Source Contribution Estimates Research Articles

Factors Affecting Mercury Stable Isotopic Distribution in Piscivorous Fish of the Laurentian Great Lakes

Identifying the sources of methylmercury (MeHg) and tracing the transformations of mercury (Hg) in the aquatic food web are important components of effective strategies for managing current and legacy Hg sources. In our previous work, we measured stable isotopes of Hg (δ202Hg, Δ199Hg, and Δ200Hg) in the Laurentian Great Lakes and estimated source contributions of Hg to bottom sediment. Here, we identify isotopically distinct Hg signatures for Great Lakes trout ( Salvelinus namaycush) and walleye ( Sander vitreus), driven by both food-web and water-quality characteristics. Fish contain high values for odd-isotope mass independent fractionation (MIF) with averages ranging from 2.50 (western Lake Erie) to 6.18‰ (Lake Superior) in Δ199Hg. The large range in odd-MIF reflects variability in the depth of the euphotic zone, where Hg is most likely incorporated into the food web. Even-isotope MIF (Δ200Hg), a potential tracer for Hg from precipitation, appears both disconnected from lake sedimentary sources and comparable in fish among the five lakes. We suggest that similar to the open ocean, water-column methylation also occurs in the Great Lakes, possibly transforming recently deposited atmospheric Hg deposition. We conclude that the degree of photochemical processing of Hg is controlled by phytoplankton uptake rather than by dissolved organic carbon quantity among lakes.

Understanding erosion processes using rare earth element tracers in a preformed interrill-rill system

Tracking sediment source and movement is essential to fully understanding soil erosion processes. The objectives of this study were to identify dominant erosion process and to characterize the effects of upslope interrill erosion on downslope interrill and rill erosion in a preformed interrill-rill system. A coarse textured soil with 2% clay and 20% silt was packed into a physical model of a scaled small watershed, which was divided into eight topographic units and was tagged with eight rare earth element (REE) oxides. Three 30-min rains were made at the sequential intensities of 60, 90, and 120mmh−1, and runoff and sediment were collected every 2min at the outlet. REE concentration in sediment was measured and used to estimate source contributions after fine-enrichment correction. Results showed that interrill erosion rate and sediment concentration increased with downslope distance, indicating that sediment transport might have controlled interrill erosion rates. In contrast, rill erosion rate was limited by rill detachment and development process. Rill erosion contributed most soil loss; however, the proportion decreased from 78 to 61% as rainfall intensity increased and rill network matured over three rains. Interrill erosion was more sensitive than rill erosion to rainfall intensity increases. The former was mostly affected by rainfall intensity in this experimental setup, while the latter was controlled by flow discharge, gradient, and rill evolution stage. The greatest sediment concentration and delivery rate occurred in the stage of the fastest rill development. The increased sediment delivery from interrill areas appeared to suppress rill detachment by concentrated flow. This study enhanced our understanding of interrill and rill erosion processes and provided the scientific insights for improving soil erosion models.

Association of Cardiovascular Responses in Mice with Source-apportioned PM2.5 Air Pollution in Beijing

ABSTRACT In this study, factor analysis and mass regression were used to identify four fine particulate matter sources and estimate their contributions to the ambient air pollution in Beijing. The identified sources were traffic re-suspended soil, mixed industrial sources, oil combustion, and secondary sulfate. The estimated source contributions were then introduced into two models as exposure variables to explore the relationships between cardiovascular responses in mice and PM exposures. We observed that PM2.5 has a small negative acute effect on heart rate, but the individual source factors showed much more significant effects. Traffic re-suspended soil had the most significant effect on heart rate, with a positive contribution on the day of exposure and a negative one on day lag 1. Acute heart rate variability outcomes were better explained by the total PM2.5 than by the source components. Chronic effects were observed as a decreased heart rate but an increased number of heart rate variability outcomes.

Sources and geographic origin of particulate matter in urban areas of the Danube macro-region: The cases of Zagreb (Croatia), Budapest (Hungary) and Sofia (Bulgaria)

The contribution of main PM pollution sources and their geographic origin in three urban sites of the Danube macro-region (Zagreb, Budapest and Sofia) were determined by combining receptor and Lagrangian models. The source contribution estimates were obtained with the Positive Matrix Factorization (PMF) receptor model and the results were further examined using local wind data and backward trajectories obtained with FLEXPART. Potential Source Contribution Function (PSCF) analysis was applied to identify the geographical source areas for the PM sources subject to long-range transport. Gas-to-particle transformation processes and primary emissions from biomass burning are the most important contributors to PM in the studied sites followed by re-suspension of soil (crustal material) and traffic. These four sources can be considered typical of the Danube macro-region because they were identified in all the studied locations. Long-range transport was observed of: a) sulphate-enriched aged aerosols, deriving from SO2 emissions in combustion processes in the Balkans and Eastern Europe and b) dust from the Saharan and Karakum deserts. The study highlights that PM pollution in the studied urban areas of the Danube macro-region is the result of both local sources and long-range transport from both EU and no-EU areas.

