This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is a...This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban scale SO 2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor re...Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3and PM2.5,which are formed by multiple precursors under complex physical and chemical processes;this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions.Different from the traditional sensitivity analysis and source apportionment methods,the reduced form models(RFMs)based on chemical transport models(CTMs)are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost.Here we review recent approaches used in RFMs and compare their structures,advantages and disadvantages,performance and applications.In general,RFMs are classified into three types including(1)sensitivity-based models,(2)models with simplified chemistry and physical processes,and(3)statistical models,with considerable differences in principles,characteristics and application ranges.The prediction of nonlinear responses by RFMs enables more in-depth analysis,not only in terms of real-time prediction of concentrations and quantification of human exposure,health impacts and economic damage,but also in optimizing control policies.Notably,data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management.In future studies,improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses.Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes,which pose great challenges to the quantification of response relationships.Additionally,with rising requirements for fine-scale air quality management,improving the performance of urban-scale simulations is worth considering.In short,accurate predictions of the response of air quality to emissions,though challenging,holds great promise for the present as well as for future scenarios.展开更多
Air pollution control policies in China have been experiencing profound changes,highlighting a strategic transformation from total pollutant emission control to air quality improvement,along with the shifting targets ...Air pollution control policies in China have been experiencing profound changes,highlighting a strategic transformation from total pollutant emission control to air quality improvement,along with the shifting targets starting from acid rain and NO_(x)emissions to PM_(2.5)pollution,and then the emerging O_(3)challenges.The marvelous achievements have been made with the dramatic decrease of SO_(2)emission and fundamental improvement of PM_(2.5)concentration.Despite these achievements,China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality,which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas(GHG)emissions.Against this background,an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China’s long-term air quality improvement strategy.This paper systematically reviews the technical system for developing the air quality improvement roadmap,which was integrated from the research output of China’s National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies(hereafter Special NKP),covering mid-and long-term air quality target setting techniques,quantitative analysis techniques for emission reduction targets corresponding to air quality targets,and pathway optimization techniques for realizing reduction targets.The experience and lessons derived from the reviews have implications for the reformation of China’s air quality improvement roadmap in facing challenges of synergistic mitigation of PM_(2.5)and O_(3),and the coupling with climate change mitigation.展开更多
Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring ...Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM<sub>10</sub> and PM<sub>2.5</sub> surpassed the WHO guidelines and Mexican standards. O<sub>3</sub> and SO<sub>2</sub> in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO<sub>2</sub> did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM<sub>10</sub> and PM<sub>2.5</sub> concentrations were the main problem. Others pollutants that influenced the AQ were O<sub>3</sub>, NO<sub>2</sub> and SO<sub>2</sub> in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO<sub>2</sub> and SO<sub>2</sub> are attributed mainly to road transport.展开更多
Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were freque...Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.展开更多
Accurate multi-step PM_(2.5)(particulate matter with diameters≤2.5 um)concentration prediction is critical for humankinds’health and air populationmanagement because it could provide strong evidence for decisionmaki...Accurate multi-step PM_(2.5)(particulate matter with diameters≤2.5 um)concentration prediction is critical for humankinds’health and air populationmanagement because it could provide strong evidence for decisionmaking.However,it is very challenging due to its randomness and variability.This paper proposed a novel method based on convolutional neural network(CNN)and long-short-term memory(LSTM)with a space-shared mechanism,named space-shared CNN-LSTM(SCNN-LSTM)for multi-site dailyahead multi-step PM_(2.5)forecasting with self-historical series.The proposed SCNN-LSTM contains multi-channel inputs,each channel corresponding to one-site historical PM_(2.5)concentration series.In which,CNN and LSTM are used to extract each site’s rich hidden feature representations in a stack mode.Especially,CNN is to extract the hidden short-time gap PM_(2.5)concentration patterns;LSTM is to mine the hidden features with long-time dependency.Each channel extracted features aremerged as the comprehensive features for future multi-step PM_(2.5)concentration forecasting.Besides,the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing.Therefore,the final features are the fusion of short-time gap,long-time dependency,and space information,which enables forecasting more accurately.To validate the proposed method’s effectiveness,the authors designed,trained,and compared it with various leading methods in terms of RMSE,MAE,MAPE,and R^(2)on four real-word PM_(2.5)data sets in Seoul,South Korea.The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM_(2.5)concentration only using self-historical PM_(2.5)concentration time series and running once.Specifically,the proposed method obtained averaged RMSE of 8.05,MAE of 5.04,MAPE of 23.96%,and R^(2)of 0.7 for four-site daily ahead 10-hourPM_(2.5)concentration forecasting.展开更多
Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term ob...Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term obser-vations by monitoring stations,cannot provide customized data services and in-time emergency response under urgent situations(gas leakage incidents).Therefore,we first review the up-to-date approaches(often machine learning and optimization methods)with respect to urban air quality monitoring and hazardous gas source anal-ysis.To bridge the gap between present solutions and practical requirements,we design a conceptual framework,namely MAsmed(Multi-Agents for sensing,monitoring,estimating and determining),to provide fine-grained concentration maps,customized data services,and on-demand emergency management.In this framework,we leverage the hybrid design of wireless sensor networks(WSNs)and mobile crowdsensing(MCS)to sense urban air quality and relevant data(e.g.traffic data,meteorological data,etc.);Using the sensed data,we can create a fine-grained air quality map for the authorities and relevant stakeholders,and provide on-demand source term estimation and source searching methods to estimate,seek,and determine the sources,thereby aiding decision-makers in emergency response(e.g.for evacuation).In this paper,we also identify several potential opportunities for future research.展开更多
In the recent years, photocatalytic self-cleaning and ＂depolluting＂ materials have been suggested as a remediation technology mainly for NOx and aromatic VOCs in urban areas. A number of products incorporating the a...In the recent years, photocatalytic self-cleaning and ＂depolluting＂ materials have been suggested as a remediation technology mainly for NOx and aromatic VOCs in urban areas. A number of products incorporating the aforementioned technology have been made commercially available with the aim to improve urban air quality. These commercial products are based on the photocatalytic properties of a thin layer of TiO2 at the surface of the material （such as glass, pavement, etc.） or embedded in paints or concrete. The use of TiO2 photocatalysts as an emerging air pollution control technology has been reported in many locations worldwide. However, up to now, the effectiveness measured in situ and theexpected positive impact on air quality of this relatively new technology has only been demonstrated in a limited manner. Assessing and demonstrating the effectiveness of these depolluting techniques in real scale applications aims to create a real added value, in terms of policy making （i.e., implementing air quality strategies） and economics （by providing a demonstration of the actual performance of a new technique）.展开更多
In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-paramete...In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming （ICTP） method. The ICTP method had several advantages： （i） its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; （ii） it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; （iii） it was useful for helping decision makers to analyze the trade-offs between cost and risk; and （iv） it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.展开更多
Toxic air pollutants(TAPs)are a class of airborne chemicals known or suspected to cause serious health issues.This study,applying positive matrix factorization and inhalation unit risk estimates of TAPs,quantifies the...Toxic air pollutants(TAPs)are a class of airborne chemicals known or suspected to cause serious health issues.This study,applying positive matrix factorization and inhalation unit risk estimates of TAPs,quantifies the changes in significant sources contributing to inhalation cancer risks(ICRs)from 2000 to 2020 in Hong Kong,China.Total ICR decreased from 1701 to 451 cases per million between 2000−2004 and 2016−2020,largely attributed to the reduction in diesel particulate matter(DPM),gasoline and solvent use-related volatile organic compounds(VOCs),and coal/biomass combustion-related polycyclic aromatic hydrocarbons and metal(loid)s.The regional contribution of VOCs associated with industrial and halogenated solvent sources increased substantially,representing the largest non-DPM ICR contributor(37%)in 2016−2020,stressing the need for a more comprehensive risk evaluation across the fast-growing and densely populated Greater Bay Area(GBA).ICRs in Hong Kong and the GBA will likely remain over 100 cases per million by 2050.The contributions to ozone formation potential of VOC/carbonyl sources were quantified,which show a notable shift from being solvent/gasoline-dominant in 2000−2004 to being more evenly shared by various sources in 2016−2020.Establishing a similar TAP monitoring network in the GBA is anticipated to provide the monitoring data needed to facilitate the development of more informed air quality management strategies.展开更多
文摘This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban scale SO 2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金supported by the National Key R&D program of China(Nos.2019YFC0214800 and 2018YFC0213805)the National Natural Science Foundation of China(No.41907190)Shanghai Science and Technology Commission Scientific Research Project(No.19DZ1205006)。
文摘Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3and PM2.5,which are formed by multiple precursors under complex physical and chemical processes;this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions.Different from the traditional sensitivity analysis and source apportionment methods,the reduced form models(RFMs)based on chemical transport models(CTMs)are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost.Here we review recent approaches used in RFMs and compare their structures,advantages and disadvantages,performance and applications.In general,RFMs are classified into three types including(1)sensitivity-based models,(2)models with simplified chemistry and physical processes,and(3)statistical models,with considerable differences in principles,characteristics and application ranges.The prediction of nonlinear responses by RFMs enables more in-depth analysis,not only in terms of real-time prediction of concentrations and quantification of human exposure,health impacts and economic damage,but also in optimizing control policies.Notably,data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management.In future studies,improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses.Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes,which pose great challenges to the quantification of response relationships.Additionally,with rising requirements for fine-scale air quality management,improving the performance of urban-scale simulations is worth considering.In short,accurate predictions of the response of air quality to emissions,though challenging,holds great promise for the present as well as for future scenarios.
