Exploring the alteration of environmental radioactivity in terms of compositional elements of heavy minerals in an anthropogenically affected urban river: Radiological and ecological risks assessment.

This study explored the alteration of naturally occurring radioactive materials (NORMs: 226Ra (≈238U), 232Th, 40K) in an anthropogenically disrupted urban river-basin (Turag, Bangladesh) in terms of constitutional substances (Sc, Ti, V, Fe, La, Ce, Sm, Eu, Tb, Dy, Ho, Yb, Lu, Hf, Ta, W, Th, U) of heavy-minerals. Average activity concentrations of 226Ra (≈238U), 232Th, and 40K were 41.5 ± 12.9, 72.1 ± 27.1, and 639 ± 100 Bqkg-1, respectively which were relatively higher compared to crustal origin. ∑REEs, Ta, W, Th, and U were ~2 times higher compared to crustal values with Ce and Eu-anomalies. APCS-MLR and PMF receptor models were used to determine the various anthropogenic and/or geogenic sources of NORMs and elements. Layer-wise variations of NORMs and elements were observed to trace the response of sedimentary processes towards the incoming pollution load. Presence of REEs indicates moderate degree of ecological risk to aquatic biota. However, carcinogenic risk (3.84 × 10-4 Sv-1) were significantly higher than threshold limit.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus.

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.

Isolating pollen signals from laser diode aerosol Optical Particle Counter (OPC) data through positive matrix factorization (PMF) and Unmix receptor models.

Pollen, a significant natural bioaerosol and allergen for sensitized individuals, is expected to increase in prevalence due to climate change. Mitigating allergy symptoms involves avoiding pollen exposure and pre-medication, emphasizing the importance of real-time knowledge of localized ambient air pollen concentrations. Laser diode Optical Particle Counters (OPCs) are commonly used for monitoring particle number concentrations in ambient air. This study explores the hypothesis that OPCs can monitor pollen but may struggle to distinguish them from other particles. We aimed to isolate the pollen signal from collective particle number concentrations using source apportionment models, specifically Positive Matrix Factorization (PMF) and Unmix, applied to multiple bin OPC data. The pollen signals isolated using PMF show slightly better correlation values than those isolated using Unmix. PMF-derived pollen signals exhibit strong correlations with Holoptelea (r = 0.64) and total pollen (r = 0.54) concentrations, while a moderate correlation is observed with Poaceae (r = 0.47). Exclusion of low pollen events strengthens correlations for Holoptelea and Poaceae to very strong (r = 0.87) and strong (r = 0.67), respectively. Although both model types effectively isolate the pollen signal, metrics suggest that Unmix has the potential for more accurate predictions of both moderate and extreme pollen events simultaneously. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE) metrics for Holoptelea are 46.2 grains m-3, 72.4 grains m-3, and 15.3; for Poaceae, 3.9 grains m-3, 4.9 grains m-3, and 13.0; and for total pollen, 43.5 grains m-3, 72.1 grains m-3, and 14.1. This study represents a significant development in the use of source apportionment models and ambient OPCs for real-time pollen monitoring, offering a cost-effective alternative to conventional automated pollen sensors. Despite challenges, the proposed methodology provides a practical and accessible solution for pollen monitoring, contributing to the advancement of bioaerosol monitoring technologies.

Vertical variation of source-apportioned PM2.5 and selected volatile organic compounds near an elevated expressway in an urban area.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are associated with adverse health effects and show spatial variation in three dimensions. The present study attempted to evaluate source contributions of PM2.5 and toxic VOCs in a metropolitan area focusing on the associated vertical variations. A special emphasis is put on the effects of the elevated expressway on the vertical variability of contribution estimates of the identified sources. Nine source factors, i.e., soil dust, sea salt/oil combustion, secondary nitrate, industrial emission, aged VOCs/secondary aerosol, traffic-related I, solvent use/industrial process, secondary sulfate, and traffic-related II, were identified using positive matrix factorization (PMF). The main contributors to PM2.5 were secondary sulfate (19.1%) and traffic-related emissions (traffic-related I and II, 16.1%), whereas the largest contributors to VOCs were traffic-related emissions (37.6%). The influence of the elevated expressway is suggested to be particularly critical on vertical variations of traffic-related emissions, including aging and secondary formation of locally accumulated air pollutants near roads. Increasing the building porosity under the viaduct could reduce the accumulation of air pollutants caused by the shelter effect. Additionally, in-street barriers would be beneficial in reducing population exposure to traffic-related emissions by altering the airflows near roads.

