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Source apportionment of trace elements and black carbon in an urban industrial area (Portland, Oregon)
Atmospheric Pollution Research 10 (2019) 784–794 HOSTED BY
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Source apportionment of trace elements and black carbon in an urban industrial area (Portland, Oregon)
T
A.J. Millera,1, D.M. Radumab, L.A. Georgeb, J.L. Frya,∗ a b
Department of Chemistry and Environmental Studies Program, Reed College, Portland, OR, USA Environmental Sciences and Management, Portland State University, Portland, OR, USA
ARTICLE INFO
ABSTRACT
Keywords: Source apportionment Trace elements Urban air quality Diesel Particulate matter Black carbon Positive matrix factorization X-ray fluorescence Fuel emissions Coal Portland Oregon
This paper reports a current source apportionment study of trace elements and black carbon in particulate matter in industrial Southeast Portland, Oregon. The study aimed to determine whether metal pollution hotspots identified in the area in 2016 had been successfully mitigated by baghouse installation, or whether industrial sources continued to dominate local particulate matter. Particulate matter was filter-collected nearly continuously in 24-hour intervals between October 2017 and March 2018 (101 total filters). Filters were analyzed for 30 elements by x-ray fluorescence; black carbon was measured continuously during filter sampling using an aethalometer. EPA's Positive Matrix Factorization 5.0 was used for source apportionment modeling, yielding a 5factor optimal solution. The source identities were resolved to be diesel and fuel emissions, sea salt, soil dust, secondary sulfates, and metals industry. The metals industry source was much less significant than expected, suggesting effective emissions reductions from the local factory. The source profiles' correlation with wind direction and speed using bivariate polar plots was examined to give further insight into the source identities and their locations. The NOAA HYSPLIT model was also used for air flow back-trajectory analysis. The results of the HYSPLIT and bivariate polar plots suggest that the coal power plant in eastern Oregon is a significant source of sulfates and mercury emissions. Using PMF on particulate matter data from a second industrial location in Southeast Portland (98 filters) revealed the same major sources except for the metals industry source, supporting the conclusion that regional sources dominate particulate matter metals composition, despite measurements made in an industrial urban location.
1. Introduction Air pollution is a costly global environmental and human welfare issue, with an estimated 7 million deaths each year worldwide linked to air pollution exposure (World Health Organization, 2014). Particulate matter (PM) is a component of air pollution that has been shown to have considerable effects on health and mortality: daily mortality increases by about 1% per 10 μg/m3 increase in PM10 (Harrison and Yin, 2000). Trace metals and black carbon (BC) are two components of particulate matter (PM) that are particularly harmful (Harrison and Yin, 2000; Jaishankar et al., 2014; Tchounwou et al., 2012). Trace metals are emitted by a variety of industrial processes (e.g. fuel combustion, vehicular emissions, metals fabrication facilities, mining and smelting, building construction) and natural processes (e.g. weathering, wildfires, sea spray, volcanoes) (Pacyna and Pacyna, 2001; HEI Panel on the
Health Effects of Traffic-Related Air Pollution, 2010; Nriagu, 1989). BC is emitted by combustion of carbonaceous fuels and is known to be strongly correlated with diesel combustion (Schauer, 2003; Hansen, 2005; Cheung et al., 2010). In 2016, the US Forest Service Pacific Northwest Research Station published a study on atmospheric metal pollution in Portland using moss collected across the city in 2013 and created a map of hotspots, finding 15 elements with high concentrations in collected moss samples from at least one location (Gatziolis et al., 2016; Donovan et al., 2016). One notable hotspot was in the industrial Southeast Portland Brooklyn neighborhood, showing high concentrations of cadmium (Cd), arsenic (As), and other metals used in stained glass manufacturing as coloring agents. While identifying spatial locations of hotspots, their study did not directly measure airborne metals concentrations. In 2015, the Oregon Department of Environmental Quality (DEQ) performed air
Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control. ∗ Corresponding author. E-mail address: [email protected] (J.L. Fry). 1 Current address: Department of Environmental Systems Science, Swiss Federal Institute of Technology Zurich (ETHZ), Zurich, Switzerland. https://doi.org/10.1016/j.apr.2018.12.006 Received 16 July 2018; Received in revised form 30 November 2018; Accepted 15 December 2018 Available online 28 December 2018 1309-1042/ © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V.
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toxics monitoring by filter sampling at the site of the Cd and As hotspot, and found high levels of these same metals (Cd average concentration of 0.029 μg/m3, maximum of 0.195 μg/m3, and As average concentration of 0.032 μg/m3, maximum of 0.101 μg/m3; (State of Oregon Department of Environmental Quality, 2015)). This present study has made new and more comprehensive direct air measurements, now after the stained glass manufacturer has installed a baghouse device (Zarkhin, 2016) to reduce toxic metal emissions. Source apportionment modeling with positive matrix factorization (PMF) is a technique commonly used to resolve source identities and contributions of species in a relatively unknown mixture. Kim and Hopke (2008) used PMF for source apportionment of particulate matter in Portland, including many elemental species, inorganic ions, and organic and elemental carbon (OC and EC). They found that wood smoke, secondary sulfates, secondary nitrates, sea salt, airborne soil, gasoline vehicles, diesel emissions, construction, and metal processing were sources of PM. This data, however, is now almost 15 years old and was collected very near the Portland International Airport, rather than in heavy populated Southeast Portland. In other previous studies, PMF results have also been combined with auxiliary information, such as local wind speed and direction, to aid in source identification (Kim and Hopke, 2008; Carslaw et al., 2006; Westmoreland et al., 2007; Yu et al., 2004). By applying source apportionment modeling to our new atmospheric pollution measurements in conjunction with wind analysis, we seek to determine the major sources of airborne metals in this industrial SE Portland location now that the former hotspot toxic metal emitter has controlled emissions.
an ARA Inc Sampler. 98 total filters were used for analysis. Table 1 summarizes the sampling at BRY and LSE. 2.2. Offline filter analysis with x-ray fluorescence (XRF) Sampled filters were analyzed for elemental concentrations by a Thermo Scientific ARL Quant'x Energy-Dispersive X-Ray Fluorescence Analyzer, following the commonly used methods as set by the EPA (Kellog and Winberry, 1999; Yatkin et al., 2012). Irradiation conditions are given in Table S1. Unsampled, clean filters were analyzed by XRF at the same time as the sampled filters to serve as a blank control. The XRF analysis gives the mass of the element per filter area; these values are converted to mass per volume of air (μg/m3). Species averages and standard deviations of samples, as well as the average uncertainty for each element as determined by the XRF analysis, and average blank concentrations are reported in Table 2 for BRY sampling and in Table 3 for LSE sampling. A detection limit was determined using these average uncertainties for each element. Species that have an average or blank concentration below the average uncertainty are reported as “BDL” for “below detection limit”. The uncertainties are given as output of XRF analysis and are determined based on two main sources of error: 1) the peak overlap between elements with similar fluorescence emissions, and 2) calibration uncertainties. For PMF analysis, all samples are used, including those with concentrations below detection limit, because the uncertainties are accounted for in the model (Brown et al., 2015). There was no duplicate sampling nor duplicate analysis of these filters. As an alternative method to show the reliability of our XRF measurements, five filters collected at a separate sampling site were analyzed by the XRF instrument used here and by an XRF of an external private lab. Good agreement (slope = 0.97, R2 = 0.931) was found for many of the elements of interest to our results, including Al, Ca, Cu, Fe, S, Si, and Pb.
2. Methods 2.1. Filter sampling in Southeast Portland 2.1.1. Brooklyn rain yard site Particulate matter of diameter up to 10 μm was collected at the Brooklyn Rail Yard (BRY, see Fig. 1). The site is located at 45.483511, −122.638592, 1.3 km to the Willamette River and 13.3 km to the Columbia River. The site is directly adjacent to railroad tracks operated by the Union Pacific Railroad, and there are nearby streets and highways with small to large vehicles and trucks. Local industries include metals processing companies and a glass factory. Particulate matter was collected on 47 mm diameter Teflon filters with 0.2 μm pore size using a Partisol Plus Sequential Air Sampler Model 2025. Sampling took place for continuous 24-hour periods (midnight to midnight) from October 15, 2017 through March 7, 2018, with some breaks, for a total of 101 filters. The Partisol was programmed to sample continuously with a constant flow rate of 16.7 L min−1. At any given time, there were between 4 and 10 filters stored in the Partisol, either awaiting sampling or after they had been sampled. Filters were stored in a freezer until analysis.
2.3. Black carbon (BC) measurements with aethalometer colocated with filter sampler BC, defined by its optical property of absorbing infrared light, was measured in real-time using a Magee Scientific Dual Wavelength Aethalometer Model AE31, from December 17, 2017 through March 7, 2018. The aethalometer was colocated with the Partisol filter sampler at the Brooklyn Rail Yard. It measures at 880 nm (IR) and 370 nm (UV). The IR absorption is assumed to be due to only BC and the UV absorption to both BC and organic carbon aerosols (Hansen, 2005); only BC measurements from the IR channel are used in this study. The aethalometer measured in 1-minute intervals; 24-hour averages were calculated to match the filter sampling timebase. 2.4. Meteorological analyses 2.4.1. Local wind Weather information for the BRY site was captured with a Davis Instruments Vantage Vue Weather Station, collocated with the Partisol and the aethalometer. The weather station was mounted on a roof, approximately 6 meters above ground to attempt to obtain winds representative of larger-scale winds. Wind speed and direction were recorded every 15 min for the entirety of the sampling period. In order to match the wind speed and direction with the filter samples, the wind data was averaged over 24-hour periods. Vector averaging was used in order to most accurately represent the wind direction over a day, and scalar averaging was used for speeds. Calculations were done in R using the openair package (Carslaw and Ropkins, 2012). To ensure our local measurements are reasonably similar to regional climate, we compare our averages to averages typical of Portland. In the winter months in Portland, wind typically comes from the east and southeast on average, and in the summer months from the north and northwest (Sharp and Mass, 2004). We see, on average, southerly winds in November,
2.1.2. Lower Southeast site Filter sampling was done in 48-hour periods between April 23, 2017 and July 3, 2017 at four other sites within a 1.4 km radius area in another industrial lower Southeast (LSE) Portland neighborhood (see Fig. 1). The four sites are located at the following coordinates: a) 45.464883, −122.610037, b) 45.458925, −122.614280, c) 45.459956, −122.598290, and d) 45.456065, −122.632850. Data from the four sites were grouped together for later analyses because each site individually does not have enough samples for PMF modeling, and we assume that the sites are close enough that they will have the same sources impacting the particulate matter measurements. A central point at 45.459529, −122.615404 was used for distance estimates and HYSPLIT analyses. The sites are about 2 km from the Willamette River and 15.7 km from the Columbia River. The site is located in an area with metal processing companies and with streets for small to large vehicles and trucks. 47 mm diameter Teflon filters were sampled using 785
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Fig. 1. Sampling locations in Portland, Oregon: Brooklyn Rail Yard (BRY) and Lower Southeast (LSE). The Pacific Ocean is 100 km to the west and the coal power plant is 220 km to the east along the Columbia River.
species measured at BRY are shown in Section S2.
