Application of APCA-MLR receptor model for source apportionment of char and soot in sediments

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China

The chlor-alkali industry (CAI) is crucial for global chemical production; however, its operation has led to widespread heavy metal (HM) contamination at numerous sites, which has not been thoroughly investigated. This study analysed 122 soil and groundwater samples from a typical CAI site in Kaifeng, China. Our aim was to assess the ecological and health risks, identify the sources, and examine the migration characteristics of HMs at this site using Monte Carlo simulation, absolute principal component score-multiple linear regression (APCS-MLR), and the potential environmental risk index (Ei). Our findings revealed that the exceedance rates for Cd, Pb, Hg, and Ni were 71.96%, 45.79%, 49.59%, and 65.42%, respectively. Mercury (Hg) displayed the greatest coefficient of variation across all the soil layers, indicating a significant anthropogenic influence. Cd and Hg were identified as having high and extremely high potential environmental risk levels, respectively. The spatial distributions of the improved Nemerow index (INI), total ecological risk (Ri), and HM content varied considerably, with the most contaminated areas typically associated with the storage of raw and auxiliary materials. Surface aggregation and significant vertical transport were noted for HMs; As and Ni showed substantial accumulation in subsoil layers, severely contaminating the groundwater. Self-organizing maps categorized the samples into two different groups, showing strong positive correlations between Cd, Pb, and Hg. The APCS-MLR model suggested that industrial emissions were the main contributors, accounting for 60.3% of the total HM input. Elevated hazard quotient values for Hg posed significant noncarcinogenic risks, whereas acceptable levels of carcinogenic risk were observed for both adults (96.60%) and children (97.83%). This study significantly enhances historical CAI pollution data and offers valuable insights into ongoing environmental and health challenges.

Occurrence, efficiency of treatment processes, source apportionment and human health risk assessment of pharmaceuticals and xenoestrogen compounds in tap water from some Ghanaian communities

The occurrence of pharmaceuticals and xenoestrogen compounds (PXCs) in drinking water presents a dire human health risk challenge. The problem stems from the high anthropogenic pollution load on source water and the inefficiencies of the conventional water treatment plants in treating PXCs. This study assessed the PXCs levels and the consequential health risks of exposure to tap water from selected Ghanaian communities as well as that of raw water samples from the respective treatment plants. Thus the PXCs treatment efficiency of two drinking water treatment plants in the metropolises studied was also assessed. The study also conducted source apportionment of the PXCs in the tap water. Twenty six (26) tap and raw water samples from communities in the Cape Coast and Sekondi-Takoradi metropolises were extracted using SPE cartridges and analysed for PXCs using Ultra-fast-HPLC-UV instrument. Elevated levels of PXCs up to 24.79 and 22.02 μg/L were respectively recorded in raw and tap water samples from the metropolises. Consequently, elevated non-cancer health risk (HI > 1) to residential adults were found for tap water samples from Cape Coast metropolis and also for some samples from Sekondi-Takoradi metropolis. Again, elevated cumulative oral cancer risks >10-5 and dermal cancer risk up to 4 × 10-5 were recorded. The source apportionment revealed three significant sources of PXCs in tap water samples studied. The results revealed the inefficiency of the treatment plants in removing PXCs from the raw water during treatments. The situation thus requires urgent attention to ameliorate it, safeguarding public health. It is recommended that the conventional water treatment process employed be augmented with advanced treatment technologies to improve their efficacy in PXCs treatment.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Trace elements in surface sediments from Xinyanggang River of Jiangsu Province, China: Spatial distribution, risk assessment and source appointment

The Xinyanggang River in Yancheng City, one of the essential rivers entering the Yellow sea, has imported abundant pollutants to the coast of Jiangsu Province. Trace elements (Cr, Ni, Cu, Zn, As, Rb, Sr, Mo, Pb, Th, U, Sc, Ga, Se, Zr, Nb, and Sn) in surface sediments in the Xinyanggang River were measured to analyze the spatial distribution, risk assessment, and source appointment. The results showed that the parts of 17 trace elements presented high average values in river sediments, such as Zr (309.19 mg/kg), Sr (182.72 mg/kg), Zn (77.67 mg/kg), and Cr (70.63 mg/kg). Compared with some coastal rivers, the Xinyanggang River was polluted by certain trace elements, such as Cr, Zn, and As. Based on the analysis of the enrichment factor (EF), the contamination factor (CF), the pollution load index (PLI), and the geoaccumulation index (I_geo), trace elements in sediments showed unpolluted to moderate contamination and mild to moderate enrichment. Among them, Zn, Pb, and Sn were highly polluted. The multivariate analysis, the principal component analysis-multiple linear regression (APCS-MLR) model, and the Unmix model identified four contributing trace element sources. Cr, Th, U, Se, Zr, and Nb originated from manufacturing industries and hydrodynamic transport erosion. Ni, Rb, Sc, and Ga were attributed to natural source. Cu, Zn, Mo, Pb, and Sn were contributed from mixed sources including industrial wastewater and transportation emissions. As and Sr were associated mainly with mixed sources of agriculture and combustion. These four sources of metals accounted for 22.5 %, 5.7 %, 15.3 %, and 11.1 % by using the APCS-MLR model, whereas 22.9 %, 39.7 %, 17.5 %, and 19.9 % by using the Unmix model, respectively.

