Islam Raisul, Sinha Alok, Hussain Athar, Usama Mohammad, Ali Shahjad, Ahmed Salman, Gani Abdul, Hassan Najmaldin Ezaldin, Mohammadi Ali Akbar, Deshmukh Kamlesh
Department of Civil Engineering, GLA University Mathura, India.
Department of Environmental Science and Engineering, IIT, (ISM), Dhanbad, Jharkhand, India.
Heliyon. 2024 Dec 4;10(24):e40887. doi: 10.1016/j.heliyon.2024.e40887. eCollection 2024 Dec 30.
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 R 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.
地下水中氟化物污染是一个相当严重的公共卫生问题，影响着全球数百万人。城市化的快速发展导致了地下水污染的增加。健康风险评估关注急性和慢性健康后果，因为它调查了通过受污染地下水接触氟化物的程度和影响。氟化物暴露，尤其是在地方性流行地区，会产生严重的健康后果，包括牙齿和骨骼氟中毒。准确评估这些危害对于公共卫生规划和缓解行动至关重要。本研究使用蒙特卡罗模拟（MCS）和人工神经网络（ANN）模型对氟化物流行地区的人群进行概率健康风险评估。对研究结果的分析表明，卡斯根杰地区的氟化物浓度范围为0.58至3.80毫克/升，平均为2.30毫克/升，高于印度标准局（BIS）和世界卫生组织（WHO）规定的允许限值。儿童的最高危害商值为3.29，出现在杜尔加殖民地地区，而成人的最低危害商值为0.31，出现在纳德拉伊门地区。健康风险评估显示，饮用含氟地下水导致非致癌疾病的可能性很高。ANN模型在训练中的R值为0.9989，在测试中的R值为0.9870，而训练和测试中的均方根误差（RMSE）值分别为0.02230和0.0267。研究结果表明，印度北方邦卡斯根杰的地下水在使用前需要进行处理并使其可饮用。结果强调了持续监测、公共教育倡议以及实施可行的缓解技术以降低氟化物暴露的迫切需求。研究结果表明，这种混合模型在解决与氟化物使用相关的众多不确定性方面表现出色，从而提高了氟化物流行地区健康风险估计的可靠性。研究结果为政策制定者和当地卫生官员制定针对性措施以保障卡斯根杰的公众健康提供了重要信息。
