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 characteristics of fine particulate pollution(PM10 and PM2.5) were measured at urban and suburban sites in Jinan during the 2008-2009 heating and non-heating seasons.The results showed that PM10 and PM2.5 pollutio...The characteristics of fine particulate pollution(PM10 and PM2.5) were measured at urban and suburban sites in Jinan during the 2008-2009 heating and non-heating seasons.The results showed that PM10 and PM2.5 pollution was quite serious,and PM mass concentration was higher during the heating season than the non-heating season.PM was the highest in the chemical factory and lowest in the development zone.The mass concentrations of PM10 and PM2.5 were linearly related,and the mass concentration ratio of PM2.5/PM10 was up to 0.59 in urban areas.PM pollution in Jinan was related to local meteorological factors:PM2.5 mass concentration and humidity were positively correlated,and PM2.5 mass concentration was negatively correlated with both click on the temperature and wind speed,although wind speed varied more.展开更多
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
Haze (known as “Mai” 霾 in Chinese) threatens the health of billions of people across the globe. To begin solving this problem without severely slowing down the economy, one has to mechanistically and geographically...Haze (known as “Mai” 霾 in Chinese) threatens the health of billions of people across the globe. To begin solving this problem without severely slowing down the economy, one has to mechanistically and geographically pinpoint the sources of these pollutants, the key of which is to thoroughly characterize and fingerprint the particulates. Here we present a broad survey and classification of thousands of individual airborne particu-lates by using the Scanning Electron Microscope (SEM) to measure their diverse mor-phologies and chemistries, which could eventually be organized into a “haze finger-print database”. For instance, one collection in Xi’an City, China during March-April 2014 yielded 494 airborne particulates that settled on silicon wafers placed outside the window of a 3<sup>rd</sup> floor office. These 494 particulates were manually imaged with high resolution (down to 2 nm), elementally mapped using Energy-dispersive X-ray Spec-troscopy (EDS), and were identified and categorized into presumed source classes such as construction activities (Ca, Al, Si-O), coal burning (sulfates), biologic (pollen, bac-teria), automotive, mining, steel making, and etc. About 20% of the particulates have mysterious origins, as it is still unclear how they were formed, and a fraction of them contained clearly hazardous elements such as lead and chromium. For future work, we propose using unmanned aerial vehicles with a special “rolling film” substrate that can autonomously collect airborne particulates, a customized SEM auto-imaging system, and machine learning software to establish an online open-access database. The end goal would be to monitor and analyze the particulate pollutants that are pumped into our atmosphere every day, and precisely track down their sources so we can better model and police the quality of the air around us.展开更多
The Nested Air Quality Prediction Model System(NAQPMS)was used to investigate the temporal and spatial variations of PM2.5over tropospheric central eastern China in January 2013.The impact of regional transport and it...The Nested Air Quality Prediction Model System(NAQPMS)was used to investigate the temporal and spatial variations of PM2.5over tropospheric central eastern China in January 2013.The impact of regional transport and its implications on pollution prevention and control were also examined.Comparison between simulated and observed PM2.5showed NAQPMS was able to reproduce the evolution of PM2.5during heavy haze episodes.The results indicated that regional transport of PM2.5played an important role in regional haze episodes in the city cluster including Hebei,Beijing and Tianjin(HBT).The cross-city clusters transport outside HBT and transport among cities inside HBT contributed 20%–35%and 26%–35%of PM2.5as compared with local emission,in HBT respectively.To meet the Air Quality Standards for Grade II,90%,90%and65%of emissions would have to be cut down in Hebei,Tianjin and Beijing,if non-control strategy was taken in the surrounding city clusters of HBT.This implicated that control of emissions in one city cluster is not sufficient to reduce regional haze events,and joint efforts among city clusters are essential.Besides regional transports,two-way feedback between boundary-layer evolution and PM2.5also significantly contributed to the formation of heavy hazes,which contributed 30%of monthly average PM2.5concentration in HBT.展开更多
Hotan Prefecture is located at the southwestern edge of Taklimakan Desert, the world＇s largest shifting sand desert, of China. The desert is one of the main sources for frequent sand-dust weather events which strongl...Hotan Prefecture is located at the southwestern edge of Taklimakan Desert, the world＇s largest shifting sand desert, of China. The desert is one of the main sources for frequent sand-dust weather events which strongly affect the air quality of Hotan Prefecture. Although this region is characterized by the highest annual mean PMlo concentration values that are routinely recorded by environmental monitoring stations across China, both this phenomenon and its underlying causes have not been adequately addressed in previous researches. Reliable pollutant PM_10 data are currently retrieved using a tapered element oscillating microbalance （TEOM） 1400a, a direct real-time monitor, while additional concentration values including for PM_2.5, sulfur dioxide （SO_2）, nitrogen dioxide （NO_2）, carbon monoxide （CO） and ozone （O_3） have been collected in recent years by the Hotan Environmental Monitoring Station. Based on these data, this paper presents a comparison of the influences of different kinds of sand-dust weather events on PM_10 （or PM_2.5） as