Objective To investigate the relation between air pollution exposure and preterm birth in Shanghai, China. Methods We examined the effect of ambient air pollution on preterm birth using time-series approach in Shangha...Objective To investigate the relation between air pollution exposure and preterm birth in Shanghai, China. Methods We examined the effect of ambient air pollution on preterm birth using time-series approach in Shanghai in 2004. This method can eliminate potential confounding by individual risk factors that do not change over a short period of time. Daily numbers of preterm births were obtained from the live birth database maintained by Shanghai Municipal Center of Disease Control and Prevention. We used the generalized additive model (GAM) with penalized splines to analyze the relation between preterm birth, air pollution, and covariates. Results We observed a significant effect of outdoor air pollution only with 8-week exposure before preterm births. An increase of 10 μg/m^3 of 8-week average PM10, SO2, NO2, and O3 corresponded to 4.42% (95%CI 1.60%, 7.25%), 11.89% (95%CI 6.69%, 17.09%), 5.43% (95%CI 1.78%, 9.08%), and 4.63% (95%CI 0.35%, 8.91%) increase of preterm birth. We did not find any significant acute effect of outdoor air pollution on preterm birth in the week before birth. Conclusion Ambient air pollution may contribute to the risk of preterm birth in Shanghai. Our analyses also strengthen the rationale for further limiting air pollution level in the city.展开更多
Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model (GAM) was used to evaluate the...Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model (GAM) was used to evaluate the specific influences of air pollutants (PM10, SO2, and NO2) on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.展开更多
During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to main...During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.展开更多
In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many me...In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many methods in time ser-ies prediction and deep learning models to estimate the severity of air pollution.Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality.This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter(PM)PM2.5.To perform experimental analysis the data from the Central Pollution Control Board(CPCB)is used.Prediction is car-ried out for Chennai with seven locations and estimated PM’s using the weighted ensemble method.Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction.Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting.Computational time of ensemble decreases with paral-lel processing in each sub model.Weighted ensemble model shows high perfor-mance in long term prediction when compared to the traditional time series models like Vector Auto-Regression(VAR),Autoregressive Integrated with Mov-ing Average(ARIMA),Autoregressive Moving Average with Extended terms(ARMEX).Evaluation metrics like Root Mean Square Error(RMSE),Mean Absolute Error(MAE)and the time to achieve the time series are compared.展开更多
Air pollution poses a health hazard in all countries.However,complete data on ambient particulate matter(PM)concentrations are not available in all world regions.Reanalysis data is already a valuable source of exposur...Air pollution poses a health hazard in all countries.However,complete data on ambient particulate matter(PM)concentrations are not available in all world regions.Reanalysis data is already a valuable source of exposure data in epidemiological studies examining the relationship between temperature and health.Nevertheless,the performance of reanalysis data in assessing the short-term health effects of particulate air pollution remains unclear.We assessed the performance of CAMS reanalysis(EAC4)data from the European Centre for Medium-Range Weather Forecasts,compared with daily PM concentrations from field monitoring stations,to estimate short-term exposure to PM with an aerodynamic diameter less than 10μm(PM_(10))on daily mortality in 33 Spanish provincial capital cities using a two-stage time series regression design.The shape of the PM_(10)distribution varied substantially between PM observations and CAMS global reanalysis of atmospheric composition(EAC4)reanalysis data,with correlation ranging from 0.21 to 0.58.The pooled mortality risk for a 10μg/m^(3)increase in PM_(10)showed similar estimates using PM concentrations{relative risks(RR)=1.007,95%confidence intervals(95%CI)=[1.002,1.011]}and EAC4 reanalysis data(RR=1.011,95%CI=[1.006,1.015]).However,the city-specific PM_(10)beta coefficients estimated using PM concentrations and EAC4 reanalysis data showed a low correlation(r=0.22).The use of reanalysis data should be approached with caution when assessing the association between particulate matter air pollution and health outcomes,particularly in cities with small populations.展开更多
