 1, and SUB 2 represent the reference sites due to missing measurements in these sites. For PM 2.5 , the mean concentrations at URBAN, TRAFFIC, and INDU sites were not markedly different from the reference sites (impact points range, 1.00  1.14). Similarly, mean concentration at TRAFFIC was 13% higher than that at CENTRAL, while the SUB 1 and SUB 2 sites had mean SO 2 were 9% and 7% lower than that at CENTRAL. Holding all other variables constant, the mean concentration of NO 2 was 42% higher than that in CENTRAL. 3.3.2. Year Effect As shown in Figure 9, yearly trend of PM 10 and NO 2 remained relatively constant over 2000  2010 period. The results of regression models (Table A 1) and the concentration impact factor show that the mean concentrations of PM 10 and NO 2 remained overall constant throughout the monitoring period. Specifically, concentration impact factors for PM 10 ranged between 0.88 and 1.03 over the period 2000–2010 or, differed from the reference point by 1% per year. The yearly effect of 2009 on PM 2.5 shows that there was a 3% increase in mean concentration, after accounting for other variables, including temperature and wind speed. For NO 2 , the concentration impact factors remained near 0.99 throughout the monitoring period. In contrast, there was a dramatic decrease in annual mean SO 2 concentration over the same period (Figure 8). Considering year 2010 as the reference point (impact point, 1), the impact factor of SO 2 steadily decreased from 1.37 in 2000 to 0.90 in year 2009. The yearly trend of O 3 was 20%  23% lower than the reference year 2010. However, the impact factor increased by 7%  25% in year 2002  2003 period. Subsequently, it leveled off towards unity in subsequent years.

Int. J. Environ. Res. Public Health 2015 , 12 4984 Such PM 10 observation is consistent with a more recent analysis by Junninen et al. (2009), which has not shown a clear long-term PM trend in peak ambient levels for PM since our investigation during 2000  2002 period [4]. For example, during the winter of 2005, the peak ambient concentrations for PM with an aerodynamic diameter <10 µm (PM 10 ) was 400  g/m 3 and peak ambient benzo[ a ]pyrene was 200  g/m 3 [4]. 3.3.3. Month Effect As shown in Figure 10, the effect of the month is strongly correlated with season for all pollutants of our interest. Considering December as the reference point (impact factor, 1) monthly concentration impact factors reach their lowest points during the May, June, July and August for PM 2.5 , PM 10 , SO 2 , and NO 2 . Specifically, July was associated with 14% decrease in mean NO 2 . When the temperature and wind speed variables were excluded from the regression models, the month of July was associated with 7% decrease in mean NO 2 . For O 3 , the effect of the month was in opposite direction. Between January and May period, the impact factor steadily increased from 1.18 to 1.57. Suring summer, the impact factor peaked between 1.52 and 1.61. It subsequently subsided from 1.35 to 0.98 during September to November period. Figure 10. Monthly concentration impact factors 3.3.4. Weekday Effect As shown in Figure 11, weekday played most visible role in NO 2 and O 3 concentrations, but not in PM 2.5 and PM 10 concentrations. During weekdays (Monday  Friday), the mean concentration of NO 2 increased approximately 6% compared to the reference day (Saturday). For O 3 , the same period was associated with impact factor decrease by 6% compared to the reference (Saturday). Contrary to our expectation, our analysis demonstrates overall poor ambient air quality in Kraków, with little improvement during the 11-year period. Such a pattern reflects the complex interplay of the sources, valley setting, and meteorological factors [4]. Clear seasonal trends of PM 10 , PM 2.5 , SO 2 , and NO 2 suggest the importance of the both coal-burning as well as traffic sources. The levels of PM 10 and PM 2.5 seen in this study reflect vast improvement in air quality of Kraków, compared to that during the Communist regime. For example, average annual concentration of PM 10 changed from 154 µg/m 3 in

