Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitably consumes significant amounts of time. To eliminate the influence of sample preparation on PXRF measurements, this study evaluates the performance of pXRF measurements in the prediction of eight heavy metals’ contents through machine learning algorithm linear regression (LR) and multivariate adaptive regression spline (MARS) models. Soil samples were collected from five industrial sites and separated into high-value and low-value datasets with pXRF measurements above or below the background values. The results showed that for Cu and Cr, the MARS models were better than the LR models at prediction (the MARS-R² values were 0.88 and 0.78; the MARS-RPD values were 2.89 and 2.11). For the pXRF low-value dataset, the multivariate MARS models improved the pXRF measurement accuracy, with the R² values improved from 0.032 to 0.39 and the RPD values increased by 0.02 to 0.37. For the pXRF high-value dataset, the univariate MARS models predicted the content of Cu and Cr with less calculation. Our study reveals that machine learning methods can better predict the Cu and Cr of large samples from multiple contaminated sites.

Chlorinated aliphatic hydrocarbons (CAHs) are widely used in agriculture and industries and have become one of the most common groundwater contaminations. With the excellent performance of the deep learning method in predicting, LSTM and XGBoost were used to forecast dichloroethene (DCE) concentrations in a pesticide-contaminated site undergoing natural attenuation. The input variables included BTEX, vinyl chloride (VC), and five water quality indicators. In this study, the predictive performances of long short-term memory (LSTM) and extreme gradient boosting (XGBoost) were compared, and the influences of variables on models’ performances were evaluated. The results indicated XGBoost was more likely to capture DCE variation and was robust in high values, while the LSTM model presented better accuracy for all wells. The well with higher DCE concentrations would lower the model’s accuracy, and its influence was more evident in XGBoost than LSTM. The explanation of the SHapley Additive exPlanations (SHAP) value of each variable indicated high consistency with the rules of biodegradation in the real environment. LSTM and XGBoost could predict DCE concentrations through only using water quality variables, and LSTM performed better than XGBoost.

Asbestos has been confirmed as a major pollutant in asbestos-mining areas that are located in western China. In general, asbestos-fibre dust will is released into the environment due to the effect of intensive industrial activities and improper environmental management, such that the health of residents in and around mining areas is jeopardised. A typical asbestos mining area served as an example in this study to analyse the content and fibre morphology of asbestos in soil and air samples in the mining area. The effects of asbestos pollution in and around the mining areas on human health were also assessed based on the U.S. Superfund Risk Assessment Framework in this study. As indicated by the results, different degrees of asbestos pollutions were present in the soil and air, and they were mainly concentrated in the mining area, the ore-dressing area, and the waste pile. The concentration of asbestos in the soil ranged from 0.3% to 91.92%, and the concentration of asbestos fibres in the air reached 0.008–0.145 f·cc⁻¹. The results of the scanning-electron microscope (SEM) energy suggested that the asbestos was primarily strip-shaped, short columnar, and granular, and the asbestos morphology of the soils with higher degrees of pollution exhibited irregular strip-shaped fibre agglomeration. The excess lifetime cancer risk (ELCR) associated with the asbestos fibres in the air of the mining area was at an acceptable level (10⁻⁴–10⁻⁶), and 40.6% of the monitoring sites were subjected to unacceptable non-carcinogenic risks (HQ > 1). Moreover, the waste pile was the area with the highest non-carcinogenic risk, followed by the ore dressing area, a residential area, and a bare-land area in descending order. In the three scenarios of adult offices or residences in the mining area, adults’ outdoor activities in the peripheral residence areas, and children’s outdoor activities, the carcinogenic-and non-carcinogenic-risk-control values in the air reached 0.1438, 0.2225 and 0.1540 f·cc⁻¹, and 0.0084, 0.0090 and 0.0090 f·cc⁻¹, respectively. The results of this study will lay a scientific basis for the environmental management and governance of asbestos polluted sites in China.

The bioavailability and mobility of copper (Cu) in soil play a crucial role in its toxicity and impact on soil organisms. Humic substances, with their abundant functional groups and unique pore structure, have demonstrated the ability to effectively mitigate the toxic effects of heavy metals in soil. This study explores the potential of a soluble humic substance (HS) derived from leonardite for Cu removal from contaminated soils. The effects of various washing conditions, such as concentration and washing cycles, on removal efficiency were assessed. The results showed that a single washing with HS solution achieved an optimal removal efficiency of 37.5% for Cu in soil, with a subsequent reuse achieving a removal efficiency of over 30.5%. To further enhance Cu removal efficiency, a two-step soil washing approach using a chemical reductant NH₂OH·HCl coupled with an HS solution (NH₂OH·HCl + HS) was employed, resulting in an increased removal efficiency to 53.0%. Furthermore, this approach significantly reduced the plant availability and bioaccessibility of Cu by 13.6% and 11.4%, respectively. Compared to a single washing with NH₂OH·HCl, both HS and NH₂OH·HCl + HS increased the soil pH and organic matter content. These findings suggest that the two-step soil-washing approach using NH₂OH·HCl + HS effectively removed Cu from polluted soil. This study demonstrates the potential of humic substances as environmentally friendly materials for remediating heavy metal-polluted soil, promoting green and sustainable applications in soil remediation practices.

Nitrogen fertilizer supports agricultural intensification, but its manufacturing results in substantial contaminated sites. Ammonia nitrogen is the main specific pollutant in retired nitrogen fertilizer sites with potential human health and odor risks. However, few studies focus on ammonia nitrogen risk assessment at contaminated sites, particularly considering its solid-liquid partitioning process (K_d) and ammonium/ammonia equilibrium process (R) in the soil. This study took a closed nitrogen fertilizer factory site as an example and innovatively introduced K_d and R to scientifically assess the human health and odor risk of ammonia nitrogen. The risk control values (RCVs) of ammonia nitrogen based on human health and odor risk were also derived. The maximum concentration of ammonia nitrogen was 3380 mg/kg in the unsaturated soil, which was acceptable for human health because the health RCVs were 5589 ∼ 137,471 mg/kg in various scenarios. However, odor risk was unacceptable for RCVs were 296 ∼ 1111 mg/kg under excavation scenarios and 1118 ∼ 35,979 mg/kg under non-excavation scenarios. Of particular concern, introducing K_d and R in calculation increased the human health and odor RCVs by up to 27.92 times. Despite the advancements in ammonia risk assessment due to the introduction of K_d and R, odor risk during excavation remains a vital issue. These findings inform a more scientific assessment of soil ammonia risk at contaminated sites and provide valuable insights for the management and redevelopment of abandoned nitrogen fertilizer plant sites.