Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network

Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic

The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery waste, chemical fertilizers, and sole application of tannery waste on soil and rice (Oryza sativa L.) plants. The results revealed that T3, which received high-quality vermicomposted tannery waste as an amendment, exhibited superior enzymatic characteristics compared to tannery sludge amended (TWS) treatments (T8, T9). After harvesting, vermicomposted tannery waste treatment (T3) showed a more significant decrease in PMs bioavailability. Accumulation of PMs in rice was minimal across all treatments except T8 and T9, where toxic tannery waste was present, resulting in a high-risk classification (class 5 < 0.01) according to the SAMOE risk assessment. Results from Fuzzy-TOPSIS, ANN, and Sobol sensitivity analyses (SSA) further indicated that elevated concentrations of PMs (Ni, Pb, Cr, Cu) adversely impacted soil-plant health synergy, with T3 showing a minimal risk in comparison to T8 and T9. According to SSA, microbial biomass carbon and acid phosphatase activity were the most sensitive factors affected by PMs concentrations in TWS. The results from the ANN assay revealed that the primary contributing factor of toxicity on the TWS was the exchangeable fraction of Cr. Correlation statistics underscored the significant detrimental effect of PMs' bioavailability on microbial and enzymatic parameters. Overall, the findings suggest that vermicomposting of tannery sludge waste shows potential as a viable organic amendment option in the near future.

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India

An increase in population expansion, urban sprawling environment, and climate change has resulted in increased food demand, water scarcity, environmental pollution, and mismanagement of water resources. Groundwater, i.e., one of the most precious and mined natural resources is used to address a variety of environmental demands. Among all, irrigation is one of the leading consumers of groundwater. Various natural heterogeneities and anthropogenic activities have impacted the groundwater quality. As a result, monitoring groundwater quality and determining its suitability are critical for the sustainable long-term management of groundwater resources. In this study, groundwater samples from 35 different sampling stations were collected and tested for various parameters associated with irrigation water quality. Hybrid MCDM (fuzzy-AHP) method was used to determine the groundwater suitability for irrigation purposes. The suitability map obtained using spatial overlay analysis was classified into low, moderate, and high irrigation water suitability zones. Along with suitability analysis, various regression-based machine learning models such as multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) were used and compared to predict irrigation water suitability. Results depicted that the ANN model with the highest R2 value of 0.990 and RMSE value near to zero (0) has outperformed all other models. The present methodology could be found useful to predict irrigation water suitability in the region where regular sampling and analysis are quite challenging.

GIS-based multicriteria and artificial neural network (ANN) investigation for the assessment of groundwater vulnerability and pollution hazard in the Braga shallow aquifer (Central Tunisia): A critical review of generic and modified DRASTIC models

Groundwater vulnerability and pollution hazard in the Braga shallow aquifer were assessed through an integrated GIS-based multicriteria analysis and Artificial Neural Network (ANN) approach, using DRASTIC and DRASTIC-LU models. The DRASTIC model integrates seven geological parameters. The DRASTIC-LU model includes an eighth parameter in addition to the previous ones. This parameter is the land use that represents the human source of groundwater pollution. The DRASTIC map showed four classes: very low (12.06%), low (81.88%), moderate (5.16%) and high (0.9%), where the vulnerability index ranged between 43 and 159. The DRASTIC-LU vulnerability index ranged between 53 and 204 and showed five classes: very low (3.10%), low (14.06%), moderate (17.11%), high (27.08%) and very high (38.65%). The DRASTIC-LU vulnerability map indicated that the high pollution risk is imposed by the intensive vegetable cultivation and the domestic wastewater. The pollution hazard index (PHI) was calculated based on the ANN modelling, using the land-use as an input and the vulnerability as a hidden layer. The DRASTIC model-based PHI map showed six classes: rare hazard (8.6%), very low (30.97%), low (6.18%), moderate (51.45%), high (2.43%) and very high (0.37%). While, The DRASTIC-LU model-based PHI map (PHILU) showed seven classes: rare hazard (2.91%), very low (11.9%), low (12.33%), moderate (13.78%), high (9.23%), very high (15.46%) and extremely hazardous (34.39%). The validation of these maps indicated that the DRSTIC-LU-based PHI is more reliable as it accurately identifies the hazardous zones.