Int. J. Environ. Res. Public Health 2015 , 12 4985 1993 to 49 µg/m 3 in 2007 [36]. During 1992–1999 period, ambient concentration of lead ranged between 0.006 and 0.434 μ g/m 3 (near residential area); 0.016–0.739 μ g/m 3 (near the industrial area); and 0.021–1.147 μ g/m 3 near roads [20]. Recent analysis estimated >50% of PM 10 in Kraków are contributed by coal burning for residential heating, and rest to automobile traffic and industrial power plants [4]. As recently as 2005, 24-hour mean concentration of airborne benzo[a]pyrene (B[a]P) at 200 ng/m 3 has been observed during winter [4]. Small domestic stoves/boilers for heating represent the primary contributors of airborne PM and polycyclic aromatics hydrocarbons during winter [4,5]. Krakow also receives air pollution from the Upper Silesia coal region [37]. Figure 11. Weekday concentration impact factors. However, the ambient levels of PAH and heavy metals continue to be high in the central section of the city, because of increasing traffic (especially diesel) and continued coal burning by industrial and residential sources [4]. In addition, transition to a market economy expanded the vehicular fleet in Kraków. Traffic density in the city center is estimated at 2500–3000 cars/hour between 7 am to 5 pm, and subsequently decreases to 200–500 cars/hour during the night [38]. In residential area, mean traffic is estimated at 50 cars/hour [38]. Our analysis suggests that NO 2 represents a dominant species in TRAFFIC site. TRAFFIC was the only site for which NO 2 /PM 10 ratio consistently remained greater than unity. In addition, the spatial concentration variability ratios (PM 10 /PM 10 , SO 2 /SO 2 , and NO 2 /NO 2 , considering CENTRAL concentration as the denominator) suggest that NO 2 and other vehicular emission factors are considerably higher only at the TRAFFIC site. Three sites in northwestern portion of the city (CENTRAL, URBAN, and TRAFFIC) are associated with the highest SO 2 /PM 10 ratios (range, 0.5  0.6), while SUB 1 and INDU site have SO 2 /PM 10 ratios < 0.4. While CENTRAL, URBAN, and TRAFFIC in northwestern portion of the city comprises high pollution and southeastern section of (comprised of INDU, SUB 1, and SUB 2) had overall lower pollution level and higher seasonal fluctuation in all of the pollutants. Our present observation is consistent with growing emissions from the mobile sources [20] as well as rising secondary particle formation since 1989 [4]. While domestic coal-burning boilers and local heating facilities without an abatement strategy have been replaced by gas-burning boilers [3], it remains unclear how effective they are as remediation strategy. Overall steady reduction in coal-burning related emission has failed to produce corresponding decrease in a

Int. J. Environ. Res. Public Health 2015 , 12 4986 number of childhood morbidity outcomes [3,36]. Furthermore, the average prevalence of childhood asthma has increased by 9% during 1993  2003 period in Poland [36]. More effective strategies for air quality improvement are needed for the protection of the health of the population in Krakow. 4. Conclusions Air quality in Krakow did not improve during the 2000  2010 period. Such a pattern is observed in spite of a dramatic decline in ambient SO 2 concentrations over the 11-year period. The site-combined annual mean PM 10 remained overall constant and considerably higher than the annual value reported for other urban background levels in other European cities. Both PM 10 and PM 2.5 exhibited clear season-dependent and site-specific variability in their mean concentration. Specifically, PM 10 and PM 2.5 concentrations due to vehicular emissions during a given season contributed to the largest spatial variability in their concentrations at the TRAFFIC site, relative to the reference site. Although the PM 2.5 data were available only for years 2009  2010, annual mean concentrations of PM 2.5 were approximately fourto five-times higher than the annual mean EU standard. Furthermore, PM 2.5 /PM 10 ratio based on only 2-year long data suggest PM 2.5 comprises major proportion PM 10 concentration. This is of concern given the profound human health relevance of PM 2.5 exposure. Furthermore, PM 10 is associated with highly accurate (>92%) and precise (>91%) estimation of ambient PM 2.5 concentration in all sites except the TRAFFIC site. In contrast, while no clear seasonal variability was seen for NO 2 , the median concentration was particularly elevated near the traffic sites. Median tropospheric ozone concentration remained well-below the EU standard value throughout the 2000  2010 period. During the years 2000  2010, the air quality of Kraków demonstrates an overall city-wide decline in ambient SO 2 level, which is counterbalanced by the contributions of automobile traffic-related air pollution. Acknowledgments This work is supported by The National Institute of Environmental Health Sciences (NIEHS) (grant numbers 5 P 01 ES 009600, R 01 ES 014939, 5 R 01 ES 008977, 5 R 01 ES 11158, 5 R 01 ES 012468, 5 R 01 ES 10165, and ES 00002), the U.S. Environmental Protection Agency (EPA) (grant numbers R 827027, 82860901, RD-832141), and the National Research Service Award (T 32 ES 07069), and an anonymous Foundation. The authors thank Voivodship Inspectorate for Environmental Protection in Kraków for kindly sharing the data. The authors also thank Frederica Perera, Wies ł aw A. Jedrychowski, Renata Majewska, Elzbieta Mroz, Elzbieta Flak, Agata Sowa, and Ryszard Jacek for their respective contributions to study design, data collection, and management. Finally, the authors are indebted to the anonymous reviewers whose comments improved the quality of the analysis. Author Contributions Hyunok Choi conducted all statistical analyses. Steven Melly compiled multiple data, conducted geographic information analyses. John Spengler oversaw design, implementation, and statistical analysis.

