A study on source identification of contaminated soil with total petroleum hydrocarbons (aromatic and aliphatic) in the Ahvaz oil field.

The oil industry in Khuzestan province (Southwest Iran) is one of the main reasons contributing to the pollution of the environment in this area. TPH, including both aromatic and aliphatic compounds, are important parameters in creating pollution. The present study aimed to investigate the source of soil contamination by TPH in the Ahvaz oil field in 2022. The soil samples were collected from four oil centers (an oil exploitation unit, an oil desalination unit, an oil rig, and a pump oil center). An area outside the oil field was determined as a control area. Ten samples with three replicates were taken from each area according to the standard methods. Aromatic and aliphatic compounds were measured by HPLC and GC methods. The positive matrix factorization (PMF) model and isomeric ratios were used to determine the source apportionment of aromatic compounds in soil samples. The effects range low and effects range median indices were also used to assess the level of ecological risk of petroleum compounds in the soil samples. The results showed that Benzo.b.fluoranthene had the highest concentration with an average of 5667.7 ug/kg in soil samples in the Ahvaz oil field. The highest average was found in samples from the pump oil center area at 7329.48 ug/kg, while the lowest was found in control samples at 1919.4 ug/kg-1. The highest level of aliphatic components was also found in the pump oil center, with a total of 3649 (mg. Kg-1). The results of source apportionment of petroleum compounds in soil samples showed that oil activities accounted for 51.5% of the measured PAHs in soil. 38.3% of other measured compounds had anthropogenic origins, and only 10.1% of these compounds were of biotic origin. The results of the isomeric ratios also indicated the local petroleum and pyrogenic origin of PAH compounds, which is consistent with the PMF results. The analysis of ecological risk indices resulting from the release of PAHs in the environment showed that, except for fluoranthene, other PAHs in the oil exploitation unit area were above the effects range median level (ERM) and at high risk. The results of the study showed that soil pollution by total petroleum hydrocarbons (TPH), both aromatic and aliphatic, is at a high level, and is mainly caused by human activities, particularly oil activities.

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations.

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1. The random forest (RF) model, achieving an AUC score of 0.95, was employed to predict PTE pollution at 372 unsampled gas stations. Additionally, a Positive Matrix Factorization (PMF) model indicated that gas station operations and vehicle emissions were responsible for 70 % of the lead (Pb) enrichment. Probabilistic health risk assessments showed that the carcinogenic risk (CR) and noncarcinogenic risk (NCR) for PTE pollution to adult females were the highest, at 0.451 and 1.61E-05 respectively, but still within acceptable levels. For adult males at contaminated sites, the Pb-associated CR and NCR were approximately twice as high as those at uncontaminated sites, with increases of 107 % and 81 %, respectively. This study provides new insights for managing pollution caused by gas stations.

Morphological Enrichment and Environmental Factors Correlation of Heavy Metals in Dominant Plants in Typical Manganese Ore Areas in Guizhou, China.

Bioavailable heavy metal and their efficient phytoremediation in mining areas have major implications for environmental and human health. In this study, we investigated 12 dominant plants in a typical Mn ore area of Zunyi, Guizhou Province, China, to determine the heavy metal contents, morphologies, and environmental factors affecting Mn, Cd, Pb, Cu, Zn, and Cr in the plant parts and rhizosphere soil. The bioavailabilities and degrees of metals were evaluated using the ratios of the secondary to primary phase distributions and potential ecological risk indices. Principal component analysis, cluster analysis, positive matrix factorisation modelling, and redundancy analysis were used to trace the origins and correlations among the metals. The results indicate that the bioavailabilities were the highest for Mn and Cd in the study area, and all of the target heavy metals had bioavailabilities above the moderate ecological harm level. Statistical modelling indicates that there are four main pollution sources: mining, smelting, processing operations, and atmospheric deposition. The dominant plants had high heavy metal enrichments, bioconcentration factors, and translocation factors for Mn, Cu, Cr, Cd, and Zn. The redundancy analysis indicates that soil total N, total P, and pH affect metal absorption and distributions in Compositae and non-Compositae plants in low-N, low-P, and slightly alkaline mining environments. This study provides a feasible basis for the screening of heavy metal enrichment plants and the improvement of remediation technology in manganese ore area under the extreme environment of poor nutrition.

