scholarly journals A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

2017 ◽  
Vol 9 (6) ◽  
pp. 911 ◽  
Author(s):  
Qingyong Wang ◽  
Hong-Ning Dai ◽  
Hao Wang
Keyword(s):  
2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.


2021 ◽  
pp. 193672442110017
Author(s):  
Sherrie M. Steiner ◽  
Jordan M. Marshall ◽  
Atefeh Mohammadpour ◽  
Aaron W. Thompson

The purpose of this engaged public sociology study was to use social science to bring resident stakeholders into the process of governing pollution production in a rural community. The community has cancer clusters. Residents have concerns about direct exposure to pollution production in their neighborhood by a steel recycling plant that has been cited numerous times for environmental violations. The facility has been under voluntary remediation since 2009, but neighborhood residents were marginalized from the governance process. This case study details how social science was used to bring neighborhood residents’ concerns about direct exposure to toxic air pollution into remediation governance. A curricula-as-research model was developed to provide an engagement framework that guided the case study as it progressed through a series of six stages over five years. The principal investigator maintained this collaboration by integrating the project into courses, securing small grants, developing an affordable air pollution monitoring method, and convening multiple community meetings. The air monitoring results are analyzed and discussed. Finally, the impact of the case study on the company, the state environmental management agency, local government, the nonprofit partner, and residents’ sense of human agency is evaluated.


2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using Nonlinear AutoregRessive network with eXogenous inputs (NARX) method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.


Author(s):  
Hone-Jay Chu ◽  
Muhammad Zeeshan Ali

Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration–duration–frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control.


2020 ◽  
Vol 6 (2) ◽  
pp. 47-52
Author(s):  
Abdullah Al Nayeem ◽  
◽  
Ahmad Kamruzzaman Majumder ◽  
Md. Sahadat Hossain ◽  
William S Carter ◽  
...  

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162655 ◽  
Author(s):  
Xiuwei Liu ◽  
Hongyong Sun ◽  
Til Feike ◽  
Xiying Zhang ◽  
Liwei Shao ◽  
...  

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