Development of indoor environmental index: Air quality index and thermal comfort index

Saad, Saidah; Shakaff, Ali Yeon Md; Saad, Abdul Rahman Mohd; Yusof, Azita Laily; Andrew, A. M.; Zakaria, A.; Adom, Abdul Hamid

doi:10.1063/1.4975276

Cited by 37 publications

(36 citation statements)

References 12 publications

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“…A review of standards and guidelines for the IAQ parameters are given in [25]. Besides the six concentrations for the AQI as mentioned above, additional pollutants are needed [26] to calculate the indoor air quality index (IAQI). These include carbon dioxide (CO 2 ), volatile organic compounds (VOCs), radon and formaldehyde, which are known to cause concerns of health risk [27].…”

Section: A Indoor Air Quality Index (Iaqi)mentioning

confidence: 99%

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Metia

Phung

2020

IEEE Sensors J.

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Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a realtime basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.

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Section: A Indoor Air Quality Index (Iaqi)mentioning

confidence: 99%

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Metia

Phung

2020

IEEE Sensors J.

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“…For example, the Environmental Protection Agency in the United States (EPA-US) only calculates the AQI for four pollutants: ozone, particulate matter, CO, and sulfur dioxide [14]. Some researchers [15,16] have tried to overcome these limitations and to establish an indoor AQI for several pollutants. Saad et al [15] developed a breakpoints table for indoor pollutants such as CO 2 , CO, NO 2 , and TVOCs, by which AQI for indoor environments can be calculated.…”

Section: Indoor Air Quality Index (Aqi)mentioning

confidence: 99%

“…Some researchers [15,16] have tried to overcome these limitations and to establish an indoor AQI for several pollutants. Saad et al [15] developed a breakpoints table for indoor pollutants such as CO 2 , CO, NO 2 , and TVOCs, by which AQI for indoor environments can be calculated.…”

Section: Indoor Air Quality Index (Aqi)mentioning

confidence: 99%

“…This study adopted the indoor AQI breakpoints categories table developed by Saad et al [15] for several pollutants, including CO 2 , CO, NO 2 , and TVOCs in which a sub-index is calculated from a segmented linear function that transforms indoor targeted concentrations onto a scale ranging from 100 to 0, as shown in Table 5. Each sub-index i is calculated by using a segmented linear function that relates pollutant concentration X i to sub-index value I i .…”

Section: Indoor Air Quality Index (Aqi)mentioning

confidence: 99%

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Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait

Almutairi¹,

Alsanad²,

Alhelailah³

2019

Applied Sciences

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Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize the quality of the indoor air environment of the Co-Ops supermarkets in Kuwait based on investigation of CO2, CO, NO2, H2S, TVOCs, and NMHC. On-site measurements were conducted to evaluate these parameters in three locations at the selected Co-Ops, and the perceived air quality (PAQ) was determined to quantify the air’s pollutants as perceived by humans. Moreover, the indoor air quality index (AQI) was constructed for the selected locations, and the ANOVA test was used to analyze the association between the observed concentrations among these environmental parameters. At least in one spot at each Co-Op, the tested environmental parameters exceeded the threshold limit set by the environmental agencies. The PAQ for Co-Op1, 2, and 3 are 1.25, 1.00, and 0.75 respectively. CO2 was significantly found in an association with CO, H2S, and TVOCs, and its indoor-outdoor concentrations were significantly correlated with R2 values ranges from 0.40 to 0.86 depending on the tested location.

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“…Here, PM 2.5 refers to the Particulate Matter with diameter less than 2.5 micrometers. AQI is a numerical scale with a corresponding color code and it is divided into several specific ranges [11]. In addition to the pollution level, this index warns citizens of potential health risk, which is critical for children, elderly people, and people with respiratory diseases.…”

mentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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Development of indoor environmental index: Air quality index and thermal comfort index

Cited by 37 publications

References 12 publications

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

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