Spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Marmara Region based on GIS and space–time cube analysis

Air pollution remains one of the most critical environmental problems affecting human health, ecosystems, and sustainable urban development, particularly in rapidly urbanizing and industrialized regions. Particulate matter (PM10) and sulfur dioxide (SO₂) are among the most critical air pollutants because of their adverse effects on human health, atmospheric processes, and ecosystem integrity. The increasing availability of long-term, high-frequency air quality monitoring data has introduced a big data dimension to environmental studies, necessitating advanced spatial and spatiotemporal analytical approaches to effectively capture the pollution dynamics. This study investigated the spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Eastern Marmara Region for the periods 2014, 2020, and 2024. Daily air quality measurements were aggregated into seasonal and annual averages to assess the long-term changes in pollution levels. Geographic Information System (GIS)-based spatial analysis techniques were employed to generate continuous pollution surfaces. In addition, space–time cube modeling was applied to integrate spatial and temporal dimensions, enabling the identification of emerging trends, persistent hotspots, and temporal shifts in the air pollution levels. The results revealed distinct spatial contrasts and seasonal variations across the study area, with elevated concentrations generally associated with industrial zones and major transportation corridors, whereas lower levels were observed in coastal and less urbanized areas. The integration of GIS-based methods and space–time cube analysis demonstrates the effectiveness of big-data-driven spatiotemporal approaches in understanding complex air-pollution dynamics. It supports informed decision-making for regional air-quality management.