The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianji...The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianjin Hebei experienced a seven-day extreme haze pollution episode with peak PM2.5(particulate matter(PM)with an aerodynamic diameter≤2.5μm)concentration of 727μg m 3.Considering the in uence of meteorological conditions on pollu-tant evolution,the e ects of varying initial conditions and lateral boundary conditions(LBCs)of the WRF-Chem model on PM2.5 concentration variation were investigated through ensemble methods.A control run(CTRL)and three groups of ensemble experiments(INDE,BDDE,INBDDE)were carried out based on difierent initial conditions and LBCs derived from ERA5 reanalysis data and its 10 ensemble members.The CTRL run reproduced the meteorological conditions and the overall life cycle of the haze event reasonably well,but failed to capture the intense oscillation of the instantaneous PM2.5 concentration.However,the ensemble forecasting showed a considerable advantage to some extent.Compared with the CTRL run,the root-mean-square error(RMSE)of PM2.5 concentration decreased by 4.33%,6.91%,and 8.44%in INDE,BDDE and INBDDE,respectively,and the RMSE decreases of wind direction(5.19%,8.89%and 9.61%)were the dominant reason for the improvement of PM2.5 concentration in the three ensemble experiments.Based on this case,the ensemble scheme seems an e ective method to improve the prediction skill of wind direction and PM2.5 concentration by using the WRF-Chem model.展开更多
Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention a...Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.展开更多
The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on...The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.展开更多
Air pollution is one of the top environmental concerns and causes of deaths and variousdiseases worldwide. In this paper, PM2.5 observation data at 1,497 automatic air quality-monitoringstations in 367 cities of China...Air pollution is one of the top environmental concerns and causes of deaths and variousdiseases worldwide. In this paper, PM2.5 observation data at 1,497 automatic air quality-monitoringstations in 367 cities of China in 2015 were utilized, and the study on spatiotemporal distribution of PM2.5concentration found that the average annual concentration of PM2.5 in urban China in 2015 was 49.74 μg/m3and exceeded the annual average limit in 287 cities. PM2.5 concentrations were highest in winter and lowestin summer in most cities, but it reached the highest in spring in the cities around Taklimakan Desert. Therewere 320 fi ne days in 2015 and the maximum PM2.5 was prone to appear at night, the minimum was usuallyin the afternoon, but in the early morning in Lhasa, and the minimum in winter was even in the earlymorning, midday and afternoon in some cities. Higher concentrations of PM2.5 were found in the EastChina Plain and the cities around Taklimakan Desert, preceded by the Yangtze River Delta economic zone,Chengdu-Chongqing economic zone and Harbin-Changchun megalopolis, while the lower values coveredthe northwestern region of Xinjiang, Heilongjiang Xing’an Mountains region, northeast high latitudes ofInner Mongolia, southwest high altitudes in the border area of Sichuan, Yunnan, Qinghai-Tibet Plateau andthe southeast coastal areas.展开更多
This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentr...This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.展开更多
The chemical characteristics（water-soluble ions and carbonaceous species） of PM2.5 in Guangzhou were measured during a typical haze episode.Most of the chemical species in PM2.5 showed significant difference between...The chemical characteristics（water-soluble ions and carbonaceous species） of PM2.5 in Guangzhou were measured during a typical haze episode.Most of the chemical species in PM2.5 showed significant difference between normal and haze days.The highest contributors to PM2.5 were organic carbon（OC）,nitrate,and sulfate in haze days and were OC,sulfate,and elemental carbon（EC） in normal days.The concentrations of secondary species such as,NO3^-,SO4^2-,and NH4^＋ in haze days were 6.5,3.9,and 5.3 times higher than those in normal days,respectively,while primary species（EC,Ca^2＋,K^＋） show similar increase from normal to haze days by a factor about 2.2-2.4.OC/EC ratio ranged from 2.8 to 6.2 with an average of 4.7 and the estimation on a minimum OC/EC ratio showed that SOC（secondary organic carbon） accounted more than 36.6% for the total organic carbon in haze days.The significantly increase in the secondary species（SOC,NO3^-,SO4^2-,and NH4^＋）,especially in NO3^-,caused the worst air quality in this region.Simultaneously,the result illustrated that the serious air pollution in haze episodes was strongly correlated with the meteorological conditions.During the sampling periods,air pollution and visibility had a good relationship with the air mass transport distance;the shorter air masses transport distance,the worse air quality and visibility in Guangzhou,indicating the strong domination of local sources contributing to haze formation.High concentration of the secondary aerosol in haze episodes was likely due to the higher oxidation rates of sulfur and nitrogen species.展开更多
基金supported by the National Basic Research(973)Program of China [grant number2015CB954102]the National Natural Science Foundation of China [grant number 41475043]the National Key R&D Program of China [grant number 2018YFC1507403]
文摘The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianjin Hebei experienced a seven-day extreme haze pollution episode with peak PM2.5(particulate matter(PM)with an aerodynamic diameter≤2.5μm)concentration of 727μg m 3.Considering the in uence of meteorological conditions on pollu-tant evolution,the e ects of varying initial conditions and lateral boundary conditions(LBCs)of the WRF-Chem model on PM2.5 concentration variation were investigated through ensemble methods.A control run(CTRL)and three groups of ensemble experiments(INDE,BDDE,INBDDE)were carried out based on difierent initial conditions and LBCs derived from ERA5 reanalysis data and its 10 ensemble members.The CTRL run reproduced the meteorological conditions and the overall life cycle of the haze event reasonably well,but failed to capture the intense oscillation of the instantaneous PM2.5 concentration.However,the ensemble forecasting showed a considerable advantage to some extent.Compared with the CTRL run,the root-mean-square error(RMSE)of PM2.5 concentration decreased by 4.33%,6.91%,and 8.44%in INDE,BDDE and INBDDE,respectively,and the RMSE decreases of wind direction(5.19%,8.89%and 9.61%)were the dominant reason for the improvement of PM2.5 concentration in the three ensemble experiments.Based on this case,the ensemble scheme seems an e ective method to improve the prediction skill of wind direction and PM2.5 concentration by using the WRF-Chem model.
