Atmospheric Environment 71 (2013) 131e139
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Spatial and temporal variability of PM10 sources in Augsburg, Germany Jianwei Gu a, b, Jürgen Schnelle-Kreis c, *, Mike Pitz a, b,1, Jürgen Diemer d, Armin Reller b, Ralf Zimmermann c, e, Jens Soentgen b, Annette Peters a, Josef Cyrys a, b a
Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 86754 Neuherberg, Germany b Environment Science Center, University of Augsburg, Universitätsstr. 1a, 86159 Augsburg, Germany c Cooperation Group “Comprehensive Molecular Analytics” Joint Mass Spectrometry Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 86754 Neuherberg, Germany d Bavarian Environment Agency, Bürgermeister-Ulrich-Str. 160, 86179 Augsburg, Germany e Joint Mass Spectrometry Center, Chair of Analytical Chemistry, University Rostock, Dr.-Lorenz-Weg 1, 18051 Rostock, Germany
h i g h l i g h t s < Six particulate sources were identified in Augsburg using PMF method. < Combustion and traffic source profiles had larger differences between two winters. < Particulate sources of different origins showed distinct spatial variability. < Traffic source was the main cause of elevated PM10 concentrations at traffic site.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 October 2012 Received in revised form 21 January 2013 Accepted 22 January 2013
Source apportionment of ambient particulate matter (PM10) was carried out using daily chemical composition data collected in winter 2006/07 and winter 2007/08 in Augsburg, Germany. Six factors have been identified and were associated with secondary nitrate, secondary sulfate, residential and commercial combustion, NaCl, re-suspended dust and traffic emissions. Comparing the source profiles between winter 2006/07 and winter 2007/08 showed that they were similar for both winters, except the combustion and traffic emissions factors. The spatial variation of particulate sources was evaluated by analysis of data collected at eight sampling sites during a one-month intensive campaign in winter 2007/ 08. Secondary nitrate, secondary sulfate as well as residential and commercial combustion factors showed strong correlations and low coefficient of divergence (COD) values among eight sites, indicating that they are uniformly distributed in urban area. By contrast, traffic emissions factor and NaCl were highly heterogeneously distributed. These two factors were enhanced greatly at the traffic site and are the cause of elevated PM10 mass concentration at traffic site. It means that for some specific sources of particles showing pronounced spatial variability a central monitoring site could not assess the absolute concentrations across an urban area. Thus, cautions should be taken when approximating average human exposure to these particle sources in long-term epidemiological studies. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Source apportionment Particulate matter PMF Spatial variability Augsburg
1. Introduction Ambient particulate matter (PM) has been found to be associated with adverse health effects by many studies (Peters et al., 2000; Brunekreef and Holgate, 2002; Schwartz et al., 2002; * Corresponding author. Tel.: þ49 89 3187 4605; fax: þ49 89 3187 3371. E-mail address:
[email protected] (J. Schnelle-Kreis). 1 Now at Bavarian Environment Agency, Bürgermeister-Ulrich-Str. 160, 86179 Augsburg, Germany. 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.01.043
Dominici et al., 2005; WHO, 2006). While PM is a complex mixture of components from a variety of sources, studies have been undertaken to better understand the relationship between sourcespecific PM and health effects (Laden et al., 2000; Ito et al., 2006; Andersen et al., 2007; Yue et al., 2007). There is clear evidence that air pollution stemming from transport is an important contributor to these effects (WHO, 2005; Morgenstern et al., 2007). However, the current European Union (EU) legislation controls only the mass concentrations of PM with aerodynamic diameters below 10 mm (PM10) and 2.5 mm (PM2.5) (EC, 2008). These air quality