Investigation of groundwater quality in the Southern Coast of the Black Sea: application of computational health risk assessment in Giresun, Türkiye.

Potentially toxic elements (PTEs), especially arsenic in drinking water, pose significant global health risks, including cancer. This study evaluates the groundwater quality in Giresun province on the Black Sea coast of Türkiye by analyzing twelve groundwater resources. The mean concentrations of macronutrients (mg/L) were: Ca (10.53 ± 6.63), Na (6.81 ± 3.47), Mg (3.39 ± 2.27), and K (2.05 ± 1.10). The mean levels of PTEs (µg/L) were: Al (40.02 ± 15.45), Fe (17.65 ± 14.35), Zn (5.63 ± 2.59), V (4.74 ± 5.85), Cu (1.57 ± 0.81), Mn (1.02 ± 0.76), As (0.93 ± 0.73), Cr (0.75 ± 0.57), Ni (0.41 ± 0.18), Pb (0.36 ± 0.23), and Cd (0.10 ± 0.05). All PTE levels complied with WHO drinking water safety guidelines, and overall water quality was excellent. The heavy metal evaluation index (HEI < 10) and heavy metal pollution index (HPI < 45) indicate low pollution levels across all stations. Irrigation water quality was largely adequate, as shown by the magnesium hazard (MH), sodium adsorption ratio (SAR), Na%, and Kelly's ratio (KR). The total hazard index (THI) values consistently remained below 1, indicating no non-carcinogenic health risks. However, at station 10 (city center), the cancer risk (CR) for adults due to arsenic was slightly above the threshold (1.44E-04). Using principal component analysis (PCA), positive matrix factorization (PMF), and geographic information system (GIS) mapping, the study determined that most PTEs originated from natural geological formations or a combination of natural and human sources, with minimal impact from human activities. These findings highlight the safety and reliability of the groundwater sources studied, emphasizing their potential as a long-term, safe water supply for nearby populations.

Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province.

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 µg/m3 and 9.0 µg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach.

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Contamination source identification for the prompt management of a gastroenteritis outbreak caused by norovirus in drinking water in Northern Italy.

In June 2022, a gastroenteritis outbreak occurred in a town in Northern Italy, possibly associated with the ingestion of norovirus from public drinking water. Noroviruses are highly infectious RNA viruses, with high stability in the environment. They are the primary cause of non-bacterial gastroenteritis worldwide, and despite the fact that the disease is mainly self-limiting, norovirus infection can lead to severe illness in the immunocompromised, the elderly and children. Immediately after the notification of the suspected norovirus outbreak, faecal specimens were collected from hospitalised patients, and water samples were collected from public drinking fountains in the affected area, to confirm the presence of norovirus. Norovirus was detected in 80 % (95 % CI 0.58-0.91) of the faecal specimens, and in 50 % (95 % CI 0.28-0.72) of the water samples using RT (reverse transcription) Real-time PCR. The identification of GII genotype in all samples confirmed public drinking water as the source of norovirus contamination. In addition, in one faeces and one water sample, the co-presence of genotypes GI and GII was detected. The strains were typed by sequencing, with most of them belonging to the genotype GII.3. Immediately after the confirmation of norovirus contamination in public drinking water, the local competent authorities applied safety measures, resulting in a decline in number of cases. Moreover, after the application of disinfection protocols in the water plant, the sampling was repeated with negative results for norovirus in the affected area. However, positive samples were found in the neighbouring area (prevalence 10.00 %, 95 % CI 0.02-0.40) and in the water spring (prevalence 50.00 %, 95 % CI 0.21-0.78), suggesting norovirus persistence and spread from the water source. The prompt identification of the source of contamination, and collaboration with the local authorities guided the implementation of proper procedures to control viral spread, resulting in the successful control of the outbreak.

Odor Fingerprinting of Chitosan and Source Identification of Commercial Chitosan: HS-GC-IMS, Multivariate Statistical Analysis, and Tracing Path Study.

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R2X = 0.866, R2Y = 0.996, Q2 = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications.

Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model.