Estimation of different source contributions to sediment organic matter in an agricultural-forested watershed using end member mixing analyses based on stable isotope ratios and fluorescence spectroscopy

The two popular source tracing tools of stable isotope ratios (δ13C and δ15N) and fluorescence spectroscopy were used to estimate the relative source contributions to sediment organic matter (SeOM) at five different river sites in an agricultural-forested watershed (Soyang Lake watershed), and their capabilities for the source assignment were compared. Bulk sediments were used for the stable isotopes, while alkaline extractable organic matter (AEOM) from sediments was used to obtain fluorescent indices for SeOM. Several source discrimination indices were fully compiled for a range of the SeOM sources distributed in the catchments of the watershed, which included soils, forest leaves, crop (C3 and C4) and riparian plants, periphyton, and organic fertilizers. The relative source contributions to the river sediment samples were estimated via end member mixing analysis (EMMA) based on several selected discrimination indices. The EMMA based on the isotopes demonstrated that all sediments were characterized by a medium to a high contribution of periphyton ranging from ~30% to 70% except for one site heavily affected by forest and agricultural fields with relatively high contributions of terrestrial materials. The EMMA based on fluorescence parameters, however, did not show similar results with low contributions from forest leaf and periphyton. The characteristics of the studied watershed were more consistent with the source contributions determined by the isotope ratios. The discrepancy in the EMMA capability for source assignments between the two analytical tools can be explained by the limited analytical window of fluorescence spectroscopy for non-fluorescent dissolved organic matter (FDOM) and the inability of AEOM to represent original bulk particulate organic matter (POM).

Source apportionments of ambient fine particulate matter in Israeli, Jordanian, and Palestinian cities

This manuscript evaluates spatial and temporal variations of source contributions to ambient fine particulate matter (PM2.5) in Israeli, Jordanian, and Palestinian cities. Twenty-four hour integrated PM2.5 samples were collected every six days over a 1-year period (January to December 2007) in four cities in Israel (West Jerusalem, Eilat, Tel Aviv, and Haifa), four cities in Jordan (Amman, Aqaba, Rahma, and Zarka), and three cities in Palestine (Nablus, East Jerusalem, and Hebron). The PM2.5 samples were analyzed for major chemical components, including organic carbon and elemental carbon, ions, and metals, and the results were used in a positive matrix factorization (PMF) model to estimate source contributions to PM2.5 mass. Nine sources, including secondary sulfate, secondary nitrate, mobile, industrial lead sources, dust, construction dust, biomass burning, fuel oil combustion and sea salt, were identified across the sampling sites. Secondary sulfate was the dominant source, contributing 35% of the total PM2.5 mass, and it showed relatively homogeneous temporal trends of daily source contribution in the study area. Mobile sources were found to be the second greatest contributor to PM2.5 mass in the large metropolitan cities, such as Tel Aviv, Hebron, and West and East Jerusalem. Other sources (i.e. industrial lead sources, construction dust, and fuel oil combustion) were closely related to local emissions within individual cities. This study demonstrates how international cooperation can facilitate air pollution studies that address regional air pollution issues and the incremental differences across cities in a common airshed. It also provides a model to study air pollution in regions with limited air quality monitoring capacity that have persistent and emerging air quality problems, such as Africa, South Asia and Central America.

Investigation of source locations and contributions using an integrated trajectory-source apportionment method with multiple time resolution data

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) coexist in ambient air and contribute to adverse health effects in human populations. Thus, it is helpful to identify the contributions of air pollutants from different sources in order to design effective control strategies. Nevertheless, different sampling time schedules for VOCs and PM2.5 result in difficulties for conventional receptor modeling. Additionally, a receptor model is unable to link the retrieved factors directly with actual source locations. To address these gaps, this study integrated back-trajectory data into an improved source apportionment model suitable for multiple time resolution data to estimate the locations of the regional transport-related factor. Within six potential source regions (PSRs) outlined by the above method, PSR 5 was suggested the primary one located near the industrial regions in the northeastern China. Constrained model results showed that the source contribution estimates with back trajectories passing over the PSRs were 3 and 9% of the selected VOCs and PM2.5 mass, respectively.

