Heavy metal pollution in Mongolian-Manchurian grassland soil and effect of long-range dust transport by wind

Accurate prediction of spatial distribution of soil heavy metal in complex mining terrain using an improved machine learning method

Accurate prediction of heavy metals (HMs) spatial distribution in mining areas is crucial for pollution management. However, predicting the spatial distribution of HMs remains a significant challenge in mining areas with complex terrain and variable contaminant transport pathways. This study aims to optimize the spatial prediction of arsenic (As) distribution in the Shimen realgar mining area, the largest in Asia, by integrating machine learning models with kriging interpolation and feature selection techniques. The results show that the Random Forest (RF) model achieved the best performance in predicting soil As concentration, with an R2 of 0.84 for the test data. Incorporating environmental variables improved the spatial prediction accuracy, with RF (R2 = 0.76, RMSE = 24.68 mg/kg) and Random Forest Regression Kriging (RFRK) (R2 = 0.78, RMSE = 23.46 mg/kg) outperforming ordinary kriging and geographically weighted regression kriging. Importance analysis and recursive feature elimination further optimized the model, leading to a 5 % increase in R2 and a reduction of RMSE by 8 %-12.4 %. The optimized RFRK model accurately captured the spatial distribution of As in the mining area, revealing the outward diffusion pattern of As from the smelting plant. The findings highlight the critical role of feature selection in improving prediction accuracy in highly polluted and complex terrain regions, an aspect that has often been overlooked in previous studies. This study provides a practical framework for spatial prediction of contaminants in similar areas, enhancing the understanding of pollution distribution.

An integrated evaluation of potentially toxic elements and microplastics in the highland soils of the northeastern Qinghai-Tibetan Plateau

As gateways to the scenic Qinghai-Tibetan Plateau (QTP), some underexplored five grassland (GLs) and three farmland (FLs) soil locations of northeastern counties were investigated. Preliminary detection showed that in the grazing and agricultural soils, elemental concentrations (Fe>Zn>Cr>Cu>Pb>Co>As>Cd) were up to 37 and 10 mg/g, but within the China soil standards, except Cd, while microplastics (MPs) abundances were 200-3640 and 280-973 particles/kg, respectively. Polypropylene (PP: 40-55 %) dominated in GLs mostly as fragments, whereas polyethylene (PE: 72-92 %) in FLs as films. Adsorption results demonstrated that potentially toxic elements (PTEs)-MPs' interaction may chiefly depend on their types and speciation in soils, the physiochemical structure of MPs, and surrounding conditions. The integrated two-dimensional risk assessment categorized three of five GLs under Risk Level VI (high pollution), whereas one of three FLs displayed Risk Level III (moderate pollution). Correlation analysis revealed that altitude, organic matter, soil clay content, and precipitation significantly affected PTEs (p ≤ 0.01), whereas MPs were influenced by altitude, soil clay content, precipitation (p ≤ 0.001), and population density (p ≤ 0.05). Comparison with low-land soils globally designated QTP as a vulnerable region to MPs due to the expanding development. Overall, our study provides a data set to understand the pollution scenario of highlands for its targeted management.

Exogenous γ-aminobutyric acid (GABA) effectively alleviates the synergistic inhibitory effect of freeze-thaw and copper combined stress on rye seedling growth

Rye (Secale cereale L.) may suffer from combined stress due to freeze-thaw cycles and copper (Cu) pollution during cultivation in grasslands. This study aims to investigate the effects of combined freeze-thaw and Cu stress on the physiological characteristics of rye seedlings and to assess the potential of exogenous GABA in mitigating these effects. The results indicated that root length, shoot length, fresh weight, and dry weight of rye seedlings were significantly reduced under combined stress, indicating synergistic inhibition. Combined stress impaired photosynthesis, increased MDA and H2O2 levels, and reduced endogenous GABA, thereby exacerbating physiological damage in rye seedlings. Treatment with exogenous GABA effectively alleviated growth inhibition and enhanced photosynthesis. Furthermore, exogenous GABA underscores its role in adaptation to combined stress by increasing soluble protein content, activating the antioxidant system, regulating GABA metabolism, and enhancing metal detoxification capacity, thereby improving stress tolerance. This finding not only contributes to enhancing the stability of grassland ecosystems but also provides a theoretical foundation for the future application of GABA in agricultural production, particularly in crop protection under environmental pollution and extreme climatic conditions.