Source apportionment for indoor air pollution: Current challenges and future directions.

Source apportionment (SA) for indoor air pollution is challenging due to the multiplicity and high variability of indoor sources, the complex physical and chemical processes that act as primary sources, sinks and sources of precursors that lead to secondary formation, and the interconnection with the outdoor environment. While the major indoor sources have been recognized, there is still a need for understanding the contribution of indoor versus outdoor-generated pollutants penetrating indoors, and how SA is influenced by the complex processes that occur in indoor environments. This paper reviews our current understanding of SA, through reviewing information on the SA techniques used, the targeted pollutants that have been studied to date, and their source apportionment, along with limitations or knowledge gaps in this research field. The majority (78 %) of SA studies to date focused on PM chemical composition/size distribution, with fewer studies covering organic compounds such as ketones, carbonyls and aldehydes. Regarding the SA method used, the majority of studies have used Positive Matrix Factorization (31 %), Principal Component Analysis (26 %) and Chemical Mass Balance (7 %) receptor models. The indoor PM sources identified to date include building materials and furniture emissions, indoor combustion-related sources, cooking-related sources, resuspension, cleaning and consumer products emissions, secondary-generated pollutants indoors and other products and activity-related emissions. The outdoor environment contribution to the measured pollutant indoors varies considerably (<10 %- 90 %) among the studies. Future challenges for this research area include the need for optimization of indoor air quality monitoring and data selection as well as the incorporation of physical and chemical processes in indoor air into source apportionment methodology.

Application of a hybrid GEM-CMB model for source apportionment of PAHs in soil of complex industrial zone.

Accurate source apportionment is essential for preventing the contamination of pervasive industrial zones. However, a limitation of traditional receptor models is their negligence of transmission loss, which consequently reduces their accuracy. Herein, chemical mass balance (CMB) and generic environmental model (GEM) was fused into a new method, which was employed to determine the traceability of polycyclic aromatic hydrocarbons (PAHs) in a complex zone containing three coking plants, two steel plants, and one energy plant. Five categories of fingerprints comprising various compounds were established for the six plant sources where seven PAHs with low-high rings were screened as the best. Considering volatilization, dry deposition, and advective and dispersive transport, the GEM model generated 232 "compartments" in multimedia to capture subtle variations of PAHs during transmission. More than 90 % of the transmission of the seven PAHs varied between 0.4 % and 6.0 %. Over pure CMB model, acceptable results and best-fit results improved by 1.6-44.4 % and 0.3-80.8 % in the GEM-CMB model. Additionally, the coking, steel, and energy industries accounted for 36.4-56.1 %, 25.6-41.7 %, and 18.3-23.6 % of PAHs sources at four receptor points, respectively. Furthermore, quantifying contaminant loss rendered the traceability results more realistic, judged by distances and discharge capacities. Accordingly, these outcomes can help in precisely determining soil contamination.

Contamination and source apportionment of metals in urban road dust (Jinan, China) integrating the enrichment factor, receptor models (FA-NNC and PMF), local Moran's index, Pb isotopes and source-oriented health risk.