well as the concentrations of other gaseous pollutants in Hotan Prefecture. It is revealed that the highest monthly average PM_10 concentrations are observed in the spring because of the frequent occurrence of three distinct kinds of sand-dust weather events at this time, including dust storms, blowing dust and floating dust. The floating dust makes the most significant contribution to PM_10 （or PM_2.5） concentration in this region, a result that differs from eastern Chinese cities where the heaviest PM_10 pollution occurs usually in winter and air pollution results from the excess emission of local anthropogenic pollutants. It is also shown that PM_10 concentration varies within wpical dust storms. PM_10 concentrations vary among 20 dust storm events within Hotan Prefecture, and the hourly mean concentrations tend to sharply increase initially then slowly decreasing over time. Data collected from cities in eastern China show the opposite with the hourly mean PM_10 （or PM_2.5） concentration tending to slowly increase then sharply decrease during heavy air pollution due to the excess emission of local anthropogenic pollutants. It is also found that the concentration of gaseous pollutants during sand-dust weather events tends to be lower than those cases under clear sky conditions. This indicates that these dust events effectively remove and rapidly diffuse gaseous pollutants. The analysis also shows that the concentration of SO_2 decreases gradually at the onset of all three kinds of sand-dust weather events because of rapidly increasing wind velocity and the development of favorable atmospheric conditions for diffusion. In contrast, changes in O_3 and NO_2 concentrations conformed to the opposite pattern during all three kinds of sand-dust weather events within this region, implying the inter transformation of these gas species during these events.展开更多
This study aimed to determine the amount of total polluting matter emitted into the atmosphere from heating and industrial-based emissions and the total pollution bulk of Tokat city center. The annual cycles of some h...This study aimed to determine the amount of total polluting matter emitted into the atmosphere from heating and industrial-based emissions and the total pollution bulk of Tokat city center. The annual cycles of some heavy metal in particulate matters have been investigated at this area in order to elucidate temporal variations as well as major sources processes responsible for their formation. Air particulate samples were collected from three different locations situated around Tokat. These samples were determined for heavy metals by using Flame or graphite-furnace Atomic absorption spectroscopy. Particulate matter concentrations up to 52.43μg/m^3 were observed in sampling area. The initial results of the chemical analysis showed that concentration values of heavy metals in air particles observed were higher than the World Health Organization （WHO） guideline limit values.展开更多
In Western countries, research works on air quality have reinforced in recent years because of the links between the level of particulate pollution in numerous cities and the appearing of various health disorders incl...In Western countries, research works on air quality have reinforced in recent years because of the links between the level of particulate pollution in numerous cities and the appearing of various health disorders including cardio-respiratory pathologies, acute bronchopneumonia, lung cancer, etc. In sub-Saharan Africa countries, particularly Burkina Faso, there is very few similar research. In the present work, the pollution levels of airborne particle in the city of Ouagadougou have been assessed through two campaigns of in situ measurements of suspended particulate matter concentrations. These measurements which have concerned PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub> were performed using a portable device (AEROCET531S) at nine sites in 2018 and at ten sites in 2019. These sites are located on roadside, administrative services, secondary education establishments and outlying districts. The results show that: 1) the PM1 concentrations values presented no significant variation between days, seasons or sampling sites;2) the 24-hour PM<sub>2.5</sub> concentrations often exceeding WHO recommended concentrations and, 3) the 24-hour PM<sub>10</sub> concentrations exceed WHO recommended concentrations regardless of the season or the sampling site. In indeed, the average 24-hour concentrations are 20 ± 4, 87 ± 16 and 951 ± 266 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup> for the PM1, PM<sub>2.5</sub> and PM<sub>10</sub>, respectively. They are 17 ± 3, 29 ± 5 and 158 ± 43 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup>, respectively, in 2018 dry season and, 12 ± 1, 22 ± 9 and 187 ± 67 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup>, respectively, in 2019 rainy season.展开更多
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Supported by Natural Science Foundation of Shandong Province(Grant No.Z2008E04)"Austria-China"international government cooperation project"Control of Fine Particles"(Nr.CN10/2007)Dr.Foundation of Shandong Jianzhu University(XNBS0920)
文摘The characteristics of fine particulate pollution(PM10 and PM2.5) were measured at urban and suburban sites in Jinan during the 2008-2009 heating and non-heating seasons.The results showed that PM10 and PM2.5 pollution was quite serious,and PM mass concentration was higher during the heating season than the non-heating season.PM was the highest in the chemical factory and lowest in the development zone.The mass concentrations of PM10 and PM2.5 were linearly related,and the mass concentration ratio of PM2.5/PM10 was up to 0.59 in urban areas.PM pollution in Jinan was related to local meteorological factors:PM2.5 mass concentration and humidity were positively correlated,and PM2.5 mass concentration was negatively correlated with both click on the temperature and wind speed,although wind speed varied more.
