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Atmospheric removal of PM 2.5 by the Three Northern Regions Shelter Forest (TNRSF) – the so called Green Great Wall (GGW) in northern China through dry deposition process was estimated using a bulk big-leaf model and a vegetation collection model. Decadal trend of PM 2.5 dry deposition flux from 1999 to 2010 was calculated from modeled dry deposition velocity and air concentration retrieved from the satellite remote sensing. Dry deposition velocities of PM 2.5 calculated using the two deposition models increased in many places of the TNRSF over the last decade due to increasing vegetation coverage of the TNRSF. Both increasing deposition velocity due to forest expansion and PM 2.5 atmospheric level contributed to the increasing deposition flux of PM 2.5 . The highest atmospheric deposition flux of PM 2.5 was found in the Central-north region covering Beijing-Tianjin-Hebei area, followed by the Northwestern and the Northeastern regions of the TNRSF. While greater collection of PM 2.5 by vegetation was identified in the Northeastern region of the TNRSF due to higher forest coverage over this region, the most significant incline of the PM 2.5 atmospheric removal due to vegetation collection was discerned in the Central-north region because of the most rapid increase in the vegetation coverage in this region. A total mass of 2.85 × 10 7 t PM 2.5 was estimated to be removed from the atmosphere through dry deposition process over the TNRSF from 1999 to 2010. The two deposition models simulated similar magnitude and spatial patterns of PM 2.5 dry deposition fluxes. Our results suggest that the TNRSF plays a moderate role in PM 2.5 uptake, but enhances PM 2.5 atmospheric removal by 30% in 2010 than in 1980. [ABSTRACT FROM AUTHOR]

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM 2.5 ) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM 2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM 2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM 2.5 anomaly as a time node, we examine changes in PM 2.5 before and after the time node to assess the response of PM 2.5 to the COVID-19 lockdown. The rate of decrease of PM 2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM 2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM 2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM 2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

PKU-FUEL is a recently developed gridded global emission inventory for multiple air pollutants that uses a bottom-up approach. The inventory includes data collected monthly for the period of 1960 to 2014 and at a 0.1° × 0.1° latitude/longitude resolution. In an effort to evaluate and improve this emission inventory, the PKU-FUEL Sulfur Dioxide (SO 2 ) emission inventory was compared to other currently available and widely used global SO 2 emission inventories constructed based on bottom-up and top-down approaches, including CEDS and OMI-HTAP. While PKU-FUEL is capable of capturing SO 2 emissions across the globe and particularly in Asia, it misses 41 industrial point sources globally, accounting for 9.3% of Ozone Monitoring Instrument (OMI) remote sensing-measured industrial point sources. Most of these missing point sources are identified in Latin America, the Middle East (~60%), and some remote places. To improve the PKU-FUEL SO 2 inventory, we applied OMI-measured emissions to sources missing from PKU-FUEL. GEOS-Chem model simulations were performed to evaluate original and improved PKU-FUEL SO 2 inventories against measured SO 2 concentrations across the world. Results were further compared to GEOS-Chem modeled SO 2 concentrations using the CEDS inventory. We show that the modeled SO 2 concentrations determined using both CEDS and improved PKU-FUEL inventories to a large extent corroborate sampled data and that the improved PKU-FUEL performs better for those regions lacking monitoring data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)