Source Contribution Estimates Research Articles

Spatiotemporal variation of polycyclic aromatic hydrocarbons in Tibetan lake sediment cores reveals the influence of forest fires

Despite declining anthropogenic emissions of polycyclic aromatic hydrocarbons (PAHs) due to global control strategies, forest fire emissions have been increasing, significantly affecting PAH dynamics in global sinks. This study investigated the spatiotemporal variations of sedimentary PAHs in three Tibetan lakes—Yiong Tso, Yamdrok Yumtso, and Urru Tso—to determine the influence of forest fires on PAH levels and historical trends. Yiong Tso Lake, located in a fire-affected watershed, exhibited the highest PAH concentrations (average of 43.4 ± 25.7 ng/g) with significant fluctuations since the 1920s, peaking in the 1960s (46.3 ng/g) and 1980s (91.3 ng/g), corresponding to periods of intense forest fires. This pattern aligned with source contribution estimates using the modified Cohen's d (mcd), indicating the dominance of forest fires as a PAH source until the 1990s. PAH concentrations decreased with increasing distance from the southeastern Tibetan Forest, as observed in Yamdrok Yumtso (average of 36.1 ± 19.9 ng/g) and Urru Tso (average of 16.4 ± 6.9 ng/g). Temporal variations in PAH concentrations and mcd values from these lakes also reflected a response to forest fires during the 1960s, suggesting a widespread influence of forest-fire-derived PAHs across the plateau. The impact of forest fires on sedimentary PAHs was expected to persist for decades, with an estimated half-life of approximately 11–12 years. These findings highlight significant emissions of PAHs from forest fires in the Tibetan Plateau, potentially transforming regional PAH dynamics and influencing global cycling.

Different variations in PM2.5 sources and their specific health risks in different periods in a heavily polluted area of the Beijing-Tianjin-Hebei region of China

The temporal variations in source-specific health risks of airborne particles and the differences in estimated source contributions to PM2.5 mass and PM2.5 exposure in severely polluted areas with the implementation of control measures remain unclear. In this study, PM2.5 samples were collected from a city of heavy pollution in the Beijing-Tianjin-Hebei region of China in two autumn-winter seasons during 2017–2019. The dominant species of PM2.5 was SNA (sum of sulfate, nitrate, and ammonium; 34 %), organic matter (OM; 23 %), and fine soil (15 %). PM2.5 concentration decreased across the years, whereas trace elements and OM showed the largest increases. A positive matrix factorization model combined with a health risk assessment method identified six sources, and quantified their specific health risks. The major sources of PM2.5 mass were secondary source (37 %) and coal combustion (20 %). The health risks (non-cancer risk for children, and cancer risk for children and adults) exceeded the guidelines, and were dominated by industrial processes (38 % and 40 %) and coal combustion (22 % and 52 %). The interannual decline in PM2.5 concentration was mainly due to reductions in biomass burning and soil dust, but the significant increase in health risks was attributed to the enhancement of industrial processes and coal combustion. On clean days, both PM2.5 mass and health risks were significantly affected by industrial processes and soil dust. As PM2.5 pollution increased, secondary source and coal combustion contributed significantly to PM2.5 mass. Coal combustion posed high health risks during pollution and the heating periods, especially in 2018–2019. Mn, Cr, and As were the dominant elements responsible for source-specific health risks. The quantitative result highlights the different responses of PM2.5 mass and health risks to recent mitigation measures, and suggests that both source concentrations and source-specific health risks should be of concern in the future.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Source Analysis and Contribution Estimation of Heavy Metal Contamination in Agricultural Soils in an Industrial Town in the Yangtze River Delta, China

