Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global we...Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global wealth inequality has intensified in recent years,yet the relationship between wealth inequality and health risks related to PM_(2.5) pollution remains poorly understood.In this study,we evaluated the global mortality and health cost at-tributable to PM_(2.5) exposure from 2017 to 2021,and analyzed the relationship between wealth inequality,PM_(2.5) pollution,and the associated health risks across regions with varying economic levels.We found a consistent decline in mortalities and health costs attributable to PM_(2.5) exposure from 2017 to 2020,followed by a rebound after 2020,driven primarily by the resurgence of PM_(2.5) concentrations and a deceleration in the reduction of baseline mortality rates.We also found that the average PM_(2.5) concentration and associated risks decrease as domestic wealth inequality decreases and national income level increases.However,regions with extremely high levels of wealth inequality consistently show lower national average PM_(2.5) concentrations and health risks.These findings highlight the need to consider healthcare security during emergencies,as well as policy fairness across economic regions,in the formulation of global PM_(2.5) pollution control measures to promote sustainable,more equitable economic growth and coordinated air pollution management.展开更多
Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China ...Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China in winter and spring of 2021.The results show that the average concentration of PM_(2.5) decreased by 47%from winter to spring,while volume-normalized and mass-normalized OP(i.e.,OP_(v) and OP_(m))increased by 6%and 69%,respectively.It suggests that the decline of PM_(2.5) may not necessarily decrease the health risks and the intrinsic toxicity of PM_(2.5).Variations of OP_(v) and OP_(m) among different periods were related to the different source contributions and environmental conditions.The positive matrix factorization model was used to identify the major sources of OP_(v).OP_(v) was mainly contributed by biomass burning/industrial emissions(29%),soil/road dust(20%),secondary sulfate(14%),and coal combustion(13%)in winter.Different major sources were resolved to be secondary sulfate(36%),biological sources(21%),and marine vessels(20%)in spring,presenting the substantial contribution of biological sources.The analysis shows strong associations between OP_(v) and both live and dead bacteria,further confirming the important contribution of bioaerosols to the enhancement of OP.This study highlights the importance of understanding OP in ambient PM_(2.5) in terms of public health impact and provides a new insight into the biological contribution to OP.展开更多
Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tra...Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tracers.This study comprehensively investigates chemical composition of PM_(2.5),including both inorganic and organic compounds throughout haze and post-haze periods in 2019 at a rural site of Northern Thailand.Average PM_(2.5) concentrations during haze and post-haze period were 87±36 and 21±11μg/m^(3),respectively.Organic matter was the dominant contributor in PM_(2.5) mass,followed by water soluble inorganic ions and mineral dust.Molecular markers,including levoglucosan,dehydroabietic acid,and 4-nitrocatechol,and ions(Cl^(-),and K^(+)),were used to characterize low haze(PM_(2.5)<100μg/m^(3))and episodic haze(PM_(2.5)>100μg/m^(3)).Low haze is associated with local aerosols from agricultural waste burning,while episodic haze is linked to aged aerosols from mixed agricultural waste,softwood,and hardwood burning.Source apportionment incorporating these molecular markers in receptor modelling(Positive matrix factorization),identified three distinct biomass burning sources:mixed,local,and aged biomass burnings,contributing 31,19 and 13%of PM_(2.5) during haze period.During post-haze period,contributions shifted,with local biomass burning(32%)comparable to secondary sulfate(34%)and mixed dust and traffic sources(26%).These findings demonstrate that both regional and local sources con-tribute to severe haze,highlighting the need for integrated policies for cross-border cooperation as well as stricter regulations to reduce biomass burning in Northern Thailand and Southeast Asia.展开更多