Contamination and source identifications of metals in urban road dust are critical for remediation and health protection. Receptor models are commonly used for metal source identification, whereas the results are usually subjective and not verified by other indicators. Here we present and discuss a comprehensive approach to study metal contamination and sources in urban road dust (Jinan) in spring and winter by integrating the enrichment factor (EF), receptor models (positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC)), local Moran's index, traffic factors and Pb isotopes. Cadmium, Cr, Cu, Pb, Sb, Sn and Zn were the main contaminants, with mean EFs of 2.0-7.1. The EFs were 1.0-1.6 times higher in winter than in spring but exhibited similar spatial trends. Chromium contamination hotspots occurred in the northern area, with other metal contamination hotspots in the central, southeastern and eastern areas. The FA-NNC results indicated Cr contamination primarily resulting from industrial sources and other metal contamination primarily originating from traffic emissions during the two seasons. Coal burning emissions also contributed to Cd, Pb and Zn contamination in winter. FA-NNC model-identified metal sources were verified via traffic factors, atmospheric monitoring and Pb isotopes. The PMF model failed to differentiate Cr contamination from other detrital metals and the above anthropogenic sources, largely due to the model grouping metals by emphasizing hotspots. Considering the FA-NNC results, industrial and traffic sources accounted for 28.5 % (23.3 %) and 44.7 % (28.4 %), respectively, of the metal concentrations in spring (winter), and coal burning emissions contributed 34.3 % in winter. Industrial emissions primarily contributed to the health risks of metals due to the high Cr loading factor, but traffic emissions dominated metal contamination. Through Monte Carlo simulations, Cr had 4.8 % and 0.4 % possibilities posing noncarcinogenic and 18.8 % and 8.2 % possibilities posing carcinogenic risks for children in spring and winter, respectively.

Source apportionment of serum PFASs in two highly exposed communities.

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals that are ubiquitous in environmental and biological systems, including human serum. PFASs are used in many products and industrial processes and are tied to numerous health effects. Due to multiple sources and exposure pathways, methods are needed to identify PFAS sources in communities to develop targeted interventions. We assessed effectiveness of three source apportionment methods (UNMIX, positive matrix factorization [PMF], and principal component analysis - multiple linear regression [PCA-MLR]) for identifying contributors to human serum PFAS concentrations in two highly exposed populations in Colorado and North Carolina where drinking water was contaminated via upstream sources, including a Space Force base and a fluorochemical manufacturing plant. UNMIX and PMF models extracted three to four potential PFAS exposure sources in the Colorado and North Carolina cohorts while PCA-MLR classified two in each cohort. No sources were characterized in NHANES (National Health and Nutrition Examination Study). Results suggest that these three methods can successfully identify sources in highly exposed populations. Future PFAS exposure research should focus on analyzing serum for an expanded PFAS panel, identifying cohorts with other distinct point source exposures, and combining biological and environmental data to better understand source apportionment results in the context of PFAS toxicokinetic behavior.

In Silico profiling of the Angiotensin converting enzyme binding affinities, toxicity and Pharmacokinetics of compounds from the nuts of Areca Catechu, Linn. and its bioisosteres

Background@#Hypertension is a worldwide epidemic that has been recognized as the most leading global risk for mortality, with its prevalence associated with increased blood pressure, concomitant risks of cardiovascular and kidney diseases, and major commonality in individuals advanced in age. With the current treatment options for hypertension management, there is still a need to develop therapies that directly target receptors that aid in hypertension treatment.@*Methodology@#The study focused on the in-silico profiling of the reported compounds from Areca catechu L. (fam. Arecaceae) towards the n-domain and c-domain angiotensin converting enzyme (ACE) receptor models. Bioisosteric replacement was used to create bioisosteres investigated for similar binding affinity.@*Results@#Some A. catechu compounds exhibited favorable binding energies towards the n- and c-domain receptor models of ACE, binding in the same ACE ligand binding site as lisinopril, benazepril, and sampatrilat via similar interactions and amino acid residues. The majority of A. catechu compounds with favorable ACE binding energies belong to the phytochemical classes of flavonoids, polyphenols and phenolics, glycosides, and steroids. After in silico toxicity and pharmacokinetic profiling, the bioisosteres Leuco-DM02-39, Leuco-DM02-66, Leuco-DM05-60, Querc-DM09-63, and Querc-DM14-31 with binding energies higher than their parent compounds and comparable to lisinopril, benazepril, and sampatrilat were deemed the best.@*Conclusion@#A. catechu compounds have the potential to target ACE n-domain and c-domain receptor models. Three leucocyanidin and two quercetin bioisosteres exhibited favorable binding to the n-domain and c-domain ACE receptor models and could be further optimized to derive a promising antihypertensive agent through ACE inhibition.

