Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm-Reference-Cited by-同舟云学术

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Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm

Published:2022-02 Issue: Volume:806 Page:150149
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Zhang Joyce J.Y.,Sun Liu,Rainham Daniel,Dummer Trevor J.B.,Wheeler Amanda J.,Anastasopolos Angelos,Gibson Mark,Johnson Markey

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference89 articles.

1. A land use regression model for ultrafine particles in Vancouver, Canada;Abernethy;Environ. Sci. Technol.,2013

2. In-use gaseous and particulate matter emissions from a modern ocean going container vessel;Agrawal;Atmos. Environ.,2008

3. Air quality in Canadian port cities after regulation of low-sulphur marine fuel in the North American Emissions Control Area;Anastasopolos;Sci. Total Environ.,2021

4. Impact of emissions from the Los Angeles port region on San Diego air quality during regional transport events;Ault;Environ. Sci. Technol.,2009

5. A framework for identifying distinct multipollutant profiles in air pollution data;Austin;Environ. Int.,2012

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4. State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods;Environment, Development and Sustainability;2024-04-02

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