Precipitation plays a pivotal role in wet deposition,significantly affecting aerosol purification.The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates invo...Precipitation plays a pivotal role in wet deposition,significantly affecting aerosol purification.The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates involved.However,further research is necessary to fully understand how precipitation impacts PM_(2.5)components.This study utilized high-temporalresolution data on PM_(2.5),its components and meteorological factors to examine varying responses influenced by precipitation intensity and duration.The findings indicate that increased rainfall intensity and duration enhance PM_(2.5)and its constituents removal efficiency.Specifically,longer precipitation periods significantly improve PM_(2.5)purification,especially with drizzle and light rain.Moreover,there is a direct correlation between preprecipitation PM_(2.5)levels and its scavenging rates,with drizzle potentially exacerbating PM_(2.5)pollution under cleaner conditions(≤35μg/m^(3)).Seasonally,the efficacy of removing PM_(2.5)components varies notably in response to drizzle and light rain.In spring,higher PM_(2.5)levels after drizzlewere primarily due to increased organic carbon concentrations favored by higher relative humidity and lower pH conditions compared to other seasons,conducive to secondary organic aerosol production.Lower wind speeds and higher temperatures further contribute to water-soluble organic carbon accumulation.Daytime and nighttime precipitation exerted differing influences on PM_(2.5)components,particularly in spring where daytime drizzle and light rain significantly increased PM_(2.5)and its constituents,notably NO_(3)-,potentially associated with phase distribution changes between gas and aerosol phases in low-temperature,high-RH conditions compared to nighttime.These results propose a dualimpact mechanism of precipitation on PM_(2.5)and provide scientific basis for designing effective control strategies.展开更多
As urbanization accelerates globally,air pollution-particularly PM_(2.5)-is becoming an increasingly significant threat,not only to public health but also the environment.In-depth research on the impact of China’s Ze...As urbanization accelerates globally,air pollution-particularly PM_(2.5)-is becoming an increasingly significant threat,not only to public health but also the environment.In-depth research on the impact of China’s Zero Waste City pilot policy on PM_(2.5)concentration offers valuable insights into the policy’s effectiveness and provides a potential model for environmental governance worldwide.This study employs panel data from 293 Chinese cities from 2014 to 2022 to systematically analyze the impact of the Zero-Waste City policy on PM_(2.5)concentration using a difference-in-differences model.The findings indicate that the policy not only directly reduces PM_(2.5)concentration but also indirectly curbs PM_(2.5)emissions by enhancing green innovation and green economic efficiency.Moreover,the policy’s effects are found to be positively moderated by urban energy dependence and digital financial inclusion,while they are negatively moderated by the government debt ratio.Based on these findings,this study suggests that cities should actively develop their digital economy,reduce government debt,promote green innovation,and improve green economic efficiency,as doing so will enhance their implementation of environmental policies and promote sustainable urban development.展开更多
In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))conce...In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))concentrations.To assess the effectiveness of this strategy,we conducted an ablation experiment on the congestion index and implemented a multi-scale input model.Compared with conventional models,the strategy reduces the root mean square error(RMSE)of all benchmark models by>6.07%on average,and the bestperforming model reduces it by 12.06%,demonstrating excellent performance improvement.In addition,evenwith high traffic emissions,the RMSE during peak hours is still below 9.83μg/m^(3),which proves the effectiveness of the strategy by effectively addressing pollution hotspots.This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.展开更多
Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–...Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.展开更多
Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the l...Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its trends.In this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate analysis.Daily PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach.Data from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the model.Evaluation indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to 2021.The highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.展开更多
Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due...Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.展开更多
基金supported by the National Natural Science Foundation of China(Nos.42305089 and 42175106)the Self-supporting Program of Guangzhou Laboratory(No.SRPG22-007)+1 种基金the Youth Science and Technology Fund Project of Gansu(No.22JR5RA512)the Fundamental Research Funds for the Central Universities(No.lzujbky-2022-pd05).
