Mobility and risk assessment of heavy metals in benthic sediments using contamination factors, positive matrix factorisation (PMF) receptor model, and human health risk assessment

Two-dimensional Monte Carlo simulation coupled with multilinear regression modeling of source-specific health risks from groundwater

Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO3-, and F-, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types. Three pollution sources were identified: agricultural practices, traffic, and natural processes. Moreover, the results revealed NO3- from an agricultural source as the primary control contaminant. Additionally, both adults and children in the study area face significant non-carcinogenic health risks. To mitigate these risks, this study recommends maximum consumption levels of 1.44 L/day for adults and 0.35 L/day for children. Furthermore, adults weighing > 68.1 kg and children weighing > 15.9 kg are likely to be at reduced risk of experiencing adverse health effects. Compared to deterministic health risk assessment and one-dimensional Monte Carlo simulation of health risks, two-dimensional Monte Carlo simulation showed improved performance, providing better accuracy and higher precision in health risk assessment results. Thus, this research is expected to enhance the understanding of health risk assessment related to groundwater and to provide valuable guidance for managing groundwater pollution.

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a significant challenge. This study employed machine learning (ML) techniques to model heavy metal pollution in soils within this critical ecosystem. A total of 91 standardized soil samples were analyzed to predict the accumulation of eight heavy metals using four distinct ML algorithms. Among them, random forest model outer performed in predicting As (0.79), Cd (0.89), Cr (0.63), Ni (0.56), Cu (0.60), and Zn (0.52), achieving notable R squared values. The feature attribute analysis identified As-K, Pb-K, Cd-S, Zn-Fe2O3, Cr- Fe2O3, Ni-Al2O3, Cu-P, and Mn- Fe2O3 relationships resembled with correlation coefficients among them. The developed models revealed that the contamination factor for metals in soils indicated extremely high levels of Pb contamination (CF ≥ 6). In conclusion, this research offers a robust framework for predicting heavy metal pollution in coal mining soils, highlighting critical areas that require immediate conservation efforts. These findings emphasize the necessity for targeted environmental management and mitigation to reduce heavy metal pollution in mining sites.

Source apportionment and probabilistic health risk assessment of polycyclic aromatic hydrocarbons in sediment from an urban shallow lake

Polycyclic aromatic hydrocarbons (PAHs) in sediment from urban shallow lake Bubanj (Serbia) were investigated in terms of their concentration, distribution, and potential effects on the environment and human health. The concentration of 16 PAHs (ΣPAHs) ranged from 24.4 to 107 ng/g dw. Low-molecular-weight PAHs (2- and 3-aromatic rings) were dominant, accounting for 71% of the total. Multivariate statistical analyses revealed that the main source of PAHs was the incomplete combustion of organic sources. Diagnostic ratios and positive matrix factorization (PMF) methods further indicated that combustion of organic matter and petroleum leakage were the dominant sources. The toxic equivalent quantity (TEQ) of ΣPAHs varied from 0.5 to 17.6 ng-TEQ/g-dry. BaP had the highest contribution to TEQ, followed by Ant, DahA, and BaA, with respective shares of 74.3%, 6.4%, 5.6%, and 5.3%. Dermal contact and ingestion were the primary exposure routes for PAHs, whereas inhalation posed negligible cancer risk. The incremental lifetime cancer risk (ILCR) values for adults were in the range of 3.9 × 10-9-1.4 × 10-7 and for children 5.3 × 10-9-1.9 × 10-7, indicating negligible carcinogenic health risks. Monte Carlo simulation showed that ILCR values for adults and children remained below the permissible limit of 1.0 × 10-6, even at the 90th percentile.