Spatial distribution of multi-elements in moss revealing heavy metal precipitation in London Island, Svalbard, Arctic.

The Arctic is a sink for major pollutants in the Northern Hemisphere, and is an ideal place to investigate the migration of concerned metals on the local environment. In this study, 13 elements including Li, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, As, Cd, Hg, and Pb were determined in mosses (Dicranum angustum) from London Island in Ny-Ålesund. The results showed that the concentrations of different elements varied greatly at different altitudes, while their distributions in low (0-200 m) and high (200-300 m) altitudes based on cluster analysis were significantly different. Among them, Li, Ti, V, Cr, Mn, Fe, Co, Cu, and As showed significant positive correlations with elevation. This result may be due to the influence of key environmental factors such as elements transported by the airborne dust carried by winds, and surface runoff from snow meltwater. Multiple receptor models (PCA, PMF, and UNMIX) were employed to discuss the sources of metals in mosses from London Island. Elements that showed positive correlation with altitude were attributed to natural sources, and Zn, Cd, Hg, and Pb, which lacked apparent correlation with elevation, were interpreted as from anthropogenic sources by the models. Among them, Zn, Cd, and Hg were from long-range deposition, while Pb was from mixed industrial sources.

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in a sediment core from Lake Dagze Co, Tibetan Plateau, China: Comparison of three receptor models.

Receptor models are a useful tool for identifying sources of polycyclic aromatic hydrocarbons (PAHs) in multiple environmental media. In this study, three different receptor models (including the principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix models) were used to apportion the sources of 16 priority PAHs in a sediment core of Lake Dagze Co. The ∑PAHs (sum of all 16 measured PAHs) concentrations ranged from 51.89 to 132.82 ng/g with an average of 80.39 ng/g. The ∑PAHs were dominated by 2-3 ring PAHs, accounting for 80.12% on average, thereby indicating that they mainly originated from biomass and coal combustion and/or from long-range atmospheric transportation. The three models produced consistent source apportionment results. The greatest contributor to ∑PAHs was biomass combustion, followed by coal combustion, vehicle emissions, and petrogenic sources. Moreover, the temporal variation of the common sources was well-correlated among models. The multi-method comparison and evaluation results showed that all three models were useful tools for source apportionment of PAHs, with the PMF model providing better results than the PCA-MLR and Unmix models. The temporal trends of factor contributions were verified by PAHs with different ring numbers. Significant correlations were found between the simulated concentrations of each source factor and the PAHs with different ring numbers (P<0.01), except for the petrogenic source identified by the Unmix model (P>0.05). This study can provide useful information for further investigation of source apportionment of PAHs in the sediment cores.

Source apportionment of children daily exposure to particulate matter.

The present study aims to investigate the sources of particulate pollution in indoor and outdoor environments, with focus on determining their contribution to the exposure of children to airborne particulate matter (PM). To this end, parallel indoor and outdoor measurements were carried out for a selection of 40 homes and 5 schools between September 2017 and October 2018. PM2.5 and PM2.5-10 samples were collected during five days in each microenvironment (ME) and analysed by X-Ray Fluorescence (XRF), for the determination of elements, and by a thermal-optical technique, for the measurement of organic and elemental carbon. The source apportionment analysis of the PM composition data, by means of the receptor model SoFi (Source Finder) 8 Pro, resulted in the identification of nine sources: exhaust and non-exhaust emissions from traffic, secondary particles, heavy oil combustion, industry, sea salt, soil, city dust, and an indoor source characterized by high levels of organic carbon. Integrated daily exposure to PM2.5 was on average 21 µg/m3. The organic matter, resulting from cleaning, cooking, smoking and biological material, was the major source contributing by 31% to the PM2.5 exposure. The source city dust, which was highly influenced by the resuspension of dust in classrooms, was the second main source (26%), followed by traffic (24%). The major sources affecting the integrated exposure to PM10, which was on average 33 µg/m3, were the city dust (39%), indoor organics (24%) and traffic (16%). This study provides important information for the design of measures to reduce the exposure of children to PM.

