National Land Use Regression Model for NO2Using Street View Imagery and Satellite Observations

Meng Qi, Kuldeep Dixit, Julian D. Marshall, Wenwen Zhang, Steve Hankey

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO2. Our results suggest that street view imagery alone may provide sufficient information to explain NO2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI) a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

Original languageEnglish (US)
Pages (from-to)13499-13509
Number of pages11
JournalEnvironmental Science and Technology
Volume56
Issue number18
DOIs
StatePublished - Sep 20 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Environmental Chemistry

Keywords

  • air quality
  • computer vision
  • empirical models
  • exposure assessment
  • image sampling and processing
  • machine learning

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