Source Apportionment Models Research Articles

Chemical Composition of Particulate Matter from Traffic Emissions in a Road Tunnel in Xi’an, China

ABSTRACT Chemical compositions of particulate matter (PM) from traffic emissions vary by region and with time. Therefore, it is necessary to obtain local mobile source profiles of PM to support regional researches for vehicle emission control policy, source apportionment modeling, etc. In this study, PM2.5 and PM10 samples were collected from a highway tunnel in Xi’an in northwestern China. The chemical composition, specifically, the OC, EC, water-soluble ions, and elements, was analyzed in detail to (1) provide local PM profiles for a mixed vehicle fleet, (2) identify the origins of different elements in the tunnel environment, and (3) determine the associated factors influencing the profiles. The PM2.5 profiles in the tunnel were identified as OC (34.10%), EC (11.96%), water-soluble ions (18.22%), and elements (27.73%), while the PM10 profiles included OC (28.48%), EC (8.59%), water-soluble ions (14.17%), and elements (33.36%), respectively. The origins of the elements in the tunnel were classified into three categories by the receptor modeling approach: resuspended road dust and brake wear, vehicle exhaust and tire wear, and tailpipe emissions from diesel vehicles (DV). The mass fractions of OC, EC, and elements originating from resuspended road dust and brake wear were mainly affected by vehicle driving conditions (i.e., uphill/downhill and speed), whereas the mass content of bromine (Br) was influenced by the proportion of DV in the fleet.

Source apportionment of trace elements and black carbon in an urban industrial area (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 5-factor 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.

Assessment of forest fire impacts on carbonaceous aerosols using complementary molecular marker receptor models at two urban locations in California's San Joaquin Valley

Two hundred sixty-three fine particulate matter (PM2.5) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM2.5 OC. Similar source categories were found from the results of the CMB and PMF models to PM2.5 OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing

In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

Source apportionment of an epiphytic lichen biomonitor to elucidate the sources and spatial distribution of polycyclic aromatic hydrocarbons in the Athabasca Oil Sands Region, Alberta, Canada

The sources and spatial distribution of polycyclic aromatic hydrocarbons (PAHs) atmospheric deposition in the boreal forests surrounding bitumen production operations in the Athabasca Oil Sands Region (AOSR), Alberta, Canada were investigated as part of a 2014 passive in-situ bioindicator source apportionment study. Epiphytic lichen species Hypogymnia physodes samples (n = 127) were collected within a 150 km radius of the main surface oil sand production operations and analyzed for total sulfur, total nitrogen, forty-three elements, twenty-two PAHs, ten groups of C1-C2-alkyl PAHs and dibenzothiophenes (polycyclic aromatic compounds; PACs), five C1- and C2-alkyldibenzothiophenes, and retene. The ΣPAH + PAC in H. physodes ranged from 54 to 2778 ng g−1 with a median concentration of 317 ng g−1. Source apportionment modeling found an eight-factor solution that explained 99% of the measured ΣPAH + PAC lichen concentrations from four anthropogenic oil sands production sources (Petroleum Coke, Haul Road Dust, Stack Emissions, Raw Oil Sand), two local/regional sources (Biomass Combustion, Mobile Source), and two lichen biogeochemical factors. Petroleum Coke and Raw Oil Sand dust were identified as the major contributing sources of ΣPAH + PAC in the AOSR. These two sources accounted for 63% (43.2 μg g−1) of ΣPAH + PAC deposition to the entire study domain. Of this overall 43.2 μg g−1 contribution, approximately 90% (39.9 μg g−1) ΣPAH + PAC was deposited within 25 km of the closest oil sand production facility. Regional sources (Biomass Combustion and Mobile Sources) accounted for 19% of ΣPAH + PAC deposition to the entire study domain, of which 46% was deposited near-field to oil sand production operations. Source identification was improved over a prior lichen-based study in the AOSR through incorporation of PAH and PAC analytes in addition to inorganic analytes.

