Source apportionment of and potential health risks posed by trace elements in agricultural soils: A case study of the Guanzhong Plain, northwest China

Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region

Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10-4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

Homology and heterogeneity of soil trace elements of coal power production bases in arid and semi-arid areas of Northwest China

Coal power activities could cause regional fluctuations of trace elements, but the distribution information of these trace elements in arid and semi-arid areas is insufficient. In this study, the soil trace elements (As, B, Be, Cd, Co, Cr, Cu, Fe, Ga, Ge, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Ti, Tl, and Zn) of Ningdong Coal Power Production Base in China were monitored. Results showed that the concentrations of B, Tl, Mn, Pb, Cr, K, Cu, and Co exceeded background values. The maximum risk index reached 265.66, while the trace elements posed a cancer risk to children. Combining correlation analyses (CA), principal component analysis (PCA), and positive matrix factorization (PMF) techniques, it indicated that trace elements were mainly coming from coal combustion (34.15%), livestock farming (17.44%), traffic emissions (12.42%), and natural factors (35.99%). This study reveals the sources and potential ecological risks of soil trace elements in the Ningdong Coal and Power Production Base. It provides a scientific basis for developing targeted environmental management measures and reducing human health risks.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

Ecological and contamination assessment of soil in the region of coal-fired thermal power plant

This study was carried out to determine the heavy metal pollution and possible sources of agricultural soils in Tavşanlı district, Which energy power plant is located. Total 83 soil samples were taken and 8 (Cu, Cr, Pb, Co, Fe, Mn, Ni, and Zn) heavy metals were analyzed in soil samples The mean concentration of heavy metals were determined as Cu (32.89 mg kg-1), Cr (285.69 mg kg-1), Co (36.37 mg kg-1), Mn (860.20 mg kg-1), Ni (457.59 mg kg-1), Pb (22.14 mg kg-1), Fe (30,250 mg kg-1) and Zn (65.05 mg kg-1), were determined. The mean concentrations of Cu, Cr Co, Mn and Ni found to be higher than both the upper continental crust values and the European soil mean values. Contamination factor Co (2.1), Cr (3.10) and Ni (9.73), enrichment factor Co (2.73), Cr (3.75) and Ni (11.42) and geoaccumulation index Co (0.18), Cr (0.50) and Ni (1.98) values showed that the soils were polluted by Co, Cr, and Ni. In addition, it was determined that Ni (48.65) poses a "moderate ecological risk" in the study area. Pearson correlation anaysis and principal component analysis determined that Cr, Co and Ni have both lithogenic and anthropogenic origin.

Contamination, ecological-health risks, and sources of potentially toxic elements in road-dust sediments and soils of the largest urban riverfront scenic park in China

Identifying the contamination and sources of potentially toxic elements (PTEs) in road-dust sediment (RDS) and the surrounding greenspace soil of urban environments and understanding their ecological-health risks are important for pollution management and public health. The contamination characteristics, ecological and probabilistic health risks, and source apportionment of eight PTEs (Cd, Pb, Cr, Cu, Ni, As, Zn, and Hg) in the Yellow River Custom Tourist Line of Lanzhou, which is the largest open urban riverfront scenic park in China, were investigated. The results showed that all the RDS PTE mean concentrations exceeded their soil background values, whereas for the surrounding greenspace soils, the concentrations of the PTEs, except for Cr and Ni, were also higher than their local background levels. Moreover, the RDS-soil system was mainly contaminated by Cd, Zn, Pb, Cu, and Hg to varying degrees, and the integrated ecological risks of PTEs in the RDS and soil were high and considerable at most sites, respectively. The probabilistic health risk assessment results demonstrated that the non-carcinogenic hazard risk for humans was negligible, but the total carcinogenic risks should be considered. Source apportionment using a positive matrix factorization model combined with multivariate statistical analyses revealed that Cr, Ni, and As in the RDS-soil system were from natural and industrial sources, Cd, Pb, Zn, Cu came from vehicle emissions and pesticide and fertilizer applications, and Hg was from natural and industrial sources and utilization of pesticides with fertilizers. This work provides scientific evidence for urban planning and human health protection in urban environments.