Impact of Lead Pollution from Vehicular Traffic on Highway-Side Grazing Areas: Challenges and Mitigation Policies

One major environmental concern is the lead (Pb) pollution from automobile traffic, especially in highway-side grazing areas. Sheep grazing in Pb-contaminated areas are particularly vulnerable because Pb exposure from soil, water, and feed can have harmful effects that impair their general health, reproductive capability, and immune systems. Long-term hazards to cattle from persistent Pb exposure include neurotoxicity, hematological abnormalities, reproductive health problems, and immunosuppression. These can have serious consequences, such as reduced productivity and even mortality. Additionally, through the food chain, Pb bioaccumulation in lamb tissues directly endangers human health. Pb poisoning is caused by a variety of intricate mechanisms, including disturbances in calcium-dependent processes, oxidative stress, and enzyme inhibition. To mitigate these risks, an interdisciplinary approach is essential, combining expertise in environmental science, toxicology, animal husbandry, and public health. Effective strategies include rotational grazing, alternative foraging options, mineral supplementation, and soil remediation techniques like phytoremediation. Additionally, the implementation of stringent regulatory measures, continuous monitoring, and community-based initiatives are vital. This review emphasizes the need for comprehensive and multidisciplinary methodologies to address the ecological, agricultural, and public health impacts of Pb pollution. By integrating scientific expertise and policy measures, it is possible to ensure the long-term sustainability of grazing systems, protect livestock and human health, and maintain ecosystem integrity.

Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning

PM2.5 bound mercury (PBM2.5) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM2.5 on large scales are still lacking. To explore the driving factors, spatiotemporal pollution distribution, and associated health risks, we compiled a comprehensive dataset consisting of PBM2.5 concentrations and spatiotemporal information across China from 2000 to 2023 that was collected from the published scientific literature with valid data. By incorporating corresponding multidimensional predicting variables, the best-fitted random forest model was applied to predict PBM2.5 concentrations with a high spatial resolution of 0.25° × 0.25°, and the health risk assessment model was used for subsequent health risk assessment. Our results indicated that population density and PM2.5 emissions from power generation were the main contributors to PBM2.5 concentrations. In 2020, the pollution was primarily concentrated in northern, central, and eastern China, with the highest annual average concentration of 815.91 pg/m3 in Shanghai. Beijing experienced the most significant seasonal increase, with PBM2.5 concentrations rising by 146.92 % from summer to winter. Nationally, the annual average PBM2.5 pollution decreased extensively and markedly from 2015 to 2020. The non-carcinogenic risk of PBM2.5 alone was negligible in 2020, with HQ values generally <0.02 in winter. This study may provide an important assessment of the effectiveness of China's measures against mercury pollution and offer valuable insights for future prevention and control of PBM2.5 pollution.

Preparation of multi-modified/carbonized/gelatinized starch and its de-risking effect on Cd(II) and hymexazol in wastewater

