scholarly journals CO2 sensing under ambient conditions using metal–organic frameworks

2020 ◽  
Vol 5 (6) ◽  
pp. 1071-1076
Author(s):  
Bohui Ye ◽  
Andreea Gheorghe ◽  
Roy van Hal ◽  
Marcel Zevenbergen ◽  
Stefania Tanase
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Metal Organic Frameworks ◽  
Ambient Conditions ◽  
Metal Organic

Determining accurately CO2 levels is highly relevant when monitoring indoor air quality.

Metal–organic framework-based nanofiber filters for effective indoor air quality control

2018 ◽  
Vol 6 (32) ◽  
pp. 15807-15814 ◽  
Author(s):  
Ye Bian ◽  
Rutao Wang ◽  
Shijie Wang ◽  
Chenyu Yao ◽  
Wei Ren ◽  
...  
Keyword(s):  
Quality Control ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Metal Organic Framework ◽  
Dual Role ◽  
Synthetic Strategy ◽  
Air Quality Control ◽  
Metal Organic ◽  
Organic Framework

MOF-based nanofiber filters via a scalable synthetic strategy serve a dual role in removing both PM2.5 and formaldehyde effectively.

scholarly journals Adsorption of Carbon Dioxide by Metal Organic Framework for Indoor Air Quality Enhancement

2021 ◽  
Vol 1192 (1) ◽  
pp. 012024
Author(s):  
K.C. Chong ◽  
S.S. Lee ◽  
S.O. Lai ◽  
H.S. Thiam ◽  
P.S. Ho ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Metal Organic Framework ◽  
Organic Ligand ◽  
Quality Enhancement ◽  
Trimesic Acid ◽  
Metal Organic ◽  
Adsorption Capability

Abstract Air pollution has become a severe environmental issue among millions of people around the globe. However, the risk of exposure to indoor air pollution is much higher than outdoor air pollution. The most effective way to improve indoor air quality (IAQ) by reducing the indoor CO2 content is by capturing and storing. There are several types of adsorbents used to capture CO2, namely physical adsorbents and chemical adsorbents. Metal-Organic Framework (MOF) is one of the recent interests arising physical adsorbents which possesses high adsorption capability. In this study, MOFs fabricated with different metals and organic ligands were used to evaluate their performance in CO2 adsorption under an enclosed office space. Magnesium, chromium, and copper metals were used as the main element in the MOF fabrication coupled with trimesic acid as an organic ligand. The MOFs’ morphologies generally illustrated that magnesium MOF exhibited a dispersed nanorod flask crystal, chromium MOF showed agglomeration crystal, whereas fine crystal rod was observed in copper MOF. The elemental analysis from EDX and XRD confirmed that the metals were successfully embedded with the organic ligand, which is similar to the literature studies. The CO2 gas adsorption study suggested that magnesium MOF fabricated with trimesic acid possess superior CO2 adsorption capability as the recorded CO2 concentration reduced from 960 ± 73 ppm to 895 ± 57 under 2 hours continuous sampling time. The CO2 adsorption study reveals that the magnesium MOF with trimesic acid ligand yields a promising result on indoor CO2 concentration reduction. This result suggested that the MOF possesses a great potential to be applied in the indoor air quality enhancement with the integration to the existing air purification and/or filtration system.

scholarly journals Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 184
Author(s):  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
Muhammad Zeeshan Shakir ◽  
Uferah Shafi ◽  
Muhammad Moeez Malik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Ambient Conditions ◽  
Public Facilities

Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

Olfactory Evaluation of Indoor Air Quality

1978 ◽  
Author(s):  
Birgitta Berglund ◽  
Thomas Lindvall
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air

Feinstaubfiltrierung: Saubere Luft atmen

2017 ◽  
Vol 22 (07/08) ◽  
pp. 106-107
Author(s):  
Marc Lichtenthäler
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Eine Hohe

Viele Studien belegen, dass durch eine hohe Indoor Air Quality die Produktivität gesteigert, Fehlzeiten abgebaut und Herz-Kreislauf-Erkrankungen vermieden werden können. Neben Behandlungs-, OP- und Pflegebereichen eines Klinikums sollten deshalb auch Bereiche mit gut aufbereiteter Raumluft bedacht werden, in denen sich ausschließlich Mitarbeiter aufhalten.

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