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 (PM_2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM_2.5 concentration with population distribution to provide the personal daily PM_2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM_2.5 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 PM_2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM_2.5 concentration was 26.5 microg/m³. The internal dose based on the assimilated indoor/outdoor PM_2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.