key: cord-1053511-33sf809e authors: Zhang, Yanqing; Ma, Zhaokun; Gao, Yi; Zhang, Meigen title: Impacts of the meteorological condition versus emissions reduction on the PM2.5 concentration over Beijing–Tianjin–Hebei during the COVID-19 lockdown date: 2020-12-14 journal: nan DOI: 10.1016/j.aosl.2020.100014 sha: 6e1eee7ffa21cf91a4b63b91ee0ac9eb152de542 doc_id: 1053511 cord_uid: 33sf809e The impacts of the meteorological condition and emissions reduction on the aerosol concentration over the Beijing–Tianjin–Hebei (BTH) region during the COVID-19 lockdown were analyzed by conducting three numerical experiments, including one with the meteorological field in 2019 and MEIC-2019 (2019 monthly Multi-resolution Emissions Inventory for China), one with the meteorological field in 2020 and MEIC-2019, and one with the meteorological field in 2020 and MEIC-2020, via a WRF-Chem model. The numerical experiments were performed from 3–16 February in 2019 and in 2020, during which a severe fog–haze event (3–16 February 2020) occurred in the BTH region, with a simulated maximum daily PM2.5 of 245 μg m−3 in Tangshan and 175 μg m−3 in Beijing. The results indicate that the daily PM2.5 decreased by 5–150 μg m−3 due to the emissions reduction and increased by 10–175 μg m−3 due to the meteorological condition in Beijing, Shijiazhuang, Cangzhou, Handan, Hengshui, Chengde, Zhangjiakou, and Tangshan from 7–14 February. For the horizontal distribution, PM2.5 and different aerosol species concentrations from 7–14 February 2020 increased compared with those during the same period in 2019, indicating that the accumulation of pollutants caused by the unfavorable meteorological condition offset the decreases caused by the emissions reduction, leading to the high aerosol concentration during the COVID-19 lockdown. 摘要 本研究从气象条件和减排两方面探讨了COVID-19封锁期间严重雾霾发生的原因[研究目的], 并定量的分析了气象条件和减排对PM2.5浓度的相对影响[创新点].使用WRF-Chem在2019年2月3日–16日和2020年2月3日–16日进行了三个数值模拟试验, 分别为2019年气象场与MEIC-2019 (2019年中国多分辨率月度排放清单) ,2020年气象场与MEIC-2019,2020年气象场与MEIC-2020[研究方法].结果表明COVID-19封锁期间严重雾霾的原因是不利气象条件增加的PM2.5浓度大于减排减少的PM2.5浓度[重要结论]. There was a case of pneumonia caused by coronavirus in Wuhan city, Hubei Province, China, at the end of December 2019. Subsequently, the coronavirus was officially named Coronavirus Disease 2019 by the World Health Organization (WHO 2020), and it quickly spread to Hubei Province and other parts of the country. During the Chinese New Year holiday, the government formulated policies to reduce the spread of the virus by reducing contact and increasing physical distance. As a part of these social policies, the Chinese government encouraged people to stay at home rather than attending mass gatherings, canceled or postponed large-scale public events, and delayed the opening of universities and the resumption of factory work. Only a limited part of the urban public transportation system was in operation, and all interprovincial bus routes were suspended (Tian et al. 2020; Chen et al. 2020) . These measures reduced the number of vehicles on the road, as well as industrial operations and restaurants in operation, which are the emission sources of air pollution, especially in winter. Human and industrial activities were reduced to basic or minimal levels (Zhang et al. 2019) . Compared with that at the beginning of January 2020, the NO 2 concentration in China dropped sharply in mid-February 2020, according to NASA satellite data (NASA 2020) . The data provided by the pollution monitoring agency showed that the PM 2.5 decreased by an average of 15-17 μg m −3 d −1 in February 2020 compared with that in January 2020. The quantity of carbon dioxide emissions, e.g., emissions from coal and crude oil, decreased by 25% during the COVID-19 lockdown compared to the same period in 2019 in the North China Plain region (Kerimray et al. 2020) . The effect of these measures on reducing pollutant concentrations during the COVID-19 lockdown has been widely discussed (Wang et al. 2020; Collivignarelli et al. 2020; Li et al. 2020) . Despite the significant reduction in emissions, several serious fog-haze events occurred in the North China Plain region during the COVID-19 lockdown, arousing great concern. Specifically, the PM 2.5 reached 181-208 μg m −3 in Beijing on 10-13 February (Nichol et al. 2020 ) and increased by approximately 30%-50% compared to that during the period before the COVID-19 lockdown over the Beijing-Tianjin-Hebei (BTH) region . Therefore, to evaluate the impact of the meteorological condition and emissions reduction on the near-surface PM 2.5 during the COVID-19 lockdown, three numerical experiments with different meteorological fields and emission sources were conducted by using a coupled meteorology and aerosol/chemistry model (WRF-Chem). The online coupled model WRF-Chem simultaneously simulates the meteorological field and the concentrations of gases and aerosols (Grell et al. 2005) . In this study, WRF-Chem (v4.1.2) was configured to cover the east part of China with 100 (south-north)  90 (west-east) grid points and a 27-km horizontal resolution centered on central China (38.0°N, 116.5°E). The vertical direction was divided into 36 layers, and the average height of the first layer was approximately 30 m. The model domain is shown in Figure S1 in the supplementary file. The meteorological initial and lateral boundary conditions used in WRF-Chem were from the NCEP FNL (final) analysis data with a 1° × 1° spatial resolution. Four-dimensional data assimilation (Werner et al. 2018 ) was used to improve