Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis

Abstract

Abstract. There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) “inline”, there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) “static” on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO2, daily maximum ozone, and daily average PM2.5 patterns, with a higher agreement and correlation with daily ground observations.

DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite

“…This approach is also well-founded as it aligns with the notion that the diurnal variation of emissions is often correlated with changes in meteorological conditions. This concept finds support in models like the meteorology driven emission modeling for both biogenic (e.g., MEGAN 49 ) and anthropogenic emissions (Met-Emis 50 ). With this in mind, DeepSAT4D effectively interpolates NO 2 concentrations for the other 23 hours between two OMI-retrieved time slots.…”

“…60 Biogenic emissions were estimated using the Model for Emissions of Gases and Aerosols from Nature (MEGAN). 49 It is noteworthy that we have assessed the performance of WRF and CMAQ in simulating meteorological variables and air pollutant concentrations through extensive comparisons with observational data in our previous studies. [58][59] The horizonal domain covers most of East Asia and is represented by grids of 182 rows and 232 columns with a horizontal resolution of 27 km by 27 km.…”