气候变化领域集成服务门户(CCPortal): Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations

CCPortal

DOI	10.1016/j.atmosenv.2021.118337
	Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations
	Fritsch M.; Behm S.
发表日期	2021
ISSN	1352-2310
卷号	253
英文摘要	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. © 2021 Elsevier Ltd
关键词	Additive regression smoothers Counterfactual analysis Exposure to air pollution Land use regression Nitrogen dioxide Spatial cross-validation
语种	英语
scopus关键词	Additives; Agglomeration; Land use; Population statistics; Regression analysis; Roads and streets; Specifications; Additive regression; Additive regression smoother; Counterfactual analyse; Exposure to air pollution; Land use regression; Land-use regression models; Model-based OPC; Nitrogen dioxides; NO $-2$; Spatial cross validations; Nitrogen oxides; nitrogen dioxide; agglomeration; atmospheric modeling; atmospheric pollution; concentration (composition); data set; estimation method; infrastructure; land use change; pollution monitoring; regression analysis; road traffic; air pollutant; air pollution; Article; construction work and architectural phenomena; controlled study; cross validation; data science; diagnostic test; evaluation study; Germany; land use; parametric test; population density; predictive value; predictor variable; priority journal; regression analysis; spatial autocorrelation analysis; traffic; Germany
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248476
作者单位	Chair of Statistics and Data Analytics, University of Passau, Germany
推荐引用方式 GB/T 7714	Fritsch M.,Behm S.. Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations[J],2021,253.
APA	Fritsch M.,&Behm S..(2021).Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations.ATMOSPHERIC ENVIRONMENT,253.
MLA	Fritsch M.,et al."Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations".ATMOSPHERIC ENVIRONMENT 253(2021).