A hybrid approach to predict daily NO2 concentrations at city block scale

Xueying Zhang, Allan C. Just, Hsiao Hsien Leon Hsu, Itai Kloog, Matthew Woody, Zhongyuan Mi, Johnathan Rush, Panos Georgopoulos, Robert O. Wright, Annemarie Stroustrup

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015–2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75–0.79, RMSE: 3.9–4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.

Original languageEnglish (US)
Article number143279
JournalScience of the Total Environment
Volume761
DOIs
StatePublished - Mar 20 2021

All Science Journal Classification (ASJC) codes

  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Environmental Chemistry

Keywords

  • Air pollution
  • Dispersion model
  • Land use regression
  • Nitrogen dioxide

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