Quantitative analysis of the main sources of pollutants in the soils around key areas based on the positive matrix factorization method

Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries

Heavy metal (HM) pollution in agricultural soils has arisen sharply in recent years. However, the impact of main factors on available HMs concentrations in agricultural soils of the three main industries (smelting, chemical and mining industry) is unclear. Herein, soil properties (pH, cation exchange capacity (CEC) and texture (sand, slit, clay)), total and available concentrations were concluded based on the results of 165 research papers from 2000 to 2023 in Web of Science database. In the three industries, the correlation and redundancy analysis were used to study the correlation between main factors and available concentrations, and quantitatively analyzed the contribution of each factor to available concentrations with gradient boosting decision tree model. The results showed that different factors had varying degrees of impact on available metals in the three main industries, and the importance of same factors varied in each industry, as for soil pH, it was most important for available Pb and Zn in the chemical industry, but the total concentrations were most important in the smelting and mining industry. There was no significant correlation between total and available concentrations. Soil properties involved in this paper (especially soil pH) were negatively correlated with available concentrations. This study provides effective guidance for the formulation of soil pollution control and risk assessment standards based on industry classification in the three major industrial impact areas.

Phthalate ester levels in agricultural soils of greenhouses, their potential sources, the role of plastic cover material, and dietary exposure calculated from modeled concentrations in tomato

Soil samples collected from 50 greenhouses (GHs) cultivated with tomatoes (plastic-covered:24, glass-covered:26), 5 open-area tomato growing farmlands, and 5 non-agricultural areas were analyzed in summer and winter seasons for 13 PAEs. The total concentrations (Σ13PAEs) in the GHs ranged from 212 to 2484 ng/g, wheeas the concentrations in open-area farm soils were between 240 and 1248 ng/g. Σ13PAE in non-agricultural areas was lower (35.0 - 585 ng/g). PAE exposure through the ingestion of tomatoes cultivated in GH soils and associated risks were estimated with Monte Carlo simulations after calculating the PAE concentrations in tomatoes using a partition-limited model. DEHP was estimated to have the highest concentrations in the tomatoes grown in both types of GHs. The mean carcinogenic risk caused by DEHP for tomato grown in plastic-covered GHs, glass-covered GHs, and open-area soils were 2.4 × 10-5, 1.7 × 10-5 and 1.1 × 10-5, respectively. Based on Positive Matrix Factorization results, plastic material usage in GHs (including plastic cover material source for plastic-GHs) was found to be the highest contributing source in both types of GHs. Microplastic analysis indicated that the ropes and irrigation pipes inside the GHs are important sources of PAE pollution. Pesticide application is the second highest contributing source.

Source apportionment and suitability evaluation of seasonal VOCs contaminants in the soil around a typical refining-chemical integration park in China

Accurate source apportionment of volatile organic compounds (VOCs) in soil nearby petrochemical industries prevailing globally, is critical for preventing pollution. However, in the process, seasonal effect on contamination pathways and accumulation of soil VOCs is often neglected. Herein, Yanshan Refining-Chemical Integration Park, including a carpet, refining, synthetic rubber, and two synthetic resin zones, was selected for traceability. Season variations resulted in a gradual decrease of 31 VOCs in soil from winter to summer. A method of dry deposition resistance model coupling partitioning coefficient model was created, revealing that dry deposition by gas phase was the primary pathway for VOCs to enter soil in winter and spring, with 100 times higher flux than by particle phase. Source profiles for five zones were built by gas sampling with distinct substance indicators screened, which were used for positive matrix factorization factors determination. Contributions of the five zones were 14.9%, 20.8%, 13.6%, 22.1%, and 28.6% in winter and 33.4%, 12.5%, 10.7%, 24.9%, and 18.5% in spring, respectively. The variation in the soil sorption capacity of VOCs causes inter-seasonal differences in contribution. The better correlation between dry deposition capacity and soil storage of VOCs made root mean square and mean absolute errors decrease averagely by 8.8% and 5.5% in winter compared to spring. This study provides new perspectives and methods for the source apportionment of soil VOCs contamination in industrial sites.