基金supported by the China’s National Key R&D Program(Nos.2019YFC0214804 and 2019YFC0214205)。
文摘Air pollution control policies in China have been experiencing profound changes,highlighting a strategic transformation from total pollutant emission control to air quality improvement,along with the shifting targets starting from acid rain and NO_(x)emissions to PM_(2.5)pollution,and then the emerging O_(3)challenges.The marvelous achievements have been made with the dramatic decrease of SO_(2)emission and fundamental improvement of PM_(2.5)concentration.Despite these achievements,China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality,which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas(GHG)emissions.Against this background,an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China’s long-term air quality improvement strategy.This paper systematically reviews the technical system for developing the air quality improvement roadmap,which was integrated from the research output of China’s National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies(hereafter Special NKP),covering mid-and long-term air quality target setting techniques,quantitative analysis techniques for emission reduction targets corresponding to air quality targets,and pathway optimization techniques for realizing reduction targets.The experience and lessons derived from the reviews have implications for the reformation of China’s air quality improvement roadmap in facing challenges of synergistic mitigation of PM_(2.5)and O_(3),and the coupling with climate change mitigation.
文摘Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM<sub>10</sub> and PM<sub>2.5</sub> surpassed the WHO guidelines and Mexican standards. O<sub>3</sub> and SO<sub>2</sub> in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO<sub>2</sub> did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM<sub>10</sub> and PM<sub>2.5</sub> concentrations were the main problem. Others pollutants that influenced the AQ were O<sub>3</sub>, NO<sub>2</sub> and SO<sub>2</sub> in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO<sub>2</sub> and SO<sub>2</sub> are attributed mainly to road transport.
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(No.NIER-2019-04-02-039)supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.
基金This work was supported by a Research Grant from Pukyong National University(2021).
文摘Accurate multi-step PM_(2.5)(particulate matter with diameters≤2.5 um)concentration prediction is critical for humankinds’health and air populationmanagement because it could provide strong evidence for decisionmaking.However,it is very challenging due to its randomness and variability.This paper proposed a novel method based on convolutional neural network(CNN)and long-short-term memory(LSTM)with a space-shared mechanism,named space-shared CNN-LSTM(SCNN-LSTM)for multi-site dailyahead multi-step PM_(2.5)forecasting with self-historical series.The proposed SCNN-LSTM contains multi-channel inputs,each channel corresponding to one-site historical PM_(2.5)concentration series.In which,CNN and LSTM are used to extract each site’s rich hidden feature representations in a stack mode.Especially,CNN is to extract the hidden short-time gap PM_(2.5)concentration patterns;LSTM is to mine the hidden features with long-time dependency.Each channel extracted features aremerged as the comprehensive features for future multi-step PM_(2.5)concentration forecasting.Besides,the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing.Therefore,the final features are the fusion of short-time gap,long-time dependency,and space information,which enables forecasting more accurately.To validate the proposed method’s effectiveness,the authors designed,trained,and compared it with various leading methods in terms of RMSE,MAE,MAPE,and R^(2)on four real-word PM_(2.5)data sets in Seoul,South Korea.The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM_(2.5)concentration only using self-historical PM_(2.5)concentration time series and running once.Specifically,the proposed method obtained averaged RMSE of 8.05,MAE of 5.04,MAPE of 23.96%,and R^(2)of 0.7 for four-site daily ahead 10-hourPM_(2.5)concentration forecasting.
