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ISPRS Int. J. Geo-Inf. 2017, 6(9), 270; doi:10.3390/ijgi6090270

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

Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran
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Author to whom correspondence should be addressed.
Academic Editors: Jamal Jokar Arsanjani and Wolfgang Kainz
Received: 6 May 2017 / Revised: 16 July 2017 / Accepted: 27 August 2017 / Published: 31 August 2017
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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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 (PM2.5) 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). PM2.5 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. View Full-Text
Keywords: spatial autocorrelation; spatial clusters; Moran’s I; Getis-Ord; air pollution; Tehran spatial autocorrelation; spatial clusters; Moran’s I; Getis-Ord; air pollution; Tehran
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Habibi, R.; Alesheikh, A.A.; Mohammadinia, A.; Sharif, M. An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran. ISPRS Int. J. Geo-Inf. 2017, 6, 270.

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