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<p>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 (SO<sub>2</sub>) are among the most critical air pollutants due to 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 large, high-dimensional environmental dataset into environmental studies, necessitating advanced spatial and spatiotemporal analytical approaches to effectively capture pollution dynamics. This study investigated spatiotemporal patterns and temporal shifts in PM<sub>10</sub> and SO<sub>2</sub> concentrations in the Eastern Marmara Region for 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 (STC) modeling was applied to integrate spatial and temporal dimensions, enabling the visualization of spatiotemporal variability in 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 results further indicated that the maximum PM10 concentration decreased from 116&#xa0;µg/m<sup>3</sup> in 2014 to 54&#xa0;µg/m<sup>3</sup> in 2020, then rebounded to 91&#xa0;µg/m<sup>3</sup> in 2024. In contrast, SO<sub>2</sub> concentrations showed a relatively stable long-term pattern, with annual averages decreasing from approximately 16 to 9&#xa0;µg/m<sup>3</sup> over the same period. Trend analysis revealed a statistically significant decline in PM<sub>10</sub>, whereas SO<sub>2</sub> did not exhibit a significant long-term trend across most districts. The integration of GIS-based methods and space–time cube analysis demonstrates the effectiveness of spatiotemporal analysis of large environmental datasets for understanding complex air-pollution dynamics. It supports informed decision-making for regional air-quality management.</p>

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Spatiotemporal trends of air pollutants using space–time cube (STC) analysis: a case study in the Eastern Marmara Region, Türkiye

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Arif Çağdaş Aydınoğlu,
  • Ali Doğan Gümüşsoy,
  • Arzu Erener

摘要

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 due to 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 large, high-dimensional environmental dataset into environmental studies, necessitating advanced spatial and spatiotemporal analytical approaches to effectively capture pollution dynamics. This study investigated spatiotemporal patterns and temporal shifts in PM10 and SO2 concentrations in the Eastern Marmara Region for 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 (STC) modeling was applied to integrate spatial and temporal dimensions, enabling the visualization of spatiotemporal variability in 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 results further indicated that the maximum PM10 concentration decreased from 116 µg/m3 in 2014 to 54 µg/m3 in 2020, then rebounded to 91 µg/m3 in 2024. In contrast, SO2 concentrations showed a relatively stable long-term pattern, with annual averages decreasing from approximately 16 to 9 µg/m3 over the same period. Trend analysis revealed a statistically significant decline in PM10, whereas SO2 did not exhibit a significant long-term trend across most districts. The integration of GIS-based methods and space–time cube analysis demonstrates the effectiveness of spatiotemporal analysis of large environmental datasets for understanding complex air-pollution dynamics. It supports informed decision-making for regional air-quality management.