Detecting local climate zone change and its effects on PM10 distribution using fuzzy machine learning in Tehran, Iran

Abstract

Urban land use and land cover (LULC) change and climate affect a wide range of environmental health and human wellbeing issues, such as air pollution and soil erosion. However, it is challenging to quantify the effects of LULC change on Particulate Matter (PM) at a metropolitan scale. In this study, LULC maps of Tehran metropolitan region were prepared for the years 2013, 2017 and 2021 using Landsat 8 images. PM10 maps were extracted from an integrated approach of the Local Climate Zone (LCZ) classification scheme and Landsat images using Random Forest algorithm and fuzzy algorithm on the desired dates. The results show extensive changes of PM distributions in the Tehran metropolitan region. The most change occurred in the north of the city and the least change did in the city center. The most growth was LCZ 4 which had grown about 57 times in 8 years. The highest level of PM10 was found in the west and southwest regions while the lowest level was in the north of the city. Also, the analysis of highest average PM10 was in LCZ 10 and the lowest in LCZ G. The analysis of the PM10 average in LCZs and the comparison of PM10 time-series maps showed that the LCZs changes which occurred during the study period have reduced the concentration of air pollution in the Tehran metropolitan region.