Hyperlocal PM2.5 Exposure and Health Burden Disparities Revealed by a Taxi-Based Mobile Monitoring Platform in Shenzhen
7 affiliations
- 1Sustainable Energy & Environment Thrust, Function Hub
- 2The Hong Kong University of Science and Technology (Guangzhou)
- 3Meta, 1 Meta Way, Menlo Park, CA, United States, 94025
- 4Soarability Technologies, Kexing Science Park, Building D, 24F, Nanshan District, Shenzhen, Guangdong, 518057
- 5Division of Natural and Applied Sciences, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu, China, 215316
- 6Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Ministry of Ecology and Environment
- 7Shanghai Academy of Environmental Sciences
Abstract
Although fine particles (PM 2.5 ) concentrations in China have declined over the past decade, exposure disparities persist at hyperlocal scales. We conducted a six-month mobile monitoring campaign in Shenzhen, where four taxis with calibrated low-cost sensors gathered 24.6 million 1 Hz measurements (~3,700 hours) across 550 km² and 7.3 million residents. We developed a novel application of a multi-source XGBoost (MS-XGB) framework that integrates mobile and fixed-site station data to reconstruct a road-network PM 2.5 dataset at 30 m × 1 h resolution, filling 98% of missing observations. Validation demonstrates strong agreement with both mobile observations (R 2 = 0.97) and data from fixed regulatory monitoring stations (Pearson r = 0.82). We used the road-network dataset to generate a 100 m × 100 m population grid exposure map and benchmarked it against two conventional exposure assessment approaches that rely solely on fixed-site regulatory stations. Relative to MS-XGB, conventional methods systematically underestimated long-term PM 2.5 exposure, with grid-level differences of up to 15 μg/m³. Population-weighted mean exposure was higher in urban villages (16.51 µg/m³) than in other urban areas (14.41 µg/m³), with hotspots 52.5% more likely to occur in urban villages. In Longhua, high exposure was concentrated specifically in urban villages, indicating that district-level averages can mask substantial intra-district heterogeneity. In Nanshan, 40% of elderly residents live in high-exposure areas, compared with 34% of children aged 0–14 and 33% of adults aged 15–59, suggesting that older adults bear a disproportionate exposure burden and face elevated health risks. Our findings demonstrate that mobile-monitoring-based hyperlocal exposure assessment is essential for revealing true PM 2.5 exposure inequalities across population groups and spatial environments that are overlooked by conventional fixed-site approaches.
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BibTeX
@misc{guo2026hyperlocal,
title = {{Hyperlocal PM2.5 Exposure and Health Burden Disparities Revealed by a Taxi-Based Mobile Monitoring Platform in Shenzhen}},
author = {Zilin Guo and Ziyang Jiang and Shutong Jiang and Tongshu Zheng and Yutong Liang},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009050/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009050/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Hyperlocal PM2.5 Exposure and Health Burden Disparities Revealed by a Taxi-Based Mobile Monitoring Platform in Shenzhen
AU - Zilin Guo
AU - Ziyang Jiang
AU - Shutong Jiang
AU - Tongshu Zheng
AU - Yutong Liang
PY - 2026
DA - 2026/09/18
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009050/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009050/v1
AB - Although fine particles (PM 2.5 ) concentrations in China have declined over the past decade, exposure disparities persist at hyperlocal scales. We conducted a six-month mobile monitoring campaign in Shenzhen, where four taxis with calibrated low-cost sensors gathered 24.6 million 1 Hz measurements (~3,700 hours) across 550 km² and 7.3 million residents. We developed a novel application of a multi-source XGBoost (MS-XGB) framework that integrates mobile and fixed-site station data to reconstruct a road-network PM 2.5 dataset at 30 m × 1 h resolution, filling 98% of missing observations. Validation demonstrates strong agreement with both mobile observations (R 2 = 0.97) and data from fixed regulatory monitoring stations (Pearson r = 0.82). We used the road-network dataset to generate a 100 m × 100 m population grid exposure map and benchmarked it against two conventional exposure assessment approaches that rely solely on fixed-site regulatory stations. Relative to MS-XGB, conventional methods systematically underestimated long-term PM 2.5 exposure, with grid-level differences of up to 15 μg/m³. Population-weighted mean exposure was higher in urban villages (16.51 µg/m³) than in other urban areas (14.41 µg/m³), with hotspots 52.5% more likely to occur in urban villages. In Longhua, high exposure was concentrated specifically in urban villages, indicating that district-level averages can mask substantial intra-district heterogeneity. In Nanshan, 40% of elderly residents live in high-exposure areas, compared with 34% of children aged 0–14 and 33% of adults aged 15–59, suggesting that older adults bear a disproportionate exposure burden and face elevated health risks. Our findings demonstrate that mobile-monitoring-based hyperlocal exposure assessment is essential for revealing true PM 2.5 exposure inequalities across population groups and spatial environments that are overlooked by conventional fixed-site approaches.
ER -