Heavy metals in sediments of the river-lake system in the Dianchi basin, China: Their pollution, sources, and risks

The accumulation of heavy metals in river and lake sediments in basins seriously threatens ecological safety and human health. To manage the basin effectively, it is crucial to understand pollution levels and identify and quantify the sources and risks of heavy metals in rivers and lakes separately for targeted control. In this study, 34 sediment samples were collected from the Dianchi Basin, China, and the pollution, sources, and risks in the river-lake system sediments were systematically analysed for cadmium (Cd), chromium (Cr), arsenic (As), mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni). The results showed that at least half of the heavy metals in the lakes and rivers exceeded the local soil background values during the flood and dry seasons. Heavy metal concentrations were generally higher in the lakes, with high concentrations in the lakes and nearby rivers. Through positive matrix factorisation and Geodetector traceability discovery, agricultural activities were found to be the main source of heavy metals in river sediments, whereas urban activities were the main source in lake sediments. Ecological risk assessments indicated that Hg and Cd were the main risk factors, causing pollution in lakes due to atmospheric deposition and traffic emissions and moderate pollution in rivers due to atmospheric deposition and agricultural production. Health risk assessments revealed that As and Ni were the main carcinogenic risks, originating from human and industrial activities in lakes, and from agricultural and natural sources in rivers. Children faced higher carcinogenic risks than adults, possibly because of their behaviour and physiology. Overall, the presence of heavy metals, along with their ecological and health risks, was significantly higher in the lakes than in the rivers. This study provides a comprehensive overview of the pollution, sources, and risks of eight heavy metals in the river-lake system of the Dianchi Basin.

Pollution level assessment, source apportionment, and health hazards of heavy metals and rare earth elements in the sediment core from the coast of Karachi, Pakistan

Evaluating the elemental composition of sediment cores is essential for understanding environmental changes, including depositional variations, soil formation processes, and human influences. Such investigations offer insights into the biological, geochemical, and industrial impacts on sediment quality and the health of marine ecosystems. This study evaluates the pollution levels and their sources along the coast of Karachi, Pakistan, as well as the effects of pollution on human and ecological health. The core sediment's elemental composition was determined by Neutron Activation Analysis. The mean values in mg/kg of the elements are Al (34800), As (11.15), Ba (371), Br (18.40), Ca (118850), Ce (41.43), Co (10.29), Cr (62.41), Cs (5.27), Eu (0.80), Fe (22855), Hf (2.43), K (11210), La (20.84), Lu (0.26), Mg (21750), Mn (416), Na (8350), Nd (18.92), Rb (66.35), Sb (1.04), Sc (8.31), Se (8.23), Sm (3.88), Sn (17.05), Ta (0.77), Th (7.17), U (3.96), V (71.80), Yb (1.28) and Zn (581). Various pollution indices were used to assess the pollution level of these elements in the sediment core. Statistical tools like Pearson's correlation matrix and Factor analysis were utilized for source apportionment of these elements. Source apportionment showed the sources of heavy metals and rare earth elements are Ship breaking facilities, different types of refuse effluents carried by the Malir river into the sea and the geology of the area. Carcinogenic and non-carcinogenic health hazards associated with exposure to toxic metals were also calculated. The ecological risk factor and sediment quality index showed As and Zn may harm the marine environment. In conclusion, this study found that the sediment of Karachi's coast is polluted with high levels of As, Br, Ca, Cr, Sn, and Zn; with As, Cr and Zn posing a threat to the marine ecology as well as human health.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23-30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.

Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China

The contamination of heavy metal has permeated many parts of China, especially in densely populated and industrialized southern China. This study focused on the degree of pollution in farmland soil heavy metals (HMs), and its spatial distribution characteristics and source apportionment. Meanwhile, we conducted an evaluation of the health risks attributed to soil HMs and analyzed the factors that impact them. We found that the distribution of five heavy metals is mainly concentrated in the east-central and southern parts of the study area. Specifically, Cd and Hg have high levels of pollution and present potential ecological risks. The pollution sources of five HMs were analyzed utilizing positive matrix factorization. The results revealed that the contribution of different sources keeps the following order: natural source (42.42%), agricultural activities (29.93%), industrial pollution source (20.49%), and atmospheric deposition pollution (7.16%). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were relatively high. Children and the elderly are more vulnerable to the negative effects of Cr, As. Using structural equation modeling, we found soil property is a vital factor affecting soil contamination, with the soil organic matter and cation exchange capacity having a relatively greater impact on heavy metals pollution. Our study provides some data reference and guidance for soil ecological protection and restoration.

