Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district.

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand.

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

A comprehensive review of human health risks of arsenic and fluoride contamination of groundwater in the South Asia region.

The present study found that ∼80 million people in India, ∼60 million people in Pakistan, ∼70 million people in Bangladesh, and ∼3 million people in Nepal are exposed to arsenic groundwater contamination above 10 µg/L, while Sri Lanka remains moderately affected. In the case of fluoride contamination, ∼120 million in India, >2 million in Pakistan, and ∼0.5 million in Sri Lanka are exposed to the risk of fluoride above 1.5 mg/L, while Bangladesh and Nepal are mildly affected. The hazard quotient (HQ) for arsenic varied from 0 to 822 in India, 0 to 33 in Pakistan, 0 to 1,051 in Bangladesh, 0 to 582 in Nepal, and 0 to 89 in Sri Lanka. The cancer risk of arsenic varied from 0 to 1.64 × 1-1 in India, 0 to 1.07 × 10-1 in Pakistan, 0 to 2.10 × 10-1 in Bangladesh, 0 to 1.16 × 10-1 in Nepal, and 0 to 1.78 × 10-2 in Sri Lanka. In the case of fluoride, the HQ ranged from 0 to 21 in India, 0 to 33 in Pakistan, 0 to 18 in Bangladesh, 0 to 10 in Nepal, and 0 to 10 in Sri Lanka. Arsenic and fluoride have adverse effects on animals, resulting in chemical poisoning and skeletal fluorosis. Adsorption and membrane filtration have demonstrated outstanding treatment outcomes.

Wastewater treatment with nanomaterials for the future: A state-of-the-art review.

Aquatic and terrestrial ecosystems are both threatened by toxic wastewater. The unique properties of nanomaterials are currently being studied thoroughly for treating sewage. Nanomaterials also have the advantage of being capable of removing organic matter, fungi, and viruses from wastewater. Advanced oxidation processes are used in nanomaterials to treat wastewater. Additionally, nanomaterials have a large effective area of contact due to their tiny dimensions. The adsorption and reactivity of nanomaterials are strong. Wastewater treatment would benefit from the development of nanomaterial technology. Second, the paper provides a comprehensive analysis of the unique characteristics of nanomaterials in wastewater treatment, their proper use, and their prospects. In addition to focusing on their economic feasibility, since limited forms of nanomaterials have been manufactured, it is also necessary to consider their feasibility in terms of their technical results. According to this study, the significant adsorption area, excellent chemical reaction, and electrical conductivity of nanoparticles (NPs) contribute to the successful treatment of wastewater.

An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India.

The main objective of the present study is to perform risk assessment of groundwater contaminated by nitrate (NO3-) and evaluate the suitability of groundwater for domestic purposes in the Palani region of South India. Thirty groundwater samples were collected in the study area. Various groundwater quality analysis parameters such as the pH, electrical conductivity, total dissolved solids, total hardness, major cations (Ca2+, Mg2+, Na+, and K+), and major anions (Cl-, SO42-, F-, CO32-, and HCO3-) were adopted in this study to evaluate the drinking water suitability according to 2011 World Health Organization (WHO) standards. Piper and Gibbs's diagrams for the tested groundwater indicated that, due to the influence of rock-water interactions, evaporation, and reverse ion exchange, the chemical composition of groundwater varied. According to water quality index (WQI) mapping results, 46.67% of the sample locations was identified as contaminated zones via GIS spatial analysis. Multivariate statistical analysis methods, such as principal component analysis, cluster analysis, and the Pearson correlation matrix, were applied to better understand the relationship between water quality parameters. The results demonstrated that 40% of the samples could be identified as highly affected zones in the study region due to a high nitrate concentration. The noncarcinogenic health risks among men, women, and children reached 40, 50, and 53%, respectively. The results illustrated that children and women occurred at a higher risk than did men in the study region. The major sources of contamination included discharge from households, uncovered septic tanks, leachate from waste dump sites, and excess utilization of fertilizers in the agricultural sector. Furthermore, using the nitrate health hazard integrated method with the conventional indexing approach ensures that groundwater reliability can be guaranteed, contamination can be explored, and appropriate remedial measures can be implemented.