Spatial distribution, sources, and risk assessment of potentially toxic elements in cultivated soils using isotopic tracing techniques and Monte Carlo simulation

Potentially toxic elements (PTEs) in cultivated lands pose serious threats to the environment and human health. Therefore, improving the understanding of their distinct sources and environmental risks by integrating various methods is necessary. This study investigated the distribution, sources, and environmental risks of eight PTEs in cultivated soils in Lishui City, eastern China, using digital soil mapping, positive matrix factorisation (PMF), isotopic tracing, and Monte Carlo simulation. The results showed that Pb and Cd are the main pollutants, which posed higher ecological risks in the study area than the other PTEs. Natural, mining, traffic, and agricultural sources were identified as the four determinants of PTE accumulation via a PMF model combined with Pearson correlation analysis, showing that their contribution rates were 22.6 %, 45.7 %, 15.2 %, and 16.5 %, respectively. Stable isotope analysis further confirmed that local mining activities affected the HM accumulation. Additionally, non-carcinogenic and carcinogenic risk values for children were 3.18 % and 3.75 %, respectively, exceeding their acceptable levels. We also identified that mining activities were the most important sources of human health risks (55.7 % for adults and 58.6 % for children) via Monte Carlo simulations coupled with the PMF model. Overall, this study provides insights into the PTE pollution management and health risk control in cultivated soils.

Identification of factors controlling heavy metals/metalloid distribution in agricultural soils using multi-source data

Understanding the factors that controlling the agricultural soil heavy metals/metalloids distribution is vital for cropland soil remediation and management. For this objective, 227 agricultural soils were sampled in the Guanzhong Plain, China, to measure the concentration of five heavy metals (Pb, Cd, Ni, Zn, and Cu) and one metalloid (As) by X-ray fluorescence spectrometer, meanwhile, 24 possible influencing factors to agricultural soil heavy metals/metalloid distribution were collected and grouped into three categories. A sequential multivariate statistical analysis was carried out to provide insight into the controlling factors of soil heavy metals/metalloid distribution, then stepwise multiple linear regression (SMLR) and partial least squares regression (PLS) were used to predict heavy metals/metalloid concentrations in agricultural soil based on the result of soil heavy metals/metalloid controlling factors identification. The results demonstrated the types of soil and land use did not have a substantial effect on soil heavy metals/metalloid distribution, except Zn and Cu. The soil properties category played a major role in influencing the soil heavy metals/metalloid concentration. The concentrations of Mn and Fe, which are the main constitute elements of soil inorganic colloid, were the most significant factors, followed by the concentrations of P, K and Ca. Soil pH and soil organic matter (SOM) content, which are often considered as important factors for soil heavy metals/metalloid distribution, were not important in the present study. The SMLR was more effective than the PLS for predicting soil heavy metals/metalloid content. The results of this study enlighten that future soil heavy metals/metalloid contamination treatment in regions with high pH and low SOM content should concentrate on inorganic colloid particles, which have strong adsorption capacity for soil heavy metals/metalloid and are environmentally friendly. Moreover, the combination of successive multivariate statistical analysis and SMLR provide an effective tool to predict and monitor agricultural soil heavy metals/metalloid distribution, and facilitate the improvement of environmental and territorial management.

A spatial distribution - Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil

With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution.

Heavy metals and their sources, potential pollution situations and health risks for residents in Adıyaman province agricultural lands, Türkiye

In this study, the contents of heavy metals (HMs) such as Al, Cd, Co, Cr, Cu Fe, Mn, Ni, Pb and Zn in soil samples collected from 403 sampling locations of the agricultural lands of Adıyaman Province (Türkiye) were determined by Inductively Coupled Plasma‒Optical Emission Spectrometry (ICP‒OES). The mean concentrations of Al, Cd, Co Cr, Cu Fe, Mn, Ni, Pb and Zn HMs were detected 28,986, 3.60, 15, 127, 52.67, 45,830, 817, 62.40, 10.75 and 66.25 mg kg^-1, respectively. These results showed that the average concentrations of Cd, Cr, Cu, Fe, Mn and Ni exceeded the Upper continental crust average. To determine and to evaluate the contamination status and distribution of HMs in agricultural soils, metal pollution parameters such as enrichment factor (EF), geoaccumulation index (I_geo), contamination factor (Cf), pollution load index, potential ecological risk factor (Er), and potential ecological risk index (RI) were used. Factor analyses (FA) and principal component analyses (PCA) indicated that Cd, Cr and Ni levels were influenced by anthropogenic sources, Fe by both lithological and anthropogenic sources, and other HMs by lithogenic origins. For both children and adults, the hazard index (HI) and total hazard index (THI) values of HMs were < 1, suggesting that non-carcinogenic health risks to residents through ingestion, inhalation pathways, and dermal contact were currently absent. In addition, the cumulative carcinogenic risk (CCR) results were within the acceptable risk range (10^-4 to 10^-6). The results showed that children were more sensitive to the non-carcinogenic and carcinogenic effects of HMs.

