Temporal characterization of minute-level PM2.5 variation within a local monitoring network using DWT-DTW-Reference-Cited by-同舟云学术

Temporal characterization of minute-level PM2.5 variation within a local monitoring network using DWT-DTW

Published:2021-11 Issue: Volume:205 Page:108221
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Fan Yuli^ORCID,Zhan Qingming^ORCID,Tang Lujia^ORCID,Liu Huimin^ORCID,Gao Sihang^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference71 articles.

1. Quantifying the air pollutants emission reduction during the 2008 olympic games in beijing;Wang;Environ. Sci. Technol.,2010

2. Impact of license plate restriction policy on emission reduction in Hangzhou using a bottom-up approach;Pu;Transport. Res. Part D,2015

3. Control of mercury emissions from coal-fired electric utility boilers;Srivastava;Environ. Sci. Technol.,2006

4. Effect of emission standard of air pollutants for thermal power plants on regional acid deposition: a numerical simulation;Wang;Zhongguo Huanjing Kexue/China Environmental Science,2014

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Air quality monitoring on university campuses as a crucial component to move toward sustainable campuses: An overview;Urban Climate;2023-11

2. A Robot Learning from Demonstration Method Based on Neural Network and Teleoperation;Arabian Journal for Science and Engineering;2023-06-05

3. High temporal resolution records of outdoor and indoor airborne microplastics;Environmental Science and Pollution Research;2023-01-04

4. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data;Urban Informatics;2022-09-09

5. A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder;Renewable Energy;2022-07