Pre-procedural SARS-CoV-2 PCR testing in the pulmonary function laboratory at a tertiary government hospital in Qatar: A clinical audit.

BACKGROUND: Prior to pulmonary function testing (PFT), local and international recommendations advise pre-procedural screening. Pulmonary function tests generate aerosol droplets containing millions of viruses, significantly increasing the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission not only to the individuals in and around the PFT office, but also to subsequent patients who undergo the test later in the same room. METHODS: This clinical audit was carried out to establish the rate of positive pre-procedural SARS-CoV-2 PCR testing before a PFT. The data were obtained over a 6-week period from our ATS accredited pulmonary function laboratory at the Hamad General Hospital, Qatar (December 01, 2021, to January 10, 2022). The PFT laboratory was closed from January 10, 2022, till the date of this report (January 27, 2022) owing to an increase in COVID cases in the community in Qatar during the fourth wave. RESULTS: All the patients scheduled for PFT were asymptomatic of COVID-19. A total of 331 individuals were scheduled for PFT, and 221 PFTs were performed. There were 109 no-shows for both the PCR and the PFT. Between weeks 1 and 4, all the pre-procedural SARS-CoV-2 PCR tests were negative. The weekly average number of COVID-19 cases in Qatar increased from 157 per 100,000 population in week 1 to 2,918 in week 6.2 There was a similar trend in the pre-procedural SARS-CoV-2 PCR tests that increased and resulted in identifying 9 cases with positive SARS-CoV-2 PCR test over weeks 5 and 6 (Figure 1). CONCLUSION: As the number of documented positive SARS-CoV-2 PCR tests in the community grew, so did the pre-procedural COVID-19 PCR positivity and the number of no-shows. The large number of no-shows may indicate greater worry or concern about contracting COVID-19 when visiting the hospital amid peak community cases. Our findings further call into question the utility of routinely performing pre-procedural PCR screening in asymptomatic cases when the prevalence of COVID-19 is low in the local population. Perhaps, it is time to consider replacing this with on-the-spot quick antigen testing for more effective use of resources.

Identifying influencing groundwater parameter on human health associate with irrigation indices using the Automatic Linear Model (ALM) in a semi-arid region in India.

Quality of water for the purposes of irrigation is a serious threat to the sustainable development of the agriculture sector. The main objective of this study is to evaluate the suitability of groundwater for irrigation purposes using various irrigation indices such as: Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Percentage Sodium (%Na), Magnesium Hazards (MH), Permeability Index (PI), Potential Salinity (PS), Residual Sodium Bicarbonate (RBSC), Kelly's Ratio (KR), Synthetic Harmful Coefficient (K), and Exchangeable Sodium Percentage (ESP). A total of 30 samples were collected from the bore well of agricultural farmland and analysed for cations and anions. MH reveal that 53.33 % of samples exceed the permissible level. PS shows that 43.33 % of samples are marginally affected and 33.33 % of samples are unsuitable for use in irrigation. About 76 % of the groundwater samples were suitable for irrigation and the remainder require treatment before use. Automatic Linear Modelling (ALM) is used to predict the major influence parameter for MH and PS are RBSC, RSC and K value of groundwater. ALM shows that excess magnesium concentration and salinity are the primary factors that affect the suitability of groundwater for irrigation use. This integrated technique showed that water from approximately 25 % of the sample locations would require treatment before use. This study will improve the pattern of irrigation, identify sources of contamination and highlight the importance of organic fertilizers to develop and enhance the sustainable practices in the study region.

Lignocellulosic biomass for biochar production: A green initiative on biowaste conversion for pharmaceutical and other emerging pollutant removal.

