Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations

Abstract

Abstract Established land use regression (LUR) techniques such as linear regression utilize extensive selection of predictors and functional form to fit a model for every data set on a given pollutant. In this paper, an alternative to established LUR modeling is employed, which uses additive regression smoothers. Predictors and functional form are selected in a data-driven way and ambiguities resulting from specification search are mitigated. The approach is illustrated with nitrogen dioxide (NO2) data from German monitoring sites using the spatial predictors longitude, latitude, altitude and structural predictors; the latter include population density, land use classes, and road traffic intensity measures. The statistical performance of LUR modeling via additive regression smoothers is contrasted with LUR modeling based on parametric polynomials. Model evaluation is based on goodness of fit, predictive performance, and a diagnostic test for remaining spatial autocorrelation in the error terms. Additionally, interpretation and counterfactual analysis for LUR modeling based on additive regression smoothers are discussed. Our results have three main implications for modeling air pollutant concentration levels: First, modeling via additive regression smoothers is supported by a specification test and exhibits superior in- and out-of-sample performance compared to modeling based on parametric polynomials. Second, different levels of prediction errors indicate that NO2 concentration levels observed at background and traffic/industrial monitoring sites stem from different processes. Third, accounting for agglomeration and infrastructure effects is important: NO2 concentration levels tend to increase around major cities, surrounding agglomeration areas, and their connecting road traffic network.