文摘Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global wealth inequality has intensified in recent years,yet the relationship between wealth inequality and health risks related to PM_(2.5) pollution remains poorly understood.In this study,we evaluated the global mortality and health cost at-tributable to PM_(2.5) exposure from 2017 to 2021,and analyzed the relationship between wealth inequality,PM_(2.5) pollution,and the associated health risks across regions with varying economic levels.We found a consistent decline in mortalities and health costs attributable to PM_(2.5) exposure from 2017 to 2020,followed by a rebound after 2020,driven primarily by the resurgence of PM_(2.5) concentrations and a deceleration in the reduction of baseline mortality rates.We also found that the average PM_(2.5) concentration and associated risks decrease as domestic wealth inequality decreases and national income level increases.However,regions with extremely high levels of wealth inequality consistently show lower national average PM_(2.5) concentrations and health risks.These findings highlight the need to consider healthcare security during emergencies,as well as policy fairness across economic regions,in the formulation of global PM_(2.5) pollution control measures to promote sustainable,more equitable economic growth and coordinated air pollution management.
基金supported by the National Natural Science Foundation of China(No.41975156)and the Fundamental Research Funds for the Central Universities.
文摘Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China in winter and spring of 2021.The results show that the average concentration of PM_(2.5) decreased by 47%from winter to spring,while volume-normalized and mass-normalized OP(i.e.,OP_(v) and OP_(m))increased by 6%and 69%,respectively.It suggests that the decline of PM_(2.5) may not necessarily decrease the health risks and the intrinsic toxicity of PM_(2.5).Variations of OP_(v) and OP_(m) among different periods were related to the different source contributions and environmental conditions.The positive matrix factorization model was used to identify the major sources of OP_(v).OP_(v) was mainly contributed by biomass burning/industrial emissions(29%),soil/road dust(20%),secondary sulfate(14%),and coal combustion(13%)in winter.Different major sources were resolved to be secondary sulfate(36%),biological sources(21%),and marine vessels(20%)in spring,presenting the substantial contribution of biological sources.The analysis shows strong associations between OP_(v) and both live and dead bacteria,further confirming the important contribution of bioaerosols to the enhancement of OP.This study highlights the importance of understanding OP in ambient PM_(2.5) in terms of public health impact and provides a new insight into the biological contribution to OP.
文摘Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tracers.This study comprehensively investigates chemical composition of PM_(2.5),including both inorganic and organic compounds throughout haze and post-haze periods in 2019 at a rural site of Northern Thailand.Average PM_(2.5) concentrations during haze and post-haze period were 87±36 and 21±11μg/m^(3),respectively.Organic matter was the dominant contributor in PM_(2.5) mass,followed by water soluble inorganic ions and mineral dust.Molecular markers,including levoglucosan,dehydroabietic acid,and 4-nitrocatechol,and ions(Cl^(-),and K^(+)),were used to characterize low haze(PM_(2.5)<100μg/m^(3))and episodic haze(PM_(2.5)>100μg/m^(3)).Low haze is associated with local aerosols from agricultural waste burning,while episodic haze is linked to aged aerosols from mixed agricultural waste,softwood,and hardwood burning.Source apportionment incorporating these molecular markers in receptor modelling(Positive matrix factorization),identified three distinct biomass burning sources:mixed,local,and aged biomass burnings,contributing 31,19 and 13%of PM_(2.5) during haze period.During post-haze period,contributions shifted,with local biomass burning(32%)comparable to secondary sulfate(34%)and mixed dust and traffic sources(26%).These findings demonstrate that both regional and local sources con-tribute to severe haze,highlighting the need for integrated policies for cross-border cooperation as well as stricter regulations to reduce biomass burning in Northern Thailand and Southeast Asia.
基金supported by the National Natural Science Foundation of China(No.42175124)the Science and Technology Department of Sichuan Province(No.23YFS0383)the Fundamental Research Funds for the Central Universities,China(No.2023CDSN-18).
