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In this paper, index for indoor air quality (also known as IAQI) and thermal comfort index (TCI) have been developed. The IAQI was actually modified from previous outdoor air quality index (AQI) designed by the United States Environmental Protection Agency (US EPA). In order to measure the index, a real-time monitoring system to monitor indoor air...
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... This equation was calculated based on air pollutant concentration data and breakpoint table shown in Table 1 below. From the table, it shows that the index was divided into six categories with specific color-coded and range: Good (0-50), Normal (51-100), Unhealthy for sensitive groups , Unhealthy (151-200), Very unhealthy (201-300) and Hazardous (>300). ...
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Citations
... The five primary pollutantssulfur dioxide (SO 2 ), carbon monoxide (CO), NO 2 , ozone (O 3 ), and PM 10 and PM 2.5 are the foundation for the US EPA's definition of AQI. First, concentration data from the linear interpolation algorithm and reference concentration data are used to generate each pollutant's unique index, as shown in the equation below [30,31]: ...
The city of Al-Najaf encounters significant challenges pertaining to traffic congestion within its street network. The aim of this study is to evaluate specific sustainable indictors of Al-Najaf’s main urban roads. The objective of this study is to determine the level of traffic performance, traffic pollution, noise, and public transportation. Field data were gathered using video cameras to measure traffic flow, while portable sound meters measured the accompanying noise levels and air quality detectors and Grey Wolf devices were employed to evaluate pollution emissions. Arc GIS Pro 3.2 has been used for facilitating the required information of road length and point data location. The results indicated that the sustainable indicators such as the level of pollution is up to the unhealthy level. Whereas the average noise level exceeds the acceptable level by 15%. Finally, the indicator of public transportation is remarkably low, as it was noted that there was a complete absence of public transportation, and the percentage of buses was 1%. This study suggests adding a green zone along with the major road in the city.
... It is a key component of IEQ, significantly affecting occupant comfort, including sleep quality, which is particularly important for hotel guests (Olesen, 2007;Okamoto-Mizuno and Mizuno, 2012). Thermal comfort is influenced by both personal factors (e.g., physical condition and clothing) and environmental factors (e.g., air temperature, velocity, and humidity) (Mamani et al., 2022;Saad et al., 2017;Hua et al., 2014). Ensuring thermal comfort is essential for creating a comfortable indoor environment and enhancing guest satisfaction in green hotels. ...
This study examines the impact of Indoor Environmental Quality (IEQ) on guest comfort and satisfaction in former Green Building Index (GBI)-certified green hotels in Malaysia’s historic cities, including Kuala Lumpur, Melaka, and Penang. With many hotels moving away from certification, it highlights the need to maintain high environmental and comfort standards. The research evaluates IEQ performance, suggests additional parameters, and explores how comfort mediates the relationship between IEQ and satisfaction. Eight hypotheses were tested, focusing on indoor air quality (IAQ), thermal comfort, lighting, acoustics, visual comfort, building features, decoration, and indoor greenery. A survey of 700 hotel guests resulted in 384 valid responses, confirming that IEQ significantly influences comfort and satisfaction. Among the factors, acoustic/noise (Beta = 0.305), IAQ (Beta = 0.221), and building characteristics (Beta = 0.167) were the most impactful, followed by thermal comfort, lighting, decoration, visual comfort, and indoor greenery. Regression analysis showed a strong link between guest comfort and satisfaction, with comfort as a key mediator. Challenges included noise, thermal discomfort, and lighting problems. The study emphasizes the importance of air quality, thermal comfort, and noise management while balancing aesthetic elements like greenery and decoration to improve guest experiences. It offers valuable insights for hotel operators, advancing sustainable practices and guest satisfaction in green-certified hotels.
... Apart from the AQI and TCI, the main pollutant contributing to the generated AQI is also recorded. The implemented AQI and TCI are based on the work by Saad et al. [68]. ...