The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Application of machine learning in groundwater quality modeling - A comprehensive review

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model's performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Health‑risk assessment and distribution characteristics of fluoride in groundwater in six basins of Shanxi Province, Middle China

High-fluoride (F) pollution is an environmental problem that severely affects the growth of human beings, animals and plants. High-fluoride groundwater severely harms the health of Shanxi residents and leads to frequent endemic diseases. This study aims to analyze the differences and possible health risks of fluoride among six basins in Shanxi Province, North China, using 338 groundwater samples collected from wells, infer the main sources of fluoride in the groundwater, and provide valuable suggestions for fluoride contamination in regional groundwater. The results revealed that F in the Yuncheng basin had the highest health risk. In addition to the Changzhi basin, the groundwater at the sampling points in other basins had adverse effects on human health. The main source of fluoride in groundwater is the dissolution of fluoride-containing minerals, which has little to do with human activities. The groundwater in Shanxi Province tends to be alkaline, and the fluorite saturation index is less than 0 in most circumstances, indicating that fluorite is in an unsaturated state, and fluoride will continue to dissolve into groundwater under suitable conditions. Clustering analysis shows that the high-fluoride areas are mainly distributed in the Yuncheng basin and the southern part of the Xinzhou basin. Fluoride-rich groundwater in a basin often exists only in a certain area, and the distribution of confined water and unconfined water in high-fluoride areas is different. Fluoride contamination in the Changzhi basin is not severe. For the high-fluoride areas in the Datong basin, Xinzhou basin, Taiyuan basin and Linfen basin, utilizing water from other areas or exploiting groundwater from other aquifers to diminish the harm of high-fluoride groundwater intake for a long period is suggested. For the Yuncheng basin, adopting membrane-based processes or variable temperature drop fluoride technology to make groundwater contaminated by excessive fluoride potable is recommended.

Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China

Algal blooms caused by climate change and human activities have received considerable attention in recent years. Since chlorophyll a (Chl-a) can be used as an indicator of phytoplankton biomass, it has been selected as a direct indicator for monitoring and early warning of algal blooms. With the development of artificial intelligence, data-driven approaches with small sample data and high accuracy prediction have been gradually applied to water quality prediction. This study aimed at using environment factors (water quality and meteorological data) to assist the prediction of Chl-a concentration based on the optimization support vector machine (SVM) model. The most relevant environment factors were extracted from the commonly used environment factors according to the method of cosine similarity. The traditional particle swarm optimization (PSO) algorithm was adopted to optimize the ANN and SVM models, respectively. Then, the better prediction model PSO-SVM can be obtained according to the results of three scientific evaluation indicators. The latest optimization algorithm of grey wolf optimizer (GWO) was also proposed to optimize the SVM to realize high-accuracy Chl-a concentration predication. The GWO-SVM model achieved higher accuracy than the other models both in training and validation processes. Therefore, the dimension of the input vector could be reduced with using the cosine similarity method, and the prediction of Chl-a concentration in high accuracy and the early warning of algal blooms in the study area of this paper could also achieved.

Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

Studies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availability and solubility of fluoride minerals as well as hydrogeological and geochemical conditions. Chemical methods usually accomplish fluoride analysis in drinking water. The chemical methods are expensive, labor-intensive and time-consuming in general although accurate and reliable results are obtained. An alternative cost-effective approach based on machine learning (ML) technique is investigated in this study. Furthermore, most effective input parameters are selected via proposed Simulated Annealing (SA) search scheme. Selected subset (SAR, K⁺, NO₃^-, NO₂^-, Mn, Ba and Fe) by SA algorithm exhibited high correlation coefficient values of 0.731 and strong t test scores of 5.248. On the other hand, most frequently selected individual features were identified as NO₃^-, NO₂^-, Fe and SAR by vote map. The results of experiments revealed that selected feature subset improves the prediction performance of the learning models while feature size is reduced substantially. Thus it eventually enabled determination of fluoride in a cheap, fast and feasible way.

