A Nonlinear Land Use Regression Approach for Modelling NO2 Concentrations in Urban Areas—Using Data from Low-Cost Sensors and Diffusion Tubes

Said Munir,Lyudmila S Mihaylova,Martin Mayfield,Daniel Coca

doi:10.3390/atmos11070736

Said Munir, Lyudmila S Mihaylova + Show 2 more

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https://doi.org/10.3390/atmos11070736

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Journal: Atmosphere	Publication Date: Jul 11, 2020
Citations: 6	License type: CC BY 4.0

Affiliation: University of Sheffield

Abstract

Land Use Regression (LUR) based on multiple linear regression model is one of the techniques used most frequently for modelling the spatial variability of air pollution and assessing exposure in urban areas. In this paper, a nonlinear generalised additive model is proposed for LUR and its performance is compared to a linear model in Sheffield, UK for the year 2019. Pollution models were estimated using NO2 measurements obtained from 188 diffusion tubes and 40 low-cost sensors. Performance of the models was assessed by calculating several statistical metrics including correlation coefficient (R) and root mean square error (RMSE). High resolution (100 m × 100 m) maps demonstrated higher levels of NO2 in the city centre, eastern side of the city and on major roads. The results showed that the nonlinear model outperformed the linear counterpart and that the model estimated using NO2 data from diffusion tubes outperformed the models using data from low-cost sensors or both low-cost sensors and diffusion tubes. The proposed method provides a basis for further application of advanced nonlinear modelling approaches to constructing LUR models in urban areas which enable quantifying small scale variability in pollution levels.

Highlights

Air pollution is one of the serious environmental issues that affects human health and may cause mortality
In this study both multiple linear regression models (MLRM) and Generalised Additive Model (GAM) are employed to model the spatial variability of NO2 concentrations in Sheffield
Both GAM and MLRM were fitted using all predictor variables first, and stepwise regression was used for model specification aiming to select only those predictors which had a significant effect

Summary

Introduction

Air pollution is one of the serious environmental issues that affects human health and may cause mortality. Several air pollutants are shown to have negative impacts on human health, among gaseous pollutants nitrogen dioxide (NO2 ) is considered the most serious pollutant causing both chronic and acute respiratory diseases including asthma, hospital admission and mortality [2]. It is important to carry out different monitoring and modelling investigations to analyse its spatial variability, especially micro-level variability. This could be done by developing high resolution maps in urban areas that help in understanding the main drivers of NO2 levels and quantifying exposure to elevated NO2 concentrations in urban areas.

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