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Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO

Daily maximum 8-hour average (MDA8) ozone (O(3)) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O(3) concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O(3) concentrations due to changes in precursor emissions such as nitrogen oxides (NO(X)) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O(3) trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90(th) and 98(th) percentiles of the distribution of MDA8 O(3) concentrations, allowing for a better understanding of the effects of local meteorology on peak O(3) levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90(th) percentile, and 98(th) percentile MDA8 O(3) concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O(3) levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O(3) National Ambient Air Quality Standards (NAAQS).

Prognostic meteorological models such as Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) are often used to supply inputs for retrospective air quality modeling done to support ozone and PM2.5 emission control demonstrations. In this study, multiple configurations of the WRF model are applied at 4 km grid resolution and compared to routine meteorological measurements and special study measurements taken in California during May–June 2010. One configuration is routinely used by US EPA to generate meteorological inputs for regulatory air quality modeling and another that is used by research scientists for evaluating meteorology and air quality. Mixing layer heights estimated from airborne High Spectral Resolution Lidar (HSRL) measurements of aerosol backscatter are compared with WRF modeled planetary boundary layer (PBL) height estimates. Both WRF configurations generally capture the variability in HSRL mixing height between days, hour-to-hour, and between surface features such as terrain and land–water interfaces. Fractional bias over all flights and both model configurations range from −38% to 32% and fractional error ranges from 22% to 58%. Surface and upper level measurements of temperature, water mixing ratio, and winds are generally well characterized by both WRF model configurations, often more closely matching surface observations than the input analysis data (12-NAM). The WRF model generally captures orographic and mesoscale meteorological features in the central Valley (bifurcation of wind flow from the San Francisco bay into the Sacramento and San Joaquin valleys) and Los Angeles air basin (ocean-land flows) during this summer period.

An accurate representation of meteorological processes is important to the accurate predictions of meteorology and air quality. In this study, the Weather Research and Forecasting model with Chemistry (WRF/Chem) is utilized to examine the sensitivity of air quality predictions to two planetary boundary layer (PBL) schemes and three land-surface models (LSMs). Model simulations with different PBL schemes and LSMs are conducted over the Houston–Galveston area for a 5-day summer episode from the 2000 Texas Air Quality Study (TexAQS-2000). Sensitivity to horizontal grid spacing (12 vs. 4 km) and nesting methods (1- or 2-way) is also studied. Model predictions are evaluated with available surface and aircraft observations. Both meteorological and chemical predictions at the surface and aloft show stronger sensitivity to LSMs than to the PBL schemes. The model predictions also show a stronger sensitivity to horizontal grid spacing using 1-way nesting than 2-way nesting and to the nesting method at 4 km than 12 km. The benefits (or disbenefits) of using more complex meteorological schemes, finer horizontal grid spacing, and more sophisticated 2-way nesting may vary and must be evaluated for specific episodes. The results from this study also indicate a need to refine model treatments at a fine grid spacing and the current 2-way nesting method used in WRF/Chem for improvement of model performance.

Gas and aerosol predictions from CMAQ simulations with horizontal grid spacings of 8- and 32-km are evaluated against available observations from CASTNet, IMPROVE, AIRS-AQS, SOS99/SOS99NASH, SEARCH, and ARIES for the southeastern US for the period of 1–10 July 1999. The predictions evaluated in this work include mixing ratios of O3 (hourly, maximum 1-h, and 8-h average), NOx, HNO3, NOy, and mass concentrations of PM10, PM2.5, and PM2.5 components. Our evaluation has shown that CMAQ tends to underpredict maximum 1-h O3 mixing ratios on high O3 days at some sites. It overpredicts the maximum and minimum hourly O3 mixing ratios for most low O3 days, the daytime and nighttime hourly, and the maximum 8-h average O3 mixing ratios on most days at all sites. The model performance for hourly O3 mixing ratios generally meets EPA's criteria but deteriorates for maximum 1- and 8-h average O3 mixing ratios. CMAQ underpredicts the mass concentrations of PM10, PM2.5, and PM2.5 composition and fails to reproduce their temporal variations (except for sulfate). Largest underpredictions occur for organic matter (OM2.5) and nitrate2.5 among all PM components. These underpredictions and overpredictions may be caused by inaccurate meteorological predictions (e.g., the PBL height, wind speed/direction, vertical mixing, temperature, and relative humidity) and boundary conditions for chemical species (e.g., O3), underestimation in emissions (e.g., NOx, NH3, and primary OM), as well as uncertainties in model assumptions and treatments in aerosol chemistry and microphysics.

National ambient air quality standards (NAAQS) have been set for PM_2.5 due to its association with adverse health effects. PM_2.5 design values in the South Coast Air Basin (SoCAB) and San Joaquin Valley of California exceed NAAQS levels, and NH^(+)_(4) and NO^(-)_(3) make up the largest fraction of total PM2.5 mass on polluted days. Here we evaluate fine-scale simulations of PM_(2.5) NH^(+)_(4) and NO^(-)_(3) with the Community Multiscale Air Quality model using measurements from routine networks and the California Research at the Nexus of Air Quality and Climate Change 2010 campaign. The model correctly simulates broad spatial patterns of NH^(+)_(4) and NO^(-)_(3) including the elevated concentrations in eastern SoCAB. However, areas for model improvement have been identified. NH_3 emissions from livestock and dairy facilities appear to be too low, while those related to waste disposal in western SoCAB may be too high. Analyses using measurements from flights over SoCAB suggest that problems with NH3 predictions can influence NO^(-)_(3) predictions there. Offline ISORROPIA II calculations suggest that overpredictions of NH_x in Pasadena cause excessive partitioning of total nitrate to the particle phase overnight, while underpredictions of Na^+ cause too much partitioning to the gas phase during the day. Also, the model seems to underestimate mixing during the evening boundary layer transition leading to excessive nitrate formation on some nights. Overall, the analyses demonstrate fine-scale variations in model performance within and across the air basins. Improvements in inventories and spatial allocations of NH_3 emissions and in parameterizations of sea spray emissions, evening mixing processes, and heterogeneous ClNO_2 chemistry could improve model performance.