A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application
Abstract. Top-down atmospheric inversion infers surface-atmosphere fluxes from spatially distributed observations of atmospheric compositions, which is a vital means for quantifying large-scale anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting/Community Multiscale Air Quality Modeling System (WRF/CMAQ) model, the three-dimensional variational (3DVAR) algorithm and the ensemble square root filter (EnSRF) algorithm. It is capable to simultaneously assimilate spatially distributed hourly in-situ measurements of CO, SO2, NO2, PM2.5 and PM10 concentrations to quantitatively optimize gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5) and coarse PM10 (PMC) on regional scale. RAPAS includes two subsystems, initial field assimilation (IA) subsystem and emission inversion (EI) subsystem, which are used to generate a "perfect" chemical initial condition (IC), and conduct inversions of anthropogenic emissions, respectively. A "two-step" inversion scheme is adopted in the EI subsystem in its each data assimilation (DA) window, in which the emission is inferred in the first step, and then, it is input into the CMAQ model to simulate the initial field of the next window, meanwhile, it is also transferred to the next window as the prior emission. The chemical IC is optimized through the IA subsystem, and the original emission inventory is only used in the first DA window. Besides, a "super-observation" approach is implemented based on optimal estimation theory to decrease the computational costs and observation error correlations and reduce the influence of representativeness errors.
With this system, we estimated the emissions of CO, SO2, NOx, PPM2.5 and PMC in December 2016 over China using the corresponding nationwide surface observations. The 2016 Multi-resolution Emission Inventory for China (MEIC 2016) was used as the prior emission. The system was run from 26 November to 31 December, in which the IA subsystem was run in the first 5 days, and the EI subsystem was run in the following days. The optimized ICs at the first 5 days and the posterior emissions in December were evaluated against the assimilated and independent observations. Results showed that the root mean squared error (RMSE) decreased by 50.0–73.2%, and the correlation coefficient (CORR) increased to 0.78–0.92 for the five species compared to the simulations without 3DVAR. Additionally, the RMSE decreased by 40.1–56.3 %, and the CORR increased to 0.69–0.87 compared to the simulations without optimized emissions. For the whole mainland China, the uncertainties were reduced by 44.4 %, 45.0 %, 34.3 %, 51.8 % and 56.1 % for CO, SO2, NOx, PPM2.5 and PMC, respectively. Overall, compared to the prior emission (MEIC 2016), the posterior emissions increased by 129 %, 20 %, 5 %, and 95 % for CO, SO2, NOx and PPM2.5, respectively, indicating that there was significant underestimation in the MEIC inventory. The posterior PMC emissions, including anthropogenic and natural dust contributions, increased by 1045 %. A series of sensitivity tests were conducted with different inversion processes, prior emissions, prior uncertainties, and observation errors. Results showed that the "two-step" scheme clearly outperformed the simultaneous assimilation of ICs and emissions ("one-step" scheme), and the system is rather robust in estimating the emissions using the nationwide surface observations over China. Our study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and near-real time.
Shuzhuang Feng et al.
Status: final response (author comments only)
CEC1: 'Comment on gmd-2021-134', Juan Antonio Añel, 12 Oct 2021
AC1: 'Reply on CEC1', Shuzhuang Feng, 13 Oct 2021
- CEC2: 'Reply on AC1', Juan Antonio Añel, 13 Oct 2021
- AC1: 'Reply on CEC1', Shuzhuang Feng, 13 Oct 2021
RC1: 'Comment on gmd-2021-134', Anonymous Referee #1, 15 Oct 2021
- AC2: 'Reply on RC1', Shuzhuang Feng, 28 Mar 2022
- AC4: 'Reply on RC1', Shuzhuang Feng, 08 May 2022
RC2: 'Comment on gmd-2021-134', Anonymous Referee #2, 21 Oct 2021
- AC3: 'Reply on RC2', Shuzhuang Feng, 28 Mar 2022
- AC5: 'Reply on RC2', Shuzhuang Feng, 08 May 2022
Shuzhuang Feng et al.
Anthropogenic air pollutant emissions over China inferred by Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) https://doi.org/10.5281/zenodo.4718290
Shuzhuang Feng et al.
Viewed (geographical distribution)
After checking your manuscript, it has come to our attention that it does not comply with our Code and Data Policy.
In the Code and Data Availability section, you state, "The code of this system can be obtained on request from the corresponding author". However, this is not acceptable for papers intending to be published in Geosc. Mod. Dev. You must publish the RAPAS v1.0 code in one of the appropriate repositories.
In this way, before the Discussions stage is closed, you must reply to this comment with the link to the repository for the code and the corresponding DOI.
Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code. Also, please, include a license for RAPAS v1.0. If you do not include a license, the code continues to be your property and can not be used by others. Therefore, when uploading the model's code to the repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options: GPLv2, Apache License, MIT License, etc.
In the meantime, please reply as soon as possible to this comment with its link so that it is available for the peer-review process, as it should be.
Juan A. Añel
Geosc. Mod. Dev. Executive Editor