A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India.

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling.

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO3-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.

Inferences on metal pollution in the natural spawning zone of Bangladesh river and pollution management strategies.

The purpose of this study was to evaluate the metal concentrations in the Halda River in Bangladesh to determine the quality of the water and sediment in the natural spawning zone. Fe > Zn > Cr > Cd > Cu was the order of the metals in water, whereas Fe > Zn > Cd > Cu was the order in sediments. Almost all of the heavy metals in the water and sediment had been found within the established limits, with the exception of Cr and Fe in the river and Cu in the sediment. In the case of water, Cr vs. Zn was found to have the strongest correlation (r = 0.96). Due to the coagulation and adsorption processes, it was shown that Fe and Zn had a substantial correlation of 0.96, Cu and Cd of 0.91, and Cr of 0.78 with Zn. Hazard quotient values of Cd show the not potable nature of Halda river surface water and might give adverse health effects for all age groups except Cu and Zn. Pollution load index values indicated the uncontaminated nature of the river bottom sediments. Natural and human activities were the key factors influencing the accumulation and movement of heavy metals in the water and sediments. Contamination sources are industrial effluents, garbage runoff, farming operations, and oil spills from fishing vessels which are comparable according to multivariate statistical analysis. Ion exchange, absorption, precipitation, complexation, filtration, bio-absorption, redox reaction, and reverse osmosis were considered to be effective for the degradation of metal concentrations. The feasibility of the suggested metal reduction procedures has to be studied to know which is optimally appropriate for this river region. It is expected that this study could provide a useful suggestion to decrease the metal pollution in the river.

Seasonal behavior and accumulation of some toxic metals in commercial fishes from Kirtankhola tidal river of Bangladesh - A health risk taxation.

Contamination of fish with heavy metals (Heavy metals) is one of the most severe environmental and human health issues. However, the contamination levels in tropical fishes from Bangladesh are still unknown. To this end, the evaluated concentrations of arsenic (As), chromium (Cr), cadmium (Cd), and lead (Pb) in 12 different commercially important fish species (Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Glossogobius giuris, Pseudeutropius atherinoides, Polynemus paradiseus, Sillaginopsis panijus, Corica soborna, Amblypharyngodon mola, Trichogaster fasciata, and Wallago attu) were collected from the Kirtankhola River assess human health risk for the consumers, both in the summer and winter seasons. Toxic metals surpassed the acceptable international limits in P. atherinoides, P. paradiseus, S. panijus, C. soborna, and W. attu. The target hazard quotient (THQ) revealed that non-carcinogenic health effects (HI < 1) for children and adults, and the carcinogenic risk (CR) indicated safety. Results show that children are more susceptible to carcinogenic and non-carcinogenic hazards from higher As. The multivariate analysis justified that heavy metals were from anthropogenic actions. The lessening of toxic metals might need strict rules and regulations as metal enrichment would continue to increase in this tidal river from both the anthropogenic and natural sources.

Site selection of check dams using geospatial techniques in Debre Berhan region, Ethiopia - water management perspective.

Remote sensing and GIS technology were very helpful to determine an appropriate location of freshwater storage in Amhara, Ethiopia. The techniques were used to investigate the impact of lithology, surface geomorphology, slope parameters, drainage flow, drainage density, lineament density, land cover parameters on relief, and aerial and linear features and to understand their interrelationships. Morphometric parameters such as mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), mean bifurcation ratio (Rbm), relief ratio (Rh), drainage density (Dd), stream frequency (Fs), drainage texture (Rt), form factor (Rf), circularity ratio (Rc), and elongation ratio (Re) were calculated. Spatial maps of morphometric parameters were produced by using AHP (analytical hierarchy process) of ArcGIS 10.3. Final priority map was generated by the overlay of those parameters with five categories of poor (16.6%), low (41.63%), moderate (29.61%), high (8.88%), and very high (3.28%) storage locations. The map showed that this study area belonged to the low to moderate storage location. The results exhibit precision-based assessment of the suitability for the dam construction sites of 6, 7, and 9 sub-basin zones. The outcome of this study strengthens the knowledge of geospatial analysis for water resources vulnerability and also allows policymakers in this drought-prone area to sustainably manage water supplies.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

Groundwater pollution index (GPI) and GIS-based appraisal of groundwater quality for drinking and irrigation in coastal aquifers of Tiruchendur, South India.

We assessed groundwater pollution index (GPI) and groundwater quality of coastal aquifers from Tiruchendur in South India for drinking and irrigation by evaluating the physico-chemical parameters of 35 samples of mainly Na-Cl type in an area of 470 km2 with respect to the World Health Organization (WHO) standard as well as by estimating different indices such as total hardness (TH), sodium percentage (Na%), magnesium ratio (MR), Kelley's ratio index (KR), potential salinity (PS), Langelier saturation index (LSI), residual sodium carbonate (RSC), sodium adsorption rate (SAR), permeability index (PI), and the irrigation water quality index (IWQI). Minimal influence of aquifer lithology and the dominant influence of evaporation on groundwater chemistry reflected the semi-arid climate of the study area. Electrical conductivity (EC) of about 89% of the samples across 418 km2 exceeded the permissible limit and Ca values of 74% of samples, however, remained within the allowable limit for drinking. More chloride was caused by influx of seawater and salt leaching and higher K was due to excessive fertilizer usage for agriculture. The spatial distribution map created using inverse distance weighting (IDW) method shows that the suitable groundwater is present close to the river basin. GPI values between 0.40 and 4.7, with an average of 1.5, classify insignificant pollution in 43% of the study region and the groundwater suitable for drinking purposes. In addition, 17% of the groundwater samples are also marginally suitable for drinking. The irrigation water quality indices provided contradictory assessments. Indices of TH, Na%, MR, PS, and LSI suggested 32-95% of the samples as unsuitable for irrigation, whereas the indices of RSC, SAR, and PI grouped 72-100% samples as permissible for irrigation. The IWQI map, however, indicated that the groundwater from more than half of the study area are not apt for irrigation and the groundwater of about one-third of the area could only be applied to salt-resistant plants.

ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area.

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular models. ANFIS-MOA models were also useful for resolving the subjectivity of physical and hydrogeological parameters in original DRASTIC method (ODM) and MDM. It is expected that ANFIS-PSO models will produce the excellent results in assessing groundwater contamination vulnerability and that they can greatly contribute to the groundwater security in other areas of the world as well as Miryang City of South Korea.

Processes and characteristics of hydrogeochemical variations between unconfined and confined aquifer systems: a case study of the Nakdong River Basin in Busan City, Korea.

This study is to assess the hydrogeochemical characteristics of groundwater at the deltaic region of the Nakdong River Basin in the Busan Metropolitan City of Korea. The study area is covered by the Quaternary sedimentary deposits and the Cretaceous granites associated with unconformity. The thick sedimentary deposits consists of two aquifers, i.e., unconfined and confined aquifers on the basis of clay deposit. Groundwater samples were collected from seven boreholes: two from unconfined aquifer and five from confined aquifer systems during the wet season of 2017 year. ORP and DO indicates that the groundwater of the unconfined aquifer exists in the oxidization condition and that of the confined aquifer pertains in the reduction condition. Piper's trilinear diagram shows CaSO4 type for groundwater of the unconfined aquifer, and NaCl type for that of the confined aquifer. Ionic concentrations of groundwater increase in the confined aquifer because of direct and reverse ion exchange processes. Carbonate weathering and evaporation are other mechanisms in the water-rock interaction. Saturation indices of dolomite and calcite are observed as oversaturated, while halite reveals undersaturation. Hierarchical cluster analysis (HCA) exhibits that cluster 1 and cluster 2 represents the properties of groundwater in unconfined and confined aquifers, respectively. Factor analysis shows that groundwater of the confined aquifer is much influenced by seawater, and includes heavy metals of iron and aluminum. Groundwater samples in unconfined and confined aquifers are located at the rock weathering and evaporation zones in the Gibbs diagram. Inverse geochemical modeling of PHREEQC code suggests that carbonate dissolution and ion exchange of major ions are the prevailing geochemical processes. This comprehensive research provides the distinguished hydrogeochemical characteristics of groundwater in confined and unconfined aquifer systems of the Nakdong River Basin in Busan City, Korea.

Factors controlling groundwater quality in the Yeonjegu District of Busan City, Korea, using the hydrogeochemical processes and fuzzy GIS.

The hydrogeochemical processes and fuzzy GIS techniques were used to evaluate the groundwater quality in the Yeonjegu district of Busan Metropolitan City, Korea. The highest concentrations of major ions were mainly related to the local geology. The seawater intrusion into the river water and municipal contaminants were secondary contamination sources of groundwater in the study area. Factor analysis represented the contamination sources of the mineral dissolution of the host rocks and domestic influences. The Gibbs plot exhibited that the major ions were derived from the rock weathering condition. Piper's trilinear diagram showed that the groundwater quality was classified into five types of CaHCO3, NaHCO3, NaCl, CaCl2, and CaSO4 types in that order. The ionic relationship and the saturation mineral index of the ions indicated that the evaporation, dissolution, and precipitation processes controlled the groundwater chemistry. The fuzzy GIS map showed that highly contaminated groundwater occurred in the northeastern and the central parts and that the groundwater of medium quality appeared in most parts of the study area. It suggested that the groundwater quality of the study area was influenced by local geology, seawater intrusion, and municipal contaminants. This research clearly demonstrated that the geochemical analyses and fuzzy GIS method were very useful to identify the contaminant sources and the location of good groundwater quality.

Microbial copper reduction method to scavenge anthropogenic radioiodine.

Unexpected reactor accidents and radioisotope production and consumption have led to a continuous increase in the global-scale contamination of radionuclides. In particular, anthropogenic radioiodine has become critical due to its highly volatile mobilization and recycling in global environments, resulting in widespread, negative impact on nature. We report a novel biostimulant method to effectively scavenge radioiodine that exhibits remarkable selectivity for the highly difficult-to-capture radioiodine of >500-fold over other anions, even under circumneutral pH. We discovered a useful mechanism by which microbially reducible copper (i.e., Cu(2+) to Cu(+)) acts as a strong binder for iodide-iodide anions to form a crystalline halide salt of CuI that is highly insoluble in wastewater. The biocatalytic crystallization of radioiodine is a promising way to remove radioiodine in a great capacity with robust growth momentum, further ensuring its long-term stability through nuclear I(-) fixation via microcrystal formation.