Farmland soil quality is a crucial determinant for agricultural productivity, food safety, and human well-being. Among the various contaminants, heavy metals have emerged as pervasive factors significantly impacting farmland quality, attracting widespread societal concern. In this study, we systematically applied multivariate statistical analysis, geostatistical methods, and the positive matrix factorization (PMF) source apportionment technique to elucidate the sources and contributions of eight heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, Ni) in farmland soils within an industrialized town. Our findings reveal that Cd, Hg, Pb, and Zn exhibit pollution or enrichment in farmland soils compared to natural background levels, with Hg and Cd surpassing 164.3% and 50.2%, respectively. Notably, Zn demonstrates discernible point-source pollution. Source apportionment results highlight industrial point sources, coal combustion, and agriculture as the primary anthropogenic contributors to heavy metal contamination, with zinc-plating enterprises being the predominant industrial point sources. Addressing the specific issue of point-source pollution from Zn in industrial activities, further analysis establishes a correlation between soil Zn content and the distance from zinc-plating enterprises. Utilizing an atmospheric transport model, we observe that the impact of industrial activities on soil Zn is limited when the distance exceeds 1.5 km, emphasizing the importance of monitoring Zn pollution within areas less than 1.5 km. This study pioneers a progressive source apportionment approach, considering the origins of different heavy metals, pollution levels, distance factors, and the cost-effectiveness of environmental measures. The insights gained provide scientifically sound strategies for future decision making in environmental protection.

Millennial-scale variability of Greenland dust provenance during the last glacial maximum as determined by single particle analysis

Greenland ice core records exhibited 100-fold higher dust concentrations during the Last Glacial Maximum (LGM) than during the Holocene, and dust input temporal variability corresponded to different climate states in the LGM. While East Asian deserts, the Sahara, and European loess have been suggested as the potential source areas (PSAs) for Greenland LGM dust, millennial-scale variability in their relative contributions within the LGM remains poorly constrained. Here, we present the morphological, mineralogical, and geochemical characteristics of insoluble microparticles to constrain the provenance of dust in Greenland NEEM ice core samples covering cold Greenland Stadials (GS)-2.1a to GS-3 (~ 14.7 to 27.1 kyr ago) in the LGM. The analysis was conducted on individual particles in microdroplet samples by scanning electron microscopy with energy dispersive X-ray spectroscopy and Raman microspectroscopy. We found that the kaolinite-to-chlorite (K/C) ratios and chemical index of alteration (CIA) values were substantially higher (K/C: 1.4 ± 0.7, CIA: 74.7 ± 2.9) during GS-2.1a to 2.1c than during GS-3 (K/C: 0.5 ± 0.1, CIA: 65.8 ± 2.8). Our records revealed a significant increase in Saharan dust contributions from GS-2.1a to GS-2.1c and that the Gobi Desert and/or European loess were potential source(s) during GS-3. This conclusion is further supported by distinctly different carbon contents in particles corresponding to GS-2.1 and GS-3. These results are consistent with previous estimates of proportional dust source contributions obtained using a mixing model based on Pb and Sr isotopic compositions in NEEM LGM ice and indicate millennial-scale changes in Greenland dust provenance that are probably linked to large-scale atmospheric circulation variabilities during the LGM.

Atmospheric CO2 Isotopic Variations, with Estimation of Ocean and Plant Source Contributions

This analysis uses both atmospheric CO2 concentrations and the accompanying δ13C isotopic measurements of CO2 over 40 years from 1978 to 2015 observed at ten different latitudes from 90° S to 82 °N. Atmospheric CO2 is separated into two components of CO2 attributable to deep ocean and to plant (including fossil fuel) sources. The isotopic values assigned to the two components are δ13C = 0‰ and −26‰, respectively. The latitude variations in residual source component CO2 show the ocean source component peaking at the equator. This contrasts with the residual plant source component that peaks in the Arctic Circle region. Seasonal comparisons show no change in the ocean component peaking at the equator and no significant changes in its variation with latitude, while the plant component shows seasonal changes of the order of 15 ppm at high latitudes. The ocean component shows clear anomalous behavior in the three years following the 1989 Pacific Ocean regime shift (a shift independently identified from the changed biological time series). By contrast, the residual plant component shows a correlation in the timing of maxima in its annual variations with the timing of El Nino events over the time span of 1985–2015. It also shows a discontinuity in annual variation coinciding with the 1995 AMO phase change. We conclude that the ocean and plant components of atmospheric CO2 relate to independent sources of atmospheric CO2 and have approximately equal magnitudes. The observations are consistent with a hypothesis that variations in the ocean components have an origin from upwelling water from deep ocean currents, and variations in plant components are dominated by a combination of fossil fuel CO2, phytoplankton productivity, and forest and peat fires, which primarily occur in the northern hemisphere.

