Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm

doi:10.1016/j.scitotenv.2021.150149

Predicting intraurban airborne PM_1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm

科研成果: 期刊稿件 › 文章 › 同行评审

源语言	英语
文章编号	150149
期刊	Science of the Total Environment
卷	806
DOI	https://doi.org/10.1016/j.scitotenv.2021.150149
出版状态	已出版 - 2月 1 2022
已对外发布	是

!!!ASJC Scopus Subject Areas

污染
废物管理和处置
环境工程学
环境化学

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1016/j.scitotenv.2021.150149

其它文件与链接

引用此

@article{07cd6e2b59ed4bcc9a2c0c32a0d0e12a,

title = "Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm",

keywords = "Land-use regression (LUR), Machine learning, Outdoor air pollution, PM trace elements, Particulate matter (PM), Pollution exposure",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2022",

month = feb,

day = "1",

doi = "10.1016/j.scitotenv.2021.150149",

language = "English",

volume = "806",

journal = "Science of the Total Environment",

issn = "0048-9697",

publisher = "Elsevier",

}

TY - JOUR

T1 - Predicting intraurban airborne PM1.0-trace elements in a port city

T2 - Land use regression by ordinary least squares and a machine learning algorithm

PY - 2022/2/1

Y1 - 2022/2/1

KW - Land-use regression (LUR)

KW - Machine learning

KW - Outdoor air pollution

KW - PM trace elements

KW - Particulate matter (PM)

KW - Pollution exposure

UR - http://www.scopus.com/inward/record.url?scp=85115810387&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85115810387&partnerID=8YFLogxK

U2 - 10.1016/j.scitotenv.2021.150149

DO - 10.1016/j.scitotenv.2021.150149

M3 - Article

C2 - 34583078

AN - SCOPUS:85115810387

SN - 0048-9697

VL - 806

JO - Science of the Total Environment

JF - Science of the Total Environment

M1 - 150149

ER -