Many state-of-the-art air quality sensor nodes feature a very high-power consumption. This limits them to being either mains powered or having a very short battery longevity. Moreover, a detailed study on their power consumption is not yet presented. Despite their high manufacturing cost, their accuracy and sensing functionality are often limited too. This chapter presents the design of an innovative low-power and low-cost air quality monitoring wireless sensor node with extensive measurement capabilities. The design adapts the LoRa transmission protocol by configuring and optimising the bandwidth and the spreading factor values. An optimal balance between data rate, range, and power was achieved. In addition to providing a thorough literature and market survey on available solutions, the work carried out on a scalable low-cost big data capture and analysis system is also discussed. The proposed sensor node can accurately measure carbon dioxide, volatile organic compounds, particulate matter, temperature, relative humidity, and atmospheric pressure. The device features an average energy consumption of 327 μAh and a 40-month autonomy with a 10,500 mAh battery. The low-cost factor enables the provision of a large-scale system. Multiple nodes, distributed across a university campus, provide extensive location-based data and LoRa metadata, which enable comprehensive data analysis.
... It plays a key role in IEQ as it influences the comfort level of occupants. It measures the satisfaction of occupants with the thermal surroundings inside the building as well as heating and cooling systems [80,81]. The thermal environment is influenced by many conditions, which can be categorized into personal and environmental factors. ...
Recent studies have focused on different aspects of green management, practices, and green consumption in the hotel industry. However, there is a need to explore and better understand the association between indoor environmental quality (IEQ) and green hotel guest’s comfort. Therefore, it is essential to explore the effects of IEQ on the comfort and satisfaction of green hotel guests. This study conducts a comprehensive review of the effects of various IEQ parameters, including indoor air quality, thermal comfort, lighting, visual/view, acoustic comfort, building characteristics, decoration, and indoor greenery, on guest’s comfort and satisfaction in green hotels. Based on previous literature, it was also revealed that most current green building schemes lack of comprehensive evaluation of the performance of IEQ dimensions in green hotels. It was also observed that these IEQ parameters show a significant influence on the hotel guest’s comfort and satisfaction. Based on the findings of the literature review, a conceptual model was developed to represent the relationship between the IEQ parameters and guest’s comfort and satisfaction. The proposed conceptual model can be implemented by the hotel management for a comprehensive assessment of guests’ perceptions toward the IEQ in green hotels. The novelty of this study is based on its findings that establish a more effective IEQ evaluation method and serve as the reference scenario of IEQ, which can be a useful tool for both academician and practitioners and contribute to improving the indoor environmental performance of green hotels through highlighting the key IEQ parameters, which affect the comfort and satisfaction of hotel guests.
... This includes factors such as temperature, humidity, ventilation, and the presence of pollutants, allergens, and other contaminants in the air. According to the United States Environmental Protection Agency (US EPA) 2006, indoor air pollution levels can be 2 to 5 times higher than outdoor air pollution levels [1]. It is also reported that exposure to indoor air pollution can cause health effects such as headaches, dizziness, and fatigue. ...
Introduction: A proper investigation on indoor pollution level in economical apartments is important, where the provision for ventilation is limited. A series of experiments were conducted in the houses within an existing economical apartment in Bhubaneswar, India to evaluate various pollutant levels. Materials and methods: Temperature, relative humidity, CO, CO2, Particulate Matter (PM10 and PM2.5), Formaldehyde and Total volatile organic compounds in the bedrooms and kitchens are measured by pollution meters. The experiments were conducted with doors and windows closed conditions. The readings were taken on a four consecutive working day in February 2023 at an interval of 3 h (9:00 am to 9:00 pm). Results: The day wise temperature and humidity variations inside the bedroom and kitchen shows a reverse trend. At the afternoon, the indoor temperature becomes high, while during the night time humidity becomes the highest. The day wise indoor CO2 and CO variation trend is pretty similar. Both CO2 and CO concentrations in bedrooms are the highest in the evening. In contrast to that CO2 and CO concentrations in kitchens becomes maximum during noon time. High particulate matter concentration at outdoor and indoor is observed at the evening time. Higher formaldehyde (HCHO) and Total Volatile Organic Compounds (TVOCs) concentration at the indoor is observed at noon and afternoon time. Conclusion: The results obtained were compared with the recommended values of World Health Organization (WHO) and National Ambient Air Quality Standards (NAQIS). The results revealed that all measured parameters are at a higher level than the recommended values except the indoor CO2 concentrations.
