Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements

Kaixu Bai; Ke Li; Ni Bin Chang; Wei Gao

doi:10.1016/j.envpol.2019.113047

Advancing the prediction accuracy of satellite-based PM_2.5 concentration mapping: A perspective of data mining through in situ PM_2.5 measurements

Kaixu Bai^*
, Ke Li
, Ni Bin Chang
, Wei Gao

^*Corresponding author for this work

School of Geographic Sciences

East China Normal University
University of Central Florida
Colorado State University

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

Main finding: The inclusion of ground-based PM_2.5 information in neighboring spaces can significantly improve satellite-based PM_2.5 mapping accuracy via data mining.

Original language	English
Article number	113047
Journal	Environmental Pollution
Volume	254
DOIs	https://doi.org/10.1016/j.envpol.2019.113047
State	Published - Nov 2019

Keywords

Aerosol optical depth
Air quality
PM
Random forest
Spatiotemporal interpolation

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10.1016/j.envpol.2019.113047

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@article{08ca8e5dc6bd4d0c9ab551bb9de2261f,

title = "Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements",

abstract = "Main finding: The inclusion of ground-based PM2.5 information in neighboring spaces can significantly improve satellite-based PM2.5 mapping accuracy via data mining.",

keywords = "Aerosol optical depth, Air quality, PM, Random forest, Spatiotemporal interpolation",

author = "Kaixu Bai and Ke Li and Chang, \{Ni Bin\} and Wei Gao",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Ltd",

year = "2019",

month = nov,

doi = "10.1016/j.envpol.2019.113047",

language = "英语",

volume = "254",

journal = "Environmental Pollution",

issn = "0269-7491",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping

T2 - A perspective of data mining through in situ PM2.5 measurements

AU - Bai, Kaixu

AU - Li, Ke

AU - Chang, Ni Bin

AU - Gao, Wei

PY - 2019/11

Y1 - 2019/11

N2 - Main finding: The inclusion of ground-based PM2.5 information in neighboring spaces can significantly improve satellite-based PM2.5 mapping accuracy via data mining.

AB - Main finding: The inclusion of ground-based PM2.5 information in neighboring spaces can significantly improve satellite-based PM2.5 mapping accuracy via data mining.

KW - Aerosol optical depth

KW - Air quality

KW - PM

KW - Random forest

KW - Spatiotemporal interpolation

UR - https://www.scopus.com/pages/publications/85071133489

U2 - 10.1016/j.envpol.2019.113047

DO - 10.1016/j.envpol.2019.113047

M3 - 文章

C2 - 31465903

AN - SCOPUS:85071133489

SN - 0269-7491

VL - 254

JO - Environmental Pollution

JF - Environmental Pollution

M1 - 113047

ER -

Advancing the prediction accuracy of satellite-based PM_2.5 concentration mapping: A perspective of data mining through in situ PM_2.5 measurements

Abstract

Keywords

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