Objective To investigate the short-term association between outdoor air pollution and outpatient visits for acute bronchitis,which is a rare subject of research in the mainland of China.Methods A time-series analysis ...Objective To investigate the short-term association between outdoor air pollution and outpatient visits for acute bronchitis,which is a rare subject of research in the mainland of China.Methods A time-series analysis was conducted to examine the association of outdoor air pollutants with hospital outpatient visits in Shanghai by using two-year daily data(2010-2011).Results Outdoor air pollution was found to be associated with an increased risk of outpatient visits for acute bronchitis in Shanghai.The effect estimates of air pollutants varied with the lag structures of the concentrations of the pollutants.For lag06,a 10 μg/m3 increase in the concentrations of PM10,SO2,and NO2 corresponded to 0.94%(95% CI:0.83%,1.05%),11.12%(95% CI:10.76%,11.48%),and 4.84%(95% CI:4.49%,5.18%) increases in hospital visits for acute bronchitis,respectively.These associations appeared to be stronger in females(P〈0.05).Between-age differences were significant for SO2(P〈0.05),and between-season differences were also significant for SO2(P〈0.05).Conclusion Our analyses have provided the first evidence that the current air pollution level in China has an effect on acute bronchitis and that the rationale for further limiting air pollution levels in Shanghai should be strengthened.展开更多
Background: Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. The air quality is influenced by locations of the air pollution sources, their performance capacity, the technology ...Background: Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. The air quality is influenced by locations of the air pollution sources, their performance capacity, the technology used, the composition of waste generated and geographical and climate conditions. In this study, a time-series analysis was conducted to estimate the association of short-term exposure to ambient air pollutants and hospitalization due to asthma in Ulaanbaatar. Objectives: We estimate the short-term associations between daily changes in ambient air pollutants and daily asthma in Ulaanbaatar, Mongolia. Methods: This is a time-series cross over study. All asthma hospital admission and air pollution data of 2008-2017 was used for this assessment. Data analyzed by using the program STATA-12. For testing the differences of the results were used appropriate non-parametric tests. Result: The daily mean of sulfur dioxide concentration was 35.22 mg/m3 in the cold season, which was 7.57 times higher than the mean of the hot season. The mean annual PM 10 concentration was 182.73 μg/m3. Most of the cases of asthma were among women, aged between 5 - 64 years old, registered during winter and spring. 3.8 people admitted to the hospital mostly on weekdays. In all Lag of SO2, in Lag of NO2, in all Lag of PM 10, in PM 2.5 and in all Lag except for Lag 2 of CO, Lag 0 - 2 of O3 the incidence is likely to increase by 0.3% - 6.1% per 10 units of pollutants. Conclusion: The air pollution especially PM 10, PM 2.5, and CO are the most harmful air pollutants to asthma in Ulaanbaatar. The correlation mainly between asthma admission cases with meteorological parameters is because of the cold winter condition.展开更多
目的探讨合肥市极端气温对居民循环系统疾病死亡的影响及不同人群的敏感性分析。方法收集合肥市2016—2021年逐日气象资料、大气污染物监测资料及循环系统疾病死亡数据。采用基于广义相加模型的分布滞后非线性模型(distributed lag non-...目的探讨合肥市极端气温对居民循环系统疾病死亡的影响及不同人群的敏感性分析。方法收集合肥市2016—2021年逐日气象资料、大气污染物监测资料及循环系统疾病死亡数据。采用基于广义相加模型的分布滞后非线性模型(distributed lag non-linear model,DLNM),评估极端气温对不同性别、年龄人群循环系统疾病死亡影响以及对循环系统主要疾病死亡的滞后效应和累积效应。以日均气温中位数(17.7℃)为对照,计算极端气温的相对危险度(RR)。结果合肥市极端气温对居民循环系统疾病死亡具有显著影响。极端低温对循环系统疾病死亡影响滞后时间长,lag4时达到最大,RR(95%CI)为1.067(1.039,1.095),且不同人群的死亡风险均明显增加。极端高温对循环系统疾病死亡的影响在当天达到最大,RR(95%CI)为1.088(1.020,1.160),持续时间短;≥65岁、女性和脑血管病患者也均在当日效应最大,且效应具有统计学意义,而对其他人群无明显影响。极端气温对不同人群的冷效应均高于热效应,低温对<65岁人群的死亡风险明显高于≥65岁人群,热效应则相反;女性冷效应和热效应均高于男性;脑血管病人群冷效应和热效应也均高于缺血性心脏病人群。结论合肥市极端气温可能增加居民循环系统疾病死亡风险,冷效应影响更大,不同人群对冷热效应的敏感性有差别。展开更多
The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated predi...The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.展开更多
基金The current work was co-funded by China National Science Foundation through grant 30500397 (PI: Y. Zhang)ShanghaiRising-Star Program for Young Investigators through grant 04QMX1402 (PI: H. Kan).