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standards and limit values are currently being exceeded at many locations in Europe (Mol et al., 2011). The understanding of the source-specific contribution to the overall PM10 burden in ambient air will be helpful by selecting the most effective measure aiming achievement of better air quality in a given city area (Juda-Rezler et al., 2011). Results of source apportionment analyses of PM10 in Augsburg have been published recently (Schnelle-Kreis et al., 2007; Gu et al., 2011). In the latter study PMF method was applied separately to two different data sets (particulate chemical composition (PCC) and particle size distribution (PSD) data) collected at two different monitoring sites in winter 2006/07. The study was focused rather on the comparison of the two approaches: PCC vs. PSD data. In this study, source apportionment was carried out for winter 2006/07 and winter 2007/08 using PCC data only. A comparison of factors was made between both winter seasons. In addition, a one-month intensive campaign was conducted in Augsburg starting on February 13, 2008, during which PCC data were collected at additional seven monitoring sites spreading out over the whole city area. By extension of the source apportionment to the seven additional sites we are able to evaluate the spatial variability of PM sources across the urban area. The results are especially important in view of epidemiological studies conducted in the city of Augsburg. 2. Methods 2.1. Sampling sites and periods The measurements were carried out in Augsburg, an average sized city in southern Germany with a population of about 265,000. The prevailing frequency of the wind direction in winter 2007/08 was 60.5% from the southwest and 24.8% from the northeast, respectively. This wind direction distribution is very similar to the wind direction distribution observed typically in winter seasons in Augsburg (the averaged wind direction frequency for the six winter seasons from 2004/2005 until 2009/2010 was 57% for the southwesterly wind and 26% for the northeasterly wind. Fig. 1 shows the locations of the measurement sites. The main monitoring site of our study was located at Königsplatz (KP) in the city center, whereas the seven satellite sites were spreading out over the whole city area. KP is an official air quality monitoring station operated by Bavarian Environment Agency (LfU). It is located next to roads with high traffic density (30,500 cars per day in year 2000) and is considered as an urban traffic site. The satellite site at Bourgesplatz (BP) is located in a small park and is about 1 km north of KP site. As a major road (and a tram line) is located about 70 m north of BP site, this site is considered as a traffic influenced urban background site. LO (Lorenz Stötter Weg) site is located in a residential area where home heating is expected to have great impact on PM10 levels in winter season (LfU, 2009). Another official air quality monitoring station (LfU) is located on the premises of Bavarian Environment Agency (LfU), approximately 4 km south of the city centre. HO (Hotel tower) site is on the top of the hotel tower, which is about 100 m above the ground. HO site is normally within the well mixed boundary layer and due to the long distance to busy roads and other local sources for particles it represents the urban background. Note that the coarse particle fraction measured at HO site may be “suppressed” to some extent depending on dispersion condition. WE (Wellenburg) is a site in southwestern Augsburg and is next to the natural park. The natural park is located to the west of Augsburg and covers an area of 1200 km2, of which 45% is covered by forest (http://www.naturpark-augsburg.de). Bi (bifa) site is close to the “bifa Environmental Institute”, within an industrial area at the outskirts of Augsburg. The industry area is located in the northeast of Augsburg, downwind of the city. Ki (Kissing) site is about 1 km
Fig. 1. Locations of measurement sites in Augsburg.
north of the town Kissing, which is next to, but not belonging to city Augsburg. The measurement was carried out at KP site between December 12, 2006 and March 23, 2007 (winter 2006/07) as well as between November 14, 2007 and March 31, 2008 (winter 2007/08). In addition, a one-month intensive campaign was conducted at eight sites (including KP) between February 13, 2008 and March 12, 2008. 2.2. Sampling and measurements Table 1 shows the available data of the study. All samples were collected and analyzed in the same way as described by Gu et al. (2011) for winter 2006/07. Briefly, the samples were collected on quartz filters (Whatmann) by high volume samplers (HVS, Digitel DHA 80, Switzerland) at a flow rate of 500 L min1 for 24 h from 0:00 to 24:00. Trace elements, water-soluble ions, elemental carbon (EC), organic carbon (OC), as well as levoglucosan (LEV) were analyzed using subsamples of the original quartz filters. Trace elements including As, Ca, Cd, Ce, Co, Cr, Cu, Fe, K, La, Mg, Mn, Ni, Pb, Sb, Ti, Tl and Zn were measured by inductively coupled plasmamass spectrometry (ICP-MS). Water-soluble ions including Cl, þ þ 2þ 2þ and NHþ SO2 4 , NO3 , Na , K , Ca , Mg 4 were measured by ion chromatography (IC). EC and OC were measured according to VDI 2465 Bl.1. Particulate organic compounds (POC) were analyzed by direct thermal desorption (DTD) gas chromatography-time-of flight mass spectrometry (Orasche et al., 2011), however, only levoglucosan data were used in this study. Detailed descriptions of the measurements and analytical methods can be found in Gu et al. (2011) and LfU (2009). In addition, continuous PM10 mass concentration was measured by beta-attenuation (FH 62 C14 Series, Thermo Scientific, U.S.) at six
