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

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.

Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

“…The results show higher levels of pollution in the city centre as expected, as can also be confirmed by similar studies in other urban areas [55,66]. For London in particular, our results can also be validated with those presented in [67] where PM 2.5 distribution is studied and higher levels of pollution are concentrated in central and inner London, as well as major road arteries.…”

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

“…In this paper, NO 2 concentrations are analysed from the AQMN in Sheffield. In the first part of the paper, spatial variability of NO 2 concentrations (µg/m 3 ) is analysed using three modelling approaches: kriging interpolation, Airviro dispersion model [13] and LUR model [12]. In the second part, NO 2 concentrations measured by various sensors are fused with the NO 2 concentrations estimated by both Airviro and LUR models.…”

“…A summary of the NO 2 data from the network is provided in Table 1. Further details on the network can be found in [12].…”

“…The LUR map used here is adopted from Munir et al [12]. LUR is a widely employed approach for the estimation of air pollution exposure using geographical information systems (GIS) and sta tistical analysis to determine the association between geographical features and measured atmos pheric pollutant concentrations [15].…”

“…In this study, firstly, the maps produced by the LUR and Airviro dispersion models are compared for analysing the spatial variability of NO 2 concentrations in Sheffield [12,13]. Secondly, using the universal kriging technique, modelled NO 2 concentrations are fused with the measured concentrations in a view to further improve the spatial maps of NO 2 .…”