Distribution and ecological risk assessment of trace elements in the paddy soil-rice ecosystem of Punjab, Pakistan

Trace elements (TEs) contamination of agricultural soils requires suitable criteria for regulating their toxicity limits in soil and food crops, which depends on their potential ecological risk spanning regional to global scales. However, no comprehensive study is available that links TE concentrations in paddy soil with ecological and human health risks in less developed regions like Pakistan. Here we evaluated the data set to establish standard guidelines for defining the hazard levels of various potentially toxic TEs (such as As, Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Se, Zn) in agricultural paddy soils of Punjab, Pakistan. In total, 100 topsoils (at 0-15 cm depth) and 204 rice plant (shoot and grain) samples were collected from five ecological zones of Punjab (Gujranwala, Hafizabad, Vehari, Mailsi, and Burewala), representing the major rice growing regions in Pakistan. The degree of contamination (C_d) and potential ecological risk index (PERI) established from ecological risk models were substantially higher in 100% and 97% of samples, respectively. The positive matrix factorization (PMF) model revealed that the elevated TEs concentration, notably Cd, As, Cr, Ni, and Pb, in the agricultural paddy soil was attributed to the anthropogenic activities and groundwater irrigation. Moreover, the concentration of these TEs in rice grains was higher than the FAO/WHO's safe limits. This study provided a baseline, albeit critical knowledge, on the impact of TE-allied ecological and human health risks in the paddy soil-rice system in Pakistan; and it opens new avenues for setting TEs guidelines in agro-ecological zones globally, especially in underdeveloped regions.

Arsenic and trace metal concentrations in different vegetable types and assessment of health risks from their consumption

The levels of 12 trace metal (loid)s (TMs) in 10 vegetable types including leafy vegetables (purslane, purple basil and parsley) and fruiting vegetables (pepper, tomato, eggplant, cucumber, zucchini, green bean and melon) and in maize grown in Malatya province (Turkey) were investigated and non-carcinogenic health risks from consumption of these crops were assessed. The levels of TMs were measured by inductively coupled plasma-optical emission spectrometry. The mean levels of Ni, Cu, Cd, Cr, As and Zn in all crops were below maximum permissible concentrations (MPCs), while those of Pb in pepper (0.109 mg/kg fw), eggplant (0.103 mg/kg fw) and green bean (0.177 mg/kg fw) slightly exceeded MPCs (0.1 mg/kg fw). Leafy vegetables had relatively higher concentrations of Al, As, Ba, Cr, Cu, Fe and Mn compared to fruiting vegetables and maize. The estimated daily intake value of each TM estimated for each crop was found to be below the tolerable daily intake value. The target hazard quotients of all TMs in all crops did not exceed the acceptable non-carcinogenic risk level. However, hazard index (HI) value (1.57) in tomato was found to be above the threshold value of 1, indicating non-carcinogenic risks for consumers due to the intake of combined TMs in tomato. The THQ values of As, Co and Pb contributed 46.4%, 24.5% and 16.4% of the HI value of tomato, respectively. High daily consumption amount of tomato likely resulted in high HI value. The findings obtained in this study reveal that even if the levels of TMs in vegetables are safe, more attention should be paid to non-carcinogenic risks associated with TMs as a result of high vegetable intake.