Aged polycyclic aromatic hydrocarbons as stratigraphic marker in the Anthropocene: Evidence from Tibetan Lake sediments

Polycyclic aromatic hydrocarbons (PAHs) were supposed to serve as combustion marker reflecting the past energy use, but it was unclear whether their sediment records in the Anthropocene were “weathered” due to aging-induced formation of bound residues. In this study, the total concentration of PAHs (the sum of rapid desorption, slow desorption, and bound residue fractions) were determined in four dated sediment cores from eastern to central Tibet using multi-step sequential extraction method. The total 16 PAH concentrations were 11.8, 13.5, 18.9, and 29.4 ng/g dw (in average) in the Co Ngoin, Pung Co, Ahung Co, and Putok lakes, respectively. The stratigraphic records and estimated source contributions of PAHs in different areas of Tibet exhibited a coherent change in the mid-20th century in response to the Holocene-Anthropocene transition. The sediment PAHs also displayed a comparable pattern when the bound residue fraction was not accounted for, suggesting their effective retainability under natural aging conditions. This may be elucidated by the enduring forward and back conversions between slow desorption and bound residue fractions, which manifested similar time-dependent variations across PAH congeners. The distinct conversion tendencies of different congeners were predicted by the binding affinity of congeners to surface/inner regions of organic matter using molecular docking simulations. Our findings demonstrate the persistence of sediment PAH records under natural aging and validate the use of PAH documentary evidence for investigating the Anthropocene.

Source apportionment of measured volatile organic compounds in Maricopa County, Arizona

ABSTRACT With the goal of corroborating existing emissions inventories of volatile organic compounds (VOCs), a statistical analysis was undertaken on measured ambient VOC concentrations in Maricopa County, Arizona. The Chemical Mass Balance (CMB) model was used to generate emissions source contribution estimates based on ambient VOC concentrations collected at the JLG Supersite in Phoenix, Arizona, and emissions source profiles obtained from EPA’s SPECIATE database. With trial-and-error, optimal model performance using a combination of emissions source profiles yielded source contribution estimates which could be compared to existing regulatory engineering-based emissions inventories. The ultimate objective of this study is to offer a comparison to the “top-down” emissions modeling via CMB and the “bottom-up” modeling traditionally used in preparing emission inventories to identify possible discrepancies and help direct future investigations to better understand local air quality. The methods used to develop the “bottom-up” inventory rely upon sound modeling developed to accurately capture emissions from various source categories. The results show discrepancies between the “bottom-up” and “top-down” emission inventory for VOC emissions from biogenic and natural gas combustion sources, suggesting that the emission strength from these source categories should be further investigated. Implications: The following implication statement has been prepared for the manuscript titled Source Apportionment of Measured Volatile Organic Compounds in Maricopa County, Arizona. The purpose of preparing such a study was to independently corroborate the findings of Maricopa County Air Quality Department (MCAQD) on source contribution estimates of VOC emissions as published in their 2020 Periodic Emissions Inventory for Ozone Precursors. The goal of preparing the findings in the study was to provide additional commentary on the significance of various VOC emissions sources to tropospheric ozone formation in Maricopa County through an alternate air quality modeling approach. The findings from this study are significant to the environment and health of Maricopa County as they offer additional insights into the pathways by which tropospheric ozone may form.

Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment

Ambient air pollution is a major risk to health and wellbeing in European cities. We aimed to estimate spatial and sector-specific contributions of emissions to ambient air pollution and evaluate the effects of source-specific reductions in pollutants on mortality in European cities to support targeted source-specific actions to address air pollution and promote population health. We conducted a health impact assessment of data from 2015 for 857 European cities to estimate source contributions to annual PM2·5 and NO2 concentrations using the Screening for High Emission Reduction Potentials for Air quality tool. We evaluated contributions from transport, industry, energy, residential, agriculture, shipping, and aviation, other, natural, and external sources. For each city and sector, three spatial levels were considered: contributions from the same city, the rest of the country, and transboundary. Mortality effects were estimated for adult populations (ie, ≥20 years) following standard comparative risk assessment methods to calculate the annual mortality preventable on spatial and sector-specific reductions in PM2·5 and NO2. We observed strong variability in spatial and sectoral contributions among European cities. For PM2·5, the main contributors to mortality were the residential (mean contribution of 22·7% [SD 10·2]) and agricultural (18·0% [7·7]) sectors, followed by industry (13·8% [6·0]), transport (13·5% [5·8]), energy (10·0% [6·4]), and shipping (5·5% [5·7]). For NO2, the main contributor to mortality was transport (48·5% [SD 15·2]), with additional contributions from industry (15·0% [10·8]), energy (14·7% [12·9]), residential (10·3% [5·0]), and shipping (9·7% [12·7]). The mean city contribution to its own air pollution mortality was 13·5% (SD 9·9) for PM2·5 and 34·4% (19·6) for NO2, and contribution increased among cities of largest area (22·3% [12·2] for PM2·5 and 52·2% [19·4] for NO2) and among European capitals (29·9% [12·5] for PM2·5 and 62·7% [14·7] for NO2). We estimated source-specific air pollution health effects at the city level. Our results show strong variability, emphasising the need for local policies and coordinated actions that consider city-level specificities in source contributions. Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making 2023-2026 Horizon Europe project.

Sediment source fingerprinting and the temporal variability of source contributions

Sediment source fingerprinting has been progressively developed and refined over the past 40 years or more and now represents a widely used and valuable technique, with important practical applications. However, relatively little attention has been given to the target samples and the extent to which they are able to provide meaningful information on short- or longer-term relative source contributions for a given study catchment. A key issue here is the inherent short- and longer-term temporal variability of source contributions and the extent to which such variability is taken into account by the target samples. The objective of this study was to investigate the temporal variation of source contributions from the Qiaozi West catchment, a small (1.09 km2) gully catchment located within the Loess Plateau of China. The target samples represented a suite of 214 spot suspended sediment samples collected during eight representative wet season rainfall events occurring over two years. A suite of geochemical properties was used as fingerprints and standard source apportionment calculations indicated that the gully walls contributed the most sediment (load-weighted mean 54.5%) and, together with cropland (load-weighted mean 37.3%), and gully slopes (load-weighed mean 6.6%) were the main sediment sources. The 214 individual target samples indicated that the contribution of cropland sources varied between 8.3% and 60.4%, gully walls between 22.9% and 85.8% and gully slopes between 1.1% and 30.7%, representing ranges of 52.1%, 62.9% and 29.6% respectively. In order to explore whether the temporal variability of source contributions demonstrated by the study catchment should be seen as typical, equivalent information was abstracted from 14 published studies for other catchments of varying size and located in different environments worldwide. This information demonstrated similar temporal variability of the relative contributions of the major sources, which were typically characterized by ranges of the order of 30–70%. The temporal variability associated with the estimates of relative source contributions provided by target samples has important implications for the uncertainty associated with such estimates derived using source fingerprinting techniques based on a limited number of target samples. Further attention needs to be directed to the design of sampling programmes used to collect such samples and to taking account of such uncertainty in source apportionment calculations.

