Forecasting of Surface Ozone Concentration by Using Artificial Neural Networks in Rural and Urban Areas in Central Poland

Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models

Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.

Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology

To optimize the accuracy of ozone (O3) concentration prediction, this paper proposes a combined prediction model of O3 hourly concentration, FC-LsOA-KELM, which integrates multiple machine learning methods. The model has three parts. The first part is the feature construction (FC), which is based on correlation analysis and incorporates time-delay effect analysis to provide a valuable feature set. The second part is the kernel extreme learning machine (KELM), which can establish a complex mapping relationship between feature set and prediction object. The third part is the lioness optimization algorithm (LsOA), which is purposed to find the optimal parameter combination of KELM. Then, we use air pollution data from 11 cities on Fenwei Plain in China from 2 January 2015 to 30 December 2019 to test the validity of FC-LsOA-KELM and compare it with other prediction methods. The experimental results show that FC-LsOA-KELM can obtain better prediction results and has a better performance.

Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania

This study focuses on the Vilnius (capital of Lithuania) agglomeration, which is facing the issue of air pollution resulting from the city’s physical expansion. The increased number of industries and vehicles caused an increase in the rate of fuel consumption and pollution in Vilnius, which has rendered air pollution control policies and air pollution management more significant. In this study, the differences in the pollutants’ means were tested using two-sided t-tests. Additionally, a 2-layer artificial neural network and a pollution data were both used as tools for predicting and warning air pollution after loop traffic has taken effect in Vilnius Old Town from July of 2020. Highly accurate data analysis methods provide reliable data for predicting air pollution. According to the validation, the multilayer perceptron network (MLPN1), with a hyperbolic tangent activation function with a 4-4-2 partition, produced valuable results and identified the main pollutants affecting and predicting air quality in the Old Town: maximum concentration of sulphur dioxide per 1 hour (SO2_1 h, normalized importance = 100%); carbon monoxide (CO) was the second pollutant with the highest indication of normalized importance, equalling 59.0%.

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 models 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.

The Effect of Emission Inventory on Modelling of Seasonal Exposure Metrics of Particulate Matter and Ozone with the WRF-Chem Model for Poland

In Poland, high concentrations of particulate matter (with a diameter smaller than 2.5 or 10 μm) exceeding the WHO threshold values are often measured in winter, while ozone (O3) concentrations are high in spring. In winter high PM2.5 and PM10 concentrations are linked to high residential combustion and road transport. The main objective of this study was to assess performance of the Weather Research and Forecasting model with Chemistry (WRF-Chem) model in reproducing observations for a period of 2017-2018 covering various meteorological conditions. We compare modelled and observed exposure metrics for PM2.5, PM10 and O3 for two sets of the WRF-Chem model runs: with coarse and fine resolution emission inventory (European Monitoring and Evaluation Programme (EMEP) and Chief Inspectorate of Environmental Protection (CIEP), respectively). CIEP run reduces the negative bias of PM2.5 and PM10 and improves the model performance for number of days with exceedance of WHO (World Health Organization) threshold for PM2.5 and PM10 24-h mean concentration. High resolution emission inventory for primary aerosols helps to better distinguish polluted urban areas from non-urban ones. There are no large differences for the model performance for O3 and secondary inorganic aerosols, and high-resolution emission inventory does not improve the results in terms of 8-h rolling mean concentrations of ozone.

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.