Revealing the sources of arsenic in private well water using Random Forest Classification and Regression

ArsenicNet: An efficient way of arsenic skin disease detection using enriched fusion Xception model

Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh's rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is critical for mitigating long-term health issues. However, these approaches are not widely accepted. In this study, we proposed a fusion approach for the detection of arsenic skin disease. The proposed model is a combination of the Xception model with the Inception module in a deep learning architecture named "ArsenicNet." The model was trained and tested on a publicly available image dataset named "ArsenicSkinImageBD" which contains only 1287 samples and is based on Bangladeshi people. The proposed model achieved the best accuracy through proper experimentation compared to several state-of-the-art deep learning models, including InceptionV3, VGG19, EfficientNetV2B0, ResNet152V2, ViT, and Xception. The proposed model achieved an accuracy of 97.69% and an F1 score of 97.63%, demonstrating superior performance. This research indicates that our proposed model can detect complex patterns in which arsenic skin disease is present, leading to a superior detection performance. Moreover, data augmentation techniques and earlystoping function were used to prevent models overfitting. This study highlights the potential of sophisticated deep learning methodologies to enhance the accuracy of arsenic detection and prevent premature interventions in the diagnosis of arsenic-related illnesses in people. This research contributes to ongoing efforts to develop robust and scalable solutions to monitor and manage arsenic contamination-related health issues.

Effects of polystyrene microplastics on the distribution behaviors and mechanisms of metalloid As(III) and As(V) on pipe scales in drinking water distribution systems

Pipe scales have long been considered the primary adsorption medium for trace heavy metals in drinking water distribution systems (DWDSs). Microplastics (MPs) potentially affect the distribution of metalloid arsenic (As) pollutants in DWDSs. Herein, the accumulation behaviors of As(Ⅲ) and As(V) on pipe scales and polystyrene microplastics (PSMPs) under different water conditions were studied. Additionally, As(Ⅲ) and As(V) accumulation behaviors on pipe scales coexisting with PSMPs were investigated. Results showed that pipe scales played a key role in the accumulation of As (pipe scales = 1.08-4.80 mg/g > PSMPs = 0.02-3.38 mg/g). The adsorption amount of As(Ⅲ) on PSMPs was higher than that of As(V). The addition of PSMPs promoted the accumulation of As(Ⅲ) on pipe scales at pH = 3-8 while inhibiting the accumulation of As(V) on pipe scales at pH = 3-10 due to the competitive adsorption. The oxidation of As(III) and the reduction of As(V) occurred during the accumulation of As(Ⅲ) and As(V) on pipe scales. Notably, PSMPs accumulated on pipe scales were beneficial to the oxidation of As(Ⅲ), potentially reducing the As-related risks. Overall, our results provide new insights into the hazards posed by MPs in DWDSs.

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 μg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations.

Micropollutant rejection by nanofiltration membranes: A mini review dedicated to the critical factors and modelling prediction

Nanofiltration (NF) membranes, extensively used in advanced wastewater treatment, have broad application prospects for the removal of emerging trace organic micropollutants (MPs). The treatment performance is affected by several factors, such as the properties of NF membranes, characteristics of target MPs, and operating conditions of the NF system concerning MP rejection. However, quantitative studies on different contributors in this context are limited. To fill the knowledge gap, this study aims to assess critical impact factors controlling MP rejection and develop a feasible model for MP removal prediction. The mini-review firstly summarized membrane pore size, membrane zeta potential, and the normalized molecular size (λ = rs/rp), showeing better individual relationships with MP rejection by NF membranes. The Lindeman-Merenda-Gold model was used to quantitatively assess the relative importance of all summarized impact factors. The results showed that membrane pore size and operating pressure were the high impact factors with the highest relative contribution rates to MP rejection of 32.11% and 25.57%, respectively. Moderate impact factors included membrane zeta potential, solution pH, and molecular radius with relative contribution rates of 10.15%, 8.17%, and 7.83%, respectively. The remaining low impact factors, including MP charge, molecular weight, logKow, pKa and crossflow rate, comprised all the remaining contribution rates of 16.19% through the model calculation. Furthermore, based on the results and data availabilities from references, the machine learning-based random forest regression model was trained with a relatively low root mean squared error and mean absolute error of 12.22% and 6.92%, respectively. The developed model was then successfully applied to predict MPs' rejections by NF membranes. These findings provide valuable insights that can be applied in the future to optimize NF membrane designs, operation, and prediction in terms of removing micropollutants.

