A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application

IF 4 3区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Model Development Pub Date : 2023-10-20 DOI:10.5194/gmd-16-5949-2023

Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, Weimin Ju

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Abstract

Abstract. Top-down atmospheric inversion infers surface–atmosphere fluxes from spatially distributed observations of atmospheric composition in order to quantify 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 (WRF–CMAQ) modeling system model, the three-dimensional variational (3D-Var) algorithm, and the ensemble square root filter (EnSRF) algorithm. This system can simultaneously assimilate hourly in situ CO, SO2, NO2, PM2.5, and PM10 observations to infer gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5), and coarse PM10 (PMC) on a regional scale. In each data assimilation window, we use a “two-step” scheme, in which the emissions are inferred first and then input into the CMAQ model to simulate initial conditions (ICs) of the next window. The posterior emissions are then transferred to the next window as prior emissions, and the original emission inventory is only used in the first window. Additionally, a “super-observation” approach is implemented to decrease the computational costs, observation error correlations, and influence of representative errors. Using this system, we estimated the emissions of CO, SO2, NOx, PPM2.5, and PMC in December and July 2016 over China using nationwide surface observations. The results show that compared to the prior emissions (2016 Multi-resolution Emission Inventory for China – MEIC 2016)), the posterior emissions of CO, SO2, NOx, PPM2.5, and PMC in December 2016 increased by 129 %, 20 %, 5 %, 95 %, and 1045 %, respectively, and the emission uncertainties decreased by 44 %, 45 %, 34 %, 52 %, and 56 %, respectively. With the inverted emissions, the RMSE of simulated concentrations decreased by 40 %–56 %. Sensitivity tests were conducted with different prior emissions, prior uncertainties, and observation errors. The results showed that the two-step scheme employed in RAPAS is robust in estimating emissions using nationwide surface observations over China. This study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and in near-real time.

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用于排放估算的区域多大气污染物同化系统(RAPAS v1.0):系统开发与应用

摘要自上而下的大气反演从大气成分的空间分布观测推断地表大气通量，以便量化人为和自然排放。本研究基于天气研究与预报-社区多尺度空气质量(WRF-CMAQ)建模系统模型、三维变分(3D-Var)算法和集合平方根滤波(EnSRF)算法开发了区域多大气污染物同化系统(RAPAS v1.0)。该系统可以同时吸收每小时的CO、SO2、NO2、PM2.5和PM10现场观测数据，从而推断出区域尺度上CO、SO2、NOx、初级PM2.5 (PPM2.5)和粗PM10 (PMC)的网格化排放。在每个数据同化窗口中，我们使用“两步”方案，首先推断排放，然后输入CMAQ模型来模拟下一个窗口的初始条件(ICs)。后排放作为前排放转移到下一个窗口，原始排放清单仅在第一个窗口中使用。此外，还实现了一种“超观测”方法，以降低计算成本、观测误差相关性和代表性误差的影响。利用该系统估算了2016年12月和7月中国地区CO、SO2、NOx、PPM2.5和PMC的排放量。结果表明，与前期排放(2016年中国多分辨率排放清单- MEIC 2016)相比，2016年12月CO、SO2、NOx、PPM2.5和PMC的后验排放量分别增加了129%、20%、5%、95%和1045%，排放不确定性分别降低了44%、45%、34%、52%和56%。在反向排放的情况下，模拟浓度的RMSE降低了40% ~ 56%。灵敏度试验采用不同的先前排放、先前不确定度和观测误差进行。结果表明，RAPAS采用的两步方案在利用中国全国地面观测数据估算排放量方面是稳健的。该研究为大尺度、近实时地准确量化多物种人为排放提供了有用的工具。

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来源期刊

Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-

CiteScore

8.60

自引率

9.80%

发文量

352

审稿时长

6-12 weeks

期刊介绍： Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.