Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models-Reference-Cited by-同舟云学术

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models

Published:2021-06-16 Issue:13 Volume:55 Page:8631-8641
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Jain Sakshi¹,Presto Albert A.²^ORCID,Zimmerman Naomi¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

2. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

Funder

Heinz Endowments

U.S. Environmental Protection Agency

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.1c02653

Reference50 articles.

1. Health Effects Institute. Institute for Health Metrics and Evaluation’s Global Burden of Disease; State of Global Air 2019. Special Report, 2019; p 24.

2. World Health Organization. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease; World Health Organization, 2016; pp 1–131.

3. Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

4. Application of mobile sampling to investigate spatial variation in fine particle composition

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