An integrated multidisciplinary-based framework for characterizing environmental risks of heavy metals and their effects on antibiotic resistomes in agricultural soils

In this study, a new integrated multidisciplinary-based framework has been proposed to better understand the environmental risks of heavy metals (HMs) in agricultural soils. The source apportionment results revealed by a multilinear engine model were incorporated into the geochemical indexes and the probabilistic health risk assessment models for identifying the source-oriented risks of HMs in the environment. High-throughput sequencing-based metagenomic assembly analysis was used for characterizing the prevalence and dissemination risk of antibiotic resistomes and their associations with the geochemical enrichment of HMs in the soils. Results showed agricultural and industrial activities were the main sources of HMs in the environment. Although the soils were contaminated moderately by HMs and the health risks posed by soil metals were negligible for both adult and children, source-oriented risk evaluation suggested agricultural activities contributed relatively higher contamination and health risks than the other sources. Notably, abundant and diverse antibiotic resistant genes, mobile gene elements, virulence factors, and antibiotic-resistant bacterial pathogens were identified in the agricultural soils, as well as their co-occurrences on the same contigs, implying a non-negligible resistome risk. Further, statistical and network analyses showed the geochemical enrichment of HMs exerted significant effects on the antibiotic resistomes in the environment.

Environmental Background Values and Ecological Risk Assessment of Heavy Metals in Watershed Sediments: A Comparison of Assessment Methods

The distribution and assessment of heavy metal pollution in sediments have been extensively studied worldwide. Risk assessment methods based on total content, background values, and sediment quality guidelines are widely applied but have never been compared. We systematically sorted out these evaluation methods, obtained evaluation results using actual monitoring data, and compared their applicability. The results showed that the background values of different metals are significantly different, which may depend on their mobility. Geoaccumulation index (Igeo) and enrichment factor (EF) values invariably decreased with the increase of background values for individual heavy metal enrichment risk assessment. Compared with EF, Igeo also showed a significant positive linear correlation with heavy metal content. Pollution load index (PLI), modified contamination degree (mCd), and potential ecological risk index (RI) showed significant differences in response to background values and evaluation levels for the comprehensive risk of heavy metal enrichment, but their distribution trends along with the sampling points were basically identical. Toxic risk index (TRI), mean ERM quotient (mERMQ), and contamination severity index (CSI) were used to evaluate the damage degree of complex heavy metals to aquatic organisms and shared a similar whole-process distribution trend. The modified hazard quotient (mHQ), which is used to evaluate the toxicity of a single heavy metal to aquatic organisms, showed a significant positive linear correlation with the total content of each heavy metal, indicating that the toxic effect on organisms can be predicted through the direct monitoring. The results of this study have important guiding significance for the selection of evaluation methods for heavy metal pollution in sediments.

A combination of finite mixture distribution model with geo-statistical models to study spatial patterns and hazardous areas of heavy metals in cropland soils of the Guanzhong Plain, Northwest China

An extensive cropland soil investigation was conducted to determine the pollution thresholds and hazardous zones of heavy metals (HMs) in the Guanzhong Plain, by using an integrated approach that combines finite mixture distribution model (FMDM) and geo-statistical analysis. FMDM results demonstrated that Pb, Cr, Ni, and Cu were fitted by binary mixture distributions representing the background and moderate pollution distributions, and Zn was fitted by a triple mixture distribution representing the background, moderate and high contamination distributions. The moderate pollution thresholds of Pb, Cr, Ni, Zn and Cu calculated by FMDM were 29.75, 80.15, 38.60, 81.48 and 27.10 mg kg^-1, whereas the cutoff value of Zn high contamination was 97.49 mg kg^-1. The moderately polluted thresholds of all five HMs were higher than their background values in the study area, and lower than the corresponding national standards. The indicator kriging simulation showed Pb, Cr, Ni, Zn had <0.1%, 2.6%, <0.1%, 2.9% of total areas exceed contamination cutoff values, whereas the hazardous area of Cu was contiguous, and covered 17.3% of the total area. Overall, 17.5% of the total area surpassed the moderate contamination threshold. The pollution hot spots and hazardous zones of soil HMs were located in the southern part of the Guanzhong Plain, where population and industrial activities are centralized, indicating that anthropogenic activities played a critical role in HMs accumulation in high-risk regions. The combination of geo-statistical and FMDM delineate the thresholds and hazardous area for HMs pollution reliably, and facilitate the improvement of soil environmental management.

