Emergent Method For Exposure Assessment To Air Pollutants Using Air Quality Monitoring Networks In Mexico

2015 ◽  
Vol 2015 (1) ◽  
pp. 3640
Author(s):  
José Luis Texcalac Sangrador ◽  
Karla Cervantes Martínez ◽  
Adrian Giovani Trejo González ◽  
Leonora Rojas Bracho ◽  
Horacio Riojas Rodríguez
Keyword(s):  
Air Quality ◽  
Exposure Assessment ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

scholarly journals Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

2018 ◽  
Vol 7 (12) ◽  
pp. 468 ◽  
Author(s):  
Shivam Gupta ◽  
Edzer Pebesma ◽  
Auriol Degbelo ◽  
Ana Costa
Keyword(s):  
Air Quality ◽  
Exposure Assessment ◽  
Urban Areas ◽  
Quality Monitoring ◽  
Sensor Technology ◽  
Prediction Errors ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Missed Opportunities ◽  
The City

Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities.

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

scholarly journals THE ANALYSE OF AIR ENVIRONMENT QUALITY MONITORING IMPROVEMENT POSSIBILITIES

2015 ◽  
Vol 1 ◽  
pp. 273
Author(s):  
Gotfrīds Noviks ◽  
Andris Skromulis
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Dust Particles ◽  
Action Program ◽  
Air Quality Monitoring ◽  
Environment Quality ◽  
Air Quality Measurements ◽  
Quality Measurements

Paper presents the results of air pollution analyses during last 8 years in Rezekne city. There is carried out a research of atmospheric dust particles, found correlations between concentrations of different air pollutants. Is given overview about air quality measurements in other countries, pointed out air ionization importance on air quality evaluation. The aim of the research – to ground the extension of air quality monitoring indicators including parameters of the air ionisation and to work out an action program to improve an air quality in working areas and recreating zones.

scholarly journals Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

2019 ◽  
Vol 136 ◽  
pp. 05001 ◽  
Author(s):  
Ziyuan Ye
Keyword(s):  
Air Quality ◽  
Hybrid Model ◽  
Air Pollutants ◽  
Moving Average ◽  
Mixing Time ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

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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