Table 1 Sampling summary for BRY and LSE.
filter sampling
BC sampling
Brooklyn Rail Yard (BRY)
Lower Southeast (LSE)
October 2017–March 2018 1 site
April 2017–July 2017 4 sites, overlapping sampling 48-hour sampling n = 98 none
24-hour sampling n = 101 December 2018–March 2018 1-minute sampling, averaged over 24hr n = 51
2.4.2. Regional airflow analysis The National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory's (ARL) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for airmass back-trajectory analysis (Stein et al., 2015). Methods are consistent with previous HYSPLIT analysis in the Columbia River Gorge (Jaffe and Reidmiller, 2009). The NARR 32 km-resolution meteorological database was used to model vertical velocity, and 24-hour back-trajectories were calculated at 2-hour intervals, 12 times to cover the entire 24-hour sampling period, each arriving at 0 m above ground level at the Brooklyn Rail Yard. For the Lower Southeast site, 24-hour back-trajectories at 2-hour intervals, 24 times were modeled to capture the entire 48-hour sampling period for each filter. Example figures are shown in Figs. S6 and S7 for BRY and LSE, respectively. The back-trajectories were highly variable, but in general, the back-trajectories for the October–March BRY samples indicate air flow from the south and southwest, and the back-trajectories for the April–July LSE samples indicate air flow from the northwest, consistent with local climatology (Sharp and Mass, 2004).
January, February, and March, indicating reasonable agreement with larger-scale wind patterns. The October and December winds are especially easterly, but this agrees with the monthly wind averages for 2017 recorded at the Portland International Airport (National Weather Service, 2017). Bivariate polar plots were used to visually represent concentrations of elements and of sources as they depend on both wind speed and wind direction, inspired by studies by Carslaw et al. (2006), Uria-Tellaetxe and Carslaw (2014), and Bari et al. (2017), who used similar analysis to aid in source apportionment. The bivariate polar plots were created in R using the openair package. Bipolar plots of each 786
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Table 2 BRY Summary: Average and standard deviation of sample concentration, average uncertainty in concentration as reported by the XRF, average blank concentration, and percent of samples below detection limit (BDL) for particulate sampling at BRY. The average uncertainty for each species is used as the detection limit.
Table 3 LSE Summary: average and standard deviation of sample concentration, average uncertainty in concentration as reported by the XRF, average blank concentration, and percent of samples below detection limit (BDL) for particulate sampling at LSE. The average uncertainty for each species is used as the detection limit.
Element
Sample Average (μg/m3)
Sample Standard Deviation (μg/m3)
Uncertainty Average (μg/m3)
Blank Average (μg/m3)
% of samples BDL
Element
Sample Average (μg/m3)
Sample Standard Deviation (μg/m3)
Uncertainty Average (μg/m3)
Blank Average (μg/m3)
% of samples BDL
Na Mg Al Si P S Cl K Ca Ti V Cr Mn Fe Co Ni Cu Zn As Se Br Rb Sr Mo Ag Cd Sn Sb Ba Hg Pb
0.2365 0.0544 0.1846 0.3692 0.0128 0.2187 0.2422 0.1000 0.1248 0.0184 0.0014 0.0037 0.0118 0.2595 0.0033 0.0049 0.0373 0.0481 BDL 0.0033 0.0046 BDL 0.0025 0.0092 0.1205 BDL 0.2971 BDL 0.0177 0.0077 0.0179
0.2486 0.0394 0.1124 0.3590 0.0076 0.1467 0.3002 0.0513 0.1165 0.0159 0.0013 0.0021 0.0148 0.1861 0.0033 0.0027 0.0063 0.0129 0.0027 0.0027 0.0028 BDL 0.0026 0.0071 0.0222 BDL 0.0735 BDL 0.0128 0.0058 0.0062
0.0029 0.0009 0.0044 0.0034 0.0014 0.0018 0.0013 0.0007 0.0008 0.0007 0.0007 0.0007 0.0007 0.0036 0.0022 0.0025 0.0021 0.0026 0.0017 0.0025 0.0024 0.0018 0.0024 0.0085 0.0285 0.0016 0.0949 0.0813 0.0013 0.0062 0.0062
0.0201 0.0131 0.0572 0.0066 BDL 0.0044 0.0073 0.0054 0.0063 BDL BDL 0.0009 0.0010 0.0415 BDL 0.0049 0.0288 0.0290 0.0019 0.0031 BDL BDL BDL BDL 0.1237 BDL 0.2456 BDL BDL 0.0080 0.0110
0 0 0 0 4 0 0 0 0 0 18 0 2 0 52 20 0 0 74 40 21 68 58 51 0 97 0 68 0 41 2
Na Na Mg Al Si P S Cl K Ca Ti V Cr Mn Fe Co Ni Cu Zn As Se Br Rb Sr Mo Ag Cd Sn Sb Ba Hg Pb
0.2587 0.2587 0.0520 0.1605 0.4051 0.0124 0.2299 0.2080 0.1027 0.1047 0.0215 0.0014 0.0030 0.0073 0.1892 0.0024 0.0021 0.0144 0.0129 BDL 0.0021 0.0019 BDL 0.0024 BDL 0.0268 BDL 0.0924 BDL 0.0150 0.0024 0.0061
0.1642 0.1642 0.0377 0.1028 0.2698 0.0043 0.1197 0.1815 0.2362 0.0580 0.0128 0.0008 0.0024 0.0044 0.1051 0.0014 0.0011 0.0124 0.0028 BDL 0.0023 0.0009 BDL 0.0059 BDL 0.0064 BDL 0.0223 BDL 0.0213 0.0020 0.0017
0.0015 0.0015 0.0005 0.0022 0.0019 0.0006 0.0010 0.0007 0.0004 0.0004 0.0002 0.0002 0.0003 0.0002 0.0013 0.0008 0.0006 0.0007 0.0007 0.0012 0.0008 0.0007 0.0009 0.0010 0.0023 0.0092 0.0020 0.0305 0.0416 0.0005 0.0022 0.0017
0.0084 0.0084 0.0041 0.0058 0.0032 BDL 0.0012 0.0033 0.0022 0.0029 0.0005 BDL 0.0003 0.0005 0.0105 BDL 0.0008 0.0099 0.0070 BDL 0.0013 BDL BDL BDL BDL 0.0259 BDL 0.0991 BDL BDL BDL 0.0044
0 0 0 0 0 0 0 0 0 0 0 6 1 0 0 14 3 0 0 76 28 11 69 47 61 0 94 0 85 0 51 0
2.5. Source apportionment with positive matrix factorization(PMF)
model. After running the model, many species were down-weighted based on poor model fit. In the end, 3 species (As, Cd, and Sb) were excluded from the model. 14 species (BC, Ag, Br, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Se, Sn, Sr, V, and Zn) were marked as “weak”. 12 species (Al, Ba, Ca, Cl, Fe, K, Mg, Na, P, S, Si, Ti) were marked “strong”. Many of the “weak” species were not well-modeled, based on their residuals and the observed/predicted correlation coefficients, but they were not excluded from the model because they did not have a significant effect on the solution either way. Since BC sampling began partway through the study, the days which have missing values for BC were kept in the model with BC values set to equal the BC median concentration for the days it was measured on, consistent with suggested methods (Brown et al., 2015). We did not measure total particulate mass and thus did not include missing mass in the model. Solutions with 3–10 factors were explored. The Q-values decreased with increasing number of factors, but the uncertainty estimates increased with increasing factors. The 10 factor solution had a Q closest to the theoretical Q, but the factor profiles were not physically realistic nor well-resolved. The 4-, 5-, and 6-factor solutions were the final contenders due to realistic profiles and good species fits. The 5-factor solution was ultimately chosen as the best solution because of good quality of species fits and for having the most interpretable source profiles. The solution was found to be stable based on all runs (n = 100) converging and 88% having the same Q value. The G-space plots, which show one factor versus another factor, showed no edges, meaning each factor was unique and there was negligible rotational ambiguity. Therefore, rotational tools were not investigated (Paatero et al., 2005). Constraints were also not
EPA's Positive Matrix Factorization 5.0 was used for source apportionment. PMF is described in detail elsewhere (Paatero et al., 1994; Hopke, 2016; Reff et al., 2007) and in Section S4. Briefly, the model uses a bilinear model to express the observations as the sum of contributions from several source profiles. The profiles are then interpreted to identify source types. The best solutions were determined based on (Reff et al., 2007; Masiol et al., 2017; Brown et al., 2015): (a) how realistic the solution was according to knowledge of the area; (b) the Qvalue and its stability over multiple runs; (c) how well-modeled each species was, using the correlation coefficient between the model-predicted concentration and the predicated concentration; and (d) the solution profile uncertainties, as calculated by error estimation methods displacement (DISP), bootstrap (BS), and BS-DISP. DISP analyses account for rotational ambiguity, BS accounts for random errors, and BSDISP accounts for both rotational ambiguity and random errors (Brown et al., 2015). 3. Results 3.1. Source apportionment of Brooklyn Rail Yard The data matrix contained 101 samples and 32 species. Following the EPA PMF 5.0 user guide (Norris et al., 2014), species were first classified as “strong”, “weak”, and “bad” based on their S/N ratios.“Weak” species have tripled uncertainties in the model to downweight their contributions and “bad” species are excluded from the 787
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Fig. 2. PMF 5-factor solution for sampled PM10 at the Brooklyn Rail Yard in Portland, OR. The 5 sources were determined to be sea salt, soil dust, secondary sulfates, diesel and fuel emissions, and metals industry.
