Impacts of emission changes in China from 2010 to 2017 on domestic and intercontinental air quality and health effect

Abstract

Abstract. China has seen dramatic emission changes from 2010, especially after the implementation of Clean Air Action in 2013, with significant air quality and human health benefits observed. Air pollutants, such as PM2.5 and surface ozone, as well as their precursors, have long enough lifetime in the troposphere which can be easily transported downwind. So emission changes in China will not only change the regional air quality domestically, but also affect the air quality in downwind regions. In this study, we use a global chemistry transport model to simulate the influence on both domestic and foreign air quality from the emission change from 2010 to 2017 in China. By applying the health impact functions derived from epidemiology studies, we then quantify the changes in air pollution-related (including both PM2.5 and O3) mortality burdens at regional and global scales. The majority of air pollutants in China reach their peak values around 2012 and 2013. Compared with the year 2010, the population-weighted annual PM2.5 in China increases till 2011 (94.1 μg m−3), and then begins to decrease. In 2017, the population-weighted annual PM2.5 decreases by 17.6 %, compared with the values in 2010 (84.7 μg m−3). The estimated national PM2.5 concentration changes in China are comparable with previous studies using fine-resolution regional models, though our model tends to overestimate PM2.5 from 2013 to 2017 when evaluated with surface observation in China during the same periods. The emission changes in China increased the global PM2.5-related mortality burdens from 2010 to 2013, by 27,700 (95 %CI: 23,900–31, 400) deaths yr−1 in 2011, and 13, 300 (11,400–15,100) deaths yr−1 in 2013, among which at least 93 % occurred in China. The sharp emission decreases after 2013 bring significant benefits for reduced avoided premature mortality in 2017, reaching 108, 800 (92,800–124,800) deaths yr−1 globally, among which 92 % happening in China. Different trend as PM2.5, the annual maximum daily 8-hr ozone in China increased, and also the ozone-related premature deaths, ranging from 3,600 (2,700–4,300) deaths yr−1 in 2011 (75 % of global total increased premature deaths), and 8,500 (6,500–9,900) deaths yr−1 in 2017 (143 % of the global total). Downwind regions, such as South Korea, Japan, and U.S. generally see a decreased O3-related mortality burden after 2013 as a combination of increased export of ozone and decreased export of ozone precursors. In general, we conclude that the sharp emission reductions in China after 2013 bring benefits of improved air quality and reduced premature deaths associated with air pollution at global scale. The benefits are dominated by the PM2.5 decreases since the ozone is shown to actually increase with the emission decrease.

A comparative analysis for deep learning model (hyDL-CO v1.0) and Kalman Filter to predict CO in China

“…For example, found the modeled spatial variability of nitrogen dioxides (NO2) matches well with surface observations but with a large bias in their concentrations. Zhang et al (2021) exhibited a difference between modeled and observed surface fine particulate matter (PM2.5) and ozone (O3): the modeled PM2.5 and O3 concentrations are higher than observations by about 40% and 15% in China in 2013-2017, respectively. Based on CTMs, data assimilation techniques integrate simulations and observations and thus can improve the modeled atmospheric compositions. For instance, Feng et al (2018) found the assimilation of surface PM2.5 observations can effectively reduce the uncertainties in PM2.5 forecasts.…”

Observational Evidence of Aerosol Radiation Modifying Photochemical Ozone Profiles in the Lower Troposphere

“…Since 2013, elevated ambient levels of particulate matter over China have been largely alleviated by effective emission controls (Zhang et al., 2021). However, an opposite trend has been observed in the concentrations of surface ozone over China, which have rapidly increased (Li et al., 2019; Lu, Zhang, 2020).…”