Source Apportionment of Heavy Metals Based on Multiple Approaches for a Proposed Subway Line in the Southeast Industrial District of Beijing, China

Apportioning the sources of heavy metals (HMs) in soil is of great importance for pollution control. A total of 64 soil samples from 13 sample points at depths of 0-21 m were collected along a proposed subway line in the southeast industrial district of Beijing. The concentrations, distribution characteristics, and sources of eight HMs were investigated. The results showed that the concentrations of Hg, Cd, Cu, Pb, As, and Zn in the topsoil (0-2 m) exceeded the Beijing soil background values. Three sources were identified and their respective contribution rates calculated for each of the HMs using multiple approaches, including correlation analysis (CA), top enrichment factor (TEF), principal component analysis (PCA), and positive matrix factor (PMF) methods. As (63.11%), Cr (61.67%), and Ni (70.80%) mainly originated from natural sources; Hg (97.0%) was dominated by fossil fuel combustion and atmospheric deposition sources; and Zn (72.80%), Pb (69.75%), Cu (65.36%) and Cd (53.08%) were related to traffic sources. Multiple approaches were demonstrated to be effective for HM source apportionment in soil, whilst the results using PMF were clearer and more complete. This work could provide evidence for the selection of reasonable methods to deal with soils excavated during subway construction, avoiding the over-remediation of the soils with heavy metals coming from natural sources.

Nonhomologous Black Carbon Decoupled Char and Soot Sequestration Based on Stable Carbon Isotopes in Tibetan Plateau Lake Sediment

Combustion-derived black carbon (BC) is an important component of sedimentary carbon pool. Due to different physicochemical properties, determining the source of char and soot is crucial for BC cycling, especially for nonhomologous char and soot in the Tibetan Plateau (TP). This study analyzed the sequestration and source of BC, char, and soot in the Dagze Co (inner TP) sediment core via the content and δ¹³C, revealing the biomass and fossil fuel driving on nonsynchronous char and soot and their response to local anthropogenic activities and atmospheric transmission. The results showed that BC concentration increased from 1.19 ± 0.35 mg g^-1 (pre-1956) to 2.03 ± 1.05 mg g^-1 (after 1956). The variation of char was similar to BC, while nonhomologous growth was detected in char and soot (r = 0.29 and p > 0.05). The source apportionment showed that biomass burning for 71.52 ± 10.23% of char and promoted char sequestration. The contribution of fossil fuel combustion to soot (46.67 ± 14.07%) is much higher than char (28.48 ± 10.23%). Redundancy analysis confirmed that local anthropogenic activities significantly influenced BC burial and atmospheric transport from outside TP-regulated BC burial. The contribution of biomass and fossil fuels to nonsynchronous char and soot is conducive to understanding the anthropogenic effect on BC burial in the TP.

Source appointment of potentially toxic elements (PTEs) at an abandoned realgar mine: Combination of multivariate statistical analysis and three common receptor models

Identifying pollution sources and quantifying their contributions are of great importance for proposing management and control strategies of potentially toxic elements (PTEs) in soil. In this study, multivariate statistical analysis and receptor models were combined to identify potential pollution sources and apportion their contributions at an abandoned realgar mine. Principal component analysis (PCA) result shows that three factors are responsible for PTEs, which is also supported by cluster analysis (CA). Correlation analysis and spatial analysis also show that the heavy metals from the same pollution source are of higher correlation coefficients and similar spatial distribution. Three receptor models were combined to apportion contributions of pollution sources. Three pollution sources were detected by absolute principal component analysis-multiple linear regression (APCA-MLR). In contrast, four sources were identified by positive matrix factorization (PMF) and UNMIX. Soil parent material was heavily loaded on Cr, Cu, Ni and Zn, occupying the largest average contribution (30%-43%). Cadmium was mainly derived from agricultural activities with contribution higher than 60%. Arsenic accumulation was mainly associated with mining and smelting activity with contribution higher than 80%. PMF and UNMIX models showed that more than half of Pb concentrations were influenced by industrial activities. Comparatively speaking, APCA-MLR was a well-performing model for all PTEs even though it only detected three pollution sources. The study showed that it was a good choice to apply multiple receptor models in order to achieve more reliable and objective conclusions of source appointment.

