Methodology for designing air quality monitoring networks: II. Application to Las Vegas, Nevada, for carbon monoxide

1986 ◽  
Vol 6 (1) ◽  
pp. 13-34 ◽  
Author(s):  
J. L. McElroy ◽  
J. V. Behar ◽  
T. C. Meyers ◽  
M. K. Liu
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Las Vegas ◽  
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

scholarly journals Gridded global surface ozone metrics for atmospheric chemistry model evaluation

2015 ◽  
Vol 8 (2) ◽  
pp. 603-647 ◽  
Author(s):  
E. D. Sofen ◽  
D. Bowdalo ◽  
M. J. Evans ◽  
F. Apadula ◽  
P. Bonasoni ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Climate Model ◽  
Surface Ozone ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Chemistry Research ◽  
Consistent Manner

Abstract. The concentration of ozone at the Earth's surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research. We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent dataset for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total dataset of approximately 6600 sites and 500 million hourly observations from 1971–2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regional background locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This dataset is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily eight-hour average (MDA8), SOMO35, AOT40, and metrics related to air quality regulatory thresholds. Gridded datasets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi:10.5285/08fbe63d-fa6d-4a7a-b952-5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner.

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