Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Recent Advances in Low-cost Particul...
~
Li, Jiayu.
Linked to FindBook
Google Book
Amazon
博客來
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application./
Author:
Li, Jiayu.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:
382 p.
Notes:
Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
Contained By:
Dissertations Abstracts International80-10B.
Subject:
Engineering. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13861835
ISBN:
9781392069684
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application.
Li, Jiayu.
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application.
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 382 p.
Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
Thesis (Ph.D.)--Washington University in St. Louis, 2019.
This item must not be sold to any third party vendors.
Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment.
ISBN: 9781392069684Subjects--Topical Terms:
586835
Engineering.
Subjects--Index Terms:
Air pollution monitoring
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application.
LDR
:04242nmm a2200397 4500
001
2265870
005
20200529130306.5
008
220629s2019 ||||||||||||||||| ||eng d
020
$a
9781392069684
035
$a
(MiAaPQ)AAI13861835
035
$a
(MiAaPQ)wustl:12812
035
$a
AAI13861835
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Li, Jiayu.
$3
3543050
245
1 0
$a
Recent Advances in Low-cost Particulate Matter Sensor: Calibration and Application.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2019
300
$a
382 p.
500
$a
Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
500
$a
Publisher info.: Dissertation/Thesis.
500
$a
Advisor: Biswas, Pratim.
502
$a
Thesis (Ph.D.)--Washington University in St. Louis, 2019.
506
$a
This item must not be sold to any third party vendors.
520
$a
Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment.
590
$a
School code: 0252.
650
4
$a
Engineering.
$3
586835
650
4
$a
Environmental engineering.
$3
548583
653
$a
Air pollution monitoring
653
$a
Light scattering
653
$a
Low-cost sensor
653
$a
Machine learning
653
$a
Particulate matter
653
$a
Pollution mapping
690
$a
0537
690
$a
0775
710
2
$a
Washington University in St. Louis.
$b
Energy, Environmental & Chemical Engineering.
$3
1669538
773
0
$t
Dissertations Abstracts International
$g
80-10B.
790
$a
0252
791
$a
Ph.D.
792
$a
2019
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13861835
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9418104
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login