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

Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China

Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %∼86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %∼61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R2 of 0.357-0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.

Combined use of positive matrix factorization and 13C15N stable isotopes to trace organic matter-bound potential toxic metals in the urban mangrove sediments

Coastal sediments act as sinks of sediment organic matter (SOM) and metals because of their special land-sea location and depositional properties. However, there are few reports on the correlation between the sources of organic matter (OM) and associated potential toxic metals (PTMs). In this study, we combined CN stable isotope analysis and positive matrix factorization to identify the matter and metal sources of OM and glomalin-related soil protein (GRSP) in an estuary under several decades of urbanization. The results of the positive matrix factorization (PMF) reveal a correlation between the sources of total sediment metals and the sources of OM-related metals. The sources of both SOM-bound PTMs and GRSP-bound PTMs are significantly related to the sources of total PTMs. OM sources were elucidated through 13C-15 N stable isotopes, and the potential sources of different types of OM differed. In addition, there is a significant correlation between OM-associated PTMs and organic matter sources. Interestingly, the functional groups of SOM were mainly influenced by multiple PTM sources but no OM source, while the functional groups of GRSP were regulated by a single metal source and OM source. This study deepened the understanding of the coupling between PTMs and SOM. The possibility of combined use of positive matrix factorization and 13C-15 N stable isotope tracing of metals as well as the sources of each metal fractions has been evaluated, which will provide new insights for the transportation of PTMs.

Kindergarten dust heavy metal(loid) exposure associates with growth retardation in children

Heavy metal contamination from electronic waste recycling sites is present in dust found in indoor kindergartens located in e-waste recycling areas, and its potential impact on child health is a significant concern. The association between heavy metal(loid)s and the child developmental indicators is still unclear. In 2019 and 2020, we enrolled 325 and 319 children in an e-waste recycling town, respectively. Corresponding 61 and 121 dust samples were collected from roads, houses, and kindergartens in the two years. The median concentrations of metals, including Cr, Ni, Cu, Zn, and Pb exceeded the allowable limits. The highest amount of cumulative enrichment (cEF) was observed in indoor kindergarten dust (cEF = 112.3400), followed by house dust (cEF = 76.6950) and road dust (cEF = 39.7700). Children residing in the e-waste town had below-average height and weight compared to their Chinese peers. Based on linear regression analysis, the daily intake of Cd, V, Mn, and Pb in indoor kindergarten dust was found to be negatively associated with head circumference (HeC) (P < 0.05). Similarly, the daily intake of As, Cd, and Ba in indoor kindergarten dust was found to be negatively associated with chest circumference (ChC) (P < 0.05). In addition, the daily intake of As, Cd, and Ba in indoor kindergarten dust was negatively correlated with body mass index (BMI), as per the results of the study (P < 0.05). Cross-product term analysis revealed a negative correlation between daily intake of heavy metal(loid)s and HeC, ChC, and BMI, with age and sex serving as influencing factors. In conclusion, exposure to heavy metal(loid)s in indoor kindergarten dust increases the risk of growth retardation and developmental delay in children.

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

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

An improved nonnegative matrix factorization with the imputation method model for pollution source apportionment during rainstorm events

Data scarcity caused by extreme conditions during storms adds difficulties in performing pollution source apportionment. This study integrated nonnegative matrix factorization with the imputation method (NMF-IM) to fill in missing data (NAs) and conduct source apportionment. A total of 367 river samples and 35 runoff samples were taken from the Banqiao and Nanfei River basins located in Hefei, China, during four rainfall events from June to August 2020. Sixteen indicators were quantified and used for source diagnostics using NMF-IM. The results showed that total phosphorus (TP) had higher concentrations and more violent fluctuations than total nitrogen (TN) in river samples taken from rain. NMF-IM was shown to recover the value distribution of NAs approximately. The source profiles and contribution rates calculated by NMF-IM with NAs were close to the original results calculated by NMF without NAs, with root mean square error of less than 2.3% and differences less than 9.5%. Multiple forms of nitrogen and phosphorus indicators benefit reaching reasonable source diagnostics results. At least four indicators were needed to reach the same contribution rates as 16 indicator diagnostics. The two good indicator combination groups are nitrate (NO₃-N), nitrite (NO₂-N), ammonia nitrogen (NH₃-N), and total suspended solids (TSS) and NO₃-N, NO₂-N, phosphorus (PO₄-P), and TSS. The pollution source contributions changed with the Antecedent dry period (ADPs) of rain events. Treated tailwater and untreated sewage were major sources, contributing more than 80% of the total pollution of the rainstorm events with short ADPs. Dust wash became the dominant contributor after 60 min and contributed 36% of the total pollution of rainstorm events with long ADPs. The average source contribution rates for rainfall events in the Banqiao River were treated tailwater (41%) > untreated sewage (27%) > dust wash (19%) > other sources (16%). The pollution source diagnostics results were verified to be reasonable by simulation using tested run-off data and literature results.

Effect of Normalization Methods on Accuracy of Estimating Low- and High-Molecular Weight PAHs Distribution in the Soils of a Coking Plant

Mapping spatial distribution of soil contaminants at contaminated sites is the basis of risk assessment. Hotspots can cause strongly skewed distribution of the raw contaminant concentrations in soil, and consequently can require suitable normalization prior to interpolation. In this study, three normalization methods including normal score, Johnson, and Box-Cox transformation were performed on the concentrations of two low-molecular weight (LMW) PAHs (i.e., acenaphthene (Ace) and naphthalene (Nap)) and two high-molecular weight (HMW) PAHs (i.e., benzo(a)pyrene (BaP) and benzo(b)fluoranthene (BbF)) in soils of a typical coking plant in North China. The estimating accuracy of soil LMW and HMW PAHs distribution using ordinary kriging with different normalization methods was compared. The results showed that all transformed data passed the Kolmogorov-Smirnov test, indicating that all three data transformation methods achieved normality of raw data. Compared to Box-Cox-ordinary kriging, normal score-, and Johnson-ordinary kriging had higher estimating accuracy of the four soil PAHs distribution. In cross-validation, smaller root-mean-square error (RMSE) and mean error (ME) values were observed for normal score-ordinary kriging for both LMW and HMW PAHs compared to Johnson- and Box-Cox-ordinary kriging. Thus, normal score transformation is suitable for alleviating the impact of hotspots on estimating accuracy of the four selected soil PAHs distribution at this coking plant. The findings can provide insights into reducing uncertainty in spatial interpolation at PAHs-contaminated sites.