... (32), Wagdi et al.(33), or Dionova et al. (34) are worth mentioning. However, in the case of the present study, an adaptation of it was chosen to reflect the needs of the study.The following section details the mathematical formula of the indoor microclimate quality index (IMQI) (Equation 1), highlighting how these values are integrated and related to ./fpubh. . ...
Indoor air quality (IAQ) and indoor air pollution are critical issues impacting urban environments, significantly affecting the quality of life. Nowadays, poor IAQ is linked to respiratory and cardiovascular diseases, allergic reactions, and cognitive impairments, particularly in settings like classrooms. Thus, this study investigates the impact of indoor environmental quality on student health in a university classroom over a year, using various sensors to measure 19 environmental parameters, including temperature, relative humidity, CO2, CO, volatile organic compounds (VOCs), particulate matter (PM), and other pollutants. Thus, the aim of the study is to analyze the implications of the indoor microclimate for the health of individuals working in the classroom, as well as its implications for educational outcomes. The data revealed frequent exceedances of international standards for formaldehyde (HCHO), VOC, PM2.5, NO, and NO2. HCHO and VOCs levels, often originating from building materials and classroom activities, were notably high. PM2.5 levels exceeded both annual and daily standards, while NO and NO2 levels, possibly influenced by inadequate ventilation, also surpassed recommended limits. Even though there were numerous exceedances of current international standards, the indoor microclimate quality index (IMQI) score indicated a generally good indoor environment, remaining mostly between 0 and 50 for this indicator. Additionally, analyses indicate a high probability that some indicators will exceed the current standards, and their values are expected to trend upwards in the future. The study highlighted the need for better ventilation and pollutant control in classrooms to ensure a healthy learning environment. Frequent exceedances of pollutant standards can suggest a significant impact on student health and academic performance. Thus, the present study underscored the importance of continuous monitoring and proactive measures to maintain optimal indoor air quality.
... There are many indices developed for IAQ and indoor environmental quality (IEQ) assessment. Among these, indoor environmental index (IEI) based on analytic hierarchy process (IEIAHP) (Chiang and Lai, 2002), IEI calculated with indoor air pollution index (IAPI) and indoor discomfort index (IDI) (Moschandreas and Sofuoğlu, 2004), IEQ (Mui and Chan, 2005), IAQ (Leyva et al., 2016), IAQ and IEI calculated with Thermal comfort index (TCI) (Saad et al., 2017), IEQ calculated considering EN 15251 (Piasecki et al., 2017), IAQ based on field measurement and survey research (Wu et al., 2018), using a low-cost monitoring platform IEQ (Mujan et al., 2021) are major studies. These indices are usually based on indoor measurements and/or user opinion surveys. ...
This is the first study to evaluate the indoor air quality of markets using the “Indoor Environmental Index (IEI)”. In the study, carbon dioxide (CO2), relative humidity, temperature, particulate matter, and total volatile organic compounds were measured as indoor air quality parameters in four different markets in Istanbul during the COVID-19 pandemic. Data were analyzed and evaluated using IBM SPSS Statistics 22 program. While CO2, Paticulate matters (PM2.5, PM10), humidity, and temperature had a statistically significant difference in different markets, no statistically significant difference was found for NO2 and total volatile organic compounds (p>0.05). Considering the different hours in a day, it was determined that there was a statistically significant difference for all parameters. The highest and strongest correlation between the parameters was found between PM2.5 and PM10 (r=0.703, p<0.01). The IEI values for 4 different markets in different time intervals in a day were found as 6.862, 6.775, 8.816, and 6.244, respectively. The highest and lowest Indoor Environmental Index values were calculated in market 2 (7.525) and market 4 (4.936), respectively. Indoor air quality parameters had an impact on the IEI results as they affected the pollution index and the discomfort index. As a result of the study, it was seen that the density of customers and products, the size of the closed area of the markets, and the capacity of ventilation equipment affect the indoor air quality. All these results were evaluated and suggestions were made about the visit times to the markets.