Int. J. Environ. Res. Public Health 2015 , 12 4987 Appendix Table A 1. Mixed-Effects models of PM 10 , PM 2.5 , SO 2 , NO 2 , and O 3 . IF stands for impact factor. Predictors PM 10 PM 2.5 SO 2 NO 2 O 3 β SE IF β SE IF β SE IF β SE IF β SE IF Intercept 1.7 0.0 5.6 1.7 0.0 5.4 0.9 0.0 2.6 1.5 0.0 4.3 1.2 0.1 3.2 Year 2000 − 0.1 0.0 0.9 0.3 0.0 1.4 0.0 0.0 1.0 − 0.3 0.1 0.8 2001 − 0.2 0.0 0.8 0.3 0.0 1.4 0.0 0.0 1.0 − 0.2 0.1 0.8 2002 0.1 0.0 1.1 0.3 0.0 1.3 0.0 0.0 1.0 0.1 0.0 1.1 2003 0.0 0.0 1.0 0.3 0.0 1.3 0.0 0.0 1.0 0.2 0.0 1.3 2004 0.0 0.0 1.0 0.2 0.0 1.2 0.0 0.0 1.0 0.1 0.0 1.1 2005 0.0 0.0 1.0 0.2 0.0 1.2 0.0 0.0 1.0 0.1 0.0 1.1 2006 0.0 0.0 1.0 0.2 0.0 1.2 0.0 0.0 1.0 0.0 0.0 1.0 2007 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 − 0.1 0.0 1.0 2008 − 0.1 0.0 1.0 − 0.1 0.0 1.0 − 0.1 0.0 1.0 0.0 0.0 1.0 2009 0.0 0.0 1.0 0.0 0.0 1.0 − 0.1 0.0 0.9 0.0 0.0 1.0 0.0 0.0 1.0 2010 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 Month January 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 0.2 0.0 1.2 February 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 0.3 0.0 1.3 March − 0.1 0.0 1.0 − 0.2 0.0 0.8 − 0.1 0.0 0.9 0.0 0.0 1.0 0.4 0.0 1.5 April − 0.1 0.0 0.9 − 0.3 0.0 0.7 − 0.2 0.0 0.8 0.0 0.0 1.0 0.5 0.0 1.6 May − 0.3 0.0 0.7 − 0.5 0.0 0.6 − 0.4 0.0 0.7 − 0.1 0.0 0.9 0.5 0.0 1.6 June − 0.4 0.0 0.7 − 0.6 0.0 0.5 − 0.4 0.0 0.7 − 0.1 0.0 0.9 0.5 0.0 1.6 July − 0.4 0.0 0.7 − 0.6 0.1 0.6 − 0.5 0.0 0.6 − 0.2 0.0 0.9 0.5 0.0 1.6 August − 0.3 0.0 0.7 − 0.6 0.0 0.5 − 0.4 0.0 0.7 − 0.1 0.0 0.9 0.4 0.0 1.5 September − 0.2 0.0 0.8 − 0.4 0.0 0.7 − 0.4 0.0 0.7 − 0.1 0.0 0.9 0.3 0.0 1.4 October − 0.1 0.0 0.9 − 0.2 0.0 0.8 − 0.2 0.0 0.8 0.0 0.0 1.0 0.2 0.0 1.2 November − 0.1 0.0 0.9 − 0.1 0.0 0.9 − 0.2 0.0 0.9 0.0 0.0 1.0 0.0 0.0 1.0 December 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

Int. J. Environ. Res. Public Health 2015 , 12 4988 Table A 1. Cont. Predictors PM 10 PM 2.5 SO 2 NO 2 O 3 β SE IF β SE IF β SE IF β SE IF β SE IF Intercept 1.7 0.0 5.6 1.7 0.0 5.4 0.9 0.0 2.6 1.5 0.0 4.3 1.2 0.1 3.2 Day Sunday 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 − 0.1 0.0 1.0 0.0 0.0 1.0 Monday 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 Tuesday 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 − 0.1 0.0 0.9 Wednesday 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 − 0.1 0.0 0.9 Thursday 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 0.1 0.0 1.1 − 0.1 0.0 0.9 Friday 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 − 0.1 0.0 0.9 Saturday 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 Site URBAN 0.0 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 0.0 0.0 1.0 0.1 0.0 1.1 SUB 1 0.0 0.0 1.0 − 0.1 0.0 0.9 0.0 0.0 1.0 0.5 0.1 1.7 TRAFFIC 0.2 0.0 1.2 0.1 0.0 1.1 0.1 0.0 1.1 0.4 0.0 1.4 INDU 0.1 0.0 1.1 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 SUB 2 − 0.1 0.0 1.0 − 0.1 0.0 0.9 0.0 0.0 1.0 0.1 0.1 1.1 CENTRAL 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 Temperature < 4.9 − 0.1 0.0 1.0 0.0 0.0 1.0 0.1 0.0 1.1 − 0.1 0.0 0.9 − 0.1 0.0 0.9 (°C) 4.9  14.6 − 0.1 0.0 0.9 0.0 0.0 1.0 0.0 0.0 1.0 − 0.1 0.0 1.0 -0.1 0.0 0.9 ≥ 14.6 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 Wind speed < 0.90 0.2 0.0 1.2 0.2 0.0 1.3 0.1 0.0 1.1 0.1 0.0 1.1 − 0.1 0.0 0.9 (m/sec) ≥ 0.90 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

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