Abstract
Understanding the spatiotemporal analysis of air pollutants is crucial for identifying hotspots, local sources, and devising mitigation strategies, but this requires faster, more efficient approaches to support decision-making. For the first time in this study, Spatiotemporal Trend, Emerging Hot Spot (EHSA) and Time Series Cluster (TSC) analysis have been performed by creating a Space Time Cube (STC) at the neighbourhood level. The analyses were conducted for key air pollutants (PM10, PM2.5, NO2) measured between 2015 and 2023 in Istanbul. For three pollutants, 9855 concentration maps were generated using Inverse Distance Weighted (IDW) for each day. The regions classified as Oscillating Hot Spot for all pollutants are generally 4 times higher than the intersection cluster of Anselin Local Moran’s I (LISA) and Optimised Hot Spot (OHSA). Although there is a downward trend in the majority of the urban area of Istanbul, increasing trends and hot spots are evident in urban transformation, dense traffic-industrial and touristic areas. NO2, PM2.5 and PM10 values decreased by 43%, 8.9% and 31.6%, respectively, when the NDVI value increased approximately 2 times. Through this approach, sociospatial variables at the neighbourhood level can be synthesised with the spatiotemporal consequences of air pollution. This research identifies key areas contributing to environmental justice, providing decision-makers with detailed, comprehensive data to advance critical social and environmental justice initiatives.