Management of air quality monitoring using principal component and cluster analysis—Part II: CO, NO2 and O3

Pires, José C.M.; Sousa, Sofia; Pereira, Maria do Carmo; Alvim-Ferraz, M.C.M.; Martins, F.G.

doi:10.1016/j.atmosenv.2007.10.041

Cited by 90 publications

(41 citation statements)

References 43 publications

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“…Indeed, tropospheric ozone is an irritating and reactive gas which rather harmful for human health, and affects other important parts of our daily life such as climate, farming, tourism etc. [6,7]. Moreover, it is responsible for increases in mortality rates during episodes of high concentrations [8].…”

Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

“…A wide range of statistical approaches was presented in the previous studies to predict O 3 concentrations. On the one hand Ortiz-García et al [8] used principal component analysis, on the other hand Yi and Prybutok, Spellman, Gómez-Sanchis et al and Pires et al [7,[13][14][15] used artificial neural network. In a more recent work, Feng et al [16] combine neural network with support vector machines.…”

Section: Related Workmentioning

confidence: 99%

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Ozone Concentration Forecasting Using Statistical Learning Approaches

Ishak¹,

Daoud²,

Trabelsi³

2017

JMES

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In this paper, we are interested in the statistical modeling and forecasting of the daily maximum ozone concentration in three monitoring stations from Tunisia. A large number of explicative variables has been considered in our study. We have focused our attention on the problem of variable selection in order to improve the forecasting performance. To achieve our goal, we have used Support Vector Regression (SVR) and Random Forests (RF). The main novelties of this paper are: the variety and originality of the approaches for variable selection in regression, and the audaciousness to deal with a sticky situation characterized by a relatively big pannier of explicative variables compared to the number of observations. The experimental results demonstrate that Random Forests outperform Support Vector Regression in variable ranking and selection. Finally, it was shown that the forecasting accuracy is at least preserved, for the three stations, when using only the selected variables.

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Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ozone Concentration Forecasting Using Statistical Learning Approaches

Ishak¹,

Daoud²,

Trabelsi³

2017

JMES

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“…Intense solar radiation, high temperature, minimal rainfall, low wind speed, and low relative humidity can raise O 3 concentration (Toh et al, 2013). Substantial research also realized the possibilities of adopting multivariate analysis to produce prediction and forecasting models of O 3 concentrations (Pires et al, 2008;Kovac-Andric et al, 2009;Jimenez-Hornero et al, 2010;Ghozdic et al, 2011). Ishi et al (2007) and Mohammed et al (2013) examined the phytotoxic effects of O 3 on crop production.…”

Section: Introductionmentioning

confidence: 99%

High Nighttime Ground-Level Ozone Concentrations in Kemaman: NO and NO2 Concentrations Attributions

Awang

Ramli

Yahaya

et al. 2015

Aerosol Air Qual. Res.

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High nighttime ozone (O 3 ) concentration levels were observed in Kemaman, Terengganu, and results were compared with those in other places in Malaysia. In this study, the contribution of precursors [nitric oxide (NO) and nitrogen dioxide (NO 2 )] and meteorological factors wind speed, and wind direction) toward long-term high nighttime O 3 over the period of 1999 to 2010 was evaluated. During this period, the recorded highest nighttime O 3 ground level was 89 ppb with more than 25% surpassing 20 ppb. Analysis shows that minimal decreasing trends were measured in Kemaman. Lower nitrogen oxide (NO x ) concentrations restricted the sinking agents; thus, reducing the depletion rates allowed O 3 to remain in the atmosphere. Minimal associations were observed between the O 3 concentration level and the speed and direction of wind. Accordingly, the largest contributor toward high nighttime O 3 ground level concentration in Kemaman was most probably NO x concentration.

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“…In regression analysis, this is particularly very useful and become good input as predictors in a regression model since they optimize spatial patterns and remove complexity due to multicollinearity [8], [9]. ICR and PCR have been widely used in particular for plant study [9], dam deformation study [10], air pollutants in subway [11], air quality management [2], [3], [12], and O 3 prediction [1].…”

Section: Introductionmentioning

confidence: 99%