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.

Machine learning predictions of chlorophyll-a in the Han river basin, Korea

This study developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus.

The Impact of Refugees on Income Inequality in Developing Countries by Using Quantile Regression, ANN, Fixed and Random Effect

Refugees affect the hosting countries both politically and economically, but the size of impact differs among these societies. While this effect emerges mostly in the form of cultural cohesion, security, and racist discourses in developed societies, it mostly stands out with its economic dimension such as unemployment, growth, and inflation in developing countries. Although different reflections exist in different societies, the reaction is expected to be higher if it affects social welfare negatively. Accordingly, one of the parameters that should be addressed is the effect of refugees on income distribution since the socio-economic impact is multifaceted. In this study, the effect of refugees on income inequality is analyzed by using quantile regression with fixed effects and Driscoll–Kraay Fixed Effect (FE)/Random Effect (RE) methods for the period of 1991 to 2020 in the 25 largest refugee-hosting developing countries. According to the findings of the study, the functional form of the relationship between refugees and income inequality in the countries is N-shaped. Accordingly, refugees first increase income inequality, decrease it after reaching a certain level, and then start increasing it, albeit at a low level.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks

The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward neural networks were developed to determine whether including land use variables would improve the model results. In the first network, easily measurable field data were used, i.e., pH, electrical conductivity, water temperature, air temperature, and aquifer level. This model achieved a fairly good simulation based on the root mean squared error (RMSE in mg/L) and the Nash–Sutcliffe Model Efficiency (NSE) indicators (RMSE = 26.18, NSE = 0.54). In the second model, the percentages of different land uses in a radius of 1000 m from each well was included in an attempt to obtain a better description of nitrate transport in the aquifer system. When these variables were used, the performance of the model increased significantly (RMSE = 15.95, NSE = 0.70). For the development of the models, data from chemical and physical analyses of groundwater samples from wells located in the Kopaidian Plain and the wider area of the Asopos River Basin, both in Greece, were used. The simulation that the models achieved indicates that they are a potentially useful tools for the estimation of groundwater contamination by nitrates and may therefore constitute a basis for the development of groundwater management plans.

Environmental arsenic exposure and its contribution to human diseases, toxicity mechanism and management

Arsenic is a well-recognized environmental contaminant that occurs naturally through geogenic processes in the aquifer. More than 200 million people around the world are potentially exposed to the elevated level of arsenic mostly from Asia and Latin America. Many adverse health effects including skin diseases (i.e., arsenicosis, hyperkeratosis, pigmentation changes), carcinogenesis, and neurological diseases have been reported due to arsenic exposure. In addition, arsenic has recently been shown to contribute to the onset of non-communicable diseases, such as diabetes mellitus and cardiovascular diseases. The mechanisms involved in arsenic-induced diabetes are pancreatic β-cell dysfunction and death, impaired insulin secretion, insulin resistance and reduced cellular glucose transport. Whereas, the most proposed mechanisms of arsenic-induced hypertension are oxidative stress, disruption of nitric oxide signaling, altered vascular response to neurotransmitters and impaired vascular muscle calcium (Ca²⁺) signaling, damage of renal, and interference with the renin-angiotensin system (RAS). However, the contributions of arsenic exposure to non-communicable diseases are complex and multifaceted, and little information is available about the molecular mechanisms involved in arsenic-induced non-communicable diseases and also no suitable therapeutic target identified yet. Therefore, in the future, more basic research is necessary to identify the appropriate therapeutic target for the treatment and management of arsenic-induced non-communicable diseases. Several reports demonstrated that a daily balanced diet with proper nutrient supplements (vitamins, micronutrients, natural antioxidants) has shown effective to reduce the damages caused by arsenic exposure. Arsenic detoxication through natural compounds or nutraceuticals is considered a cost-effective treatment/management and researchers should focus on these alternative options. This review paper explores the scenarios of arsenic contamination in groundwater with an emphasis on public health concerns. It also demonstrated arsenic sources, biogeochemistry, toxicity mechanisms with therapeutic targets, arsenic exposure-related human diseases, and onsets of cardiovascular diseases as well as feasible management options for arsenic toxicity.