Contamination and source-specific health risk assessment of polycyclic aromatic hydrocarbons in soil from a mega iron and steel site in China

The iron and steel industry has always been a key and difficult point of environmental pollution control. In the present study, 493, 175, 153, 72, and 42 soil samples were collected from the soil depths of 0-0.5, 0.5-2, 2-3, 3-4, and 4-5 m (herein called the layers) of the Shougang Steel site, respectively. Compared with the evaluation criteria, the Shougang Steel surface soil was severely polluted by polycyclic aromatic hydrocarbons (PAHs). Inverse distance-weighted interpolation and the Kruskal-Wallis H test revealed that the soil PAH pollution in the iron-making area, especially the coking area, was severer than those in other areas. The PAH concentrations first decreased, and then, increased with the increase of depth. With the increase in depth, the contributions of 2- and 3-ring PAHs increased, while those of 4-, 5-, and 6-ring PAHs decreased. The bivariate local indicators of spatial association (LISA) analysis was used to identify the areas prone to soil PAH pollution due to atmospheric deposition of industrial waste gas and traffic emissions. The method could be used to analyze the impact of anthropogenic activities on soil's PAH pollution for other contaminated sites. Three main pollution sources of soil PAHs, the backfill source, the combustion of coal, and the traffic emissions, were identified based upon three diagnostic ratios, positive matrix factorization and the bivariate LISA analysis, and accounted for 53.8%, 23.5%, and 22.7%, respectively. The combination of bivariate LISA analysis and other source analysis methods could improve the accuracy of source analysis. Benzo[a]pyrene contributed the most to the total health risk among sixteen PAHs. The health risks related to the three pollution sources decreased in the order of backfill sources > coal combustion > traffic emissions. The incremental life-time carcinogenic risks were all below 10-4, indicating negligible or acceptable risks.

Influencing factors identification and the nested structure analysis of heavy metals in soils in entire city and surrounding the multiple pollution sources

Identifying the sources of pollutants and analyzing the nested structure of heavy metals is vital for the prevention and control of soil pollution. However, there is a lack of research on comparison the main sources and the nested structure at different scales. In this study, two spatial extent scales were taken as the research objects, the results showed that, (1) the point exceeding standard rate of As, Cr, Ni, and Pb is higher at the entire city scale; (2) As and Pb, while Cr, Ni, and Zn, have weaker spatial variability at the entire scale and surrounding the pollution sources, respectively; (3) the contribution of the larger structure of Cr and Ni, while Cr, Ni, and Zn, at the entire scale and surrounding the pollution sources, respectively, is bigger to the total variability. The representation of semivariogram is better when the general spatial variability is weaker and the contribution of the smaller structure is lower; (4) various factors with different influencing distance could lead to nested structure even at a small extent spatial scale. The results provide a basis for the determination of remediation and prevention objectives at different spatial scales.

Quantitative analysis of the contribution of sources, diffusion pathways, and receptor attributes for the spatial distribution of soil heavy metals and their nested structure analysis in China

Investigation of heavy metal pollution degree, pollution sources, and spatial distribution structure is crucial for the country's soil pollution prevention, but relevant research is lacking. In this study, As, Cd, Cr, Cu, Pb and Zn in the national scope are taken as research objects. Among them, Cd has the highest pollution level. Four sources were quantitatively allocated as soil type, mining and dressing industry, GDP, and NDVI, which accounted for 92.93, 97.81, 99.30 and 96.24 % of Cr, Cd, Zn and As contamination, respectively. In addition, according to the geographical detector, the spatial distribution of As was affected by three diffusion pathways, whose influence degree were 0.822-0.947, especially the slope. Cadmium was primarily affected by both receptor attributes and diffusion pathways, with an influence degree of 0.010-0.175, especially soil water content and slope; Cr and Pb were affected by receptor attributes, with an influence degree of 0.886-0.986 and 0.007-0.288, respectively, especially for soil water content and soil organic carbon; Cu and Zn were affected by receptor attributes, with an influence degree of 0.182-0.823 and 0.002-0.150, respectively, especially for soil texture. There are two spatial distribution structures with nested scales in east-west and north-south directions. The large spatial structure has a more significant impact on the spatial distribution of heavy metals, especially in the east-west direction. Overall, the mining and dressing industry is the main source in Hunan, Yunnan, and Liaoning, where many mines exist and mining activities are frequent. GDP was the main source in Shanghai and Zhejiang areas, where the economy is developed. NDVI was the main source in Guangdong and Anhui areas, where agriculture is relatively developed. These results provide a basis for determining remediation and prevention objectives in soil pollution remediation and prevention in the national scope.