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Table 1 Summary of available data in this study.a Sampling sites
Site typeb
Parameters
Start timec
End time
d
Königsplatz (KP)
TS
PM10, HVS , NO, NO2, CO, SO2
12.21.2006 11.14.2007
03.23.2007 03.31.2008
BourgesPlatz (BP) Lorenz Stötter Weg (LO) Bavarian Environment Agency (LfU)
T-UB UB UB
PM10, HVS, NO, NO2 HVSe PM10, HVS T, RH, WS, WD, NO, NO2, CO, SO2
02.13.2008 02.13.2008 02.13.2008 12.21.2006 11.14.2007
03.12.2008 03.12.2008 03.12.2008 03.23.2007 03.31.2008
Hotel tower (HO) Wellenburg (WE) Bifa (Bi) Kissing (Ki)
Tower site Suburban site UB UB
HVS PM10, HVS, NO, NO2, SO2 PM10, HVS PM10, HVSf, NO, NO2, SO2
02.13.2008 02.14.2008 02.13.2008 02.14.2008
03.12.2008 03.12.2008 03.12.2008 03.12.2008
a b c d e f
Table modified from LfU (2009). Site type: TS: traffic site; T-UB: traffic influenced urban background; UB: urban background. Time format: mm.dd.yyyy. HVS: high volume sampler. HVS failure at LO site between 02.17.008 and 02.20.2008. HVS failure at Ki site between 03.01.2008 and 03.05.2008.
sites. As shown in Table 1, gaseous pollutants including NO, NO2, CO and SO2 were measured at KP and some of the satellite sites by commercial gas monitors. Meteorological parameters including temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WD) were obtained at LfU site. PM10 mass concentration and gaseous pollutants concentrations were averaged to 24 h. 2.3. PMF analysis PMF is a widely used receptor model for source apportionment studies (Kim et al., 2003; Song et al., 2006; Pere-Trepat et al., 2007; Pitz et al., 2011). It was developed by Paatero (1997, 1999) and can decompose the data matrix into two sub-data matrixes, the factor profiles and factor contributions, without detailed prior knowledge on source inventories. In this study we used EPA-PMF 3.0 (http://www.epa.gov/heasd/ products/pmf/pmf.html) model for source apportionment analysis. Source apportionment was carried out using data collected at KP site in winter 2007/08, and data from eight sampling sites collected during the one-month intensive campaign (February to March, 2012), respectively. The data and uncertainties were treated in the same way for both analyses. Missing data were replaced with the mean values of that species, and their uncertainties were set to three times of the mean values to reduce their importance. For data below the limit of quantification (LOQ), the values were replaced with half of the LOQ and the uncertainties were set to 5/6 of the LOQ (Polissar et al., 1998). For other data, the uncertainties were set according to equation
h i0:5 u ¼ ðerror fraction concentrationÞ2 þ LOQ 2
(1)
where error fraction is the percentage of uncertainty, LOQ is the limit of quantification (Norris et al., 2008). Error fraction is estimated from sampling error and analytical error. In PMF analysis we assumed 8% for trace elements (from ICP-MS) and 12% for watersoluble ions (from IC). 2.3.1. Winter 2007/08 at KP site 134 daily samples between November, 2007 and March 2008 were used (Five samples were excluded as outliers). For each sample the concentrations of the following 25 species were included: Ca, Cd, Ce, Co, Cr, Cu, Fe, K, La, Mg, Mn, Ni, Pb, Sb, Ti, Tl, Zn, Cl, þ þ SO2 4 , NO3 , Na , NH4 , EC, OC and PM10. Ce, Co, La and Tl were
characterized as weak which automatically increased the uncertainties by three times because they were poorly modeled by PMF. PM10 mass concentration was also included in PMF to apportion factor mass concentration, however, to reduce the influence of PM10 on PMF solution it was characterized as weak. 2.3.2. FebruaryeMarch, 2012 at eight sampling sites Data at eight sampling sites were combined and analyzed together in PMF. In total, 218 daily samples between February 13 and March 12, 2008 were used (Five samples were excluded as outliers). Concentrations of 25 species were pre-included, however, Co, Cr and Tl were excluded due to a large percentage of missing data (data coverage < 40%). Ni, Zn (poorly modeled species) and PM10 were characterized as weak. For both analyses a variety of factor numbers between 5 and 9 have been tested in PMF. In 5-factor method, re-suspended dust factor and traffic emission factors were combined as one factor. The 7-factor method produced an unknown factor with high loading of La (FebeMar, 2008 eight sites), or a factor with high loadings of Cd (winter 2006/07 KP site), or a dust factor primarily consists of Ca and Mg (winter 2007/08 KP site). The 6-factor method was then chosen as the most meaningful result. Bootstrap runs were performed and the factors showed good stabilities. 