Predicting Hourly Ozone Concentration Time Series in Dali Area of Taichung City Based on Seven Types of GM (1, 1) Model

Pai, Tzu‐Yi; Lin, Su-Hwa; Yang, Pei-Yu; Chang, Dyi-Huey; Kuo, Jui-Ling

doi:10.1007/978-3-642-33439-9_17

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…Other studies with the same modeling method showed that R 2 value were relatively higher than this study such as Turkey during warming seasons (0.92 [8]), Taiwan (0.86 [12]), Kuwait(0.69 [11]), Greece (0.653 [22]), Croatia during summer season (0.8 [23]). Many factors could cause the differences.…”

Section: Iii3supporting

confidence: 46%

“…It should be noted however, that the complexity of tropospheric ozone formation reaction, meteorological conditions in urban areas and uncertainties in the measurement of all parameters cause difficulty of ozone modeling [8]). Moreover, the relationship between ozone and its precursor is complex because meteorological conditions and the rate of chemical reactions change very rapidly to very slowly [12]. Therefore, to simplify the complex processes of ozone formation, multivariate statistical methods is applied to model the air quality monitoring data in urban area, Jakarta, Indonesia.…”

mentioning

confidence: 99%

“…Therefore, to simplify the complex processes of ozone formation, multivariate statistical methods is applied to model the air quality monitoring data in urban area, Jakarta, Indonesia. [12], [13]. In this study, MLR is useful to model the relationship between ozone concentration (the dependent variable) with the precursors concentrations and meteorological conditions (the independent variables).…”

mentioning

confidence: 99%

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Modelling of Tropospheric Ozone Concentration in Urban Environment

Wasi’ah

Driejana

2017

IJPS

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Abstract Tropospheric ozone is harmful to human health and plants. It is resulted from photochemical processes involving NO x and VOCs from reactions of motor vehicle emissions and solar radiation in polluted urban environment. Historical data in Jakarta indicated that ozone concentrations often exceeded ambient standard threshold. To minimize its impact to human health it is important to predict its concentration. This paper reports the use of multivariate statistical method to predict ozone concentration, using precursor concentration and meteorological parameters. CH 4 , CO, NMHC, NO, NO 2 , THC data concentration, wind direction and speed, temperature, solar radiation and relative humidity during 2011 -2012 were used to build the model. Multiple linear regressions were applied to predict ozone concentration at Thamrin Station, Jakarta. These data were used as predictors at time (t) to estimate the ozone concentration at time (t +1). Meteorological conditions were found to strongly affect the concentration of ozone. The strongest relationship was found between ozone and temperature (0.513, p = 0.000). Weaker but significant positive correlations were found for solar radiation and NO 2 (r = 0.242, p= 0.000),. NMHC and NO correlation (r= 0.353, p= 0.000). Both NO and NMHC are freshly emitted from exhaust gas. Correlations between humidity, wind speed and direction were negative. Methana, NMHC, were negatively correlated with ozone due to their roles for producing NO 2 as the main precursor, while NO was for its scavenging reaction with O 3 . Based on Adjusted R 2 value, all predictors could explain variation in ozone concentration of approximately 46.32%. These findings will be useful as input in urban transportation planning and management in cities with tropical climate like Indonesia, as all precursors are emitted from vehicle combustion.

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Section: Iii3supporting

confidence: 46%

mentioning

confidence: 99%

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confidence: 99%

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Modelling of Tropospheric Ozone Concentration in Urban Environment

Wasi’ah

Driejana

2017

IJPS

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“…The MLR is a modelling technique that widely used to investigate the relationship between two or more independent variables and the dependent variable by fitting a linear equation observe the data [14,35]. In this study, it was employed to justify the relationship between the ambient air quality parameters and their impact on the API.…”

Section: Multiple Liner Regressionmentioning

confidence: 99%

Air quality modelling using chemometric techniques

Azid¹,

Rani²,

Samsudin³

et al. 2018

J. Fundam and Appl Sci.

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The datasets of air quality parameters for three years (2012 the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven paramet the most significant variables after stepwise backward mode. PCA identifies the major sour of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively.

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“…This technique has been used for investigating the relationship among various independent and dependent variables by fitting a linear equation to observed data (Pai et al, 2009;Ul-Saufi et al, 2011) and gives the percentage of the contribution of each parameter to the atmospheric pollution (Aertsen et al, 2010). In this study, it was used to justify the relationship between the air quality parameters and total API data.…”

Section: Multiple Linear Regressions (Mlr)mentioning

confidence: 99%

Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric

Azid

Juahir

Ezani

et al. 2015

Aerosol Air Qual. Res.

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This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January-December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters -low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM 10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.

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Predicting Hourly Ozone Concentration Time Series in Dali Area of Taichung City Based on Seven Types of GM (1, 1) Model

Cited by 7 publications

References 18 publications

Modelling of Tropospheric Ozone Concentration in Urban Environment

Modelling of Tropospheric Ozone Concentration in Urban Environment

Air quality modelling using chemometric techniques

Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric

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