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain

Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R²) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this study, the role of the health system in the fight against Covid-19 is discussed. In this context, the number of hospital beds, the number of doctors, life expectancy at 60, universal health service and the share of health expenditures in GDP were used as health indicators. In the study, firstly 2020 data was estimated by using the Artificial Neural Networks simulation method and this year was used in the analysis. The model, with the data of 124 countries, was estimated using the cross-sectional OLS regression method. The estimation results show that the number of hospital beds, number of doctors and life expectancy at the age of 60 have statistically significant and positive effects on the ratio of Covid-19 recovered/cases. Universal health service and share of health expenditures in GDP are not significant statistically on the cases and recovered. Hospital bed capacity is the most effective variable on the recovered/case ratio.

Prevalence of Anemia and Its Associate Factors among Women of Reproductive Age in Lao PDR: Evidence from a Nationally Representative Survey

Introduction

Anemia continues to be a major public health problem significant among women of reproductive age (WRA) in developing countries, including Lao People's Democratic Republic (Lao PDR), where the prevalence of anemia among women remains high. This study aimed to assess the prevalence of anemia and its associated factors among WRA 15–49 years in Lao PDR.

Methods

We conducted a cross-sectional study, using the Lao Social Indicator Survey II, 2017 dataset. A total of 12,519 WRA tested for anemia were included in this study, through multistage sampling approaches. Binary logistic regression was used to determine the associated factors of anemia.

Results

Of 12,519 women, 4,907 (39.2%) were anemic. Multivariate logistic regression revealed that living in central provinces (aOR: 2.16, 95% CI: 1.96–2.38), rural area (aOR: 1.1, 95% CI: 1.00–1.20), large family size with more than 6 persons (aOR: 1.14, 95% CI: 1.01–1.29), pregnancy (aOR: 1.46, 95% CI: 1.22–1.74), having any adverse pregnancy outcomes (aOR: 1.14, 95% CI: 1.03–1.25), poor drinking water (aOR: 1.24, 95% CI: 1.10–1.39), and poor sanitation facility (aOR: 1.15, 95% CI: 1.03–1.28) were significantly associated with an increased risk of anemia. Conversely, four factors were associated with anemia preventively, including being aged 25–34 years (aOR: 0.81, 95% CI: 0.74–0.90), postsecondary education (aOR: 0.76, 95% CI: 0.60–0.97), Hmong-Mien ethnicity (aOR: 0.48, 95% CI: 0.39–0.59), and watching television almost daily (aOR: 0.84, 95% CI: 0.75–0.95).

Conclusion

Anemia continues to be a major public health challenge in Lao PDR. Interventions should be considered on geographic variations, improving safe water and sanitation facility, promoting of iron supplements during pregnancy, and health education through mass media for women in rural areas.

Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network

In the Mediterranean area, the high water demand frequently leads to an excessive exploitation of the water resource, which involves a qualitative degradation of the freshwaters stored in coastal karst aquifers, as a result of phenomena such as sea saltwater intrusion. In this study, the NARX network was used to predict the daily groundwater level fluctuation for 76 monitored wells located on the Apulian territory. A preliminary analysis on reference wells was performed in order to assess the impact on the groundwater level prediction of two input parameters, rainfall and evapotranspiration, and the sensitivity to changes of training algorithm and input time delay. Based on the findings of the preliminary analysis, a comprehensive regional analysis and extensive sub-regional analyses were performed, proving the reliability of the NARX-BR network for the groundwater level prediction in wells located on different hydrogeological structures. The accurate results obtained for the Apulia region suggest the NARX network application for groundwater level prediction in other areas affected by groundwater resource management issues.

Using convolutional neural network for predicting cyanobacteria concentrations in river water

Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images.