... The US EPA bases its definition of AQI on the five leading pollutants: sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM10 and PM2.5) (SO2). The index of each pollutant individually , as specified in the equation below, is first calculated using concentration data from the linear interpolation formula and reference concentration data [21]: ...
It is urgently necessary to improve the state of transportation and related infrastructure, especially given that the most important indicators of urban development gauge a city's progress. One of the most influential of these indicators is what is known as "smart transport," which refers to transportation that utilizes modern communication and information technology technologies to address various challenges in various transportation sectors. The holy city of Najaf smart transportation strategy seeks to reduce dangerous levels of traffic-related noise and air pollution while enhancing various aspects of mobility and traffic flow indicators. This study aims to evaluate the performance of the existing traffic network and public transport in Najaf City and its interference with noise and air pollution at selected points for data gathering by using field measurements using cameras, noise meters, and pollution measurement devices. The study states that the public transport sector in Al-Najaf city is significantly poor as private cars are dominant by about 65% of traffic mix with values of pollution and noise above the standards. One of the most effective solutions to traffic problems is the implementation of intelligent transportation systems. Part of these strategies is establishing a tram network and raising road classes' strategies by proposing some geometric design editing, U-turns reducing, and raised ramps additions.
... Meanwhile, many studies used the concentrations of PM 2.5 , PM 10 , NO 2 , O 3 , CO, VOCs, and fungi to describe IAQ, both in schools and other buildings [21,[63][64][65][66][67]. Considering the relationship between pollutant emissions and environmental parameters such as temperature and humidity [68,69], some research added those environmental parameters to IAQ indicators [70][71][72][73][74], while others presented them as contextual factors. However, there is a lack of clear consensus on which or what combination of pollutant parameters should be used to describe IAQ [75]. ...
Background:
Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures.
Methods:
This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion.
Results:
After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models.
Conclusions:
This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.
... The IAQI developed in this research work was modified using outdoor AQI designed by the USEPA. The developed index showed that the system is able to justify the indoor environmental setting like smoking, operation of air condition and window positioning [10]. Most of the researchers investigated indoor Environmental Quality (IEQ) considering indoor air, thermal comfort, acoustic and illumination aspects, in various types of buildings like offices, mechanically ventilated buildings, elderly daycare centers, sports centers and commercial complexes. ...
... So, the reference value of PM 10 and PM 2.5 as 50 µg/m 3 and 25 µg/m 3 (WHO standard), which was seen to be stringent to achieve, even in ambient environments. At the same time, the majority of the countries have suggested 150 µg/m 3 as a reference value for PM10 . So, in the present study reference value of 150 µg/m 3 is considered in place of the WHO value of PM 10 50 µg/m 3 . ...
Introduction: Due to various components, materials, and processes, industrial indoor air quality differs from building indoor air. Air quality and the working environment impact health, performance, and comfort. This study developed an Indoor Work Environmental Air Quality Index (IWEAQI) to assess and characterize industrial work environments.
Materials and methods: Surat “Textile city” is situated in the western partof India in Gujarat state. The small-scale dyeing and printing industry has been selected as a study area. The industry locations like Jet dyeing machine area, stenter machine area, printing machine area, looping machine area and washing basin area has been selected. Various chemicals, adhesives, solvents, dyes, and varied temperature and humidity conditions are used to transform the raw cloth into the finished product. CO, CO2, SO2, NO2, O3, Total Volatile Organic compounds (TVOC), Formaldehyde, Particulate Matters (PM10, PM2.5), WBGT index, humidity, noise, and light were considered to construct IWEAQI. Continuous observations were recorded at minute intervals with a real-time monitoring system. To account for all contributing aspects, United States Environmental Protection Agency (USEPA) air quality index technique was updated for index formulation. IWEAQI was validated using
the Pollution Index approach.
Results: The proposed approach calculated IWEAQI from results. Both approaches gave an index value of 46-80. The developed approach and pollution index method were compared using regression analysis. All study locations had regression values between 0.93 and 0.99.
Conclusion: The technique classifies IWEAQI as excellent (0-20), good (21-40), moderate (41-60), poor (61-80), and very poor (81-100). From the developed index value, which parameters are influencing the most can be judged.