[PM2.5 Source Apportionment Based on a Variety of New Receptor Models].

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

Source apportionment of potentially toxic elements in soils of the Yellow River Delta Nature Reserve, China: The application of three receptor models and geostatistical independent simulation.

The Yellow River Delta (YRD) wetland, the most important estuary wetland in eastern China, has an important ecosystem service function. Rapid and intensive development has inevitably led to the accumulation of potentially toxic elements (PTEs) in soils. Therefore, identifying quantitative sources and spatial distributions of PTEs is essential for soil environmental protection in the YRD. A total of 240 topsoil samples (0-20 cm) were collected in the Yellow River Delta Nature Reserve (YRDNR) and analyzed the PTE contents. To avoid the biases of the single receptor model, positive matrix factorization, factor analysis with nonnegative constraints, and maximum likelihood principal component analysis-multivariate curve resolution-alternating least squares were used for source apportionment of soil PTEs. To promote the efficiency of multivariate geostatistical simulation, a minimum/maximum autocorrelation factor-sequential Gaussian simulation was built to map the spatial patterns of PTEs. Three factors were derived by the three receptor models, and their contributions to the source explanation were similar. As, Cr, Cu, Mn, Ni, and Zn originated from natural sources, with contributions of 85.6%-96.4 %. A total of 61.5 % of Hg was associated with atmospheric deposition of coal combustion and wastewater from upstream. Agricultural activities and oil exploitation contributed 33.5 % and 15.9 % of the Cd and Pb concentrations. Spatial distributions of soil PTEs were controlled by sedimentary grain size. A total of 47.2 % of the total study area was identified as hazardous area for Cd, 10.3 % for As, and 5.4 % for Hg. This work is expected to provide references for soil pollution assessment and management of YRDNR.

Evaluating source-oriented human health risk of potentially toxic elements: A new exploration of multiple age groups division.

Effective source-oriented human health risk assessment (HHRA) for people in different life stages will guide pollution control and risk prevention. This work integrated three receptor models of positive matrix factorization, Unmix, and factor analysis with nonnegative constraints for accurate source-oriented HHRA of potentially toxic elements in 6 age groups of populations (0-<1 year, 1-<6 years, 6-<12 years, 12-<18 years, 18-<44 years, and 44+ years). Four sources were identified. Natural source controlled As, Cr, and Ni in dust and soil as well as Pb and Zn in soil. Industrial-traffic emissions contributed most of Cd in dust and soil as well as Pb and Zn in dust. Hg in both dust and soil originated from coal combustion. Construction works contributed more to PTEs in soil than in dust. Noncarcinogenic and carcinogenic risk for both dust and soil changed in similar trends by age. The noncancer risk reduced with increasing age for people below 44 years. Carcinogenic risk of females over 44 years were the highest, while children from 0 to 1 year faced the lowest carcinogenic risk. Among the four origins of PTEs, natural sources contributed most to health risk of PTEs, followed by industrial-traffic sources, construction works, and coal combustion. Based on sequential Gaussian simulation (SGS), the susceptible population and risk areas were identified. Children from 0 to 6 years were identified as susceptible population. The areas with noncancer risk in dust were 19.15 km2 for 0-<1 year and 3.14 km2 for children from 1 to <6 years, and noncancer risk areas in soil were 30.26 km2 for 0-<1 year and 0.85 km2 for 1-<6 years. Relevant control and management works were demanded on children from 0 to 6 years and noncancer risk areas.

