東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing./
Author:	Brehmer, Collin.
Description:	1 online resource (71 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-07.
Contained By:	Masters Abstracts International84-07.
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29322211click for full text (PQDT)
ISBN:	9798368426341

Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing.
Brehmer, Collin.

Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing. - 1 online resource (71 pages)

Source: Masters Abstracts International, Volume: 84-07.

Thesis (M.S.)--Colorado State University, 2022.

Includes bibliographical references

Policymakers and non-governmental organizations have been implementing policies and interventions designed to reduce exposure to hazardous air pollution. Having knowledge of how non-policy related factors (i.e., meteorology) impact air pollution concentrations in a given study area can better inform longitudinal studies of the effects of the policy on air pollution and health. In this study, we apply a random forest machine learning approach to evaluate how meteorological factors including temperature, relative humidity, wind speed, wind direction, and boundary layer height influence daily PM2.5 concentrations in rural Beijing villages during heating months (January and February of 2019 and 2020). Ten-fold cross validation indicated good model performance with an overall r2 of 0.85 for season 1, and 0.93 for season 2. The models were able to identify variables that were the most important for predicting PM2.5 concentrations both field seasons (relative humidity) and variables that had changes in relative importance between seasons (temperature and boundary layer height). Additionally, examination of one and two-way partial dependence plots as well as interactions through Friedman's H-statistic granted insight into how meteorology variables influence PM2.5 concentrations. Findings from this work provide a basis for adjusting for meteorological variability in important indicators of air quality like PM2.5 concentrations in an ongoing real-world policy evaluation of a province-wide ban on household use of coal for space heating in Beijing, which is critical for isolating (to the extent possible) changes in measured pollutant concentrations attributable to the policy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798368426341Subjects--Topical Terms:

548583
Environmental engineering.
Subjects--Index Terms:

Air pollutionIndex Terms--Genre/Form:

542853
Electronic books.

Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing.
LDR:03134nmm a2200433K 4500 001 2355425
005 20230512095512.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798368426341
035 $a (MiAaPQ)AAI29322211
035 $a AAI29322211
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Brehmer, Collin. $3 3695841
245 1 0 $a Understanding Meteorological Impacts on Ambient PM2.5 Concentrations Using Random Forest Models in Beijing.
264 0 $c 2022
300 $a 1 online resource (71 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-07.
500 $a Advisor: Carter, Ellison.
502 $a Thesis (M.S.)--Colorado State University, 2022.
504 $a Includes bibliographical references
520 $a Policymakers and non-governmental organizations have been implementing policies and interventions designed to reduce exposure to hazardous air pollution. Having knowledge of how non-policy related factors (i.e., meteorology) impact air pollution concentrations in a given study area can better inform longitudinal studies of the effects of the policy on air pollution and health. In this study, we apply a random forest machine learning approach to evaluate how meteorological factors including temperature, relative humidity, wind speed, wind direction, and boundary layer height influence daily PM2.5 concentrations in rural Beijing villages during heating months (January and February of 2019 and 2020). Ten-fold cross validation indicated good model performance with an overall r2 of 0.85 for season 1, and 0.93 for season 2. The models were able to identify variables that were the most important for predicting PM2.5 concentrations both field seasons (relative humidity) and variables that had changes in relative importance between seasons (temperature and boundary layer height). Additionally, examination of one and two-way partial dependence plots as well as interactions through Friedman's H-statistic granted insight into how meteorology variables influence PM2.5 concentrations. Findings from this work provide a basis for adjusting for meteorological variability in important indicators of air quality like PM2.5 concentrations in an ongoing real-world policy evaluation of a province-wide ban on household use of coal for space heating in Beijing, which is critical for isolating (to the extent possible) changes in measured pollutant concentrations attributable to the policy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Environmental engineering. $3 548583
650 4 $a Environmental studies. $3 2122803
650 4 $a Forestry. $3 895157
650 4 $a Meteorology. $3 542822
650 4 $a Southeast Asian studies. $3 3344898
653 $a Air pollution
653 $a Machine learning
653 $a Policymakers
653 $a Meteorology
653 $a Beijing
653 $a Meteorological Impacts
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0775
690 $a 0477
690 $a 0478
690 $a 0222
690 $a 0557
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Colorado State University. $b Civil and Environmental Engineering. $3 2094665
773 0 $t Masters Abstracts International $g 84-07.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29322211 $z click for full text (PQDT)