Urban particulate matter 2.5(PM2.5)pollution and public health are closely related,and concerns regarding PM2.5 are widespread.Of the underlying factors,the urban morphology is the most manageable.Therefore,investigat...Urban particulate matter 2.5(PM2.5)pollution and public health are closely related,and concerns regarding PM2.5 are widespread.Of the underlying factors,the urban morphology is the most manageable.Therefore,investigations of the impact of urban three-dimensional(3D)morphology on PM2.5 concentration have important scientific significance.In this paper,39 PM2.5 monitoring sites of Beijing in China were selected with PM2.5 automatic monitoring data that were collected in 2013.This data set was used to analyze the impacts of the meteorological condition and public transportation on PM2.5 concentrations.Based on the elimination of the meteorological conditions and public transportation factors,the relationships between urban 3D morphology and PM2.5 concentrations are highlighted.Ten urban 3D morphology indices were established to explore the spatial-temporal correlations between the indices and PM2.5 concentrations and analyze the impact of urban 3D morphology on the PM2.5 concentrations.Results demonstrated that road length density(RLD),road area density(RAD),construction area density(CAD),construction height density(CHD),construction volume density(CVD),construction otherness(CO),and vegetation area density(VAD)have positive impacts on the PM2.5 concentrations,whereas water area density(WAD),water fragmentation(WF),and vegetation fragmentation(VF)(except for the 500 m buffer)have negative impacts on the PM2.5 concentrations.Moreover,the correlations between the morphology indices and PM2.5 concentrations varied with the buffer scale.The findings could lay a foundation for the high-precision spatial-temporal modelling of PM2.5 concentrations and the scientific planning of urban 3D spaces by authorities responsible for controlling PM2.5 concentrations.展开更多
Objective To investigate the antagonistic effects of different doses of Lianhua Qingwen on pulmonary injury induced by fine particulates PM2.5 in rats. Methods Fine particulates suspended in the environment were colle...Objective To investigate the antagonistic effects of different doses of Lianhua Qingwen on pulmonary injury induced by fine particulates PM2.5 in rats. Methods Fine particulates suspended in the environment were collected. Forty-eight healthy adult wistar rats were randomly divided into 6 groups with 8 rats in each group. Four groups of rats were exposed to PM2.5 by intratracheally dripping suspensions of fine particulates PM2.5(7.5 mg/kg) as dust-exposed model rats. Among them 24 rats in three groups received Lianhua Qingwen treatment(crude drug) at a dose of 2 g/kg, 4 g/kg, 8 g/kg per day for 3 days before dust exposure and were defined as low-dose, middle-dose and high-dose Lianhua Qingwen treatment groups respectively. The other dust-exposed model rats without treatment were assigned as PM2.5 control group. The un-exposed rats were set as saline control group(1.5 ml/kg saline) and blank control group. All rats were killed after 24 hours of the exposure. Lung tissue, serum and bronchoalveolar lavage fluid(BALF) were collected. The levels of malonaldehyde(MDA), lactate dehydrogenase(LDH), and glutathione peroxidase(GSH-PX) in blood serum and BALF, and superoxide dismutase(SOD) in blood surum were measured using fluorescent quantitation PCR; Expression of NF-E2-related factor 2(NRF-2), heme oxygenase 1(HO-1) and quinone oxidoreductase 1(NQO1) in lung tissues were measured using Western blot. Pathological changes of lung tissues in each group were also examined. Results Pathology revealed thickened alveolar septum, congestion of capillary, interstitial edema and infiltration of lymphocyte and neutrophil surrounding bronchiole in the PM2.5 control group, which weresignificantly relieved in the Lianhua Qingwen treatment groups. Compared to the blank and saline control groups, the PM2.5 control group had significantly higher levels of LDH and MDA(p<0.01) and lower level of GSH-PS(p<0.01) in BALF, significantly higher levels of LDH and MDA(p<0.05) and lower level of GSH-PS(p<0.05) in rat serum. The levels of MDA in blood serum and BALF were significantly lower in each treatment group than that in PM2.5 control group(all P<0.05). In both middle-dose and high-dose treatment group the measurements of LDH in serum and BALF as well as GSH-PX in serum were significant difference from those of PM2.5 control group(all P<0.05). Expressions of NRF-2, HO-1 and NQO1 in lung tissues were significantly different among middle-dose and high-dose treatment group compared with those in PM2.5 control group(all P<0.05). Conclusion Fine particulates PM2.5 in environment may induce pulmonary oxidative lesions in rats. Middle-dose and high-dose Lianhua Qingwen has antagonist effece on the injuries induced by fine particulates.展开更多