Izmir, Turkey's third most populous city, is in an important position in terms of both agriculture and industry. The province, which contributes 9.3% to the country's industrial production, also has an important potential in terms of olive cultivation. However, until now, no research has been undertaken to analyze the content of trace elements (TEs) in the soil of olive orchards in Izmir. This study was carried out to determine the pollution level and ecological risks of TEs in the olive orchards soils of Izmir province, to reveal their potential sources and to evaluate their health risks. Among the TEs, the average content of only Ni (37.9 mg/kg) exceeded the world soil average content (29 mg/kg), while the average content of only Cd (0.176 mg/kg) exceeded the upper continental crust content (0.09 mg/kg). Enrichment factor revealed that there was significant enrichment for Cd in 73.6%, Ni in 11.6% and Cr in 5.4% of olive orchards, respectively, due to polluted irrigation water and agrochemicals. Similarly, ecological risk factor indicated that there were moderate and considerable ecological risks for Cd in 48.8% and 23.3% of olive orchards, respectively. Absolute principal component scores-multiple linear regression (APCS-MLR) model showed that Ni and Cr in the study area are affected by agricultural sources, Al, Co, Cu, Fe, Mn, Pb and Zn originate from lithogenic sources, and Cd originates from mixed sources. Based on health risk evaluation methods, non-carcinogenic and carcinogenic effects would not be expected for residents. This study provides significant knowledge for evaluating soil TE pollution in olive orchards and serves a model for source apportionment and human health risk evaluation of TEs in other agricultural regions.

The oxidative potential of fine ambient particulate matter in Xinxiang, North China: Pollution characteristics, source identification and regional transport.

Atmospheric fine particulate matter (PM2.5) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (∼24 h) ambient PM2.5 samples within an urban region of North China during four seasons of 2019-2020. PM2.5 samples were examined for carbonaceous components, water-soluble ions, and elements, together with their oxidative potential (represent ROS-producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 ± 1.26 nmol min-1 m-3), whereas the DTTm was the highest in summer (40.6 ± 8.7 pmol min-1 µg-1). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed-effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM2.5 OP was more susceptible to the influence of short-distance transport clusters. Discerning the influence of chemicals on health-pertinent attributes of PM2.5, such as OP, could facilitate a deep understanding of the cause-and-effect relationship between PM2.5 and impacts.

Comprehensive source identification of heavy metals in atmospheric particulate matter in a megacity: A case study of Hangzhou.

Megacities face significant pollution challenges, particularly the elevated levels of heavy metals (HMs) in particulate matter (PM). Despite the advent of interdisciplinary and advanced methods for HM source analysis, integrating and applying these approaches to identify HM sources in PM remains a hurdle. This study employs a year-long daily sampling dataset for PM1 and PM1-10 to examine the patterns of HM concentrations under hazy, clean, and rainy conditions in Hangzhou City, aiming to pinpoint the primary sources of HMs in PM. Contrary to other HMs that remained within acceptable limits, the annual average concentrations of Cd and Ni were found to be 20.6 ± 13.6 and 46.9 ± 34.8 ng/m³, respectively, surpassing the World Health Organization's limits by 4.1 and 1.9 times. Remarkably, Cd levels decreased on hazy days, whereas Ni levels were observed to rise on rainy days. Using principal component analysis (PCA), enrichment factor (EF), and backward trajectory analysis, Fe, Mn, Cu, and Zn were determined to be primarily derived from traffic emissions, and there was an interaction between remote migration and local emissions in haze weather. Isotope analysis reveals that Pb concentrations in the Hangzhou region were primarily influenced by emissions from unleaded gasoline, coal combustion, and municipal solid waste incineration, with additional impact from long-range transport; it also highlights nuanced differences between PM1 and PM1-10. Pb isotope and PCA analyses indicate that Ni primarily stemmed from waste incineration emissions. This explanation accounts for the observed higher Ni concentrations on rainy days. Backward trajectory cluster analysis revealed that southern airflows were the primary source of high Cd concentrations on clean days in Hangzhou City. This study employs a multifaceted approach and cross-validation to successfully delineate the sources of HMs in Hangzhou's PM. It offers a methodology for the precise and reliable analysis of complex HM sources in megacity PM.

Characterization of atmospheric nitroaromatic compounds in Southwest China by direct injection liquid chromatography-tandem mass spectrometry analysis of aerosol extracts.

