• Title/Summary/Keyword: Air Pollution Modeling

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Air pollution study using factor analysis and univariate Box-Jenkins modeling for the northwest of Tehran

  • Asadollahfardi, Gholamreza;Zamanian, Mehran;Mirmohammadi, Mohsen;Asadi, Mohsen;Tameh, Fatemeh Izadi
    • Advances in environmental research
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    • v.4 no.4
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    • pp.233-246
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    • 2015

  • High amounts of air pollution in crowded urban areas are always considered as one of the major environmental challenges especially in developing countries. Despite the errors in air pollution prediction, the forecasting of future data helps air quality management make decisions promptly and properly. We studied the air quality of the Aqdasiyeh location in Tehran using factor analysis and the Box-Jenkins time series methods. The Air Quality Control Company (AQCC) of the Municipality of Tehran monitors seven daily air quality parameters, including carbon monoxide (CO), Nitrogen Monoxide (NO), Nitrogen dioxide ($NO_2$), $NO_x$, ozone ($O_3$), particulate matter ($PM_{10}$) and sulfur dioxide ($SO_2$). We applied the AQCC data for our study. According to the results of the factor analysis, the air quality parameters were divided into two factors. The first factor included CO, $NO_2$, NO, $NO_x$, and $O_3$, and the second was $SO_2$ and $PM_{10}$. Subsequently, the Box- Jenkins time series was applied to the two mentioned factors. The results of the statistical testing and comparison of the factor data with the predicted data indicated Auto Regressive Integrated Moving Average (0, 0, 1) was appropriate for the first factor, and ARIMA (1, 0, 1) was proper for the second one. The coefficient of determination between the factor data and the predicted data for both models were 0.98 and 0.983 which may indicate the accuracy of the models. The application of these methods could be beneficial for the reduction of developing numbers of mathematical modeling.

  • https://doi.org/10.12989/aer.2015.4.4.233 인용 KSCI

Methodology of Application to Air Quality Model to Evaluate the Results of the Enforcement Plan in Seoul Metropolitan Area (수도권 지역의 대기환경관리 시행계획 추진결과 평가를 위한 대기질 모델링 적용 방법)

  • Yoo, Chul;Lee, Dae-Gyun;Lee, Yong-Mi;Lee, Mi-Hyang;Hong, Ji-Hyung;Lee, Seok-Jo
    • Journal of Environmental Science International
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    • v.20 no.12
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    • pp.1647-1661
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    • 2011

  • The Government had devised legislation of Special Act and drew up guidelines for improving air quality in Seoul Metropolitan area. In 2007 local government of Seoul, Incheon and Gyeonggi conducted the results of application policy by reduced air pollutants emission for the first time. Although there was reduction of air pollutant emission in each local government, it was ineffective as expected using air pollution monitoring database. Therefore we worked out a way to prepare modeling input data using the results of enforcement plan. And we simulated surface $NO_2$ and PM10 before and after decrease in air pollutants emission and examine reduction effects of air pollution according to enforcement regulation except other influence, by using MM5-SMOKE-CMAQ system. Each local government calculated the amount of emission reduction under application policy, and we developed to prepare input data so as to apply to SMOKE system using emission reduction of enforcement plan. Distribution factor of emission reduction were classified into detailed source and fuel codes using code mapping method in order to allocate the decreased emission. The code mapping method also included a way to allocate spatial distribution by CAPSS distribution. According to predicted result using the reduction of NOx emission, $NO_2$ concentration was decreased from 19.1 ppb to 18.0 ppb in Seoul. In Gyeonggi and Incheon $NO^2$ concentrations were down to 0.65 ppb and 0.68 ppb after application of enforcement plan. PM10 concentration was reduced from 18.2 ${\mu}g/m^3$ to 17.5 ${\mu}g/m^3$ in Seoul. In Gyeonggi PM10 concentration was down to 0.51 ${\mu}g/m^3$ and in Incheon PM10 concentration was decreased about 0.47 ${\mu}g/m^3$ which was the lower concentration than any other cities.

  • https://doi.org/10.5322/JES.2011.20.12.1647 인용 PDF KSCI

Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square

  • Yu, Suk-Hyun;Kwon, Hee-Yong
    • Journal of Korea Multimedia Society
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    • v.16 no.12
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    • pp.1465-1474
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    • 2013

  • In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

  • https://doi.org/10.9717/kmms.2013.16.12.1465 인용 PDF KSCI KPUBS HTML