RAPID: Development of a Nonlinear Activity Response Model for Coronavirus (COVID-19) Scenario Projections Based on the Observations of the Shutdown-Reopening Cycle of China

RAPID：基于对中国关闭-重新开放周期的观察，开发冠状病毒（COVID-19）情景预测的非线性活动响应模型

• 批准号: 2030425
• 负责人: Yuhang Wang
• 金额: $ 19.98万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030425&HistoricalAwards=false
• 关键词: RAPID; Development; Nonlinear; Activity; Response

This RAPID project will investigate the cycle of shutdown and reopening of businesses in China due to COVID-19, and assess the usefulness of studying this cycle for providing guidance for policymaking in the U.S., European countries, and the countries where the medical testing capability is severely limited. The response to COVID-19 has varied significantly from the epicenter (Wuhan and Hunan province) to the other 25 provinces, 4 provincial-level megacities (Beijing, Shanghai, Tianjin, and Chongqing), and 5 autonomous regions for minority ethnic groups. The proxy data for response activities includes the processed TROPOMI tropospheric vertical NO2 column data and inverse modeling of daily NOx emissions in China. The hypotheses of this project are that (1) the COVID-19 infection data (including coronavirus positive test, hospitalization, and mortality data) and government policies largely shape the responses by the society and businesses; (2) near real-time monitoring of tropospheric column NO2 provide timely high spatiotemporal data for gauging the activity responses by the society and businesses, which are unavailable through conventional means; (3) with the large datasets of varying degrees of COVID-19 infection, governmental policy, and activity responses in different regions of China, a nonlinear response model will be developed and this model can later be corrected with data from US and other countries to provide policymaking guidance through scenario analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该RAPID项目将调查中国因COVID-19而关闭和重新开业的企业周期，并评估研究该周期的有用性，为美国、欧洲国家和医疗检测能力较差的国家的政策制定提供指导。受到严格限制。从疫情中心（武汉和湖南省）到其他 25 个省份、4 个省级特大城市（北京、上海、天津和重庆）以及 5 个少数民族自治区，对 COVID-19 的反应差异很大。响应活动的代理数据包括处理后的TROPOMI对流层NO2垂直柱数据和中国每日NOx排放量的反演模型。 该项目的假设是：（1）COVID-19感染数据（包括冠状病毒阳性检测、住院和死亡数据）和政府政策在很大程度上决定了社会和企业的反应； （2）对流层NO2柱的近实时监测为衡量社会和企业的活动响应提供了及时的高时空数据，这是传统手段无法获得的； （3）利用中国不同地区不同程度的COVID-19感染、政府政策和活动反应的大型数据集，将建立一个非线性反应模型，并且该模型随后可以用美国和其他国家的数据进行修正通过情景分析提供政策制定指导。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

中国长江三角洲地区气溶胶和微量气体的时空特征：跨界污染和气象的影响

• DOI: 10.1186/s12302-022-00668-2
• 发表时间: 2022-09-08
• 期刊: Environmental Sciences Europe
• 影响因子: 5.9
• 作者: Zeeshan Javed;M. Bilal;Z. Qiu;Guanlin Li;O. S;hu;hu;Khalid Mehmood;Yu Wang;Md. Arfan Ali
• 通讯作者: Md. Arfan Ali

Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model

使用 4D-Var 对初始浓度和排放源进行优化估计，用于 2D 传输模型中的空气污染预测

• DOI: 10.1016/j.scitotenv.2021.145580
• 发表时间: 2021-06
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Liu, Caili;Zhang, Shaoqing;Gao, Yang;Wang, Yuhang;Sheng, Lifang;Gao, Huiwang;Fung, J.C.H.
• 通讯作者: Fung, J.C.H.

Wintertime Formation of Large Sulfate Particles in China and Implications for Human Health

中国冬季大硫酸盐颗粒的形成及其对人体健康的影响

• DOI: 10.1021/acs.est.3c05645
• 发表时间: 2023-12-05
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Zhang, Qianru;Wang, Yuhang;Liu, Maodian;Zheng, Mingming;Yuan, Lianxin;Liu, Junfeng;Tao, Shu;Wang, Xuejun
• 通讯作者: Wang, Xuejun

NOx Emission Reduction and Recovery during COVID-19 in East China

华东地区 COVID-19 期间氮氧化物减排和回收

• DOI: 10.3390/atmos11040433
• 发表时间: 2020-04-01
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Ruixiong Zhang;Yuzhong Zhang;Haipeng Lin;Xu Feng;T. Fu;Yuhang Wang
• 通讯作者: Yuhang Wang

Quantifying the Impacts of COVID-19 Lockdown and Spring Festival on Air Quality over Yangtze River Delta Region

量化 COVID-19 封锁和春节对长三角地区空气质量的影响

• DOI: 10.3390/atmos12060735
• 发表时间: 2021-06-08
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Z. Javed;Aimon Tanvir;Yuhang Wang;A. Waqas;M. Xie;A. Abbas;O. S;hu;hu;Cheng Liu
• 通讯作者: Cheng Liu