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<p>Rapid urbanization, coupled with limited monitoring infrastructure, continues to significantly challenge fine-scale air pollution assessment in developing cities. This study examines spatiotemporal variability in PM<sub>2.5</sub> using a network of 20 low-cost sensors (LCS) deployed across diverse land-use settings in Surat, India (October 2022–September 2023). A framework integrating quality assurance, calibration, and multi-criteria hotspot identification was applied. Hotspots were defined by the intersection of concentration magnitude (SOE), temporal persistence (FOE), and spatial clustering (Getis-Ord Gi*). The city’s annual mean (± standard deviation) PM<sub>2.5</sub> concentration was 64.6 ± 46.25&#xa0;µg/m<sup>3</sup>, exceeding national standards, and showed a clear seasonal pattern: winter (116.2 ± 42.0&#xa0;µg/m<sup>3</sup>) &gt; post-monsoon (86.6 ± 46.7&#xa0;µg/m<sup>3</sup>) &gt; summer (57.6 ± 25.6&#xa0;µg/m<sup>3</sup>) &gt; monsoon (29.3 ± 8.3&#xa0;µg/m<sup>3</sup>). Industrial and traffic-dominated locations emerged as major PM<sub>2.5</sub> hotspots. Sensitivity analyses confirmed the robustness of hotspot classification. Meteorological influences were assessed using boundary-layer analysis, source direction techniques, and air-mass back trajectories. Significant negative correlations between PM<sub>2.5</sub> and planetary boundary layer height were observed during winter (ρ = -0.40) and post-monsoon (ρ = -0.31), indicating strong atmospheric control on pollutant accumulation. CBPF analysis, Bivariate polar plots, and clustering indicated dominant source regions from the northeast-east and southeast sectors, though these are indicative and based on meteorological patterns rather than source apportionment. Back-trajectory analysis revealed a shift from marine influence during monsoon to continental transport during high-pollution seasons. The study demonstrates that integrated analysis using calibrated LCS networks provides reliable insights into urban air pollution dynamics and supports targeted mitigation strategies.</p>

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Spatiotemporal Variation and Hotspot Identification of PM2.5 Using Low-Cost Sensors: A Shifting Paradigm for Urban Smart Cities

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Kiran Suryawanshi,
  • Namrata Jariwala

摘要

Rapid urbanization, coupled with limited monitoring infrastructure, continues to significantly challenge fine-scale air pollution assessment in developing cities. This study examines spatiotemporal variability in PM2.5 using a network of 20 low-cost sensors (LCS) deployed across diverse land-use settings in Surat, India (October 2022–September 2023). A framework integrating quality assurance, calibration, and multi-criteria hotspot identification was applied. Hotspots were defined by the intersection of concentration magnitude (SOE), temporal persistence (FOE), and spatial clustering (Getis-Ord Gi*). The city’s annual mean (± standard deviation) PM2.5 concentration was 64.6 ± 46.25 µg/m3, exceeding national standards, and showed a clear seasonal pattern: winter (116.2 ± 42.0 µg/m3) > post-monsoon (86.6 ± 46.7 µg/m3) > summer (57.6 ± 25.6 µg/m3) > monsoon (29.3 ± 8.3 µg/m3). Industrial and traffic-dominated locations emerged as major PM2.5 hotspots. Sensitivity analyses confirmed the robustness of hotspot classification. Meteorological influences were assessed using boundary-layer analysis, source direction techniques, and air-mass back trajectories. Significant negative correlations between PM2.5 and planetary boundary layer height were observed during winter (ρ = -0.40) and post-monsoon (ρ = -0.31), indicating strong atmospheric control on pollutant accumulation. CBPF analysis, Bivariate polar plots, and clustering indicated dominant source regions from the northeast-east and southeast sectors, though these are indicative and based on meteorological patterns rather than source apportionment. Back-trajectory analysis revealed a shift from marine influence during monsoon to continental transport during high-pollution seasons. The study demonstrates that integrated analysis using calibrated LCS networks provides reliable insights into urban air pollution dynamics and supports targeted mitigation strategies.