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Exposure to particulate matter (PM) could increase both susceptibility to SARS-CoV-2 infection and severity of COVID-19 disease. Prior studies investigating associations between PM and COVID-19 morbidity have only considered PM 2.5 or PM 10 , rather than PM 1. We investigated the associations between daily-diagnosed COVID-19 morbidity and average exposures to ambient PM 1 starting at 0 through 21 days before the day of diagnosis in 12 cities in China using a two-step analysis: a time-series quasi-Poisson analysis to analyze the associations in each city; and then a meta-analysis to estimate the overall association. Diagnosed morbidities and PM 1 data were obtained from National Health Commission in China and China Meteorological Administration, respectively. We found association between short-term exposures to ambient PM 1 with COVID-19 morbidity was significantly positive, and larger than the associations with PM 2.5 and PM 10. Percent increases in daily-diagnosed COVID-19 morbidity per IQR/10 PM 1 for different moving averages ranged from 1.50% (−1.20%, 4.30%) to 241% (95%CI: 80.7%, 545%), with largest values for exposure windows starting at 17 days before diagnosis. Our results indicate that smaller particles are more highly associated with COVID-19 morbidity, and most of the effects from PM 2.5 and PM 10 on COVID-19 may be primarily due to the PM 1. This study will be helpful for implementing measures and policies to control the spread of COVID-19. • The first attempt to examine the association between PM1 and COVID-19 morbidity. • Short-term exposure to ambient PM1 is associated with COVID-19 morbidity. • Smaller particles are more highly associated with COVID-19 morbidity. [ABSTRACT FROM AUTHOR]

High PM 2.5 concentration threats ecosystem functions but limited quantitative studies have recognized PM 2.5 pollution as an individual stressor in evaluating ecological risk. In this study, we applied a machine-learning-based simulation model incorporating full-coverage satellite-driven PM 2.5 dataset to estimate high-resolution ground PM 2.5 concentration for the Golden Triangle of Southern Fujian Province, China (GTSF) in 2030 under two Representative Concentration Pathways (RCPs). Based on the simulation output, the ecological risk's spatiotemporal change and the risk for different land cover types, which were caused by PM 2.5 pollution, were assessed. We found that the PM 2.5 levels and ecological risk in the GTSF under RCP 4.5 would be reduced while those under RCP 8.5 would continue to increase. The regions with the highest ecological risk under RCP 4.5 are the most urbanized and industrialized districts, while those with the highest ecological risk under RCP 8.5 are of the highest rate in urbanization and the greatest decrease in planetary potential layer height. For both base years and 2030 under two RCPs, the ecological risk on developed land is the highest, while that on the forest is the lowest. Our study can provide useful information for environmental policy risk assessment. • Model simulations captured spatio-temporal trends of PM 2.5 in 2030 under two RCPs. • We assessed the ecological risk of PM 2.5 considering regional disparities. • The variation of land cover, population and climate change will induce polarized ecological risk under two RCPs. • The developed land in the GTSF has the highest ecological risk and the forest the lowest. [ABSTRACT FROM AUTHOR]