Contamination characteristics and source analysis of potentially toxic elements in dustfall-soil-crop systems near non-ferrous mining areas of Yunnan, southwestern China

Potentially toxic elements (PTEs) in the dustfall-soil-crop system pose a serious threat to the ecological environment and agricultural production. However, there is still a knowledge gap in terms of better understanding the distinctive sources of PTEs by integrating various models and technologies. In this study, we comprehensively investigated the concentrations, distribution, and sources of seven PTEs in a dustfall-soil-crop system (424 samples in total) near a typical non-ferrous mining area, using absolute principal component score/multiple linear regression (APCS/MLR) combined with X-ray diffraction (XRD) and microscopy techniques. Our results showed that the mean values of As, Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 211, 14, 105, 91, 65, 232, and 325 mg/kg, respectively. These values were significantly higher than the background soil values in Yunnan. Except for Ni and Cr, all elements in the soil were significantly higher than the screening values of agricultural lands in China. The spatial distribution of PTE concentrations was similar among the three media. The ACPS/MLR, XRD, and microscopy analyses further indicated that soil PTEs mainly originated from industrial activities (37 %), vehicle emissions and agricultural activities (29 %), respectively. Dustfall PTEs mainly originated from vehicle emissions and industrial activities, accounting for 40 % and 37 %, respectively. Crop PTEs mainly originated from vehicle emissions and soil (57 %), and agricultural activities (11 %), respectively. PTEs seriously threaten the safety of agricultural products and the ecological environment once they settle from the atmosphere to soil and crop leaves, further accumulate in crops, and spread through the food chain. Therefore, our study provides scientific evidence for government regulators to control PTE pollution and reduce their environmental risks in dustfall-soil-crop systems.

Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, Geodetector, and multiple linear regression of distance

The accurate identification of sources for soil heavy metal(loid) is difficult, especially for multi-functional parks, which include multiple pollution sources. Aiming to identify the apportionment and location of heavy metal(loid)s pollution sources, this study established a method combining principal component analysis (PCA), Geodetector, and multiple linear regression of distance (MLRD) in soil and dust, taking a multi-functional industrial park in Anhui Province, China, as an example. PCA and Geodetector were used to determine the type and possible location of the source. Source apportionment of individual elements is achieved by MLRD. The detection results quantified the spatial explanatory power (0.21 ≤ q ≤ 0.51) of the potential source targets (e.g., river and mining area) for the PCA factors. A comparative analysis of the regression equation (Model 1 and Model 3) indicated that the river (0.50 ≤ R² ≤0.78), main road (0.47 ≤ R² ≤ 0.81), and mine (0.14 ≤ R² ≤ 0.92) (p < 0.01) were the main sources. Different from the traditional source apportionment methods, the current method could obtain the exact contributing sources, not just the type of source (e.g., industrial activities), which could be useful for pollution control in areas with multiple sources.

