Estimating the impacts on PM_(2.5)pollution and CO_(2)emissions by human activities in different urban regions is important for developing efficient policies.In early 2020,China implemented a lockdown policy to contai...Estimating the impacts on PM_(2.5)pollution and CO_(2)emissions by human activities in different urban regions is important for developing efficient policies.In early 2020,China implemented a lockdown policy to contain the spread of COVID-19,resulting in a significant reduction of human activities.This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution.Here,we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets,the Suomi-NPP/VIIRS and the CO_(2)emissions from the Carbon Monitor project.Our study shows that PM_(2.5)concentrations in the spring of 2020 decreased by 41.87%in the Yangtze River Delta(YRD)and 43.30%in the Pearl River Delta(PRD),respectively,owing to the significant shutdown of traffic and manufacturing industries.However,PM_(2.5)concentrations in the Beijing-Tianjin-Hebei(BTH)region only decreased by 2.01%because the energy and steel industries were not fully paused.In addition,unfavorable weather conditions contributed to further increases in the PM_(2.5)concentration.Furthermore,CO_(2)concentrations were not significantly affected in China during the short-term emission reduction,despite a 19.52%reduction in CO_(2)emissions compared to the same period in 2019.Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO_(2)emissions.展开更多
Coronavirus disease 2019(COvID-19)is a severe global public health emergency that has caused a major cri-sis in the safety of human life,health,global economy,and social order.Moreover,CovID-19 poses significant chall...Coronavirus disease 2019(COvID-19)is a severe global public health emergency that has caused a major cri-sis in the safety of human life,health,global economy,and social order.Moreover,CovID-19 poses significant challenges to healthcare systems worldwide.The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics.However,because of the complexity of epidemics,predicting infectious diseases on a global scale faces significant challenges.In this study,we developed the second version of Global Prediction System for Epidemiological Pandemic(GPEP-2),which combines statis-tical methods with a modified epidemiological model.The GPEP-2 introduces various parameterization schemes for both impacts of natural factors(seasonal variations in weather and environmental impacts)and human so-cial behaviors(government control and isolation,personnel gathered,indoor propagation,virus mutation,and vaccination).The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%.It also provided prediction and decision-making bases for several regional-scale CovID-19 pandemic outbreaks in China,with an average accuracy rate of 89.3%.Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread.The predicted results could serve as a reference for public health planning and policymaking.展开更多
基金supported by the National Science Foundation of China(Grant.No.41521004)the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development(Grant No.2019ZX-06)the Fundamental Research Funds for the Central Universit-ies(lzujbky-2021-kb12)。
文摘Estimating the impacts on PM_(2.5)pollution and CO_(2)emissions by human activities in different urban regions is important for developing efficient policies.In early 2020,China implemented a lockdown policy to contain the spread of COVID-19,resulting in a significant reduction of human activities.This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution.Here,we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets,the Suomi-NPP/VIIRS and the CO_(2)emissions from the Carbon Monitor project.Our study shows that PM_(2.5)concentrations in the spring of 2020 decreased by 41.87%in the Yangtze River Delta(YRD)and 43.30%in the Pearl River Delta(PRD),respectively,owing to the significant shutdown of traffic and manufacturing industries.However,PM_(2.5)concentrations in the Beijing-Tianjin-Hebei(BTH)region only decreased by 2.01%because the energy and steel industries were not fully paused.In addition,unfavorable weather conditions contributed to further increases in the PM_(2.5)concentration.Furthermore,CO_(2)concentrations were not significantly affected in China during the short-term emission reduction,despite a 19.52%reduction in CO_(2)emissions compared to the same period in 2019.Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO_(2)emissions.
基金This work was jointly supported by the National Natural Science Foundation of China[grant numbers 41521004 and 41875083]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
基金This work was jointly supported by the National Natural Science Foundation of China[grant number 41521004]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
基金the Collaborative Research Project of the National Natural Science Foundation of China(L2224041)the Chinese Academy of Sciences(XK2022DXC005)+1 种基金Frontier of Interdisciplinary Research on Monitoring and Prediction of Pathogenic Microorganisms in the Atmosphere,Self-supporting Program of Guangzhou Laboratory(SRPG22–007)Gansu Province Intellectual Property Program(Oriented Organization)Project(22ZSCQD02).
文摘Coronavirus disease 2019(COvID-19)is a severe global public health emergency that has caused a major cri-sis in the safety of human life,health,global economy,and social order.Moreover,CovID-19 poses significant challenges to healthcare systems worldwide.The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics.However,because of the complexity of epidemics,predicting infectious diseases on a global scale faces significant challenges.In this study,we developed the second version of Global Prediction System for Epidemiological Pandemic(GPEP-2),which combines statis-tical methods with a modified epidemiological model.The GPEP-2 introduces various parameterization schemes for both impacts of natural factors(seasonal variations in weather and environmental impacts)and human so-cial behaviors(government control and isolation,personnel gathered,indoor propagation,virus mutation,and vaccination).The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%.It also provided prediction and decision-making bases for several regional-scale CovID-19 pandemic outbreaks in China,with an average accuracy rate of 89.3%.Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread.The predicted results could serve as a reference for public health planning and policymaking.
基金jointly supported by the National Natural Science Foundation of China (41521004)the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development (2019ZX-06)。
