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

Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils

The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties in pollution evaluation, failing to accurately quantify source-specific contributions and associated risks. This study combines multivariate statistical methods, machine learning techniques, and positive matrix factorization (PMF) with Monte Carlo simulation to investigate HM sources, probabilistic pollution levels, source-based ecological risks, and population-specific health hazards in seven urban parks in a nickel-based mining city in China. Results showed that average concentrations of Cd (0.53 mg/kg), Cr (77.72 mg/kg), Cu (171.15 mg/kg), Hg (0.03 mg/kg), Ni (125.42 mg/kg), Pb (27.13 mg/kg), and Zn (81.97 mg/kg) exceeded their background values, except for As (11.85 mg/kg), particularly for Cd, Cu, and Ni, with exceedance rates of 98.46%, 100.00%, and 100.00%, respectively. Probabilistic assessments revealed that pollution levels were particularly high due to Cd, Cu, and Ni. Source apportionment using PMF, correlation analysis, hierarchical cluster analysis, and super-clustering of self-organizing maps identified fertilizers and pesticides (19.33%), industrial atmospheric deposition (21.13%), mining and agrochemicals (16.41%), and mining and transport activities (43.13%) as the major pollution sources. Probabilistic ecological risk assessments showed significant risks from Cd, Hg, and Cu. Non-carcinogenic risks were negligible, while carcinogenic risks were cautionary, especially for children. Mining and transportation activities were the main contributors to ecological risks, while fertilizers, pesticides, and Ni were the primary health risk factors. This study provides a robust framework to improve the accuracy of risk evaluation and offers valuable guidance for targeted interventions and sustainable management of urban soils.

Uncertainty in soil elemental prediction using machine learning and hyperspectral remote sensing

Potentially toxic elements (PTE) in soils pose significant environmental risks due to their persistence and bioaccumulation. Integrating hyperspectral remote sensing with machine learning models is a promising approach for quantifying and mapping soil PTE distributions, enabling advanced pollution monitoring. However, comprehensive evaluations of model accuracy remain limited. Here, we conducted a meta-analysis using data from 87 studies across 97 locations, 7 soil elements, 42 spectral transformation methods, 16 band optimization methods, and 34 ML techniques. Our findings indicate that among transformation methods, first derivative (FD), second derivative (SD), wavelet transform (WT), and continuum removal (CR) achieve superior model accuracy. For band optimization methods, principal component correlation (PCC), principal component analysis (PCA), expert knowledge (EK), and the combination (C_2) method effectively enhance predictive performance. In terms of model algorithms, random forest (RF), support vector machine (SVM), artificial neural networks (ANN), extreme learning machine (ELM), and partial least squares regression (PLSR) achieve high accuracy. Furthermore, the selection of FD transformation, PCC method, and RF algorithm yields R² of 79.55 % ± 13.26 %, 82.55 % ± 9.81 %, and 79.55 % ± 13.26 %, respectively. While environmental conditions, sampling design, and covariates influence model accuracy, optimizing preprocessing is key to accurate predictions. Applying scientifically optimized preprocessing methods to field sampling data can significantly enhance model performance by maximizing the utility of available samples. Therefore, we highly recommend that researchers prioritize the FD-PCC-RF strategy combination for exploratory modeling after acquiring soil samples. This study highlights the importance of advanced preprocessing and model integration for soil PTE predictions, enhancing environmental risk assessment and management. Future research should focus on adaptive preprocessing approaches based on element spectral characteristics, hybrid optimization frameworks, advances in predictive algorithms, and multimodal environmental data fusion modeling to enhance model robustness and accuracy.