Development of an on-line source-tagged model for sulfate, nitrate and ammonium: A modeling study for highly polluted periods in Shanghai, China

An on-line source-tagged model coupled with an air quality model (Nested Air Quality Prediction Model System, NAQPMS) was applied to estimate source contributions of primary and secondary sulfate, nitrate and ammonium (SNA) during a representative winter period in Shanghai. This source-tagged model system could simultaneously track spatial and temporal sources of SNA, which were apportioned to their respective primary precursors in a simulation run. The results indicate that in the study period, local emissions in Shanghai accounted for over 20% of SNA contributions and that Jiangsu and Shandong were the two major non-local sources. In particular, non-local emissions had higher contributions during recorded pollution periods. This suggests that the transportation of pollutants plays a key role in air pollution in Shanghai. The temporal contributions show that the emissions from the “current day” (emission contribution from the current day during which the model was simulating) contributed 60%–70% of the sulfate and ammonium concentrations but only 10%–20% of the nitrate concentration, while the previous days’ contributions increased during the recorded pollution periods. Emissions that were released within three days contributed over 85% averagely for SNA in January 2013. To evaluate the source-tagged model system, the results were compared by sensitivity analysis (emission perturbation of −30%) and backward trajectory analysis. The consistency of the comparison results indicated that the source-tagged model system can track sources of SNA with reasonable accuracy.

PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment.

USEPA's UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM2.5 measurements, sampled longitudinally during 2012-2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown threefold higher relative contribution of solid fuel burning emissions to indoor PM2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.

Comparison of source apportionment of PM2.5 using receptor models in the main hub port city of East Asia: Busan

The contributions of various PM2.5 emission sources to ambient PM2.5 levels during 2013 in the main hub port city (Busan, South Korea) of East Asia was quantified using several receptor modeling techniques. Three receptor models of principal component analysis/absolute principal component score (PCA/APCS), positive matrix factorization (PMF), and chemical mass balance (CMB) were used to apportion the source of PM2.5 obtained from the target city. The results of the receptor models indicated that the secondary formation of PM2.5 was the dominant (45–60%) contributor to PM2.5 levels in the port city of Busan. The PMF and PCA/APCS suggested that ship emission was a non-negligible contributor of PM2.5 (up to about 10%) in the study area, whereas it was a negligible contributor based on CMB. The magnitude of source contribution estimates to PM2.5 levels differed significantly among these three models due to their limitations (e.g., PM2.5 emission source profiles and restrictions of the models). Potential source contribution function and concentration-weighted trajectory analyses indicated that long-range transport from sources in the eastern China and Yellow Sea contributed significantly to the level of PM2.5 in Busan.

A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

Community Multi-Scale Air Quality (CMAQ) estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority of PM2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is −0.28 (OC), 0.11 (NO3), 0.05 (NH4), and −0.08 (SO4). The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO3, NH4, and SO4 after applying the secondary bias correction. Ten-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.

Estimating runoff from a glacierized catchment using natural tracers in the semi‐arid Andes cordillera

AbstractThis paper presents a methodology for hydrograph separation in mountain watersheds, which aims at identifying flow sources among ungauged headwater sub‐catchments through a combination of observed streamflow and data on natural tracers including isotope and dissolved solids. Daily summer and bi‐daily spring season water samples obtained at the outlet of the Juncal River Basin in the Andes of Central Chile were analysed for all major ions as well as stable water isotopes, δ18O and δD. Additionally, various samples from rain, snow, surface streams and exfiltrating subsurface water (springs) were sampled throughout the catchment. A principal component analysis was performed in order to address cross‐correlation in the tracer dataset, reduce the dimensionality of the problem and uncover patterns of variability. Potential sources were identified in a two‐component U‐space that explains 94% of the observed tracer variability at the catchment outlet. Hydrograph separation was performed through an Informative‐Bayesian model. Our results indicate that the Juncal Norte Glacier headwater sub‐catchment contributed at least 50% of summer flows at the Juncal River Basin outlet during the 2011–2012 water year (a hydrologically dry period in the Region), even though it accounts for only 27% of the basin area. Our study confirms the value of combining solute and isotope information for estimating source contributions in complex hydrologic systems, and provides insights regarding experimental design in high‐elevation semi‐arid catchments. The findings of this study can be useful for evaluating modelling studies of the hydrological consequences of the rapid decrease in glacier cover observed in this region, by providing insights into the origin of river water in basins with little hydrometeorological information. Copyright © 2016 John Wiley & Sons, Ltd.

