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

Research output: Contribution to journalJournal articlepeer-review

Standard

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. / Di, Qian; Amini, Heresh; Shi, Liuhua; Kloog, Itai; Silvern, Rachel; Kelly, James; Sabath, M. Benjamin; Choirat, Christine; Koutrakis, Petros; Lyapustin, Alexei; Wang, Yujie; Mickley, Loretta J.; Schwartz, Joel.

In: Environment International, Vol. 130, 104909, 2019.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Di, Q, Amini, H, Shi, L, Kloog, I, Silvern, R, Kelly, J, Sabath, MB, Choirat, C, Koutrakis, P, Lyapustin, A, Wang, Y, Mickley, LJ & Schwartz, J 2019, 'An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution', Environment International, vol. 130, 104909. https://doi.org/10.1016/j.envint.2019.104909

APA

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 PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, [104909]. https://doi.org/10.1016/j.envint.2019.104909

Vancouver

Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J et al. An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International. 2019;130. 104909. https://doi.org/10.1016/j.envint.2019.104909

Author

Di, Qian ; Amini, Heresh ; Shi, Liuhua ; Kloog, Itai ; Silvern, Rachel ; Kelly, James ; Sabath, M. Benjamin ; Choirat, Christine ; Koutrakis, Petros ; Lyapustin, Alexei ; Wang, Yujie ; Mickley, Loretta J. ; Schwartz, Joel. / An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. In: Environment International. 2019 ; Vol. 130.

Bibtex

@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",
doi = "10.1016/j.envint.2019.104909",
language = "English",
volume = "130",
journal = "Environment international",
issn = "0160-4120",
publisher = "Pergamon Press",

}

RIS

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

Y1 - 2019

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

VL - 130

JO - Environment international

JF - Environment international

SN - 0160-4120

M1 - 104909

ER -

ID: 228252741