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
In fall–winter, 2007–2013, visibility and light scattering coefficients（b sp） were measured along with PM_（2.5）mass concentrations and chemical compositions at a background site in the Pearl River Delta（PRD） r...In fall–winter, 2007–2013, visibility and light scattering coefficients（b sp） were measured along with PM_（2.5）mass concentrations and chemical compositions at a background site in the Pearl River Delta（PRD） region. The daily average visibility increased significantly（p 〈 0.01） at a rate of 1.1 km/year, yet its median stabilized at ~13 km. No haze days occurred when the 24-hr mean PM_（2.5）mass concentration was below 75 μg/m^3. By multiple linear regression on the chemical budget of particle scattering coefficient（b sp）, we obtained site-specific mass scattering efficiency（MSE） values of 6.5 ± 0.2, 2.6 ± 0.3, 2.4 ± 0.7 and 7.3 ± 1.2 m2/g,respectively, for organic matter（OM）, ammonium sulfate（AS）, ammonium nitrate（AN） and sea salt（SS）. The reconstructed light extinction coefficient（b ext） based on the Interagency Monitoring of Protected Visual Environments（IMPROVE） algorithm with our site-specific MSE revealed that OM, AS, AN, SS and light-absorbing carbon（LAC） on average contributed 45.9% ± 1.6%,25.6% ± 1.2%, 12.0% ± 0.7%, 11.2% ± 0.9% and 5.4% ± 0.3% to light extinction, respectively.Averaged b ext displayed a significant reduction rate of 14.1/Mm·year（p 〈 0.05）; this rate would be 82% higher if it were not counteracted by increasing relative humidity（RH） and hygroscopic growth factor（f（RH）） at rates of 2.5% and 0.16/year-1（p 〈 0.01）, respectively, during the fall–winter, 2007–2013. This growth of RH and f（RH） partly offsets the positive effects of lowered AS in improving visibility, and aggravated the negative effects of increasing AN to impair visibility.展开更多
Daily PM_（2.5）（particulate matter with an aerodynamic diameter of below 2.5 μm） mass concentrations were measured by gravimetric analysis in Chinese Research Academy of Environmental Sciences（CRAES）, in the nor...Daily PM_（2.5）（particulate matter with an aerodynamic diameter of below 2.5 μm） mass concentrations were measured by gravimetric analysis in Chinese Research Academy of Environmental Sciences（CRAES）, in the northern part of the Beijing urban area, from December 2013 to April 2015. Two pairs of Teflon（T1/T2） and Quartz（Q1/Q2） samples were obtained, for a total number of 1352 valid filters. Results showed elevated pollution in Beijing,with an annual mean PM_（2.5）mass concentration of 102 μg/m^3. According to the calculated PM_（2.5）mass concentration, 50% of our sampling days were acceptable（PM_（2.5）〈 75 μg/m^3）, 30% had slight/medium pollution（75–150 μg/m^3）, and 7% had severe pollution（〉 250 μg/m^3）. Sampling interruption occurred frequently for the Teflon filter group（75%） in severe pollution periods,resulting in important data being missing. Further analysis showed that high PM_（2.5）combined with high relative humidity（RH） gave rise to the interruptions. The seasonal variation of PM_（2.5）was presented, with higher monthly average mass concentrations in winter（peak value in February, 422 μg/m^3）, and lower in summer（7 μg/m^3 in June）. From May to August, the typical summer period, least severe pollution events were observed, with high precipitation levels accelerating the process of wet deposition to remove PM_（2.5）. The case of February presented the most serious pollution, with monthly averaged PM_（2.5）of 181 μg/m^3 and 32% of days with severe pollution. The abundance of PM_（2.5）in winter could be related to increased coal consumption for heating needs.展开更多
Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitori...Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.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funded by Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDB05010200)the Natural Science Foundation of China (Nos.41025012,41121063)the Bureau of Science,Technology and Information of Guangzhou (No.201300000130)
文摘In fall–winter, 2007–2013, visibility and light scattering coefficients（b sp） were measured along with PM_（2.5）mass concentrations and chemical compositions at a background site in the Pearl River Delta（PRD） region. The daily average visibility increased significantly（p 〈 0.01） at a rate of 1.1 km/year, yet its median stabilized at ~13 km. No haze days occurred when the 24-hr mean PM_（2.5）mass concentration was below 75 μg/m^3. By multiple linear regression on the chemical budget of particle scattering coefficient（b sp）, we obtained site-specific mass scattering efficiency（MSE） values of 6.5 ± 0.2, 2.6 ± 0.3, 2.4 ± 0.7 and 7.3 ± 1.2 m2/g,respectively, for organic matter（OM）, ammonium sulfate（AS）, ammonium nitrate（AN） and sea salt（SS）. The reconstructed light extinction coefficient（b ext） based on the Interagency Monitoring of Protected Visual Environments（IMPROVE） algorithm with our site-specific MSE revealed that OM, AS, AN, SS and light-absorbing carbon（LAC） on average contributed 45.9% ± 1.6%,25.6% ± 1.2%, 12.0% ± 0.7%, 11.2% ± 0.9% and 5.4% ± 0.3% to light extinction, respectively.Averaged b ext displayed a significant reduction rate of 14.1/Mm·year（p 〈 0.05）; this rate would be 82% higher if it were not counteracted by increasing relative humidity（RH） and hygroscopic growth factor（f（RH）） at rates of 2.5% and 0.16/year-1（p 〈 0.01）, respectively, during the fall–winter, 2007–2013. This growth of RH and f（RH） partly offsets the positive effects of lowered AS in improving visibility, and aggravated the negative effects of increasing AN to impair visibility.
基金supported by the State Environmental Protection Commonweal Trade Scientific Research,Ministry of Environmental Protection of China (No.2013467010)The financial support of this special fund for the public service sector and research support from the staff of Chinese Research Academy of Environmental Sciences (CRAES) (Z141100002714002)
文摘Daily PM_（2.5）（particulate matter with an aerodynamic diameter of below 2.5 μm） mass concentrations were measured by gravimetric analysis in Chinese Research Academy of Environmental Sciences（CRAES）, in the northern part of the Beijing urban area, from December 2013 to April 2015. Two pairs of Teflon（T1/T2） and Quartz（Q1/Q2） samples were obtained, for a total number of 1352 valid filters. Results showed elevated pollution in Beijing,with an annual mean PM_（2.5）mass concentration of 102 μg/m^3. According to the calculated PM_（2.5）mass concentration, 50% of our sampling days were acceptable（PM_（2.5）〈 75 μg/m^3）, 30% had slight/medium pollution（75–150 μg/m^3）, and 7% had severe pollution（〉 250 μg/m^3）. Sampling interruption occurred frequently for the Teflon filter group（75%） in severe pollution periods,resulting in important data being missing. Further analysis showed that high PM_（2.5）combined with high relative humidity（RH） gave rise to the interruptions. The seasonal variation of PM_（2.5）was presented, with higher monthly average mass concentrations in winter（peak value in February, 422 μg/m^3）, and lower in summer（7 μg/m^3 in June）. From May to August, the typical summer period, least severe pollution events were observed, with high precipitation levels accelerating the process of wet deposition to remove PM_（2.5）. The case of February presented the most serious pollution, with monthly averaged PM_（2.5）of 181 μg/m^3 and 32% of days with severe pollution. The abundance of PM_（2.5）in winter could be related to increased coal consumption for heating needs.
基金supported by the Anhui Science Foundation for Distinguished Young Scholars (No.1908085J24)the Natural Science Foundation of China (No.62072427)the Jiangsu Natural Science Foundation (No. BK20191193)
文摘Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.
