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Capturing the invisible: hyperlocal variability of size-resolved particulate matter in a smart city
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DOI：10.1007/s10661-026-15746-8.png)
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Abstract
En 中文
Street canyon cities experience amplified pollution retention and exhibit sharp variations over short distances, posing heightened health risks from exposure to particulate matter (PM). Despite these concerns, Indian cities lack comprehensive, hyperlocal air quality assessments. Most existing studies rely on stationary monitoring, leaving a gap in mobile monitoring–based assessments that reflect localized exposure dynamics. This study presents a hyperlocal mobile monitoring dataset of PM₁, PM2.5, and PM₁₀ collected in Madurai, Tamil Nadu, during the retreat of the northeast monsoon, when long-range transport strongly influences air quality in peninsular India. Measurements were recorded every 6 s using a mounted mobile platform across ~ 2.4 km2 at a 50 m × 50 m grid resolution, improving within-grid aggregation and reducing outlier influence. Local enhancements above background concentrations for the study route showed that PM₁₀ recorded the highest values (188.91 ± 149.07 µg/m3), indicating a strong influence from road dust and construction. Particle size distribution analysis was performed using raw measured concentrations, which revealed that the evening PM₁ levels (42.10 µg/m3) exceeded morning PM2.5, highlighting the health relevance of submicron particles. A bimodal size distribution revealed peaks in the coarse (16.77–32.37 µm) and submicron (0.275–0.53 µm) ranges. A robust spatial hotspot analysis (Getis–Ord Gi*) was used to identify the pollution hotspots. From the analyses, persistent pollution hotspots were observed along traffic-heavy corridors, while restricted access zones consistently remained as low-concentration cold spots. SEM-EDX analysis of 22 elements further linked particle size, chemistry, and sources, underscoring the need for real-time, mobile-based PM monitoring in Indian cities. These findings demonstrate the value of hyperlocal mobile monitoring for identifying localized exposure hotspots and supporting evidence-based urban air quality management.
Keywords:
Urban air quality
Particulate matter
Mobile monitoring
Pollution hotspots
Size distribution
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