Size-segregated chemical source profiles and potential health impacts of multiple sources of fugitive dust in and around Bhopal, central India.

PM2.5 and PM10 fugitive dust samples from multiple sources (construction, demolition, industrial, agricultural fields, and bare ground) were collected in triplicate for each size bin, from 18 distinct locations in and around Bhopal, central India. The dust samples were dried, sieved, and re-suspended in a chamber fitted with a suitable sampling system, to collect PM2.5 and PM10 samples onto Teflon and Quartz filters. The filters were subjected to gravimetric and chemical analyses. Trace elements, water-soluble ions, and thermal-optical carbon fractions were quantified using a variety of analyses. These species were then used to develop PM10 and PM2.5 chemical source profiles of the fugitive dust sources. As expected, crustal species were abundant in all source categories. For industrial dust, Fe contribution to mass in both size fractions was about 11.4% and above the upper continental crustal abundance. Further, the source profiles generated for each source were different from their counterparts in the US EPA SPECIATE database and profiles reported in literature. Thus, it will be useful to utilize profiles generated in this study to enhance receptor model performance for the study region. However, collinearity analysis of the profiles revealed that PM10 agricultural and bare ground dust; and PM2.5 construction and demolition dust profile pairs may not be separated by receptor models. Finally, a human health risk assessment revealed that construction and industrial dust may pose significant risk to the population. The Incremental Lifetime Cancer Risk (ILCR) metric revealed that adults (2 × 10-5) and children (1 × 10-5) were susceptible to cancer risk from exposure to metals in PM2.5 fugitive dust. Further, children were more vulnerable than adults. This finding merits further investigation of oxidation state and solubility/bioavailability of Cr and Ni in fugitive dusts.

A review on analysis methods, source identification, and cancer risk evaluation of atmospheric polycyclic aromatic hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) have gained attention because of their environmental persistence and effects on ecosystems, animals, and human health. They are mutagenic, carcinogenic, and teratogenic. The review provides background knowledge about their sources, metabolism, temporal variations, and size distribution in atmospheric particulate matter. The review article briefly discusses the analytical methods suitable for the extraction, characterization, and quantification of nonpolar and polar PAHs, addressing the challenges. Herein, we discussed the molecular diagnostic ratios (DRs), stable carbon isotopic analysis (SCIA), and receptor models, with much emphasis on the positive matrix factorization (PMF) model, for apportioning PAH sources. Among which, DRs and PCA identified as the most widely employed method, but their accuracy for PAH source identification has received global criticism. Therefore, the review recommends compound-specific isotopic analysis (CSIA) and PMF as the best alternative methods to provide detailed qualitative and quantitative source analysis. The compound-specific isotopic signatures are not affected by environmental degradation and are considered promising for apportioning PAH sources. However, isotopic fractions of co-eluted compounds like polar PAHs and aliphatic hydrocarbons make the PAHs isotopic fractions interpretation difficult. The interference of unresolved complex mixtures is a limitation to the application of CSIA for PAH source apportionment. Hence, for CSIA to further support PAH source apportionment, fast and cost-effective purification techniques with no isotopic fractionation effects are highly desirable. The present review explains the concept of stable carbon isotopic analysis (SCIA) relevant to PAH source analysis, identifying the techniques suitable for sample extract purification. We demonstrate how the source apportioned PAHs can be applied in assessing the health risk of PAHs using the incremental lifetime cancer risk (ILCR) model, and in doing so, we identify the key factors that could undermine the accuracy of the ILCR and research gaps that need further investigation.

A review of the main strategies used in the interpretation of similar chemical profiles yielded by receptor models in the source apportionment of particulate matter.

