Screening and prioritizing substances in groundwater in the Beijing-Tianjin-Hebei region of the North China Plain based on exposure and hazard assessments

Seasonal Effects of Constructed Wetlands on Water Quality Characteristics in Jinshan Lake: A Gate Dam Lake (Zhenjiang City, China)

Urban lakes commonly suffer from nutrient over-enrichment, resulting in water quality deterioration and eutrophication. Constructed wetlands are widely employed for ecological restoration in such lakes but their efficacy in water purification noticeably fluctuates with the seasons. This study takes the constructed wetland of Jinshan Lake as an example. By analyzing the water quality parameters at three depths during both summer and winter, this study explores the influence of the constructed wetland on the water quality of each layer during different seasons and elucidates the potential mechanisms underlying these seasonal effects. The results indicate that the constructed wetland significantly enhances total nitrogen (TN) concentration during summer and exhibits the capacity for nitrate-nitrogen removal in winter. However, its efficacy in removing total phosphorus (TP) is limited, and may even serve as a potential phosphorus (P) source for the lake during winter. Water quality test results of different samples indicated they belong to Class III or IV. Restrictive factors varied across seasons: nitrate-nitrogen and BOD5 jointly affected water quality in winter, whereas TP predominantly constrained water quality in summer. These results could provide a reference for water quality monitoring and management strategies of constructed wetlands in different seasons in Jiangsu Province.

Determining soil conservation strategies: Ecological risk thresholds of arsenic and the influence of soil properties

The establishment of ecological risk thresholds for arsenic (As) plays a pivotal role in developing soil conservation strategies. However, despite many studies regarding the toxicological profile of As, such thresholds varying by diverse soil properties have rarely been established. This study aims to address this gap by compiling and critically examining an extensive dataset of As toxicity data sourced from existing literature. Furthermore, to augment the existing information, experimental studies on As toxicity focusing on barley-root elongation were carried out across various soil types. The As concentrations varied from 12.01 to 437.25 mg/kg for the effective concentrations that inhibited 10% of barley-root growth (EC10). The present study applied a machine-learning approach to investigate the complex associations between the toxicity thresholds of As and diverse soil properties. The results revealed that Mn-/Fe-ox and clay content emerged as the most influential factors in predicting the EC10 contribution. Additionally, by using a species sensitivity distribution model and toxicity data from 21 different species, the hazardous concentration for x% of species (HCx) was calculated for four representative soil scenarios. The HC5 values for acidic, neutral, alkaline, and alkaline calcareous soils were 80, 47, 40, and 28 mg/kg, respectively. This study establishes an evidence-based methodology for deriving soil-specific guidance concerning As toxicity thresholds.

Characteristics of groundwater microbial communities' structure under the impact of elevated nitrate concentrations in north China plain

In groundwater environments, the interaction between microbial communities and the hydrogeochemical parameters have been investigated extensively in the past years. However, little is known whether the maximum contamination level (MCL) is a threshold value that dictates the microbial composition. In this study, we analyzed 10 groundwater samples for their nitrate, nitrite, COD and sulfate concentrations, and characterized their microbial compositions using 16 S rRNA based high-throughput sequencing methods. All the 10 samples had oxygen demands higher than the corresponding MCL of China (10 mg L^-1); moreover, 4 out of 10 samples also had nitrate concentrations higher than the corresponding MCL, which indicated that the groundwater quality was negatively impacted by anthropogenic activities. Comparing the microbial composition of groundwater that had higher-than-MCL nitrate concentrations to those that had lower-than-MCL nitrate concentrations, no significant differences were detected in communities' richness and diversity. However, the non-metric multi-dimensional analysis suggested that the 4 groundwater samples whose nitrate concentration exceed MCL are distinctly different from those of the rest 6 samples, indicating that MCL does have a significant impact on microbial structures. Pearson's correlation analysis suggested that none of the four analyzed hydrochemical parameters had significant impact on microbial communities' richness and diversity; however, at the genus level, the correlation results suggested that JG30-KM-CM45, Sphingomonas and Rhodococcus are closely correlated with nitrate concentration. The findings of this study deepened our understanding with respect to the relationships between the environmental quality indices and the microbial compositions of groundwater.

Screening priority pesticides for drinking water quality regulation and monitoring by machine learning: Analysis of factors affecting detectability

Proper selection of new contaminants to be regulated or monitored prior to implementation is an important issue for regulators and water supply utilities. Herein, we constructed and evaluated machine learning models for predicting the detectability (detection/non-detection) of pesticides in surface water as drinking water sources. Classification and regression models were constructed for Random Forest, XGBoost, and LightGBM, respectively; of these, the LightGBM classification model had the highest prediction accuracy. Furthermore, its prediction performance was superior in all aspects of Recall, Precision, and F-measure compared to the detectability index method, which is based on runoff models from previous studies. Regardless of the type of machine learning model, the number of annual measurements, sales quantity of pesticide for rice-paddy field, and water quality guideline values were the most important model features (explanatory variables). Analysis of the impact of the features suggested the presence of a threshold (or range), above which the detectability increased. In addition, if a feature (e.g., quantity of pesticide sales) acted to increase the likelihood of detection beyond a threshold value, other features also synergistically affected detectability. Proportion of false positives and negatives varied depending on the features used. The superiority of the machine learning models is their ability to represent nonlinear and complex relationships between features and pesticide detectability that cannot be represented by existing risk scoring methods.

Thermal desorption optimization for the remediation of hydrocarbon-contaminated soils by a self-built sustainability evaluation tool

Current thermal desorption practices of hydrocarbon-contaminated soils focus on remediation efficiency and cost, with little systematic assessment of the reuse value of treated soils. This study evaluated various integrated indices of treatment cost and reuse of treated soils at three desorption temperatures. Various typical engineering and ecological characteristics closely related to soil reusability were selected to analyze the changes in various treated soils, including shear strength, Atterberg limits, particle size distribution, permeability, soil carbon, and soil biomass. A sustainability evaluation tool was developed for the greener disposal of hazardous soils considering both the treatment cost and reuse indices. Such an evaluation led to the conclusion that the contaminated soils treated at 350 °C generated the highest soil reusability with an excellent remediation efficiency. The sensitivity analysis confirmed that the tool had better stability in a common situation where the weight of the remediation cost was heavier than the soil reusability. Meanwhile, published data were input into the tool to validate its applicability under different scenarios. The results were consistent with the qualitative assessment of the literature. The tool can quantitatively select a more sustainable desorption method for the disposal and reuse of hazardous soils.