Understanding the driving mechanisms of site contamination in China through a data-driven approach

China currently faces significant environmental risks stemming from contaminated sites. The driving mechanism of site contamination, influenced by various drivers, remain obscured due to a dearth of quantitative methodologies and comprehensive data. Here, we used a data-driven causality inference approach to construct an interpretable random forest (RF) model. Results show that: (1) the trained RF model demonstrated remarkable predictive accuracy for identifying contaminated sites, with an accuracy rate of 0.89. In contrast to conventional correlation analysis, the RF model excels in discerning the key drivers through non-linear and genuine causal relationships between these drivers and site contamination. (2) Among the 25 potential drivers, we identified 18 key drivers of site contamination. These drivers encompass a broad spectrum of factors, including production and operational data, pollutant control level, site protection capability, pollutant characteristics, and physical-geographical conditions. (3) Each key driver exerts varying impacts on site pollution, with diverse directions, intensities, and underlying patterns. The partial dependence plots (PDPs) illuminate the role of each key driver, its critical value contributing to site pollution, and the interplay between these drivers. The key drivers facilitate the realization of three primary contamination processes: uncontrolled release, effective migration, and persistent accumulation. In light of our findings, environmental managers can proactively prevent site contamination by regulating single, dual, and multiple key drivers to disrupt critical pollution processes. This research offers valuable insights for devising targeted strategies and interventions aimed at mitigating environmental risks associated with contaminated sites in China.

Spatiotemporal heterogeneity of ecosystem service interactions and their drivers at different spatial scales in the Yellow River Basin

Accurately understanding ecosystem service (ES) interactions and an analysis of the complex, multiscale driving mechanisms are foundational prerequisites for implementing effective multiscale ES management. This study dives into the spatial and temporal variations of ES interactions in the Yellow River Basin across four spatial scales. The eXtreme Gradient Boosting (XGBoost) model is later deployed to pinpoint the key drivers of ecosystem services and their indirect pathways to ESs are illuminated utilizing Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results indicate that (1) The synergistic effect between ES pairs in the Yellow River Basin surpasses that of trade-offs. Various types of ecosystem service bundles have transformed into each other from 2000 to 2020, and the spatial patterns of ES interactions bear resemblances at different scales. (2) The factors driving habitat quality (HQ), carbon sequestration (CS), and landscape aesthetics (LA) are mainly the landscape configuration and biophysical conditions. The factor driving food production (FP) is mainly the level of urbanization, whereas soil conservation (SC) and water yield (WY) are mainly subject to climate. (3) When biophysical conditions and level of urbanization serve as mediating variables in pathways, driving factors invariably have negative indirect effects on ESs. When landscape configuration serves as a mediating variable, biophysical conditions positively influence HQ and CS, and negatively impact FP, WY, and LA. Conversely, the level of urbanization negatively affects all ESs. (4) The combination of XGBoost and PLS-SEM offers a comprehensive and innovative lens for analyzing ESs driving mechanisms. Based on our findings, scientific management of ESs should account not only for the direct impacts of driving elements but also for their scale and indirect effects.

