Temporally Resolved Sectoral and Regional Contributions to Air Pollution in Beijing: Informing Short-Term Emission Controls
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Atmos. Chem. Phys., 21, 4471–4485, 2021 https://doi.org/10.5194/acp-21-4471-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls Tabish Umar Ansari1,a, Oliver Wild1, Edmund Ryan2, Ying Chen1,b, Jie Li3, and Zifa Wang3 1Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom 2School of Mathematics, University of Manchester, Manchester, United Kingdom 3State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China anow at: Campus Fryslân, University of Groningen, Leeuwarden, the Netherlands bnow at: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom Correspondence: Tabish Umar Ansari ([email protected]) and Oliver Wild ([email protected]) Received: 3 August 2020 – Discussion started: 28 September 2020 Revised: 10 February 2021 – Accepted: 10 February 2021 – Published: 23 March 2021 Abstract. We investigate the contributions of local and re- emulators to determine the sensitivity of PM2:5 concentra- gional emission sources to air pollution in Beijing to inform tions to different emission sources and the interactions be- the design of short-term emission control strategies for miti- tween them, including for secondary PM, and to create pollu- gating major pollution episodes. We use a well-evaluated ver- tant response surfaces for daily average PM2:5 concentrations sion of the WRF-Chem model at 3 km horizontal resolution in Beijing. We use these surfaces to identify the short-term to determine the daily accumulation of pollution over Bei- emission controls needed to meet the national air quality tar- −3 jing from local and regional sources in October 2014 under get of daily average PM2:5 less than 75 µg m for pollution a range of meteorological conditions. Considering feasible episodes of different intensities. We find that for heavily pol- −3 emission reductions across residential, transport, power, and luted days with daily mean PM2:5 higher than 225 µg m , industrial sectors, we find that 1 d controls on local emissions even emission reductions of 90 % across all sectors over Bei- have an immediate effect on PM2:5 (particulate matter with jing and surrounding provinces may be insufficient to meet diameter less than 2.5 µm) concentrations on the same day the national air quality standards. These results highlight the but can have lingering effects as much as 5 d later under stag- regional nature of PM pollution and the challenges of tack- nant conditions. One-day controls in surrounding provinces ling it during major pollution episodes. have the greatest effect in Beijing on the day following the controls but may have negligible effects under northwest- erly winds when local emissions dominate. To explore the contribution of different emission sectors and regions, we 1 Introduction perform simulations with each source removed in turn. We find that residential and industrial sectors from neighbour- Beijing, located at the foot of the Yan Mountains on the ing provinces dominate PM2:5 levels in Beijing during major northern edge of the heavily populated North China Plain, pollution episodes but that local residential emissions and in- has consistently been named among the most polluted cap- dustrial or residential emissions from more distant provinces ital cities in the world (State of Global Air, 2019; Global can also contribute significantly during some episodes. We burden of disease, 2016). Key air pollutants including car- then perform a structured set of perturbed emission simula- bon monoxide, sulfur dioxide, nitrogen oxides, ozone, and tions to allow us to build statistical emulators that represent PM2:5 (particulate matter with diameter less than 2.5 µm) of- the relationships between emission sources and air pollution ten breach the World Health Organization (WHO) and na- in Beijing over the period. We use these computationally fast tional air quality standards, posing risks to human health, vis- ibility, and climate (Lelieveld et al., 2015; Luan et al., 2018; Published by Copernicus Publications on behalf of the European Geosciences Union. 4472 T. U. Ansari et al.: Contributions to air pollution in Beijing United Nations Environment Programme and World Meteo- demonstrate how they can be used to guide the development rological Organization, 2011). To address the severe ambient of future short-term emission control policies in the city. air pollution in Chinese cities, the State Council of China launched the Air Pollution Prevention and Control Action Plan in 2013 setting targets to reduce PM2:5 concentrations 2 Modelling approaches and motivation over the Beijing–Tianjin–Hebei region by 25 % from their 2013 levels by 2017 and to reduce annual mean PM2:5 con- We use the Weather Research and Forecasting model with centrations in Beijing to 60 µg m−3 (Zheng et al., 2018; Wei Chemistry (WRF-Chem) version 3.7.1 at a horizontal reso- et al., 2017). The mitigation strategies focused on