investigated. There was good agreement between the predicted concentrations and measured concentrations for all the species designated as “strong”, with an r2 range of 0.76–1. There was less agreement between predicted and measured concentrations for the “weak” species, with an r2 range of 0.01–0.61, but this is reasonable because of the high uncertainties used for these species. DISP, BS, and BS-DISP error estimation methods revealed little uncertainty in the chosen 5-factor solution. Detailed results are provided in Tables S2, S3, and S4, but briefly: (a) no factor swaps occurred for DISP analysis and the largest percent decrease in Q was 0.002%; (b) over 100 BS runs, 4 factors mapped 98% and 1 factor mapped at 81%; (c) for BS-DISP analysis, 79% of cases were accepted, the largest percent decrease in Q was 0.07%, there were 18 swaps in DISP, and there were ≤4 swaps per factor. The factor profiles for the five factors are shown in Fig. 2 with their identities labeled. These figures show both the mass concentration of each species in each factor as well as the percent of the species that is accounted for in that factor. Both were employed in determining factor identities. Bivariate polar plots of the total contributions of each factor as they vary by day with wind speed and direction are shown in Fig. 3. These plots help in source identification because they indicate factor dependence on airmass origin. Factor contribution is plotted by color
against wind direction and wind speed, where wind speed increases radially from the center. Because of the somewhat limited sample size, these plots are not as filled in as they are in studies with larger sample sizes or higher time resolution (Carslaw et al., 2006). 3.1.1. Sea salt Factor 1 represents sea salt and accounts for 22% of the measured mass. This factor was assigned as sea salt because of the high concentrations and percentage of Na, Cl, and Mg. 74% of Na, 57% of Mg, and 86% of Cl are mapped into this factor. The [Na]/[Cl] ratio is 0.81, which agrees with the accepted Na/Cl ratio of continental sea salt, 0.86 (Maeller, 1990). There are also high fractions of Ag (21%), Cu (18%), Pb (15%), Zn (12%), S (12%), and Ca (10%). These species may be included in the factor due to marine aerosol picking up other emissions during their transport from the Pacific Ocean, which is 100 km to the west of the sampling site. The bivariate polar plot of sea salt concentration with wind speed and direction (Fig. 3) further supports the assignment as sea salt from the Pacific. The plot shows increasing concentration from the center towards the western direction, and decreasing concentration to the eastern direction. As mentioned previously, because of the limited sample size, the polar plot is not filled 788
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Fig. 3. Bivariate polar plots of the 5 factors (sea salt, soil dust, secondary sulfates, diesel and fuel emissions, and metals industry) as they depend on wind speed and direction. Sea salt appears to be dominant with west winds, soil dust with north winds, secondary sulfates with north east winds, fuel emissions with local east winds, and metals industry with north winds.
out in every direction, but if the pattern were to continue, this factor would likely still show a strong directional dependence to the west.
et al., 2010). The Boardman coal-fired power plant lies 220 km to the east-northeast of Portland on the Columbia River (see Fig. 1). The coal plant released an estimated 7440 tons of sulfur dioxide (SO2) in 2014, according to the EPA National Emissions Inventory, which is the largest SO2 emissions in the state (U.S. Environmental Protection Agency, 2016). The plant installed mercury scrubbers in 2011 that filter out 90% of mercury (Corson, 2011), so there is substantially less mercury emitted than previously, however the small amount remaining appears to occasionally reach Portland. There is another coal-fired power plant in Centralia, Washington, located 140 km north-northwest of the sampling site, that emitted about 3040 tons of SO2 in 2014 (U.S. Environmental Protection Agency, 2016). It is likely contributing to the source of secondary sulfates, but because of its location to the north, the Centralia plant is not consistent with the identified source of both sulfur and mercury. In addition to the local wind directions indicating a source in the distant northeast, the HYSPLIT models for the days with high measured mercury also show air flow from along the Columbia River Gorge. Samples were ranked from highest to lowest Hg concentration, and HYSPLIT back-trajectories for the top and bottom 6 (all bottom 6 have zero measured Hg) were categorized as “1” having air flow from the Columbia Gorge or “2” having air flow from elsewhere. A summary of their dates, Hg concentrations, wind speed and direction from The Dalles Municipal Airport in the Columbia River Gorge (between Portland and the coal plant), and their HYSPLIT category are shown in Table 4. As an example, Fig. 5 shows the HYSPLIT 24-hour back-trajectories for December 7, 2017, which had the third highest mercury concentration, clearly showing air flow from the Gorge. The top 6 days have winds from the east and 5 of them have back-trajectories from the east along the river, while the bottom 6 days have winds primarily not from east and back-trajectories not from along the river. The PMF factor profile, the polar plots of local BRY data, The Dalles wind information, and the HYSPLIT back-trajectories all support the identification of the coal-fired power plant on the Columbia River Gorge as the source of mercury and secondary sulfates measured at the Rail Yard.
3.1.2. Soil dust Factor 2 represents airborne soil dust and accounts for 27% of the measured mass. The factor was assigned as soil dust because of the high concentrations and percentage of Si, Al, and Ti. 76% of Si, 51% of Al, and 68% of Ti were mapped into this factor. There were also significant contributions of Mn (56%), Fe (49%), Ba (48%), Ca (32%), P (24%), K (22%), and BC (16%). Many of these elements are found in high abundance in the Earth's crust (Taylor, 1964). The bivariate polar plot of this factor indicates a dependence on winds from the north. 3.1.3. Secondary sulfates Factor 3 represents secondary sulfate emissions and accounts for 16% of the measured mass. This factor is dominated by sulfur, with 68% of S attributed to this factor. There is also a significant amount of Pb (33%), Sn (27%), Zn (27%), Ag (26%), and Cu (20%). Nearly all of the modeled Hg is mapped to this factor (94%), although the concentration of Hg is predicted to be negligible. This suggests that even though there is little mercury, any mercury that may be present is correlated with secondary sulfates. The bivariate plot of the sulfate factor plot shows a strong dependence on northeast winds at high windspeed, indicating a source far to the northeast. The bivariate polar plot of mercury concentrations (Fig. 4) also shows a dependence on northeast winds, suggesting that the measured mercury may have the same source as the secondary sulfates. The mercury and sulfates together suggest a coal-burning source somewhere northeast of the sampling site, as coal-plants are well-known large sources of secondary sulfate and of mercury (Stevens et al., 2012; Richaud et al., 1998; Wang
3.1.4. Fuel emissions Factor 4 was identified as diesel and fuel emissions and accounts for 26% of the measured mass. Most of the BC (51%) was mapped to this factor, which is considered a tracer for diesel (Schauer, 2003; Kleeman et al., 2000). This factor also has contributions of Se (92%), Cr (55%), K (52%), P (48%), Cu (47%), Sn (45%), Pb (44%), Zn (42%), Ag (41%), Ba (30%), and Fe (29%). Some of these elements (Cr, Mn, Fe) are characteristic of rail emissions (Bukowiecki et al., 2007; Abbasi et al., 2013) and may be coming from the diesel-powered trains running near the sampling site (Union Pacific, 2018). K is a signature element of biodiesel combustion (Cheung et al., 2010; Hildemann et al., 1991), which may be from the Portland TriMet busses that run on biodiesel throughout the city (York, 2017). The bivariate polar plot of this factor indicates higher contributions when the wind is at lower speeds and when it comes from the more eastern directions. The slow wind speeds indicate a nearer source, which supports the identity of local fuel emission sources. The reason for the eastern directionality is less clear because there are major roadways and highways in all directions from the site. 3.1.5. Metals industry Factor 5 accounts for 9% of the measured mass and is labeled “metals industry” because it is similar to the “metal processing” factor from a previous Portland source apportionment study (Kim and Hopke, 2008) and a previous Seattle, WA source apportionment study (Larson
Fig. 4. Bivariate polar plot of mercury concentrations as they depend on wind speed and direction. 790
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Table 4 Summary of the 6 highest- and 6 lowest-measured-Hg days at BRY. Their ranks, dates, and concentrations are listed along with the daily average wind speed and direction at The Dalles Municipal Airport in the Columbia River Gorge, and their HYSPLIT category (1 = air flow from the Columbia Gorge; 2 = air flow from elsewhere). Rank
Date
Hg conc. (μg/m3)
wind speed (m/s)
wind dir.
