Predicting ambient PM
Air Pollution
Environmental Monitoring
Exposure Modeling
Particulate Matter
Journal
Journal of exposure science & environmental epidemiology
ISSN: 1559-064X
Titre abrégé: J Expo Sci Environ Epidemiol
Pays: United States
ID NLM: 101262796
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
23
01
2020
accepted:
23
07
2020
revised:
22
07
2020
pubmed:
5
8
2020
medline:
7
8
2021
entrez:
5
8
2020
Statut:
ppublish
Résumé
Accurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution, mostly due to sparse monitoring networks with a lack of consistent data. We evaluated the performance of six different machine learning algorithms in predicting fine particulate matter (PM Random forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R Our results provide evidence of the advantage and feasibility of machine learning approaches in predicting ambient PM
Sections du résumé
BACKGROUND
Accurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution, mostly due to sparse monitoring networks with a lack of consistent data.
METHODS
We evaluated the performance of six different machine learning algorithms in predicting fine particulate matter (PM
RESULTS
Random forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R
CONCLUSION
Our results provide evidence of the advantage and feasibility of machine learning approaches in predicting ambient PM
Identifiants
pubmed: 32747729
doi: 10.1038/s41370-020-0257-8
pii: 10.1038/s41370-020-0257-8
pmc: PMC9871862
mid: NIHMS1614534
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
699-708Subventions
Organisme : NIEHS NIH HHS
ID : D43 ES022862
Pays : United States
Références
J Stat Softw. 2010;33(1):1-22
pubmed: 20808728
Environ Sci Technol. 2018 Apr 3;52(7):4173-4179
pubmed: 29537833
J Air Waste Manag Assoc. 2013 Jun;63(6):659-70
pubmed: 23858992
Environ Sci Technol. 2013 Oct 15;47(20):11369-77
pubmed: 23980922
Environ Int. 2017 Feb;99:293-302
pubmed: 28038970
Environ Sci Technol. 2020 Feb 18;54(4):2152-2162
pubmed: 31927908
Air Qual Atmos Health. 2013 Mar;6(1):137-150
pubmed: 23450113
Environ Sci Technol. 2017 Jun 20;51(12):6936-6944
pubmed: 28534414
Environ Int. 2019 Sep;130:104909
pubmed: 31272018
Circulation. 2010 Jun 1;121(21):2331-78
pubmed: 20458016
Environ Int. 2018 Jul;116:286-299
pubmed: 29704807
Environ Res. 2018 Nov;167:7-14
pubmed: 30005199
J Expo Sci Environ Epidemiol. 2007 May;17(3):279-87
pubmed: 17006435
Sci Rep. 2019 May 16;9(1):7497
pubmed: 31097728
Am J Epidemiol. 2019 Dec 31;188(12):2222-2239
pubmed: 31509183
Environ Sci Technol. 2015 Mar 17;49(6):3887-96
pubmed: 25648639
N Engl J Med. 2017 Jun 29;376(26):2513-2522
pubmed: 28657878
Environ Health Prev Med. 2019 Nov 27;24(1):66
pubmed: 31775603
Environ Pollut. 2018 Nov;242(Pt B):1417-1426
pubmed: 30142557
Environ Res. 2020 Apr;183:108924
pubmed: 31831155
Environ Res. 2020 Jan;180:108810
pubmed: 31630004
BMC Pregnancy Childbirth. 2014 Apr 23;14:146
pubmed: 24758249
Environ Pollut. 2017 Feb;221:491-500
pubmed: 28012666
Lancet. 2017 May 13;389(10082):1907-1918
pubmed: 28408086
Environ Pollut. 2019 Nov;254(Pt A):112792
pubmed: 31421571
Res Rep Health Eff Inst. 2000 Aug;(95):5-72, discussion 73-82
pubmed: 11246487