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil conditions and adversely affects the health of living organisms. Visible and near-infrared spectroscopy (VNIRS) shows a potential feasibility for estimating heavy metal elements in soil. Moreover, deep learning models have been shown to successfully deal with complex multi-dimensional and multivariate nonlinear data. Thus, this study implemented a deep learning method on reflectance spectra of soil samples to estimate heavy metal concentrations. A convolutional neural network (CNN) was adopted to estimate arsenic (As), copper (Cu), and lead (Pb) concentrations using measured soil reflectance. In addition, a convolutional autoencoder was utilized as a joint method with the CNN for dimensionality reduction of the reflectance spectra. Furthermore, artificial neural network (ANN) and random forest regression (RFR) models were built for heavy metal estimation. Principal component analysis was utilized for dimensionality reduction of the ANN and RFR models. Among these models, the CNN model with convolutional autoencoder showed the highest accuracies for As, Cu, and Pb estimates, having R² values of 0.86, 0.74, and 0.82, respectively. The convolutional autoencoder disentangled the relevant features of multiple heavy metal elements and delivered robust features to the CNN model, thereby generating relatively accurate estimates.

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

Chemometrics for environmental monitoring: a review

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

Machine Learning Models of Groundwater Arsenic Spatial Distribution in Bangladesh: Influence of Holocene Sediment Depositional History

Recent advances in machine learning methods offer the opportunity to improve risk assessment and to decipher factors influencing the spatial variability of groundwater arsenic ([As]_gw). A systematic comparison reveals that boosted regression trees (BRT) and random forest (RF) outperform logistic regression. The probability of [As]_gw exceeding 5 μg/L (approximate median value of Bangladesh [As]_gw), 10 μg/L (WHO provisional guideline value), and 50 μg/L (Bangladesh drinking water standard) is modeled by BRT and RF methods for Bangladesh and its four subregions demarcated by major rivers. Of the 109 geo-environmental and hydrochemical predictor variables, phosphorus and iron emerge as the most important across spatial scales, consistent with known As mobilization mechanisms. Well depth is significant only when hydrochemical parameters are not considered, consistent with prior studies. A peak of probability of [As]_gw exceedance at ∼30 m depth is evident in the partial dependence plots (PDPs) for spatial-parameter-only models but not in the equivalent all-parameter models, suggesting that sediment depositional history explains interdependent spatial patterns of groundwater As-P-Fe in Holocene aquifers. The South region exhibits a decrease of probability of [As]_gw exceedance below 150 m depth in PDPs for spatial-parameter-only and all-parameter models, supporting that the deeper Pleistocene aquifer is a low-As water resource.

Assessment of a green roof practice using the coupled SWMM and HYDRUS models

Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R²) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10⁻⁵. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R² of 0.99.

Arsenic toxicity and its mitigation in ectomycorrhizal fungus Hebeloma cylindrosporum through glutathione biosynthesis

Arsenic (As) contamination is one of the most daunting environmental problem bothering the whole world. Exploring a suitable bioremediation technique is an urgent need of the hour. The present study focusses on scrutinizing the ectomycorrhizal (ECM) fungus for its potential role in As detoxification and understanding the molecular mechanisms responsible for its tolerance. When exposed to increasing concentrations of external As, the ECM fungus H. cylindrosporum accumulated the metalloid intracellularly, inducing the glutathione biosynthesis pathway. The genes coding for GSH biosynthesis enzymes, γ-glutamylcysteine synthetase (Hcγ-GCS) and glutathione synthetase (HcGS) were highly regulated by As stress. Arsenic coordinately upregulated the expression of both Hcγ-GCS and HcGS genes, thus resulting in increased Hcγ-GCS and HcGS protein expressions and enzyme activities, with substantial increase in intracellular GSH. Functional complementation of the two genes (Hcγ-GCS and HcGS) in their respective yeast mutants (gsh1^Δ and gsh2^Δ) further validated the role of both enzymes in mitigating As toxicity. These findings clearly highlight the potential importance of GSH antioxidant defense system in regulating the As induced responses and its detoxification in ECM fungus H. cylindrosporum.

Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network

Sludge pyrolysis is a complex process including complicated reaction chemistry, phase transition, and transportation phenomena. To better evaluate the use of syngas, the monitoring and prediction of a higher heating value (HHV) is necessary. This study developed an artificial neural network (ANN) model to predict the HHV of syngas, with the process variables (i.e., sludge type, catalyst type, catalyst amount, pyrolysis temperature, and moisture content) as the inputs. In the first step, through optimizing various sets of parameters, a three-layer network including 8 input neurons, 15 hidden neurons, and 1 output neuron was established. Then, in the second step, an ANN model has been successfully used to predict the HHV of syngas, with a fitting correlation coefficient of 0.97 and a root mean square error (MSE) value of 14.62. The relative influence of input variables showed that the pyrolysis temperature and moisture content were the determining factors that affected the HHV of syngas. The results of optimization experiments showed that when temperature was 895 °C and the moisture content was 45.63 wt%, the highest HHV can be obtained as 438.22 kcal/m³-N. Moreover, the ANN model showed a higher prediction accuracy than other models like multiple linear regression and principal component regression. The model developed in this work may be used to predict the HHV of syngas using conventional operational parameters measured from in situ experiments, thus further providing predictive information for the use of syngas as energy and fuel.

Hydrogeochemical controls on arsenic contamination potential and health threat in an intensive agricultural area, northern China

The contamination of ground water with arsenic is a great public health concern. This paper discusses the possible formation mechanism of high As groundwater; identify the main influences of natural and anthropogenic factors on As occurrence in groundwater; and finally estimates As-induced potential health hazards in an intensive agricultural region, Datong Basin (Northern China). Our findings indicate that the predominant controlling factors of As in groundwater can be divided into natural factors and anthropogenic activities. Natural factors can be classified as natural potential source of As, environmental geological characteristics and hydrochemical conditions; anthropogenic activities are manifested in industrial coal mining, domestic coal burning, agricultural irrigation return flow and excessive application of fertilizers, and groundwater exploitation. Microbial and/or chemical reduction desorption of arsenate from Fe-oxide/hydroxide and/or clay minerals, As-bearing Fe-oxide/hydroxide reduction coupled with sulfate reduction, and competition with phosphorus are postulated to be the major process dominating As enrichment in the alkaline and anoxic groundwater. In addition, age-dependent human health risk assessment (HHRS) was performed, and high risk values reveal a high toxic and carcinogenic risk of As contaminate for population who is subject to the continuous and chronic exposure to elevated As.

Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment

The Indo-Gangetic alluvium is prime region for intensive agricultural. In some areas of this region, groundwater is now becoming progressively polluted by contamination with poisonous substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell results in the formation of As(III) which is more toxic. Thus groundwater quality assessment of Gangetic basin has become essential for its safer use. Therefore we under took study on the spatial variability of arsenic by collecting georeferred groundwater samples on grid basis from various water sources like dug well, bore and hand pumps covering the river bank region of Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na, K, Ca, Mg, SAR, SSP, CO₃, HCO₃, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe As contamination in ground water of three sites of the study area. ARC GIS software is now able to process maps along with tabular data and compare them well, to provide the spatial visualization of information and using this tool, the Geographical Information System (GIS) of arsenic was developed. It was noticed from spatial maps that concentration of arsenic was more near the meandering points of Ganga.

Prediction of Algal Chlorophyll-a and Water Clarity in Monsoon-Region Reservoir Using Machine Learning Approaches

