A B S T R A C TIn the study, a long-term set of data collected at the air monitoring station located in Lodz (central Poland) was analysed. Two air pollutants-O 3 and CO-were chosen in order to carry out the prediction procedure. The prediction was performed using regression neural networks. The modelled concentrations were compared to the actual ones in order to assess the prediction accuracy. Approximation errors were calculated for the entire range of concentrations and also separately for several concentration sub ranges. The following measures of error were considered: the mean absolute error, the mean squared error, the root mean squared error, the mean absolute relative error, Pearson's correlation coefficient and Willmott's indexes of agreement. Values of errors and their variabilities in different ranges were analysed. It was stated that only some error measures properly reflect the difficulties in modelling concentrations in the entire range of concentrations as well as in different sub ranges of concentrations. The use of a single error measure may lead to incorrect interpretation.
Long-term collection of data, recorded at several air monitoring stations located in Central Poland, was analyzed. The main objective of the analysis was to choose optimum modelling methods for concentration of specified air pollutants. For this purpose accuracies of various groups of autonomous models were compared. Prediction of any air pollutants was performed using three different modelling methods. The modelled value was instantaneous concentration of specified pollutant. The models varied in the number and type of the explanatory variables and the modelling technique. It was presumed that there is a need for modelling the measurement gap, comprising a selected extract of the time series of a chosen pollutant. For successive cases in the gap, prediction errors of various methods of modelling were compared.
Combustion of energy fuels or organic waste is associated with the emission of harmful gases and aerosols into the atmosphere, which strongly affects air quality. Air quality monitoring devices are unreliable and measurement gaps appear quite often. Missing data modeling techniques can be used to complete the monitoring data. Concentrations of monitored pollutants can be approximated with regression modeling tools, such as artificial neural networks. In this study, a long-term set of data from the air monitoring station in Zabrze (Silesia, South Poland) was analyzed. Concentration prediction was tested for the main air pollutants, i.e., O3, NO, NO2, SO2, PM10, CO. Multilayer perceptrons were used to model the concentrations. The predicted concentrations were compared to the observed ones to evaluate the approximation accuracy. Prediction errors were calculated separately for the whole concentration range as well as for the specified concentration subranges. Some different measures of error were estimated. It was stated that the use of a single measure of the approximation accuracy may lead to incorrect interpretation. The application of one neural network to the entire concentration range results in different prediction accuracy in various concentration subranges. Replacing one neural network with several networks adjusted to specific concentration subranges should improve the modeling accuracy.
The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations.
Air quality is assessed on the basis of air monitoring data. Monitoring data are often not complete enough to carry out an air quality assessment. To fill the measurement gaps, predictive models can be used, which enable the approximation of missing data. Prediction models use historical data and relationships between measured variables, including air pollutant concentrations and meteorological factors. The known predictive air quality models are not accurate, so it is important to look for models that give a lower approximation error. The use of artificial neural networks reduces the prediction error compared to classical regression methods. In previous studies, a single regression model over the entire concentration range was used to approximate the concentrations of a selected pollutant. In this study, it was assumed that not a single model, but a group of models, could be used for the prediction. In this approach, each model from the group was dedicated to a different sub-range of the concentration of the modeled pollutant. The aim of the analysis was to check whether this approach would improve the quality of modeling. A long-term data set recorded at two air monitoring stations in Poland was used in the examination. Hourly data of basic air pollutants and meteorological parameters were used to create predictive regression models. The prediction errors for the sub-range models were compared with the corresponding errors calculated for one full-range regression model. It was found that the application of sub-range models reduced the modeling error of basic air pollutants.
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