ABSTRACT
Fine particulate matter (PM2.5) is the largest environmental risk factor impacting human health. While PM2.5 has been measured widely across the world, there has been no high-resolution and gapless global PM2.5 data on a daily scale. We generate a global daily PM2.5 concentration at 1 km resolution using satellite gap-filled aerosol products and machine learning. Daily PM2.5 retrievals agreed well with ground measurements, with sample-, space-, and time-based cross-validated correlations of 0.93, 0.89, and 0.88, respectively. This enables us to unprecedentedly monitor the day-to-day variations of PM2.5, exposure risk, and mortality burden around the globe. More than 96% of the days exceeded the World Health Organization (WHO) recommended daily air quality guidelines (AQG) level (15 μg m-3) in 2020, and 99% of populated areas were exposed to PM2.5 risk at least one day; in particular, the proportions are 91% and 64% similarly in 7 and 30 days, respectively. The annual population-weighted mean PM2.5 concentration was 27.6 μg m-3 (~5.5 times higher than the WHO annual AQG level of 5 μg m-3), resulting in estimated premature deaths of ~4.2 million people and accounting for ~6.6% of total global deaths. Substantial differences are noted in many parts of the world between 2019 and 2020 associated with widespread episodes of wildfires or the COVID-19 shutdowns. The overall air quality in 2020 was significantly better than in 2019 in more than 70% of major cities. The global population-weighted mean PM2.5 decreased by ~5.1%, and the associated number of premature deaths dropped by 56,700.
Subject(s)
COVID-19 , DeathABSTRACT
Electric vehicles (EVs) represent a major path for global decarbonization, improvement of air quality and promotion of renewable energy. To tackle the COVID-19 pandemic, China imposed full lockdowns and thorough travel restrictions. This event represents an unprecedented inadvertent geoengineering experiment in vehicle emissions, emulating transition to EVs. Here we novelly exploited observations of air quality during the full lockdown to constrain predictions of a comprehensive chemical transport model. Large traffic flux reductions were near-linearly linked to reductions of NO 2 and PM 2.5 (correlation = 0.491 ~ 0.626). Extrapolating to a full conversion to EV results in a large reduction of PM 2.5 (30 ~ 70%) in most of central and south eastern China. A smaller reduction of PM 2.5 (10 ~ 20%) in Beijing and Tianjin was achievable due to the presence of major industrial emission sources which cause haze at a regional scale. The potential reductions in NO 2 were 40 ~ 90% in most of the megacities. At this present crossroads of policy, our findings reveal timely evidence supporting the transition towards renewable energy.Funding: This study is supported by the Department of Science and Technology of China (No. 2016YFC0202702, 2018YFC0213506 and 2018YFC0213503), National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107) and National Natural Science Foundation of China (No. 21577126, 41561144004, 21625701). Pengfei Li is supported by Initiation Fund for Introducing Talents of Hebei Agricultural University (412201904) and Youth Top Fund of Universities in Hebei Province (BJ2020032). Conflict of Interest: Authors declare no competing interests.
Subject(s)
COVID-19 , Electric InjuriesABSTRACT
Background To develop and evaluate the prognostic machine-learning model for mortality in patients with coronavirus disease 2019 (COVID-19).Methods Clinical data of confirmed COVID-19 were retrospectively collected from Wuhan between 18th January and 29th March 2020. Gradient Boosting Decision Tree (GBDT), logistic regression (LR) model, and simplified LR with selected 5 features (LR-5) model were built to predict the mortality of COVID-19. 5-fold area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated and compared between models.Results A total of 2,924 patients were included in the final analysis, 257(8.8%) of whom died during hospitalization and 2,667 (91.2%) survived. There were 21(0.7%) mild cases, 2,051(70.1%) moderate case, 779(26.6%) severe cases, and 73(2.5%) critically severe cases of COVID-19 on admission. The overall 5-fold AUC was observed highest in GBDT model (0.941), followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracy were 0.889 in GBDT, 0.868 in LR and 0.887 in LR-5. GBDT model also showed the highest sensitivity (0.899) and speciality (0.889). The NPV of all three models exceeded 97%, while the PPV were relatively low in all models, 0.381 for LR, 0.402 for LR-5 and 0.432 for GBDT. In subgroups analysis with severe cases only, GBDT model also performed the best with a accuracy of 0.799 and 5-fold AUC (0.918).Conclusion The finding revealed that mortality prediction performance of the GBDT was superior to the LR models in confirmed cases of COVID-19, regardless of disease severity.
Subject(s)
COVID-19ABSTRACT
Social and mental stressors associated with the COVID-19 pandemic may promote long-term effects on child development. However, reports aimed at identifying the relationship between pandemics and child health are limited. We conducted a retrospective study to evaluate the severe acute respiratory syndrome (SARS) pandemic in 2003 and its relationship to child development indicators using a representative sample across China. Our study involved longitudinal measurements of 14,647 children, 36% of whom (n = 5216) were born before or during the SARS pandemic. Cox models were utilized to examine the effects of SARS on preterm birth and four milestones of development: age to (1) walk independently, (2) say a complete sentence, (3) count from 0 to 10, and (4) undress him/herself for urination. Mixed effect models were utilized to associate SARS with birthweight, body weight and height. Our results show that experiencing SARS during early childhood was significantly associated with delayed milestones, with adjusted hazard ratios of 3.17 [95% confidence intervals (CI): 2.71, 3.70], 3.98 (3.50, 4.53), 4.96 (4.48, 5.49), or 5.57 (5.00, 6.20) for walking independently, saying a complete sentence, counting from 0 to 10, and undressing him/herself for urination, respectively. Experiencing SARS was also associated with reduced body weight. This effect was strongest for preschool children [a weight reduction of 4.86 (0.36, 9.35) kg, 5.48 (-0.56, 11.53) kg or 5.09 (-2.12, 12.30) kg for 2, 3, 4 year-olds, respectively]. We did not identify a significant effect of maternal SARS exposure on birthweight or gestational length. Collectively, our results showed that the SARS pandemic was associated with delayed child development and provided epidemiological evidence to support the association between infectious disease epidemics and impaired child health. These results provide a useful framework to investigate and mitigate relevant impacts from the COVID-19 pandemic.