Surface ozone regulation policies rely heavily on air quality models, such as CAMx, as important guiding tools. Comparison with observations is crucial to validating a model’s ability to represent ozone production chemistry. Identifying factors influencing surface ozone formation is complicated because ozone photochemical production rates are non-linearly dependent on concentrations of precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs). We compare ozone production regimes (OPRs) identified from satellite observations and model simulations, as defined by the ratio of column formaldehyde to nitrogen dioxide (FNR, HCHO/NO2). We perform CAMx simulations for June-July-August 2016 over the Contiguous United States (CONUS) and compared these outputs against two OMI NO2and HCHO retrievals. Our analysis spans diurnal and altitudinal variations of OPRs, offering important insights for effective policy formulation. At the time of the OMI overpass (~1:30 PM LT), OPR is NOx-limited over most of the CONUS, as determined from OMI column ratios. Analysis of CAMx column ratios shows similar results. In contrast, more regions are VOC-limited when we constrain our ratio to within the Planetary Boundary Layer (PBL). In the morning (~9 AM LT), the CAMx PBL column ratios shift towards VOC-limited regime. We highlight areas of the CONUS for which satellite measurements of FNR may not be an accurate indicator of near-surface OPRs. Air quality regulations based on satellite observations should consider the diurnal variations of surface OPRs and assess how well their ratios represent near-surface OPR. Our results have implications for interpretation of TEMPO data for policy relevant applications.

We studied atmospheric methane observations from November 2016 to October 2017 from one rural and two urban towers in the Baltimore-Washington region (BWR). Methane observations at these three towers display distinct seasonal and diurnal cycles with maxima at night and in the early morning, reflecting local emissions and boundary layer dynamics. Peaks in winter concentrations and vertical gradients indicate strong local anthropogenic wintertime methane sources in urban regions. In contrast, our analysis shows larger local emissions in summer at the rural site, suggesting a dominant influence of wetland emissions. We compared observed enhancements (mole fractions above the 5th percentile) to simulated methane enhancements using the WRF-STILT model driven by two EDGAR inventories. When run with EDGAR 5.0, the low bias of modeled versus measured methane was greater (ratio of 1.9) than the bias found when using the EDGAR 4.2 emission inventory (ratio of 1.3). However, the correlation of modeled versus measured methane was stronger (~1.2 times higher) for EDGAR 5.0 compared to results found using EDGAR 4.2. In winter, the inclusion of wetland emissions using WETCHARTs had little impact on the mean bias, but during summer, the low bias for all hours using EDGAR 5.0 improved by from 63 to 23 nanomoles per mole of dry air or parts per billion (ppb) at the rural site. We conclude that both versions of EDGAR underestimate the regional anthropogenic emissions of methane, but version 5.0 has a more accurate spatial representation.

Ozone (O3) is an important trace and greenhouse gas in the atmosphere yet, and it threatens the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct measurements whose accuracy and density vary considerably. To overcome these limitations, we employed the ensemble learning method of the extremely randomized trees model by utilizing the spatiotemporal information of a large number of input variables from ground-based observations, remote sensing, atmospheric reanalysis, and model simulation products to estimate ground-level O3. This method yields uniform, long-term and continuous spatiotemporal information of daily maximum eight-hour average (MDA8) O3 over China (called ChinaHighO3) from 2013 to 2020 at a 10 km resolution without any missing values (spatial coverage = 100%). Evaluation against observations indicates that our O3 estimations and predictions are reliable with an average out-of-sample (out-of-station) coefficient of determination (CV-R2) of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 [units here are at standard conditions (273K, 1013hPa)], and are also robust at varying spatial and temporal scales in China. This high-quality and full-coverage O3 dataset allows us to investigate the exposure and trends in O3 pollution at both long- and short-term scales. Trends in O3 concentrations varied substantially but showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020 in China. Most areas show an increasing trend since 2015, especially in summer ozone over the North China Plain. Our dataset accurately captured a recent national and regional O3 pollution event from 23 April to 8 May in 2020. Rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.