文摘Objective To investigate the relation between air pollution exposure and preterm birth in Shanghai, China. Methods We examined the effect of ambient air pollution on preterm birth using time-series approach in Shanghai in 2004. This method can eliminate potential confounding by individual risk factors that do not change over a short period of time. Daily numbers of preterm births were obtained from the live birth database maintained by Shanghai Municipal Center of Disease Control and Prevention. We used the generalized additive model (GAM) with penalized splines to analyze the relation between preterm birth, air pollution, and covariates. Results We observed a significant effect of outdoor air pollution only with 8-week exposure before preterm births. An increase of 10 μg/m^3 of 8-week average PM10, SO2, NO2, and O3 corresponded to 4.42% (95%CI 1.60%, 7.25%), 11.89% (95%CI 6.69%, 17.09%), 5.43% (95%CI 1.78%, 9.08%), and 4.63% (95%CI 0.35%, 8.91%) increase of preterm birth. We did not find any significant acute effect of outdoor air pollution on preterm birth in the week before birth. Conclusion Ambient air pollution may contribute to the risk of preterm birth in Shanghai. Our analyses also strengthen the rationale for further limiting air pollution level in the city.
基金supported by the Gong-Yi Program of China Meteorological Administration(GYHY201106034)the Fundamental Research Funds for the Central Universities(lzuibky-2013-m03)+2 种基金National Natural Science Foundation of China(41075103)National Natural Science Foundation of China(41075102)National Natural Science Foundation of China(41305105)
文摘Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model (GAM) was used to evaluate the specific influences of air pollutants (PM10, SO2, and NO2) on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.
文摘During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.
文摘In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many methods in time ser-ies prediction and deep learning models to estimate the severity of air pollution.Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality.This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter(PM)PM2.5.To perform experimental analysis the data from the Central Pollution Control Board(CPCB)is used.Prediction is car-ried out for Chennai with seven locations and estimated PM’s using the weighted ensemble method.Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction.Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting.Computational time of ensemble decreases with paral-lel processing in each sub model.Weighted ensemble model shows high perfor-mance in long term prediction when compared to the traditional time series models like Vector Auto-Regression(VAR),Autoregressive Integrated with Mov-ing Average(ARIMA),Autoregressive Moving Average with Extended terms(ARMEX).Evaluation metrics like Root Mean Square Error(RMSE),Mean Absolute Error(MAE)and the time to achieve the time series are compared.
文摘Air pollution poses a health hazard in all countries.However,complete data on ambient particulate matter(PM)concentrations are not available in all world regions.Reanalysis data is already a valuable source of exposure data in epidemiological studies examining the relationship between temperature and health.Nevertheless,the performance of reanalysis data in assessing the short-term health effects of particulate air pollution remains unclear.We assessed the performance of CAMS reanalysis(EAC4)data from the European Centre for Medium-Range Weather Forecasts,compared with daily PM concentrations from field monitoring stations,to estimate short-term exposure to PM with an aerodynamic diameter less than 10μm(PM_(10))on daily mortality in 33 Spanish provincial capital cities using a two-stage time series regression design.The shape of the PM_(10)distribution varied substantially between PM observations and CAMS global reanalysis of atmospheric composition(EAC4)reanalysis data,with correlation ranging from 0.21 to 0.58.The pooled mortality risk for a 10μg/m^(3)increase in PM_(10)showed similar estimates using PM concentrations{relative risks(RR)=1.007,95%confidence intervals(95%CI)=[1.002,1.011]}and EAC4 reanalysis data(RR=1.011,95%CI=[1.006,1.015]).However,the city-specific PM_(10)beta coefficients estimated using PM concentrations and EAC4 reanalysis data showed a low correlation(r=0.22).The use of reanalysis data should be approached with caution when assessing the association between particulate matter air pollution and health outcomes,particularly in cities with small populations.
