The control of anthropogenic emissions contributed to 80 % of the decrease in PM_2.5 concentrations in Beijing from 2013 to 2017

Abstract

<p><strong>Abstract.</strong> With the completion of the Beijing Five-year Clean Air Action Plan by the end of 2017, the annual mean PM<sub>2.5</sub> concentrations in Beijing dropped dramatically to 58.0 μg/m<sup>3</sup> in 2017 from 89.5 μg/m<sup>3</sup> in 2013. However, controversies exist to argue that favorable meteorological conditions in 2017 that helped pollution dispersion were the major factor for such rapid decrease in PM<sub>2.5</sub> concentrations. To comprehensively evaluate this five-year plan, we employed Kolmogorov-Zurbenko (KZ) filtering and a WRF-CMAQ model to quantify the relative contribution of meteorological conditions and the control of anthropogenic emissions to PM<sub>2.5</sub> reduction in Beijing from 2013 to 2017. For these five years, the relative contribution of emission-reduction measures to the decrease of PM<sub>2.5</sub> concentrations in Beijing calculated by KZ filtering and the WRF-CMAQ model was 80.6 % and 78.6 % respectively. The WRF-CMAQ model further revealed that local and regional emission-reduction measures contributed to 53.7 % and 24.9 % of the PM<sub>2.5</sub> reduction respectively. For local emission-reduction measures, the regulation of coal boilers, increasing clean fuels for residential use, industrial restructuring, the regulation of raise dust and vehicle emissions contributed to 20.1 %, 17.4 %, 10.8 %, 3.0 % and 2.4 % of PM<sub>2.5</sub> reduction respectively. Both models suggested that the control of anthropogenic emissions contributed to around 80 % of the total decrease in PM<sub>2.5</sub> concentrations in Beijing, indicating that emission control was crucial for the notable improvement in air quality in Beijing from 2013 to 2017. Therefore, such long-term air quality clean plan should be continued for the future years to further reduce PM<sub>2.5</sub> concentrations in Beijing. Considering that different emission-reduction measures exert distinct effects on PM<sub>2.5</sub> reduction and existing emission-reduction measures work poorly to reduce ozone concentrations, future strategies for emission-reduction should be designed and implemented accordingly.</p>

Effects of air pollution control policies on PM<sub>2.5</sub> pollution improvement in China from 2005 to 2017: a satellite-based perspective

“…It should be noted that interannual variation in meteorology has also contributed to the changes in PM 2.5 . A recent study shows that meteorological conditions contributed approximately 20 % of the PM 2.5 reduction in Beijing from 2013 to 2017, while the control of anthropogenic emissions contributed 80 % (Chen et al, 2019). In addition, the slowdown of economic development after the financial crisis in 2008 might contribute to the PM 2.5 emissions reduction.…”

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

“…This later translated into a stringent and binding target of a maximum annual mean PM 2.5 concentration of 60 µg m −3 in 2017 for Beijing, which was ultimately reached (the 2017 concentration was 58.5 µg m −3 ) (Beijing Municipal Ecology and Environment Bureau, 2013). However, several studies estimated that the concentration would have exceeded this target in Beijing were it not for meteorological conditions in the winter 2017 that favored pollution reductions (Vu et al, 2019;Chen et al, 2019;Cheng et al, 2019). The European Union and US Environmental Protection Agency (EPA) use a 3-year average of the PM 2.5 concentration to determine compliance with air quality standards (European Union, 2020; US Environmental Protection Agency, 2019).…”

“…A related approach is to combine MLR with techniques that can decompose time series of observed concentrations into long-term, seasonal, and short-term components (e.g., Kolmogorov-Zurbenko (KZ) filters, Zurbenko, 1994). Ma et al (2016) and Chen et al (2019) use KZ filters to calculate the long-term component of observed PM 2.5 and then apply MLR to separate the impacts of long-term meteorological changes on the concentrations. Henneman et al (2015) apply MLR to the short-term component (identified by KZ filters) of air pollu-tant concentrations near Atlanta during 2000 to 2012 to separate the impact of short-term meteorological variability and then estimate the long-term trend in air quality.…”

Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

“…These data show 30 %-40 % decreases in PM 2.5 across eastern China over the 2013-2017 period (Chinese State Council, 2018a;Zhang et al, 2019). However, interpretation of these trends in terms of emission controls may be biased by interannual variability and trends in meteorology Wang et al, 2014;Zhu et al, 2012;Jia et al, 2015;Yang et al, 2018Yang et al, , 2016Liang et al, 2016;Cheng et al, 2019;Chen et al, 2019;Silver et al, 2018). Here we use a stepwise multilinear regression (MLR) model to separate the effects of meteorological variability and emission controls on the 2013-2018 trends in PM 2.5 across China.…”