Evaluation of Variations in Ground-Level Ozone (O<sub>3</sub>) Concentrations

Souza, Amaury de; Aristone, Flávio; Arsić, Milica; Kumar, Ujjwal

doi:10.1080/01919512.2017.1398633

Cited by 5 publications

(10 citation statements)

References 27 publications

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“…Although the summer level of ozone is of interest due to high temperature and speed of ozone formation (in correlation with NO x and VOC) [6,19,20,24,43], during the survey, the ozone value was the highest recorded during winter and spring. The reason for this is emission of air pollutants and high values NO x and VOC in Banja Luka.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Ilić

Popović

Markić³

2020

Ecological Chemistry and Engineering S

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The paper presents results of the measurements of the tropospheric ozone (O3) concentration and meteorological parameters: temperature, air pressure, relative humidity, speed and wind direction. The data were collected from January 2016 to December 2016 at station located in locality Centre (Banja Luka), Republic of Srpska, Bosnia and Herzegovina. Ozone is one of the most harmful pollutants to plants and health and highly reactive secondary pollutant. The present study covers investigation of the relationship between the concentration of ozone and meteorological parameters as well as time variations of ozone concentration (by hours, months, seasons). This topic has not been studied up to now in this region, although the recent research data indicates that there is a correlation between them and previously obtained from the world’s relevant scientific centres, as already cited above. Statistical analysis confirms string of rolls, which shows directional connection between tropospheric ozone and meteorological parameters, specially temperature (r = 0.148), air pressure (r = –0.292) and relative humidity (r = –0.292). These parameters are the most important meteorological factors influencing the variation in ozone levels during the research. The correlation ozone concentrations with speed and direction of wind is not significant, like other parameters.

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Section: Resultsmentioning

confidence: 99%

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Ilić

Popović

Markić³

2020

Ecological Chemistry and Engineering S

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“…In addition, the decomposition process and ozone concentration are influenced by meteorological factors with very dynamic changes (Wasi’ah and Driejana 2017 ). Air temperature, solar radiation, and air pressure can increase ozone formation, while air humidity can reduce ozone concentrations (Souza et al 2018 ). In addition, the ozone concentration in the future also tends to be correlated or influenced by the ozone concentration in the past.…”

Section: Introductionmentioning

confidence: 99%

Using Harris hawk optimization towards support vector regression to ozone prediction

Kurniawan¹,

Setiawan

Caraka

et al. 2022

Stoch Environ Res Risk Assess

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As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimizationsupport vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R 2 ), Variance Ratio (VR), and Diebold-Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R 2 around 0.97, and VR around 0.98. Then, the results of the Diebold-Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.

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“…The concentration of photochemical oxidizers may be reduced throughout the control of their precursors, which are nitrogen oxides NOx (NO and NO2) and VOCs. [2][3][4][5][6][7][8] In Campo Grande, some studies and climate monitoring campaigns have been carried out, 2,[9][10][11][12][13][14][15][16][17] for studying the atmospheric dispersion modelling to explore the results of climate change.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions assessment for modeling gas concentration in Campo Grande, MS, Brazil

Souza¹,

Olaofe²,

Kodicherla³

et al. 2018

ECB

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The predominant air pollutants in urban cities are (NOx = (NO + NO2), O3 and (OX = (O3 + NO2). This research focused on pollutant variables that cause damage to human health as well as to the environment. Thus, seven statistical models {Weibull (W), Gamma (G), Lognormal (L), Frechet (Fr), Burr (Bur), Rayleigh (R) and Rician (Ri)} were chosen to fit the observations of the air pollutants. An average hourly data from one year to 2015 were considered. In addition, performance indicators {Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE)} were applied, to determine the quality criteria for adjustment of the frequency distributions. The best distribution that adapts to the observations of the variables was the RICIAN distribution, the log-normal distribution for COD. The probabilities of the concentration of exceedances were calculated,(predicted) from the cumulative density function (cdf) obtained from the best fit distributions.

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Evaluation of Variations in Ground-Level Ozone (O₃) Concentrations

Cited by 5 publications

References 27 publications

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Using Harris hawk optimization towards support vector regression to ozone prediction

Probability distributions assessment for modeling gas concentration in Campo Grande, MS, Brazil

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Evaluation of Variations in Ground-Level Ozone (O3) Concentrations

Cited by 5 publications

References 27 publications

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Assessment of Meteorological Effects and Ozone Variation in Urban Area

Using Harris hawk optimization towards support vector regression to ozone prediction

Probability distributions assessment for modeling gas concentration in Campo Grande, MS, Brazil

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Evaluation of Variations in Ground-Level Ozone (O₃) Concentrations