Lignocellulosic waste generation and their improper disposal has accelerated the problems associated with increased greenhouse gas emissions and associated environmental pollution. Constructive ways to manage and mitigate the pollution associated with lignocellulosic waste has propelled the research on biochar production using lignocellulose-based substrates. The sustainability of various biochar production technologies in employing lignocellulosic biomass as feedstock for biochar production not only aids in the lignocellulosic biomass valorization but also helps in carbon neutralization and carbon utilization. Functionalization of biochar through various physicochemical methods helps in improving their functional properties majorly by reducing the size of the biochar particles to nanoscale and modifying their surface properties. The usage of engineered biochar as nano adsorbents for environmental applications like dye absorption, removal of organic pollutants and endocrine disrupting compounds from wastewater has been the thrust areas of research in the past few decades. This review presents a comprehensive outlook on the up-to-date research findings related to the production and engineering of biochar from lignocellulosic biomass and their applications in environmental remediation especially with respect to wastewater treatment. Further a detailed discussion on various biochar activation methods and the future scope of biochar research is presented in this review work.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning.

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 µg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Effectiveness of potassium ferrate (VI) as a green agent in the treatment and disinfection of carwash wastewater.

Carwash wastewater treatment with potassium ferrate (VI) (K2FeO4) was optimized by response surface methodology. The optimum conditions for chemical oxygen demand removal were established a pH 3.5, 0.328 g/L dose of K2FeO4, and with a process duration of 48 min. At these conditions, chemical oxygen demand, total organic carbon, total nitrogen, and total phosphorus decreased by 70.3, 58.9, 73.3, 82.0%, respectively; and the putrid odor was reduced. Simultaneously, the total viable count, total coli count, most probable number of fecal enterococci, and the total proteolytic bacteria count decreased by 89.5, 93.1, 92.9, and 95.0 %, respectively. Comparatively, an application of 0.450 g/L FeCl3·6H2O corresponding to the iron content in 0.328 g/L of K2FeO4 resulted in a decrease of total viable count, total coli count, most probable number of fecal enterococci and the total proteolytic bacteria count only by 38.1, 31.2, 42.9, and 58.0%, respectively. Therefore, flocculation with polyacrylamide anionic flocculant combined with potassium ferrate (VI) oxidation is a more effective alternative to coagulation with FeCl3 and the same flocculant. The use of potassium ferrate (VI) is a viable option for the treatment of carwash wastewater.

Effectiveness of grey and green engineered solutions for protecting the low-lying muddy coast of the Chao Phraya Delta, Thailand.

Coastal protection measures can be categorized into grey and green solutions in terms of their ecosystem impacts. As the use of grey solutions has become a serious issue due to environmental consequences during the last few decades, green/nature-based solutions have become prioritized. This study evaluates the effectiveness of grey and green solutions applied along the eastern Chao Phraya Delta (ECPD) based on historical shoreline change analysis and coastal observations using Light Detection and Ranging technology. The results from shoreline analysis indicate that nearshore breakwaters installed 100-250 m from the shoreline have successfully reclaimed the coastline with a sedimentation rate of 17-23 cm/y. Meanwhile, sand-sausage-submerged breakwaters were ineffective at stabilizing the coastline during 2002-2010 due to land subsidence. With a low subsidence rate, the rubble-mound-submerged breakwaters can reduce the shoreline retreat rate with a vertical deposition rate of about 5 cm/y. In contrast, use of a bamboo fence, a green solution widely used along muddy coasts, traps sediment at a rate of less than 1.3 cm/y and typically lasts only for 2-3 years after installation. Decomposed bamboo causes environmental degradation so local communities disapprove of the approach. Results reveal that grey solutions are more effective for stabilizing the ECPD coastline and result in less coastal environmental impact than the nature-based solution using a bamboo fence.

Geochemical evaluation and human health risk assessment of nitrate-contaminated groundwater in an industrial area of South India.