Development and assessment of a receptor source apportionment model based on four nonnegative matrix factorization algorithms

This study developed a receptor model, comprising four non-negative matrix factorization algorithms: the multiplicative update method; the optimal gradient method; the highly efficient, monotonic, fixed-point method; and the conjugate gradient method. The feasibility and performance of the developed model for emission source apportionment were assessed, using both a synthetic dataset, and an ambient PM2.5 dataset. The results from the US EPA's positive matrix factorization (PMF) 5.0 model were used for the assessment. Modeled results for the synthetic data showed that the range of factor contributions to most matrix elements solved by the four algorithms covered actual values. Modeled results, using the ambient dataset as the input, showed that the four algorithms in the developed model, and the PMF model, identified the same eight emission sources, and apportioned similar source contributions to PM2.5. Comparisons between the modeled organic carbon, and the elemental carbon source apportionments and radiocarbon measurements, suggested that combined application of multiple algorithms could satisfactorily apportion emission source contributions for one, or a few, specified samples among a receptor dataset, thus confirming the excellent source apportionment ability of the proposed model.

Developing an EFDC and Numerical Source-Apportionment Model for Nitrogen and Phosphorus Contribution Analysis in a Lake Basin

The numerical source-apportionment model is an efficient and useful method for analyzing water-quality responses to nutrient loading in rivers and lakes. In this study, the Environmental Fluid Dynamic Code (EFDC) and numerical source-apportionment model were applied to Lake Bali in Jiujiang City, China to predict the contributions of various pollution sources to the lake at any time and position. We calibrated and validated the model by comparing its predictions with observed hydrodynamic and water-quality parameters from 2014 to 2015. Application of the calibrated model to simulate water-quality responses to a pollution source showed that the contribution of a pollution source to water quality in the lake has strong spatial heterogeneity. The results provide useful information for the optimization of pollution load reduction in Lake Bali and can be used to determine the most effective implementation of its pollution-control plan. The model built in this study can also be used for pollution source-apportionment in other urban lakes and is superior to other traditional source-apportionment models.

Resonant Synchrotron X-ray Diffraction determines markers for iron-rich atmospheric particulate matter in urban region

Particulate matter driven health problems are strongly associated with its chemical composition. Despite the benefits of using source apportionment models for air quality management, limitations such as collinearity effects, restrict their application or compromise the accurate separation of sources, particularly for particulate matter with similar chemical profiles. Receptors models also depend on the operator expertise to appropriately classified sources, a subjective process that can lead to biased results. For highly correlated sources, the identification of specific markers is still the best way to achieve proper source apportionment. In this study, Resonant Synchrotron X-ray Diffraction has been applied to the analysis of atmospheric particles to determine markers for industrial and vehicular sources in the Region of Greater Vitória, Brazil. Total suspended particulate matter, PM10, and PM2.5 samples were analyzed by Resonant Synchrotron X-ray Diffraction showing high levels of iron-based crystalline phases. In comparison to the use of chemical elemental species, the identification of the crystalline phases provided an enhanced approach to classify specific iron-based source markers. For this study, α-Fe2O3 was identified with iron-based sources such as iron ore, pelletizing, and sintering; metallic Fe was inferred with blast furnaces and steelmaking; FeS2 was correlated with coal deposits; and K2Fe2O4 was associated to sintering emissions. Elemental carbon with different X-ray diffraction patterns enabled the differentiation of industrial and vehicular sources. The attribution of crystal rather than elemental composition in the identification of sources improves the accuracy of source apportionment studies.

Exploration of PM<sub>2.5</sub> sources on the regional scale in the Pearl River Delta based on ME-2 modeling