The prediction of algal chlorophyll-a and water clarity in lentic ecosystems is a hot issue due to rapid deteriorations of drinking water quality and eutrophication processes. Our key objectives of the study were to predict long-term algal chlorophyll-a and transparency (water clarity), measured as Secchi depth, in spatially heterogeneous and temporally dynamic reservoirs largely influenced by the Asian monsoon during 2000–2017 and then determine the reservoir trophic state using a multiple linear regression (MLR), support vector machine (SVM) and artificial neural network (ANN). We tested the models to analyze the spatial patterns of the riverine zone (Rz), transitional zone (Tz) and lacustrine zone (Lz) and temporal variations of premonsoon, monsoon and postmonsoon. Monthly physicochemical parameters and precipitation data (2000–2017) were used to build up the models of MLR, SVM and ANN and then were confirmed by cross-validation processes. The model of SVM showed better predictive performance than the models of MLR and ANN, in both before validation and after validation. Values of root mean square error (RMSE) and mean absolute error (MAE) were lower in the SVM model, compared to the models of MLR and ANN, indicating that the SVM model has better performance than the MLR and ANN models. The coefficient of determination was higher in the SVM model, compared to the MLR and ANN models. The mean and maximum total suspended solids (TSS), nutrients (total nitrogen (TN) and total phosphorus (TP)), water temperature (WT), conductivity and algal chlorophyll (CHL-a) were in higher concentrations in the riverine zone compared to transitional and lacustrine zone due to surface run-off from the watershed. During the premonsoon and postmonsoon, the average annual rainfall was 59.50 mm and 54.73 mm whereas it was 236.66 mm during the monsoon period. From 2013 to 2017, the trophic state of the reservoir on the basis of CHL-a and SD was from mesotrophic to oligotrophic. Analysis of the importance of input variables indicated that WT, TP, TSS, TN, NP ratios and the rainfall influenced the chlorophyll-a and transparency directly in the reservoir. These findings of the algal chlorophyll-a predictions and Secchi depth may provide key clues for better management strategy in the reservoir.

Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India

Arsenic (As) contamination of groundwater is a public health concern, impacting the lives of approximately 100 million people in India. Chronic exposure to As significantly increases mortality due to the occurrence of several types of cancer, respiratory and cardiac diseases. Uttar Pradesh is a part of the middle Indo-Gangetic plains and has been found to be severely affected by As contamination of groundwater, as established by several small-scale studies. The current study incorporates a hybrid method based on a random forest ensemble algorithm and univariate feature selection using 1473 data points for predicting As in the region. Twenty direct/proxy predictor variables were considered to describe the geochemical environment, aquifer conditions and topography that are responsible for As enrichment in groundwater. The map of As predicted through the hybrid random forest ensemble model shows an overall accuracy of 84.67%. The hybrid random forest model performs better than the univariate, logistic, fuzzy, adaptive fuzzy and adaptive neuro fuzzy inference systems, which have been widely used for As prediction. The projected number of rural populations at risk due to high As exposure is 12% of the total population of the region, which accounts for 23.48 million people who are at risk. The predictive map provides insight for the regions where future testing campaigns and interventions for mitigation should be prioritized by policymakers.

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and surface water sources. The Regina landfill is built on top of the Condie aquifer, without an engineered liner. Missing data and inconsistent sampling make a traditional groundwater assessment difficult. An integrated statistical approach using principle component analysis, correlation analysis, ion plots, and multiple linear regression is used to study groundwater contamination at the Regina landfill. Geological locations of the water samples were explicitly considered. The abundance of cations in the groundwater was Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ > Mn²⁺; and for anions SO₄^2- > HCO₃^- > Cl^-. Correlation analysis and ion plots pointed to gypsum and halite dissolution being the main factors affecting groundwater chemistry. Principal component analysis yielded three principal components, responsible for 80.7% of the total variance. For all monitoring well groups, the sodium absorption ratio was generally less than one. The variation in the ratio from monitoring well groups suggests possible groundwater contamination from landfill operation. Wilcox diagrams indicate groundwater near the landfill is unsuitable for irrigation. A two-step multiple linear regression was used to develop a model for total hardness prediction.

Living in poisoning environments: Invisible risks and human adaptation

This article describes the hidden natural chemical contaminants present in a unique desert environment and their health consequences on ancient populations. Currently, millions of people are affected worldwide by toxic elements such as arsenic. Using data gathered from Atacama Desert mummies, we discuss long-term exposure and biocultural adaptation to toxic elements. The rivers that bring life to the Atacama Desert are paradoxically laden with arsenic and other minerals that are invisible and tasteless. High intake of these toxic elements results in severe health and behavioral problems, and even death. We demonstrate that Inca colonies, from Camarones 9 site, were significantly affected by chemical contaminants in their food and water. It appears however, some modern-day Andean populations resist the elevated levels of arsenic exposure as a result of positive selection mediated via the arsenic methyltransferase enzyme and display more tolerance to high chemical doses. This article further debate the effects of natural pollution and biocultural adaptation of past populations.

