A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Generalized additive models: Building evidence of air pollution, climate change and human health
Abstract 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.
Graphical abstract Display Omitted
Highlights Shows application of GAM in air pollution, climate change, and health Provide an overview of regression models and emerging models Discuss ‘gam’ in R environment with ‘mgcv’ package having complete insight Merits and limitations of GAM are highlighted. Build a GAM conceptual framework for evidence-based policies and risk reduction
Generalized additive models: Building evidence of air pollution, climate change and human health
Abstract 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.
Graphical abstract Display Omitted
Highlights Shows application of GAM in air pollution, climate change, and health Provide an overview of regression models and emerging models Discuss ‘gam’ in R environment with ‘mgcv’ package having complete insight Merits and limitations of GAM are highlighted. Build a GAM conceptual framework for evidence-based policies and risk reduction
Generalized additive models: Building evidence of air pollution, climate change and human health
Ravindra, Khaiwal (author) / Rattan, Preety (author) / Mor, Suman (author) / Aggarwal, Ashutosh Nath (author)
2019-06-30
Article (Journal)
Electronic Resource
English
GAM , Generalized Additive Model , GLM , Generalized Linear model , PM , Particulate Matter PM<inf>10</inf>:Particulate Matter 10 μm or less in diameter , PM<inf>2.5</inf> , Particulate Matter 2.5 μm or less in diameter , NO<inf>2</inf> , Nitrogen dioxide , CO , Carbon Monoxide , O<inf>3</inf> , Ozone , Time series analysis , Mortality , Splines & lag , Regression models and climate change
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