In this study, starch (S) was gelatinized and carbonized to prepare carbonized/gelatinized S (CGS) as the research material. Then, peat extract (Pe) and surfactants with different ratios were single- and multi-modified on CGS, respectively, to prepare Pe-modified CGS (Pe-CGS) and multi-modified CGS, respectively. The microscopic morphology of multi-modified CGS was studied using various testing methods. The de-risking effect on Cd(II) and hymexazol in wastewater was investigated, and the effects of temperature, pH, and ionic strength were compared. The spheroidal structure of S was destroyed after carbonization, and Pe and surfactants were modified on the surface and changed the surface properties of CGS. The adsorption processes of Cd(II) and hymexazol were suitable to be described by the Langmuir and Freundlich models, respectively. The maximum adsorption capacities (qm) of Cd(II) and adsorption capacity parameter (k) of hymexazol on different modified CGSs presented the peak value at BS/Pe-CGS. With the increase in the modification ratio of Pe, BS, and SDS, qm and k increased, which showed a high value at 100 % modification. Increases in temperature and pH were beneficial to Cd(II) adsorption but were not conducive to hymexazol adsorption. The adsorption amount decreased for Cd(II) and increased first and then reduced for hymexazol with the rise in ionic strength. The adsorption process exhibited spontaneity, endothermic behavior for Cd(II), exothermic behavior for hymexazol, and an entropy-increasing reaction. The adsorption amount of Cd(II) and hymexazol by multi-modified CGS maintained approximately 81 % of the original sample after three rounds of regeneration.

Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features

Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consuming and ineffective. In this study, machine learning (ML) models were developed to investigate the intricate interactions among soil properties, climate features, and available Cd, aiming to identify the key influencing factors. The optimal model was obtained through a combination of stratified sampling, Bayesian optimization, and 10-fold cross-validation. It was further explained through the utilization of permutation feature importance, 2D partial dependence plot, and 3D interaction plot. The findings revealed that pH, surface pressure, sensible heat net flux and organic matter content significantly influenced the Cd accumulation in the soil. By utilizing historical soil surveys and climate change data from China, this study predicted the spatial distribution trend of available Cd in the Chinese region, highlighting the primary areas with heightened Cd activity. These areas were primarily located in the eastern, southern, central, and northeastern China. This study introduces a novel methodology for comprehending the process of available Cd accumulation in soil. Furthermore, it provides recommendations and directions for the remediation and control of soil Cd pollution.

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R2C > 0.80 and R2P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Meta-analysis of impacts of microplastics on plant heavy metal(loid) accumulation

The co-occurrence of microplastics (MPs) and heavy metal(loid)s (HMs) has attracted growing scientific interest because of their wide distribution and environmental toxicity. Nevertheless, the interactions between MPs and HMs in soil-plant systems remain unclear. We conducted a meta-analysis with 3226 observations from 87 independent studies to quantify the impact of MPs addition on the plant biomass and HMS accumulation. Co-occurrence of MPs and HMs (except for As) induced synergistic toxicity to plant growth. MPs promoted their uptake in the shoot by 11.0% for Cd, 30.0% for Pb, and 47.1% for Cu, respectively. In contrast, MPs caused a significant decrease (22.6%, 17.9-26.9%) in the shoot As accumulation. The type and dose of MPs were correlated with the accumulation of HMs. MPs increased available concentrations of Cd, Pb, and Cu, but decreased available As concentration in soils. Meanwhile, MPs addition significantly lowered soil pH. These findings may provide explanations for MPs-mediated effects on influencing the accumulation of HMs in plants. Using a machine learning approach, we revealed that soil pH and total HMs concentration are the major contributors affecting their accumulation in shoot. Overall, our study indicated that MPs may increase the environmental risks of HMs in agroecosystems, especially metal cations.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Impacts of atmospheric deposition on the heavy metal mobilization and bioavailability in soils amended by lime

Atmospheric deposition is an important source of heavy metal in agricultural soils, but there is limited research on the mobility of these metals in soil and their impact on soil amendment. Here, we performed a dust incubation experiment in soils in the laboratory and a factorial transplant experiment at three field sites with a gradient of atmospheric deposition to examine the impacts of atmospherically deposited heavy metals (Cu, Cd, and Pb) on the mobility and bioavailability in soils with and without lime applications. Results showed that the atmospherically deposited heavy metals showed high mobility and were primarily presented in the soluble ionic fractions in the wet part and acid-exchangeable and reducible fractions in the dry part of atmospheric deposition. Atmospheric dust addition caused the 2p3/2 and 2p1/2 electrons of Cu atoms in uncontaminated soils to transition the 3d vacant states, resulting in similar copper absorption peaks as atmospheric particles by the observation of X-ray absorption near-edge spectroscopy (XANES). In the field, atmospheric deposition can only increase the mobile fractions in the surface soils, but not in the deeper layers. However, the deposition can increase the soluble and diffusive gradients in thin films (DGT)-measured bioavailable fractions in profile along with the soil depth. Lime applications cannot significantly reduce the mobile fractions of heavy metals in the surface soils exposed to atmospheric deposition, but significantly reduce the heavy metal concentrations in soil solutions and the DGT-measured bioavailable concentrations, particularly in the deeper layer (6-10 cm). The major implication is that atmospherically deposited heavy metals can significantly increase their bioavailable concentrations in the plough horizon of soil and constrain the effects of soil amendments on heavy metal immobilization, thereby increasing the risks of crop uptake.

