The study investigates the public health impacts of climate change and pollution in Delhi, NCR, focusing on the correlation between environmental factors and respiratory diseases.Data was collected from various monitoring stations and health records from 2010 to 2020.The analysis employed Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) models to predict the incidence of respiratory diseases based on pollutant levels and climatic variables.Results indicate a significant correlation between increased levels of PM2.5, NO2, and temperature fluctuations with the rise in respiratory disease cases.The SVM model exhibited an accuracy of 89%, the RF model 92%, and the LR model 85%.The ROC curves for the models demonstrated the robustness of the predictions, with AUC values of 0.91 for SVM, 0.94 for RF, and 0.87 for LR.These findings underscore the urgent need for stringent pollution control measures and public health interventions to mitigate the adverse health effects of climate change and pollution in Delhi, NCR.The study also identifies some of the key factors that moderate this relationship, such as socio-demographic variables, perception of health risks, and coping strategies.The findings of this study have important implications for policymakers, urban planners, and public health officials, who must consider the impact of air pollution on health while formulating policies and interventions to address this critical issue.
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A 2024 study studied this question.
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