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

• Published in: Atmosphere Vol. 11; no. 7; p. 736
• Main Authors: Munir, Said, Mayfield, Martin, Coca, Daniel, Mihaylova, Lyudmila S
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2020
• Subjects: Additives; Air pollution; air quality modelling; City centres; Correlation coefficient; Correlation coefficients; Diffusion; Geographic information systems; Geography; Interpolation; Land use; land use regression; Low cost; low-cost sensors; Modelling; Nitrogen dioxide; nonlinear regression; Outdoor air quality; Pollutants; Pollution levels; Population density; Regression models; Roads & highways; Root-mean-square errors; Sensors; Sheffield; spatial analysis; Spatial variability; Spatial variations; Statistical analysis; Tubes; Urban areas; United Kingdom > UK
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos11070736

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.