HYSPLIT category
1 2 3 4 5 6 96 97 98 99 100 101
03/06/18 12/23/17 12/07/17 11/29/17 10/28/17 12/31/17 12/12/17 11/28/17 11/20/17 11/13/17 11/02/17 10/20/17
0.0251 0.0216 0.0210 0.0203 0.0196 0.0189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1.79 0.89 2.24 1.79 0.00 1.34 1.79 0.89 1.34 3.13 2.24 3.58
E ESE E SE n/a E E NW NW N NW NW
1 1 1 2 n/a 1 2 2 2 2 2 2
et al., 2006). This factor has many elements contributing as well as a modest amount of BC (17%). 100% of V, Co, Ni, and Br were mapped to this factor, as well as 92% of Mo and 88% of Sr, although the predicted concentrations of Co, Br, Sr, and Mo is zero. Other elements mapped to this factor include Ca (38%), S (19%), Mn (15%), and P (15%). The bivariate polar plot for this factor shows some correlation with faster north and northeasterly winds, which is very similar to the polar plot for the soil dust factor. In Portland, there are many industrial sites on the north edges of the city, near the Columbia River and the airport. It is surprising, however, that the local sources of metals from the sites
directly surrounding the sampling area, did not contribute a unique factor in this study, given the prior evidence of local pollution as noted in the introduction. Most notably, Cd and As do not appear at all, as both were excluded from the model due to very low concentrations and high uncertainties. In contrast to the high levels measured during the 2015 DEQ sampling (see above, average Cd concentration of 0.029 μg/ m3 and maximum of 0.195 μg/m3; average As of 0.032 μg/m3 and maximum of 0.101 μg/m3), the concentrations measured in this study are significantly lower (Cd average and maximum of 0.0001 μg/m3 and 0.0070 μg/m3; As of 0.001 μg/m3 and 0.015 μg/m3). These differences
Fig. 5. HYSPLIT 24-hour back-trajectories arriving at Brooklyn Rail Yard on December 7, 2017 at 2-hour intervals. The black stars at the bottom represent the arrival time of each back-trajectory in UTC. The big yellow star on the map is the location of the coal-fired power plant. These trajectories show air flow from the east along the Columbia River Gorge from the coal plant. 791
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Fig. 6. PMF 4-factor solution for the lower Southeast Portland sites. The 4 sources were identified as sea salt, soil dust, secondary sulfates, and diesel/fuel emissions.
in Cd and As from 2015 to 2018 suggest significantly reduced toxic emissions after the baghouse installation at the stained glass manufacturer, positively impacting air quality in the surrounding neighborhoods.
results from BS and DISP error estimation analyses. The 4-factor solution was determined to be the optimal solution due to solution stability, realistic factor profiles, and reasonably good diagnostics and error estimation results. There was good agreement between the predicted concentrations and measured concentrations for all the species designated as “strong”, with an r2 range of 0.72–1, but less good agreement between predicted and measured concentrations for the “weak” species, with an r2 range of 0.01–0.58. DISP analysis showed little to no rotational ambiguity, with only 0.003% decrease in Q and zero swaps by factor. BS analysis showed some random error: 3 factors mapped at or above 99% and one factor mapped at 69%. BS-DISP analysis revealed more uncertainty, with 72 of 100 swaps in DISP, between 1 and 11 swaps by factor, and 0.4% decrease in Q. DISP, BS, and BS-DISP results are detailed in Tables S5, S6, and S7, respectively. These error estimation results indicate some uncertainty in the solution and that the factor profiles should be loosely interpreted. The factor profiles for the 4-factor solution are shown in Fig. 6, showing species concentrations and percent of species.
3.2. Source apportionment with PMF of another industrial lower Southeast Portland site 3.2.1. Finding the optimal solution EPA PMF 5.0 was used for source apportionment of speciated PM from LSE, following the same methods as were used with the BRY data. The data from the 4 sites were compiled to create a big enough sample size for PMF, with a total of 98 samples and 31 species in the matrix. 3 species (As, Cd, Sb) were excluded from the model. 15 species (Ag, Br, Co, Cr, Cu, Hg, Mo, Ni, P, Pb, Rb, Se, Sn, Sr, Zn) were marked as “weak” to down-weight their contribution to the model. 13 species (Al, Ba, Ca, Cl, Fe, K, Mg, Mn, Na, S, Si, Ti, V) were classified as “strong”. There were three samples from July 4th at three sites with very high concentrations of S, K, Cu, Sr, and Ba, corresponding to fireworks emissions (Lin, 2016). These samples were excluded from the model because the high concentrations skewed the data (Reff et al., 2007). Solutions with 2–10 factors were explored. As with the BRY solutions, solutions with more factors had Q values closer to Qexp but had significantly worse
3.2.2. Comparison of this solution to the rail yard solution The 4 factors at LSE were identified as sea salt, soil dust, secondary sulfates, and fuel emissions. Sea salt accounted for 19% of the total mass of the measured species, soil dust accounted for 36%, secondary 792
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sulfates 29%, and fuel emissions 15%. These are the same factors as the BRY PMF solution except the industrial metals factor. There are fewer factors likely because the sampling time was two times longer at this site, making it harder to resolve infrequent sources, and because black carbon was not measured, which would resolve a larger fraction of total particulate matter and could allow for better separation of sources. Although the error estimation methods revealed that there is some uncertainty in the solution, the factor profiles were easily interpretable and very similar to the BRY solution. The LSE sea salt factor profile is nearly identical to the BRY sea salt factor, defined by high contributions of Cl (77%), Na (43%), and Mg (42%) with some Al, Is, K, Ca, Fe, Cu, Zn, Ag, and Sn. The LSE soil dust factor profile is also very similar to BRY solution, defined by high concentrations of Si (71%), Al (62%), Ti (63%), Ca (59%), and Fe (60%) and smaller amounts of Na, Mg, P, S, K, Mn, Zn, and Ba. The secondary sulfates factor is similarly defined by S (67%) with contributions of Na, Mg, Al, Si, P, Cl, K, Ca, Ti, Fe, Cu, Zn, Ag, Sn, and Pb. Mercury is not clearly associated with the sulfates factor at the LSE site; this is not surprising given the prevailing wind directions during the April–July sampling season at this site. The fuel emissions factor is similarly high in K (51%), P (51%), Pb (50%), Sn (51%), Ag (50%), Cu (51%), Zn (49%), and Se (56%).
Acknowledgments The authors gratefully acknowledge funding from EPA-OAROAQPS-17-03 and from Portland State University Institute for Sustainable Solutions, Multnomah County, and City of Portland for the XRF instrument grant. We thank Anthony Barnack (Oregon DEQ), Gary Norris (US EPA), Annelise Hill (Reed College, BA 2017), and Philip Hopke (University of Rochester) for valuable discussions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apr.2018.12.006. References Abbasi, S., Jansson, A., Sellgren, U., Olofsson, U., Jan. 2013. Particle emissions from rail traffic: a literature review. Crit. Rev. Environ. Sci. Technol. 43 (23), 2511–2544. http://www.tandfonline.com/doi/abs/10.1080/10643389.2012.685348. Bari, M.A., Kindzierski, W.B., Oct. 2017. Ambient fine particulate matter (PM 2.5 ) in Canadian oil sands communities: levels, sources and potential human health risk. Sci. Total Environ. 595, 828–838. https://linkinghub.elsevier.com/retrieve/pii/ S0048969717308513. Brown, S.G., Eberly, S., Paatero, P., Norris, G.A., Jun. 2015. Methods for estimating uncertainty in PMF solutions: examples with ambient air and water quality data and guidance on reporting PMF results. Sci. Total Environ. 518–519, 626–635. http:// www.sciencedirect.com/science/article/pii/S004896971500025X. Bukowiecki, N., Gehrig, R., Hill, M., Lienemann, P., Zwicky, C.N., Buchmann, B., Weingartner, E., Baltensperger, U., Feb. 2007. Iron, manganese and copper emitted by cargo and passenger trains in Zrich (Switzerland): size-segregated mass concentrations in ambient air. Atmos. Environ. 41 (4), 878–889. http://linkinghub. elsevier.com/retrieve/pii/S1352231006008739. Carslaw, D., Ropkins, K., 2012. openair: an R package for air quality data analysis. Environ. Model. Softw. 27–28, 52–61. Carslaw, D.C., Beevers, S.D., Ropkins, K., Bell, M.C., Sep. 2006. Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmos. Environ. 40 (28), 5424–5434. http://www. sciencedirect.com/science/article/pii/S1352231006004250. Cheung, K.L., Ntziachristos, L., Tzamkiozis, T., Schauer, J.J., Samaras, Z., Moore, K.F., Sioutas, C., Jun. 2010. Emissions of particulate trace elements, metals and organic species from gasoline, diesel, and biodiesel passenger vehicles and their relation to oxidative potential. Aerosol Sci. Technol. 44 (7), 500–513. https://doi.org/10.1080/ 02786821003758294. Corson, S., 2011. PGE Reports Progress Implementing Boardman Power Plant Emissions Reduction Strategy. . http://investors.portlandgeneral.com/news-releases. Donovan, G.H., Jovan, S.E., Gatziolis, D., Burstyn, I., Michael, Y.L., Amacher, M.C., Monleon, V.J., Jul. 2016. Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution. Sci. Total Environ. 559, 84–93. http:// www.sciencedirect.com/science/article/pii/S0048969716306052. Gatziolis, D., Jovan, S., Donovan, G., Amacher, M., Monleon, V., Jun. 2016. Elemental Atmospheric Pollution Assessment via Moss-based Measurements in Portland, Oregon. General Technical Report PNW-GTR-938. U.S.D.A. Forest Service, Pacific Northwest Research Station. https://www.treesearch.fs.fed.us/pubs/51076. Hansen, A., 2005. Magee Scientific: the Aethalometer. Magee Scientific Company, Berkeley, CA. Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci. Total Environ. 249 (1), 85–101. HEI Panel on the Health Effects of Traffic-Related Air Pollution, 2010. Traffic-related Air Pollution: a Critical Review of the Literature on Emissions, Exposure, and Health Effects. HEI Special Report 17. Health Effects Institute, Boston, MA. Hildemann, L.M., Markowski, G.R., Cass, G.R., Apr. 1991. Chemical composition of emissions from urban sources of fine organic aerosol. Environ. Sci. Technol. 25 (4), 744–759. https://doi.org/10.1021/es00016a021. Hopke, P.K., Mar. 2016. Review of receptor modeling methods for source apportionment. J. Air Waste Manag. Assoc. 66 (3), 237–259. https://doi.org/10.1080/10962247. 2016.1140693. Jaffe, D.A., Reidmiller, D.R., Oct. 2009. Now you see it, now you don't: impact of temporary closures of a coal-fired power plant on air quality in the Columbia River Gorge National Scenic Area. Atmos. Chem. Phys. 9 (20), 7997–8005. https://www.atmoschem-phys.net/9/7997/2009/. Jaishankar, M., Tseten, T., Anbalagan, N., Mathew, B.B., Beeregowda, K.N., Jan. 2014. Toxicity, mechanism and health effects of some heavy metals. Interdiscipl. Toxicol. 7 (2). http://www.degruyter.com/view/j/intox.2014.7.issue-2/intox-2014-0009/ intox-2014-0009.xml. Kellog, B., Winberry, W., 1999. 