Approximation of imission level at air monitoring stations by means of autonomous neural models

Hoffman, Szymon

doi:10.37190/epe120210

Cited by 3 publications

(5 citation statements)

References 2 publications

(2 reference statements)

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“…In the past, classical regression or autoregressive methods were used [38][39][40]. Since the 1990s, artificial intelligence techniques have been increasingly used to model air pollution concentrations [41][42][43][44][45][46][47][48][49][50]. The most popular were models that provide prediction without any data from outside of the monitoring system.…”

Section: Introductionmentioning

confidence: 99%

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Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Hoffman,

Jasiński,

Baran

2024

Energies

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Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

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Section: Introductionmentioning

confidence: 99%

“…The most popular were models that provide prediction without any data from outside of the monitoring system. They are sometimes called autonomous models [48,49]. Very complex neural models, including deep learning methods, are increasingly used to predict air pollution concentrations [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Hoffman,

Jasiński,

Baran

2024

Energies

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“…Missing data can be supplemented by introducing modeled concentrations in the measurement gaps [ 24 , 25 , 26 , 27 ]. The time series data obtained with air monitoring have specific characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…If historical data from the selected air monitoring station are available, they can be used to explore the knowledge hidden in them. Autonomous models of this type can be accurate and have a very significant advantage: the approximation of concentrations does not require external data from outside the monitoring system [ 26 , 27 ]. In the first models, classical statistical regression techniques were used [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Air Quality Modeling with the Use of Regression Neural Networks

Hoffman

Filak

Jasiński

2022

IJERPH

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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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“…The most popular are modeling techniques, which provide prediction without recourse to any data coming from outside of the monitoring system. Such models may be called autonomous models [18,19]. Concentrations of pollutants measured at air monitoring stations can be modeled using prediction techniques based on regression analysis, time-series analysis, or other statistical methods [20,21].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Prediction Error in Regression Air Quality Models

Hoffman

2021

Energies

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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.

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Approximation of imission level at air monitoring stations by means of autonomous neural models

Cited by 3 publications

References 2 publications

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Air Quality Modeling with the Use of Regression Neural Networks

Estimation of Prediction Error in Regression Air Quality Models

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