Nitroaromatic compounds (NACs) in ambient particles are of great concern due to their adverse effects on human health and climate. However, investigations on the characteristics and potential sources of NACs in Southwest China are still scarce. In this study, a field sampling campaign was carried out in the winter of 2022 at a suburban site in Mianyang, Southwest China. A direct injection liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to rapidly determine 10 NACs in fine particulate matter (PM2.5) extracts. The method was sensitive for the quantification of the NACs, with a limit of quantification (LOQ) in the range of 0.092-0.52 ng mL-1. Then, the developed method was applied to determine the concentrations of nitrophenols (NPs), nitrocatechols (NCs), nitrosalicylic acids (NSAs), and nitronaphthol in PM2.5 in Mianyang. The average concentration of total NACs was 78.2 ± 31.2 ng m-3, with daily concentrations ranging from 20.7 to 127.9 ng m-3. Among the measured NACs, 4-nitrocatechol was the most abundant, accounting for 57.8% of the NACs in winter. The five NPs compounds together contributed to 14% of the NACs, which was lower than in other Chinese cities due to the warm climate in winter in Southwest China. NSAs and nitronaphthol each accounted for less than 5% of the NACs. Three major sources of NACs were identified based on the principal component analysis, including vehicle emissions, biomass burning, and secondary formation. The significant correlation between individual NACs and NO2 supported their secondary formation sources. The good correlation between NPs and cloud amount further suggested that gas-phase oxidation was the possible NPs formation mechanism. Our findings revealed the important role of nitrocatechols in NACs in Southwest China, implying that more measures should be taken to control biomass burning and aromatic volatile organic compounds emissions to reduce the level of NACs.

Typical emerging contaminants in sewage treatment plant effluent, and related watersheds in the Pearl River Basin: Ecological risks and source identification.

Emerging contaminants pose a potential risk to aquatic ecosystems in the Pearl River Basin, China, owing to the high population density and active industry. This study investigated samples from eight sewage treatment plants, and five surface water bodies of related watersheds. To screen the risk of emerging contaminants (ECs), and clarify their sources, this study calculated the risk quotient of detected chemical and performed source identification/apportionment using the positive matrix factorization method. In total, 149 organic pollutants were identified. Pharmaceuticals showed significant concentrations in sewage treatment plant samples (120.87 ng/L), compared with surface water samples (1.13 ng/L). The ecological risk assessment identified three chemicals with a heightened risk to aquatic organisms: fipronil sulfide, caffeine, and roxithromycin. Four principal sources of contaminants were identified: pharmaceutical wastewater, domestic sewage, medical effluent, and agricultural runoff. Pharmaceutical wastewater was the primary contributor (60.4 %), to the cumulative EC concentration and to ECs in sewage treatment plant effluent. Agricultural drainage was the main source of ECs in surface water. This study provides a strategy to obtain comprehensive information on the aquatic risks and potential sources of EC species in areas affected by artificial activities, which is of substantial importance to pollutant management and control.

Trace elements in the Upper Indus River Basin (UIRB) of Western Himalayas: Quantification, sources modeling, and impacts.

This study conducted a comprehensive analysis of trace element concentrations in the Upper Indus River Basin (UIRB), a glacier-fed region in the Western Himalayas (WH), aiming to discern their environmental and anthropogenic sources and implications. Despite limited prior data, 69 samples were collected in 2019 from diverse sources within the UIRB, including mainstream, tributaries, and groundwater, to assess trace element concentrations. Enrichment factor (EF) results and comparisons with regional and global averages suggest that rising levels of Zn, Cd, and As may pose safety concerns for drinking water quality. Advanced multivariate statistical techniques such as principal component analysis (PCA), absolute principal component scores (APCS-MLR), Monte Carlo simulation (MCS), etc were applied to estimate the associated human health hazards and also identified key sources of trace elements. The 95th percentile of the MCS results indicates that the estimated total cancer risk for children is significantly greater than (>1000 times) the USEPA's acceptable risk threshold of 1.0 × 10-6. The results classified most of the trace elements into two distinct groups: Group A (Li, Rb, Sr, U, Cs, V, Ni, TI, Sb, Mo, Ge), linked to geogenic sources, showed lower concentrations in the lower-middle river reaches, including tributaries and downstream regions. Group B (Pb, Nb, Cr, Zn, Be, Al, Th, Ga, Cu, Co), influenced by both geogenic and anthropogenic activities, exhibited higher concentrations near urban centers and midstream areas, aligning with increased municipal waste and agricultural activities. Furthermore, APCS-MLR source apportionment indicated that trace elements originated from natural geogenic processes, including rock-water interactions and mineral dissolution, as well as anthropogenic activities. These findings underscore the need for targeted measures to mitigate anthropogenic impacts and safeguard water resources for communities along the IRB and WH.

