Spatial Analysis of the PM2.5 Air Pollution in Iraq by using R Language

Abstract

Abstract Remote sensing refers to the acquisition of data and information about a phenomenon and a territory, without a direct contact with it. It is alternative to in-situ observation. Due to its wide coverage, multi-temporal and multispectral capability, remote sensing satellites have been used for studying and monitoring the spatial distribution of the aerosol pollution (like PM2.5) in the atmosphere. PM2.5 refers to atmospheric particulate matter (PM) that has a diameter of less than 2.5 micrometers, which is about 3% the diameter of a human hair. The current study concentrates on the using of R programming language to process and analyze the retrieval PM2.5 dataset over Iraq for the period (January 1, 2020 to December 31, 2020), these data were downloaded from the European Union’s Earth observation Copernicus program through the Copernicus Atmosphere Monitoring Service (CAMs). The study presented output results of the PM2.5 concentration levels by developing an automatic tool that restructuring the netCDF file into excel file using R language. In addition, it produces different valuable charts that help in practical dataset interpretation and decision-making. The results of the study showed the variation in the spatial distribution of PM2.5 in the study area due to the diversity of pollution sources from one region to another.