ArticleEnvironmental science & technology2022
A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements.
Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Hybrid Transfer Learning Approach for Ambient PMACS ES&T air · 2026Article
- Influence of on-road mobile monitoring design on ultrafine particle exposure models and cognitive health inferences.Journal of exposure science & environmental epidemiology · 2026Article
- Ambient PMCurrent environmental health reports · 2026Review
- Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images.Environmental science & technology · 2025Article
- Optimizing Air Pollution Exposure Assessment with Application to Cognitive Function.Research report (Health Effects Institute) · 2025Article
- High-Resolution Geospatial Database: National Criteria-Air-Pollutant Concentrations in the Contiguous U.S., 2016-2020.Geoscience data journal · 2025Article
- Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.Environmental science & technology · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO
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