Abstract Modeling air quality has always been a challenge in global models constrained by coarse grids. Here, the variable‐resolution Community Atmosphere Model with full chemistry based on the scalable spectral element (SE) dynamical core (MUSICAv0) is applied in simulating air pollution with a finer grid resolution of ∼0.25° over East Asia (SE_VR), in contrast to the same model with a uniform resolution of ∼1.0° (SE_UR). Two nudging experiments and four free‐running experiments are conducted to investigate the capabilities of SE_VR in modeling the air pollution and aerosol‐planetary boundary layer (PBL) interactions over China. Results show the regional refinement in SE_VR is essential for simulating haze events over complex terrain areas attributed to its better performance in representing the local vertical and horizontal dispersion conditions. SE_VR shows prominent advantages over SE_UR in simulating surface ozone because of better resolving spatial segregation of NO x and volatile organic compounds (VOC) chemical regimes and subsequently representing more detailed chemical processes related to ozone formation, although the model generally overestimates surface ozone over China. Further analysis of SE_VR shows the daytime radiative effect of black carbon (BC) aerosols lowers PBL height by 12.0% (17.9%), and leads to an increase of PM 2.5 by 14.5% (10.8%) under the moderate (severe) air pollution conditions over Sichuan Basin. However, SE_UR has deficiencies in simulating BC‐PBL interactions due to its inability to reproduce the strong inverse temperature structure caused by BC aerosols in the lower atmosphere layer. Our results highlight the value of variable‐resolution global models for simulating air pollution and its interactions with climate.
Abstract. Non-methane volatile organic compounds (NMVOC) are important ozone and secondary organic aerosol precursors and play important roles in tropospheric chemistry. In this work, we estimated the total and speciated NMVOC emissions from China’s anthropogenic sources during 1990–2017 by using a bottom-up emission inventory framework, and investigated the main drivers behind the trends. We found that, anthropogenic NMVOC emissions in China have been increased continuously since 1990 due to the dramatic growth in activity rates and absence of effective control measures. We estimated that, anthropogenic NMVOC emissions in China increased from 9.76 Tg in 1990 to 28.5 Tg in 2017, mainly driven by persistent growth from the industry sector and solvent use. In the meanwhile, emissions from the residential and transportation sectors declined after 2005, partly offset the total emission increase. During 1990–2017, mass-based emissions of alkanes, alkenes, alkynes, aromatics, oxygenated VOCs (OVOC) and other species increased by 274 %, 88 %, 4 %, 387 %, 91 %, and 231 % respectively. Following the growth in total NMVOC emissions, the corresponding ozone formation potential (OFP) increased form 38.2 Tg-O 3 in 1990 to 99.7 Tg-O 3 in 2017. We estimated that aromatics accounted for the largest share (43 %) of total OFP, followed by alkenes (37 %) and OVOC (10 %). Growth in China's NMVOC emissions were mainly driven by the transportation sector before 2000, while industrial sector and solvent use dominated the emission growth during 2000–2010. After 2010, although emissions from the industry sector and solvent use kept growing, strict control measures on transportation and fuel transition in residential stoves have successfully slowed down the increase trend, especially after the implementation of China's clean air action since 2013. However, compared to large emission decreases of other criteria air pollutants in China (e.g., SO 2 , NO x , and primary PM) during 2013–2017, the relatively flat trend in NMVOC emissions and OFP revealed the absence of effective control measures, which might have contributed to the increase of ozone during the same period. Given their high contributions to emissions and OFP, tailored control measures for solvent use and industrial sources should be developed, and collaborative control strategies should be designed to mitigate both PM 2.5 and ozone pollution simultaneously.
As Earth's primary energy source, surface downward solar radiation (Rs) determines the solar power potential and usage for climate change mitigation. Future projections of Rs based on climate models have large uncertainties that interfere with the efficient deployment of solar energy to achieve China's carbon-neutrality goal. Here we assess 24 models in the latest Coupled Model Intercomparison Project Phase 6 with historical observations in China and find systematic biases in simulating historical Rs values likely due to model biases in cloud cover and clear-sky radiation, resulting in largely uncertain projections for future changes in Rs. Based on emergent constraints, we obtain credible Rs with narrowed uncertainties by ∼56% in the mid-twenty-first century and show that the mean Rs change during 2050-2069 relative to 1995-2014 is 30% more brightening than the raw projections. Particularly in North China and Southeast China with higher power demand, the constrained projections present more significant brightening, highlighting the importance of considering the spatial changes in future Rs when locating new solar energy infrastructures.
To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading to large reductions in economic activities and associated emissions. Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in eastern China, raising questions about the well-established relationship between human activities and air quality. Here, using comprehensive measurements and modeling, we show that the haze during the COVID lockdown was driven by enhancements of secondary pollution. In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter. Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants.
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.
Abstract. We estimated the changes in chemical composition of ambient PM2.5 over China during 2005–2012 using satellite-based aerosol optical depth (AOD) data and the GEOS-Chem chemical transport model, and investigated the driving forces behind the changes by examining the changes in precursor emissions using a bottom-up emission inventory. We found that the national population-weighted mean PM2.5 concentration increased from 63.9 μg/m3 in 2005 to 75.2 μg/m3 in 2007 (+18.19 % per year), and subsequently decreased to 66.9 μg/m3 from 2007 to 2012 (−2.67 % per year), composing a flat trend of population-weighted mean PM2.5 concentration during 2005–2012. Variations in PM2.5 concentrations are mainly driven by the changes in sulfate and nitrate concentrations. Population-weighted mean sulfate concentration increased by 10.72 % from 2005–2006 (from 14.4 μg/m3 to 15.9 μg/m3) and then decreased by 4.30 % per year from 2006–2012, dominating the variations of total PM2.5 concentrations. The decrease of sulfate concentration is partly offset by the increase of nitrate concentration: population-weighted mean nitrate concentration increased by 3.39 % per year during 2005–2012 (from 9.8 μg/m3 to 12.2 μg/m3). The changes in sulfate and nitrate concentrations were in line with the changes in SO2 and NOx emissions during the same period. By examining the emission data from the MEIC emission inventory, we found that the desulfurization regulation enforced around 2005 in power plants was the primary contributor to the SO2 emissions reduction since 2006. In contrast, growth of energy consumption and lack of control measures for NOx resulted in persistent increase in NOx emissions until the installation of denitrification devices on power plants late in 2011, which began to take effect in 2012. The results of this work indicate that the synchronized abatement of emissions for multi-pollutants are necessary for reducing ambient PM2.5 concentrations over China.
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