Receptor model-oriented sources and risks evaluation of metals in sediments of an industrial affected riverine system in Bangladesh

Toxic metals in river sediments may represent significant ecological concerns, although there has been limited research on the source-oriented ecological hazards of metals in sediments. Surface sediments from an industrial affected Rupsa River were utilized in this study to conduct a complete investigation of toxic metals with source-specific ecological risk assessment. The findings indicated that the average concentration of Ni, Cr, Cd, Zn, As, Cu, Mn and Pb were 50.60 ± 10.97, 53.41 ± 7.76, 3.25 ± 1.73, 147.76 ± 36.78, 6.41 ± 1.85, 59.78 ± 17.77, 832.43 ± 71.56 and 25.64 ± 7.98 mg/kg, respectively and Cd, Ni, Cu, Pb and Zn concentration were higher than average shale value. Based on sediment quality guidelines, the mean effective range median (ERM) quotient (1.29) and Mean probable effect level (PEL) quotient (2.18) showed medium-high contamination in sediment. Ecological indexes like toxic risk index (20.73), Nemerow integrated risk index (427.59) and potential ecological risk index (610.66) posed very high sediment pollution. The absolute principle component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) model indicated that Zn (64.21%), Cd (51.58%), Cu (67.32%) and Ni (58.49%) in APCS-MLR model whereas Zn (49.5%), Cd (52.7%), Cu (57.4%) and Ni (44.6%) in PMF model were derived from traffic emission, agricultural activities, industrial source and mixed sources. PMF model-based Nemerow integrated risk index (NIRI) reported that industrial emission posed considerable and high risks for 87.27% and 12.72% of sediment samples. This work will provide a model-based guidelines for identifying and assessing metal sources which would be suitable for mitigating future pollution hazards in Riverine sediments in Bangladesh.

Quantitative source apportionment and associated driving factor identification for soil potential toxicity elements via combining receptor models, SOM, and geo-detector method

Quantitative source apportionment of soil potential toxicity elements (PTEs) and associated driving factor identification are critical for prevention and control of soil PTEs. In this study, 421 soil samples from a typical area in southeastern Yunnan Province of China were collected to evaluate the pollution level of soil PTE using pollution factors, pollution load index, and enrichment factors. Positive matrix factorization (PMF), absolute principal component score/multiple line regression (APCS/MLR), edge analysis (UNMIX) and self-organizing map (SOM) were applied for source apportionment of soil PTEs. The geo-detector method (GDM) was used to identify the driving factor to PTE pollution sources, which assisted in source interpretation derived from receptor models. The results showed that the geometric mean of As, Cd, Cu, Cr, Ni, Pb, and Zn were 94.94, 1.02, 108.6, 75.40, 57.14, 160.2, and 200.3 mg/kg, which were significantly higher than their corresponding background values (P < 0.00). Particularly, As and Cd were 8.71 and 12.75 times higher than their corresponding background values, respectively. SOM yielded four clusters of soil PTEs: AsCd, PbZn, CrNi, and Cu. APCS/MLR was regarded as the preferred receptor model for source apportionment of soil PTEs due to its optimal performance. The results of ACPS/MLR revealed that 36.64% of Pb and 38.30% of Zn were related to traffic emissions, Cr (92.64%) and Ni (82.51%) to natural sources, As (85.83%) and Cd (87.04%) to industrial discharge, and Cu (42.78%) to agricultural activities. Distance to road, lithology, distance to industries, and land utilization were the respective major driving factor influencing these four sources, with the q values of 0.1213, 0.1032, 0.2295 and 0.1137, respectively. Additionally, GDM revealed that nonlinear interactions between anthropogenic and natural factors influencing PTEs sources. Based on these results, comprehensive prevention and control strategies should be considered for pollution prevention and risk controlling.

Discharge Patterns of Potentially Harmful Elements (PHEs) from Coking Plants and Its Relationship with Soil PHE Contents in the Beijing–Tianjin–Hebei Region, China

