Neighborhood-Scale O3, NO2, and PM2.5 in Washington, D.C. from a 1 × 1 km2 WRF–SMOKE–CMAQ Framework

Abstract

Quantifying air pollution concentrations at fine spatial scales is important for resolving intra-urban exposure variability and identifying sources that disproportionately impact population groups across locations. We developed a neighborhood-scale resolution (1 × 1 km²) WRF–SMOKE–CMAQ modeling framework to characterize O₃, NO₂, and PM_2.5 concentrations in Washington, D.C. in 2017. In evaluations against EPA Air Quality System observations and the National Climatic Data Center meteorological data, WRF reproduced the temporal variability of temperature and specific humidity with reasonable agreement (average r = 0.8 and 0.7, respectively), although biases varied across seasons. CMAQ reproduced seasonal and diurnal patterns of O₃ and PM_2.5 with r up to 0.7 for hourly O₃, though NO₂ was negatively biased (−14% to −29%), reflecting challenges in resolving near-road gradients. We identified pronounced intra-urban concentration variability of NO₂, whose coefficient of variation was greater than that of O₃ and more than of three times that of PM_2.5. O₃ concentrations were lower in all months by 4% to 11% in the urban core compared to outside the urban core due to NO_x titration/inhibition. Relative to 4 × 4 km² resolution, 1 × 1 km² simulations increased population-weighted exposure (PWE) for O₃ (+0.5% to +1.5%) but decreased PWE for NO₂ (−3% to −6%) and PM_2.5 (−1.5% to −3%). VOC/NO_x ratios indicated predominantly NO_x-saturated chemistry within the urban core. This work presents the value and the remaining challenges of high-resolution regional air quality modeling in heterogeneous urban settings.