The primary goal of this study is to evaluate the groundwater quality and conduct a non-carcinogenic risk assessment of nitrate contamination in an industrialized and high-density region of South India. A total of 40 sampling sites were identified in and around the industrial area, and samples were collected during the pre-monsoon and post-monsoon seasons. Piper and Gibbs' diagram shows that rock-water interaction, lithological characteristics and ion-exchange processes are the primary factors determining groundwater quality. The novel entropy water quality index (EWQI) indicated that 32 and 37.5% of the water in the study area were unsuitable for drinking purposes during both the pre-monsoon and post-monsoon seasons, respectively. Due to landfill leachate and modern agricultural activity, the nitrate concentration in groundwater post-monsoon had increased by 17.11%. The nitrate pollution index (NPI) value of groundwater exceeded the contaminated level by 22.77%. The non-carcinogenic human health risk assessment revealed that 35 and 40% of adult males, 37.5 and 52.5% of adult females and 42.5 and 55% of children during the pre-monsoon and post-monsoon periods were exposed to an increased concentration of nitrate in groundwater. The non-carcinogenic risk level to the exposed population in the study region descends in the following order: children > > females > males. The study suggests that low body weight in children is a direct result of consumption of low-quality water and that adult men and women suffer less severe consequences.

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models.

Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model's accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.

Integration of multi criteria decision analysis and GIS for evaluating the site suitability for aquaculture in southern coastal region, India.

Deterioration of water and soil quality, poor infrastructure facilities and improper maintenance are the major factors that govern aquaculture growth and production in major part of India. In the present study aims to identify the suitable land for aquaculture growth and suggest the sustainable practice to improvise the growth of aquaculture in study region. With use of analytical hierarchy process (AHP) the various significant parameters such as geology, pH, salinity, soil media, slope, geomorphology, land use land cover, distance to water, settlement and road networks were analyzed and based on these characteristics, thematic maps were prepared. The results are revealed that, that 882.13 km2 area was most suitable, 1264.88 km2 area was suitable and 14.00 km2 area was unsuitable for aquaculture in the study region. The study results will helpful to decision makers and to make a design plan for aquaculture growth in the study region.

Effects of age, gender, and environmental exposures on exhaled nitric oxide level in healthy 12 to 18 years Qatari children.

CONTEXT: Fractional exhaled nitric oxide (FENO) is a useful noninvasive diagnostic tool for asthma and some other pediatric respiratory diseases. Factors affecting FENO level are variable in different populations and studies. AIMS: To estimate the normal values of exhaled nitric oxide for Qataris 12 to 18 years of age. Other objectives were to measure the correlation of anthropometric and other potential factors with FENO levels. SETTINGS AND DESIGN: Community-based, cross-sectional study. METHODS: A total of 438 Qatari national school children from both genders were randomly recruited in cross-sectional study. Of them, 203 were non-atopic and hence included in the statistical analysis. Questionnaires including personal data, demographic data, and other factors that may affect FENO level were distributed. STATISTICAL ANALYSIS USED: Comparison of means done using t-test. We performed Spearman's rho test to measure correlations. Data analysis was done using PASW 18.0 Release 18.0.0, 2009. RESULTS: The geometric mean of FENO levels for all subjects was 14.1 ppb (upper level CI 95% - 36.3 ppb). FENO was significantly higher in males (R(2) = -0.254, P<0.0001) and was negatively correlated with increasing age for the whole study population (P=0.036). This decline was interrupted by a significant upraise at the age of 15 years (P=0.0462) which seems to be driven by the males (P=0.0244). FENO levels were lower in subjects exposed to cats (P=0.019). We could not find significant correlation between FENO and other factors studied. CONCLUSIONS: Estimated FENO level with 95% CI in Qatari children, which is probably close to those in other Gulf countries, will be helpful clinically. The lower level of FENO with female gender, increasing age, and exposure to cats needs to be further studied to establish the association and to understand the underlying mechanisms.