Abstract. The Pearl River Delta (PRD) of China, which has a population of more than 58 million people, is one of the largest agglomerations of cities in the world and had severe PM2.5 pollution at the beginning of this century. Due to the implementation of strong pollution control in recent decades, PM2.5 in the PRD has continuously decreased to relatively lower levels in China. To comprehensively understand the current PM2.5 sources in the PRD to support future air pollution control strategies in similar regions, we performed regional-scale PM2.5 field observations coupled with a state-of-the-art source apportionment model at six sites in four seasons in 2015. The regional annual average PM2.5 concentration based on the 4-month sampling was determined to be 37 µg m−3, which is still more than 3 times the WHO standard, with organic matter (36.9 %) and SO42- (23.6 %) as the most abundant species. A novel multilinear engine (ME-2) model was first applied to a comprehensive PM2.5 chemical dataset to perform source apportionment with predetermined constraints, producing more environmentally meaningful results compared to those obtained using traditional positive matrix factorization (PMF) modeling. The regional annual average PM2.5 source structure in the PRD was retrieved to be secondary sulfate (21 %), vehicle emissions (14 %), industrial emissions (13 %), secondary nitrate (11 %), biomass burning (11 %), secondary organic aerosol (SOA, 7 %), coal burning (6 %), fugitive dust (5 %), ship emissions (3 %) and aged sea salt (2 %). Analyzing the spatial distribution of PM2.5 sources under different weather conditions clearly identified the central PRD area as the key emission area for SO2, NOx, coal burning, biomass burning, industrial emissions and vehicle emissions. It was further estimated that under the polluted northerly air flow in winter, local emissions in the central PRD area accounted for approximately 45 % of the total PM2.5, with secondary nitrate and biomass burning being most abundant; in contrast, the regional transport from outside the PRD accounted for more than half of PM2.5, with secondary sulfate representing the most abundant transported species.

Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in different seasons from Shanghai, China

PM2.5 samples in four representative periods were collected from a highly industrialized district in Shanghai, China. The concentrations of PM2.5 and PM2.5-bound PAHs were analyzed. Positive matrix factorization (PMF) model was used to identify the potential sources. Relationship between PAHs distribution and meteorological parameters was assessed meanwhile. The incremental lifetime cancer risks (ILCRs) model was applied to quantitatively evaluate the exposure risk of PAHs. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLT) model was used to track the potential pollution area of PM2.5 along with a Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) methods. The results showed concentrations of PM2.5 and PAHs ranged from 14.83 to 185.58 μg/m3, 2.58 to 123.62 ng/m3, respectively. The source apportionment model indicated that traffic emissions were the most important sources in each sampling season, which accounted for 38.44%, 34.48%, 39.04% and 45.03%, respectively. Spearman correlation coefficient showed that PAHs had negative correlation with ambient temperature and relative humidity in some periods, while had no significant correlation with atmospheric pressure and visibility. The average estimated lifetime cancer risk for total PAHs reached 4.7 × 10−5, 4.5 × 10−5 and 4.1 × 10−5, 4.0 × 10−5 to exposed children and adults in winter and autumn, respectively, meaning that PM2.5-bound PAHs had high potential risk. HYSPLIT model suggested that monsoon greatly influenced the air quality in both winter and autumn.

Visibility impacts at Class I areas near the Bakken oil and gas development

ABSTRACTOil and gas activities have occurred in the Bakken region of North Dakota and nearby states and provinces since the 1950s but began increasing rapidly around 2008 due to new extraction methods. Three receptor-based techniques were used to examine the potential impacts of oil and gas extraction activities on airborne particulate concentrations in Class I areas in and around the Bakken. This work was based on long-term measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network. Spatial and temporal patterns in measured concentrations were examined before and after 2008 to better characterize the influence of these activities. A multisite back-trajectory analysis and a receptor-based source apportionment model were used to estimate impacts. Findings suggest that recent Bakken oil and gas activities have led to an increase in regional fine (PM2.5—particles with aerodynamic diameters <2.5 µm) soil and elemental carbon (EC) concentrations, as well as coarse mass (CM = PM10–PM2.5). Influences on sulfate and nitrate concentrations were harder to discern due to the concurrent decline in regional emissions of precursors to these species from coal-fired electric generating stations. Impacts were largest at sites in North Dakota and Montana that are closest to the most recent drilling activity.Implications: The increase in oil and gas activities in the Bakken region of North Dakota and surrounding areas has had a discernible impact on airborne particulate concentrations that impact visibility at protected sites in the region. However, the impact has been at least partially offset by a concurrent reduction in emissions from coal-fired electric generating stations. Continuing the recent reductions in flaring would likely be beneficial for the regional visual air quality.