Assessing the evolution and attribution of watershed resilience in arid inland river basins, Northwest China

Water scarcity significantly limits the sustainable development of oasis economies in arid inland river basins. Quantifying watershed resilience and its drivers is a major focus in the fields of hydrology and water resources. In this study, the resilience indicator pi represents watershed resilience, while meteorological, hydrological, socioeconomic, and ecological factors are used to investigate the spatial and temporal patterns of resilience and important driving factors in the Hotan River Basin from 1958 to 2020 by combining principal component analysis and random forest model. Results show that the overall resilience of the Hotan River Basin is low, decreasing from the upper (upstream) to the middle and lower (downstream) reaches, and that the intensity of human activities has a negative impact on resilience. Rivers are more likely to reach maximum resilience after experiencing periods of wet and dry conditions, although there is a lag in this progress. The random forest machine learning algorithm was used to accurately predict the resilience levels of the two upstream tributaries Yurungkash and Karakash Rivers, and the downstream Hotan River, with classification accuracies of 84.2 %, 71.4 %, and 87 %, respectively. The factors affecting the resilience of the Yurungkash River are the 30-day maximum, base flow index, low pulse duration, median streamflow in May, median streamflow in August, median streamflow in October, and 7-day maximum. The set of factors used to classify the resilience of the Karakash River include the 7-day maximum, 1-day maximum, median streamflow in June, 30-day maximum, 3-day maximum, median streamflow in February, and autumn temperature. The factors affecting the resilience of the Hotan River are the watershed inflow, Xiaota station runoff, population growth rate, and effective irrigated area. The findings of this study provide a theoretical basis for integrated water resource management and the sustainable development of the oasis economy in the Hotan River Basin.

Assessment and prediction of hexavalent chromium vulnerability in groundwater by Geochemical modelling, NOBLES Index and Random Forest Model

Unregulated chromite mining causes enrichment of hexavalent chromium in the groundwater. Due to unpredictable monsoonal recharge and anthropogenic dependencies on groundwater, the depth and extent of chromium pollution becomes extremely difficult to demarcate. For this specific objective, the present study was carried out in order to explore the potential of a coupled surface and sub-surface modelling approach in Sukinda valley, which accounts for 97-98 % of the total chromite reserve of India. Through ionic speciation, saturation state and clustering analysis, the most probable source and corresponding mineral stability state was investigated. In order to trace the extent, status and severity of the problem, both hydrogeologic parameters as well as the geogenic soil parameters were taken into account to develop DRASTIC, DRASTIC-L as well as NOBLES Index. While DRASTIC and DRASTIC-L model provided assessment of vulnerability due to surface leaching of contaminants, NOBLES index, speciation analysis and geochemical model provided sub-surface assessment of vulnerability due to chromium. MRSA and SPSA sensitivity analysis were applied in order to understand the most critical factor that can dominantly control the surface contamination in the groundwater. Random Forest (RF) based machine learning techniques were applied in order to integrate the sub-surface as well as surface characteristics for the purpose of prediction of chromium in the groundwater. The present study therefore presents a novel methodology of risk assessment for regions where either extensive mining activities are operational or in regions with abandoned mines with operative acid mine drainage.

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria

In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%-45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.

An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants

With the prevalence of digitization, smart operation has become mainstream in future wastewater treatment plants. This requires substantial and complete historical data for model construction. However, the data collected from the front-end sensor contained numerous missing dissolved oxygen (DO) values. Therefore, this study proposed a framework that adaptively adjusted the structure of embedded filling models according to the missing situation. Long short-term memory and gated recurrent units (GRU) were embedded for experiments, and some standard filling methods were selected as benchmarks. The experimental dataset indicated that the K-nearest neighbor could achieve good filling results by traversing the parameters. The effect obtained by the method proposed in this study was slightly better, and GRU was better among the three embedded models. Analysis of the filling results for each DO column revealed that the effect was highly correlated with the dispersion of DO data. The experimental results for the entire dataset demonstrated that the filling effect of the proposed method was significantly better and more stable than the others. The proposed model suffered from the problem of insufficient interpretability and long training time. This study provides an efficient and practical method to solve the intricate missing DO and lays the foundation for the smart operation of wastewater treatment plants.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.