the accuracy of the simulation results. The nudging coefficients of temperature, water vapor, and horizontal wind components were 3  10 −4 s −1 , 1  10 −5 s −1 , and 3  10 −4 s −1 , respectively (Otte 2008) . The chemical initial and boundary conditions were from the global chemical forecast output of the Whole Atmosphere Community Climate Model (Marsh et al. 2013) . Details regarding the parameterization schemes used in this study are listed in Table S1 . (Guenther et al. 2006 ). The biomass burning emissions were based on the Fire Inventory from NCAR (Wiedinmyer, Yokelson, and Gullett, 2014) . Dust emissions were calculated online according to the method of Shao et al. (2011) . Sea salt emissions were calculated online with reference to Gong, Barrie, and Blanchet (1997) . To investigate the causes of the high near-surface PM 2.5 concentration in the BTH region during the COVID-19 lockdown, three experiments were conducted from 20 January to 16 The impacts of the meteorological condition and emissions reduction on the near-surface PM 2.5 concentration during the COVID-19 lockdown are discussed in this section. Figure 1 shows the time series of the modeled daily near-surface PM 2.5 concentration during 3-16 February 2019 (19M19E) and 3-16 February 2020 (20M20E), the differences between the observed PM 2.5 in 2020 and 2019 (OBS_change), and the modeled PM 2.5 between 20M20E and 19M19E (ALL), between 20M19E and 19M19E (MET) , and between 20M20E and 20M19E (EMI), which was averaged at the sites in BJ, SJZ, CZ, HD, HS, CD, ZJK, and TS. It can be seen that the daily PM 2.5 was higher than 100 μg m −3 at BJ, SJZ, CZ, HD, HS, and Table S4 shows the linear fitting coefficients between T, RH, WS, PBLH changes and PM 2.5 changes of 19M19E and 20M19E. It can be seen that the fitting coefficients of T and RH changes are positive, indicating that the PM 2.5 in different cities increases with the increase in temperature, but the proportions are different. A higher temperature and relative humidity usually accelerate the formation of secondary aerosols by accelerating chemical reactions (Li et al. 2018; Wang et al. 2019) . The fitting coefficient of WS and PBLH is negative, indicating that the lower WS in the BTH region inhibits the diffusion of air pollutants and the lower PBLH enhances atmospheric stability (Liu et al. 2017 ). For BJ, CD and TS, the absolute value of the fitting coefficient of WS is maximum, indicating that WS changes are the dominant contributor for PM 2.5 changes. RH is the dominant contributor of PM 2.5 change in SJZ, CZ, and HS, and T is the dominant contributor in HD and ZJK. As we know, atmospheric stability has a great influence on PM 2.5 (Han et al. 2014) . The profile of the temperature can be used to characterize the stability of the atmosphere. Figure S6 shows the temperature (℃) profiles for 19M19E and 20M19E at 0200 LST, 0800 LST, 1400 LST, and 2000 LST, averaged for the BJ, SJZ, CD, and ZJK sites and the period 7-14 February. As seen in Figure S6 , the temperature in 2020 was higher than that in 2019, by about 10°C. Compared with 2019, there were strong temperature inversions near the surface for BJ, SJZ, CD, and ZJK (under 950 hPa, 950 hPa, 900 hPa and 850 hPa, respectively) in 2020. This suggests that the atmosphere in 2020 was more stable, which would have been conducive to the accumulation of pollutants. Therefore, these unfavorable meteorological conditions offset the impact of the emissions reduction and contributed to the high PM 2.5 concentration from 7-14 February 2020. In this study, the impacts of the meteorological condition and emissions reduction on the COVID-19 control in China during mass population movements at New Year Lockdown for CoViD-2019 in Milan: What are the effects on air quality Fully coupled "online" chemistry within the WRF model Modeling sea-salt aerosols in the atmosphere: 1. Model development Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature) Characteristics and formation mechanism of a winter haze-fog episode in Tianjin, China Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation Characteristics and formation mechanism of regional haze episodes in the Pearl River Delta of China Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China's Jing-Jin-Ji area Climate change from 1850 to 2005 simulated in CESM1 (WACCM) Airborne nitrogen dioxide plummets over China Air pollution scenario over China during COVID-19 The impact of nudging in the meteorological model for retrospective air quality simulations. Part I: Evaluation against national observation networks Parameterization of size-resolved dust emission and validation with measurements An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China Dynamic projection of anthropogenic emissions in China: methodology and 2015-2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios A novel coronavirus outbreak of global health concern The impacts of the meteorology features on PM2. 5 levels during a severe haze episode in central-east China Aerosol-radiation feedback and PM 10 air concentrations over Poland Global emissions of trace gases, particulate matter, and hazardous air pollutants from open burning of domestic waste Coronavirus disease 2019 (COVID-19): situation report Impact of Meteorological Conditions on PM2.5 Pollution in China during Winter Pollution Characteristics and Regional Transport of Atmospheric Particulate Matter in Beijing from We acknowledge the free use of the emissions data from the MEIC model (http://www.meicmodel.org).