Air pollution control has always been a global challenge, and significant progress has been made in recent years in controlling air pollutants. However, in some major cities, air pollutant concentrations still exceed ...Air pollution control has always been a global challenge, and significant progress has been made in recent years in controlling air pollutants. However, in some major cities, air pollutant concentrations still exceed the standards. Some scholars have used linear models or conditional autoregressive iterative models to apply the VaR method to predict pollutant concentrations. However, traditional methods based on quantile regression estimation can lead to inadequate risk estimates. Therefore, we propose a method based on the Conditional Autoregressive Value at Risk (CAViaR) model, which uses the kth power expectile regression to estimate VaR. This method does not specify the type of the distribution of data, is easier to calculate the asymptotic variance, more sensitive to extreme values. Applying our method to the data of PM10 in Beijing, we investigate the fitting effects in the case of k = 1, k = 2, and k = 1.9 through predictive tests. The results show that the kth power expectile regression estimates are better than quantile and expectile regression estimates to some extent.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
This research study quantifies the PM<sub>10</sub> emission rates (g/s) from cement silos in 25 concrete batching facilities for both controlled and uncontrolled scenarios by applying the USEPA AP-42 guide...This research study quantifies the PM<sub>10</sub> emission rates (g/s) from cement silos in 25 concrete batching facilities for both controlled and uncontrolled scenarios by applying the USEPA AP-42 guidelines step-by-step approach. The study focuses on evaluating the potential environmental impact of cement dust fugitive emissions from 176 cement silos located in 25 concrete batching facilities in the M35 Mussafah industrial area of Abu Dhabi, UAE. Emission factors are crucial for quantifying the PM<sub>10</sub> emission rates (g/s) that support developing source-specific emission estimates for areawide inventories to identify major sources of pollution that provide screening sources for compliance monitoring and air dispersion modeling. This requires data to be collected involves information on production, raw material usage, energy consumption, and process-related details, this was obtained using various methods, including field visits, surveys, and interviews with facility representatives to calculate emission rates accurately. Statistical analysis was conducted on cement consumption and emission rates for controlled and uncontrolled sources of the targeted facilities. The data shows that the average cement consumption among the facilities is approximately 88,160 (MT/yr), with a wide range of variation depending on the facility size and production rate. The emission rates from controlled sources have an average of 4.752E<sup>-04</sup> (g/s), while the rates from uncontrolled sources average 0.6716 (g/s). The analysis shows a significant statistical relationship (p < 0.05) and perfect positive correlation (r = 1) between cement consumption and emission rates, indicating that as cement consumption increases, emission rates tend to increase as well. Furthermore, comparing the emission rates from controlled and uncontrolled scenarios. The data showed a significant difference between the two scenarios, highlighting the effectiveness of control measures in reducing PM<sub>10</sub> emissions. The study’s findings provide insights into the impact of cement silo emissions on air quality and the importance of implementing control measures in concrete batching facilities. The comparative analysis contributes to understanding emission sources and supports the development of pollution control strategies in the Ready-Mix industry.展开更多
The concentration of air PM10 in six types of green lands in campus of Tsinghua university was measured. The results showed that PM10 concentration exhibited obvious daily and monthly variation,and also had remarkable...The concentration of air PM10 in six types of green lands in campus of Tsinghua university was measured. The results showed that PM10 concentration exhibited obvious daily and monthly variation,and also had remarkable difference among different seasons. However there was no remarkable difference in concentration of PM10 within the same season over all types of green lands. As for average concentration,the value was lower in arbor land of spring,autumn and winter,and in lawn of summer. The concentration of PM10 was 43% higher in cloudy days than that of sunny ones. Moreover,the difference increased to 137% between cloudy day after raining and sunny day after raining because of the clarification of rain.展开更多
文摘Air pollution control has always been a global challenge, and significant progress has been made in recent years in controlling air pollutants. However, in some major cities, air pollutant concentrations still exceed the standards. Some scholars have used linear models or conditional autoregressive iterative models to apply the VaR method to predict pollutant concentrations. However, traditional methods based on quantile regression estimation can lead to inadequate risk estimates. Therefore, we propose a method based on the Conditional Autoregressive Value at Risk (CAViaR) model, which uses the kth power expectile regression to estimate VaR. This method does not specify the type of the distribution of data, is easier to calculate the asymptotic variance, more sensitive to extreme values. Applying our method to the data of PM10 in Beijing, we investigate the fitting effects in the case of k = 1, k = 2, and k = 1.9 through predictive tests. The results show that the kth power expectile regression estimates are better than quantile and expectile regression estimates to some extent.
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This research study quantifies the PM<sub>10</sub> emission rates (g/s) from cement silos in 25 concrete batching facilities for both controlled and uncontrolled scenarios by applying the USEPA AP-42 guidelines step-by-step approach. The study focuses on evaluating the potential environmental impact of cement dust fugitive emissions from 176 cement silos located in 25 concrete batching facilities in the M35 Mussafah industrial area of Abu Dhabi, UAE. Emission factors are crucial for quantifying the PM<sub>10</sub> emission rates (g/s) that support developing source-specific emission estimates for areawide inventories to identify major sources of pollution that provide screening sources for compliance monitoring and air dispersion modeling. This requires data to be collected involves information on production, raw material usage, energy consumption, and process-related details, this was obtained using various methods, including field visits, surveys, and interviews with facility representatives to calculate emission rates accurately. Statistical analysis was conducted on cement consumption and emission rates for controlled and uncontrolled sources of the targeted facilities. The data shows that the average cement consumption among the facilities is approximately 88,160 (MT/yr), with a wide range of variation depending on the facility size and production rate. The emission rates from controlled sources have an average of 4.752E<sup>-04</sup> (g/s), while the rates from uncontrolled sources average 0.6716 (g/s). The analysis shows a significant statistical relationship (p < 0.05) and perfect positive correlation (r = 1) between cement consumption and emission rates, indicating that as cement consumption increases, emission rates tend to increase as well. Furthermore, comparing the emission rates from controlled and uncontrolled scenarios. The data showed a significant difference between the two scenarios, highlighting the effectiveness of control measures in reducing PM<sub>10</sub> emissions. The study’s findings provide insights into the impact of cement silo emissions on air quality and the importance of implementing control measures in concrete batching facilities. The comparative analysis contributes to understanding emission sources and supports the development of pollution control strategies in the Ready-Mix industry.
文摘The concentration of air PM10 in six types of green lands in campus of Tsinghua university was measured. The results showed that PM10 concentration exhibited obvious daily and monthly variation,and also had remarkable difference among different seasons. However there was no remarkable difference in concentration of PM10 within the same season over all types of green lands. As for average concentration,the value was lower in arbor land of spring,autumn and winter,and in lawn of summer. The concentration of PM10 was 43% higher in cloudy days than that of sunny ones. Moreover,the difference increased to 137% between cloudy day after raining and sunny day after raining because of the clarification of rain.
