Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods-Reference-Cited by-同舟云学术

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Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods

Published:2023-07 Issue: Volume:228 Page:115836
ISSN:0013-9351
Container-title:Environmental Research
language:en
Short-container-title:Environmental Research

Author:

Yuan Zhendong^ORCID,Kerckhoffs Jules^ORCID,Shen Youchen,de Hoogh Kees^ORCID,Hoek Gerard,Vermeulen Roel^ORCID

Publisher

Elsevier BV

Subject

General Environmental Science,Biochemistry

Reference39 articles.

1. Air Quality e-Reporting (AQ e-Reporting) — European Environment Agency . https://www.eea.europa.eu/data-and-maps/data/aqereporting-9 (accessed 2021-11-23).

2. Long-term exposure to outdoor air pollution and the prevalence of asthma: meta-analysis of multi-community prevalence studies;Anderson;Air Qual. Atmosphere Health,2013

3. High-resolution air pollution mapping with Google street View cars: exploiting big data;Apte;Environ. Sci. Technol.,2017

4. Effect of the number of measurement sites on land use regression models in estimating local air pollution;Basagaña;Atmos. Environ.,2012

5. Spatial and spatiotemporal variability of regional background ultrafine particle concentrations in The Netherlands;van de Beek;Environ. Sci. Technol.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lur Modeling of Long-Term Average Hourly Concentrations of No2 Using Hyperlocal Mobile Monitoring Data;2023

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