Spatiotemporal modeling of airborne fine particulate matter distribution in Isfahan

Abstract

Aims: Urban expansion has caused lots of problems such as air pollution, which endanger the health of residents. In this research, the spatiotemporal trend of atmospheric fine particulate matter (PM2.5) of Isfahan was studied and modeled using distributed space–time expectation–maximization (D-STEM) software in 2017. Materials and Methods: This software uses a flexible hierarchical space–time model that can deal with multiple variables and massive loads of missing data. Model estimation is based on the expectation–maximization algorithm. The effects of confounder variables such as holidays, altitude, average temperature and relative humidity, rainfall, wind speed, and direction were considered in the modeling. The hourly measured ambient PM2.5concentration data were obtained from seven air pollution monitoring stations installed in different zones of Isfahan and operated by the department of environment. Results: The distribution map of the pollutant demonstrated two polluted areas located in southwest and southeast regions of the city that are high traffic and densely populated area. PM2.5concentration was significantly increased (P