Ecological and human health risks of potentially toxic elements (PTEs) in street dust of Al-Hillah City, Iraq using Monte Carlo simulation

Street dust is a primary source of metal pollution in urban environments, posing a significant threat to human health through chronic exposure via inhalation, ingestion, and skin contact. This study used deterministic and Monte Carlo simulation to assess the health risks of potential toxic elements (PTEs) in the street dust of Al-Hillah City. The average concentrations of elements in the samples followed the order: Al > Fe > S > K > Sr > Mn > Cr > Ba > Zn > Ni > Pb > Cu > Co > As > Sn > Sb > Cd. In the study area, all the measured elements exceeded UCC values except for Al, Ba, Fe, and K. The results for the enrichment factor (EF), geo accumulation index (Igeo), and contamination factor (CF) revealed that the most sampled locations were polluted with sulfur (S), arsenic (As), and chromium (Cr). The highest values of the pollution load index were not for a solely land use class; they were identified at different sampling stations. According to the potential ecological risk rating, As and Cd pose a medium risk, while Cr, Cu, Ni, Pb, and Zn have low risks. The probabilistic Monte Carlo simulation highlighted the significant health risks from PTEs in street dust, especially for children, with HI values of 2.01, 3.24, and 5.26 at the 5th, 50th, and 95th percentiles, respectively. In comparison, HI values for adults were much lower at 0.29, 0.41, and 0.58, remaining within safe limits. Lifetime Cancer Risk (LTCR) estimates showed that 99.7 % of adults and 97 % of children exposed to levels exceeding the safe threshold 1E-4. Sensitivity analysis revealed that chromium (Cr) and nickel (Ni) were the main PTEs contributing to health risks in children and adults' groups.

Determination of priority control factors for risk management of heavy metal(loid)s in park dust in Mianyang City

In order to determine the priority control elements and sources of heavy metal(loid)s (HMs) pollution in park dust, this study collected dust samples from 25 parks in the urban area of Mianyang City and measured the contents of 10 HMs. Based on Monte Carlo simulation, the probabilistic pollution levels and ecological-health risks of HMs were assessed. We found that the average contents of Zn, Co, Pb, and Cr were much higher than their background values in local soil and were influenced by artificial activities. The pollution assessment found that 5 parks were moderately polluted. The comprehensive pollution of HMs in the dust was mainly caused by Zn and Cr, and industrial source was the main contributor to Zn and Cr pollution. The contribution of As, Co, and Pb to the comprehensive ecological risk was high, accounting for 54.6%. Co, Pb, and As were the priority control HMs of ecological risk, while mixed source and industrial source were the priority control sources. HMs presented potential carcinogenic health risks to both children and adults. The non-carcinogenic risk to adults was within safety level, while some parks showed non-carcinogenic risk to children, which should be paid attention to. The source-specific health risk assessment showed that Cr and As were the priority control HMs for human health, while mixed source and industrial source were the priority control sources.

Source-specific ecological and human health risk analysis of topsoil heavy metals in urban greenspace: a case study from Tianshui City, northwest China

Soil contamination of heavy metals in urban greenspaces can exert detrimental impacts on ecological biodiversity and the health of inhabitants through cross-media migration-induced risks. Here, a total of 72 topsoil samples were collected from greenspaces in the popular tourist city of Tianshui, ranging from areas with parks, residential, road, industrial and educational soils. The study aimed to evaluate an integrated source-specific ecological and human health risk assessment of heavy metals. Among the analyzed heavy metals, except Cr (mean), all exceeded the local background values by 1.30-5.67-fold, and Hg, Cd, Pb and As were the metals with large CV values. The Igeo and CF results showed Hg, Cd, As and Pb exhibited significantly high pollution levels and were the primary pollution factors. The mean PLI values indicated moderate pollution in educational (2.21), industrial (2.07), and road (2.02) soils but slight pollution in park (1.84) and residential (1.39) greenspaces. The Igeo, CF, and PLI results also revealing that these heavy metals are more likely to be affected by human activity. Four primary source factors were identified based on PMF model: coal combustion (25.57%), agricultural sources (14.49%), atmospheric deposition (20.44%) and mixed sources (39.50%). In terms of ecological risk, the mean IRI values showed considerable risks in educational soils (287.52) and moderate risks in road (215.09), park (151.27) and residential (136.71) soils. And the contribution ratio of atmospheric deposition for park, residential, road, industrial and educational greenspaces were 57.72%, 65.41%, 67.69%, 59.60% and 75.76%, respectively. In terms of human health risk, the HI (below 1) and CR (below 1.00E-04) for adults from soils of all land use types was negligible. However, children have more significant non-carcinogenic and carcinogenic hazards especially in residential soils, the HI (above 1) and CR (above 1.00E-04) revealed the significance of regarding legacy As contamination from coal combustion when formulating risk mitigation strategies in this area. The proposed method for source and risk identification makes the multifaceted concerns of pollution and the different relevant risks into a concrete decision-making process, providing robust support for soil contamination control.