3. Results and discussion The mean PM10 mass concentration in the study period (November 14, 2007eMarch 31, 2008) was 36.3 mg m3, which is entirely in the same range as the PM10 mass concentration observed during winter seasons in Augsburg from 2004/05 until 2009/2010 (33e47 mg m3). In Supplementary material A we present further results of descriptive analysis for PM10 and major species (Table S1) as well as the diurnal variations of PM10 and gaseous pollutants during the study period (Fig. S1). About 80% of PM10 mass is accounted for the measured chemical species. In the first section of this chapter 3.1 the characterization of the particulate sources in winter 2007/08 at KP site is presented and the source profiles are compared with those already obtained for winter 2006/07 in the analysis described by Gu et al. (2011). In Section 3.2 the spatial and temporal variability of sources is presented for the intensive campaign period from February to March, 2008. In Section 3.3 we discuss the implications of spatial and temporal variability of particulate sources for epidemiological studies. Factors and sources both represent “sources” obtained from PMF analysis, and in this manuscript they are used interchangeably.
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3.1. Factor characterization in winter 2007/08 at KP site and comparison of source profiles between winter 2006/07 and winter 2007/08 In winter 2007/08, six factors were resolved and were associated with (1) secondary nitrate, (2) secondary sulfate, (3) residential and commercial combustion, (4) NaCl, (5) re-suspended dust and (6) traffic emissions. These findings are in line with the current emission inventory for the City of Augsburg (Government of Swabia, 2004). The most important local sources for PM10 are large scale industry (contributing 44% to the total PM10 emission in Augsburg), small scale industry and domestic heating (36%) and traffic emissions (20%). These sources contribute mostly to the factors (3), (5) and (6). The long-range transported secondary aerosol is represented mostly by factors (1) and (2) and contributes to the regional and urban background. Note that in Augsburg about 25% (38 t/a) of local PM10 is emitted by wood combustion (Brandt et al., 2011). Fig. 2 illustrates the factor profiles by concentration (g g1) and percentage of species mass (%). In addition to factor profiles, correlations between PMF factors and gaseous pollutants are used to better characterize the factors. Table 2 shows the Spearman rank correlation coefficients between the factors and gaseous pollutants, as well as levoglucosan (LEV). We also consider possible PM sources in Augsburg and well established tracers for specific sources from other studies when interpreting the PMF factors. The secondary nitrate factor was characterized by nitrate and ammonium. Secondary sulfate was characterized by sulfate and
Table 2 Spearman rank correlations between PMF factors and gaseous pollutants and levoglucosan and factor mass concentrations. Spearman’s r
Nitrate
Sulfate
Combustion
NaCl
Dust
Traffic
NO NO2 CO SO2 LEV
0.44 0.47 0.58 0.48 0.65
0.30 0.25 0.49 0.42 0.64
0.74 0.75 0.71 0.63 0.84
0.03 0.12 0.04 0.13 0.01
0.23 0.39 0.11 0.01 0.05
0.40 0.32 0.17 0.04 0.06
Mass conc. (mg m3)
7.2
8.2
6.2
3.0
8.0
3.0
ammonium. Residential and commercial combustion factor was characterized by OC, EC, K and Cd. This factor is moderately correlated with NO, NO2 and CO, indicating that it is mixed with traffic exhaust emissions. NaCl factor was identified by high loadings of Naþ and Cl, which came from the de-icing agent used in winter season. Re-suspended dust was characterized by crustal elements (Ca and Mg) and other trace elements (Fe, Cr, Mn and Ni) which were believed to come from traffic-related dust. Traffic emissions factor was characterized by EC, Cu and Sb. Detailed descriptions on sources are presented in Supplementary material B (page 2e5, line 20e94). The comparison of the above described results with the results obtained previously in the PMF analysis from winter 2006/07 (refer to Gu et al., 2011) is shown in Fig. 3. Both analyses resulted in the same number of factors and same factor types. However, there are
Fig. 2. Source profiles obtained in PMF analysis in winter 2007/08 using data from KP site (November 14, 2007eMarch 31, 2008). Concentration (g g1) is the mass of species contained in every 1 g of particles in a factor profile. Percentage of species mass (%) is the concentration of a species contained in one factor divided by total factor concentration.
