The design of cost-effective air quality monitoring networks

1981 ◽  
Vol 1 (1) ◽  
pp. 59-74 ◽  
Author(s):  
E. E. Pickett ◽  
R. G. Whiting
Keyword(s):  
Air Quality ◽  
Cost Effective ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

scholarly journals Analysis and optimal design of air quality monitoring networks using a variational approach

Tellus B ◽  
2015 ◽  
Vol 67 (1) ◽  
pp. 25385 ◽  
Author(s):  
Adolfo Henriquez ◽  
Axel Osses ◽  
Laura Gallardo ◽  
Melisa Diaz Resquin
Keyword(s):  
Air Quality ◽  
Optimal Design ◽  
Variational Approach ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

Analysis of air quality monitoring networks by functional clustering

2008 ◽  
Vol 19 (7) ◽  
pp. 672-686 ◽  
Author(s):  
R. Ignaccolo ◽  
S. Ghigo ◽  
E. Giovenali
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Functional Clustering

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

2020 ◽  
Author(s):  
Woo-Sik Jung ◽  
Woo-Gon Do
Keyword(s):  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Monitoring Network ◽  
Hierarchical Cluster ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Monitoring Stations

<p><strong>With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.</strong></p><p><strong>This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).</strong></p>

A methodology for the quantification of the net African dust load in air quality monitoring networks

2007 ◽  
Vol 41 (26) ◽  
pp. 5516-5524 ◽  
Author(s):  
M. Escudero ◽  
X. Querol ◽  
J. Pey ◽  
A. Alastuey ◽  
N. Pérez ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
African Dust ◽  
Dust Load
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