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

With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 mu g/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model. Mapping human exposure to particulate matter in Beijing using an approach that combines observational monitoring, satellite data and air quality modelling with mobile phone data indicates that exposure heterogeneity and attributed mortality are higher than coarser modelling approaches suggest.

Automated summary

By combining high-resolution PM2.5 concentration and population distribution, this study considers indoor/outdoor exposure differences and provides personal daily PM2.5 internal dose. The study utilizes assimilation methods and mobile signaling data to determine population-weighted ambient PM2.5 concentrations and exposure diversity.