An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

摘要: Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration contaminants. It may also lead to identification hotspots. The patterns then be targeted manage level pollutants. In this regard, employing autocorrelation indices as important tools is inevitable. study, general and local Moran’s I Getis-Ord statistics were assessed in their representation structural characteristics carbon monoxide (CO) fine particulate matter (PM2.5) polluted areas Tehran, Iran, which one most cities world. For purpose, a grid (200 m × 200 m) was applied across city, inverse distance weighted (IDW) interpolation method used allocate value each pixel. To compare methods detecting clusters meaningfully quantitatively, cleanliness index (PCI) established. results ascertained high pollutants study area (with 99% confidence level). PM2.5 separated city into northern southern parts, cold spots situated north half hotspots south. However, CO covered an from northeast southwest spread over rest city. Getis-Ord’s PCI suggested more quality than PCI. provides feasible methodology urban planners decision makers effectively investigate govern contaminated sites with aim reducing harmful effects on public health environment.