Determination of contamination, source, and risk of potentially toxic metals in fine road dust in a karst region of Southwest China

Understanding the pollution situation of potentially toxic metals (PTMs) in fine road dust (FRD) in emerging industrialized cities and identifying priority control factors is crucial for urban environmental management, resident health protection, and pollution control. This study conducted a comprehensive investigation on PTMs pollution in FRD in Zunyi, a representative emerging industrialized city in the karst region of southwestern China. The average contents of Ni, Cr, Mn, Cu, Zn, Ba, Pb, V, and Co in the FRD were 43.2, 127.0, 1232.1, 134.4, 506.6, 597.8, 76.1, 86.8, and 16.2 mg kg-1, respectively, which were obviously higher than the corresponding background levels of the local soil except for V and Co. The comprehensive pollution level of the determined PTMs in the FRD was very high, primarily caused by Zn and Cu. The sources of PTMs in Zunyi FRD were traffic, industrial, construction, and natural sources, accounting for 38.0, 23.7, 21.9, and 16.4% of the total PTMs content, respectively. The PTMs in Zunyi FRD exhibited a low to moderate overall ecological risk level, mainly contributed by Cu and traffic source. The cancer risks of PTMs in Zunyi FRD were high for all populations. The non-carcinogenic risk of PTMs in Zunyi FRD was acceptable for adults, but cannot be ignored for children. According to the source-specific probabilistic health risk estimation results, the priority control source is industrial source and the priority control PTM is Cr. Local governments need to give more attention to the carcinogenic risks and health hazards posed by PTMs in the FRD.

Pollution and source-specific risk analysis of potentially toxic metals in urban soils of an oasis-tourist city in northwest China

Source-specific risk apportionment for soil potentially toxic metals (PTMs) is of great significance for contamination prevention and risk management in urban environments. Eighty-five urban soil samples were obtained from an oasis-tourist city, China and examined for eight PTMs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn). The pollution levels, sources, and ecological risk of soil PTMs were quantified, and their source-specific ecological and human health effects were also estimated using the multi-proxy approaches. The results demonstrated that accumulation of Cd, Hg, Pb, Cr, Cu, and Zn in soils was observed compared to their background levels, and the soils experienced varying degrees of PTMs pollution, especially at sites with high-intensity anthropogenic activities. Natural sources, atmospheric deposition, industrial sources, vehicular emissions, and comprehensive inputs were the principal sources, with contributions of 29.28%, 25.86%, 20.13%, 16.50%, and 8.23%, respectively. The integrated ecological risks of PTMs in soils were moderate at most sites, with atmospheric deposition being the dominant contributor to ecological risks. Children exhibited pronounced non-cancer risks, but adults had no notable non-cancer risks. Moreover, there were potential carcinogenic risks for both children and adults within the study region. Non-cancer and carcinogenic risks were more significant for children than adults, and traffic emissions were the primary contributor to non-cancer risks (adults: 20.53%, children: 20.49%) and carcinogenic risks (adults: 22.95%, children: 22.08%). The industrial and traffic activities were considered as priority control sources for soil pollution control and risk management, with Hg, Cd, Zn, and Pb corresponding to the priority elements. This study highlights the source-specific ecological and human health effects of PTMs pollution in urban soils, thereby providing valuable information for targeted pollution control and priority source management.