Spatial Autocorrelation Analysis of CO and NO2 Related to Forest Fire Dynamics

The increasing frequency and severity of forest fires globally highlight the critical need to understand their environmental impacts. This study applies spatial autocorrelation techniques to analyze the dispersion patterns of carbon monoxide (CO) and nitrogen dioxide (NO2) emissions during the 2021 Manavgat forest fires in Türkiye, using Sentinel-5P satellite data. Univariate (UV) Global Moran’s I values indicated strong spatial autocorrelation for CO (0.84–0.93) and NO2 (0.90–0.94), while Bivariate (BV) Global Moran’s I (0.69–0.84) demonstrated significant spatial correlations between the two gases. UV Local Moran’s I analysis identified distinct UV High-High (UV-HH) and UV Low-Low (UV-LL) clusters, with CO concentrations exceeding 0.10000 mol/m2 and exhibiting wide dispersion, while NO2 concentrations, above 0.00020 mol/m2, remained localized near intense fire zones due to its shorter atmospheric lifetime. BV Local Moran’s I analysis revealed overlapping BV-HH (high CO, high NO2) and BV-LL (low CO, low NO2) clusters, influenced by topography and meteorological factors. These findings enhance the understanding of gas emission dynamics during forest fires and provide critical insights into the influence of environmental and combustion processes on pollutant dispersion

Autocorrelation · Geography · Remote sensing · Spatial analysis · Statistics · Atmospheric and Environmental Gas Dynamics · Atmospheric chemistry and aerosols · Environmental Science · Fire effects on ecosystems · Mathematics

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