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

Abstract

Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran's I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM 2.5 ) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM 2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord's PCI suggested a more polluted air quality than the Moran's I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.

Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)

“…Tehran, the capital city of Iran, is located in the northern part of this nation at the hillside location of the southern side of Alborz Mountains, and is spread over latitude 35°30′ to 35°51′ and longitude 51°00' to 51°40′ [10,44,45] (Figure 1). It had a population of 0.21 million in 1921 [44].…”

“…The city of Tehran is the capital of Iran and has one of the oldest histories of civilization [10,44]. The city has two major types of physiology: It is mountainous in the northern part and has a desert in the southern part [10,45,46,56]. As this city has a history of human attraction to its landscape, in the course of time, the city has experienced explosive impervious development in the form of residential, commercial, industrial, transport network, parking lots, etc., at the cost of transformation of LU/LC classes like VL, FL, and OL.…”

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

“…This article first used Moran's I to detect the spatial autocorrelation of PM 2.5 pollution in the 289 selected cities [47][48][49], and subsequently used regression analysis methods to detect the relationship between PM 2.5 pollution and the selected explanatory variables.…”

Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China

“…In spatial statistics, the Getis-Ord Gi* index has been frequently used to identify statistically significant spatial clusters of hot spots and cold spots, which has been widely used in geographical analysis [54][55][56]. By calculating the Getis-Ord Gi* index, the method generates a z-score and a p-value for each feature and then finds these features with high (low) values that are also surrounded by other features with high (low) values.…”