Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series

Abstract

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 NO2 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: R2 = 76.135%, the correlation between the actual and predicted values (rap) 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, rap = 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 (R2 = 0.996), whereas, on the test set, the results were not very good (R2 = 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 O3.

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

“…Interquartile range (IQR) is a commonly used method for outlier detection whose principle is to arrange a group of observation data from smallest to largest and then divide them into quartiles. The data in the 25th and 75th percentiles represent the lower and upper quartiles, respectively and the difference between them represents the IQR [39,40], which can be expressed as…”

Assessing Benzene and TVOC Pollution and the Carcinogenic and Noncarcinogenic Risks to Workers in an Industrial Plant in Southeastern Romania

“…In Romania, special attention is paid to aligning with the EU Directive on air quality. Therefore, the study of atmospheric pollution (as a first step in taking measures to diminish it based on informed insight) and its impact on population health has become a topic of interest for scientists [ 6 , 7 , 8 , 9 , 48 , 49 , 50 , 51 ].…”

“…In Romania, special attention is paid to aligning with the EU Directive on air quality. Therefore, the study of atmospheric pollution (as a first step in taking measures to diminish it based on informed insight) and its impact on population health has become a topic of interest for scientists [6][7][8][9][48][49][50][51]. This research's aim is twofold: (1) to assess VOC pollution levels from the secondbiggest refinery in Romania and (2) to evaluate the noncarcinogenic and carcinogenic risks of benzene and TVOC exposure to the workers.…”

On the Spatio-Temporal Characteristics of Aerosol Optical Depth in the Arabian Gulf Zone

“…Multivariate statistical analysis became one of the most utilized tools for extracting the common characteristics of big data sets issued from environmental sciences [34][35][36][37][38]. The spatio-temporal analysis of AOD is mainly performed by Principal Component Analysis (PCA, also named EOF-Empirical Orthogonal Functions), non-negative matrix factorization (NMF), and combined Principal Component Analysis (CPCA).…”