Journal article
Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
Atmospheric chemistry and physics, Vol.20(11), pp.6651-6670
06/01/2020
DOI: 10.5194/acp-20-6651-2020
Abstract
Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution ( 2 ∘× 2.5 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e3e327827f9255bbd8eb2f675554019e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00001.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00001.png"/></svg:svg> ) posterior SO2 and NOx emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these 2 ∘× 2.5 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e1aaf5b4047def0324c5db88dce1d7d1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00002.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00002.png"/></svg:svg> posterior SO2 and NOx emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS SO2 and NO2 products retrieved at 50 km×50 km at nadir and ∼ 190 km × 50 km <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="86pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="be0f90b345790fc3bfd39f735725de9f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00003.svg" width="86pt" height="10pt" src="acp-20-6651-2020-ie00003.png"/></svg:svg> at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem simulations of surface SO2 and NO2 concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale 2 ∘× 2.5 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="cb00614ce5e68aee27df96e84dc83c24"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00004.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00004.png"/></svg:svg> GEOS-Chem surface SO2 and NO2 concentrations to the resolution of 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="6c0122c5f517b964e9293f8b27e553dc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00005.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00005.png"/></svg:svg> through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="61869edc76f6434e768838cbca6ba70b"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00006.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00006.png"/></svg:svg> simulation using the prior MIX emissions has the correct spatial distribution of SO2 and NO2 concentrations but a systematic bias; (b) downscale surface NO2 simulations at 2 ∘× 2.5 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="b6313970925e1be6cb2b9210f4e4b81a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00007.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00007.png"/></svg:svg> to 0.05 ∘× 0.05 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="fb5d5ee9be6bf39a026c6c70b10bc67e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00008.svg" width="64pt" height="11pt" src="acp-20-6651-2020-ie00008.png"/></svg:svg> according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations; (c) downscale posterior emissions (DE) of SO2 and NOx to 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a686986a9b4a600f3085804a00300a9a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00009.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00009.png"/></svg:svg> with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior NOx emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior SO2 and NO2 simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the posterior NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a02d6da6fe2af4371c56aa09ccd6317e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00010.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00010.png"/></svg:svg> using the MIX-DE approach has NCRMSEs that are 58.8 % and 14.7 % smaller than the prior 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="21a7b1079ea296fad5da96f5060a3cf9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00011.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00011.png"/></svg:svg> MIX simulation for surface SO2 and NO2 concentrations, respectively, but the RMSE from the MIX-DE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both SO2 and NO2 ; (d) the NL-DE posterior NO2 simulation also improves on the prior MIX simulation at 0.25 ∘× 0.3125 ∘ <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="f9bdb3a5c0c170d06a6d15b83863860a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00012.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00012.png"/></svg:svg> , but it is worse than the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled SO2 and NO2 simulations simultaneously, using the posterior SO2 and NOx emissions from joint inverse modeling of both species is better than only using one ( SO2 or NOx ) emission from corresponding single-species inverse modeling and is similar to using the posterior emissions of SO2 and NOx emission inventories respectively from single-species inverse modeling. Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show that (a) the improvements of forecasting surface SO2 concentrations through MIX-DE and MIX-DDC are comparable; (b) for the NO2 forecast, MIX-DE shows larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE but shows larger NCRMSE; (d) the forecast of surface O3 concentrations is improved by MIX-DE downscaled posterior NOx emissions. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain SO2 and NO2 emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality modeling for the present month.
Details
- Title: Subtitle
- Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
- Creators
- Yi Wang - Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USAJun Wang - Department of Chemical and Biochemical Engineering, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USAMeng Zhou - Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USADaven K Henze - Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USACui Ge - Department of Chemical and Biochemical Engineering, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USAWei Wang - China National Environmental Monitoring Center, Beijing 100012, China
- Resource Type
- Journal article
- Publication Details
- Atmospheric chemistry and physics, Vol.20(11), pp.6651-6670
- DOI
- 10.5194/acp-20-6651-2020
- ISSN
- 1680-7316
- eISSN
- 1680-7324
- Publisher
- Copernicus Publications
- Language
- English
- Date published
- 06/01/2020
- Academic Unit
- Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984049699902771
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