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Data science in air quality monitoring

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science in air quality monitoring/ by Hui Liu, Yanfei Li, Zhu Duan.
Author:	Liu, Hui.
other author:	Li, Yanfei.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxii, 239 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Data preprocessing in air quality monitoring -- Chapter 3 Data decomposition in air quality monitoring -- Chapter 4 Data identification in air quality monitoring -- Chapter 5 Data preprocessing in air quality monitoring -- Chapter 6 Data forecasting in air quality monitoring -- Chapter 7 Data interpolation in air quality monitoring.
Contained By:	Springer Nature eBook
Subject:	Air quality - Measurement -
Online resource:	https://doi.org/10.1007/978-981-96-5777-3
ISBN:	9789819657773

Data science in air quality monitoring
Liu, Hui.

Data science in air quality monitoring[electronic resource] /by Hui Liu, Yanfei Li, Zhu Duan. - Singapore :Springer Nature Singapore :2025. - xxii, 239 p. :ill., digital ;24 cm. - Engineering applications of computational methods,v. 232662-3374 ;. - Engineering applications of computational methods ;v. 23..

Chapter 1 Introduction -- Chapter 2 Data preprocessing in air quality monitoring -- Chapter 3 Data decomposition in air quality monitoring -- Chapter 4 Data identification in air quality monitoring -- Chapter 5 Data preprocessing in air quality monitoring -- Chapter 6 Data forecasting in air quality monitoring -- Chapter 7 Data interpolation in air quality monitoring.

This book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc.

ISBN: 9789819657773

Standard No.: 10.1007/978-981-96-5777-3doiSubjects--Topical Terms:

3783667
Air quality
--Measurement

LC Class. No.: TD890

Dewey Class. No.: 628.53

Data science in air quality monitoring
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300 $a xxii, 239 p. : $b ill., digital ; $c 24 cm.
490 1 $a Engineering applications of computational methods, $x 2662-3374 ; $v v. 23
505 0 $a Chapter 1 Introduction -- Chapter 2 Data preprocessing in air quality monitoring -- Chapter 3 Data decomposition in air quality monitoring -- Chapter 4 Data identification in air quality monitoring -- Chapter 5 Data preprocessing in air quality monitoring -- Chapter 6 Data forecasting in air quality monitoring -- Chapter 7 Data interpolation in air quality monitoring.
520 $a This book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc.
650 0 $a Air quality $x Measurement $x Data processing. $3 3783667
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650 2 4 $a Pollution. $3 518679
700 1 $a Li, Yanfei. $3 3783664
700 1 $a Duan, Zhu. $3 3783665
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950 $a Earth and Environmental Science (SpringerNature-11646)