Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE
Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022038725 |
| _version_ | 1811161849027100672 |
|---|---|
| author | Aishah Al Yammahi Zeyar Aung |
| author_facet | Aishah Al Yammahi Zeyar Aung |
| author_sort | Aishah Al Yammahi |
| collection | DOAJ |
| description | Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019–2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from “very good” (MAPE of 8.64% at the Liwa station with the closed loop) to “acceptable” (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values. |
| first_indexed | 2024-04-10T06:21:47Z |
| format | Article |
| id | doaj.art-85bc2a965aed46d8ba25b74555568458 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:21:47Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-85bc2a965aed46d8ba25b745555684582023-03-02T04:59:44ZengElsevierHeliyon2405-84402023-02-0192e12584Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAEAishah Al Yammahi0Zeyar Aung1Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Corresponding author.Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Center for Catalysis and Separation (CeCaS), Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesNitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019–2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from “very good” (MAPE of 8.64% at the Liwa station with the closed loop) to “acceptable” (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.http://www.sciencedirect.com/science/article/pii/S2405844022038725Machine learningARIMASARIMALSTMNARClassical statistics |
| spellingShingle | Aishah Al Yammahi Zeyar Aung Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE Heliyon Machine learning ARIMA SARIMA LSTM NAR Classical statistics |
| title | Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE |
| title_full | Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE |
| title_fullStr | Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE |
| title_full_unstemmed | Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE |
| title_short | Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE |
| title_sort | forecasting the concentration of no2 using statistical and machine learning methods a case study in the uae |
| topic | Machine learning ARIMA SARIMA LSTM NAR Classical statistics |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022038725 |
| work_keys_str_mv | AT aishahalyammahi forecastingtheconcentrationofno2usingstatisticalandmachinelearningmethodsacasestudyintheuae AT zeyaraung forecastingtheconcentrationofno2usingstatisticalandmachinelearningmethodsacasestudyintheuae |