Exploring nitrogen dioxide spatial concentration via physics-informed multiple quantile regression

Keywords

Statistical learning
Geosciences/Protection of Land and Water Resources
Code:
32/2025
Title:
Exploring nitrogen dioxide spatial concentration via physics-informed multiple quantile regression
Date:
Wednesday 28th May 2025
Author(s):
De Sanctis, M.F.; Di Battista, I.; Arnone, E.; Castiglione, C.; Palummo, A.; Bernardi, M.; Ieva, F.; Sangalli, L.M.
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Abstract:
Understanding the spatial distribution of air pollutants, such as nitrogen dioxide (NO2), is crucial for assessing environmental and health impacts, particularly in densely populated and industrialized regions. This paper introduces a novel method for estimating multiple spatial quantiles, ensuring the monotonicity of the resulting estimates. The proposed model builds upon recent advancements in quantile regression, and incorporates physical information of the phenomenon under analysis, to address the challenges posed by anisotropy, non-stationarity and skewness, typically observed in environmental data. For instance, in the study of air pollutants concentration, the model permits the inclusion of information concerning air-circulation, and in particular the physics of wind streams, which strongly influences the pollutant concentration. Moreover, the monotone estimation of the quantile maps yields a fully nonparametric reconstruction of the pollutant probability density function, at any spatial location. This in turn enables the construction of probability maps, that quantify the likelihood of exceeding regulatory thresholds set by policymakers, offering valuable information for environmental monitoring policies, aimed at mitigating the adverse effects of air pollution.
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Published in Environmental and Ecological Statistics, 2025 https://doi.org/10.1007/s10651-025-00664-8