Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 µg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 µg m-3) and anthropogenic emissions (6.7 µg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 µg m-3) were much lower than that in 2013-2017 (5.2 µg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

Cost-effectiveness uncertainty may bias the decision of coal power transitions in China.

A transition away from coal power always maintains a high level of complexity as there are several overlapping considerations such as technical feasibility, economic costs, and environmental and health impacts. Here, we explore the cost-effectiveness uncertainty brought by policy implementation disturbances of different coal power phaseout and new-built strategies (i.e., the disruption of phaseout priority) in China based on a developed unit-level uncertainty assessment framework. We reveal the opportunity and risk of coal transition decisions by employing preference analysis. We find that, the uncertainty of a policy implementation might lead to potential delays in yielding the initial positive annual net benefits. For example, a delay of six years might occur when implementing the prior phaseout practice. A certain level of risk remains in the implementation of the phaseout policy, as not all strategies can guarantee the cumulative positive net benefits from 2018-2060. Since the unit-level heterogeneities shape diverse orientation of the phaseout, the decision-making preferences would remarkably alter the selection of a coal power transition strategy. More strikingly, the cost-effectiveness uncertainty might lead to missed opportunities in identifying an optimal strategy. Our results highlight the importance of minimizing the policy implementation disturbance, which helps mitigate the risk of negative benefits and strengthen the practicality of phaseout decisions.

Co-benefits of CO2 emission reduction from China's clean air actions between 2013-2020.

Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China's stringent clean air actions on its energy use and CO2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country's energy system. The co-benefits of China's clean air measures far outweigh the additional CO2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO2 from 2013-2020, exceeding the accumulated CO2 emission increase in China (2.03 Gt CO2) during the same period. Our study indicates that China's efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO2 reduction co-benefits deserve further attention in future policy design.

Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling.

New challenges are emerging in fine-scale air quality modeling in China due to a lack of high-resolution emission maps. Currently, only a few emission sources have accurate geographic locations (point sources), while a large part of sources, including industrial plants, are estimated as provincial totals (area sources) and spatially disaggregated onto grid cells based on proxies; this approach is reasonable to some extent but is highly questionable at fine spatial resolutions. Here, we compile a new comprehensive point source database that includes nearly 100,000 industrial facilities in China. We couple it with the frame of Multi-resolution Emission Inventory for China (MEIC), estimate point source emissions, combine point and area sources, and finally map China's anthropogenic emissions of 2013 at the spatial resolution of 30″×30″ (~1 km). Consequently, the percentages of point source emissions in the total emissions increase from less than 30% in the MEIC up to a maximum of 84% for SO2 in 2013. The new point source-based emission maps show the uncoupled distribution of emissions and populations in space at fine spatial scales, however, such a pattern cannot be reproduced by any spatial proxy used in the conventional emissions mapping. This new accurate high-resolution emission mapping approach reduces the modeled biases of air pollutant concentrations in the densely populated areas compared to the raw MEIC inventory, thus improving the assessment of population exposure.