Advances in statistical analysis in the last few decades in the area of linear models enhanced the capability of researchers to study environmental procedures. In relation to general linear models; generalized linear models (GLM) provide greater flexibility in analyzing data related to non-normal distributions. Considering this, the current review explains various applications of the generalized additive model (GAM) to link air pollution, climatic variability with adverse health outcomes. The review examines the application of GAM within the varied field, focusing on the environment and meteorological data. Further, advantages and complications of applying GAM to environmental data are also discussed. Application of GAM allowed for specification for the error pattern and found to be an appropriate fit for the data sets having non-normal distributions; this results in lower and more reliable p-values. Since most environmental data is non-normal, GAM provides a more effective analytical method than traditional linear models. This review highlights on ambient air pollutants, climate change, and health by evaluating studies related to GAM. Additionally, an insight into the application of GAM in R software is provided, which is open source software with the extensive application for any type of dataset.

The average global temperature is rising due to anthropogenic emissions. Hence, a systematic approach was used to examine the projected impacts of rising global temperatures on heatwaves in India and provide insights into mitigation and adaptation strategies. With over 24,000 deaths attributed to heatwaves from 1992 to 2015, there is an urgent need to understand India's vulnerabilities and prepare adaptive strategies under various emission scenarios.This situation is predicted to worsen as heatwaves become more frequent, intense, and long-lasting. Severe heatwaves can exacerbate chronic health conditions, vector-borne diseases, air pollution, droughts and other socio-economic pressures causing higher mortality and morbidity. Heatwaves with severe consequences have increased and are expected to become more frequent in Indian climatic and geographical conditions. As per the future projection studies, the temperature could rise ±1.2° C to ±3.5° C and will start reducing by the end of 2050. The study also provides data from the research that employs climatic models and statistical approaches for a more precise characterization of heat extremes and improved projections. Also, the study appraises the past, present and future heatwave trend projections. Most of these studies compute future projections using the Coupled Model Intercomparison Project (CMIP5) models and Representative Concentration Pathway (RCP). Limited systematic reports have been found using CMIP6, whereas the best-suited and widely used method was the RCP8.5. The study findings will aid in identifying the zones most susceptible to heatwave risk and provide actionable projections for policymakers to examine the existing evidence for developing proper planning and policy formulation, considering the future climate and temperature projections.