Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia

In this paper, we described generation and performances of feedforward neural network model that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO3) and 8-h average ozone concentration (8hO3) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott’s Index for the validation data for 1hO3 forecasting were 0.005 μg m−3, 12.149 μg m−3, 15.926 μg m−3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and − 0.565 μg m−3, 10.101 μg m−3, 12.962 μg m−3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO3 forecasting, statistical indicators were − 1.126 μg m−3, 10.614 μg m−3, 12.962 μg m−3, 0.910, and 0.948 respectively for the station Dnevnik and − 0.001 μg m−3, 8.574 μg m−3, 10.741 μg m−3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov–Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold

Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants

Air pollution has become a threat to human life around the world since researchers have demonstrated several effects of air pollution to the environment, climate, and society. The proposed research was organized in terms of National Air Quality Index (NAQI) and air pollutants prediction using data mining algorithms for particular timeframe dataset (01 January 2019, to 01 June 2021) in the industrial eastern coastal state of India. Over half of the study period, concentrations of PM2.5, PM10 and CO were several times higher than the NAQI standard limit. NAQI, in terms of consistency and frequency analysis, revealed that moderate level (ranges 101–200) has the maximum frequency of occurrence (26–158 days), and consistency was 36%–73% throughout the study period. The satisfactory level NAQI (ranges 51–100) frequency occurrence was 4–43 days with a consistency of 13%–67%. Poor to very poor level of air quality was found 13–50 days of the year, with a consistency of 9%–25%. Random Forest (RF), Support Vector Machine (SVM), Bagged Multivariate Adaptive Regression Splines (MARS) and Bayesian Regularized Neural Networks (BRNN) are the data mining algorithms, that showed higher efficiency for the prediction of PM2.5, PM10, NO2 and SO2 except for CO and O3 at Talcher and CO at Brajrajnagar. The Root Mean Square Error (RMSE) between observed and predicted values of PM2.5 (ranges 12.40–17.90) and correlation coefficient (r) (ranges 0.83–0.92) for training and testing data indicate about slightly better prediction of PM2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM2.5 RMSE (ranges 13.06–21.66) and r (ranges 0.64–0.91) at Brajrajnagar. However, PM10 (RMSE: 25.80–43.41; r: 0.57–0.90), NO2 (RMSE: 3.00–4.95; r: 0.42–0.88) and SO2 (RMSE: 2.78–5.46; r: 0.31–0.88) at Brajrajnagar are better than PM10 (RMSE: 35.40–55.33; r: 0.68–0.91), NO2 (RMSE: 4.99–9.11; r: 0.48–0.92), and SO2 (RMSE: 4.91–9.47; r: 0.20–0.93) between observed and predicted values of training and testing data at Talcher using RF, SVM, bagged MARS and BRNN models, respectively. Taylor plots demonstrated that these algorithms showed promising accuracy for predicting air quality. The findings will help scientific community and policymakers to understand the distribution of air pollutants to strategize reduction in air pollution and enhance air quality in the study region

Comparative Performance of Different Statistical Models for Predicting Ground-Level Ozone (O3) and Fine Particulate Matter (PM2.5) Concentrations in Montréal, Canada

Ground-level ozone (O3) and fine particulate matter (PM2.5) are two air pollutants known to reduce visibility, to have damaging effects on building materials and adverse impacts on human health. O3 is the result of a series of complex chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of solar radiation. PM is a class of airborne contaminants composed of sulphate, nitrate, ammonium, crustal components and trace amounts of microorganisms. PM2.5 is the respirable subgroup of PM having an aerodynamic diameter of less than 2.5 μm. Development of effective forecasting models for ground-level O3 and PM2.5 is important to warn the public about potentially harmful or unhealthy concentration levels. The objectives of this study is to investigate the applicability of Multiple Linear Regression (MLR), Principle Component Regression (PCR), Multivariate Adaptive Regression Splines (MARS), feed-forward Artificial Neural Networks (ANN) and hybrid Principal Component – Artificial Neural Networks (PC-ANN) models to predict concentrations of O3 and PM2.5 in Montréal (Canada). Air quality and meteorological data is obtained from the Réseau de surveillance de la qualité de l’air (RSQA) for the Airport Station (45°28′N, 73°44′W) and the Maisonneuve Station (45°30′N, 73°34′W) for the period January 2004 to December 2007. Air pollution data include concentration values for nitrogen monoxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO) and 142 different volatile organic compounds. Meteorological data include solar irradiation (SR), temperature (Temp), pressure (Press), dew point (DP), precipitation (Precip), wind speed (WS) and wind direction (WD). Analysis of the available volatile organic compound data expressed on a propylene-equivalent concentration indicated that m/p-xylene, toluene, propylene and (1,2,4)-trimethylbenzene were species with the most significant ozone forming potential in the study area. Different models and architectures have been investigated through five case studies. Predictive performances of each model have been measured by means of performance metrics and forecast success rates. Overall, MARS models allowing second order interaction of independent basis functions yielded lower error, higher correlation and higher forecast success rates. This study indicates that models based on statistical methods can be cost-effective tools to forecast ground-level O3 and PM2.5 in Montréal and to provide support for decision makers in protecting human health

Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US)

This study developed 12 prediction models using two types of data matrix (daily means and a selection of the mean for the first 6 h of the day). The Persistence parametric prediction technique was applied separately to these matrices, as well as semiparametric Ridge Regression and three non-parametric or artificial intelligence techniques: Support Vector Machine, Multilayer Perceptron and ELMAN networks. The target was the prediction of maximum tropospheric ozone concentrations for the next day in the Mexicali-Calexico border area. The main ozone precursors and meteorological parameters were used for the different models. The proposals were evaluated using specific performance measurements for the air quality models established in the Model Validation Kit and recommended by the US Environmental Protection Agency. Results with similar margins of error were obtained in various models developed in this study, and some of them have provided smaller margins of error than similar prediction models existing in the literature developed in other regions. For this reason, we consider it feasible to apply the prediction models developed and they could be useful for supporting decisions in the matter of ozone pollution in the region under study, as well as for use in daily forecasting in this area. © 2007 Elsevier Ltd. All rights reserved