This research assessed the environmental impact of cement silos emission on the existing concrete batching facilities in M35-Mussafah, Abu Dhabi, United Arab Emirates. These assessments were conducted using an air qua...This research assessed the environmental impact of cement silos emission on the existing concrete batching facilities in M35-Mussafah, Abu Dhabi, United Arab Emirates. These assessments were conducted using an air quality dispersion model (AERMOD) to predict the ambient concentration of Portland Cement particulate matter less than 10 microns (PM<sub>10</sub>) emitted to the atmosphere during loading and unloading activities from 176 silos located in 25 concrete batching facilities. AERMOD was applied to simulate and describe the dispersion of PM<sub>10</sub> released from the cement silos into the air. Simulations were carried out for PM<sub>10</sub> emissions on controlled and uncontrolled cement silos scenarios. Results showed an incremental negative impact on air quality and public health from uncontrolled silos emissions and estimated that the uncontrolled PM<sub>10</sub> emission sources contribute to air pollution by 528958.32 kg/Year. The modeling comparison between the controlled and uncontrolled silos shows that the highest annual average concentration from controlled cement silos is 0.065 μg/m<sup>3</sup>, and the highest daily emission value is 0.6 μg/m<sup>3</sup>;both values are negligible and will not lead to significant air quality impact in the entire study domain. However, the uncontrolled cement silos’ highest annual average concentration value is 328.08 μg/m<sup>3</sup>. The highest daily emission average value was 1250.09 μg/m<sup>3</sup>;this might cause a significant air pollution quality impact and health effects on the public and workers. The short-term and long-term average PM<sub>10</sub> pollutant concentrations at these receptors predicted by the air dispersion model are discussed for both scenarios and compared with local and international air quality standards and guidelines.展开更多
Vietnam’s economy has been developing strongly in recent years;however, it is necessary to examine the impact of its economic activities on environmental quality. This study aims to evaluate the relationship between ...Vietnam’s economy has been developing strongly in recent years;however, it is necessary to examine the impact of its economic activities on environmental quality. This study aims to evaluate the relationship between CO<sub>2</sub> emissions and economic growth, industrial production, and foreign direct investment (FDI) in Vietnam. The ARDL estimation was used to process the dataset from World Bank. Results showed that economic growth, industrial production, and FDI have an impact on CO<sub>2</sub> emissions in the long run in Vietnam. Granger Causality test also indicated that there is a causal relationship between economic growth, industrial production, and CO<sub>2</sub> emissions in Vietnam from 1990 to 2018, at 5% statistical significance level. Proposed solutions to reduce CO<sub>2</sub> emissions but still promote economic growth toward the green growth orientation and zero carbon target attainment are as follows: 1) reduce the use of fossil energy in industrial manufacturing and replace it by renewable energy sources;2) use modern technology for all production sectors in economy;and 3) develop a legal framework for FDI projects selection and choose foreign investors with modern and low carbon emission technology.展开更多
A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Marko...A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.展开更多
文摘This research assessed the environmental impact of cement silos emission on the existing concrete batching facilities in M35-Mussafah, Abu Dhabi, United Arab Emirates. These assessments were conducted using an air quality dispersion model (AERMOD) to predict the ambient concentration of Portland Cement particulate matter less than 10 microns (PM<sub>10</sub>) emitted to the atmosphere during loading and unloading activities from 176 silos located in 25 concrete batching facilities. AERMOD was applied to simulate and describe the dispersion of PM<sub>10</sub> released from the cement silos into the air. Simulations were carried out for PM<sub>10</sub> emissions on controlled and uncontrolled cement silos scenarios. Results showed an incremental negative impact on air quality and public health from uncontrolled silos emissions and estimated that the uncontrolled PM<sub>10</sub> emission sources contribute to air pollution by 528958.32 kg/Year. The modeling comparison between the controlled and uncontrolled silos shows that the highest annual average concentration from controlled cement silos is 0.065 μg/m<sup>3</sup>, and the highest daily emission value is 0.6 μg/m<sup>3</sup>;both values are negligible and will not lead to significant air quality impact in the entire study domain. However, the uncontrolled cement silos’ highest annual average concentration value is 328.08 μg/m<sup>3</sup>. The highest daily emission average value was 1250.09 μg/m<sup>3</sup>;this might cause a significant air pollution quality impact and health effects on the public and workers. The short-term and long-term average PM<sub>10</sub> pollutant concentrations at these receptors predicted by the air dispersion model are discussed for both scenarios and compared with local and international air quality standards and guidelines.
文摘Vietnam’s economy has been developing strongly in recent years;however, it is necessary to examine the impact of its economic activities on environmental quality. This study aims to evaluate the relationship between CO<sub>2</sub> emissions and economic growth, industrial production, and foreign direct investment (FDI) in Vietnam. The ARDL estimation was used to process the dataset from World Bank. Results showed that economic growth, industrial production, and FDI have an impact on CO<sub>2</sub> emissions in the long run in Vietnam. Granger Causality test also indicated that there is a causal relationship between economic growth, industrial production, and CO<sub>2</sub> emissions in Vietnam from 1990 to 2018, at 5% statistical significance level. Proposed solutions to reduce CO<sub>2</sub> emissions but still promote economic growth toward the green growth orientation and zero carbon target attainment are as follows: 1) reduce the use of fossil energy in industrial manufacturing and replace it by renewable energy sources;2) use modern technology for all production sectors in economy;and 3) develop a legal framework for FDI projects selection and choose foreign investors with modern and low carbon emission technology.
文摘A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.