Occurrence, accumulation, ecological risk, and source identification of potentially toxic elements in multimedia in a subtropical bay, Southeast China.

Potentially toxic elements (PTEs) in seawater and sediments may be amplified along the aquatic food chain, posing a health threat to humans. This study comprehensively analyzed the concentrations, distribution, potential sources, and health risk of 7 PTEs in multimedia (seawater, sediment and organism) in typical subtropical bays in southern China. The results indicated that Zn was the most abundant element in seawater, and the average concentration of Cd in sediment was 3.93 times higher than the background value. Except for As, the seasonal differences in surface seawater were not significant. The content of Zn in fishes, crustacea, and shellfish was the highest, while the contents of Hg and Cd were relatively low. Bioaccumulation factor indicated that Zn was a strongly bioaccumulated element in seawater, while Cd was more highly enriched by aquatic organisms in sediment. According to principal component analysis (PCA), and positive matrix factorization (PMF), the main sources of PTEs in Quanzhou Bay were of natural derivation, industrial sewage discharge, and agricultural inputs, each contributing 40.4 %, 24.2 %, and 35.4 %, respectively. This study provides fundamental and significant information for the prevention of PTEs contamination in subtropical bays, the promotion of ecological safety, and the assessment of human health risk from PTEs in seafood.

Characterization of biomass burning tracers in particulate matter at 12 sites in China: Significant increase of coal combustion emitted levoglucosan in northern China during winter.

Biomass burning (BB) is the largest contributor to carbonaceous aerosols globally. Specific organic tracers can track BB particles and identify BB types. At present, there is limited information on the composition of BB tracers on a continental scale. In this study, we conducted year-round sampling of particulate matter (PM) at 12 sites in China. Nine BB tracers were measured in PM with aerodynamic diameters <1.1 µm (PM<1.1), in the range of 1.1-3.3 µm (PM1.1-3.3), and > 3.3 µm (PM>3.3). The annual average concentration of these nine BB tracers (∑9 BB tracers) in the total PM was 366 ng m-3 with the majority of levoglucosan (66 %). The concentration of ∑9 BB tracers was higher in northern China than in southern China, especially in winter. ∑9 BB tracers were most enriched in PM<1.1 (50-61 % in mass), followed by PM1.1-3.3 and PM>3.3. The highest concentrations of ∑9 BB tracers were observed in winter, while satellite-recorded fire spots were intensive in autumn and spring. The mismatch of seasonal trends between them indicated that the high levels of BB tracers in winter was not due to open BB. The composition of 4-hydroxybenzoic acid, syringic acid and vanillic acid suggested that the burning of crop residues and softwoods were the major BB types in China. The ratio of levoglucosan to mannosan could neither identify the major BB types in China nor distinguish between BB and coal combustion. Correlation analysis and the PMF model demonstrated that non-BB sources contributed 7 %-58 % to levoglucosan at the 12 sites, with coal combustion being the predominant non-BB source in China, especially in northern urban sites during winter. Our findings suggest that caution should be taken in application of these organic tracers to identify BB types and estimate BB aerosols.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Occurrence, spatial distribution and ecological risk assessment of Organophosphate Esters in surface water and sediments from the Ravi River and its tributaries.

