Aerometric data analysis—time series analysis and forecast and an atmospheric smog diagram

Merz, P.H.; Painter, Louis J.; Ryason, P. R.

doi:10.1016/0004-6981(72)90199-0

Cited by 58 publications

(22 citation statements)

References 8 publications

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“…Results from the ARIMA models closely match the observed values in the training stage, although results for SO 2 were not as good as those for O 3 . All of the ARIMA models were then applied to the data in the forecast stage for both 1-stepahead and 24-step-ahead predictions.…”

Section: Forecast Model Development-time Seriesmentioning

confidence: 58%

“…Pollutants in the atmosphere may disperse or concentrate during varied time periods. Previous studies 2,3,4,5 have indicated that the data of ambient air quality are stochastic time series, thereby making it possible to make a short-term forecast on the basis of historical data. However, when applying the conventional time-series model to the ambient air pollution forecast, the pollutant level variations are generally not simple autoregressive or moving average models.…”

Section: Introductionmentioning

confidence: 99%

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Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data

Kao

Huang²

2000

Journal of the Air & Waste Management Association

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This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system. INTRODUCTIONAir pollutants exert a wide range of impacts on biological, physical, and economic systems. Their effects on human health are of particular concern. The decrease in

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Section: Forecast Model Development-time Seriesmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data

Kao

Huang²

2000

Journal of the Air & Waste Management Association

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“…The simplest way to model non-linear O 3 formation is to assume a universal non-linear relationship between O 3 levels and VOC and NO x emissions (Merz et al 1972;Schwing et al 1980;Delucchi and McCubbin 1996). The equation is shown as follows:…”

Section: Model Descriptionmentioning

confidence: 99%

Contribution of motor vehicle emissions to surface ozone in urban areas: A case study in Beijing

Liu¹,

Zhu²,

Zhao³

2008

International Journal of Sustainable Development & World Ec

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SUMMARYSurface ozone (O 3 ) pollution, a critical environmental challenge facing government agencies at all levels, is becoming more and more serious in China, especially in rapidly developing urban areas like Beijing. However, in China, few studies have evaluated the contribution of various pollution sources to surface O 3 , e.g. motor vehicles. In this paper, we combined a non-linear model with an analysis of motor vehicle emissions of NO x and VOCs in an integrated approach to estimate the contribution of motor vehicle emissions to surface O 3 . The model showed that, in urban areas of Beijing in 2000, the contribution of motor vehicle emissions to surface O 3 was 45.9%, and that elimination of 20.0% of motor vehicle emissions will lead to a 7.4% reduction in surface O 3 , while elimination of 20.0% of NO x or VOC emissions from motor vehicles will result in a 5.0% and 2.5% decline, respectively, in surface O 3 . In addition, elimination of 10,000 t of NO x from motor vehicles results in the same reduction in surface O 3 as elimination of 25,631 t of VOCs. Thus, controlling NO x emissions from motor vehicles is an effective way to control surface O 3 pollution in the study area.

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“…giving each station a polygonal "area of influence"). There turns out an approximation of the type (1) where Aj = area of Rj a\ = area of the surface common to sector Rj and to the polygon covering the grid squares near station i. First the "historical DAP time series" mentioned above has been assumed to be a (finite) realization of a three-variate stationary random process \DAP(k)\k (in fact there is no evidence of cycles or trends in the series).…”

Section: Description Of the Area Data Set And Dap Vectormentioning

confidence: 99%

“…a value of b is given by the previous step; in correspondence with such given value of 6, optimal estimates of (j) 1 and a are found through standard least squares fitting; ii. in correspondence with the values of 0 7 and a supplied by i), b is changed in accordance with a gradient technique 18 in order to improve fitting.…”

Section: A Single Input Arx Model (Lnput:temperature)mentioning

confidence: 99%

Multivariate Stochastic Models of Sulphur Dioxide Pollution in an Urban Area

Finz

Fronza

Spirito

1980

Journal of the Air Pollution Control Association

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Aerometric data analysis—time series analysis and forecast and an atmospheric smog diagram

Cited by 58 publications

References 8 publications

Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data

Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data

Contribution of motor vehicle emissions to surface ozone in urban areas: A case study in Beijing

Multivariate Stochastic Models of Sulphur Dioxide Pollution in an Urban Area

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