Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series
Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period....
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MDPI AG
2022-03-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/4/558 |
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| author | Alina Bărbulescu Cristian Stefan Dumitriu Iulia Ilie Sebastian-Barbu Barbeş |
| author_facet | Alina Bărbulescu Cristian Stefan Dumitriu Iulia Ilie Sebastian-Barbu Barbeş |
| author_sort | Alina Bărbulescu |
| collection | DOAJ |
| description | Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO<sub>2</sub> series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R<sup>2</sup> = 76.135%, the correlation between the actual and predicted values (r<sub>ap</sub>) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, r<sub>ap</sub> = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R<sup>2</sup> = 0.996), whereas, on the test set, the results were not very good (R<sup>2</sup> = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O<sub>3.</sub> |
| first_indexed | 2024-03-09T11:09:42Z |
| format | Article |
| id | doaj.art-0438f1b6778e4582a6438f784d0ec5e4 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T11:09:42Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-0438f1b6778e4582a6438f784d0ec5e42023-12-01T00:46:35ZengMDPI AGAtmosphere2073-44332022-03-0113455810.3390/atmos13040558Influence of Anomalies on the Models for Nitrogen Oxides and Ozone SeriesAlina Bărbulescu0Cristian Stefan Dumitriu1Iulia Ilie2Sebastian-Barbu Barbeş3Department of Civil Engineering, Transilvania University of Brașov, 5 Turnului Str., 900152 Brașov, RomaniaSC Utilnavorep SA, 55, Aurel Vlaicu Av., 900055 Constanta, RomaniaSiemens Romania, 13A, Bd. Garii, 900055 Brașov, RomaniaDoctoral School of Civil Engineering, Technical University of Civil Engineering, 122-124 Lacul Tei Bvd., 020396 Bucharest, RomaniaNowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO<sub>2</sub> series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R<sup>2</sup> = 76.135%, the correlation between the actual and predicted values (r<sub>ap</sub>) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, r<sub>ap</sub> = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R<sup>2</sup> = 0.996), whereas, on the test set, the results were not very good (R<sup>2</sup> = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O<sub>3.</sub>https://www.mdpi.com/2073-4433/13/4/558aberrant valuesnitrogen oxidesARIMAGRNNARIMA–GRNNisolation forest |
| spellingShingle | Alina Bărbulescu Cristian Stefan Dumitriu Iulia Ilie Sebastian-Barbu Barbeş Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series Atmosphere aberrant values nitrogen oxides ARIMA GRNN ARIMA–GRNN isolation forest |
| title | Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series |
| title_full | Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series |
| title_fullStr | Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series |
| title_full_unstemmed | Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series |
| title_short | Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series |
| title_sort | influence of anomalies on the models for nitrogen oxides and ozone series |
| topic | aberrant values nitrogen oxides ARIMA GRNN ARIMA–GRNN isolation forest |
| url | https://www.mdpi.com/2073-4433/13/4/558 |
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