Modeling spatial trends and exchange fluxes of contaminants in agricultural soil under pollution prevention measures

Modeling the long-term trends of contaminants in topsoil under controlled measures is critical for sustainable agricultural environmental management. Traditional mass balance equations cannot predict spatial variation and exchange flux of regional soil contaminants for it lacks a method of assigning input-output parameters to each simulated cell. To overcome this limitation, we allocate the estimated source contribution flux to the spatial grid cell in the regional chemical mass balance by integrated positive matrix factorization (P-RCMB) with historical trends quantification. Focusing on Cd and As, which are elements with elevated risks of food intake and volatilization/infiltration, the model is applied to 30 ha of agricultural land near the enterprise. Predictions indicate an additional 13.5% of the soil is contaminated, and approximately 2.57 ha may accrue after 100 years at the site, with an uncertainty range of 0.98-5.3 ha. Clean water irrigation (CWI) reduces contamination expansion by approximately 42%, including approximately 4813 g ha-1 yr-1 net As infiltration, playing a dominant role in preventing the formation of severely contaminated soil. Stop straw return, green fertilizers use, and reduced atmospheric deposition control the exchange flux of Cd (114.9 g ha-1 yr-1) in moderate/slight contamination areas. For the different contaminants' cumulative trends in dryland and paddy fields, achieving a net cumulative flux close to zero in marginally contaminated areas presents a viable approach to optimize current emission standards. if trade-off straw removal and additional fertilizer inputs, a straw return rate of approximately 40% in Cd-contaminated soil will yield overall benefits. This model contributes valuable insights and tools for policymaking in contaminated land sustainable utilization and emission standard optimization.

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.

Non-Negligible Ecological Risks of Urban Wetlands Caused by Cd and Hg on the Qinghai-Tibet Plateau, China

The Huangshui National Wetland Park (HNWP) is a unique national wetland park in a city on the Qinghai-Tibetan Plateau, containing three zones: Haihu, Beichuan, and Ninghu. In this study, a total of 54 soil samples (18 sampling points with depths of 0-10 cm, 10-20 cm, and 20-30 cm) were collected in these three zones, and the contents of heavy metals (Cr, Cd, Cu, Hg, Ni, Pb, Zn, and As) of each sample were determined. The ecological risk of eight kinds of heavy metals was evaluated by using the geo-accumulation index (Igeo), and the ecological risk-controlling effect of the Xining urban wetlands on heavy metals was explored by comparative analysis, and the possible sources of heavy metals in the soil were analyzed via correlation analysis and principal component analysis (PCA). The results revealed that the total heavy metal concentration order was Haihu > Beichuan > Ninghu zone. As and Cu presented vertical accumulation characteristics in the surface and lower horizon, respectively. Cr, Cd, Hg, Ni, Pb, and Zn accumulated downwards along the depth. On the spatial scale, the enrichments of Cd and Hg brought non-negligible ecological risks in plateau urban wetlands. The results of PCA indicated that soil heavy metals mainly came from compound sources of domestic and atmospheric influences, traffic pollution sources, and industrial pollution sources. The study has revealed that human activities have inevitable negative impacts on wetland ecosystems, while the HNWP provides a significant weakening effect on heavy metal pollution.