Source apportionment of PM2.5 organic carbon in the San Joaquin Valley using monthly and daily observations and meteorological clustering

Two hundred sixty-three fine particulate matter (PM2.5) samples collected on 3-day intervals over a 14-month period at two sites in the San Joaquin Valley (SJV) were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and organic molecular markers. A unique source profile library was applied to a chemical mass balance (CMB) source apportionment model to develop monthly and seasonally averaged source apportionment results. Five major OC sources were identified: mobile sources, biomass burning, meat smoke, vegetative detritus, and secondary organic carbon (SOC), as inferred from OC not apportioned by CMB. The SOC factor was the largest source contributor at Fresno and Bakersfield, contributing 44% and 51% of PM mass, respectively. Biomass burning was the only source with a statistically different average mass contribution (95% CI) between the two sites. Wintertime peaks of biomass burning, meat smoke, and total OC were observed at both sites, with SOC peaking during the summer months. Exceptionally strong seasonal variation in apportioned meat smoke mass could potentially be explained by oxidation of cholesterol between source and receptor and trends in wind transport outlined in a Residence Time Analysis (RTA). Fast moving nighttime winds prevalent during warmer months caused local emissions to be replaced by air mass transported from the San Francisco Bay Area, consisting of mostly diluted, oxidized concentrations of molecular markers. Good agreement was observed between SOC derived from the CMB model and from non-biomass burning WSOC mass, suggesting the CMB model is sufficiently accurate to assist in policy development. In general, uncertainty in monthly mass values derived from daily CMB apportionments were lower than that of CMB results produced with monthly marker composites, further validating daily sampling methodologies.Strong seasonal trends were observed for biomass and meat smoke OC apportionment, and monthly mass averages had lowest uncertainty when derived from daily CMB apportionments.

Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model

The traditional source apportionment models, such as absolute principal component scores-multiple linear regression (APCS-MLR), are usually susceptible to outliers, which may be widely present in the regional geochemical dataset. Furthermore, the models are merely built on variable space instead of geographical space and thus cannot effectively capture the local spatial characteristics of each source contributions. To overcome the limitations, a new receptor model, robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), was proposed based on the traditional APCS-MLR model. Then, the new method was applied to the source apportionment of soil metal elements in a region of Wuhan City, China as a case study. Evaluations revealed that: (i) RAPCS-RGWR model had better performance than APCS-MLR model in the identification of the major sources of soil metal elements, and (ii) source contributions estimated by RAPCS-RGWR model were more close to the true soil metal concentrations than that estimated by APCS-MLR model. It is shown that the proposed RAPCS-RGWR model is a more effective source apportionment method than APCS-MLR (i.e., non-robust and global model) in dealing with the regional geochemical dataset.

Release of Black Carbon From Thawing Permafrost Estimated by Sequestration Fluxes in the East Siberian Arctic Shelf Recipient

AbstractBlack carbon (BC) plays an important role in carbon burial in marine sediments globally. Yet the sequestration of BC in the Arctic Ocean is poorly understood. Here we assess the concentrations, fluxes, and sources of soot BC (SBC)—the most refractory component of BC—in sediments from the East Siberian Arctic Shelf (ESAS), the World's largest shelf sea system. SBC concentrations in the contemporary shelf sediments range from 0.1 to 2.1 mg g−1 dw, corresponding to 2–12% of total organic carbon. The 210Pb‐derived fluxes of SBC (0.42–11 g m−2 yr−1) are higher or in the same range as fluxes reported for marine surface sediments closer to anthropogenic emissions. The total burial flux of SBC in the ESAS (~4,000 Gg yr−1) illustrates the great importance of this Arctic shelf in marine sequestration of SBC. The radiocarbon signal of the SBC shows more depleted yet also more uniform signatures (−721 to −896‰; average of −774 ± 62‰) than of the non‐SBC pool (−304 to −728‰; average of −491 ± 163‰), suggesting that SBC is coming from an, on average, 5,900 ± 300 years older and more specific source than the non‐SBC pool. We estimate that the atmospheric BC input to the ESAS is negligible (~0.6% of the SBC burial flux). Statistical source apportionment modeling suggests that the ESAS sedimentary SBC is remobilized by thawing of two permafrost carbon (PF/C) systems: surface soil permafrost (topsoil/PF; 25 ± 8%) and Pleistocene ice complex deposits (ICD/PF; 75 ± 8%). The SBC contribution to the total mobilized permafrost carbon (PF/C) increases with increasing distance from the coast (from 5 to 14%), indicating that the SBC is more recalcitrant than other forms of translocated PF/C. These results elucidate for the first time the key role of permafrost thaw in the transport of SBC to the Arctic Ocean. With ongoing global warming, these findings have implications for the biogeochemical carbon cycle, increasing the size of this refractory carbon pool in the Arctic Ocean.

