scholarly journals Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2476 ◽  
Author(s):  
Jaehyun Ahn ◽  
Dongil Shin ◽  
Kyuho Kim ◽  
Jihoon Yang
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Sensor Data ◽  
Quality Analysis

scholarly journals Indoor air quality analysis based on Hadoop

2014 ◽  
Vol 17 ◽  
pp. 012260
Author(s):  
Wang Tuo ◽  
Sun Yunhua ◽  
Tian Song ◽  
Yu Liang ◽  
Cui Weihong
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Analysis

scholarly journals Covid-19 risk assessment in public transport using ambient sensor data and wireless communications

2020 ◽  
Vol 10 (2) ◽  
pp. 43-50
Author(s):  
Şentürk Fatih ◽  
Adar Gökhan ◽  
Stefan Panić ◽  
Časlav Stefanović ◽  
Mete Yağanoğlu ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Public Transport ◽  
Public Transportation ◽  
Carbon Dioxide Concentration ◽  
Sensor Data ◽  
Risk Level ◽  
Factors Affecting ◽  
Transportation Vehicle

Covid-19 causes one of the most alarming global health and economic crises in modern times. Countries around the world establish different preventing measures to stop or control Covid-19 spread. The goal of this paper is to present methods for the evaluation of indoor air quality in public transport to assess the risk of contracting Covid19. The first part of the paper involves investigating the relationship between Covid-19 and various factors affecting indoor air quality. The focus of this paper relies on exploring existing methods to estimate the number of occupants in public transport. It is known that increased occupancy rate increases the possibility of contamination as well as indoor carbon dioxide concentration. Wireless data collection schemes will be defined that can collect data from public transportation. Collected data are envisioned to be stored in the cloud for data analytics. We will present novel methods to analyze the collected data by considering the historical data and estimate the virus contagion risk level for each public transportation vehicle in service. The methodology is expected to be applicable for other airborne diseases as well. Real-time risk levels of public transportation vehicles will be available through a mobile application so that people can choose their mode of transportation accordingly.

Uptake Rates of Diffusive Samplers for VOC In Indoor Air Quality Analysis

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S364 ◽  
Author(s):  
D Ullrich ◽  
M Ball ◽  
K R Brenske ◽  
A Herz ◽  
R Dijkgraaf
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Analysis ◽  
Uptake Rates

Indoor air quality analysis of residential buildings in northeast China based on field measurements and longtime monitoring

2018 ◽  
Vol 144 ◽  
pp. 171-183 ◽  
Author(s):  
Kailiang Huang ◽  
Jiasen Song ◽  
Guohui Feng ◽  
Qunpeng Chang ◽  
Bian Jiang ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Northeast China ◽  
Residential Buildings ◽  
Field Measurements ◽  
Quality Analysis

scholarly journals Thermal Comfort and Indoor air Quality Analysis of a Low-energy Cooling Windcatcher

2017 ◽  
Vol 105 ◽  
pp. 2865-2870 ◽  
Author(s):  
John Kaiser Calautit ◽  
Angelo I. Aquino ◽  
Sally Shahzad ◽  
Diana S.N.M. Nasir ◽  
Ben Richard Hughes
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Energy ◽  
Quality Analysis

scholarly journals Multisensor IoT Platform for Optimising IAQ Levels in Buildings through a Smart Ventilation System

2019 ◽  
Vol 11 (20) ◽  
pp. 5777 ◽  
Author(s):  
Giacomo Chiesa ◽  
Silvia Cesari ◽  
Miguel Garcia ◽  
Mohammad Issa ◽  
Shuyang Li
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Real Time ◽  
Indoor Air ◽  
Low Cost ◽  
Ventilation System ◽  
Mobile App ◽  
Sensor Data ◽  
Pollutant Concentrations ◽  
Indoor And Outdoor

Indoor Air Quality (IAQ) issues have a direct impact on the health and comfort of building occupants. In this paper, an experimental low-cost system has been developed to address IAQ issues by using a distributed internet of things platform to control and monitor the indoor environment in building spaces while adopting a data-driven approach. The system is based on several real-time sensor data to model the indoor air quality and accurately control the ventilation system through algorithms to maintain a comfortable level of IAQ by balancing indoor and outdoor pollutant concentrations using the Indoor Air Quality Index approach. This paper describes hardware and software details of the system as well as the algorithms, models, and control strategies of the proposed solution which can be integrated in detached ventilation systems. Furthermore, a mobile app has been developed to inform, in real time, different-expertise-user profiles showing indoor and outdoor IAQ conditions. The system is implemented in a small prototype box and early-validated with different test cases considering various pollutant concentrations, reaching a Technology Readiness Level (TRL) of 3–4.

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