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Related Experiment Video

Updated: Aug 9, 2025

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
07:14

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Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE.

Aishah Al Yammahi1, Zeyar Aung1,2

  • 1Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

Heliyon
|February 16, 2023
PubMed
Summary

Accurate prediction of nitrogen dioxide (NO2) concentrations is crucial for air quality management. This study found that open-loop machine learning models generally provide better NO2 predictions than closed-loop models in the UAE.

Keywords:
ARIMAClassical statisticsLSTMMachine learningNARNO2SARIMA

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Nitrogen dioxide (NO2) is a key industrial pollutant linked to human activities.
  • NO2 concentrations are influenced by factors like industrial emissions and public health policies, as seen during the COVID-19 lockdown.
  • Accurate NO2 monitoring is vital for environmental regulation and public health protection.

Purpose of the Study:

  • To predict NO2 concentrations in the United Arab Emirates (UAE) using advanced statistical and machine learning models.
  • To evaluate the performance of different modeling architectures (open-loop vs. closed-loop) for NO2 forecasting.
  • To assess the impact of the COVID-19 lockdown on NO2 levels and prediction accuracy.

Main Methods:

  • Utilized statistical models like Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA).
  • Employed machine learning models including Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Neural Network (NAR-NN).
  • Compared open-loop and closed-loop prediction architectures, evaluating performance using Mean Absolute Percentage Error (MAPE).

Main Results:

  • NO2 concentrations were predicted for 14 ground stations in the UAE during December 2020.
  • Open-loop models generally outperformed closed-loop models, showing significantly lower MAPE values.
  • Model performance varied across stations, with MAPE ranging from 8.64% to 42.45%, and MAPE correlated with NO2 concentration variability.

Conclusions:

  • Machine learning models, particularly in an open-loop configuration, offer effective tools for NO2 concentration prediction in the UAE.
  • The findings highlight the importance of model architecture selection for accurate air pollution forecasting.
  • Understanding NO2 variability is key to improving the reliability of air quality predictions and informing environmental policy.