Characterization of groundwater nitrate exposure using Monte Carlo and Sobol sensitivity approaches in the diverse aquifer systems of an agricultural semiarid region of Lower Ganga Basin, India

Mukherjee, Indrani; Singh, Umesh Kumar

doi:10.1016/j.scitotenv.2021.147657

Cited by 42 publications

(16 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These estimations of the population exposed can be made more accurate with the inclusion of data on the availability of treated piped water supply as treated water significantly limits the nuisance caused by groundwater contamination. The identified areas of population exposure to elevated nitrate risk in this study align with several previous studies that have reported population vulnerable to groundwater nitrate contamination by calculating the human-health-risk indexes in areas of Andhra Pradesh, Chandigarh, Gujarat, Haryana, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, and West Bengal (Mukherjee and Singh). Thus, the estimated population exposed to elevated groundwater nitrate in this study shows the possible severity of high groundwater nitrate risk in the country.…”

Section: Resultssupporting

confidence: 84%

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

et al. 2022

View full text Add to dashboard Cite

Elevated groundwater nitrate poses risk to the ecosystem and human health, and delineating the extent of elevated groundwater nitrate risk is essential for effective groundwater management and public health safety. Here, using machine learning models (Random Forest, Boosted Regression Tree, and Logistic Regression) on a large, in situ dataset, we have predicted the first nationwide extent of groundwater nitrate contamination risk (concentration >45 mg/L) across India. We also aimed to delineate the intrinsic (e.g., climate, geomorphic, hydrogeologic) and extraneous (e.g., anthropogenic input) predictors for identifying groundwater pollution risk. Of these models, Random Forest performed best and was considered to develop the final prediction map of groundwater nitrate at 1 km2 resolution. Climate variables like precipitation and aridity, and anthropogenic influence, e.g., fertilizer application and population density, were identified as the most important variables for predicting groundwater nitrate risk. Dry arid and semiarid regions in the west, south, and central parts of the country contained the majority of high-risk areas. Predictions suggested that about 37% of India’s areal extent and 380 million people were exposed to elevated nitrate. The prediction model performed satisfactorily over the validation dataset that indicates the prediction ability of the model at the local scale. The study aims to provide an effective approach for identifying elevated groundwater nitrate risk and aid in the development of awareness and strategies to uphold public health safety.

show abstract

Section: Resultssupporting

confidence: 84%

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Sobol sensitivity analysis is a sophisticated technique for determining whether responses and their processes have additional effects on the overall system by evaluating the relative extent of each significant input and its interaction with the output model’s variance [ 35 ]. Sobol sensitivity analysis is extensively employed in health risk assessment, and complete evaluation methodologies have been described in the pertinent papers [ 35 , 38 , 39 ]. When the sensitivity index of the input parameters is >0.1, 0.01–0.1, and 0.01, they are categorized as very sensitive, sensitive, and insensitive parameters, respectively [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…Sobol sensitivity analysis is extensively employed in health risk assessment, and complete evaluation methodologies have been described in the pertinent papers [ 35 , 38 , 39 ]. When the sensitivity index of the input parameters is >0.1, 0.01–0.1, and 0.01, they are categorized as very sensitive, sensitive, and insensitive parameters, respectively [ 38 ]. In this work, we performed 10,000 iterations of sensitivity analysis on the parameters of the health risk assessment of Cr(VI) pollution in soil and groundwater in the study area using the SALib module in Python 3.8.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Health Risk Assessment during In Situ Remediation of Cr(VI)-Contaminated Groundwater by Permeable Reactive Barriers: A Field-Scale Study

Zhang

Zhu

et al. 2022

IJERPH

View full text Add to dashboard Cite

The presence of residual Cr(VI) in soils causes groundwater contamination in aquifers, affecting the health of exposed populations. Initially, permeable reactive barriers(PRB) effectively removed Cr(VI) from groundwater. However, as PRB clogging increased and Cr(VI) was released from upstream soils, the contamination plume continued to spread downstream. By 2020, the level of contamination in the downstream was nearly identical to that in the upstream. The study results show that during normal operation, the PRB can successfully remove Cr(VI) from contaminated groundwater and reduce the carcinogenic and non-carcinogenic risks to humans from the downstream side of groundwater. However, the remediated groundwater still poses an unacceptable risk to human health. The sensitivity analysis revealed that the concentration of the pollutant was the most sensitive parameter and interacted significantly with other factors. Ultimately, it was determined that the residual Cr(VI) in the soil of the study region continues to contaminate the groundwater and constitutes a serious health danger to residents in the vicinity. As remediated groundwater still poses a severe threat to human health, PRB may not be as effective as people believe.

show abstract

“…A sensitivity analysis was also conducted to evaluate the effects of different parameters on output results. We used a Sobol global sensitivity analysis, which is essentially a Monte Carlo method based on variance calculation (Mukherjee and Singh, 2021). In a Sobol analysis, the input value of each parameter is selected by Sobol sequence sampling.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Dietary exposure and risk assessment of European Union priority (EU 15+1) polycyclic aromatic hydrocarbons from milks and milk powders in China

Yan

2022

Journal of Dairy Science

View full text Add to dashboard Cite

Dairy products, characterized by rich nutrition and unique flavor, occupy an increasing share of the human diet with the development of higher living standards. However, trace hazardous contaminants in dairy products, such as polycyclic aromatic hydrocarbons (PAH), are arousing public concern. In this study, we investigated 82 milks and milk powders from Shanghai markets for the occurrence of 20 PAH, including 16 European Union priority (EU 15+1) PAH and 4 derivatives. Furthermore, we carried out a risk assessment of dietary exposure of PAHs from dairy products based on detection results and data on body weight and dietary intake obtained from questionnaires. Two assessment methods were used in this study: the incremental lifetime cancer risk (ILCR) and margin of exposure (MOE) methods. The ILCR of all age groups was 10 −6 to 10 −4 , indicating a potential but acceptable carcinogenic risk. The MOE of the 4 groups (divided by age and sex) was >10 4 , indicating no significant carcinogenic risk of PAH exposure from dairy products. In both methods, children had the highest risk because of their greater consumption of dairy products, followed by seniors, young adults, and middle-aged adults. Results of this study provide reference data on the exposure level and health risk of PAHs from dairy product intake.

show abstract

Characterization of groundwater nitrate exposure using Monte Carlo and Sobol sensitivity approaches in the diverse aquifer systems of an agricultural semiarid region of Lower Ganga Basin, India

Cited by 42 publications

References 45 publications

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

Health Risk Assessment during In Situ Remediation of Cr(VI)-Contaminated Groundwater by Permeable Reactive Barriers: A Field-Scale Study

Dietary exposure and risk assessment of European Union priority (EU 15+1) polycyclic aromatic hydrocarbons from milks and milk powders in China

Contact Info

Product

Resources

About