A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Envi...A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Environmental Protection Agency, USA （USEPA） in 1997 except for the Ming Tombs site. PM2.5 concentrations in winter are much higher than in summer. The 16 Polycyclic aromatic hydrocarbons （PAHs） listed as priority pollutants by USEPA in PM2.5 were completely identified and quantified by high performance liquid chromatography （HPLC） with variable wavelength detector （VWD） and fluorescence detector （FLD） employed. The PM2.5 concentrations indicate that the pollution situation is still serious in Beijing. The sum of 16 PAHs concentrations ranged from 22.17 to 5366 ng m^-3. The concentrations of the heavier molecular weight PAHs have a different pollution trend from the lower PAHs. Seasonal variations were mainly attributed to the difference in coal combustion emission and meteorological conditions. The source apportionment analysis suggests that PAHs from PM2.5 in Beijing city mainly come from coal combustion and vehicle exhaust emission. New measures about restricting coal combustion and vehicle exhaust must be established as soon as possible to improve the air pollution situation in Beijing city.展开更多
Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an au...Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.展开更多
Objective To screen the differentially expressed proteins(DEPs)in human bronchial epithelial cells(HBE)treated with atmospheric fine particulate matter(PM2.5).Methods HBE cells were treated with PM2.5 samples from She...Objective To screen the differentially expressed proteins(DEPs)in human bronchial epithelial cells(HBE)treated with atmospheric fine particulate matter(PM2.5).Methods HBE cells were treated with PM2.5 samples from Shenzhen and Taiyuan for 24 h.To detect overall protein expression,the Q Exactive mass spectrometer was used.Gene ontology(GO),Kyoto encyclopedia of genes and genomes(KEGG),and Perseus software were used to screen DEPs.Results Overall,67 DEPs were screened in the Shenzhen sample-treated group,of which 46 were upregulated and 21 were downregulated.In total,252 DEPs were screened in the Taiyuan sampletreated group,of which 134 were upregulated and 118 were downregulated.KEGG analysis demonstrated that DEPs were mainly enriched in ubiquitin-mediated proteolysis and HIF-1 signal pathways in Shenzhen PM2.5 samples-treated group.The GO analysis demonstrated that Shenzhen sample-induced DEPs were mainly involved in the biological process for absorption of various metal ions and cell components.The Taiyuan PM2.5-induced DEPs were mainly involved in biological processes of protein aggregation regulation and molecular function of oxidase activity.Additionally,three important DEPs,including ANXA2,DIABLO,and AIMP1,were screened.Conclusion Our findings provide a valuable basis for further evaluation of PM2.5-associated carcinogenesis.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
考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统...考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.展开更多
基金Under the auspices of National Key Research and Development Program of China(No.2016YFB0502504)Beijing Excellent Youth Talent Program(No.2015400018760G294)National Natural Science Foundation of China(No.41201443,41001267).
文摘Urban particulate matter 2.5(PM2.5)pollution and public health are closely related,and concerns regarding PM2.5 are widespread.Of the underlying factors,the urban morphology is the most manageable.Therefore,investigations of the impact of urban three-dimensional(3D)morphology on PM2.5 concentration have important scientific significance.In this paper,39 PM2.5 monitoring sites of Beijing in China were selected with PM2.5 automatic monitoring data that were collected in 2013.This data set was used to analyze the impacts of the meteorological condition and public transportation on PM2.5 concentrations.Based on the elimination of the meteorological conditions and public transportation factors,the relationships between urban 3D morphology and PM2.5 concentrations are highlighted.Ten urban 3D morphology indices were established to explore the spatial-temporal correlations between the indices and PM2.5 concentrations and analyze the impact of urban 3D morphology on the PM2.5 concentrations.Results demonstrated that road length density(RLD),road area density(RAD),construction area density(CAD),construction height density(CHD),construction volume density(CVD),construction otherness(CO),and vegetation area density(VAD)have positive impacts on the PM2.5 concentrations,whereas water area density(WAD),water fragmentation(WF),and vegetation fragmentation(VF)(except for the 500 m buffer)have negative impacts on the PM2.5 concentrations.Moreover,the correlations between the morphology indices and PM2.5 concentrations varied with the buffer scale.The findings could lay a foundation for the high-precision spatial-temporal modelling of PM2.5 concentrations and the scientific planning of urban 3D spaces by authorities responsible for controlling PM2.5 concentrations.