Risks to Human Health from Mercury in Gold Mining in the Coastal Region of Ecuador

Artisanal and small-scale gold mining (ASGM) plays a crucial role in global gold production. However, the adoption of poor mining practices or the use of mercury (Hg) in gold recovery processes has generated serious environmental contamination events. The focus of this study is assessing the concentration of Hg in surface waters within the coastal region of Ecuador. The results are used to conduct a human health risk assessment applying deterministic and probabilistic methods, specifically targeting groups vulnerable to exposure in affected mining environments. Between April and June 2022, 54 water samples were collected from rivers and streams adjacent to mining areas to determine Hg levels. In the health risk assessment, exposure routes through water ingestion and dermal contact were considered for both adults and children, following the model structures outlined by the U.S. Environmental Protection Agency. The results indicate elevated Hg concentrations in two of the five provinces studied, El Oro and Esmeraldas, where at least 88% and 75% of the samples, respectively, exceeded the maximum permissible limit (MPL) set by Ecuadorian regulations for the preservation of aquatic life. Furthermore, in El Oro province, 28% of the samples exceeded the MPL established for drinking water quality. The high concentrations of Hg could be related to illegal mining activity that uses Hg for gold recovery. Regarding the human health risk assessment, risk values above the safe exposure limit were estimated. Children were identified as the most vulnerable receptor. Therefore, there is an urgent need to establish effective regulations that guarantee the protection of river users in potentially contaminated areas. Finally, it is important to continue investigating the contamination caused by human practices in the coastal region.

Chemical speciation and comprehensive risk assessment of metals in sediments from Nabq protectorate, the Red Sea using individual and synergistic indices

The study evaluates metal concentrations, distributions, contamination, risk, sources, fractionation, and mobility in Nabq Protectorate sediments, revealing a metal content order of Fe, Mn, Pb, Ni, and Cd. Metals are dominated by residual fractions, with Cd (83.70 %) > Ni (82.98 %) > Pb (80.96 %) > Fe (80.31 %) > Mn (76.65 %) reflecting the natural sources of investigated metals. Mn (23.35 %) was the most abundant mobile metal, and the sediments of the protectorate had low toxicity and moderate risk according to the synergistic indices (1 ≤ mRAC<10 and ERM; 5-10). The results from the proposed individual indices showed that Mn, Fe, and Pb are the most bioavailable (BIM 0.1-0.4), Cd, Mn, Ni, Fe, and Pb are of moderate mobility (MIM 0.1-0.4), and Cd is the most available (ARIM 5-10), with Cd posing the most ecological risk. The total hazard quotient (THQ) for child was greater than one, exposure to manganese through ingestion and skin contact while swimming may endanger human health.

An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Impacts of the steel industry on sediment pollution by heavy metals in urban water system