基金This study is supported in part by the National Natural Science Foun-dation of China under Grant Nos.62173337,21808181,72071207in part by the National Social Science Foundation of China under Grant 17CGL047.
文摘Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term obser-vations by monitoring stations,cannot provide customized data services and in-time emergency response under urgent situations(gas leakage incidents).Therefore,we first review the up-to-date approaches(often machine learning and optimization methods)with respect to urban air quality monitoring and hazardous gas source anal-ysis.To bridge the gap between present solutions and practical requirements,we design a conceptual framework,namely MAsmed(Multi-Agents for sensing,monitoring,estimating and determining),to provide fine-grained concentration maps,customized data services,and on-demand emergency management.In this framework,we leverage the hybrid design of wireless sensor networks(WSNs)and mobile crowdsensing(MCS)to sense urban air quality and relevant data(e.g.traffic data,meteorological data,etc.);Using the sensed data,we can create a fine-grained air quality map for the authorities and relevant stakeholders,and provide on-demand source term estimation and source searching methods to estimate,seek,and determine the sources,thereby aiding decision-makers in emergency response(e.g.for evacuation).In this paper,we also identify several potential opportunities for future research.
文摘In the recent years, photocatalytic self-cleaning and ＂depolluting＂ materials have been suggested as a remediation technology mainly for NOx and aromatic VOCs in urban areas. A number of products incorporating the aforementioned technology have been made commercially available with the aim to improve urban air quality. These commercial products are based on the photocatalytic properties of a thin layer of TiO2 at the surface of the material （such as glass, pavement, etc.） or embedded in paints or concrete. The use of TiO2 photocatalysts as an emerging air pollution control technology has been reported in many locations worldwide. However, up to now, the effectiveness measured in situ and theexpected positive impact on air quality of this relatively new technology has only been demonstrated in a limited manner. Assessing and demonstrating the effectiveness of these depolluting techniques in real scale applications aims to create a real added value, in terms of policy making （i.e., implementing air quality strategies） and economics （by providing a demonstration of the actual performance of a new technique）.
文摘In this study, interval-parameter programming, two-stage stochastic progranaming （TSP）, and conditional value-at-risk （CVaR） were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming （ICTP） method. The ICTP method had several advantages： （i） its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; （ii） it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; （iii） it was useful for helping decision makers to analyze the trade-offs between cost and risk; and （iv） it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.
基金supported by the Hong Kong Environmental Protection Department(Project 20-00424)supported by a fellowship award from the Research Grants Council of the HKSAR,China(HKUST PDFS2223-6S10).
文摘Toxic air pollutants(TAPs)are a class of airborne chemicals known or suspected to cause serious health issues.This study,applying positive matrix factorization and inhalation unit risk estimates of TAPs,quantifies the changes in significant sources contributing to inhalation cancer risks(ICRs)from 2000 to 2020 in Hong Kong,China.Total ICR decreased from 1701 to 451 cases per million between 2000−2004 and 2016−2020,largely attributed to the reduction in diesel particulate matter(DPM),gasoline and solvent use-related volatile organic compounds(VOCs),and coal/biomass combustion-related polycyclic aromatic hydrocarbons and metal(loid)s.The regional contribution of VOCs associated with industrial and halogenated solvent sources increased substantially,representing the largest non-DPM ICR contributor(37%)in 2016−2020,stressing the need for a more comprehensive risk evaluation across the fast-growing and densely populated Greater Bay Area(GBA).ICRs in Hong Kong and the GBA will likely remain over 100 cases per million by 2050.The contributions to ozone formation potential of VOC/carbonyl sources were quantified,which show a notable shift from being solvent/gasoline-dominant in 2000−2004 to being more evenly shared by various sources in 2016−2020.Establishing a similar TAP monitoring network in the GBA is anticipated to provide the monitoring data needed to facilitate the development of more informed air quality management strategies.
