Meteorological mechanism of regional PM2.5 transport building a receptor region for heavy air pollution over Central China

In recent years, China has experienced severe and persistent air pollution associated with rapid urbanization and climate change. Three years' time series (January 2014 to December 2016) concentrations data of air pollutants including particulate matter (PM2.5 and PM10) and gaseous pollutants (SO2, NO2, CO, and O3) from over 1300 national air quality monitoring sites were studied to understand the severity of China's air pollution. In 2014 (2015, 2016), annual population-weighted-average (PWA) values in China were 65.8 (55.0, 50.7) μg m(-3) for PM2.5, 107.8 (91.1, 85.7) μg m(-3) for PM10, 54.8 (56.2, 57.2) μg m(-3) for O3_8 h, 39.6 (33.3, 33.4) μg m(-3) for NO2, 34.1 (26, 21.9) μg m(-3) for SO2, 1.2 (1.1, 1.1) mg m(-3) for CO, and 0.60 (0.59, 0.58) for PM2.5/PM10, respectively. In 2014 (2015, 2016), 7% (14%, 19%), 17% (27%, 34%), 51% (67%, 70%) and 88% (97%, 98%) of the population in China lived in areas that meet the level of annual PM2.5, PM10, NO2, and SO2 standard metrics from Chinese Ambient Air Quality Standards-Grade II. The annual PWA concentrations of PM2.5, PM10, O3_8 h, NO2, SO2, CO in the Northern China are about 40.4%, 58.9%, 5.9%, 24.6%, 96.7%, and 38.1% higher than those in Southern China, respectively. Though the air quality has been improving recent years, PM2.5 pollution in wintertime is worsening, especially in the Northern China. The complex air pollution caused by PM and O3 (the third frequent major pollutant) is an emerging problem that threatens the public health, especially in Chinese mega-city clusters. NOx controls were more beneficial than SO2 controls for improvement of annual PM air quality in the northern China, central, and southwest regions. Future epidemiologic studies are urgently required to estimate the health impacts associated with multi-pollutants exposure, and revise more scientific air quality index standards.

Abstract. In 2013, China's government published the Air Pollution Prevention and Control Action Plan (APPCAP) with a specific target for Beijing, which aims to reduce annual mean PM2.5 concentrations in Beijing to 60 µg m−3 in 2017. During 2013–2017, the air quality in Beijing was significantly improved following the implementation of various emission control measures locally and regionally, with the annual mean PM2.5 concentration decreasing from 89.5 µg m−3 in 2013 to 58 µg m−3 in 2017. As meteorological conditions were more favourable to the reduction of air pollution in 2017 than in 2013 and 2016, the real effectiveness of emission control measures on the improvement of air quality in Beijing has frequently been questioned. In this work, by combining a detailed bottom-up emission inventory over Beijing, the MEIC regional emission inventory and the WRF-CMAQ (Weather Research and Forecasting Model and Community Multiscale Air Quality) model, we attribute the improvement in Beijing's PM2.5 air quality in 2017 (compared to 2013 and 2016) to the following factors: changes in meteorological conditions, reduction of emissions from surrounding regions, and seven specific categories of local emission control measures in Beijing. We collect and summarize data related to 32 detailed control measures implemented during 2013–2017, quantify the emission reductions associated with each measure using the bottom-up local emission inventory in 2013, aggregate the measures into seven categories, and conduct a series of CMAQ simulations to quantify the contribution of different factors to the PM2.5 changes. We found that, although changes in meteorological conditions partly explain the improved PM2.5 air quality in Beijing in 2017 compared to 2013 (3.8 µg m−3, 12.1 % of total), the rapid decrease in PM2.5 concentrations in Beijing during 2013–2017 was dominated by local (20.6 µg m−3, 65.4 %) and regional (7.1 µg m−3, 22.5 %) emission reductions. The seven categories of emission control measures, i.e. coal-fired boiler control, clean fuels in the residential sector, optimize industrial structure, fugitive dust control, vehicle emission control, improved end-of-pipe control, and integrated treatment of VOCs, reduced the PM2.5 concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2 µg m−3, respectively, during 2013–2017. We also found that changes in meteorological conditions could explain roughly 30 % of total reduction in PM2.5 concentration during 2016–2017 with more prominent contribution in winter months (November and December). If the meteorological conditions in 2017 had remained the same as those in 2016, the annual mean PM2.5 concentrations would have increased from 58 to 63 µg m−3, exceeding the target established in the APPCAP. Despite the remarkable impacts from meteorological condition changes, local and regional emission reductions still played major roles in the PM2.5 decrease in Beijing during 2016–2017, and clean fuels in the residential sector, coal-fired boiler control, and optimize industrial structure were the three most effective local measures (contributing reductions of 2.1, 1.9, and 1.5 µg m−3, respectively). Our study confirms the effectiveness of clean air actions in Beijing and its surrounding regions and reveals that a new generation of control measures and strengthened regional joint emission control measures should be implemented for continued air quality improvement in Beijing because the major emitting sources have changed since the implementation of the clean air actions.