Inter-comparison of source apportionment of PM10 using PMF and CMB in three sites nearby an industrial area in central Italy

Receptor models (RMs), based on chemical composition of particulate matter (PM), such as Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF), represent useful tools for determining the impact of PM sources to air quality. This information is useful, especially in areas influenced by anthropogenic activities, to plan mitigation strategies for environmental management. Recent inter-comparison of source apportionment (SA) results showed that one of the difficulties in the comparison of estimated source contributions is the compatibility of the sources, i.e. the chemical profiles of factor/sources used in receptor models. This suggests that SA based on integration of several RMs could give more stable and reliable solutions with respect to a single model. The aim of this work was to perform inter-comparison of PMF (using PMF3.0 and PMF5.0 codes) and CMB outputs, focusing on both source chemical profiles and estimates of source contributions. The dataset included 347 daily PM10 samples collected in three sites in central Italy located near industrial emissions. Samples were chemically analysed for the concentrations of 21 chemical species (NH4+, Ca2+, Mg2+, Na+, K+, Mg2+, SO42−, NO3−, Cl−, Si, Al, Ti, V, Mn, Fe, Ni, Cu, Zn, Br, EC, and OC) used as input of RMs. The approach identified 9 factor/sources: marine, traffic, resuspended dust, biomass burning, secondary sulphate, secondary nitrate, crustal, coal combustion power plant and harbour-industrial. Results showed that the application of constraints in PMF5.0 improved interpretability of profiles and comparability of estimated source contributions with stoichiometric calculations. The inter-comparison of PMF and CMB gave significant differences for secondary nitrate, biomass burning, and harbour-industrial sources, due to non-compatibility of these source profiles that have local specificities. When these site-dependent specificities were taken into account, optimising the input source profiles of CMB, a significant improvement in the comparison of the estimated source contributions with PMF was obtained.

Source insights into the 11-h daytime and nighttime fine ambient particulate matter in China as well as the synthetic studies using the new Multilinear Engine 2-species ratios (ME2-SR) method

Modeled results are very important for environmental management. Unreasonable modeled result can lead to wrong strategy for air pollution management. In this work, an improved physically constrained source apportionment (PCSA) technology known as Multilinear Engine 2-species ratios (ME2-SR) was developed to the 11-h daytime and nighttime fine ambient particulate matter in urban area. Firstly, synthetic studies were carried out to explore the effectiveness of ME2-SR. The estimated source contributions were compared with the true values. The results suggest that, compared with the positive matrix factorization (PMF) model, the ME2-SR method could obtain more physically reliable outcomes, indicating that ME2-SR was effective, especially when apportioning the datasets with no unknown source. Additionally, 11-h daytime and nighttime PM2.5 samples were collected from Tianjin in China. The sources of the 11-h daytime and nighttime fine ambient particulate matter in China were identified using the new method and the PMF model. The calculated source contributions for ME2-SR for daytime PM2.5 samples are resuspended dust (38.91 μg m−3, 26.60%), sulfate and nitrate (38.60 μg m−3, 26.39%), vehicle exhaust and road dust (38.26 μg m−3, 26.16%) and coal combustion (20.14 μg m−3, 13.77%), and those for nighttime PM2.5 samples are resuspended dust (18.78 μg m−3, 12.91%), sulfate and nitrate (41.57 μg m−3, 28.58%), vehicle exhaust and road dust (38.39 μg m−3, 26.39%), and coal combustion (36.76 μg m−3, 25.27%). The comparisons of the constrained versus unconstrained outcomes clearly suggest that the physical meaning of the ME2-SR results is interpretable and reliable, not only for the specified species values but also for source contributions. The findings indicate that the ME2-SR method can be a useful tool in source apportionment studies, for air pollution management.