Organophosphate esters (OPEs) are widely used as substitutes for brominated flame retardants and characterized as emerging contaminants. Due to their toxicity and persistent nature, OPEs are becoming a matter of greater concern worldwide. However, information about the pollution profile of OPEs and associated ecological risks is still scarce in environmental matrices of the South Asian region, particularly Pakistan. Hence, the current study was conducted to investigate the occurrence, spatial distribution patterns, ecological risks and riverine flux of 10 organophosphate esters in surface water and sediments of Ravi River and its four tributaries. The concentrations of ∑10OPEs were in the range of 19.2 - 105 ng/L, with the dominance of chlorinated-OPEs (51 %) in surface water, whereas in case of sediments, the ∑10OPEs concentrations ranged from 20.7 to 149 ng/g dw, with high abundance of non - chlorinated alkyl-OPEs, which contributed about 56 % to total OPE concentration. The correlation analysis signified a strong positive relation of OPEs with TOC (p < 0.05, R = 0.76) in sediments; and in addition to this, field-based LogKoc values were estimated to be higher than predicted LogKoc. Moreover, a significantly positive correlation (p < 0.05, R = 0.88) was observed between LogKoc and LogKow, implying that hydrophobicity plays a significant role in OPE distribution in different environmental matrices. The global comparison revealed that contamination status of OPEs in the present study was comparatively lower than other regional findings, furthermore, principal component analysis suggested vehicular emissions, industrial discharges, household supplies and atmospheric deposition as main sources of OPEs occurrence in current study region. Furthermore, the riverine flux of ∑10OPEs was estimated to be 0.68 tons/yr and the ecological risk assessment indicated that all OPEs, except EHDPP and TCrP, showed negligible or insignificant ecological risks for aquatic organisms.

Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment.

Contaminated sediments can adversely affect aquatic ecosystems, making the identification and management of pollutant sources extremely important. In this study, we proposed machine learning approaches to reveal sources and their influential distances for heavy metal contamination of downstream sediment. We employed classification models with artificial neural networks (ANN) and random forest (RF), respectively, to predict the heavy metal contamination of stream sediments using upland environmental variables as input features. A comprehensive Korean nationwide monitoring database containing 1546 datasets was used to train and test the models. These datasets encompass the concentrations of eight heavy metals (Ar, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment samples collected from 160 stream sites across the nation from 2014 to 2018. Model's prediction accuracy was evaluated for input feature sets from different influential upland areas defined by different buffer radii and the watershed boundary for each site. Although both ANN and RF models were unsatisfactory in predicting heavy metal quartile classes, RF-classifiers with adaptive synthetic oversampling (ORFC) showed reasonably well-predicted classes of the sediment samples based on the Canada's Sediment Quality Guidelines (accuracy ranged from 0.67 to 0.94). The best influential distance (i.e., buffer radius) was determined for each heavy metal based on the accuracy of ORFC. The results indicated that Cd, Cu and Pb had shorter influential distances (1.5-2.0 km) than the other heavy metals with little difference in accuracy for different influential distances. Feature importance calculation revealed that upland soil contamination was the primary factor for Hg and Ni, while residential areas and roads were significant features associated with Pb and Zn contamination. This approach offers information on major contamination sources and their influential areas to be prioritized for managing contaminated stream sediments.

Riverine seasonal rainfall event tracing of organic pollution sources using fluorescence fingerprint difference spectrum.

Elucidating the dynamics of dissolved organic matter (DOM) transport and transformation under seasonal rainfall events is essential for the conservation of riverine ecosystems, for mitigating the effects of climate change, and for crafting informed water management strategies. Therefore, this study aimed to investigate the evolutionary characteristics of organic pollution sources during consecutive rainfall events in early spring and to quantify their relative contributions to the process of surface water pollution. The results showed seasonal rainfall induces water quality exceedances in rivers due to the combined impacts of terrestrial inputs and endogenous releases. Humic acid (HA) (region V) and fulvic acid (FA) (region III) emerged as the predominant organic matter in the water column, with their fluorescence intensity altering as rainwater flushed the riverbed. Sources of pollution include agricultural and urban domestic sources (AS + DS) (72.29 %), industrial and urban domestic and microbial sources (IS + DS + MS) (37.71 %), and agricultural and industrial sources (AS + IS) (63.32 %), indicating that agricultural surface pollution discharges contribute significantly. The gas-chromatography-mass spectrometry (GC-MS) further confirmed that exogenous inputs were predominantly comprised of particulate pollutants. This study underscores the efficacy of fluorescence difference spectrometry in delineating the migration and transformation of river pollution sources during seasonal rainfall and facilitating the implementation of targeted management strategies for river ecosystems.