Dominant role of emission reduction in PM<sub>2.5</sub> air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis

Abstract

<p><strong>Abstract.</strong> 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 PM<sub>2.5</sub> concentrations in Beijing to 60  m<sup>−3</sup> 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 PM<sub>2.5</sub> concentration decreasing from 89.5  m<sup>−3</sup> in 2013 to 58  m<sup>−3</sup> in 2017. As meteorological conditions were more favourable to the reduction of air pollution in 2017 than in 2013 and 2016, the <q>real</q> effectiveness of emission control measures on the improvement of air quality in Beijing has frequently been questioned.</p> <p> In this work, by combining a detailed bottom-up emission inventory over Beijing, the MEIC regional emission inventory, and the WRF-CMAQ model, we attribute the improvement in Beijing's PM<sub>2.5</sub> 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 PM<sub>2.5</sub> changes.</p> <p> We found that, although changes in meteorological conditions partly explain the improved PM<sub>2.5</sub> air quality in Beijing in 2017 compared to 2013 (3.8  m<sup>−3</sup>, 12.1 % of total), the rapid decrease in PM<sub>2.5</sub> concentrations in Beijing during 2013–2017 was dominated by local (20.6  m<sup>−3</sup>, 65.4 %) and regional (7.1  m<sup>−3</sup>, 22.5 %) emission reductions. The seven categories of emission control measures, i.e., Coal-fired boiler control, Clean fuels in the residential sector, Optimized industrial structure, Fugitive dust control, Vehicle emission control, Improved end-of-pipe control, and Integrated treatment of VOCs, reduced the PM<sub>2.5</sub> concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2  m<sup>−3</sup>, respectively, during 2013–2017. We also found that if the meteorological conditions in 2017 had remained the same as those in 2016, the annual mean PM<sub>2.5</sub> concentrations would have increased from 58  m<sup>−3</sup> to 63  m<sup>−3</sup>, exceeding the target established in the APPCAP. Despite the remarkable impacts from meteorological condition changes, local and regional emission reductions still played dominant roles in the PM<sub>2.5</sub> decrease in Beijing during 2016–2017, and Clean fuels in the residential sector, Coal-fired boiler control, and Optimized industrial structure were the three most effective local measures (contributing reductions of 2.1, 1.9 and 1.5  m<sup>−3</sup>, 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.</p>

Stronger policy required to substantially reduce deaths from PM2.5 pollution in China

“…Second, although changes in PM 2.5 concentration are predominantly affected by the emission of pollutants 12,15,19 , meteorological conditions such as wind also have some influence 19,51,52 . We did not consider the effects of meteorological factors but directly assumed the air quality improvement from 2013 to 2017 is the result of APPCAP.…”

Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences

“…In particular, year 2016 and 2017 witnessed the most severe surface ozone pollution in record over most Chinese cities. The summertime surface MDA8 ozone levels in 2016-2017 averaged over the major Chinese cities increased by about 20% compared to 2013-2014 (Lu et al, 2018a), despite that the Chinese Action Plan on Air Pollution Prevention and Control implemented in 2013 has significantly reduced anthropogenic emissions of nitrogen oxides (NOx=NO+NO2) and alleviated the winter haze pollution (Cheng et al, 2018;Zheng et al, 2018). Exploring source attribution to surface ozone in China is therefore critical to understand the recent ozone increase and to re-examine the air pollution control strategy.…”

Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters

“…Overall, the estimated average PM 2.5 concentration in the study area was 48.04 µg/m 3 during 2016-2018, while the average of the ground-level measurements was 45.09 µg/m 3 , indicating a tight estimation consistency. Almost all regions in the study area exceeded the annual Chinese National Ambient Air Quality Standard (CNAAQS) [66] of 35 µg/m 3 in 2016 and 2017, which resulted in a serious health concern for the YRD. When it comes to 2018, part of the south Zhejiang Province was up to the standard, while the other regions failed to reach the standard but had a decreasing trend.…”

“…These improving trends suggested that series of strict air pollution control policies and measures had indeed taken effect, as these actions included energy conservation, the promotion of cleaner fuels and the optimization of the industrial structure. All of these actions focused on reducing local emissions from human activities [66]. Subsequently, the dynamics of daily exposure PM 2.5 concentrations are illustrated in Fig.…”