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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.

Sizes and number concentrations are critical parameters for the impact of atmospheric particles on climate and human health. However, comprehensive studies focusing on size‐resolved particle number (PN) emissions from various sectors are scarce. This study aims to fill this research gap by developing sectoral size‐resolved PN emissions for major species including sulfate, organic mass (OM), and black carbon (BC). The size‐resolved emission profiles derived from various measurements in the literature were integrated with a particle mass emission inventory (EI) for 13 major sectors in the Yangtze River Delta region of China as a case study. The particle number size distribution (PNSD) of emitted particles exhibited two distinct peaks: one at approximately 10 nm and the other in the range of 40–60 nm. The primary contributors to PN emissions in the region in 2017 were power plants, gasoline vehicles, diesel vehicles, and cooking sources. In terms of species, OM dominated PN emissions, followed by primary sulfate and then BC. A regional size‐resolved aerosol model employing the size‐resolved PN EI developed here (referred to as the BIN‐SPE experiment) provided reasonably accurate temporal variations of the total PN concentration and captured the PNSD within the size range of 10–300 nm. Uncertainty analysis of sectoral PN emissions across size ranges was carried out and the performance of the BIN‐SPE experiment was compared with those of three commonly used PN emission parameterizations. Our model evaluations highlight future needs for in‐depth investigations into more advanced size‐resolved emissions and secondary OM formation mechanisms. Plain Language Summary: Various sources such as power plants, vehicles, cooking, and others emit numerous small particles. Quantifying their sizes and number emissions is crucial for understanding atmospheric particle number size distribution and its impacts on climate and health. However, few studies comprehensively address sectoral size‐resolved particle number emissions, with most focusing on global scales or single source. This study couples size‐resolved emission profiles reported in literature for 13 major sectors with mass emission inventories to quantify sectoral size‐resolved particle number emissions with speciation for the Yangtze River Delta region in China. The particle number emissions exhibit a bimodal distribution with peaks at approximately 10 and 40–60 nm. Primary contributors are power plants, gasoline vehicles, diesel vehicles, and cooking sources. In terms of species, organic matter dominates particle number emissions, followed by primary sulfate and black carbon. Notably, the peak in emissions with sizes less than 20 nm reconciles the disparity between modeled and observed distributions. To the best of our knowledge, this study represents the first comprehensive examination of sectoral particle number emissions with speciation in China. The method and data sets utilized in this study can be employed to quantify particle number emissions in other regions and are instrumental for impact assessments. Key Points: An emission profile‐based method is applied to quantify sectoral size‐resolved particle number emissions with speciationVehicles, power plants, and cooking dominate bimodal particle number emissions, with organics and primary sulfate as major speciesProminent particle number emissions with sizes less than 20 nm substantially reduce model‐observation disparities [ABSTRACT FROM AUTHOR]

Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution. [ABSTRACT FROM AUTHOR]

Severe airborne particulate matter (PM, including PM 2.5 and PM 10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM 2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µgm-3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM 2.5 and PM 10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p<0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM 2.5 concentrations declined in most regions (-0.78 µgm-3yr-1) during 2018–2022. Severe PM 2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM 2.5 and PM 10 concentrations are publicly accessible at 10.5281/zenodo.10073944 (Wang et al., 2023a). [ABSTRACT FROM AUTHOR]

Aerosol particles play critical roles in climate and human health. However, aerosol composition and evolution, particularly secondary organic aerosol (SOA), and aerosol interactions with clouds in high-altitude background areas in China remain less understood. Here, we conducted real-time measurements of submicron aerosols (PM 1) using aerosol mass spectrometers at a forested mountain site (1128 m a.s.l.) in southeastern China in November 2022. The average (±1σ) PM 1 mass concentration was 4.3±4.8 µ g m -3 , which was ubiquitously lower than that at other mountain sites in China. Organic aerosol (OA) constituted the largest fraction of PM 1 (42.9 %) and was predominantly secondary, as indicated by the high oxygen-to-carbon (O / C) ratio (0.85–0.96) and carbon oxidation state (0.21–0.49). Notably, the remarkably enhanced PM 1 concentrations observed during the daytime on cloudless days were identified to be likely to be produced from cloud evaporation. While more oxidized oxygenated OA was scavenged efficiently during cloud events, cloud evaporation was found to release a significant amount of less oxidized oxygenated OA from air masses transported from polluted regions. The distinct decrease in OA / Δ CO with the increase in O / C during the cloud evaporative period further demonstrates that OA remaining in cloud droplets is generally in a moderate oxidation state. Moreover, organic nitrates were also estimated and showed a higher contribution to the total nitrate during the cloudy period (27 %) than during the evaporative period (3 %). Overall, our results demonstrate the importance of SOA and the influences of cloud processes in regional mountain areas in southeastern China. [ABSTRACT FROM AUTHOR]