The Beijing–Tianjin–Hebei (BTH) region in China is a rapid development area with a dense population and high-pollution, high-energy-consumption industries. Despite the general idea that the coking industry contributes greatly to the total emission of potentially harmful elements (PHEs) in BTH, quantitative analysis on the PHE pollution caused by coking is rare. This study collected the pollutant discharge data of coking enterprises and assessed the risks of coking plants in BTH using the soil accumulation model and ecological risk index. The average contribution rate of coking emissions to the total emissions of PHEs in BTH was ~7.73%. Cross table analysis indicated that there was a close relationship between PHEs discharged by coking plants and PHEs in soil. The accumulation of PHEs in soil and their associated risks were calculated, indicating that nearly 70% of the coking plants posed a significant ecological risk. Mercury, arsenic, and cadmium were the main PHEs leading to ecological risks. Scenario analysis indicated that the percentage of coking plants with high ecological risk might rise from 8.50% to 20.00% as time progresses. Therefore, the control of PHEs discharged from coking plants in BTH should be strengthened. Furthermore, regionalized strategies should be applied to different areas due to the spatial heterogeneity of risk levels.

A four-way model (FEST) for source apportionment: Development, verification, and application

In studying the spatial, temporal, and particle size variations heavy metal sources, a source apportionment model for a four-way array of data is required. In this study, referencing two-way and three-way models, a four-way (particle fractions, elements, sites, and time) source apportionment model (FEST) was developed. Errors in the three-way models solving four-way problems verified the necessity of developing the FEST model. The results showed that the FEST model had a higher accuracy than the existing models, which was probably because of more constraints and input data in the FEST model. Based on the sampled data in Beijing, sources were apportioned for the four-way array of data using the FEST model, and the spatial, temporal, and particle size variations of sources were evaluated. The main sources of heavy metals were similar to those in our prior studies, whereas the contributions of sources to specific heavy metals differed. Traffic exhaust and fuel combustion contributed more to fine particles than coarse particles, indicating that the two should be controlled preferentially among all sources. The management of traffic exhaust should be focused on the central and northern areas in each season, and the control of fuel combustion should be strengthened in the southern area in winter.

An effective method for determining the optimal sampling scale based on the purposes of soil pollution investigations and the factors influencing the pollutants

There is a lack of a systematic method for determining the optimal sampling scale based on the purposes of soil pollution investigations (purpose_invest) and the factors influencing of pollutants, which could affect the accuracy of determining pollution scope of the pollution. Therefore, in this study, both the purpose_invest and the influencing factors were considered to determine the optimal sampling scale. The conclusions were obtained through geostatistical and spatial analysis. (1) The optimal sampling scale should account for 3% of the range of the pollutants, which can identify pollution information and minimize sampling costs. (2) The optimal sampling scale should be set to 3% of the range of the main factor influencing the pollutants in the absence of prior pollution information. (3) The greater the influences of the factors on the pollutants, the closer the optimal sampling scale calculated according to the influencing factors will be to that calculated based on the purpose_invest. (4) The method of determination based on both the purpose_invest and the influencing factors was concluded to be rational and reliable based on validation and advantage analysis. These results provide a method for soil pollution investigation that can minimize costs and improve the representativeness of the sample sites.

Characteristics of dust-phase phthalates in dormitory, classroom, and home and non-dietary exposure in Beijing, China

The phthalate concentrations in dust from undergraduate dormitories, classrooms, and homes in Beijing, China, were measured in April 2017. We analyzed the characteristics of phthalates in dust from three environments. In addition, we estimated the daily intake of phthalates via three pathways using Monte Carlo simulations. The detection frequency of eight phthalates in dust ranges from 74.5 to 100%. Di (2-ethylhexyl) phthalate (DEHP), di-n-butyl phthalate (DnBP), and di-isobutyl phthalate (DiBP) are the most abundant phthalates. The median proportion of DEHP in dust is the highest, ranging from 67.1 to 72.9%. The PMF results indicated that two, four, and three types of phthalate sources exist in home, dormitory, and classroom, respectively. The differences in the phthalate concentrations between sunny and shaded rooms and urban and suburban classrooms are insignificant, whereas that between male and female dormitories is significant. The total daily intake of DEHP, DnBP, and DiBP ranges from 97.3 to 336 ng/ (kg·day). The oral intake for DEHP in classrooms and the dermal intake of DnBP and DiBP in homes are the highest. The carcinogenic risk of DEHP to university students is the highest in classrooms and the total carcinogenic risk of the three environments is 4.70 × 10^-6.