Sources and characteristics of terrestrial carbon in Holocene-scale sediments of the East Siberian Sea

Abstract. Thawing of permafrost carbon (PF-C) due to climate warming can remobilise considerable amounts of terrestrial carbon from its long-term storage to the marine environment. PF-C can be then be buried in sediments or remineralised to CO2 with implications for the carbon–climate feedback. Studying historical sediment records during past natural climate changes can help us to understand the response of permafrost to current climate warming. In this study, two sediment cores collected from the East Siberian Sea were used to study terrestrial organic carbon sources, composition and degradation during the past ∼ 9500 cal yrs BP. CuO-derived lignin and cutin products (i.e., compounds solely biosynthesised in terrestrial plants) combined with δ13C suggest that there was a higher input of terrestrial organic carbon to the East Siberian Sea between ∼ 9500 and 8200 cal yrs BP than in all later periods. This high input was likely caused by marine transgression and permafrost destabilisation in the early Holocene climatic optimum. Based on source apportionment modelling using dual-carbon isotope (Δ14C, δ13C) data, coastal erosion releasing old Pleistocene permafrost carbon was identified as a significant source of organic matter translocated to the East Siberian Sea during the Holocene.

Determining the contribution of long-range transport, regional and local source areas, to PM10 mass loading in Hessen, Germany using a novel multi-receptor based statistical approach

This study uses two newly developed statistical source apportionment models, MuSAM and MuReSAM, to perform quantitative statistical source apportionment of PM10 at multiple receptor sites in South Hessen. MuSAM uses multi-site back trajectory data to quantify the contribution of long-range transport, while MuReSAM uses wind speed and direction as proxy for regional transport and quantifies the contribution of regional source areas. On average, between 7.8 and 9.1 μg/m3 of PM10 (∼50%) at receptor sites in South Hessen is contributed by long-range transport. The dominant source regions are Eastern, South Eastern, and Southern Europe. 32% of the PM10 at receptor sites in South Hessen is contributed by regional source areas (2.8–9.41 μg/m3). This fraction varies from <20% at remote sites to >40% for urban stations. Sources located within a 2 km radius around the receptor site are responsible for 7%–20% of the total PM10 mass (0.7–4.4 μg/m3). The perturbation study of the traffic flow due to the closing and reopening of the Schiersteiner Brücke revealed that the contribution of the bridge to PM10 mass loadings at two nearby receptor sites increased by approximately 120% after it reopened and became a bottleneck, although in absolute terms, the increase is small.

Ambient sediment quality conditions in Minnesota lakes, USA: Effects of watershed parameters and aquatic health implications

Surficial sediments were collected from 50 randomly selected Minnesota lakes, plus four a priori reference lakes, in 2007. The lakes encompassed broad geographic coverage of the state and included a variety of major land uses in the surrounding watersheds. Sediment samples were analyzed for a suite of metals, metalloids, persistent organic pollutants, total organic carbon, and particle size fractions. In addition, a small fish survey was conducted to assess PBDEs in both whole fish and fish tissues. Sediment quality in this set of lakes ranged from good (43%) to moderate (57%) based on an integrative measure of multiple contaminants. On an individual basis, some contaminants (e.g., arsenic, lead, DDD, and DDE) exceeded benchmark values in a small number of lakes that would be detrimental to benthic invertebrates. The sediments in two developed lakes tended to be more contaminated than sediments in lakes from other major watershed land uses. These differences were often statistically significant (p<0.05), particularly for lakes with developed versus cultivated land uses for arsenic, lead, zinc, and numerous PAH compounds. Multivariate statistical approaches were used on a subgroup of contaminants to show the two urban lakes, as well as a few northeastern Minnesota lakes, differed from the rest of the data set. Background threshold values were calculated for data with <80% nondetects. Source apportionment modeling of PAHs revealed that vehicle emissions and coal-related combustion were the most common sources. A general environmental forensic analysis of the PCDD/F data showed that ubiquitous combustion sources appeared to be important. BDE-209, a decaBDE, was detected in 84% of lake sediment samples, whereas fish at the top of the food chain (i.e., predator trophic group) had significantly higher (p<0.05) mean lipid-normalized concentrations of BDEs-47, 100, and 153 than lower trophic fish. These results will be used for future status and trends work.

Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data

Abstract. Source apportionment modeling provides valuable information on the contributions of different source sectors and/or source regions to ozone (O3) or fine particulate matter (PM2.5) concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. The Comprehensive Air quality Model with Extensions (CAMx) offers unique source attribution tools, called the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT), which track source contributions. We present results from a CAMx source attribution modeling study for a summer month and a winter month using a recently evaluated European CAMx modeling database developed for Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII). The contributions of several source sectors (including model boundary conditions of chemical species representing transport of emissions from outside the modeling domain as well as initial conditions of these species) to O3 or PM2.5 concentrations in Europe were calculated using OSAT and PSAT, respectively. A 1-week spin-up period was used to reduce the influence of initial conditions. Evaluation focused on 16 major cities and on identifying source sectors that contributed above 5 %. Boundary conditions have a large impact on summer and winter ozone in Europe and on summer PM2.5, but they are only a minor contributor to winter PM2.5. Biogenic emissions are important for summer ozone and PM2.5. The important anthropogenic sectors for summer ozone are transportation (both on-road and non-road), energy production and conversion, and industry. In two of the 16 cities, solvent and product also contributed above 5 % to summertime ozone. For summertime PM2.5, the important anthropogenic source sectors are energy, transportation, industry, and agriculture. Residential wood combustion is an important anthropogenic sector in winter for PM2.5 over most of Europe, with larger contributions in central and eastern Europe and the Nordic cities. Other anthropogenic sectors with large contributions to wintertime PM2.5 include energy, transportation, and agriculture.

A note on unusual Si/Al ratios in PM10 and PM2.5 road dust at several locations in India

The Si/Al ratios in road dust (PM10 and/or PM2.5) at several locations in India were examined and found to range between 1.6 and 84.9. The potential factors for this wide range of unusual Si/Al ratios in road dust are not known at this time. In addition to re-assessing data quality, the observations suggest the need to carefully quantify anthropogenic inputs of these elements to road dust. The findings of this study also contradict popular assumptions about minimal enrichment of crustal elements by anthropogenic sources and highlight the need to re-visit dust mass estimation using Si and Al as a surrogate. Further, characterization of Si/Al ratio in road dust at locations in India and the influence of local geology/geochemistry on it are especially important, if this ratio is to be used either for dust estimation or as an input to ambient aerosol mass source apportionment models.

An extended Bayesian sediment fingerprinting mixing model for the full Bayes treatment of geochemical uncertainties

AbstractRecent advances in sediment fingerprinting research have seen Bayesian mixing models being increasingly employed as an effective method to coherently translate component uncertainties into source apportionment results. Here, we advance earlier work by presenting an extended Bayesian mixing model capable of providing a full Bayes treatment of geochemical uncertainties. The performance of the extended full Bayes model was assessed against the equivalent empirical Bayes model and traditional frequentist optimisation. The performance of models coded in different Bayesian software (JAGS and Stan) was also evaluated, alongside an assessment of model sensitivity to reduced source representativeness and nonconservative fingerprint behaviour. Results revealed comparable accuracy and precision for the full and empirical Bayes models across both synthetic and real sediment geochemistry datasets, demonstrating that the empirical treatment of source data here represents a close approximation of the full Bayes treatment. Contrasts in the performance of models coded in JAGS and Stan revealed that the choice of software employed can impact significantly upon source apportionment results. Bayesian models coded in Stan were the least sensitive to both reduced source representativeness and nonconservative fingerprint behaviour, indicating Stan as the preferred software for future Bayesian sediment fingerprinting studies. Whilst the frequentist optimisation generally yielded comparable accuracy to the Bayesian models, uncertainties around apportionment estimates were substantially greater and the frequentist model was less effective at dealing with nonconservative behaviour. Overall, the effective performance of the extended full Bayes mixing model coded in Stan represents a notable advancement in source apportionment modelling relative to previous approaches. Both the mixing model and the software comparisons presented here should provide useful guidelines for future sediment fingerprinting studies.