Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).T...Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.展开更多
In many air pollution health studies,the time-activity pattern of individuals is often ignored largely due to lack of data.However,a better understanding of this location-based information is expected to decrease unce...In many air pollution health studies,the time-activity pattern of individuals is often ignored largely due to lack of data.However,a better understanding of this location-based information is expected to decrease uncertainties in exposure estimation.Here,we showcase the potential of iPhone’s Significant Location(iSL)data in capturing the user’s historical time-activity patterns in order to estimate exposure to ambient air pollutants.In this study,one subject carried an iPhone in tandem with a reference GPS tracking device for one month.The GPS device recorded locations in 10 second intervals while the iSL recorded the time spent in locations the subject visited frequently.Using GPS data as a reference,we then evaluated the accuracy of iSL data in capturing the subject’s time-activity patterns and time-weighted air pollution concentration within the study time period.We found the iSL data accurately captured the time the subject spent in 16 microenvironments(i.e.locations the subject visited more than once),which was 93%of the time during the study period.The average error of time-weighted aerosol optical depth value,a surrogate of particle pollution,is only 0.012%.To explore the availability of iSL data among iPhone users,an online survey was conducted.Among the 349 surveyed participants,72%of them have iSL data available.Considering the popularity of iPhones,iSL data may be available for a significant portion of the general population.Our results suggest iSL data have great potential for characterizing historical time-activity patterns to improve air pollution exposure estimation.展开更多
High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the world.As abundant inorganic components in PM_(2.5),ammonium nitrate(NH...High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the world.As abundant inorganic components in PM_(2.5),ammonium nitrate(NH_(4)NO_(3))formation includes two processes,the diffusion process(molecule of ammonia and nitric acid move from gas phase to liquid phase)and the ionization process(subsequent dissociation to form ions).In this study,we discuss the impact of meteorological factors,emission sources,and gaseous precursors on NH4NO3 formation based on thermodynamic theory,and identify the dominant factors during clean periods and haze periods.Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution.The dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze periods.And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate.The findings of this work inform the design of effective strategies to control particulate matter pollution.展开更多
基金supported by the National Natural Science Foundation of China(42077191)the National Key Research and Development Program of China(2022YFC3703400)+1 种基金the Blue Sky Foundation,Tianjin Science and Technology Plan Project(18PTZWHZ00120)Fundamental Research Funds for the Central Universities(63213072 and 63213074).
文摘Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
文摘In many air pollution health studies,the time-activity pattern of individuals is often ignored largely due to lack of data.However,a better understanding of this location-based information is expected to decrease uncertainties in exposure estimation.Here,we showcase the potential of iPhone’s Significant Location(iSL)data in capturing the user’s historical time-activity patterns in order to estimate exposure to ambient air pollutants.In this study,one subject carried an iPhone in tandem with a reference GPS tracking device for one month.The GPS device recorded locations in 10 second intervals while the iSL recorded the time spent in locations the subject visited frequently.Using GPS data as a reference,we then evaluated the accuracy of iSL data in capturing the subject’s time-activity patterns and time-weighted air pollution concentration within the study time period.We found the iSL data accurately captured the time the subject spent in 16 microenvironments(i.e.locations the subject visited more than once),which was 93%of the time during the study period.The average error of time-weighted aerosol optical depth value,a surrogate of particle pollution,is only 0.012%.To explore the availability of iSL data among iPhone users,an online survey was conducted.Among the 349 surveyed participants,72%of them have iSL data available.Considering the popularity of iPhones,iSL data may be available for a significant portion of the general population.Our results suggest iSL data have great potential for characterizing historical time-activity patterns to improve air pollution exposure estimation.
基金the National Natural Science Foundation of China(No.42077191)the Fundamental Research Funds for the Central Universities(Nos.63213072,63213074)+1 种基金the GDAS’Project of Science and Technology Development(No.2021GDASYL-20210103058)the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515012165),The Blue Sky Foundation.
文摘High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the world.As abundant inorganic components in PM_(2.5),ammonium nitrate(NH_(4)NO_(3))formation includes two processes,the diffusion process(molecule of ammonia and nitric acid move from gas phase to liquid phase)and the ionization process(subsequent dissociation to form ions).In this study,we discuss the impact of meteorological factors,emission sources,and gaseous precursors on NH4NO3 formation based on thermodynamic theory,and identify the dominant factors during clean periods and haze periods.Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution.The dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze periods.And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate.The findings of this work inform the design of effective strategies to control particulate matter pollution.