Health risk assessment of heavy metal(loid)s in the farmland of megalopolis in China by using APCS-MLR and PMF receptor models: Taking Huairou District of Beijing as an example

The quality of agricultural soils is important for agricultural production and food safety. The contamination of agricultural soils by heavy metal(loid)s (HMs) has aroused global attention. Fifty-two topsoil samples with 8 HMs were gathered to assess the health risks of farmland soil in Huairou District, Beijing. As a significantly enriched pollutant, the results revealed that Hg had greater ecological risks relative to other HMs. We found that the positive matrix factorization (PMF) model appears to be more physically plausible in identifying complex pollution sources compared to the absolute principal components score-multiple linear regression (APCS-MLR) model, which had a higher fit coefficient (r² = 0.69-0.99). Five HMs from pollution sources, including agricultural activities, traffic source, natural source, fuel burning, and industrial production, were identified by integrating the PMF model with Pearson's correlation analysis, revealing corresponding contribution rates of 29.40%, 22.54%, 20.16%, 15.20%, and 12.70%, respectively. The probabilistic health risk evaluation results showed an absence of non-carcinogenic risks in all populations, but the carcinogenic risk could not be ignored, especially in children. In addition, the source-oriented health risks showed that agricultural activities made the largest contribution to the health risks of all populations. This research provides scientific evidence for preventing HMs contamination and control of farmland.

The sources risk assessment combined with APCS/MLR model for potentially toxic elements in farmland of a first-tier city, China

With the rapid economic development, potentially toxic elements (PTEs) are continuously migrating, transforming, and enriching in farmland through atmospheric deposition and other media, posing threats to food security and human health. At present, there are few quantitative studies on the health risks of PTEs sources in farmland. In this study, absolute principal component score-multiple linear regression (APCS-MLR) receptor model was used to quantify the pollution sources of PTEs in farmland in Suzhou of Yangtze River Delta Economic Zone, China. Combined with geoaccumulation index (I_geo) and health risk assessment model, the source risk of PTEs was further quantified. The results show that Cd has reached the level of unpolluted to moderate polluted (0 < I_geo < 1); the total hazard index (THI) and total carcinogenic risk (TCR) index of PTEs are acceptable for adults, but not for children (THI > 1, TCR > 1 × 10^-4). The results of APCS-MLR source apportionment were industrial sources (25.65%), agricultural sources (20.00%), traffic sources (16.81%), and domestic pollution sources (9.71%). The I_geo values of all pollution sources were less than 0, and no ecological risk was caused. The contribution patterns of pollution sources to THI and TCR in adults and children are similar. Industrial pollution sources pose the greatest non-carcinogenic risk to humans, accounting for 47.35% and 47.26% of adults and children, respectively; for carcinogenic risks, domestic pollution sources contribute the most among all identified pollution sources, accounting for 27.71% and 27.73% of adults and children, respectively. In general, this study emphasizes the need to strengthen the supervision of industrial pollution sources and domestic pollution sources in the study area to reduce the health risks to children.

Phosphorus-arsenic interaction in the 'soil-plant-microbe' system and its influence on arsenic pollution

Elevated arsenic (As) in soil is of public concern due to the carcinogenicity. Phosphorus (P) strongly influences the adsorption, absorption, transport, and transformation of As in the soil and in organisms due to the similarity of the chemical properties of P and As. In soil, P, particularly inorganic P, can release soil-retained As (mostly arsenate) by competing for adsorption sites. In plant and microbial systems, P usually reduces As (mainly arsenate) uptake and affects As biotransformation by competing for As transporters. The intensity and pattern of PAs interaction are highly dependent on the forms of As and P, and strongly influenced by various biotic and abiotic factors. An understanding of the PAs interaction in 'soil-plant-microbe' systems is of great value to prevent soil As from entering the human food chain. Here, we review PAs interactions and the main influential factors in soil, plant, and microbial subsystems and their effects on the As release, absorption, transformation, and transport in the 'soil-plant-microbe' system. We also analyze the application potential of P fertilization as a control for As pollution and suggest the research directions that need to be followed in the future.

