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Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

Environmental Studies and Forestry

Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

Y. Wang, Q. Li, et al.

Discover groundbreaking insights into PM2.5 exposure with this innovative study combining high-resolution data and population distribution. Researchers Yongyue Wang, Qiwei Li, and their team reveal critical differences in personal exposure that traditional models overlook, highlighting indoor air quality's significant role in health risks.

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Playback language: English
Abstract
With decreasing regional-transported PM2.5 levels, health risk assessment has become insufficient to reflect local source heterogeneity's contribution to exposure differences. This study combined ultra-high-resolution PM2.5 concentration with population distribution to estimate personal daily PM2.5 internal dose, considering indoor/outdoor exposure differences. A 30-m PM2.5 assimilating method was developed, fusing multiple predictors, achieving higher accuracy (R²=0.78-0.82) than chemical transport model outputs (R² = 0.31-0.64). Weekly differences were identified from hourly mobile signaling data. Population-weighted ambient PM2.5 concentrations varied among districts but didn't fully reflect exposure differences. The average indoor PM2.5 concentration was 26.5 µg/m³. The internal dose showed high exposure diversity among subgroups, and attributed mortality increased by 24.0% compared to coarser models.
Publisher
Communications Earth & Environment
Published On
Dec 01, 2023
Authors
Yongyue Wang, Qiwei Li, Zhenyu Luo, Junchao Zhao, Zhaofeng Lv, Qiuju Deng, Jing Liu, Majid Ezzati, Jill Baumgartner, Huan Liu, Kebin He
Tags
PM2.5
health risk assessment
exposure differences
indoor air quality
population distribution
environmental modeling
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