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.
文摘Haze (known as “Mai” 霾 in Chinese) threatens the health of billions of people across the globe. To begin solving this problem without severely slowing down the economy, one has to mechanistically and geographically pinpoint the sources of these pollutants, the key of which is to thoroughly characterize and fingerprint the particulates. Here we present a broad survey and classification of thousands of individual airborne particu-lates by using the Scanning Electron Microscope (SEM) to measure their diverse mor-phologies and chemistries, which could eventually be organized into a “haze finger-print database”. For instance, one collection in Xi’an City, China during March-April 2014 yielded 494 airborne particulates that settled on silicon wafers placed outside the window of a 3<sup>rd</sup> floor office. These 494 particulates were manually imaged with high resolution (down to 2 nm), elementally mapped using Energy-dispersive X-ray Spec-troscopy (EDS), and were identified and categorized into presumed source classes such as construction activities (Ca, Al, Si-O), coal burning (sulfates), biologic (pollen, bac-teria), automotive, mining, steel making, and etc. About 20% of the particulates have mysterious origins, as it is still unclear how they were formed, and a fraction of them contained clearly hazardous elements such as lead and chromium. For future work, we propose using unmanned aerial vehicles with a special “rolling film” substrate that can autonomously collect airborne particulates, a customized SEM auto-imaging system, and machine learning software to establish an online open-access database. The end goal would be to monitor and analyze the particulate pollutants that are pumped into our atmosphere every day, and precisely track down their sources so we can better model and police the quality of the air around us.
基金supported by the CAS Strategic Priority Research Program(Grant Nos.XDB05030200 and XDB05030101)the National Natural Science Foundation of China(Grant No.41278138)
文摘The Nested Air Quality Prediction Model System(NAQPMS)was used to investigate the temporal and spatial variations of PM2.5over tropospheric central eastern China in January 2013.The impact of regional transport and its implications on pollution prevention and control were also examined.Comparison between simulated and observed PM2.5showed NAQPMS was able to reproduce the evolution of PM2.5during heavy haze episodes.The results indicated that regional transport of PM2.5played an important role in regional haze episodes in the city cluster including Hebei,Beijing and Tianjin(HBT).The cross-city clusters transport outside HBT and transport among cities inside HBT contributed 20%–35%and 26%–35%of PM2.5as compared with local emission,in HBT respectively.To meet the Air Quality Standards for Grade II,90%,90%and65%of emissions would have to be cut down in Hebei,Tianjin and Beijing,if non-control strategy was taken in the surrounding city clusters of HBT.This implicated that control of emissions in one city cluster is not sufficient to reduce regional haze events,and joint efforts among city clusters are essential.Besides regional transports,two-way feedback between boundary-layer evolution and PM2.5also significantly contributed to the formation of heavy hazes,which contributed 30%of monthly average PM2.5concentration in HBT.
