Linear regression analyses of ozone and sulphur dioxide in ambient air

Gupta, Gian; Sabaratnam, S. Vikineswary; Dadson, R. B.

doi:10.1016/0048-9697(86)90362-1

Cited by 4 publications

(1 citation statement)

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“…Thus, in the presence of O 3 under wet conditions, the SO 2 dry-deposition velocity and oxidation from SO 2 to sulphate is enhanced (Sakamoto et al 2004). Furthermore, in the presence of H 2 O 2 and wet aerosols, SO 2 does participate in the chemistry of ozone by the absorp- tion of light within the ultraviolet region (Gupta et al 1986, Ruiz Suárez et al 1995. Due to the high solar radiation and relative humidity in the area under investigation, the two former phenomena are likely to be strengthened.…”

Section: Data Collectionmentioning

confidence: 93%

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

Turias

González

Martín

et al. 2007

Environ Monit Assess

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The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO 2 ) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO 2 series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d SPM 24-ahead =0.906, d CO 1-ahead =0.891, d SO2 1-ahead =0.851).

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Section: Data Collectionmentioning

confidence: 93%

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

Turias

González

Martín

et al. 2007

Environ Monit Assess

View full text Add to dashboard Cite

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Predicting ozone levels

Abdul‐Wahab

Bouhamra

Ettouney

et al. 1996

Environ. Sci. & Pollut. Res.

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This paper presents a statistical model that is capable of predicting ozone levels from precursor concentrations and meteorological conditions during daylight hours in the Shuaiba Industrial Area (SIA) of Kuwait. The model has been developed from ambient air quality data that was recorded for one year starting from December 1994 using an air pollution mobile monitoring station. The functional relationship between ozone level and the various independent variables has been determined by using a stepwise multiple regression modelling procedure. The model contains two terms that describe the dependence of ozone on nitrogen oxides (NOx) and nonmethane hydrocarbon precursor concentrations, and other terms that relate to wind direction, wind speed, sulphur dioxide (SO2) and solar energy. In the model, the levels of the precursors are inversely related to ozone concentration, whereas SO2 concentration, wind speed and solar radiation are positively correlated. Typically, 63 % of the variation in ozone levels can be explained by the levels of NOx. The model is shown to be statistically significant and model predictions and experimental observations are shown to be consistent. A detailed analysis of the ozone-temperature relationship is also presented; at temperatures less than 27 °C there is a positive correlation between temperature and ozone concentration whereas at temperatures greater than 27 °C a negative correlation is seen. This is the first time a non-monotonic relationship between ozone levels and temperature has been reported and discussed.

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