Predicting flocculant dosage in the drinking water treatment process using Elman neural network

Predicting the flocculant dosage in the drinking water treatment process is essential for public health. However, due to the complexity of water quality and flocculation, many difficulties remain. The present study aimed to report on using artificial intelligence, namely, the Elman neural network (ENN), to predict the flocculant dosage and explore the applications of the proposed model in waterworks. The flocculation process of drinking water was introduced in this study, and four typical models were developed based on multiple linear regression (MLR), the radial basis function neural network (RBFNN), the least squares support vector machine (LSSVM), and the ENN. To improve the prediction accuracy, a mixed term including long-term data and short-term data was proposed to capture the periodic and time-varying characteristics of water quality data. The weights of each part are updated adaptively according to the comparison of effluent turbidity and set values. The results demonstrate that the proposed ENN model performed better than the other three models in terms of the prediction performance. With the ENN model of flocculant dosage, the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) of the test data were 1.8917, 5.0067, and 0.8999, which were improved by 36.9%, 41.5%, and 14.0% in comparison with the best one (RBFNN) of the other three models, respectively. The effluent turbidity of sedimentation tank was more stable under the control of proposed ENN model of flocculant dosage than the other three models. Considering its performance, the ENN model can be taken as a preferred data intelligence tool for predicting the drinking water flocculant dosage.

Health risk and source assessment of semi-volatile phenols, p-chloroaniline and plasticizers in plastic packaged (sachet) drinking water

The presence of U.S. EPA priority organic contaminants in drinking water poses a dire health risk on consumers. Packaged drinking water such as plastic sachet drinking water has significantly gained market in both developed and developing countries, especially, its dominance in the Ghanaian market. The treatment process, packaging, and storage of the sachet drinking water contribute to the levels of genotoxic semi-volatile phenols, p-chloroaniline, and plasticizers contamination in the drinking water. The study thus sought to investigate the levels of semi-volatile phenols, p-chloroaniline, and plasticizer contaminants in sachet drinking water on the Ghanaian market and the associated health risk of exposure. The study also investigated the possible sources of the contaminants. A total of thirty (30) different brands of sachet water on the Ghanaian market were studied. The samples were extracted in replicates (n = 3) using Solid Phase Extraction (SPE) cartridges and further analysed with GC-MS (SIM mode). The source apportionment was conducted using absolute principal component analysis coupled with multiple, linear regression (APCA-MLR) and automatic linear regression (APCA-MALR) modelling. The mean total levels for the phenols, p-chloroaniline, and plasticizers were between 210.2 and 18,914.9, 11.2 and 18,871.0, and 21.2 and 69,834.1 ng/L respectively. The cumulative non-cancer risk (hazard quotient) and cancer risk upon exposure were computed to range between 2.1 × 10^-3 and 1.2 and 1.5 × 10^-7 and 1.3 × 10^-4 respectively. About 37% of the samples had elevated cancer risk (>10^-6) which may contribute to the existing incidence, cause for concern. The five sources found for the contaminants were apportioned as "environmental background (major)", "water treatment/disinfectant", "plastic/plasticizers", "storage and preservation", and "residual inter-conversion/degradation sources".

Enhanced adsorption of polystyrene nanoplastics (PSNPs) onto oxidized corncob biochar with high pyrolysis temperature

Plastic pollution has become a global threat in the natural environment, and an urgent remedial measure is needed to reduce the negative effects caused by plastic pollutants. In the current study, the effects of pyrolysis temperature (500 °C, 700 °C, and 900 °C) and aging on the adsorption of polystyrene nanoplastics (PSNPs) onto corncob biochar were systematically assessed with kinetic, isotherm, pH-dependent adsorption experiments, FTIR and XPS spectroscopy, and DLVO calculations. The oxidation was done with 5% of HNO₃/H₂SO₄ to simulate long-term oxidative aging of biochar in the environment. The results showed that the specific surface area, hydrophobicity, and aromaticity of biochar increased with pyrolysis temperature, whereas the specific surface area and amounts of oxygen-containing groups increased after oxidation. The adsorption mechanism of PSNPs onto the biochar was explored based on the correlation between biochar properties and adsorption parameters derived from adsorption isotherms. Overall, the adsorption capacity of biochar for PSNPs increased with increased pyrolysis temperature and after aging. While the increase of specific surface area was considered the major factor leading to the increase of the adsorption, the variation in surface properties also played an important role. Pore filling, hydrophobic interaction, and hydrogen bonding may all be involved in PSNPs adsorption to biochar. However, the hydrophobic interaction might be more important for the fresh biochar, whereas hydrogen bonding involving oxygen-containing groups might make a bigger contribution to PSNPs adsorption to oxidized biochar. The pH experiments revealed that PSNPs adsorption decreased in general with the increase of pH, indicating that electrostatic repulsion played a vital role in the PSNPs adsorption process. The results of this study indicate that biochar can be potentially applied to immobilize plastic particles in terrestrial ecosystems such as in soil or groundwater, and the immobilization could be enhanced via artificial oxidation or aging of biochar in the natural environment.