Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data-Reference-Cited by-同舟云学术

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Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data

Published:2024-02 Issue: Volume:318 Page:120233
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Matthaios Vasileios N.^ORCID,Knibbs Luke D.,Kramer Louisa J.^ORCID,Crilley Leigh R.,Bloss William J.^ORCID

Funder

Royal Society of Chemistry

University of Birmingham

European Commission Marie Sklodowska-Curie Actions

Horizon 2020

Natural Environment Research Council

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference68 articles.

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2. Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis;Adam;Eur. Respir. J.,2015

3. Instance-based learning algorithms;Aha;Mach. Learn.,1991

4. Elevated stacks' pollutants' dispersion and its contributions to photochemical smog formation in a heavily industrialized area;Aidaoui;Air Quality, Atmosphere Health,2015

5. Urban ambient particle metrics and health: a time-series analysis;Atkinson;Epidemiology,2010

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1. Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review;Results in Engineering;2024-06

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