United States Gulf of Mexico Waters provide important nursery habitat for Mexico’s Green turtle nesting populations

Resolving natal populations for juvenile green turtles is challenging given their potential for extensive dispersal during the oceanic stage and ontogenetic shifts among nursery habitats. Mitochondrial DNA markers have elucidated patterns of connectivity between green turtle nesting populations (rookeries) and juvenile foraging aggregations. However, missing rookery baseline data and haplotype sharing among populations have often impeded inferences, including estimating origins of Gulf of Mexico juveniles. Here, we assessed genetic structure among seven foraging aggregations spanning southern Texas (TX) to southwestern Florida (SWFL), including Port Fourchon, Louisiana (LA); a surface-pelagic aggregation (SP) offshore of Louisiana and Florida; Santa Rosa Island, Florida (SRI); St. Joseph Bay, Florida (SJB); and the Big Bend region, Florida (BB). We estimated source contributions to aggregations with novel genetic data (excluding SP and BB) using a Bayesian many-to-one mixed stock analysis (MSA) approach. Haplotype frequencies for western (TX, LA, SP, SRI) and eastern (SJB, BB, SWFL) aggregations were significantly differentiated. The largest shift in haplotype frequencies between proximal nursery sites occurred between SRI and SJB, separated by only 150 km, highlighting the lack of a geographic yardstick for predicting genetic structure. In contrast to previous MSA results, there was no signal of Florida juveniles at any foraging site. Mexican contributions dominated in all aggregations, with strong connectivity between western Bay of Campeche (Tamaulipas/Veracruz) rookeries and western foraging aggregations. MSA indicated more diverse Mexican origins for eastern aggregations, with larger inputs from the eastern Bay of Campeche (Campeche/Yucatán), Campeche Bank, and Quintana Roo rookeries. These results demonstrate the significance of the Gulf of Mexico coast and offshore waters of the United States as important nursery habitat for green turtles of Mexican origin and highlight the need for international coordination for management of these populations.

Application of Sediment Fingerprinting to Apportion Sediment Sources: Using Machine Learning Models

Highlights Relative source contributions to stream bed sediment from construction sites and stream banks were quantified. Two machine-learning techniques were used to select composite fingerprinting properties. The MixSIR Bayesian model was employed for source apportionment. Statistical methods employed for fingerprinting properties selection have the potential to impact source apportionments. Management strategies to reduce sediment mobilization should be targeted depending on the dominant source of sediment in each sub-watershed. Abstract. Sediment fingerprinting is an extensively used approach for investigating sediment sources by linking in-stream sediment mixtures with watershed source materials. The overall goal of this research was to estimate the relative source contributions of stream banks and construction sites to the stream bed sediment in an urbanized watershed (Alabama, USA) using a fingerprinting technique established on composite fingerprints selected by two different machine learning techniques at a sub-watershed scale. The two statistical approaches employed to select the subset of fingerprinting properties were: (1) the Random Forest algorithm (RF) with Gini importance ranking of variables; and (2) logistic regression with the least absolute shrinkage and selection operator (LASSO). A Bayesian mixing model was then used to estimate the distribution of mixing proportions along with the associated uncertainty. The models were built based on the composite fingerprints selected using the two machine learning methods. Overall, using the subset of fingerprints selected by RF and LASSO, the relative contribution of stream banks ranged from 14±9% to 97±2% and from 24±18% to 94±5%, respectively, throughout the watershed. The stream bank contributions were compared with a previous study conducted in the watershed that utilized a two-step statistical procedure (which involved a Mann-Whitney U-test as the first step and discriminant function analysis (DFA) as the second step) to select the composite of fingerprinting properties and a frequentist mixing model to calculate the source apportionments. The relative contributions of stream banks to stream bed sediment in the previous study reported ranged from 9±8% to 100±1%. Therefore, the study demonstrated the dependence of source attributions on the statistical procedures used to select the optimum composite fingerprints for sediment fingerprinting applications. Furthermore, the results underscored the importance of using different mixing model structures to obtain reliable estimates of source contributions. Keywords: Least absolute shrinkage and selection operator (LASSO), MixSIR Bayesian model, Random Forest (RF), Statistical techniques.

Large-number detrital zircon U-Pb ages reveal global cooling caused the formation of the Chinese Loess Plateau during Late Miocene.