Bayesian nitrate source apportionment to individual groundwater wells in the Central Valley by use of elemental and isotopic tracers

AbstractGroundwater quality is a concern in alluvial aquifers that underlie agricultural areas, such as in the San Joaquin Valley of California. Shallow domestic wells (less than 150 m deep) in agricultural areas are often contaminated by nitrate. Agricultural and rural nitrate sources include dairy manure, synthetic fertilizers, and septic waste. Knowledge of the relative proportion that each of these sources contributes to nitrate concentration in individual wells can aid future regulatory and land management decisions. We show that nitrogen and oxygen isotopes of nitrate, boron isotopes, and iodine concentrations are a useful, novel combination of groundwater tracers to differentiate between manure, fertilizers, septic waste, and natural sources of nitrate. Furthermore, in this work, we develop a new Bayesian mixing model in which these isotopic and elemental tracers were used to estimate the probability distribution of the fractional contributions of manure, fertilizers, septic waste, and natural sources to the nitrate concentration found in an individual well. The approach was applied to 56 nitrate‐impacted private domestic wells located in the San Joaquin Valley. Model analysis found that some domestic wells were clearly dominated by the manure source and suggests evidence for majority contributions from either the septic or fertilizer source for other wells. But, predictions of fractional contributions for septic and fertilizer sources were often of similar magnitude, perhaps because modeled uncertainty about the fraction of each was large. For validation of the Bayesian model, fractional estimates were compared to surrounding land use and estimated source contributions were broadly consistent with nearby land use types.

Quantifying Sediment Provenance Using Multiple Composite Fingerprints in a Small Watershed in Oklahoma.

Quantitative information on sediment provenance is needed for improved calibration and validation of process-based soil erosion models. However, sediment source data are often limited due to difficulties in directly measuring source contributions at a watershed scale. Our objectives in this study were to estimate sediment source contributions in a 15-km watershed using analytical solutions to a three end-member mixing model using multiple composite fingerprints and to compare the results with those estimated with a single radionuclide, Cs. Surface soil samples were collected from 23 croplands, 19 rangelands, and 26 gully banks in the watershed, and 31 geochemical elements were analyzed for each sample. The elements served as tracers and were screened using statistical tests and range checks. The mean concentrations of all the nonconflict tracer pairs were used in the mixing model to calculate source contributions for the three sources. Results showed that although source contributions were strongly influenced by topography and land use, gully or subsoil erosion was found to be the main source of fine sediment in most subwatersheds. This study demonstrates that estimated source contributions may vary substantially among different composite fingerprints and that the use of multiple composite fingerprints greatly improves accuracy while reducing uncertainty. The source contributions estimated using multiple composite fingerprints agreed well with those estimated with Cs, with a correlation coefficient of 0.69 for gully contributions. This good agreement increases our confidence in using the multiple composite fingerprint method to identify sediment provenance in relatively small watersheds.

Sources of sediment carbon sequestered in restored seagrass meadows

Seagrass meadows accumulate carbon in sediments as a result of in situ production and sedimentation of particulate organic matter (OM). We quantified the contribution of OM sources to the sediment carbon pool in restored seagrass meadows of different ages (unvegetated and 4 and 10 yr since restoration) in the Virginia coastal bays. Using carbon (C) and nitrogen (N) stable isotopes, we estimated the contribution of seagrass (Zostera marina), benthic diatoms and sestonic particles (BD/S), and macroalgae (MA) to the sediment OM pool influenced by restoration (top 10 cm) with a Bayesian mixing model. Marsh grass was not a likely source based on C:N ratios of the sediment OM. The 4 and 10 yr seagrass meadows had similar OM source contributions to the top 10 cm of sediment, which were distinct from those of unvegetated sites. Seagrass, BD/S, and MA contributed 41, 56, and 3%, respectively, in the 10 yr age treatments and 50, 46, and 4%, respectively, in the 4 yr age treatments. Diagenesis of OM sources had little impact on the source contribution estimates. In combination with carbon accumulation rates at these sites (37 g C m −2 yr −1 ), these results indicate that 10 yr after seeding, restored seagrass meadows accumulated sea- grass carbon at a rate of 14.3 g C m −2 yr −1 and non-seagrass carbon (BD/S and MA) at a rate of 22.4 g C m −2 yr −1 . This study demonstrates how seagrass restoration contributes to the sequestra- tion of 'blue carbon' and quantifies the impact restored seagrass meadow age has on stored sedi- ment carbon.

Estimation of source contribution to nitrate loading in road runoff using stable isotope analysis

The nitrogen load was determined in road runoff during rainfall events. Moreover, nitrate isotopes analysis was conducted to determine the contribution of nitrates from atmospheric deposition and leaching from road dust. The concentrations of NO3-N in road runoff were higher than those in atmospheric deposits for each rainfall event, except one event with a long antecedent dry weather period. The δ18O-NO3 in road runoff was lower than in atmospheric deposits and higher than in leachate from road dust; however, no difference in δ15N-NO3 was observed. By using δ18O-NO3 as an indicator for evaluating NO3-N sources in road runoff, contribution ratios of NO3-N from road dust were estimated to be 14–22%, 23–25%, and 22–34% for Event 1 to Event 3, respectively. These results indicated that the NO3-N from the atmosphere accounts for more than half of the NO3-N in road runoff.