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4. Conclusions and broader implications In this source apportionment study of trace elements in particulate matter in industrial Southeast Portland, Oregon, EPA's Positive Matrix Factorization 5.0 was used to estimate source profiles and source contributions at the Brooklyn Rail Yard (BRY) as well as in industrial lower Southeast Portland (LSE). Five sources were identified at BRY: diesel and fuel emissions, sea salt, soil dust, secondary sulfates, and metals industry. Wind speed and direction were plotted with factor profiles on bivariate polar plots to further aid in identifying possible sources to particulate matter. It was surprising that there were such large regional influences in the sources of particulate matter that was collected, given the sampling location. A distinct and strong sea salt factor originates from the ocean 100 km away, and a sulfate factor likely from the coal plant 220 km away. Despite the sampling taking place in an industrial area at a rail yard with many local sources of pollution, these longrange, regional sources dominated. The PMF model for LSE site allowed identification of 4 sources: sea salt, soil dust, secondary sulfates, and fuel emissions. As with the BRY model, these sources are more heavily regionally influenced than locally, although the sampling took place in another industrial neighborhood. An important caveat to these results and conclusions is that this is not a complete compositional analysis of particulate matter. Only trace elements and black carbon were measured at BRY and only trace elements at LSE. Measuring organic carbon compounds as well as inorganic ions such as nitrate, ammonium, and sulfate would be required for a complete quantitative analysis of particulate matter sources in Portland. Including more species and total particulate mass in the source apportionment model would also likely allow for determining more than five factors and gaining a more thorough understanding of the possible sources, as is common in other studies (Kim and Hopke, 2008; Larson et al., 2006). This is instead a qualitative picture of which sources of pollution contribute to particulate matter in an urban industrial area, revealing a surprising dominance of remote regional sources. Another goal of this study was to measure airborne concentrations of toxic metals for the purpose of determining human exposure, in response to the Donovan et al. (2016) moss study. It was found that, for almost all elements measured, concentrations were always below the Oregon DEQ's Ambient Benchmark Concentrations (State of Oregon Department of Environmental Quality, 2010). Figures showing these results are in Section S7. This suggests that local sources of metals pollution are no longer as influential on southeast Portland's air quality, instead leaving an urban particulate matter composition driven primarily by regional sources. 793
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Source apportionment of trace elements and black carbon in an urban industrial area (Portland, Oregon)
T
A.J. Millera,1, D.M. Radumab, L.A. Georgeb, J.L. Frya,∗ a b
Department of Chemistry and Environmental Studies Program, Reed College, Portland, OR, USA Environmental Sciences and Management, Portland State University, Portland, OR, USA
ARTICLE INFO
ABSTRACT
Keywords: Source apportionment Trace elements Urban air quality Diesel Particulate matter Black carbon Positive matrix factorization X-ray fluorescence Fuel emissions Coal Portland Oregon
This paper reports a current source apportionment study of trace elements and black carbon in particulate matter in industrial Southeast Portland, Oregon. The study aimed to determine whether metal pollution hotspots identified in the area in 2016 had been successfully mitigated by baghouse installation, or whether industrial sources continued to dominate local particulate matter. Particulate matter was filter-collected nearly continuously in 24-hour intervals between October 2017 and March 2018 (101 total filters). Filters were analyzed for 30 elements by x-ray fluorescence; black carbon was measured continuously during filter sampling using an aethalometer. EPA's Positive Matrix Factorization 5.0 was used for source apportionment modeling, yielding a 5factor optimal solution. The source identities were resolved to be diesel and fuel emissions, sea salt, soil dust, secondary sulfates, and metals industry. The metals industry source was much less significant than expected, suggesting effective emissions reductions from the local factory. The source profiles' correlation with wind direction and speed using bivariate polar plots was examined to give further insight into the source identities and their locations. The NOAA HYSPLIT model was also used for air flow back-trajectory analysis. The results of the HYSPLIT and bivariate polar plots suggest that the coal power plant in eastern Oregon is a significant source of sulfates and mercury emissions. Using PMF on particulate matter data from a second industrial location in Southeast Portland (98 filters) revealed the same major sources except for the metals industry source, supporting the conclusion that regional sources dominate particulate matter metals composition, despite measurements made in an industrial urban location.
1. Introduction Air pollution is a costly global environmental and human welfare issue, with an estimated 7 million deaths each year worldwide linked to air pollution exposure (World Health Organization, 2014). Particulate matter (PM) is a component of air pollution that has been shown to have considerable effects on health and mortality: daily mortality increases by about 1% per 10 μg/m3 increase in PM10 (Harrison and Yin, 2000). Trace metals and black carbon (BC) are two components of particulate matter (PM) that are particularly harmful (Harrison and Yin, 2000; Jaishankar et al., 2014; Tchounwou et al., 2012). Trace metals are emitted by a variety of industrial processes (e.g. fuel combustion, vehicular emissions, metals fabrication facilities, mining and smelting, building construction) and natural processes (e.g. weathering, wildfires, sea spray, volcanoes) (Pacyna and Pacyna, 2001; HEI Panel on the
Health Effects of Traffic-Related Air Pollution, 2010; Nriagu, 1989). BC is emitted by combustion of carbonaceous fuels and is known to be strongly correlated with diesel combustion (Schauer, 2003; Hansen, 2005; Cheung et al., 2010). In 2016, the US Forest Service Pacific Northwest Research Station published a study on atmospheric metal pollution in Portland using moss collected across the city in 2013 and created a map of hotspots, finding 15 elements with high concentrations in collected moss samples from at least one location (Gatziolis et al., 2016; Donovan et al., 2016). One notable hotspot was in the industrial Southeast Portland Brooklyn neighborhood, showing high concentrations of cadmium (Cd), arsenic (As), and other metals used in stained glass manufacturing as coloring agents. While identifying spatial locations of hotspots, their study did not directly measure airborne metals concentrations. In 2015, the Oregon Department of Environmental Quality (DEQ) performed air
Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control. ∗ Corresponding author. E-mail address: [email protected] (J.L. Fry). 1 Current address: Department of Environmental Systems Science, Swiss Federal Institute of Technology Zurich (ETHZ), Zurich, Switzerland. https://doi.org/10.1016/j.apr.2018.12.006 Received 16 July 2018; Received in revised form 30 November 2018; Accepted 15 December 2018 Available online 28 December 2018 1309-1042/ © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V.
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toxics monitoring by filter sampling at the site of the Cd and As hotspot, and found high levels of these same metals (Cd average concentration of 0.029 μg/m3, maximum of 0.195 μg/m3, and As average concentration of 0.032 μg/m3, maximum of 0.101 μg/m3; (State of Oregon Department of Environmental Quality, 2015)). This present study has made new and more comprehensive direct air measurements, now after the stained glass manufacturer has installed a baghouse device (Zarkhin, 2016) to reduce toxic metal emissions. Source apportionment modeling with positive matrix factorization (PMF) is a technique commonly used to resolve source identities and contributions of species in a relatively unknown mixture. Kim and Hopke (2008) used PMF for source apportionment of particulate matter in Portland, including many elemental species, inorganic ions, and organic and elemental carbon (OC and EC). They found that wood smoke, secondary sulfates, secondary nitrates, sea salt, airborne soil, gasoline vehicles, diesel emissions, construction, and metal processing were sources of PM. This data, however, is now almost 15 years old and was collected very near the Portland International Airport, rather than in heavy populated Southeast Portland. In other previous studies, PMF results have also been combined with auxiliary information, such as local wind speed and direction, to aid in source identification (Kim and Hopke, 2008; Carslaw et al., 2006; Westmoreland et al., 2007; Yu et al., 2004). By applying source apportionment modeling to our new atmospheric pollution measurements in conjunction with wind analysis, we seek to determine the major sources of airborne metals in this industrial SE Portland location now that the former hotspot toxic metal emitter has controlled emissions.
an ARA Inc Sampler. 98 total filters were used for analysis. Table 1 summarizes the sampling at BRY and LSE. 2.2. Offline filter analysis with x-ray fluorescence (XRF) Sampled filters were analyzed for elemental concentrations by a Thermo Scientific ARL Quant'x Energy-Dispersive X-Ray Fluorescence Analyzer, following the commonly used methods as set by the EPA (Kellog and Winberry, 1999; Yatkin et al., 2012). Irradiation conditions are given in Table S1. Unsampled, clean filters were analyzed by XRF at the same time as the sampled filters to serve as a blank control. The XRF analysis gives the mass of the element per filter area; these values are converted to mass per volume of air (μg/m3). Species averages and standard deviations of samples, as well as the average uncertainty for each element as determined by the XRF analysis, and average blank concentrations are reported in Table 2 for BRY sampling and in Table 3 for LSE sampling. A detection limit was determined using these average uncertainties for each element. Species that have an average or blank concentration below the average uncertainty are reported as “BDL” for “below detection limit”. The uncertainties are given as output of XRF analysis and are determined based on two main sources of error: 1) the peak overlap between elements with similar fluorescence emissions, and 2) calibration uncertainties. For PMF analysis, all samples are used, including those with concentrations below detection limit, because the uncertainties are accounted for in the model (Brown et al., 2015). There was no duplicate sampling nor duplicate analysis of these filters. As an alternative method to show the reliability of our XRF measurements, five filters collected at a separate sampling site were analyzed by the XRF instrument used here and by an XRF of an external private lab. Good agreement (slope = 0.97, R2 = 0.931) was found for many of the elements of interest to our results, including Al, Ca, Cu, Fe, S, Si, and Pb.
2. Methods 2.1. Filter sampling in Southeast Portland 2.1.1. Brooklyn rain yard site Particulate matter of diameter up to 10 μm was collected at the Brooklyn Rail Yard (BRY, see Fig. 1). The site is located at 45.483511, −122.638592, 1.3 km to the Willamette River and 13.3 km to the Columbia River. The site is directly adjacent to railroad tracks operated by the Union Pacific Railroad, and there are nearby streets and highways with small to large vehicles and trucks. Local industries include metals processing companies and a glass factory. Particulate matter was collected on 47 mm diameter Teflon filters with 0.2 μm pore size using a Partisol Plus Sequential Air Sampler Model 2025. Sampling took place for continuous 24-hour periods (midnight to midnight) from October 15, 2017 through March 7, 2018, with some breaks, for a total of 101 filters. The Partisol was programmed to sample continuously with a constant flow rate of 16.7 L min−1. At any given time, there were between 4 and 10 filters stored in the Partisol, either awaiting sampling or after they had been sampled. Filters were stored in a freezer until analysis.
