Fine particulate matter (PM_2.5) is a severe air pollution problem in China. Observations of PM_2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM_2.5 across China over the 2013–2018 period, averaging at −5.2 µg m⁻³ a⁻¹. Trends in the five megacity cluster regions targeted by the government for air quality control are $-\mathrm{9.3}\pm \mathrm{1.8}$ µg m⁻³ a⁻¹ (±95 % confidence interval) for Beijing–Tianjin–Hebei, $-\mathrm{6.1}\pm \mathrm{1.1}$ µg m⁻³ a⁻¹ for the Yangtze River Delta, $-\mathrm{2.7}\pm \mathrm{0.8}$ µg m⁻³ a⁻¹ for the Pearl River Delta, $-\mathrm{6.7}\pm \mathrm{1.3}$ µg m⁻³ a⁻¹ for the Sichuan Basin, and $-\mathrm{6.5}\pm \mathrm{2.5}$ µg m⁻³ a⁻¹ for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO₂) and carbon monoxide (CO) show that the declines in PM_2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM_2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM_2.5 trends across China. The MLR model correlates the 10 d PM_2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM_2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM_2.5 decrease across China is −4.6 µg m⁻³ a⁻¹ in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM_2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are $-\mathrm{8.0}\pm \mathrm{1.1}$ µg m⁻³ a⁻¹ for Beijing–Tianjin–Hebei (14 % weaker than in the original data), $-\mathrm{6.3}\pm \mathrm{0.9}$ µg m⁻³ a⁻¹ for the Yangtze River Delta (3 % stronger), $-\mathrm{2.2}\pm \mathrm{0.5}$ µg m⁻³ a⁻¹ for the Pearl River Delta (19 % weaker), $-\mathrm{4.9}\pm \mathrm{0.9}$ µg m⁻³ a⁻¹ for the Sichuan Basin (27 % weaker), and $-\mathrm{5.0}\pm \mathrm{1.9}$ µg m⁻³ a⁻¹ for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM_2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.

Significance: The North China Plain experiences severe summer ozone pollution, but ozone during winter haze (particulate) pollution events has been very low. Here, we show that the abrupt decrease in nitrogen oxide (NO _x ) emissions following the COVID-19 lockdown in January 2020 drove fast ozone production during winter haze events to levels approaching the air quality standard. This fast ozone production was driven by formaldehyde originating from high emissions of volatile organic compounds (VOCs). The COVID-19 experience highlights a general 2013 to 2019 trend of rapidly increasing ozone pollution in winter–spring in China as NO _x emissions have decreased. VOC emission controls would mitigate the spreading of ozone pollution into winter–spring with benefits for public health, crop production, and particulate pollution.

Satellite observations of tropospheric NO₂ columns are extensively used to infer trends in anthropogenic emissions of nitrogen oxides (NO_x ≡ NO + NO₂), but this may be complicated by trends in NO_x lifetime. Here we use 2004–2018 observations from the Ozone Monitoring Instrument (OMI) satellite-based instrument (QA4ECV and POMINO v2 retrievals) to examine the seasonality and trends of tropospheric NO₂ columns over central–eastern China, and we interpret the results with the GEOS-Chem chemical transport model. The observations show a factor of 3 increase in NO₂ columns from summer to winter, which we explain in GEOS-Chem as reflecting a longer NO_x lifetime in winter than in summer (21 h versus 5.9 h in 2017). The 2005–2018 summer trends of OMI NO₂ closely follow the trends in the Multi-resolution Emission Inventory for China (MEIC), with a rise over the 2005–2011 period and a 25 % decrease since. We find in GEOS-Chem no significant trend of the NO_x lifetime in summer, supporting the emission trend reported by the MEIC. The winter trend of OMI NO₂ is steeper than in summer over the entire period, which we attribute to a decrease in NO_x lifetime at lower NO_x emissions. Half of the NO_x sink in winter is from N₂O₅ hydrolysis, which counterintuitively becomes more efficient as NO_x emissions decrease due to less titration of ozone at night. The formation of organic nitrates also becomes an increasing sink of NO_x as NO_x emissions decrease but emissions of volatile organic compounds (VOCs) do not.

Cloud water acidity affects the atmospheric chemistry of sulfate and organic aerosol formation, halogen radical cycling, and trace metal speciation. Precipitation acidity including post-depositional inputs adversely affects soil and freshwater ecosystems. Here, we use the GEOS-Chem model of atmospheric chemistry to simulate the global distributions of cloud water and precipitation acidity as well as the total acid inputs to ecosystems from wet deposition. The model accounts for strong acids (H₂SO₄, HNO₃, and HCl), weak acids (HCOOH, CH₃COOH, CO₂, and SO₂), and weak bases (NH₃ as well as dust and sea salt aerosol alkalinity). We compile a global data set of cloud water pH measurements for comparison with the model. The global mean observed cloud water pH is 5.2±0.9, compared to 5.0±0.8 in the model, with a range from 3 to 8 depending on the region. The lowest values are over East Asia, and the highest values are over deserts. Cloud water pH over East Asia is low because of large acid inputs (H₂SO₄ and HNO₃), despite NH₃ and dust neutralizing 70 % of these inputs. Cloud water pH is typically 4–5 over the US and Europe. Carboxylic acids account for less than 25 % of cloud water H⁺ in the Northern Hemisphere on an annual basis but 25 %–50 % in the Southern Hemisphere and over 50 % in the southern tropical continents, where they push the cloud water pH below 4.5. Anthropogenic emissions of SO₂ and NO_x (precursors of H₂SO₄ and HNO₃) are decreasing at northern midlatitudes, but the effect on cloud water pH is strongly buffered by ${\mathrm{NH}}_{\mathrm{4}}^{+}$ and carboxylic acids. The global mean precipitation pH is 5.5 in GEOS-Chem, which is higher than the cloud water pH because of dilution and below-cloud scavenging of NH₃ and dust. GEOS-Chem successfully reproduces the annual mean precipitation pH observations in North America, Europe, and eastern Asia. Carboxylic acids, which are undetected in routine observations due to biodegradation, lower the annual mean precipitation pH in these areas by 0.2 units. The acid wet deposition flux to terrestrial ecosystems taking into account the acidifying potential of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ in N-saturated ecosystems exceeds 50 $\mathrm{meq}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{a}}^{-\mathrm{1}}$ in East Asia and the Americas, which would affect sensitive ecosystems. ${\mathrm{NH}}_{\mathrm{4}}^{+}$ is the dominant acidifying species in wet deposition, contributing 41 % of the global acid flux to continents under N-saturated conditions.

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM_2.5) air quality, but this requires better understanding of the AOD–PM_2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM_2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM_2.5 composition, PM₁₀, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM_2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM_2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM_2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM_2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM_2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.