An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the...An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the study case a 67% to 74% NPS pollutant load removal rate can lead to meeting the chemical oxygen demand COD pollution control target for most watersheds.In contrast it is hardly to achieve the ammonia nitrogen NH4-N total phosphorus TP and biological oxygen demand BOD5 pollution control target by simply removing NPS pollutants. This highlights that the pollution control strategies should be taken according to different pollutant species and sources in different watersheds rather than one-size-fits-all .展开更多
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
基金The National Science and Technology Major Project of China(No.2012ZX07301-001)the Shenzhen Environmental Research Project,China Postdoctoral Science Foundation(No.2013M530642)
文摘An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the study case a 67% to 74% NPS pollutant load removal rate can lead to meeting the chemical oxygen demand COD pollution control target for most watersheds.In contrast it is hardly to achieve the ammonia nitrogen NH4-N total phosphorus TP and biological oxygen demand BOD5 pollution control target by simply removing NPS pollutants. This highlights that the pollution control strategies should be taken according to different pollutant species and sources in different watersheds rather than one-size-fits-all .
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.