2.3. Black carbon (BC) measurements with aethalometer colocated with filter sampler BC, defined by its optical property of absorbing infrared light, was measured in real-time using a Magee Scientific Dual Wavelength Aethalometer Model AE31, from December 17, 2017 through March 7, 2018. The aethalometer was colocated with the Partisol filter sampler at the Brooklyn Rail Yard. It measures at 880 nm (IR) and 370 nm (UV). The IR absorption is assumed to be due to only BC and the UV absorption to both BC and organic carbon aerosols (Hansen, 2005); only BC measurements from the IR channel are used in this study. The aethalometer measured in 1-minute intervals; 24-hour averages were calculated to match the filter sampling timebase. 2.4. Meteorological analyses 2.4.1. Local wind Weather information for the BRY site was captured with a Davis Instruments Vantage Vue Weather Station, collocated with the Partisol and the aethalometer. The weather station was mounted on a roof, approximately 6 meters above ground to attempt to obtain winds representative of larger-scale winds. Wind speed and direction were recorded every 15 min for the entirety of the sampling period. In order to match the wind speed and direction with the filter samples, the wind data was averaged over 24-hour periods. Vector averaging was used in order to most accurately represent the wind direction over a day, and scalar averaging was used for speeds. Calculations were done in R using the openair package (Carslaw and Ropkins, 2012). To ensure our local measurements are reasonably similar to regional climate, we compare our averages to averages typical of Portland. In the winter months in Portland, wind typically comes from the east and southeast on average, and in the summer months from the north and northwest (Sharp and Mass, 2004). We see, on average, southerly winds in November,
2.1.2. Lower Southeast site Filter sampling was done in 48-hour periods between April 23, 2017 and July 3, 2017 at four other sites within a 1.4 km radius area in another industrial lower Southeast (LSE) Portland neighborhood (see Fig. 1). The four sites are located at the following coordinates: a) 45.464883, −122.610037, b) 45.458925, −122.614280, c) 45.459956, −122.598290, and d) 45.456065, −122.632850. Data from the four sites were grouped together for later analyses because each site individually does not have enough samples for PMF modeling, and we assume that the sites are close enough that they will have the same sources impacting the particulate matter measurements. A central point at 45.459529, −122.615404 was used for distance estimates and HYSPLIT analyses. The sites are about 2 km from the Willamette River and 15.7 km from the Columbia River. The site is located in an area with metal processing companies and with streets for small to large vehicles and trucks. 47 mm diameter Teflon filters were sampled using 785
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Fig. 1. Sampling locations in Portland, Oregon: Brooklyn Rail Yard (BRY) and Lower Southeast (LSE). The Pacific Ocean is 100 km to the west and the coal power plant is 220 km to the east along the Columbia River.
species measured at BRY are shown in Section S2.
Table 1 Sampling summary for BRY and LSE.
filter sampling
BC sampling
Brooklyn Rail Yard (BRY)
Lower Southeast (LSE)
October 2017–March 2018 1 site
April 2017–July 2017 4 sites, overlapping sampling 48-hour sampling n = 98 none
24-hour sampling n = 101 December 2018–March 2018 1-minute sampling, averaged over 24hr n = 51
2.4.2. Regional airflow analysis The National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory's (ARL) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for airmass back-trajectory analysis (Stein et al., 2015). Methods are consistent with previous HYSPLIT analysis in the Columbia River Gorge (Jaffe and Reidmiller, 2009). The NARR 32 km-resolution meteorological database was used to model vertical velocity, and 24-hour back-trajectories were calculated at 2-hour intervals, 12 times to cover the entire 24-hour sampling period, each arriving at 0 m above ground level at the Brooklyn Rail Yard. For the Lower Southeast site, 24-hour back-trajectories at 2-hour intervals, 24 times were modeled to capture the entire 48-hour sampling period for each filter. Example figures are shown in Figs. S6 and S7 for BRY and LSE, respectively. The back-trajectories were highly variable, but in general, the back-trajectories for the October–March BRY samples indicate air flow from the south and southwest, and the back-trajectories for the April–July LSE samples indicate air flow from the northwest, consistent with local climatology (Sharp and Mass, 2004).
January, February, and March, indicating reasonable agreement with larger-scale wind patterns. The October and December winds are especially easterly, but this agrees with the monthly wind averages for 2017 recorded at the Portland International Airport (National Weather Service, 2017). Bivariate polar plots were used to visually represent concentrations of elements and of sources as they depend on both wind speed and wind direction, inspired by studies by Carslaw et al. (2006), Uria-Tellaetxe and Carslaw (2014), and Bari et al. (2017), who used similar analysis to aid in source apportionment. The bivariate polar plots were created in R using the openair package. Bipolar plots of each 786
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Table 2 BRY Summary: Average and standard deviation of sample concentration, average uncertainty in concentration as reported by the XRF, average blank concentration, and percent of samples below detection limit (BDL) for particulate sampling at BRY. The average uncertainty for each species is used as the detection limit.
Table 3 LSE Summary: average and standard deviation of sample concentration, average uncertainty in concentration as reported by the XRF, average blank concentration, and percent of samples below detection limit (BDL) for particulate sampling at LSE. The average uncertainty for each species is used as the detection limit.
Element
Sample Average (μg/m3)
Sample Standard Deviation (μg/m3)
Uncertainty Average (μg/m3)
Blank Average (μg/m3)
% of samples BDL
Element
Sample Average (μg/m3)
Sample Standard Deviation (μg/m3)
Uncertainty Average (μg/m3)
Blank Average (μg/m3)
% of samples BDL
Na Mg Al Si P S Cl K Ca Ti V Cr Mn Fe Co Ni Cu Zn As Se Br Rb Sr Mo Ag Cd Sn Sb Ba Hg Pb
0.2365 0.0544 0.1846 0.3692 0.0128 0.2187 0.2422 0.1000 0.1248 0.0184 0.0014 0.0037 0.0118 0.2595 0.0033 0.0049 0.0373 0.0481 BDL 0.0033 0.0046 BDL 0.0025 0.0092 0.1205 BDL 0.2971 BDL 0.0177 0.0077 0.0179
0.2486 0.0394 0.1124 0.3590 0.0076 0.1467 0.3002 0.0513 0.1165 0.0159 0.0013 0.0021 0.0148 0.1861 0.0033 0.0027 0.0063 0.0129 0.0027 0.0027 0.0028 BDL 0.0026 0.0071 0.0222 BDL 0.0735 BDL 0.0128 0.0058 0.0062
0.0029 0.0009 0.0044 0.0034 0.0014 0.0018 0.0013 0.0007 0.0008 0.0007 0.0007 0.0007 0.0007 0.0036 0.0022 0.0025 0.0021 0.0026 0.0017 0.0025 0.0024 0.0018 0.0024 0.0085 0.0285 0.0016 0.0949 0.0813 0.0013 0.0062 0.0062
0.0201 0.0131 0.0572 0.0066 BDL 0.0044 0.0073 0.0054 0.0063 BDL BDL 0.0009 0.0010 0.0415 BDL 0.0049 0.0288 0.0290 0.0019 0.0031 BDL BDL BDL BDL 0.1237 BDL 0.2456 BDL BDL 0.0080 0.0110
0 0 0 0 4 0 0 0 0 0 18 0 2 0 52 20 0 0 74 40 21 68 58 51 0 97 0 68 0 41 2
Na Na Mg Al Si P S Cl K Ca Ti V Cr Mn Fe Co Ni Cu Zn As Se Br Rb Sr Mo Ag Cd Sn Sb Ba Hg Pb
0.2587 0.2587 0.0520 0.1605 0.4051 0.0124 0.2299 0.2080 0.1027 0.1047 0.0215 0.0014 0.0030 0.0073 0.1892 0.0024 0.0021 0.0144 0.0129 BDL 0.0021 0.0019 BDL 0.0024 BDL 0.0268 BDL 0.0924 BDL 0.0150 0.0024 0.0061
0.1642 0.1642 0.0377 0.1028 0.2698 0.0043 0.1197 0.1815 0.2362 0.0580 0.0128 0.0008 0.0024 0.0044 0.1051 0.0014 0.0011 0.0124 0.0028 BDL 0.0023 0.0009 BDL 0.0059 BDL 0.0064 BDL 0.0223 BDL 0.0213 0.0020 0.0017
0.0015 0.0015 0.0005 0.0022 0.0019 0.0006 0.0010 0.0007 0.0004 0.0004 0.0002 0.0002 0.0003 0.0002 0.0013 0.0008 0.0006 0.0007 0.0007 0.0012 0.0008 0.0007 0.0009 0.0010 0.0023 0.0092 0.0020 0.0305 0.0416 0.0005 0.0022 0.0017
0.0084 0.0084 0.0041 0.0058 0.0032 BDL 0.0012 0.0033 0.0022 0.0029 0.0005 BDL 0.0003 0.0005 0.0105 BDL 0.0008 0.0099 0.0070 BDL 0.0013 BDL BDL BDL BDL 0.0259 BDL 0.0991 BDL BDL BDL 0.0044
0 0 0 0 0 0 0 0 0 0 0 6 1 0 0 14 3 0 0 76 28 11 69 47 61 0 94 0 85 0 51 0
2.5. Source apportionment with positive matrix factorization(PMF)
model. After running the model, many species were down-weighted based on poor model fit. In the end, 3 species (As, Cd, and Sb) were excluded from the model. 14 species (BC, Ag, Br, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Se, Sn, Sr, V, and Zn) were marked as “weak”. 12 species (Al, Ba, Ca, Cl, Fe, K, Mg, Na, P, S, Si, Ti) were marked “strong”. Many of the “weak” species were not well-modeled, based on their residuals and the observed/predicted correlation coefficients, but they were not excluded from the model because they did not have a significant effect on the solution either way. Since BC sampling began partway through the study, the days which have missing values for BC were kept in the model with BC values set to equal the BC median concentration for the days it was measured on, consistent with suggested methods (Brown et al., 2015). We did not measure total particulate mass and thus did not include missing mass in the model. Solutions with 3–10 factors were explored. The Q-values decreased with increasing number of factors, but the uncertainty estimates increased with increasing factors. The 10 factor solution had a Q closest to the theoretical Q, but the factor profiles were not physically realistic nor well-resolved. The 4-, 5-, and 6-factor solutions were the final contenders due to realistic profiles and good species fits. The 5-factor solution was ultimately chosen as the best solution because of good quality of species fits and for having the most interpretable source profiles. The solution was found to be stable based on all runs (n = 100) converging and 88% having the same Q value. The G-space plots, which show one factor versus another factor, showed no edges, meaning each factor was unique and there was negligible rotational ambiguity. Therefore, rotational tools were not investigated (Paatero et al., 2005). Constraints were also not
EPA's Positive Matrix Factorization 5.0 was used for source apportionment. PMF is described in detail elsewhere (Paatero et al., 1994; Hopke, 2016; Reff et al., 2007) and in Section S4. Briefly, the model uses a bilinear model to express the observations as the sum of contributions from several source profiles. The profiles are then interpreted to identify source types. The best solutions were determined based on (Reff et al., 2007; Masiol et al., 2017; Brown et al., 2015): (a) how realistic the solution was according to knowledge of the area; (b) the Qvalue and its stability over multiple runs; (c) how well-modeled each species was, using the correlation coefficient between the model-predicted concentration and the predicated concentration; and (d) the solution profile uncertainties, as calculated by error estimation methods displacement (DISP), bootstrap (BS), and BS-DISP. DISP analyses account for rotational ambiguity, BS accounts for random errors, and BSDISP accounts for both rotational ambiguity and random errors (Brown et al., 2015). 3. Results 3.1. Source apportionment of Brooklyn Rail Yard The data matrix contained 101 samples and 32 species. Following the EPA PMF 5.0 user guide (Norris et al., 2014), species were first classified as “strong”, “weak”, and “bad” based on their S/N ratios.“Weak” species have tripled uncertainties in the model to downweight their contributions and “bad” species are excluded from the 787
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Fig. 2. PMF 5-factor solution for sampled PM10 at the Brooklyn Rail Yard in Portland, OR. The 5 sources were determined to be sea salt, soil dust, secondary sulfates, diesel and fuel emissions, and metals industry.