The impact of the steel industry on sediment heavy metal (HM) pollution in urban aquatic environments was investigated in a major iron ore-producing area (Ma'anshan) in China. The concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 9.68 ± 3.56, 170.31 ± 82.40, 90.62 ± 19.54, 30.61 ± 6.72, 125.43 ± 63.60, and 1276.59 ± 701.90 mg/kg in the steel industry intruded upon sediments and 4.63 ± 1.41, 87.60 ± 10.96, 52.67 ± 19.99, 37.49 ± 6.17, 35.84 ± 11.41, and 189.02 ± 95.57 mg/kg in the control area, respectively. Comparing with the local soil background (0.08 mg/kg for Cd, 62.6 mg/kg for Cr, 19.3 mg/kg for Cu, 28.1 mg/kg for Ni, 26.0 mg/kg for Pb, and 58.0 mg/kg for Zn), significantly higher levels of Cd, Cr, Cu, Pb, and Zn were detected in the steel industry affected sediments. The enrichment factor and principal component analysis indicated that the heavy metals (HMs), except for Ni, were primarily derived from anthropogenic inputs, particularly from steel industrial activities. Multiple risk assessment models suggested that the sediments affected by industrial activities showed significant toxic effects for Cd, Cr, Pb, and Zn, with Cd being the main contributor to sediment toxicity. However, the alkaline nature of the sediments (pH = 7.85 ± 0.57) and the high proportion of residual fraction Cd (61.09% ± 26.64%) may help to reduce the toxic risks in the sediments. Effective measures to eliminate tinuous thethe continous input of Cd and Zn via surface runoff are crucial.

Dietary exposure risk assessment of pyrethroids in fruits and vegetables: a national scale investigation

The pyrethroids (PYRs) were extensively used to increase agriculture outputs. However, the cumulative exposures of PYRs would bring about potential risks through food intake. It is an urgent requirement to explore the cumulative exposures on the fruits and vegetables. In this study, a total of 1720 samples incorporating eight primary fruits and vegetables collected around China were investigated to assess the health risk for adults and children from eight PYRs. The relative potency factor (RPF) method was employed to reveal both chronic and acute cumulative exposure. As a result, the hazard index (HI) were 0.004 ~ 0.200% and 11.85 ~ 99.19% for chronic and acute cumulative dietary exposure, respectively. The national wide investigation indicated the cumulative assessments were not hazardous. Besides, the acute intake of pear, grape, and lettuce should be paid on more attention, particularly. This study provides compelling evidence to develop relative policy and regulation to improve the food quality and safety.

Assessing pollution and health risks from chromite mine tailings contaminated soils in India by employing synergistic statistical approaches

Potentially toxic elements (PTEs) contamination in the agricultural soil can generate a detrimental effect on the ecosystem and poses a threat to human health. The present work evaluates the PTEs concentration, source identification, probabilistic assessment of health hazards, and dietary risk analysis due to PTEs pollution in the region of the chromite-asbestos mine, India. To evaluate the health risks associated with PTEs in soil, soil tailings and rice grains were collected and studied. The results revealed that the PTEs concentration (mainly Cr and Ni) of total, DTPA-bioavailable, and rice grain was significantly above the permissible limit in site 1 (tailings) and site 2 (contaminated) as compared with site 3 (uncontaminated). The Free ion activity model (FIAM) was applied to detect the solubility of PTEs in polluted soil and their probable transfer from soil to rice grain. The hazard quotient values were significantly higher than the safe (FIAM-HQ < 0.5) for Cr (1.50E+00), Ni (1.32E+00), and, Pb (5.55E+00) except for Cd (1.43E-03), Cu (5.82E-02). Severity adjustment margin of exposure (SAMOE) results denote that the PTEs contaminated raw rice grain has high health risk [Cr_SAMOE: 0.001; Ni_SAMOE: 0.002; Cd_SAMOE: 0.007; Pb_SAMOE: 0.008] for humans except for Cu. The Positive matrix factorization (PMF) along with correlation used to apportion the source. Self-organizing map (SOM) and PMF analysis identified the source of pollution mainly from mines in this region. Monte Carlo simulation (MCS) revealed that TCR (total carcinogenic risk) cannot be insignificant and children were the maximum sufferers relative to adults via ingestion-pathway. In the spatial distribution map, the region nearer to mine is highly prone to ecological risk with respect to PTEs pollution. Based on appropriate and reasonable evaluation methods, this work will help environmental scientists and policymakers' control PTEs pollution in agricultural soils near the vicinity of mines.