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Updated: Feb 28, 2026

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
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Probabilistic forecasting for extreme NO2 pollution episodes.

José L Aznarte1

  • 1Artificial Intelligence Department, Universidad Nacional de Educación a Distancia - UNED, c/ Juan del Rosal, 16, Madrid, Spain.

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|June 13, 2017
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Quantile regression effectively predicts extreme nitrogen dioxide (NO2) concentrations by modeling the full probability distribution. This probabilistic approach offers accurate, reliable forecasts superior to traditional methods.

Keywords:
Air qualityMadridNitrogen dioxideProbabilistic forecastingQuantile regression

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Statistical Modeling

Background:

  • Accurate prediction of extreme air pollutant concentrations, such as nitrogen dioxide (NO2), is crucial for public health and environmental management.
  • Traditional point-forecasting methods provide single values, limiting the ability to assess risk associated with high pollution events.
  • Probabilistic forecasting offers a more comprehensive understanding of potential pollutant levels.

Purpose of the Study:

  • To evaluate the utility of quantile regression for predicting extreme NO2 concentrations.
  • To compare the performance of quantile regression against standard point-forecasting techniques.
  • To develop a method for calculating exceedance probabilities for air quality thresholds.

Main Methods:

  • Utilized quantile regression to model the probability distribution of NO2 concentrations.
  • Incorporated meteorological data alongside NO2 measurements from Madrid.
  • Analyzed the importance of different predictor variables across various quantiles.

Main Results:

  • Quantile regression models demonstrated superior accuracy, reliability, and sharpness in predicting extreme NO2 concentrations compared to point-forecasting.
  • Identified distinct sets of important predictor variables for median versus upper quantiles.
  • Developed and validated a method for computing probabilities of exceeding predefined thresholds.

Conclusions:

  • Quantile regression is a convenient and effective tool for probabilistic forecasting of extreme NO2 events.
  • Understanding variable importance across quantiles provides nuanced insights into pollution drivers.
  • The proposed exceedance probability method enhances the practical application of probabilistic air quality forecasts.