investigated. There was good agreement between the predicted concentrations and measured concentrations for all the species designated as “strong”, with an r2 range of 0.76–1. There was less agreement between predicted and measured concentrations for the “weak” species, with an r2 range of 0.01–0.61, but this is reasonable because of the high uncertainties used for these species. DISP, BS, and BS-DISP error estimation methods revealed little uncertainty in the chosen 5-factor solution. Detailed results are provided in Tables S2, S3, and S4, but briefly: (a) no factor swaps occurred for DISP analysis and the largest percent decrease in Q was 0.002%; (b) over 100 BS runs, 4 factors mapped 98% and 1 factor mapped at 81%; (c) for BS-DISP analysis, 79% of cases were accepted, the largest percent decrease in Q was 0.07%, there were 18 swaps in DISP, and there were ≤4 swaps per factor. The factor profiles for the five factors are shown in Fig. 2 with their identities labeled. These figures show both the mass concentration of each species in each factor as well as the percent of the species that is accounted for in that factor. Both were employed in determining factor identities. Bivariate polar plots of the total contributions of each factor as they vary by day with wind speed and direction are shown in Fig. 3. These plots help in source identification because they indicate factor dependence on airmass origin. Factor contribution is plotted by color
against wind direction and wind speed, where wind speed increases radially from the center. Because of the somewhat limited sample size, these plots are not as filled in as they are in studies with larger sample sizes or higher time resolution (Carslaw et al., 2006). 3.1.1. Sea salt Factor 1 represents sea salt and accounts for 22% of the measured mass. This factor was assigned as sea salt because of the high concentrations and percentage of Na, Cl, and Mg. 74% of Na, 57% of Mg, and 86% of Cl are mapped into this factor. The [Na]/[Cl] ratio is 0.81, which agrees with the accepted Na/Cl ratio of continental sea salt, 0.86 (Maeller, 1990). There are also high fractions of Ag (21%), Cu (18%), Pb (15%), Zn (12%), S (12%), and Ca (10%). These species may be included in the factor due to marine aerosol picking up other emissions during their transport from the Pacific Ocean, which is 100 km to the west of the sampling site. The bivariate polar plot of sea salt concentration with wind speed and direction (Fig. 3) further supports the assignment as sea salt from the Pacific. The plot shows increasing concentration from the center towards the western direction, and decreasing concentration to the eastern direction. As mentioned previously, because of the limited sample size, the polar plot is not filled 788
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Fig. 3. Bivariate polar plots of the 5 factors (sea salt, soil dust, secondary sulfates, diesel and fuel emissions, and metals industry) as they depend on wind speed and direction. Sea salt appears to be dominant with west winds, soil dust with north winds, secondary sulfates with north east winds, fuel emissions with local east winds, and metals industry with north winds.
out in every direction, but if the pattern were to continue, this factor would likely still show a strong directional dependence to the west.
et al., 2010). The Boardman coal-fired power plant lies 220 km to the east-northeast of Portland on the Columbia River (see Fig. 1). The coal plant released an estimated 7440 tons of sulfur dioxide (SO2) in 2014, according to the EPA National Emissions Inventory, which is the largest SO2 emissions in the state (U.S. Environmental Protection Agency, 2016). The plant installed mercury scrubbers in 2011 that filter out 90% of mercury (Corson, 2011), so there is substantially less mercury emitted than previously, however the small amount remaining appears to occasionally reach Portland. There is another coal-fired power plant in Centralia, Washington, located 140 km north-northwest of the sampling site, that emitted about 3040 tons of SO2 in 2014 (U.S. Environmental Protection Agency, 2016). It is likely contributing to the source of secondary sulfates, but because of its location to the north, the Centralia plant is not consistent with the identified source of both sulfur and mercury. In addition to the local wind directions indicating a source in the distant northeast, the HYSPLIT models for the days with high measured mercury also show air flow from along the Columbia River Gorge. Samples were ranked from highest to lowest Hg concentration, and HYSPLIT back-trajectories for the top and bottom 6 (all bottom 6 have zero measured Hg) were categorized as “1” having air flow from the Columbia Gorge or “2” having air flow from elsewhere. A summary of their dates, Hg concentrations, wind speed and direction from The Dalles Municipal Airport in the Columbia River Gorge (between Portland and the coal plant), and their HYSPLIT category are shown in Table 4. As an example, Fig. 5 shows the HYSPLIT 24-hour back-trajectories for December 7, 2017, which had the third highest mercury concentration, clearly showing air flow from the Gorge. The top 6 days have winds from the east and 5 of them have back-trajectories from the east along the river, while the bottom 6 days have winds primarily not from east and back-trajectories not from along the river. The PMF factor profile, the polar plots of local BRY data, The Dalles wind information, and the HYSPLIT back-trajectories all support the identification of the coal-fired power plant on the Columbia River Gorge as the source of mercury and secondary sulfates measured at the Rail Yard.
3.1.2. Soil dust Factor 2 represents airborne soil dust and accounts for 27% of the measured mass. The factor was assigned as soil dust because of the high concentrations and percentage of Si, Al, and Ti. 76% of Si, 51% of Al, and 68% of Ti were mapped into this factor. There were also significant contributions of Mn (56%), Fe (49%), Ba (48%), Ca (32%), P (24%), K (22%), and BC (16%). Many of these elements are found in high abundance in the Earth's crust (Taylor, 1964). The bivariate polar plot of this factor indicates a dependence on winds from the north. 3.1.3. Secondary sulfates Factor 3 represents secondary sulfate emissions and accounts for 16% of the measured mass. This factor is dominated by sulfur, with 68% of S attributed to this factor. There is also a significant amount of Pb (33%), Sn (27%), Zn (27%), Ag (26%), and Cu (20%). Nearly all of the modeled Hg is mapped to this factor (94%), although the concentration of Hg is predicted to be negligible. This suggests that even though there is little mercury, any mercury that may be present is correlated with secondary sulfates. The bivariate plot of the sulfate factor plot shows a strong dependence on northeast winds at high windspeed, indicating a source far to the northeast. The bivariate polar plot of mercury concentrations (Fig. 4) also shows a dependence on northeast winds, suggesting that the measured mercury may have the same source as the secondary sulfates. The mercury and sulfates together suggest a coal-burning source somewhere northeast of the sampling site, as coal-plants are well-known large sources of secondary sulfate and of mercury (Stevens et al., 2012; Richaud et al., 1998; Wang
3.1.4. Fuel emissions Factor 4 was identified as diesel and fuel emissions and accounts for 26% of the measured mass. Most of the BC (51%) was mapped to this factor, which is considered a tracer for diesel (Schauer, 2003; Kleeman et al., 2000). This factor also has contributions of Se (92%), Cr (55%), K (52%), P (48%), Cu (47%), Sn (45%), Pb (44%), Zn (42%), Ag (41%), Ba (30%), and Fe (29%). Some of these elements (Cr, Mn, Fe) are characteristic of rail emissions (Bukowiecki et al., 2007; Abbasi et al., 2013) and may be coming from the diesel-powered trains running near the sampling site (Union Pacific, 2018). K is a signature element of biodiesel combustion (Cheung et al., 2010; Hildemann et al., 1991), which may be from the Portland TriMet busses that run on biodiesel throughout the city (York, 2017). The bivariate polar plot of this factor indicates higher contributions when the wind is at lower speeds and when it comes from the more eastern directions. The slow wind speeds indicate a nearer source, which supports the identity of local fuel emission sources. The reason for the eastern directionality is less clear because there are major roadways and highways in all directions from the site. 3.1.5. Metals industry Factor 5 accounts for 9% of the measured mass and is labeled “metals industry” because it is similar to the “metal processing” factor from a previous Portland source apportionment study (Kim and Hopke, 2008) and a previous Seattle, WA source apportionment study (Larson
Fig. 4. Bivariate polar plot of mercury concentrations as they depend on wind speed and direction. 790
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Table 4 Summary of the 6 highest- and 6 lowest-measured-Hg days at BRY. Their ranks, dates, and concentrations are listed along with the daily average wind speed and direction at The Dalles Municipal Airport in the Columbia River Gorge, and their HYSPLIT category (1 = air flow from the Columbia Gorge; 2 = air flow from elsewhere). Rank
Date
Hg conc. (μg/m3)
wind speed (m/s)
wind dir.
