Human-induced arsenic pollution modeling in surface waters - An integrated approach using machine learning algorithms and environmental factors

Copper, Iron, Cadmium, and Arsenic, All Generated in the Universe: Elucidating Their Environmental Impact Risk on Human Health Including Clinical Liver Injury

Humans are continuously exposed to various heavy metals including copper, iron, cadmium, and arsenic, which were specifically selected for the current analysis because they are among the most frequently encountered environmental mankind and industrial pollutants potentially causing human health hazards and liver injury. So far, these issues were poorly assessed and remained a matter of debate, also due to inconsistent results. The aim of the actual report is to thoroughly analyze the positive as well as negative effects of these four heavy metals on human health. Copper and iron are correctly viewed as pollutant elements essential for maintaining human health because they are part of important enzymes and metabolic pathways. Healthy individuals are prepared through various genetically based mechanisms to maintain cellular copper and iron homeostasis, thereby circumventing or reducing hazardous liver and organ injury due to excessive amounts of these metals continuously entering the human body. In a few humans with gene aberration, however, liver and organ injury may develop because excessively accumulated copper can lead to Wilson disease and substantial iron deposition to hemochromatosis. At the molecular level, toxicities of some heavy metals are traced back to the Haber Weiss and Fenton reactions involving reactive oxygen species formed in the course of oxidative stress. On the other hand, cellular homeostasis for cadmium and arsenic cannot be provided, causing their life-long excessive deposition in the liver and other organs. Consequently, cadmium and arsenic represent health hazards leading to higher disability-adjusted life years and increased mortality rates due to cancer and non-cancer diseases. For unknown reasons, however, liver injury in humans exposed to cadmium and arsenic is rarely observed. In sum, copper and iron are good for the human health of most individuals except for those with Wilson disease or hemochromatosis at risk of liver injury through radical formation, while cadmium and arsenic lack any beneficial effects but rather are potentially hazardous to human health with a focus on increased disability potential and risk for cancer. Primary efforts should focus on reducing the industrial emission of hazardous heavy metals.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

Immobilizing arsenic-enriched wastewater from utilization of crude antimony oxides as scorodite using a novel multivalent iron source

Arsenic-enriched wastewater (A-EW) is a hypertoxic sewage from the utilization of crude antimony oxides in lead anode slime metallurgy. In traditional methods, the H+ accumulation inhibits the arsenic immobilization during scorodite synthesis. In this study, a novel multivalent iron source comprised of Fe(OH)3 and FeSO4·7H2O was proposed to resolve the adverse effects of pH fluctuation during immobilizing A-EW as scorodite. Various approaches, such as scanning electron microscopy and X-ray photoelectron spectroscopy, were applied to characterize the synthesized scorodite. This work was divided into two parts. In thermodynamics, HnAsO4(3-n)- (n = 1, 2, 3) and Fe(OH)n(3-n)+ (n = 0, 1, 2, 3) can feasibly coprecipitate as scorodite according to their △rGm,Tθ ranged from -111.10 kJ mol-1 to -33.53 kJ mol-1. In experimental research, A-EW was immobilized as scorodite by optimizing conditions as initial pH = 2.0, molar ratio of Fe to As = 1.2, molar ratio of Fe(II) to Fe(III) = 4:6, arsenic concentration = 40 g/L, and temperature = 95 °C. The arsenic precipitation ratio is 99.60%, and the micromorphology of synthesized scorodite presents a regular octahedron having size of 5-10 μm. The low leachability of As (0.41 mg/L) in toxicity characteristic leaching procedure (TCLP) confirmed that the prepared scorodite is nonhazardous. The solution pH is stable at 2.0 as the H+ depletion (0.5660 mol) by Fe(OH)3 dissolution and Fe2+ oxidization balanced with that (0.5657 mol) generated from As(V)-Fe(III) coprecipitation. In general, the A-EW was effectively immobilized by proposed multivalent iron source, and can be potentially applied to safely dispose other industrial effluents, such as high arsenic leachates and arsenic-bearing waste acid from nonferrous metallurgy.