long-term lution of 27 km over China with nested domains over north- measures such as a gradual phase-out of residential biofuel ern China at 9 km and the North China Plain at 3 km res- use, changes in industrial technology, improved vehicle fuel olution. Gas-phase chemistry in the model is represented standards, and the relocation of coal-fired power plants. The by the Carbon Bond mechanism version Z (CBMZ) which air quality in Beijing improved substantially during 2013– is coupled with the Model for Simulating Aerosol Interac- 2017, and the annual mean PM2:5 concentration decreased tions and Chemistry (MOSAIC) aerosol module with eight from 89.5 µg m−3 in 2013 to 58 µg m−3 in 2017, largely due aerosol size bins (Zaveri and Peters, 1999; Zaveri et al., to the implementation of these long-term local and regional 2008). We use meteorological fields from the European Cen- emission control measures (Ma et al., 2018; Cheng et al., tre for Medium-Range Weather Forecasts (ECMWF) and an- 2019; Zheng et al., 2018). thropogenic emissions from residential, transport, industry, While recent long-term emission reductions have brought power generation, and agricultural sectors from the Multi- substantial improvements in overall air quality, the region resolution Emission Inventory for China (MEIC) appropri- continues to experience frequent haze episodes character- ate for 2014 (Li et al., 2017a). We take biomass burning ized by high levels of PM2:5, particularly in winter (Dang emissions from the Fire Emissions INventory from NCAR and Liao, 2019; Xiao et al., 2020). Additional emergency (FINN; Wiedinmyer et al., 2011) and biogenic emissions measures generally lasting 3–7 d are necessary to prevent from the Model of Emissions of Gases and Aerosols from these extreme pollution episodes, especially under stable me- Nature (MEGAN; Guenther et al., 2012). Further details of teorological conditions that are conducive to the formation the model configuration and evaluation over North China are and accumulation of very high levels of particulate matter. provided in a previous study where we explored emission Such short-term emission controls have been tested, with controls in Beijing during the Asia–Pacific Economic Co- some success, during special events such as the 2008 Beijing operation (APEC) summit in November 2014 (Ansari et al., Olympics, the Asia–Pacific Economic Cooperation (APEC) 2019). In this previous study we demonstrated that the model summit in 2014, and the China Victory Day Parade in 2015 can reproduce the magnitude of and variation in key pollu- (Zhang et al., 2015; Xu et al., 2019, 2017). However, the tants over Beijing well and showed that meeting national air success of these controls has often been greatly aided by quality standards over the period was critically dependent on favourable weather conditions (Liu et al., 2017; Liang et al., the weather conditions at the time. The formulation of ef- 2017; Gao et al., 2017) and the same control strategies are fective short-term emission control policies therefore needs liable to fail under different meteorological conditions. For to account for the important role played by meteorologi- example, while national air quality standards were met dur- cal processes. In this study we focus on the same October– ing the APEC summit, we have shown that they would have November period in 2014 and investigate the key source sec- been greatly exceeded under the same emission controls had tors and regions responsible for short-term pollution episodes the summit been held 2 weeks earlier when the weather was during a range of meteorological conditions. less favourable (Ansari et al., 2019). Previous studies have Figure1 shows hourly observed and simulated PM 2:5 advocated the application of emission controls over a wider concentrations for Beijing from 12 October to 19 Novem- geographical region (Guo et al., 2016; Wen et al., 2016; ber 2014. APEC emission controls were implemented from Ansari et al., 2019) but have not identified the spatial or 3–12 November. The model has a positive mean bias of temporal scales needed for successful policy implementation 22 µg m−3 over the October period, and we have previously or proposed any general framework to devise future mitiga- demonstrated that this overestimation during episodes is tion strategies that account for differing meteorological con- principally due to insufficient boundary layer mixing (Ansari ditions. In this study we use a range of new modelling ap- et al., 2019). In the last 15 d of October, only 3 of the proaches, including 1 d emission reductions and statistical days met the daily national Class 2 air quality standard −3 emulation, to gain a detailed understanding of the magni- of 24 h average PM2:5 concentration less than 75 µg m tude and timing of local and regional source contributions (air quality index, AQI D 100), and 8 of the days exceeded −3 to PM2:5 concentrations in Beijing under varying meteoro- the higher threshold for heavy pollution of 150 µg m logical conditions.