Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach-Reference-Cited by-同舟云学术

Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach

Published:2022-11 Issue:11 Volume:13 Page:101580
ISSN:1309-1042
Container-title:Atmospheric Pollution Research
language:en
Short-container-title:Atmospheric Pollution Research

Author:

Araki Shin^ORCID,Shimadera Hikari^ORCID,Shima Masayuki^ORCID

Publisher

Elsevier BV

Subject

Atmospheric Science,Pollution,Waste Management and Disposal

Reference48 articles.

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3. Effect of spatial outliers on the regression modelling of air pollutant concentrations: A case study in Japan;Araki;Atmos. Environ.,2017

4. National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000–2010;Bechle;Environ. Sci. Technol.,2015

5. Prenatal nitrate air pollution exposure and reduced child lung function: Timing and fetal sex effects;Bose;Environ. Res.,2018

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