Receptor models have been widely used for the source apportionment of airborne particulate matter. However, in the last 10 years, the use of factor analysis-based models, such as PMF and UNMIX, has increased significantly. The results yielded by these models must be interpreted by users who must know all variables influencing the modeling, and without this knowledge, the probability of incorrect interpretation of the source profiles may increase, especially when two or more sources have similar chemical profiles. Concerning the quality of data, this work shows that a broad characterization of PM composition, including inorganic, organic, and mineralogical species can improve this process, avoiding misinterpretation and the attribution of mixed or unidentified sources. This work aims to provide readers with some answers for a question often risen during source apportionment studies: Which source markers should be used for better separation and interpretation of source profiles? This review shows there is no right answer for this because different strategies can be used for this purpose. Therefore, this review aims to compile and highlight qualitatively the key strategies already used by several experienced receptor models users, combining the use of inorganic, organic, and mineralogical markers of PM for better separation and interpretation of the profiles yielded by receptor models. Also, this work presents a compilation in tables of the main chemical species reported in the literature as markers for interpreting the source profiles.

Review of online source apportionment research based on observation for ambient particulate matter.

Particulate matter source apportionment (SA) is the basis and premise for preventing and controlling haze pollution scientifically and effectively. Traditional offline SA methods lack the capability of handling the rapid changing pollution sources during heavy air pollution periods. With the development of multiple online observation techniques, online SA of particulate matter can now be realized with high temporal resolution, stable and reliable continuous observation data on particle compositions. Here, we start with a summary of online measuring instruments for monitoring particulate matters that contains both online mass concentration (online MC) measurement, and online mass spectrometric (online MS) techniques. The former technique collects ambient particulate matter onto filter membrane and measures the concentrations of chemical components in the particulate matter subsequently. The latter technique could be further divided into two categories: bulk measurement and single particle measurement. Aerosol Mass Spectrometers (AMS) could provide mass spectral information of chemical components of non-refractory aerosols, especially organic aerosols. While the emergence of single-particle aerosol mass spectrometer (SPAMS) technology can provide large number of high time resolution data for online source resolution. This is closely followed by an overview of the methods and results of SA. However, online instruments are still facing challenges, such as abnormal or missing measurements, that could impact the accuracy of online dataset. Machine leaning algorithm are suited for processing the large amount of online observation data, which could be further considered. In addition, the key research challenges and future directions are presented including the integration of online dataset from different online instruments, the ensemble-trained source apportionment approach, and the quantification of source-category-specific human health risk based on online instrumentation and SA methods.

Source apportionment and spatial distribution of potentially toxic elements in soils: A new exploration on receptor and geostatistical models.

Potentially toxic element (PTE) pollution is considered as the main soil environmental problem in the world. Source apportionment and spatial pattern of soil PTEs are essential for soil management. US-EPA positive matrix factorization (EPAPMF) and sequential Gaussian simulation (SGS) are general modeling tools for source apportionment and spatial distribution, respectively. Factor analysis with nonnegative constraints (FA-NNC) and stochastic partial derivative equations (SPDE) provided potential tools for this issue. We compared the performance of FA-NNC with PMF and the performance of SPDE with SGS, based on a dataset containing 9 PTEs in 285 topsoil samples. Three factors were determined by the two receptor models, and the source contributions were similar, suggesting that FA-NNC can validly identify quantitative sources of soil PTEs. The average source contributions were calculated based on the PMF and FA-NNC. Natural sources dominated the contents of As, Co, Cr, Cu, Ni, and Zn and affected 56.0%, 38.7%, and 84.8% of the Cd, Hg, and Pb concentrations, respectively. A total of 59.8% of Hg and 12.0% of Pb were associated with atmospheric deposition from coal combustion, industrial and traffic emissions, respectively. Agricultural and industrial activities contributed 37.2% of Cd concentration. SPDE proved to be an effective geostatistical technique to simulate the spatial patterns of soil PTEs with higher prediction accuracy than SGS. Co, Cr, Cu, and Ni had similar spatial patterns with hotspots randomly distributed across the study area. The common hotspots of As, Cd, Hg, Pb, and Zn in central parts inherited their high geochemical background in mudstone, while intensive human inputs in these areas also contributed to the accumulation of Cd, Hg, and Pb.