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Fig. 3. Comparison of factor profiles at KP site between two winters (winter 2007/08 minus winter 2006/07). Grey bars are the differences in concentrations (g g 1). Red and blue bars are differences in the percentage of species mass (%). Red ones represent the positive values (i.e. increase in winter 2007/08) and blue ones represent negative values (i.e. increase in winter 2006/07). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
visible differences between the source profiles obtained in the two winters, particularly for traffic factor and residential and commercial combustion factor. The factor profiles of NaCl and re-suspended dust showed minor differences between two winters. Also the differences in the factor profiles for secondary nitrate and secondary sulfate factors between the two winter seasons were small. In winter 2007/08, 11% of sulfate was mixed in the secondary nitrate factor, and 20% of nitrate was mixed in the secondary sulfate factor, while in winter 2006/07 less mixture was observed. In contrast, the residential and commercial combustion factor as well as the traffic emission factor exhibited larger differences regarding their source profiles (i.e. concentration and percentage of mass of certain species). The combustion factor was characterized in winter 2007/08 by OC, EC, K and Cd, and can explain 48%, 46%, 60% and 57% of above species. The factor was strongly correlated with levoglucosan (r ¼ 0.84). In winter 2006/07 the concentrations and the percentage of those species mass were lower. OC, EC and K in this factor accounted for 26%, 18% and 27% of total species in winter 2006/07. Potassium, an inorganic tracer for biomass burning, was found evenly distributed in several factors. Moreover, correlation of this factor with levoglucosan and K was 0.76 and 0.70, respectively, not as strong as correlation with Cd (r ¼ 0.85). A sensitivity study of K was carried out to test the influence of its signal/noise (s/n) ratio on the model result in winter 2006/07. It showed that combustion factor in winter 2006/07 is sensitive to the
s/n of K, and the importance of K as a biomass burning tracer was weakened. Details of the K sensitivity analysis can be found in Supplementary material C (page 5e6, line 96e132). The same sensitivity analysis was made in winter 2007/08 and the residential and commercial combustion factor was not sensitive to K s/n ratio. The differences in contribution of OC and EC to the traffic emission and combustion factor is caused by change of correlations between OC and EC and other species between the two winter seasons. In winter 2006/07 OC and EC were strong correlated with K, Cd, Sb and Cu (r: 0.82e0.92), as well as some other trace elements. In contrast, OC and EC in winter 2007/08 were strong correlated with K and Cd (combustion tracers, r: 0.85e0.91), but weaker correlated with Sb and Cu (traffic tracers, r: 0.65e0.84). It seems that the OC and EC in winter 2007/08 were less associated with other traffic indicators, but more with combustion source indicators. The changes in the source profiles between the two winters demonstrate that PMF can be directly influenced by data uncertainties and inter-correlations of species. The uncertainties for both winters are considered the same as the samples were collected and analyzed in the same way and by the same investigators for both winters. In both winters different species were set as weak because they have low signal/noise ratio, have a large percentage of missing values, or were poorly modeled by PMF. However, we checked the influence of different settings for some specific species on final results and tested various changes of settings like changing