Regulatory Mechanisms Underlying Arsenic Uptake, Transport, and Detoxification in Rice

Arsenic (As) is a metalloid environmental pollutant ubiquitous in nature that causes chronic and irreversible poisoning to humans through its bioaccumulation in the trophic chain. Rice, the staple food crop for 350 million people worldwide, accumulates As more easily compared to other cereal crops due to its growth characteristics. Therefore, an in-depth understanding of the molecular regulatory mechanisms underlying As uptake, transport, and detoxification in rice is of great significance to solving the issue of As bioaccumulation in rice, improving its quality and safety and protecting human health. This review summarizes recent studies on the molecular mechanisms of As toxicity, uptake, transport, redistribution, regulation, and detoxification in rice. It aims to provide novel insights and approaches for preventing and controlling As bioaccumulation in rice plants, especially reducing As accumulation in rice grains.

Exploration of the factors that influence total phosphorus in surface water and an evaluation of surface water vulnerability based on an advanced algorithm and traditional index method

Due to the continuous influence of human activities, phosphorus pollution in surface water has become a persistent problem that needs to be addressed since phosphorous entails certain risks and degrees of damage to ecosystems and humans. The presence and accumulation of total phosphorus (TP) concentrations in surface waters is the result of a combined effect of many natural and anthropogenic factors, and it is often difficult to intuitively identify the individual importance of each factor in regard to the pollution of the aquatic environment. Considering these issues, this study provides a new methodology to better understand the vulnerability of surface water to TP pollution and the factors that influence TP pollution through the application of two modeling approaches. This includes the boosted regression tree (BRT), an advanced machine learning method, and the traditional comprehensive index method (CIM). Different factors, such as natural variables (including slope, soil texture, normalized difference vegetation index (NDVI), precipitation, and drainage density) and point and nonpoint source anthropogenic factors were included to model the vulnerability of surface water to TP pollution. Two methods were used to produce a vulnerability map of surface water to TP pollution. Pearson correlation analysis was used to validate the two vulnerability assessment methods. The results showed that BRT was more strongly correlated than CIM. In addition, the importance ranking results showed that slope, precipitation, NDVI, decentralized livestock farming and soil texture had a greater influence on TP pollution. Industrial activities, scale livestock farming and population density, which are all contributing sources of pollution, were all relatively less important. The introduced methodology can be used to quickly identify the area most vulnerable to TP pollution and to develop problem specific adaptive policies and measures to reduce the damage from TP pollution.

A novel framework to improve the consistency of water quality attribution from natural and anthropogenic factors

One critical question for water security and sustainable development is how water quality responses to the changes in natural factors and human activities, especially in light of the expected exacerbation in water scarcity. Although machine learning models have shown noticeable advances in water quality attribution analysis, they have limited interpretability in explaining the feature importance with theoretical guarantees of consistency. To fill this gap, this study built a modelling framework that employed the inverse distance weighting method and the extreme gradient boosting model to simulate the water quality at grid scale, and adapted the Shapley additive explanation to interpret the contributions of the drivers to water quality over the Yangtze River basin. Different from previous studies, we calculated the contribution of features to water quality at each grid within river basin and aggregated the contribution from all the grids as the feature importance. Our analysis revealed dramatic changes in response magnitudes of water quality to drivers within river basin. Air temperature had high importance in the variability of key water quality indicators (i.e. ammonia-nitrogen, total phosphorus, and chemical oxygen demand), and dominated the changes of water quality in Yangtze River basin, especially in the upstream region. In the mid- and downstream regions, water quality was mainly affected by human activities. This study provided a modelling framework applicable to robustly identify the feature importance by explaining the contribution of features to water quality at each grid.

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.

The research trend on arsenic pollution in freshwater: a bibliometric review

We conducted a quantitative and qualitative bibliometric analysis based on 8740 research articles from the Web of Science Core Collection published in the last 20 years (2000-2020) for a better understanding of the research progress and development trend of arsenic pollution in freshwater (FAP). The results showed a significant increase in the number of publications from 2007 to 2020, especially after 2015. Four of the top 10 productive authors are from China. Two of the top three research institutions are from China, and the publications of Chinese Academy of Sciences accounted for 5.40% of the total. China is also the center of the national cooperation network, indicating a greater influence of China in this scientific research field. The top three journals included Science of the Total Environmental, Environmental Science Technology, and Journal of Hazardous Materials. Besides arsenic, the high-frequency keywords in this field included adsorption, contamination, groundwater, removal, detection, and geochemistry. The researchers mainly focused on the groundwater environment, as well as the pollution hazards of arsenic in water bodies, remediation techniques, detection, migration, and transformation. Studies should pay more attention to the application and development of phytoremediation technology in the field of FAP in the future.