Using multiple composite fingerprints to quantify fine sediment source contributions: A new direction

Sediment source fingerprinting provides an essential means for estimating sediment source contributions, which are needed not only for soil conservation planning but also for erosion model evaluation/refinement. A single optimum composite fingerprint has been widely used in the literature to estimate sediment provenance. The objectives of this work are to (1) verify whether an optimum composite fingerprint exists, (2) present a new direction of using multiple composite fingerprints to improve the accuracy and reliability of source contribution estimation, and (3) evaluate the optimization model formulation and the validity of the tracer discriminatory weighting. This study shows that tracer selection greatly impacts the estimated source contributions. The optimum composite fingerprint may not exist, or at least cannot be identified simply based on ability of the tracer to discriminate sources because of the lack of correlation between ability of the tracer to discriminate and its rigor in estimating source contributions. The weak link is likely caused by (1) tracer conflicts, (2) differential tracer measurement errors, and (3) varying degree of the conservativeness of each tracer or lack of it. To overcome this shortcoming, a new approach of using multiple composite fingerprints was proposed. Contrary to the popular tracer reduction strategies, this new approach uses a maximum number of composite fingerprints, which contain non-contradictory tracers in each composite, to maximize the use of all tracer information. The new approach assumes that source proportions averaged over multiple composite fingerprints are more likely to be closer to the population means than any estimate using a single fingerprint alone. Such a ‘mean of the means’ approach has been shown to not only improve the accuracy but also reduce the uncertainty of the proportion estimates. The model of the absolute relative difference performed slightly better than the squared relative difference model in estimating source contributions, suggesting the former be preferred. The results also indicated that the tracer discriminatory weighting should be excluded as it tends to bias contribution estimates.

Regional air quality in Leipzig, Germany: detailed source apportionment of size-resolved aerosol particles and comparison with the year 2000.

A detailed source apportionment of size-resolved aerosol particles in the area of Leipzig, Germany, was performed. Sampling took place at four sites (traffic, traffic/residential, urban background, regional background) in parallel during summer 2013 and the winters 2013/14/15. Twenty-one samples were taken per season with a 5-stage Berner impactor and analysed for particulate mass, inorganic ions, organic and elemental carbon, water-soluble organic carbon, trace metals, and a wide range of organic species. The compositional data were used to estimate source contributions to particulate matter (PM) in quasi-ultrafine (up to 140 nm), accumulation mode, and coarse size ranges using Positive Matrix Factorisation (PMF) receptor modelling. Traffic (exhaust and general traffic emissions), coal combustion, biomass combustion, photochemistry, general secondary formation, cooking, fungal spores, urban dust, fresh sea/road salt, and aged sea salt were all found to contribute to different extents to observed PM concentrations. PMF derived estimates agreed reasonably with estimates from established macrotracer approaches. Quasi-ultrafine PM originated mainly from traffic (20-50%) and photochemistry (30-50%) in summer, while it was dominated by solid fuel (mainly biomass) combustion in winter (50-70%). Tentatively identified cooking aerosol contributed up to 36% on average at the residential site. For accumulation mode particles, two secondary sources typically contributed 40-90% to particle mass. In winter, biomass and coal combustion contributions were up to ca. 25% and 45%, respectively. Main sources of coarse particles were diverse and included nearly all PMF-resolved ones depending on season and air mass origin. For PM10, traffic (typically 20-40% at kerbside sites), secondary formation (30-60%), biomass combustion (10-15% in winter), and coal combustion (30-40% in winter with eastern air mass inflow) were the main quantified sources. At the residential site, contributions from biomass combustion derived up to 60% from local emissions. Coal combustion as a significant source was only present during eastern air mass inflow and showed very similar concentrations at all sites, indicating the importance of trans-boundary air pollution transport in the study area. Overall, nearly half of the PM10 mass was attributed to urban sources by a simple subtractive approach with highest reduction potentials of up to 80% for local (urban) mitigation measures in ultrafine and coarse particles. Local increments of elemental carbon have decreased by about 50% as compared to the year 2000, corroborating results from a former study on the positive effects of a low emission zone, implemented in Leipzig in 2011.