The formation and evolution of the landscape of the Chinese Loess Plateau (CLP) is debated because of uncertainties regarding dust provenance. We present a quantitative estimation of dust source contributions to the CLP, based on more than 37,100 detrital zircon U-Pb ages, combined with mineral assemblages and isotope analyses. Our results reveal that the CLP was stepwise formed by ~8 million years (Ma) and is mainly composed of material from the Northeastern Qinghai-Tibetan Plateau, with stepwise shifts in relative contributions of different eolian silt sources occurring at ~2.6 Ma and 1.5 to 1.2 Ma. We infer that these changes were driven by stepwise global cooling, which induced aridification and enhanced silt production in glaciated-cold climate dust source regions, as well as dust ablation in the expanded arid regions. We propose that global cooling, rather than regional tectonic deformation, was the main driver of the formation and evolution of the CLP during late Cenozoic.

Source Attribution of Atmospheric CO2 Using 14C and 13C as Tracers in Two Chinese Megacities During Winter

AbstractIdentifying the sources of atmospheric Carbon dioxide (CO2) is an important prerequisite for developing effective mitigation strategies. Here we conducted regular observations of the atmospheric CO2 mixing ratio and its carbon isotope compositions (i.e., Δ14C and δ13C) in Xi'an and Beijing during winter, to estimate source contributions of CO2 emissions in Chinese megacities. The results showed that CO2 emissions in both Xi'an and Beijing originated mainly from fossil‐fuel sources, which contributed 65 ± 3% and 82 ± 2% of the total CO2 enhancement, respectively, during the sampling period; the results also revealed a substantial biogenic CO2 contribution during winter. We further separated the fossil‐fuel sources into contributions from coal, oil and natural gas combustions. We found that coal combustion was the dominant anthropogenic source in Xi'an, accounting for 54 ± 4% of the total fossil‐fuel emissions, and oil and natural gas contribute almost equally to the emissions. In contrast, emission from natural‐gas combustion was the main fossil‐fuel source in Beijing, accounting for more than half of the total fossil‐fuel emissions, whereas, coal combustion contributed only 17 ± 10%. These top‐down results are generally consistent with emission inventory when seasonal variations of emissions are considered; some differences between the two methods indicated that the inventory for Xi'an might be underestimating the emissions from oil consumption. This pilot study confirms the potential of direct verification between top‐down and bottom‐up methods from the perspective of source attribution.

Impacts of tracer type, tracer selection, and source dominance on source apportionment with sediment fingerprinting

Sediment fingerprinting estimates the proportional contribution of fine sediment from distinct catchment sources delivered to downstream receiving environments. Increased attention has focused on assessing the accuracy of source contribution estimates, particularly in relation to tracer selection and statistical un-mixing procedures. However, no studies have systematically tested the impact of source combination or dominance on the accuracy of source estimates. Here, we assess sensitivity to tracer type, selection, and number of sources, and examine how variations in the dominant sediment source affect the accuracy of source apportionments using numerical mixtures. Sources were sampled according to erosion process and land cover from a New Zealand catchment. Topsoil and subsoil (landslide) samples were collected from pasture, harvested pine, kānuka scrub, and native forest, while banks were sampled along the main channel. Samples were analysed for bulk geochemistry, fallout radionuclides, and compound specific stable isotopes (CSSIs). Source apportionment accuracy tended to decrease as source number increased, which reflected decreasing source discrimination. Tracer selection showed variations in accuracy but exhibited no clear pattern overall. Source combination and particularly the dominant source had the largest impact on accuracy, reflecting the level of discrimination for each source. Notably, channel bank was frequently identified as the dominant source when using CSSI tracers. While this partly reflected lower levels of discrimination, the CSSI apportionment was particularly sensitive to the use of post-unmixing corrections routinely applied to derive soil proportional contributions from isotopic proportions. This sensitivity likely related to the low organic carbon content in bank material and the assumption that source apportionments based on isotopic proportions can be corrected using a linear relationship with organic carbon content. These results indicate that the use of CSSI tracers in catchments where erosion sources exhibit large differences in soil organic carbon content may introduce significant unquantified error in source apportionments.

Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002–2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

How to evaluate sediment fingerprinting source apportionments

PurposeEvaluating sediment fingerprinting source apportionments with artificial mixtures is crucial for supporting decision-making and advancing modeling approaches. However, artificial mixtures are rarely incorporated into fingerprinting research and guidelines for model testing are currently lacking. Here, we demonstrate how to test source apportionments using laboratory and virtual mixtures by comparing the results from Bayesian and bootstrapped modeling approaches.Materials and methodsLaboratory and virtual mixtures (n = 79) with known source proportions were created with soil samples from two catchments in Fukushima Prefecture, Japan. Soil samples were sieved at 63 µm and analyzed for colorimetric and geochemical parameters. The MixSIAR Bayesian framework and a bootstrapped mixing model (BMM) were used to estimate source contributions to the artificial mixtures. In addition, we proposed and demonstrated the use of multiple evaluation metrics to report on model uncertainty, residual errors, performance, and contingency criteria.Results and discussionOverall, there were negligible differences between source apportionments for the laboratory and virtual mixtures, for both models. The comparison between MixSIAR and BMM illustrated a trade-off between accuracy and precision in the model results. The more certain MixSIAR solutions encompassed a lesser proportion of known source values, whereas the BMM apportionments were markedly less precise. Although model performance declined for mixtures with a single source contributing greater than 0.75 of the material, both models represented the general trends in the mixtures and identified their major sources.ConclusionsVirtual mixtures are as robust as laboratory mixtures for assessing fingerprinting mixing models if analytical errors are negligible. We therefore recommend to always include virtual mixtures as part of the model testing process. Additionally, we highlight the value of using evaluation metrics that consider the accuracy and precision of model results, and the importance of reporting uncertainty when modeling source apportionments.

Effects of COVID-19 lockdown on PM10 composition and sources in the Rome Area (Italy) by elements' chemical fractionation-based source apportionment

During the national lockdown imposed by the Government of Italy (from March 9th to May 18th 2020) to counter the Covid-19 pandemic, 24-h PM10 samples were collected at three sites in the Rome area (Central Italy), two urban (Sapienza and Via Saredo, highly impacted by vehicular traffic) and one peri-urban (Montelibretti, more impacted by biomass domestic heating). Further, at Sapienza and Montelibretti PM10 daily sampling had been carried out in the period immediately before lockdown, and at Via Saredo samples were additionally collected also after the end of lockdown. PM10 was chemically speciated for main components (major elements, inorganic ions, EC, OC, levoglucosan), and trace elements. The latter were chemically fractionated and considered for their water-soluble and insoluble fractions, which proved to be more source-selective than total element. Three datasets were thus built and analyzed by Positive Matrix Factorization (PMF), with the aim of identifying and apportioning mass contributions of sources acting in the periods before, during and after lockdown, in the Rome area. Identified emission sources were mostly from long-range advection (two different contributions of mineral dust, fresh sea spray, heavy oil combustion), while local sources (vehicular traffic and biomass burning) were strongly abated during lockdown, with respect to previous sampling period, and inorganic secondary aerosol showed a progressive increment of sulfates, driven by seasonal evolution from winter to spring. Since the lockdown interrupted all non-essential productive and work activities, thus reducing the chemical fingerprinting of local sources, this occurrence allowed to clearly describe both profiles and source contribution estimates of long-range transported PM10 components. Moreover, it allowed assessing the reduction of the impact of anthropogenic sources (such as vehicular traffic) and the efficiency of mitigation measures generally taken to control PM10 mass concentration. Acidic sulfates (bisulfate and letovicite) resulted associated to mineral dust transport events, and the role of chemically fractionated elements as source-specific tracers was further confirmed.

Source Apportionment of Fine Organic Particulate Matter (PM2.5) in Central Addis Ababa, Ethiopia.

The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June–September) and short rain season (February–April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public’s health.

SIBaR: a new method for background quantification and removal from mobile air pollution measurements

Abstract. Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.