Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

Abdul‐Wahab, Sabah A.; Al-Alawi, S.M.

doi:10.1016/s1364-8152(01)00077-9

Cited by 182 publications

(83 citation statements)

References 15 publications

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“…ANN models presented slightly better performance than MLR. Abdul-Wahab and Al-Alawi [36] developed ANN models to predict O 3 concentrations through meteorological and environmental data. The contribution of the meteorological data was defined between 33% and 41%, while the remaining variation was attributed to chemical pollutants.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O 3 concentrations changes at different O 3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O 3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O 3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO 2 , and O 3 , and meteorological variables (temperature, relative humidity, and wind speed) measured from January 2012 to December 2013. Seven simulations were performed and the achieved models considered wind speed (at 4.9 m·s −1 ), temperature (at 17.5 • C) and NO 2 (at 26.6 µg·m −3 ) as the variables that determine the change of O 3 behavior. All the achieved models presented a similar fitting performance: R 2 = 0.71-0.72, RMSE = 14.5-14.7 µg·m −3 , and the index of agreement of the second order of 0.91. The combined effect of these variables on O 3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O 3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant.

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Section: Introductionmentioning

confidence: 99%

Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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“…These relations allow explaining in a good manner the latent factors that are not manifest variables. For this reason, we included these indices as input for NN, because the greatest advantage of a neural network is its ability to model a complex nonlinear relationship between independent and dependent variables [Gardner, 1999 ], [Gardner, 2000], [Abdul-Wahab, 2002].…”

Section: Pre-processingmentioning

confidence: 99%

Application of Neural Net Model to Estimate the Cardiovascular and Respiratory Diseases by Air Pollution Data in Urban Area

Pelliccioni¹,

Cotroneo²

2011

Advanced Topics in Environmental Health and Air Pollution Case Studies

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“…This method has been successfully applied to identify potential sources of air pollutants and analyse relationships between distinct air pollutants in measured data (Horel, 1981;Yu et al, 2000;Shi et al, 2009;Abhishek et al, 2010;Wang et al, 2010;Kothai et al, 2011;Amodio et al, 2013;Liang et al, 2013). Some research studies have been conducted with the aim of predicting the ozone generation based on the meteorological conditions and the pollutants-precursor appearing at a particular point in time (Abdul-Wahab et al, 2002. The HYSPLIT model, established by the NOAA (National Oceanic and Atmospheric Administration) has been widely used in many scientific research applications and emergency scenarios that require modelling the transport and dispersion of hazardous air pollutants (Draxler and Rolph, 2003;Rasheed et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment of Submicron Particle Size Distribution and PM2.5 Composition during an Asian Dust Storm Period in Two Urban Atmospheres

Liang

Lin

2015

Aerosol Air Qual. Res.

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Asian dust storms (ADS), coming from deserts of China and Mongolia, have serious environmental impact on particulate matter (PM) and other pollutants in Taiwan. This study selected two urban sites, Taipei and Kaohsiung, to evaluate the influence of ADS on air quality. During the ADS periods, the hourly PM 10 mass concentrations were 800 µg m -3 in Taipei and the 400 µg m -3 in Kaohsiung, which was three to five times higher than PM episodes during the non-ADS periods. By using the principal component analysis (PCA) manner, the potential sources, the dust storm contained, can be successfully identified during ADS periods. The other potential sources can be identified as vehicular emission and secondary organic aerosols from local area. There have been many studies conducted on the impact of ADS on airborne coarse particle concentration, but very few on fine particle concentration. This study focused on, using PCA for analysis and discussion, the impact of ADS on submicron particle size distribution. The results showed that there was no close relationship between the ADS and Aitken mode (30-100 nm or D 30-100 nm) or accumulation mode (from 0.1-1 µm). However, it was found that strong correlation existed between the ADS and nucleation mode (10-30 nm or D 10-30 nm). In addition, it was found that nucleation mode appeared first, followed by an air plume of dust particles twelve (12) hours later. The nucleation component from the PCA could be used as predictors of arrival time for an ADS. Taking into account the effects of meteorological conditions and employing technique of backward trajectories, PCA can be utilized as a powerful tool to better identify the source of dust storms and provide accurate results.

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Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

Cited by 182 publications

References 15 publications

Characterization of Surface Ozone Behavior at Different Regimes

Characterization of Surface Ozone Behavior at Different Regimes

Application of Neural Net Model to Estimate the Cardiovascular and Respiratory Diseases by Air Pollution Data in Urban Area

Source Apportionment of Submicron Particle Size Distribution and PM2.5 Composition during an Asian Dust Storm Period in Two Urban Atmospheres

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