scholarly journals Advances in Air Quality Monitoring and Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5817
Author(s):  
Thomas Maggos
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring

Air quality monitoring is a long-term assessment of pollutant levels that helps to assess the extent of pollution and provide information about air quality trends [...]

Air Quality Monitoring in Daejeon City with Long-Term NO2and SO2Passive Diffusive Samplers

2007 ◽  
Vol 16 (2) ◽  
pp. 187-195 ◽  
Author(s):  
Bong-Been Yim ◽  
Sun-Tae Kim ◽  
Jae-Ho Jung ◽  
Bum-Jin Lee
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring

Long-term air quality monitoring at the South Pole by the NOAA Program Geophysical Monitoring for Climatic Change

1988 ◽  
Vol 26 (1) ◽  
pp. 63 ◽  
Author(s):  
E. Robinson ◽  
B. A. Bodhaine ◽  
W. D. Komhyr ◽  
S. J. Oltmans ◽  
L. P. Steele ◽  
...  
Keyword(s):  
Air Quality ◽  
Climatic Change ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
South Pole ◽  
The South ◽  
Geophysical Monitoring

Long term air quality monitoring in a net-zero energy residence designed with low emitting interior products

2015 ◽  
Vol 94 ◽  
pp. 33-42 ◽  
Author(s):  
Dustin G. Poppendieck ◽  
Lisa C. Ng ◽  
Andrew K. Persily ◽  
Alfred T. Hodgson
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Zero Energy ◽  
Net Zero Energy ◽  
Net Zero

scholarly journals Predict and Measure Air Quality Monitoring System Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 2562-2571
Author(s):  
D.Saravanan , Et. al.
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Suitable Model ◽  
Air Quality Monitoring ◽  
Related Data ◽  
Multiple Tasks ◽  
Standard Regularization ◽  
Term Data

This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.

scholarly journals Nonlinear relationships between atmospheric aerosol and its gaseous precursors: Analysis of long-term air quality monitoring data by means of neural networks

2003 ◽  
Vol 3 (1) ◽  
pp. 835-866
Author(s):  
I. B. Konovalov
Keyword(s):  
Neural Networks ◽  
Air Quality ◽  
Main Idea ◽  
Empirical Models ◽  
Quality Monitoring ◽  
Nonlinear Dependence ◽  
Air Quality Monitoring ◽  
Atmospheric Conditions ◽  
Monitoring Stations

Abstract. The nonlinear features of the relationships between concentrations of aerosol and volatile organic compounds (VOC) and nitrogen oxides (NOx) in urban environments are revealed directly from data of long-term routine measurements of NOx, VOC, and total suspended particulate matter (PM). The main idea of the method is development of special empirical models based on artificial neural networks. These models, that are basically, the nonlinear extension of the commonly used linear statistical models provide the best fit for the real (nonlinear) PM-NOx-VOC relationships under different atmospheric conditions. Such models may be useful in the context of various scientific and practical problems related to atmospheric aerosols. The method is demonstrated on an example of two empirical models based on independent data-sets collected at two air quality monitoring stations at South Coast Air Basin, California. It is shown that in spite of a rather large distance between the monitoring stations (more than 50 km) and thus substantially different environmental conditions, the empirical models demonstrate several common qualitative features. Specifically, under definite conditions, a decrease in the level of NOx or VOC may lead to an increase in mass concentration of aerosol. It is argued that these features are due to the nonlinear dependence of hydroxyl radical on VOC and NOx.

Low-cost sensors as an alternative for long-term air quality monitoring

2020 ◽  
Vol 185 ◽  
pp. 109438 ◽  
Author(s):  
Xiaoting Liu ◽  
Rohan Jayaratne ◽  
Phong Thai ◽  
Tara Kuhn ◽  
Isak Zing ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Long-Term Outdoor Reliability Assessment of a Wireless Unit for Air-Quality Monitoring Based on Nanostructured Films Integrated on Micromachined Platforms

Sensors ◽  
2012 ◽  
Vol 12 (6) ◽  
pp. 8176-8192 ◽  
Author(s):  
Matteo Leccardi ◽  
Massimiliano Decarli ◽  
Leandro Lorenzelli ◽  
Paolo Milani ◽  
Petteri Mettala ◽  
...  
Keyword(s):  
Air Quality ◽  
Reliability Assessment ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Nanostructured Films
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