Spatiotemporal modeling of airborne fine particulate matter distribution in Isfahan

Document Type : Original Article


1 Department of Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

2 Department of Biostatistics, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

3 Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran


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 < 0.05) with an increase in land elevation by a coefficient of 0.93; conversely, it decreased significantly (P < 0.05) with every increase in wind speed by a coefficient of −0.226. Conclusion: Given the spatiotemporal correlations between air pollutant data, it is necessary to incorporate these correlations into model to obtain more accurate estimates. Using the statistical models and methods to manage the data, time, and volume of calculations in spatiotemporal estimations, the D-STEM program gives more accurate estimates of the desired parameters. Presenting models and maps for every desired time period are another feature of this software that can be useful in health programming and environmental management. Vehicular traffic had a significant effect on the increasing trend of the pollutant level in urban areas; however, the effects of atmospheric phenomena such as dust storms and thermal inversion cannot be ignored.


Volume 2020, July
July 2020
Pages 1-7
  • Receive Date: 26 January 2023
  • Accept Date: 26 January 2023