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PM10 from weak to bad and Ce from weak to strong. The PMF results were very stable in this sensitivity analysis for all settings, which indicates that the differences of PMF settings between two winters have little impact on the differences in PMF results for both winters. In fact, the analysis of the correlations between the species for both winter seasons shows some differences in the correlation matrix, which could be reason for the observed differences (data not shown). Also the factors resolved by PMF are rather a combination of a few true source profiles. This mixing of true source profiles in factor profiles depends upon the meteorology or changing correlations between the species, which makes the comparison of two different time periods difficult. 3.2. Variation of PM10 sources in time and space (FebruaryeMarch, 2008) PMF analysis was carried out with combined data at eight sampling sites in the intensive campaign between February 13 and March 12, 2008 (shown in Fig. 1). Six factors have been determined and the source profiles were very similar to those already presented in Section 3.1. One larger difference should be noted: the source profile for the re-suspended factor obtained in this analysis contains only crustal related elements (Ca and Mg). The other trace elements including Fe, Ni, Mn and Cr were found in traffic emissions factor. The specific factor profiles are given in Supplementary material Fig. S3. 3.2.1. Temporal variation Fig. 4 shows the temporal variations of all factors between eight monitoring sites. Fig. 5 shows the box plot of Spearman rank correlation coefficients of each factor between eight sites. In general, the medians of correlation coefficients are above 0.70 for all factors. It means that the temporal correlations between all sites are rather
strong for all factors. However, correlations between some specific sites are lower, especially for NaCl, re-suspended dust and traffic emission factors. Secondary nitrate and secondary sulfate showed very consistent temporal variation among eight sites. Secondary nitrate, secondary sulfate as well as residential and commercial combustion factors were highly correlated among all monitoring sites. However, sulfate at KP site had relatively lower (but still strong) correlations with other sites, within the range of 0.77e0.88, mostly fell in the lower 25 percentile range. Residential and commercial combustion factor generally showed similar temporal variation trends among all sampling sites. High correlation coefficients were observed for this factor, and the median correlation coefficient was 0.94. NaCl factor showed similar temporal variations among all sites except KP site. Overall NaCl factor had a median correlation coefficient of 0.88. At KP site NaCl factor exhibits the same temporal pattern as NaCl factors at other sites only in the second half of the measurement period. The correlation coefficients between KP and other sites were in the range of 0.40e0.77, whereas, the other seven sites exhibited stronger correlations between each other with correlation coefficients in the range of 0.85e0.97. The low correlation of NaCl factor between KP site and other sites was due to more intensive suspension of de-icing and also probably more deicing agent at traffic site (KP site). Re-suspended dust had a median correlation coefficient of 0.81 among eight sites. KP site showed different temporal variations than other seven satellite sites. The correlations between KP and the seven sites were in the range of 0.45e0.56, lower than the intercorrelations among the other seven sites. For traffic emissions factor, KP, BP, LO, LfU, Bi and Ki sites had moderate to strong inter-correlations (r: 0.69e0.94). HO and WE sites showed weaker correlations with other six sites (0.31e0.70). It may be due to the fact that HO is a tower site 100 m above ground
Fig. 4. Temporal variation of PMF factors at eight sampling sites between February 13, 2008 and March 12, 2008.
137
0.6 COD
0.7
0.4
0.6 0.3
0.4
0.2
0.5
Spearman’s correlation
0.8
0.9
0.8
1.0
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Nitrate
Sulfate
Combustion
NaCl
Dust
Traffic
Nitrate
Sulfate
Combustion
NaCl
Dust
Traffic
Fig. 5. Box plots of Spearman rank correlation coefficients (left) and coefficient of divergence (COD, right) for PMF factors between eight different measurement sites. (In calculating COD and correlations, there are 4 missing data for LO site, 1 for LfU and WE site, respectively, and 8 for Ki site.) The box plots indicate the maximum, 75th percentile, the median, 25th percentile, and the minimum of all the data, respectively.