文摘Precipitation plays a pivotal role in wet deposition,significantly affecting aerosol purification.The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates involved.However,further research is necessary to fully understand how precipitation impacts PM_(2.5)components.This study utilized high-temporalresolution data on PM_(2.5),its components and meteorological factors to examine varying responses influenced by precipitation intensity and duration.The findings indicate that increased rainfall intensity and duration enhance PM_(2.5)and its constituents removal efficiency.Specifically,longer precipitation periods significantly improve PM_(2.5)purification,especially with drizzle and light rain.Moreover,there is a direct correlation between preprecipitation PM_(2.5)levels and its scavenging rates,with drizzle potentially exacerbating PM_(2.5)pollution under cleaner conditions(≤35μg/m^(3)).Seasonally,the efficacy of removing PM_(2.5)components varies notably in response to drizzle and light rain.In spring,higher PM_(2.5)levels after drizzlewere primarily due to increased organic carbon concentrations favored by higher relative humidity and lower pH conditions compared to other seasons,conducive to secondary organic aerosol production.Lower wind speeds and higher temperatures further contribute to water-soluble organic carbon accumulation.Daytime and nighttime precipitation exerted differing influences on PM_(2.5)components,particularly in spring where daytime drizzle and light rain significantly increased PM_(2.5)and its constituents,notably NO_(3)-,potentially associated with phase distribution changes between gas and aerosol phases in low-temperature,high-RH conditions compared to nighttime.These results propose a dualimpact mechanism of precipitation on PM_(2.5)and provide scientific basis for designing effective control strategies.
基金The authors declare that fund support was received from National Social Science Fund of China[Grant No.23BJL010].
文摘As urbanization accelerates globally,air pollution-particularly PM_(2.5)-is becoming an increasingly significant threat,not only to public health but also the environment.In-depth research on the impact of China’s Zero Waste City pilot policy on PM_(2.5)concentration offers valuable insights into the policy’s effectiveness and provides a potential model for environmental governance worldwide.This study employs panel data from 293 Chinese cities from 2014 to 2022 to systematically analyze the impact of the Zero-Waste City policy on PM_(2.5)concentration using a difference-in-differences model.The findings indicate that the policy not only directly reduces PM_(2.5)concentration but also indirectly curbs PM_(2.5)emissions by enhancing green innovation and green economic efficiency.Moreover,the policy’s effects are found to be positively moderated by urban energy dependence and digital financial inclusion,while they are negatively moderated by the government debt ratio.Based on these findings,this study suggests that cities should actively develop their digital economy,reduce government debt,promote green innovation,and improve green economic efficiency,as doing so will enhance their implementation of environmental policies and promote sustainable urban development.
基金supported by the Enterprises Research Project(Nos.W2021JSKF0922 and W2023JSKF0116)the Key industrialization Projects of Intelligent Manufacturing Institute,Hefei University of Technology(No.IMICZ2019001).
文摘In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))concentrations.To assess the effectiveness of this strategy,we conducted an ablation experiment on the congestion index and implemented a multi-scale input model.Compared with conventional models,the strategy reduces the root mean square error(RMSE)of all benchmark models by>6.07%on average,and the bestperforming model reduces it by 12.06%,demonstrating excellent performance improvement.In addition,evenwith high traffic emissions,the RMSE during peak hours is still below 9.83μg/m^(3),which proves the effectiveness of the strategy by effectively addressing pollution hotspots.This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030708,42375138,42030608,42105128,42075079)the Opening Foundation of Key Laboratory of Atmospheric Sounding,China Meteorological Administration(CMA),and the CMA Research Center on Meteorological Observation Engineering Technology(Grant No.U2021Z03),and the Opening Foundation of the Key Laboratory of Atmospheric Chemistry,CMA(Grant No.2022B02)。
文摘Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.
基金supported by the Collaborative Research Project(CRP)grant,Nazarbayev University(Nos.11022021CRP1512,211123CRP1604)PK acknowledges the support from the NERC-funded projects ASAP-Delhi(NE/P016510/1),GreenCities(NE/P016510/1)RECLAIM Network Plus(EP/W034034/1).
文摘Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its trends.In this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate analysis.Daily PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach.Data from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the model.Evaluation indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to 2021.The highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.
基金supported by the National Natural Science Foundation of China(Nos.U23A2016,U1704241,and 42007175).
文摘Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.
