An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

Qian Di; Heresh Amini; Liuhua Shi; Itai Kloog; Rachel Silvern; James Kelly; M. Benjamin Sabath; Christine Choirat; Petros Koutrakis; Alexei Lyapustin; Yujie Wang; Loretta J. Mickley; Joel Schwartz

doi:10.1016/j.envint.2019.104909

An ensemble-based model of PM_2.5 concentration across the contiguous United States with high spatiotemporal resolution

Qian Di, Heresh Amini, Liuhua Shi, Itai Kloog, Rachel Silvern, James Kelly, M. Benjamin Sabath, Christine Choirat, Petros Koutrakis, Alexei Lyapustin, Yujie Wang, Loretta J. Mickley, Joel Schwartz

77 Citationer (Scopus)

3 Downloads (Pure)

Originalsprog	Engelsk
Artikelnummer	104909
Tidsskrift	Environment International
Vol/bind	130
Antal sider	13
ISSN	0160-4120
DOI	https://doi.org/10.1016/j.envint.2019.104909
Status	Udgivet - sep. 2019
Udgivet eksternt	Ja

FN’s Verdensmål

Dette resultat bidrager til følgende verdensmål

Adgang til dokumentet

10.1016/j.envint.2019.104909Licens: CC BY-NC-ND

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolutionForlagets udgivne version, 2,54 MBLicens: CC BY-NC-ND

Citationsformater

Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM_2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Artikel 104909. https://doi.org/10.1016/j.envint.2019.104909

@article{66c032b5783f421f9fce6fd467324dfe,

title = "An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution",

keywords = "Fine particulate matter (PM2.5), Ensemble model, Neural network, Gradient boosting, Random forest",

author = "Qian Di and Heresh Amini and Liuhua Shi and Itai Kloog and Rachel Silvern and James Kelly and Sabath, {M. Benjamin} and Christine Choirat and Petros Koutrakis and Alexei Lyapustin and Yujie Wang and Mickley, {Loretta J.} and Joel Schwartz",

year = "2019",

month = sep,

doi = "10.1016/j.envint.2019.104909",

language = "English",

volume = "130",

journal = "Environment International",

issn = "0160-4120",

publisher = "Pergamon Press",

}

TY - JOUR

T1 - An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

AU - Di, Qian

AU - Amini, Heresh

AU - Shi, Liuhua

AU - Kloog, Itai

AU - Silvern, Rachel

AU - Kelly, James

AU - Sabath, M. Benjamin

AU - Choirat, Christine

AU - Koutrakis, Petros

AU - Lyapustin, Alexei

AU - Wang, Yujie

AU - Mickley, Loretta J.

AU - Schwartz, Joel

PY - 2019/9

Y1 - 2019/9

KW - Fine particulate matter (PM2.5)

KW - Ensemble model

KW - Neural network

KW - Gradient boosting

KW - Random forest

U2 - 10.1016/j.envint.2019.104909

DO - 10.1016/j.envint.2019.104909

M3 - Journal article

C2 - 31272018

SN - 0160-4120

VL - 130

JO - Environment International

JF - Environment International

M1 - 104909

ER -

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

FN’s Verdensmål

Adgang til dokumentet

Fingeraftryk

Citationsformater

An ensemble-based model of PM_2.5 concentration across the contiguous United States with high spatiotemporal resolution