基金supported by the National Natural Science Foundation of China(91644226)the National Key Research Project of China(2016YFA0602004)the Fundamental Research Funds of Chinese Academy of Meteorological Sciences(2017Y005)
文摘Hotan Prefecture is located at the southwestern edge of Taklimakan Desert, the world＇s largest shifting sand desert, of China. The desert is one of the main sources for frequent sand-dust weather events which strongly affect the air quality of Hotan Prefecture. Although this region is characterized by the highest annual mean PMlo concentration values that are routinely recorded by environmental monitoring stations across China, both this phenomenon and its underlying causes have not been adequately addressed in previous researches. Reliable pollutant PM_10 data are currently retrieved using a tapered element oscillating microbalance （TEOM） 1400a, a direct real-time monitor, while additional concentration values including for PM_2.5, sulfur dioxide （SO_2）, nitrogen dioxide （NO_2）, carbon monoxide （CO） and ozone （O_3） have been collected in recent years by the Hotan Environmental Monitoring Station. Based on these data, this paper presents a comparison of the influences of different kinds of sand-dust weather events on PM_10 （or PM_2.5） as well as the concentrations of other gaseous pollutants in Hotan Prefecture. It is revealed that the highest monthly average PM_10 concentrations are observed in the spring because of the frequent occurrence of three distinct kinds of sand-dust weather events at this time, including dust storms, blowing dust and floating dust. The floating dust makes the most significant contribution to PM_10 （or PM_2.5） concentration in this region, a result that differs from eastern Chinese cities where the heaviest PM_10 pollution occurs usually in winter and air pollution results from the excess emission of local anthropogenic pollutants. It is also shown that PM_10 concentration varies within wpical dust storms. PM_10 concentrations vary among 20 dust storm events within Hotan Prefecture, and the hourly mean concentrations tend to sharply increase initially then slowly decreasing over time. Data collected from cities in eastern China show the opposite with the hourly mean PM_10 （or PM_2.5） concentration tending to slowly increase then sharply decrease during heavy air pollution due to the excess emission of local anthropogenic pollutants. It is also found that the concentration of gaseous pollutants during sand-dust weather events tends to be lower than those cases under clear sky conditions. This indicates that these dust events effectively remove and rapidly diffuse gaseous pollutants. The analysis also shows that the concentration of SO_2 decreases gradually at the onset of all three kinds of sand-dust weather events because of rapidly increasing wind velocity and the development of favorable atmospheric conditions for diffusion. In contrast, changes in O_3 and NO_2 concentrations conformed to the opposite pattern during all three kinds of sand-dust weather events within this region, implying the inter transformation of these gas species during these events.
文摘This study aimed to determine the amount of total polluting matter emitted into the atmosphere from heating and industrial-based emissions and the total pollution bulk of Tokat city center. The annual cycles of some heavy metal in particulate matters have been investigated at this area in order to elucidate temporal variations as well as major sources processes responsible for their formation. Air particulate samples were collected from three different locations situated around Tokat. These samples were determined for heavy metals by using Flame or graphite-furnace Atomic absorption spectroscopy. Particulate matter concentrations up to 52.43μg/m^3 were observed in sampling area. The initial results of the chemical analysis showed that concentration values of heavy metals in air particles observed were higher than the World Health Organization （WHO） guideline limit values.
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)
文摘In Western countries, research works on air quality have reinforced in recent years because of the links between the level of particulate pollution in numerous cities and the appearing of various health disorders including cardio-respiratory pathologies, acute bronchopneumonia, lung cancer, etc. In sub-Saharan Africa countries, particularly Burkina Faso, there is very few similar research. In the present work, the pollution levels of airborne particle in the city of Ouagadougou have been assessed through two campaigns of in situ measurements of suspended particulate matter concentrations. These measurements which have concerned PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub> were performed using a portable device (AEROCET531S) at nine sites in 2018 and at ten sites in 2019. These sites are located on roadside, administrative services, secondary education establishments and outlying districts. The results show that: 1) the PM1 concentrations values presented no significant variation between days, seasons or sampling sites;2) the 24-hour PM<sub>2.5</sub> concentrations often exceeding WHO recommended concentrations and, 3) the 24-hour PM<sub>10</sub> concentrations exceed WHO recommended concentrations regardless of the season or the sampling site. In indeed, the average 24-hour concentrations are 20 ± 4, 87 ± 16 and 951 ± 266 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup> for the PM1, PM<sub>2.5</sub> and PM<sub>10</sub>, respectively. They are 17 ± 3, 29 ± 5 and 158 ± 43 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup>, respectively, in 2018 dry season and, 12 ± 1, 22 ± 9 and 187 ± 67 μg<span style="white-space:nowrap;">·</span>m<sup><span style="white-space:nowrap;"><span style="white-space:nowrap;">−</span></span>3</sup>, respectively, in 2019 rainy season.