基金supported by the National Clinical Key Subject Construction for founds(occupational disease Program),the National Basic Research Program(973 program)of China(2011CB503802)National Natural Science Foundation of China(81222036)Gong-Yi Program of China Ministry of Environmental Protection(201209008)
文摘Objective To investigate the short-term association between outdoor air pollution and outpatient visits for acute bronchitis,which is a rare subject of research in the mainland of China.Methods A time-series analysis was conducted to examine the association of outdoor air pollutants with hospital outpatient visits in Shanghai by using two-year daily data(2010-2011).Results Outdoor air pollution was found to be associated with an increased risk of outpatient visits for acute bronchitis in Shanghai.The effect estimates of air pollutants varied with the lag structures of the concentrations of the pollutants.For lag06,a 10 μg/m3 increase in the concentrations of PM10,SO2,and NO2 corresponded to 0.94%(95% CI:0.83%,1.05%),11.12%(95% CI:10.76%,11.48%),and 4.84%(95% CI:4.49%,5.18%) increases in hospital visits for acute bronchitis,respectively.These associations appeared to be stronger in females(P〈0.05).Between-age differences were significant for SO2(P〈0.05),and between-season differences were also significant for SO2(P〈0.05).Conclusion Our analyses have provided the first evidence that the current air pollution level in China has an effect on acute bronchitis and that the rationale for further limiting air pollution levels in Shanghai should be strengthened.
文摘Background: Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. The air quality is influenced by locations of the air pollution sources, their performance capacity, the technology used, the composition of waste generated and geographical and climate conditions. In this study, a time-series analysis was conducted to estimate the association of short-term exposure to ambient air pollutants and hospitalization due to asthma in Ulaanbaatar. Objectives: We estimate the short-term associations between daily changes in ambient air pollutants and daily asthma in Ulaanbaatar, Mongolia. Methods: This is a time-series cross over study. All asthma hospital admission and air pollution data of 2008-2017 was used for this assessment. Data analyzed by using the program STATA-12. For testing the differences of the results were used appropriate non-parametric tests. Result: The daily mean of sulfur dioxide concentration was 35.22 mg/m3 in the cold season, which was 7.57 times higher than the mean of the hot season. The mean annual PM 10 concentration was 182.73 μg/m3. Most of the cases of asthma were among women, aged between 5 - 64 years old, registered during winter and spring. 3.8 people admitted to the hospital mostly on weekdays. In all Lag of SO2, in Lag of NO2, in all Lag of PM 10, in PM 2.5 and in all Lag except for Lag 2 of CO, Lag 0 - 2 of O3 the incidence is likely to increase by 0.3% - 6.1% per 10 units of pollutants. Conclusion: The air pollution especially PM 10, PM 2.5, and CO are the most harmful air pollutants to asthma in Ulaanbaatar. The correlation mainly between asthma admission cases with meteorological parameters is because of the cold winter condition.
文摘目的探讨合肥市极端气温对居民循环系统疾病死亡的影响及不同人群的敏感性分析。方法收集合肥市2016—2021年逐日气象资料、大气污染物监测资料及循环系统疾病死亡数据。采用基于广义相加模型的分布滞后非线性模型(distributed lag non-linear model,DLNM),评估极端气温对不同性别、年龄人群循环系统疾病死亡影响以及对循环系统主要疾病死亡的滞后效应和累积效应。以日均气温中位数(17.7℃)为对照,计算极端气温的相对危险度(RR)。结果合肥市极端气温对居民循环系统疾病死亡具有显著影响。极端低温对循环系统疾病死亡影响滞后时间长,lag4时达到最大,RR(95%CI)为1.067(1.039,1.095),且不同人群的死亡风险均明显增加。极端高温对循环系统疾病死亡的影响在当天达到最大,RR(95%CI)为1.088(1.020,1.160),持续时间短;≥65岁、女性和脑血管病患者也均在当日效应最大,且效应具有统计学意义,而对其他人群无明显影响。极端气温对不同人群的冷效应均高于热效应,低温对<65岁人群的死亡风险明显高于≥65岁人群,热效应则相反;女性冷效应和热效应均高于男性;脑血管病人群冷效应和热效应也均高于缺血性心脏病人群。结论合肥市极端气温可能增加居民循环系统疾病死亡风险,冷效应影响更大,不同人群对冷热效应的敏感性有差别。
基金Supported by Student Research Fund of Agricultural University of Hebei(cxzr2014023)Technology Fund of Agricultural University of Hebei(ZD201406)~~
文摘The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.