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 accumulation of nitrogen in groundwater in the industrial plots, especially the high ammonium, can result in a serious threat to the groundwater system in the urban area. This study monitored the dissolved inorganic nitrogen (DIN) of the polluted groundwater four times in one year in a retired nitrogenous fertilizer plant site with a production history of nearly 40 years, to analyze the spatial-temporal characteristics of DIN species (NH₄⁺-N, NO₃⁻-N, and NO₂⁻-N) and the effects of groundwater environment on their transfer and transformation. The results showed that NH₄⁺-N (<0.025 to 1310 mg/L) was the main DIN species (61.38–76.80%) with low mobility, whereas the concentration of NO₃⁻-N and NO₂⁻-N was 0.15–146 mg/L and <0.001–12.4 mg/L, accounting for 22.34–36.07% and 0.53–2.83% of total DIN, respectively. The concentration and proportion of NO₃⁻-N and NO₂⁻-N showed an upward trend with time, posing a threat to the safety of surrounding groundwater, and their high spatial-temporal variation was related to the morphological transformation and the transport. In the wet season, the pH and redox condition benefited the nitrification, and NO₃⁻-N easily migrated from the deep soil solution to groundwater, hence the NO₃⁻-N can be accumulated. Therefore, the analysis of species and behaviors of DIN in shallow groundwater is indispensable for environmental risk assessment.

BTEX (benzene, toluene, ethylbenzene, and xylene), as characteristic pollutants in chemical plant sites, are widely present in the environment and pose a serious threat to the health and safety of nearby residents. Studying the spatial distribution characteristics and transport fluxes of BTEX in soil and air at contaminated sites and the health risks they pose to humans is of great significance for fine pollution control and environmental management. This study took a typical decommissioned steel plant as a case study. A total of 23 soil and air samples were collected from different locations to investigate the spatial distribution characteristics of BTEX in soil and air. The transport and fate of BTEX in soil and air were evaluated using the fugacity model, and finally, a human health risk assessment was conducted. The results indicate a relatively severe level of benzene pollution in both soil and air. The maximum exceedance factor of benzene in soil samples is 31.5, with the concentration exceedance depth at 1.5 m. The maximum concentration of benzene in air samples is 4.98 μg·m⁻³. Benzene, at 5.9% of the site, shows a low flux with negative values, while other components at various locations all exhibit a trend of transport from the soil phase to the atmospheric phase. Benzene is the pollutant that contributes the most to the transport flux from soil to air within the site. The coking area and sewage treatment area are key areas within the steel mill where BTEX accumulate easily in the soil. The non-carcinogenic risk values of the individual components of BTEX in the soil are below the acceptable risk level. However, the carcinogenic risk value of benzene in the children’s exposure scenario exceeds the carcinogenic risk level of 10⁻⁶. The carcinogenic risk range of various components of BTEX in the air is 2.63 × 10⁻⁶~3.88 × 10⁻⁵, with 28.6% of the locations exceeding the threshold of 10⁻⁶. The range of the total HI (hazard index) is 2.08 × 10⁻⁴~1.81 × 10⁻¹, all of which is below the safety threshold of 1. The results of this study will provide scientific support for the fine pollution control and environmental management of industrial contaminated sites with BTEX as their typical pollutants.