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and ) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations ( < 0.01), whereas solar radiation was negatively correlated ( < 0.01). The performance of the ANN model for predicting ENT and at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset ( < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use.

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.

Effect on human health of the arsenic pollution and hydrogeochemistry of the Yazır Lake wetland (Çavdır-Burdur/Turkey)

In this study, the physicochemical parameters, major ions and arsenic (As) contents of water resources in the Yazır lake wetland, were evaluated. In addition, water resources in this region were investigated from the point of water quality and health risk assessment. Thirty water samples were collected from the area in dry and wet seasons. Ca-Mg-HCO₃ and Ca-HCO₃ were the dominant water types. The Gibbs diagram suggests that most of the samples fall in rock-dominance zone, which indicates the groundwater interaction between rock chemistry. When compared to drinking water guidelines established by World Health Organization and Turkey, much greater attention should be paid to As, Fe, and Mn through varied chemicals above the critical values. According to the pH-ORP diagram, the predominant species is arsenate (H₂AsO₄^-2). The high concentrations of As in the surface water and groundwater are related to oxidative and reductive dissolution reaction of Fe and Mn hydroxides within the Kızılcadağ ophiolite and melange. In addition, the seasonal changes in As concentrations depend on the increase in pH of water samples. The major toxic and carcinogenic chemical within water samples is As for groundwater and surface water. From the results of hazard index, it is verified that As which is taken by ingestion of water was the main contaminant, and toxic human risk in the study area. The obtained results will help define strategies for As problems in the water resources in future.

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance.

Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea

Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = -0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = -0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.

Bayesian modeling approach for characterizing groundwater arsenic contamination in the Mekong River basin

In the Mekong River basin, groundwater from tube-wells is a major drinking water source. However, arsenic (As) contamination in groundwater resources has become a critical issue in the watershed. In this study, As species such as total As (AsTOT), As(III), and As(V), were monitored across the watershed to investigate their characteristics and inter-relationships with water quality parameters, including pH and redox potential (Eh). The data illustrated a dramatic change in the relationship between AsTOT and Eh over a specific Eh range, suggesting the importance of Eh in predicting AsTOT. Thus, a Bayesian change-point model was developed to predict AsTOT concentrations based on Eh and pH, to determine changes in the AsTOT-Eh relationship. The model captured the Eh change-point (∼-100±15mV), which was compatible with the data. Importantly, the inclusion of this change-point in the model resulted in improved model fit and prediction accuracy; AsTOT concentrations were strongly negatively related to Eh values higher than the change-point. The process underlying this relationship was subsequently posited to be the reductive dissolution of mineral oxides and As release. Overall, AsTOT showed a weak positive relationship with Eh at a lower range, similar to those commonly observed in the Mekong River basin delta. It is expected that these results would serve as a guide for establishing public health strategies in the Mekong River Basin.

Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approach

Currently, continued urbanization and development result in an increase of impervious areas and surface runoff including pollutants. Also one of the greatest issues in pollutant emissions is the first flush effect (FFE), which implies a greater discharge rate of pollutant mass in the early part in the storm. Low impact development (LID) practices have been mentioned as a promising strategy to control urban stormwater runoff and pollution in the urban ecosystem. However, this requires many experimental and modeling efforts to test LID characteristics and propose an adequate guideline for optimizing LID management. In this study, we propose a novel methodology to optimize the sizes of different types of LID by conducting intensive stormwater monitoring and numerical modeling in a commercial site in Korea. The methodology proposed optimizes LID size in an attempt to moderate FFE on a receiving waterbody. Thereby, the main objective of the optimization is to minimize mass first flush (MFF), which is an indicator for quantifying FFE. The optimal sizes of 6 different LIDs ranged from 1.2 mm to 3.0 mm in terms of runoff depths, which significantly moderate the FFE. We hope that the new proposed methodology can be instructive for establishing LID strategies to mitigate FFE.