scholarly journals Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 952
Author(s):  
He Zhang ◽  
Ravi Srinivasan ◽  
Xu Yang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Time Series Regression ◽  
Ann Models ◽  
Mlr Model

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

Perceived indoor air quality and its relationship to air pollutants in French dwellings

Indoor Air ◽  
2017 ◽  
Vol 27 (6) ◽  
pp. 1168-1176 ◽  
Author(s):  
S. Langer ◽  
O. Ramalho ◽  
E. Le Ponner ◽  
M. Derbez ◽  
S. Kirchner ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants

Assessing Future Trends in Indoor Air Quality

1990 ◽  
Vol 6 (5) ◽  
pp. 103-115 ◽  
Author(s):  
H. J. Van De Wiel ◽  
E. Lebret ◽  
W. K. Van Der Lingen ◽  
H. C. Eerens ◽  
L.H. Vaas ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Housing Stock ◽  
International Health ◽  
Health Organizations ◽  
Dutch Housing ◽  
International Health Organizations

Several national and international health organizations have derived concentration levels below which adverse effects on men are not expected or levels below which the excess risk for individuals is less than a specified value. For every priority pollutant indoor concentrations below this limit are considered “healthy.” The percentage of Dutch homes exceeding such a limit is taken as a measure of indoor air quality for that component. The present and future indoor air quality of the Dutch housing stock is described for fourteen air pollutants. The highest percentages are scored by radon, environmental tobacco smoke, nitrogen dioxide from unvented combustion, and the potential presence of housedust mite and mould allergen in damp houses. Although the trend for all priority pollutants is downward the most serious ones remain high in the coming decades if no additional measures will be instituted.

scholarly journals Indoor Air Quality Control Using Backpropagated Neural Networks

2022 ◽  
Vol 70 (2) ◽  
pp. 3837-3853
Author(s):  
Raissa Uskenbayeva ◽  
Aigerim Altayeva ◽  
Faryda Gusmanova ◽  
Gluyssya Abdulkarimova ◽  
Saule Berkimbaeva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Control ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Quality Control

The importance of improving indoor air quality to help reduce asthma attacks

2020 ◽  
Vol 1 (1) ◽  
pp. 47-48
Keyword(s):  
Air Quality ◽  
Young People ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
School Environments ◽  
Children And Young People ◽  
Home And School

Two recent reports have warned about the importance of good air quality and issued advice on improving it in the home and school environments. This is a key consideration for children and young people with asthma, for whom air pollutants may worsen symptoms and trigger asthma attacks.

scholarly journals A Promising Technological Approach to Improve Indoor Air Quality

2019 ◽  
Vol 9 (22) ◽  
pp. 4837 ◽  
Author(s):  
Thomas Maggos ◽  
Vassiliοs Binas ◽  
Vasileios Siaperas ◽  
Antypas Terzopoulos ◽  
Panagiotis Panagopoulos ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Advanced Oxidation Processes ◽  
Environmental Science ◽  
Critical Issue ◽  
Toluene Concentration ◽  
Lab Test ◽  
Real Scale

Indoor Air quality (IAQ) in private or public environments is progressively recognized as a critical issue for human health. For that purpose the poor IAQ needs to be mitigated and immediate drastic measures must be taken. In environmental science and especially in advanced oxidation processes and technologies (AOPs-AOTs), photocatalysis has gained considerable interest among scientists as a tool for IAQ improvement. In the current study an innovative paint material was developed which exhibits intense photocatalytic activity under direct and diffused visible light for the degradation of air pollutants, suitable for indoor use. A laboratory and a real scale study were performed using the above innovative photo-paint. The lab test was performed in a special design photo-reactor while the real scale in a military’s medical building. Nitrogen Oxide (NO) and Toluene concentration was monitored between “reference” rooms (without photo paint) and “green” rooms (with photo-paint) in order to estimate the photocatalytic efficiency of the photo-paint to degrade the above pollutants. Results of the study showed a decrease up to 60% and 16% for NO and toluene respectively under lab scale tests while an improvement of air quality up to 19% and 5% under real world conditions was achieved.

scholarly journals Investigation of air pollutants in rural nursery school – a case study

2018 ◽  
Vol 28 ◽  
pp. 01022 ◽  
Author(s):  
Anna Mainka ◽  
Elwira Zajusz-Zubek ◽  
Barbara Kozielska ◽  
Ewa Brągoszewska
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Nursery School ◽  
Indoor Environments ◽  
Outdoor Air ◽  
Older Children ◽  
Nursery Schools ◽  
Public Health Challenge

Children’s exposure to air pollutants is an important public health challenge. Indoor air quality (IAQ) in nursery school is believed to be different from elementary school. Moreover, younger children are more vulnerable to air pollution than higher grade children because they spend more time indoors, and their immune systems and bodies are less mature. The purpose of this study was to evaluate the indoor air quality (IAQ) at naturally ventilated rural nursery schools located in Upper Silesia, Poland. We investigated the concentrations of volatile organic compounds (VOCs), particulate matter (PM), bacterial and fungal bioaerosols, as well as carbon dioxide (CO2) concentrations in younger and older children's classrooms during the winter and spring seasons. The concentration of the investigated pollutants in indoor environments was higher than those in outdoor air. The results indicate the problem of elevated concentrations of PM2.5 and PM10 inside the examined classrooms, as well as that of high levels of CO2 exceeding 1,000 ppm in relation to outdoor air. The characteristics of PM and CO2 levels were significantly different, both in terms of classroom occupation (younger or older children) and of season (winter or spring).

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