Source tracing of potentially toxic elements in soils around a typical coking plant in an industrial area in northern China

Coking plants are a substantial source of potentially toxic elements (PTEs) in soil. In this study, we examined the concentration of PTEs, the soil physicochemical properties, and the Pb isotopes in the soil inside and around a coking plant in an industrial city in northern China. We analyzed the spatial distribution of PTEs and the pollution risk areas by I_geo index, the enrichment factor (EF), and the Nemerow index, and we quantitatively identified the contribution of PTE pollution sources in the soil on a small- and medium-scale (plant and work section). Our results indicated that the Hg concentration inside the plant and the Cd concentration in the agricultural land around the plant were both relatively high. A comprehensive analysis of the soil in the study area was performed using the positive matrix factorization model and Pb isotope (^206/207Pb, ^208/206Pb) tracing method, based on the MixSIAR model, this analysis indicated that burning coal was the main source of Pb both inside (46.8%) and outside (26.3%) the coking plant. The pollution emission sources with significant influence on the soil outside the coking plant were diesel vehicles (12.5%), gas tanks (12.4%), and coke ovens (11.5%), while the sources inside the plant were quenching sections (11.1%), atmospheric deposition (11.0%), coke oven sections (9.6%), and diesel vehicles (6.1%). The results of PTE pollution risk zoning and Pb isotope tracing indicated that pollution is more serious in the western part of the plant, which is the area where coking and gas production takes place, and the most serious pollution outside the plant is mainly distributed to the southeast. This study provides theoretical and practical data indicating the contribution of industrial enterprises to soil pollution, and will help identify pollution responsibility and the management of pollution sources.

Pollution Characteristics, Spatial Patterns, and Sources of Toxic Elements in Soils from a Typical Industrial City of Eastern China

Soil pollution due to toxic elements (TEs) has been a core environmental concern globally, particularly in areas with developed industries. In this study, we sampled 300 surface (0–0.2 m) soil samples from Yuyao City in eastern China. Initially, the geo-accumulation index, potential ecological risk index, single pollution index, and Nemerow composite pollution index were used to evaluate the soil contamination status in Yuyao City. Ordinary kriging was then deployed to map the distribution of the soil TEs. Subsequently, indicator kriging was utilized to identify regions with high risk of TE pollution. Finally, the positive matrix factorization model was used to apportion the sources of the different TEs. Our results indicated that the mean content of different TEs kept the order: Zn > Cr > Pb > Cu > Ni > As > Hg ≈ Cd. Soil pollution was mainly caused by Cd and Hg in the soil of Yuyao City, while the content of other TEs was maintained at a safe level. Regions with high TE content and high pollution risk of TEs are mainly located in the central part of Yuyao City. Four sources of soil TEs were apportioned in Yuyao City. The Pb, Hg, and Zn contents in soil were mainly derived from traffic activities, coal combustion, and smelting. Meanwhile, Cu was mainly sourced from industrial emissions and atmospheric deposition, Cr and Ni mainly originated from soil parental materials, and Cd and As were produced by industrial and agricultural activities. Our study provides important implications for improving the soil environment and contributes to the development of efficient strategies for TE pollution control and remediation.

Modeling and Assessing the Spatial and Vertical Distributions of Potentially Toxic Elements in Soil and How the Concentrations Differ

A healthy soil is a healthy ecosystem because humans, animals, plants, and water highly depend upon it. Soil pollution by potentially toxic elements (PTEs) is a serious concern for humankind. The study is aimed at (i) assessing the concentrations of PTEs in soils under a long-term heavily industrialized region for coal and textiles, (ii) modeling and mapping the spatial and vertical distributions of PTEs using a GIS-based ordinary kriging technique, and (iii) identifying the possible sources of these PTEs in the Jizerské Mountains (Jizera Mts.) using a positive matrix factorization (PMF) model. Four hundred and forty-two (442) soil samples were analyzed by applying the aqua regia method. To assess the PTE contents, the level of pollution, and the distribution pattern in soil, the contamination factor (CF) and the pollution load index load (PLI) were applied. ArcGIS-based ordinary kriging interpolation was used for the spatial analysis of PTEs. The results of the analysis revealed that the variation in the coefficient (CV) of PTEs in the organic soil was highest in Cr (96.36%), followed by Cu (54.94%) and Pb (49.40%). On the other hand, the mineral soil had Cu (96.88%), Cr (66.70%), and Pb (64.48%) as the highest in CV. The PTEs in both the organic soil and the mineral soil revealed a high heterogeneous variability. Though the study area lies within the "Black Triangle", which is a historic industrial site in Central Europe, this result did not show a substantial influence of the contamination of PTEs in the area. In spite of the rate of pollution in this area being very low based on the findings, there may be a need for intermittent assessment of the soil. This helps to curtail any excessive accumulation and escalation in future. The results may serve as baseline information for pollution assessment. It might support policy-developers in sustainable farming and forestry for the health of an ecosystem towards food security, forest safety, as well as animal and human welfare.