Spatial Analysis of SO2, PM10, CO, NO2, and O3 Pollutants: The Case of Konya Province, Turkey

Abstract

Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO2, PM10, CO, NO2 and O3 pollutants, which cause air pollution within the borders of the municipal urban areas of Konya province, was carried out for the years 2019–2020. In this context, air pollution maps were produced using the IDW interpolation method with data obtained from the National Air Quality Monitoring Network stations, which belong to the Ministry of Environment and Urbanization, in the Konya region. The results obtained were examined with maps and graphics based on the limit values found in the Air Quality Assessment and Management Regulation published by the Ministry of Environment and Urbanization. In this context, the periods of lockdown experienced during the COVID-19 pandemic were also evaluated in terms of air pollution. From the evaluation made on the values taken from the air quality stations, it can be observed that the air pollution did not violate the national limit value much in 2019 and 2020.

Spatio-temporal characterization of PM10 concentration across Abu Dhabi Emirate (UAE)

“…Consequently, offering a suitable approach for continuously changing data across a specific area [ 29 , 55 ]. The IDW method is expressed by Equation (1) : Where Z(X0) is the value to be estimated at location X0, z(Xi) is the value measured at location Xi, r represents the weights assigned to the predicted value, and d is the distance between the known value point and the value to be estimated [ 57 ].…”

Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine

“…Although satellite images are a promising source of data for generating estimates at high spatial resolution on a local scale as they capture some spatial variability, they are limited to generalizing in certain areas [52,53]. Future work should focus on combining additional datasets that are readily available globally (e.g., additional bands of satellite data, normalized difference vegetation index (NDVI)/enhanced vegetation index (EVI), meteorology, digital elevation model (DEM)) that can be combined with meter-scale satellite images to generate better estimates of air pollutants.…”