Pollution status, potential sources and health risk assessment of arsenic and trace metals in agricultural soils: A case study in Malatya province, Turkey

We measured the concentrations of arsenic and 11 trace metals in the soils from vegetable fields in Malatya province (Turkey) and assessed health risks for residential adults and children. Also, we assessed their potential sources, contamination status and ecological risks. Median concentrations of only As, Co, Ni and Cr exceeded the world soil average values, while those of Cd, As, Ni and Cu exceeded the upper continental crust contents. Contamination factor, enrichment factor and geoaccumulation index results indicated that the study region was contaminated with Cd, As, Ni and Cu likely due to use of irrigation water contaminated with industrial wastewaters and use of fertilizers and pesticides. Also, the study region had "high potential ecological risk" for Cd, whereas "low potential ecological risk" for the other trace metal(loid)s (TMs). Factor and hierarchical cluster analyses revealed that As and Cu were from anthropogenic sources, Cd and Ni from both natural and anthropogenic sources, while other TMs from natural sources. The hazard quotient values of all TMs and total hazard index values for both children and adults were lower than the risk level of 1, indicating that non-carcinogenic health risks are not expected for residents. Also, the cumulative carcinogenic risk results were within the acceptable risk range. Our results indicated that application of multivariate statistics, pollution, ecological and health indices together provide valuable knowledge for assessing soil pollution in a particular region.

Comprehensive screen the lead and other toxic metals in total environment from a coal-gas industrial city (NW, China): Based on integrated source-specific risks and site-specific blood lead levels of 0-6 aged children

A new integrated source-specific risk model and site-specific blood lead levels (BLLs) of 0-6 children were introduced to comprehensive understand the status of the toxic metals in soil-dust-plant total environment from a Coal-Gas industrial city, NW China. 144 samples were collected and ten toxic metals (As, Ba, Co, Cr, Cu, Mn, Ni, Sr, Pb, and Zn) were screened by XRF and ICP-MS. It was found that the occurrences of toxic metals deferred in the different medium, such as Co, Cu, Pb, and Zn observed the trend of accumulating in soil and plant compared to clustered distributions of Cr, Mn and Ni preferred to accumulate in dust. However, few bioaccumulations observed in Ulmus pumila L. Toxic metals distributions in majority of sites influenced by coal combustion mixed sources and industrial activities posed the high integrated ecological risks and caused significant non-carcinogenic and carcinogenic integrated risks for local 0-6 children identified by new integrated source-specific risk model, especially observed in the priority contaminants Co and Pb. The site-specific BLLs confirmed that younger children fewer than 4 lived in the north region were more vulnerable to priority Pb pollution as their BLLs above 50 μg/L, almost up to 80 μg/L. Although proportions of source-specific risks to toxic metals changed in soil and dust, the critical sources from coal combustions and industrial activities posed the most important contribution to the local risks. Therefore, effective strategies targeting at critical sources on coal industries should be conducted to reduce risks, and mostly emphasize on the north hotspot areas.

Source apportionment of heavy metals in sediments and soils in an interconnected river-soil system based on a composite fingerprint screening approach

Heavy metal pollution has been a global concern and key points of environmental pollution prevention and control due to the growing problems of urbanization and industrialization. Rapidly and correctly apportioning sources of heavy metal is still a great challenge because of the stability of source fingerprint and complex interaction of multiple contaminants and sources. In this study, we perform a combination of optimization of pollution source fingerprint and source apportionment through jointly utilizing two machine classification and screening methods for characterizing the pollution sources of heavy metal in the sediments of an urban river and its surrounding soils. Dominance-based rough set model (DRS), content optimization tools, and multivariate curve resolution-alternating least squares model (MCR-WALS) were employed to screen representative pollution source samples, optimize pollution source fingerprint, and apportion the potential sources of heavy metals, respectively. Further, Support vector machine (SVM) was adopted to correspondence analysis results and pollution fingerprint based on the factor characteristics for achieving source apportionment accurately. Results showed that the pollution source pollution source fingerprints optimized by DRS and optimization tools are more representative and stable, and the results obtained by SVM and MCR-WALS are more accurate comparing with traditional methods. As whole, source apportionment suggested that printing and dyeing, chemical, electroplating, metal processing were the main origins of heavy metals in this area and the proportions of them in sediment and soil pollution sources were 67.05% and 28.43%, respectively. Besides, coal combustion was also the main sources of heavy metal pollution in soils, accounting about 34.16%. Results of the study can advance our knowledge to better understand the characterization of heavy metal pollution in the peri-urban ecosystem and to design effective targeted strategies for reducing heavy metal pollution diffusion.