Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multi-sensor satellite data: 2. Downscaling techniques for air quality analysis and forecasts

Abstract

Abstract. Top-down emissions estimates provide valuable up-to-date information on pollution sources; however, the computational effort involved with developing these emissions often requires 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°) posterior SO2 and NOx emissions (derived through inverse modeling in Part I of this study) for improving air quality assessment and forecasts over China in October 2013. The SO2 and NOx emission inverse modeling was conducted at the 2° × 2.5° resolution in Part I to save computational time. The prior emission inventory (MIX) as well as the posterior GEOS-Chem simulations of surface SO2 and NO2 concentrations at this 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° GEOS-Chem surface SO2 and NO2 concentrations to the resolution of 0.25° × 0.3125° through a Dynamic Downscaling Concentration (MIX-DDC) approach, which assumes that the 0.25° × 0.3125° 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° to 0.05° × 0.05° according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOMI NO2 observations; (c) Downscale posterior Emissions (DE) of SO2 and NOx to 0.25° × 0.3125° 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 compared to the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior NO2 simulation 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° using the MIX-DE approach has NCRMSEs that are 58.8 % and 14.7 % smaller than the prior 0.25° × 0.3125° 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°, 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) emissions from corresponding single-species inverse modeling and is similar to using the posterior emissions for both SO2 and inventories 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 (a) the improvements of forecasting surface SO2 concentrations through MIX-DE and MIX-DDC are comparable; (b) for NO2 forecast, MIX-DE show 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. 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 model for the present month.

Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

“…Retrievals of NO 2 from satellite ultraviolet-visible (UV-Vis) spectral measurements are powerful tools for the characterization of the spatiotemporal variation of NO x . Benchmarked by the Global Ozone Monitoring Experiment (GOME) instrument onboard the European Remote Sensing Satellite (ERS-2), global monitoring of tropospheric NO 2 has been possible since 1995 [12][13][14], which boosted the investigations of spatial variability and long-term changes of NO 2 abundance [15,16], various NO x emission from anthropogenic [17][18][19][20][21] and natural sources [22,23], NO x lifetime [24,25], etc. Moreover, a global geostationary constellation of NO 2 monitoring is emerging [26][27][28], with hourly and km-scale monitoring capabilities to further facilitate the investigation of diurnal variability of NO x -relevant processes.…”

Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multi-sensor satellite data: 1. formulation and sensitivity analysis

“…and Sentinel-4 (monitoring Europe) are to be launched in the next several years, and all of these satellites will provide hourly SO2 and NO2 observations during the daytime. Furthermore, in Part II of this work, we develop various downscale methods to apply these coarser-resolution top-down estimates of emissions for air quality forecasts and evaluate the forecasts with surface measurements, both at the finer spatial scale (Wang et al, 2019).…”

“…All these experiments use OMPS SO2 and NO2 retrievals to optimize corresponding emissions over China in October 2013 at a horizontal resolution of 2°x2.5°. Although finer resolution options such as 0.5°x0.625° or 0.25°x0.3125° are available for China, the 2°x2.5° resolution is selected to save computational time; in Part II (Wang et al, 2019) of this study, we develop downscaling tools for regional air quality modeling.…”

“…Specifically, this study aims to conduct 4D-Var assimilation of VCDs of SO2 and NO2 from OMPS to constrain SO2 and NOx emissions over China using the 95 GEOS-Chem 4D-Var inverse modeling framework. In our companion study (Wang et al, 2019), we develop approaches to downscaling the optimized emission inventories for improving air quality predictions. Despite their numerous applications for top-down estimate of SO2 and NOx emissions in the past two decades, GOME and SCIAMCHY stopped providing data in 2004 and 2012, respectively, while OMI has been suffering from a row anomaly that leads to much less spatial coverage and larger data uncertainty (Schenkeveld et al, 2017).…”

Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations

“…Existing downscaling methods include dynamical downscaling and statistical downscaling. Dynamical downscaling simulates using high-resolution physical local-area models based on low-resolution boundary conditions; however, it is computational demanding (Hong et al, 2016;Yahya et al, 2017;Wang et al, 2020). Statistical downscaling trains linear or nonlinear statistical models to estimate high-resolution information, but the downscaled variable is generally the same as the lowresolution origin (Zhu et al, 2016;Ahmed et al, 2018;Oteros et al, 2019;Khan et al, 2019).…”