Development of environmental data for land use regression models to assess fine particulate matter pollution in Ho Chi Minh City

Land use regression (LUR) model is a common method for assessing ambient air pollution in metropolis, such as fine particulate matter (PM2.5). The LUR model utilizes PM2.5 concentrations as a dependent variable and traffic, topography, and land use variables..., named as environmental data, as independent variables in multiple linear regression analysis. Currently, the PM2.5 pollution is one of the most concerning environmental issues in Ho Chi Minh City (HCMC). Particularly, in the context of current limited air pollution monitoring resources of the city, the LUR model usage for PM2.5 pollution assessment at the level of the whole city may be a possible solution. However, the environmental data for using in the LUR model in HCMC is not available. Thus, this study was conducted to prepare environmental data for applying in the LUR model in upcoming studies. The study selected potential environmental variables in HCMC and continued to deal with databases selection for developing these environmental variables. The major result of the study is that a database of environmental data in HCMC was developed, including 26 variables categorized into five groups, including (1) Meteorological data, (2) Traffic-related data, (3) Population data, (4) Land use data, and (5) Social-economic data. Moreover, the study established a conceptual framework for environmental data collection. Finally, environmental data of several fixed sites, where PM2.5 concentrations were monitored in our previous studies, was extracted as an illustration.