Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas
Groundwater contamination with fluoride is a considerable public health concern that affects millions of people worldwide. The rapid growth of urbanization has led to increase in groundwater contamination. The health risk assessment focuses on both acute and chronic health consequences as it investi...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024169186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846115961877299200 |
|---|---|
| author | Raisul Islam Alok Sinha Athar Hussain Mohammad Usama Shahjad Ali Salman Ahmed Abdul Gani Najmaldin Ezaldin Hassan Ali Akbar Mohammadi Kamlesh Deshmukh |
| author_facet | Raisul Islam Alok Sinha Athar Hussain Mohammad Usama Shahjad Ali Salman Ahmed Abdul Gani Najmaldin Ezaldin Hassan Ali Akbar Mohammadi Kamlesh Deshmukh |
| author_sort | Raisul Islam |
| collection | DOAJ |
| description | Groundwater contamination with fluoride is a considerable public health concern that affects millions of people worldwide. The rapid growth of urbanization has led to increase in groundwater contamination. The health risk assessment focuses on both acute and chronic health consequences as it investigates the extent and effects of fluoride exposure through contaminated groundwater. Fluoride exposure, especially in endemic locations, has serious health consequences, including dental and skeletal fluorosis. An accurate assessment of these hazards is essential for public health planning and mitigation actions. The present study uses Monte Carlo Simulation (MCS) and an Artificial Neural Network (ANN) model to perform a Probabilistic Health Risk Assessment on populations in fluoride-endemic areas. Analysis of the results of the study reveals that the concentration of fluoride ranged from 0.58 to 3.80 mg/L with an average of 2.30 mg/L across the Kasganj district, which was higher than permissible limits given by BIS and WHO. The highest value of hazard quotient of 3.29 for Children is found to be in the Durga Colony area, while the lowest value of the hazard quotient of 0.31 for adults is found to be in the Nadrai Gate area. The assessment of health risks revealed a high probability of non-carcinogenic disease from the consumption of groundwater containing fluoride. The ANN model has the R2 value of 0.9989 in training and 0.9870 in testing while RMSE value in training and testing was 0.02230 and 0.0267. The findings suggest that before being used, the groundwater in Kasganj, Uttar Pradesh, India, needs to be treated and made drinkable. The results emphasize the critical need for ongoing monitoring, public education initiatives, and implementing feasible mitigating techniques to lower fluoride exposure. The findings show that this hybrid model is excellent at addressing the numerous uncertainties associated with fluoride use, hence improving the reliability of health risk estimates in fluoride-endemic locations. The results offer vital information to help policymakers and local health officials create focused measures to safeguard public health in Kasganj. |
| format | Article |
| id | doaj-art-7a8d8300b2d74695b7e408c68e2a626e |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-7a8d8300b2d74695b7e408c68e2a626e2024-12-19T10:56:05ZengElsevierHeliyon2405-84402024-12-011024e40887Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areasRaisul Islam0Alok Sinha1Athar Hussain2Mohammad Usama3Shahjad Ali4Salman Ahmed5Abdul Gani6Najmaldin Ezaldin Hassan7Ali Akbar Mohammadi8Kamlesh Deshmukh9Department of Civil Engineering, GLA University Mathura, India; Department of Environmental Science and Engineering, IIT, (ISM), Dhanbad, Jharkhand, IndiaDepartment of Environmental Science and Engineering, IIT, (ISM), Dhanbad, Jharkhand, IndiaDepartment of Civil Engineering, Netaji Subhas University of Technology, New Delhi, IndiaDepartment of Environmental Science, Integral University, Lucknow, IndiaDepartment of Environmental Science, Sharda School of Smart Agriculture, Sharda University Agra, Keetham, Agra, 282007, India; Corresponding author.Interdisciplinary Department of Remote Sensing and GIS Applications, Aligarh Muslim University, Aligarh, IndiaDepartment of Civil Engineering, Netaji Subhas University of Technology, New Delhi, IndiaDepartment of Civil and Environment Engineering, University of Zakho, Kurdistan Region, IraqDepartment of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran; Workplace health research center, Neyshabur University of Medical Sciences, Neyshabur, Iran; Corresponding author. Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran.Department of Computer Science and Engineering, Anand Engineering College, Agra, IndiaGroundwater contamination with fluoride is a considerable public health concern that affects millions of people worldwide. The rapid growth of urbanization has led to increase in groundwater contamination. The health risk assessment focuses on both acute and chronic health consequences as it investigates the extent and effects of fluoride exposure through contaminated groundwater. Fluoride exposure, especially in endemic locations, has serious health consequences, including dental and skeletal fluorosis. An accurate assessment of these hazards is essential for public health planning and mitigation actions. The present study uses Monte Carlo Simulation (MCS) and an Artificial Neural Network (ANN) model to perform a Probabilistic Health Risk Assessment on populations in fluoride-endemic areas. Analysis of the results of the study reveals that the concentration of fluoride ranged from 0.58 to 3.80 mg/L with an average of 2.30 mg/L across the Kasganj district, which was higher than permissible limits given by BIS and WHO. The highest value of hazard quotient of 3.29 for Children is found to be in the Durga Colony area, while the lowest value of the hazard quotient of 0.31 for adults is found to be in the Nadrai Gate area. The assessment of health risks revealed a high probability of non-carcinogenic disease from the consumption of groundwater containing fluoride. The ANN model has the R2 value of 0.9989 in training and 0.9870 in testing while RMSE value in training and testing was 0.02230 and 0.0267. The findings suggest that before being used, the groundwater in Kasganj, Uttar Pradesh, India, needs to be treated and made drinkable. The results emphasize the critical need for ongoing monitoring, public education initiatives, and implementing feasible mitigating techniques to lower fluoride exposure. The findings show that this hybrid model is excellent at addressing the numerous uncertainties associated with fluoride use, hence improving the reliability of health risk estimates in fluoride-endemic locations. The results offer vital information to help policymakers and local health officials create focused measures to safeguard public health in Kasganj.http://www.sciencedirect.com/science/article/pii/S2405844024169186Artificial neuro networkMonte Carlo simulationProbabilistic health risk assessmentFluorosisKasganj area |
| spellingShingle | Raisul Islam Alok Sinha Athar Hussain Mohammad Usama Shahjad Ali Salman Ahmed Abdul Gani Najmaldin Ezaldin Hassan Ali Akbar Mohammadi Kamlesh Deshmukh Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas Heliyon Artificial neuro network Monte Carlo simulation Probabilistic health risk assessment Fluorosis Kasganj area |
| title | Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas |
| title_full | Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas |
| title_fullStr | Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas |
| title_full_unstemmed | Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas |
| title_short | Application of Monte Carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride-endemic areas |
| title_sort | application of monte carlo simulation and artificial neural network model to probabilistic health risk assessment in fluoride endemic areas |
| topic | Artificial neuro network Monte Carlo simulation Probabilistic health risk assessment Fluorosis Kasganj area |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024169186 |
| work_keys_str_mv | AT raisulislam applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT aloksinha applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT atharhussain applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT mohammadusama applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT shahjadali applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT salmanahmed applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT abdulgani applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT najmaldinezaldinhassan applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT aliakbarmohammadi applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas AT kamleshdeshmukh applicationofmontecarlosimulationandartificialneuralnetworkmodeltoprobabilistichealthriskassessmentinfluorideendemicareas |