The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement.However,quantifying this impact is difficult as meteorological conditions ma...The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement.However,quantifying this impact is difficult as meteorological conditions may mask the real effect of changes in emissions on the observed concentrations of pollutants.Based on the air quality and meteorological data at 35 sites in Beijing from 2015 to 2020,a machine learning technique was applied to decouple the impacts of meteorology and emissions on the concentrations of air pollutants.The results showed that the real(“deweathered”)concentrations of air pollutants(expect for O 3)dropped significantly due to lockdown measures.Compared with the scenario without lockdowns(predicted concentrations),the observed values of PM_(2.5),PM_(10),SO_(2),NO_(2),and CO during lockdowns decreased by 39.4%,50.1%,51.8%,43.1%,and 35.1%,respectively.In addition,a significant decline for NO_(2)and CO was found at the background sites(51%and 37.8%)rather than the traffic sites(37.1%and 35.5%),which is different from the common belief.While the primary emissions reduced during the lockdown period,episodic haze events still occurred due to unfavorable meteorological conditions.Thus,developing an optimized strategy to tackle air pollution in Beijing is essential in the future.展开更多
The authors evaluated and compared the behavior of PM2.5 with respect to NOx and NH3 emission changes in high(the year 2013)and low(the year 2018)SO2 emission cases.Two groups of simulations were conducted based on an...The authors evaluated and compared the behavior of PM2.5 with respect to NOx and NH3 emission changes in high(the year 2013)and low(the year 2018)SO2 emission cases.Two groups of simulations were conducted based on anthropogenic emissions from China in 2013 and 2018,respectively.In each group of simulations,a respective 25%reduction in NOx and NH3 emissions were assumed.A sensitivity factor(β)was defined as the relative change in PM2.5 concentration due to 1%change in NOx or NH3 emissions.In the high SO2 emissions case,PM2.5 was more sensitive to NH3(0.31)emissions change than NOx(0.21).Due to the significant decrease in SO2 emissions from the high to low SO2 emissions case,the sensitivity of PM2.5 to NOx increased to 0.33,while its sensitivity to NH3 decreased to 0.22.The result implies that now and in the future,PM2.5 is/will be less sensitive to NH3 emissions change,while NOx emissions control is more effective in reducing the surface PM2.5 concentration.Seasonally,in the low SO2 emissions case,the sensitivities of PM2.5 to NOx and NH3 in winter were higher than those in summer,indicating that to dealwith severewinter hazemore attention should be paid to the emissions control of inorganic PM2.5 precursors,especially NOx.展开更多
Quantification of the nonlinearities between ambient ozone(O3)and the emissions of nitrogen oxides(NOx)and volatile organic compound(VOC)is a prerequisite for an effective O3 control strategy.An Enhanced polynomial fu...Quantification of the nonlinearities between ambient ozone(O3)and the emissions of nitrogen oxides(NOx)and volatile organic compound(VOC)is a prerequisite for an effective O3 control strategy.An Enhanced polynomial functions Response Surface Model(Epf-RSM)with the capability to analyze O3-NOx-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model(ERSM)system.The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O3 concentrations to precursor emission changes.Several comparisons between Epf-RSM and pf-ERSM(polynomial functions based ERSM)were performed using out-of-sample validation,together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams.The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to overfitting in the margin areas and high biases in the transition areas.The O3 concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results.The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O3 formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January,April,and October,while more NOx-sensitive in July.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant number 42077204)the National Key Research and Development Project(Grant number 2017YFC0210103)with data support provided by the National Earth System Science Data Center,National Science&Technology Infrastructure of China(http://www.geodata.cn).
文摘The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement.However,quantifying this impact is difficult as meteorological conditions may mask the real effect of changes in emissions on the observed concentrations of pollutants.Based on the air quality and meteorological data at 35 sites in Beijing from 2015 to 2020,a machine learning technique was applied to decouple the impacts of meteorology and emissions on the concentrations of air pollutants.The results showed that the real(“deweathered”)concentrations of air pollutants(expect for O 3)dropped significantly due to lockdown measures.Compared with the scenario without lockdowns(predicted concentrations),the observed values of PM_(2.5),PM_(10),SO_(2),NO_(2),and CO during lockdowns decreased by 39.4%,50.1%,51.8%,43.1%,and 35.1%,respectively.In addition,a significant decline for NO_(2)and CO was found at the background sites(51%and 37.8%)rather than the traffic sites(37.1%and 35.5%),which is different from the common belief.While the primary emissions reduced during the lockdown period,episodic haze events still occurred due to unfavorable meteorological conditions.Thus,developing an optimized strategy to tackle air pollution in Beijing is essential in the future.
基金This work was supported by the National Natural Science Foundation of China[grant number 41805098].
文摘The authors evaluated and compared the behavior of PM2.5 with respect to NOx and NH3 emission changes in high(the year 2013)and low(the year 2018)SO2 emission cases.Two groups of simulations were conducted based on anthropogenic emissions from China in 2013 and 2018,respectively.In each group of simulations,a respective 25%reduction in NOx and NH3 emissions were assumed.A sensitivity factor(β)was defined as the relative change in PM2.5 concentration due to 1%change in NOx or NH3 emissions.In the high SO2 emissions case,PM2.5 was more sensitive to NH3(0.31)emissions change than NOx(0.21).Due to the significant decrease in SO2 emissions from the high to low SO2 emissions case,the sensitivity of PM2.5 to NOx increased to 0.33,while its sensitivity to NH3 decreased to 0.22.The result implies that now and in the future,PM2.5 is/will be less sensitive to NH3 emissions change,while NOx emissions control is more effective in reducing the surface PM2.5 concentration.Seasonally,in the low SO2 emissions case,the sensitivities of PM2.5 to NOx and NH3 in winter were higher than those in summer,indicating that to dealwith severewinter hazemore attention should be paid to the emissions control of inorganic PM2.5 precursors,especially NOx.
基金supported by the Science and Technology Program of Guangzhou,China(No.202002030188)the National Key Research and Development Program of China(No.2016YFC0207606)+2 种基金US EPA Emission,Air quality,and Meteorological Modeling Support(No.EP-D-12-044)the National Natural Science Foundation of China(Grant No.21625701),the Fundamental Research Funds for the Central Universities(Nos.D2160320,D6180330,and D2170150)the Natural Science Foundation of Guangdong Province,China(No.2017A030310279).
文摘Quantification of the nonlinearities between ambient ozone(O3)and the emissions of nitrogen oxides(NOx)and volatile organic compound(VOC)is a prerequisite for an effective O3 control strategy.An Enhanced polynomial functions Response Surface Model(Epf-RSM)with the capability to analyze O3-NOx-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model(ERSM)system.The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O3 concentrations to precursor emission changes.Several comparisons between Epf-RSM and pf-ERSM(polynomial functions based ERSM)were performed using out-of-sample validation,together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams.The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to overfitting in the margin areas and high biases in the transition areas.The O3 concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results.The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O3 formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January,April,and October,while more NOx-sensitive in July.
