Ensemble-based deep learning for estimating PM over California with multisource big data including wildfire smoke.

Introduction: Estimating PM concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.

US particulate matter air quality improves except in wildfire-prone areas.

Using data from rural monitoring sites across the contiguous United States, we evaluated fine particulate matter (PM) trends for 1988-2016. We calculate trends in the policy-relevant 98th quantile of PM using Quantile Regression. We use Kriging and Gaussian Geostatistical Simulations to interpolate trends between observed data points.

Evaluation of the KCl denuder method for gaseous oxidized mercury using HgBr2 at an in-service AMNet site.

During the summer of 2013, we examined the performance of KCl-coated denuders for measuring gaseous oxidized mercury (GOM) by calibrating with a known source of GOM (i.e., HgBr2) at the North Birmingham SouthEastern Aerosol Research and Characterization (SEARCH) site.