and WE is a suburban site, which are quite different from the other sites that are in or near the city. 3.2.2. Spatial variation Fig. 5 displays the box plots of coefficient of divergence (COD) of each factor between eight monitoring sites. The COD value was used in recent studies to examine the degree of discrepancy between data sets from two monitoring sites (Wongphatarakul et al., 1998; Cyrys et al., 2008),
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !2 u u1 Xn xij xik COD ¼ t ; i¼1 x þ x n ij ik
3.2.3. Source contributions to PM10 Fig. 6 shows the contribution of each specific particle source to the overall PM10 mass concentration at the eight monitoring site (in mg m3 and in percentage). The actual measured PM10 are well explained by modeled PM10 in PMF, with the slope of the linear regression of 0.94 and the R square of 0.97. Detailed information is provided in Supplementary material D (page 6e7, 138e150). First of all, it is striking that the levels of secondary nitrate and secondary sulfate factors were very similar at all eight sites (circa. 5.8 mg m3 for sulfate factor and 7.1 mg m3 for nitrate factor, respectively). It is in line with their strong correlations and low COD
(2)
where xij and xik are the ith observations of x at j and k sampling sites, respectively. n is the total observation number. If two sampling sites are very similar, COD approaches zero, otherwise, COD approaches one. The COD was calculated between each pair of monitoring sites. The lowest COD values were observed for long-range transported aerosol: secondary nitrate factor (median COD 0.13) and secondary sulfate (median COD 0.21), indicating that secondary aerosol is uniformly distributed over the whole study area. Higher CODs were observed for re-suspended dust (0.28), residential and commercial combustion (0.31) and NaCl (0.33) factors. NaCl had a median COD value of 0.33. Significantly higher COD values were obtained between KP and other sites (0.50e0.64, in the upper 25 percentile range). It is due to higher concentrations of this factor observed at KP site in the first half of intensive campaign before February 27 (see Fig. 4). Snowfall was recorded on 15 February 2008, followed by sunny days with no rain/snowfall. The average temperatures were relatively low (4 to 2 centigrade). The re-suspension of the de-icing agent at the major road in the vicinity of the KP site during the dry period after fresh application of de-icing salt led to the elevated concentrations of NaCl factor at this site. Re-suspended dust had a median COD value of 0.28. Resuspended dust is mainly related to the natural dust, and showed similar concentrations at eight sites. The highest COD values (median: 0.62) were found for traffic emissions factor, indicating that traffic-related air pollutants are distributed heterogeneously over the city area, with much higher concentrations at KP site throughout the whole study period. KP site was greatly impacted by traffic. The concentration of traffic emissions factor at KP was 23 times of the lowest concentration observed at WE site (18.8 vs. 0.81 mg m3).
Fig. 6. Factor contributions to PM10 mass concentration at eight measurement sites in Augsburg between February and March, 2008.
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values. The contribution of residential and commercial combustion to PM10 mass concentrations varied from 2.6 at HO site to 5.2 mg m3at LfU site. The smallest contribution of this factor to PM10 at HO site could be explained by the sampling height at 100 m above the ground. Obviously at this elevation the impact of local combustion on PM10 mass concentration is limited. The re-suspended dust factor showed rather similar concentrations at all eight sites (0.77e 0.94 mg m3) and they accounted for only a small fraction of PM10. Larger differences were observed for the contributions of NaCl and traffic emissions factors to PM10 at different sites. Especially at KP site the contribution of traffic emission (and NaCl factor) was enhanced. The elevated concentrations of the two factors at KP site are responsible for the significantly higher total PM10 mass concentration at this site. Considering the contribution of different sources to PM10 mass concentration in percentage, it becomes clear that the contribution of specific particle sources depends on the site type. Thus the contribution of the secondary sulfate and nitrate to total PM10 varied from 30% at the traffic site (KP) up to 60% at a tower site (Ho) e in spite of the similar absolute contribution expressed in mass concentrations (mg m3). Also the contribution of traffic emissions factor to PM10 at KP was 45%, whereas at other (background) sites it ranged from 4.5% to 20%. It means that local abatement measures (Low Emission Zone (LEZ) or transit ban for heavy-duty vehicles) can offer an efficient strategy for reducing PM10 levels in urban air especially at traffic sites, where the existing air quality guidelines for PM10 are mostly being exceeded and where traffic emissions contributed to a large fraction of the PM10 mass concentration. 