基金supposed by Shandong Natural Science Foundation[Grant number:ZR2016GM03]Ministry of Education[Grant number:17YJA790054]
文摘Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.
基金The work was financially supported by National Natural Science Fund of China,specific grant numbers were 61371143 and 61662033initials of authors who received the grants were respectively Z.YM,H.L,and the URLs to sponsors’websites was http://www.nsfc.gov.cn/.This paper was supported by National Natural Science Fund of China(Grant Nos.61371143,61662033).
文摘The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.
基金Supported by Guizhou Province Science and Technology Fund(LKT[2012]07,25).
文摘Air pollution is one of the top environmental concerns and causes of deaths and variousdiseases worldwide. In this paper, PM2.5 observation data at 1,497 automatic air quality-monitoringstations in 367 cities of China in 2015 were utilized, and the study on spatiotemporal distribution of PM2.5concentration found that the average annual concentration of PM2.5 in urban China in 2015 was 49.74 μg/m3and exceeded the annual average limit in 287 cities. PM2.5 concentrations were highest in winter and lowestin summer in most cities, but it reached the highest in spring in the cities around Taklimakan Desert. Therewere 320 fi ne days in 2015 and the maximum PM2.5 was prone to appear at night, the minimum was usuallyin the afternoon, but in the early morning in Lhasa, and the minimum in winter was even in the earlymorning, midday and afternoon in some cities. Higher concentrations of PM2.5 were found in the EastChina Plain and the cities around Taklimakan Desert, preceded by the Yangtze River Delta economic zone,Chengdu-Chongqing economic zone and Harbin-Changchun megalopolis, while the lower values coveredthe northwestern region of Xinjiang, Heilongjiang Xing’an Mountains region, northeast high latitudes ofInner Mongolia, southwest high altitudes in the border area of Sichuan, Yunnan, Qinghai-Tibet Plateau andthe southeast coastal areas.
文摘This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.
基金国家重点研发计划重点专项(No.2018YFC0214005)国家自然科学基金项目(No.41603102)+2 种基金南开大学环境污染过程与基准教育部重点实验室开放基金(No.201803)Supported by National Key Research and Development Program of China(No.2018YFC0214005)National Natural Science Foundation of China(No.41603012)Opening Project of Key Laboratory of Pollution Processes and Environmental Criteria,Ministry of Education,China(No.201803)
基金supported by the National Excellent Youth Foundation of China (No. 20625722)the China Postdoctoral Science Foundation (No. 20080430396)
文摘The chemical characteristics（water-soluble ions and carbonaceous species） of PM2.5 in Guangzhou were measured during a typical haze episode.Most of the chemical species in PM2.5 showed significant difference between normal and haze days.The highest contributors to PM2.5 were organic carbon（OC）,nitrate,and sulfate in haze days and were OC,sulfate,and elemental carbon（EC） in normal days.The concentrations of secondary species such as,NO3^-,SO4^2-,and NH4^＋ in haze days were 6.5,3.9,and 5.3 times higher than those in normal days,respectively,while primary species（EC,Ca^2＋,K^＋） show similar increase from normal to haze days by a factor about 2.2-2.4.OC/EC ratio ranged from 2.8 to 6.2 with an average of 4.7 and the estimation on a minimum OC/EC ratio showed that SOC（secondary organic carbon） accounted more than 36.6% for the total organic carbon in haze days.The significantly increase in the secondary species（SOC,NO3^-,SO4^2-,and NH4^＋）,especially in NO3^-,caused the worst air quality in this region.Simultaneously,the result illustrated that the serious air pollution in haze episodes was strongly correlated with the meteorological conditions.During the sampling periods,air pollution and visibility had a good relationship with the air mass transport distance;the shorter air masses transport distance,the worse air quality and visibility in Guangzhou,indicating the strong domination of local sources contributing to haze formation.High concentration of the secondary aerosol in haze episodes was likely due to the higher oxidation rates of sulfur and nitrogen species.