文摘Objective To investigate the antagonistic effects of different doses of Lianhua Qingwen on pulmonary injury induced by fine particulates PM2.5 in rats. Methods Fine particulates suspended in the environment were collected. Forty-eight healthy adult wistar rats were randomly divided into 6 groups with 8 rats in each group. Four groups of rats were exposed to PM2.5 by intratracheally dripping suspensions of fine particulates PM2.5(7.5 mg/kg) as dust-exposed model rats. Among them 24 rats in three groups received Lianhua Qingwen treatment(crude drug) at a dose of 2 g/kg, 4 g/kg, 8 g/kg per day for 3 days before dust exposure and were defined as low-dose, middle-dose and high-dose Lianhua Qingwen treatment groups respectively. The other dust-exposed model rats without treatment were assigned as PM2.5 control group. The un-exposed rats were set as saline control group(1.5 ml/kg saline) and blank control group. All rats were killed after 24 hours of the exposure. Lung tissue, serum and bronchoalveolar lavage fluid(BALF) were collected. The levels of malonaldehyde(MDA), lactate dehydrogenase(LDH), and glutathione peroxidase(GSH-PX) in blood serum and BALF, and superoxide dismutase(SOD) in blood surum were measured using fluorescent quantitation PCR; Expression of NF-E2-related factor 2(NRF-2), heme oxygenase 1(HO-1) and quinone oxidoreductase 1(NQO1) in lung tissues were measured using Western blot. Pathological changes of lung tissues in each group were also examined. Results Pathology revealed thickened alveolar septum, congestion of capillary, interstitial edema and infiltration of lymphocyte and neutrophil surrounding bronchiole in the PM2.5 control group, which weresignificantly relieved in the Lianhua Qingwen treatment groups. Compared to the blank and saline control groups, the PM2.5 control group had significantly higher levels of LDH and MDA(p<0.01) and lower level of GSH-PS(p<0.01) in BALF, significantly higher levels of LDH and MDA(p<0.05) and lower level of GSH-PS(p<0.05) in rat serum. The levels of MDA in blood serum and BALF were significantly lower in each treatment group than that in PM2.5 control group(all P<0.05). In both middle-dose and high-dose treatment group the measurements of LDH in serum and BALF as well as GSH-PX in serum were significant difference from those of PM2.5 control group(all P<0.05). Expressions of NRF-2, HO-1 and NQO1 in lung tissues were significantly different among middle-dose and high-dose treatment group compared with those in PM2.5 control group(all P<0.05). Conclusion Fine particulates PM2.5 in environment may induce pulmonary oxidative lesions in rats. Middle-dose and high-dose Lianhua Qingwen has antagonist effece on the injuries induced by fine particulates.
基金Financial support from the National Natural Science Foundation of China （Grant No. 40475049） the Natural Sciences Foundation of Beijing city （Grant No. 8032012） are acknowledged.
文摘A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Environmental Protection Agency, USA （USEPA） in 1997 except for the Ming Tombs site. PM2.5 concentrations in winter are much higher than in summer. The 16 Polycyclic aromatic hydrocarbons （PAHs） listed as priority pollutants by USEPA in PM2.5 were completely identified and quantified by high performance liquid chromatography （HPLC） with variable wavelength detector （VWD） and fluorescence detector （FLD） employed. The PM2.5 concentrations indicate that the pollution situation is still serious in Beijing. The sum of 16 PAHs concentrations ranged from 22.17 to 5366 ng m^-3. The concentrations of the heavier molecular weight PAHs have a different pollution trend from the lower PAHs. Seasonal variations were mainly attributed to the difference in coal combustion emission and meteorological conditions. The source apportionment analysis suggests that PAHs from PM2.5 in Beijing city mainly come from coal combustion and vehicle exhaust emission. New measures about restricting coal combustion and vehicle exhaust must be established as soon as possible to improve the air pollution situation in Beijing city.
基金funded by grant number 14-INF1015-10 from the National ScienceTechnology,and Innovation Plan(MAARIFAH)+1 种基金the King Abdul-Aziz City for Science and Technology(KACST)Kingdom of Saudi Arabia.We thank the Science and Technology Unit at Umm Al-Qura University for their continued logistics support.
文摘Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.
基金Supported by the basic research programs of Shenzhen Science and Technology Innovation Committee to XU Xin Yun[JCYJ20170413101713324]Shenzhen Key Medical Discipline Construction Fund[SZXK067].
文摘Objective To screen the differentially expressed proteins(DEPs)in human bronchial epithelial cells(HBE)treated with atmospheric fine particulate matter(PM2.5).Methods HBE cells were treated with PM2.5 samples from Shenzhen and Taiyuan for 24 h.To detect overall protein expression,the Q Exactive mass spectrometer was used.Gene ontology(GO),Kyoto encyclopedia of genes and genomes(KEGG),and Perseus software were used to screen DEPs.Results Overall,67 DEPs were screened in the Shenzhen sample-treated group,of which 46 were upregulated and 21 were downregulated.In total,252 DEPs were screened in the Taiyuan sampletreated group,of which 134 were upregulated and 118 were downregulated.KEGG analysis demonstrated that DEPs were mainly enriched in ubiquitin-mediated proteolysis and HIF-1 signal pathways in Shenzhen PM2.5 samples-treated group.The GO analysis demonstrated that Shenzhen sample-induced DEPs were mainly involved in the biological process for absorption of various metal ions and cell components.The Taiyuan PM2.5-induced DEPs were mainly involved in biological processes of protein aggregation regulation and molecular function of oxidase activity.Additionally,three important DEPs,including ANXA2,DIABLO,and AIMP1,were screened.Conclusion Our findings provide a valuable basis for further evaluation of PM2.5-associated carcinogenesis.
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.
文摘考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.