HYSPLIT category
1 2 3 4 5 6 96 97 98 99 100 101
03/06/18 12/23/17 12/07/17 11/29/17 10/28/17 12/31/17 12/12/17 11/28/17 11/20/17 11/13/17 11/02/17 10/20/17
0.0251 0.0216 0.0210 0.0203 0.0196 0.0189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1.79 0.89 2.24 1.79 0.00 1.34 1.79 0.89 1.34 3.13 2.24 3.58
E ESE E SE n/a E E NW NW N NW NW
1 1 1 2 n/a 1 2 2 2 2 2 2
et al., 2006). This factor has many elements contributing as well as a modest amount of BC (17%). 100% of V, Co, Ni, and Br were mapped to this factor, as well as 92% of Mo and 88% of Sr, although the predicted concentrations of Co, Br, Sr, and Mo is zero. Other elements mapped to this factor include Ca (38%), S (19%), Mn (15%), and P (15%). The bivariate polar plot for this factor shows some correlation with faster north and northeasterly winds, which is very similar to the polar plot for the soil dust factor. In Portland, there are many industrial sites on the north edges of the city, near the Columbia River and the airport. It is surprising, however, that the local sources of metals from the sites
directly surrounding the sampling area, did not contribute a unique factor in this study, given the prior evidence of local pollution as noted in the introduction. Most notably, Cd and As do not appear at all, as both were excluded from the model due to very low concentrations and high uncertainties. In contrast to the high levels measured during the 2015 DEQ sampling (see above, average Cd concentration of 0.029 μg/ m3 and maximum of 0.195 μg/m3; average As of 0.032 μg/m3 and maximum of 0.101 μg/m3), the concentrations measured in this study are significantly lower (Cd average and maximum of 0.0001 μg/m3 and 0.0070 μg/m3; As of 0.001 μg/m3 and 0.015 μg/m3). These differences
Fig. 5. HYSPLIT 24-hour back-trajectories arriving at Brooklyn Rail Yard on December 7, 2017 at 2-hour intervals. The black stars at the bottom represent the arrival time of each back-trajectory in UTC. The big yellow star on the map is the location of the coal-fired power plant. These trajectories show air flow from the east along the Columbia River Gorge from the coal plant. 791
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Fig. 6. PMF 4-factor solution for the lower Southeast Portland sites. The 4 sources were identified as sea salt, soil dust, secondary sulfates, and diesel/fuel emissions.
in Cd and As from 2015 to 2018 suggest significantly reduced toxic emissions after the baghouse installation at the stained glass manufacturer, positively impacting air quality in the surrounding neighborhoods.
results from BS and DISP error estimation analyses. The 4-factor solution was determined to be the optimal solution due to solution stability, realistic factor profiles, and reasonably good diagnostics and error estimation results. There was good agreement between the predicted concentrations and measured concentrations for all the species designated as “strong”, with an r2 range of 0.72–1, but less good agreement between predicted and measured concentrations for the “weak” species, with an r2 range of 0.01–0.58. DISP analysis showed little to no rotational ambiguity, with only 0.003% decrease in Q and zero swaps by factor. BS analysis showed some random error: 3 factors mapped at or above 99% and one factor mapped at 69%. BS-DISP analysis revealed more uncertainty, with 72 of 100 swaps in DISP, between 1 and 11 swaps by factor, and 0.4% decrease in Q. DISP, BS, and BS-DISP results are detailed in Tables S5, S6, and S7, respectively. These error estimation results indicate some uncertainty in the solution and that the factor profiles should be loosely interpreted. The factor profiles for the 4-factor solution are shown in Fig. 6, showing species concentrations and percent of species.
3.2. Source apportionment with PMF of another industrial lower Southeast Portland site 3.2.1. Finding the optimal solution EPA PMF 5.0 was used for source apportionment of speciated PM from LSE, following the same methods as were used with the BRY data. The data from the 4 sites were compiled to create a big enough sample size for PMF, with a total of 98 samples and 31 species in the matrix. 3 species (As, Cd, Sb) were excluded from the model. 15 species (Ag, Br, Co, Cr, Cu, Hg, Mo, Ni, P, Pb, Rb, Se, Sn, Sr, Zn) were marked as “weak” to down-weight their contribution to the model. 13 species (Al, Ba, Ca, Cl, Fe, K, Mg, Mn, Na, S, Si, Ti, V) were classified as “strong”. There were three samples from July 4th at three sites with very high concentrations of S, K, Cu, Sr, and Ba, corresponding to fireworks emissions (Lin, 2016). These samples were excluded from the model because the high concentrations skewed the data (Reff et al., 2007). Solutions with 2–10 factors were explored. As with the BRY solutions, solutions with more factors had Q values closer to Qexp but had significantly worse
3.2.2. Comparison of this solution to the rail yard solution The 4 factors at LSE were identified as sea salt, soil dust, secondary sulfates, and fuel emissions. Sea salt accounted for 19% of the total mass of the measured species, soil dust accounted for 36%, secondary 792
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sulfates 29%, and fuel emissions 15%. These are the same factors as the BRY PMF solution except the industrial metals factor. There are fewer factors likely because the sampling time was two times longer at this site, making it harder to resolve infrequent sources, and because black carbon was not measured, which would resolve a larger fraction of total particulate matter and could allow for better separation of sources. Although the error estimation methods revealed that there is some uncertainty in the solution, the factor profiles were easily interpretable and very similar to the BRY solution. The LSE sea salt factor profile is nearly identical to the BRY sea salt factor, defined by high contributions of Cl (77%), Na (43%), and Mg (42%) with some Al, Is, K, Ca, Fe, Cu, Zn, Ag, and Sn. The LSE soil dust factor profile is also very similar to BRY solution, defined by high concentrations of Si (71%), Al (62%), Ti (63%), Ca (59%), and Fe (60%) and smaller amounts of Na, Mg, P, S, K, Mn, Zn, and Ba. The secondary sulfates factor is similarly defined by S (67%) with contributions of Na, Mg, Al, Si, P, Cl, K, Ca, Ti, Fe, Cu, Zn, Ag, Sn, and Pb. Mercury is not clearly associated with the sulfates factor at the LSE site; this is not surprising given the prevailing wind directions during the April–July sampling season at this site. The fuel emissions factor is similarly high in K (51%), P (51%), Pb (50%), Sn (51%), Ag (50%), Cu (51%), Zn (49%), and Se (56%).
Acknowledgments The authors gratefully acknowledge funding from EPA-OAROAQPS-17-03 and from Portland State University Institute for Sustainable Solutions, Multnomah County, and City of Portland for the XRF instrument grant. We thank Anthony Barnack (Oregon DEQ), Gary Norris (US EPA), Annelise Hill (Reed College, BA 2017), and Philip Hopke (University of Rochester) for valuable discussions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apr.2018.12.006. References Abbasi, S., Jansson, A., Sellgren, U., Olofsson, U., Jan. 2013. Particle emissions from rail traffic: a literature review. Crit. Rev. Environ. Sci. Technol. 43 (23), 2511–2544. http://www.tandfonline.com/doi/abs/10.1080/10643389.2012.685348. Bari, M.A., Kindzierski, W.B., Oct. 2017. Ambient fine particulate matter (PM 2.5 ) in Canadian oil sands communities: levels, sources and potential human health risk. Sci. 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4. Conclusions and broader implications In this source apportionment study of trace elements in particulate matter in industrial Southeast Portland, Oregon, EPA's Positive Matrix Factorization 5.0 was used to estimate source profiles and source contributions at the Brooklyn Rail Yard (BRY) as well as in industrial lower Southeast Portland (LSE). Five sources were identified at BRY: diesel and fuel emissions, sea salt, soil dust, secondary sulfates, and metals industry. Wind speed and direction were plotted with factor profiles on bivariate polar plots to further aid in identifying possible sources to particulate matter. It was surprising that there were such large regional influences in the sources of particulate matter that was collected, given the sampling location. A distinct and strong sea salt factor originates from the ocean 100 km away, and a sulfate factor likely from the coal plant 220 km away. Despite the sampling taking place in an industrial area at a rail yard with many local sources of pollution, these longrange, regional sources dominated. The PMF model for LSE site allowed identification of 4 sources: sea salt, soil dust, secondary sulfates, and fuel emissions. As with the BRY model, these sources are more heavily regionally influenced than locally, although the sampling took place in another industrial neighborhood. An important caveat to these results and conclusions is that this is not a complete compositional analysis of particulate matter. Only trace elements and black carbon were measured at BRY and only trace elements at LSE. Measuring organic carbon compounds as well as inorganic ions such as nitrate, ammonium, and sulfate would be required for a complete quantitative analysis of particulate matter sources in Portland. Including more species and total particulate mass in the source apportionment model would also likely allow for determining more than five factors and gaining a more thorough understanding of the possible sources, as is common in other studies (Kim and Hopke, 2008; Larson et al., 2006). This is instead a qualitative picture of which sources of pollution contribute to particulate matter in an urban industrial area, revealing a surprising dominance of remote regional sources. Another goal of this study was to measure airborne concentrations of toxic metals for the purpose of determining human exposure, in response to the Donovan et al. (2016) moss study. It was found that, for almost all elements measured, concentrations were always below the Oregon DEQ's Ambient Benchmark Concentrations (State of Oregon Department of Environmental Quality, 2010). Figures showing these results are in Section S7. This suggests that local sources of metals pollution are no longer as influential on southeast Portland's air quality, instead leaving an urban particulate matter composition driven primarily by regional sources. 793
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