Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. L...

Erratum: “Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles”

Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles

BACKGROUND Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES We aimed to develop Europ...

Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport: a land use regression modeling study

BACKGROUND There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the im...

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Heavy air pollution, especially fine particulate matter (PM2.5), poses serious challenges to environmental sustainability in Beijing. Epidemiological studies and the identification of measures for preventing serious air pollution both require accurate PM2.5 spatial distribution data. Land use regression (LUR) models are promising for estimating the spatial distribution of PM2.5 at a high spatia...