3.3. Implications for epidemiological studies Measurements from central site monitors are often used as the metric of exposure in epidemiologic studies of the health effects of air pollution. Also in Augsburg a number of epidemiological studies using air pollutant data from one or few monitoring sites were carried out within the framework of the research platform KORA (Cooperative Health Research in the Augsburg Region) (von Klot et al., 2005, 2011; Holle et al., 2005; Rückerl et al., 2007; Peters et al., 2009; Hampel et al., 2010). The probability of exposure misclassification depends on the epidemiological study design and on the spatial and temporal variation of the air pollutants under study. For short term epidemiological studies which typically use time series of PM pollutants at one central monitoring site (or average of a set of monitoring sites) the focus is on capturing the day-to-day (or hour-to-hour) changes in pollutant concentrations for the whole study population. Therefore, if the particulate pollutants are not highly temporal correlated, exposure misclassification can happen. In long-term epidemiological studies the study population is followed over a long period of time. In the analysis typically different groups of individuals (cohorts) are compared: an exposed and an unexposed cohort. Therefore estimating exposure at the individual-level is of great concern in such studies. If the absolute concentration of air pollutants differs substantially across the study area (large spatial variation), there is a potential for exposure misclassification for this type of epidemiological study. There exist a number of studies dealing with the spatial variability of PM mass concentration in urban areas. The homogeneous assumption of PM was evidenced by many studies (Burton et al., 1996; Oglesby et al., 2000; DeGaetano and Doherty, 2004; Sajani et al., 2004). Many other studies, however, found PM to be heterogeneous in urban areas (Cyrys et al., 1998; Grivas et al., 2004; Nerriere et al., 2005). In a study of 27 U.S. urban areas, Pinto et al. (2004) found spatial distribution of PM varied for urban areas across the country, and he pointed out that the conclusion of spatial variability of PM mass concentration of an individual urban area
cannot be made until the data is investigated. One thing is certain that particulate source (local vs. regional) is an important factor controlling the intra-urban spatial variability (Monn, 2001). Our results show clearly that particulate sources differed substantially in their temporal and spatial variability. Secondary sulfate and secondary nitrate exhibited great uniformity both in temporal variation and in absolute levels across the urban area. Therefore no exposure misclassification will occur for secondary nitrate and sulfate for short term or long-term epidemiological studies. Residential and commercial combustion factor showed also good agreement in temporal variations and similar concentrations across the urban area. In contrast, traffic emissions factor varied highly across the city. The concentration level is obviously highly affected by the traffic intensity on roads close to the measurement site. Moderate to strong correlations were found between six sites in or near the city area. This means that traffic emissions showed generally good temporal variations in urban areas (not including HO and WE sites), but differed greatly in concentrations. Studies aiming at assessing the role of traffic-related particles in health effects on a spatial scale need therefore careful assessment of traffic-related exposures, for example by modeling or improved measurement strategies. 4. Conclusions Source apportionment using PMF method has been carried out in winter 2007/08. Six factors were characterized and were associated with secondary nitrate, secondary sulfate, residential and commercial combustion, NaCl, re-suspended dust and traffic emissions. We compared results of PMF analysis obtained for winter 2006/07 and winter 2007/08. In both winters it becomes apparent that the six factor method is the most suitable method. Factor profiles were in general similar between two winters. However, some pronounced differences in factor profiles were observed for residential and commercial combustion and traffic emissions factors. The changing correlations for the PM species between the two winter seasons also changed the factor profiles, making the comparison of two factors profiles and factor contributions in two different time periods difficult. Source apportionment was carried out separately using data at eight sampling sites in a one-month intensive campaign. Six factors were obtained and the spatial variability of particulate sources was evaluated. Secondary nitrate and secondary sulfate were uniformly distributed in urban area. Residential and commercial combustion factor showed similar temporal variation (high correlation coefficients) but larger differences in concentrations (higher COD). Re-suspended dust showed relatively lower correlation coefficients and lower COD values. The concentrations of traffic emissions factor varied greatly from traffic site to urban background sites, however moderate to strong correlations were found among six sites traffic and urban background sites but not for the suburban and tower sites. Acknowledgments This work was supported by the Helmholtz Zentrum München, German Research Center for Environmental Health, the Bavarian Ministry for Environment and Consumer Protection under grant U47 and China Scholarship Council. We would also thank Dr. K. Wolf for preparation of Fig. 1. Appendix A. Supplementary material Supplementary material related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2013.01.043.
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