Gridded bias correction of modeled PM2.5 for exposure assessment, and estimation of background concentrations over a coastal valley region of northwestern British Columbia, Canada

Abstract

ABSTRACT Chemical transport models (CTM) can have large biases and errors when simulating pollutant concentrations. To improve the characterization of fine particulate matter (PM2.5) over complex terrain for exposure assessments, three mathematical formulae that utilized the relationship between modeled and observed quantile concentrations at a monitor location were developed. These were then applied to 1 year of CMAQ model output of PM2.5 over the Terrace–Kitimat Valley of northwestern British Columbia, Canada. The final products enhanced the representation of ambient levels at existing monitoring stations when evaluated with conventional statistical measures. Better agreement of corrected outputs with observed compliance metrics was also found. On average, the absolute errors of amended outputs were 11% and 10% for the annual mean PM2.5 and 98th percentiles of daily concentrations, respectively, compared to 45% and 61%, respectively, in the original outputs. These improvements provided greater confidence to use the amended outputs to estimate concentrations at locations without monitors. The predominance of pristine conditions in the modeling domain was exploited to derive annual background PM2.5 concentrations over the valley, which was estimated to be 2.0–2.3 μg m−3. To our knowledge, this is the first study to calculate background PM2.5 concentrations over northern BC coastlands using bias-corrected outputs from an air quality model. Implications: Bias correction of CMAQ model output was necessary for assessing regulatory compliance for ambient PM2.5. The implications are notable. First, for low to moderate spatial heterogeneity in monitoring data, the use of regression equations that relates quantile mean concentrations of model outputs to those of observational data enhances the estimation of PM2.5 at unmonitored locations. Second, by providing spatial pollutant distribution ahead of planned industrial development in Terrace–Kitimat Valley (TKV), corrected model output offers a baseline for tracking progress in airshed management. Third, correction improved pollutant exposure classification, for which the risk was predominantly low. Finally, 2.0–2.3 μg m−3 should be considered as PM2.5 concentrations that are irreducible when setting voluntary targets for ambient levels in the area.