indoor air quality research papers

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• Published: 29 January 2020

A comprehensive review on indoor air quality monitoring systems for enhanced public health

• Jagriti Saini   ORCID: orcid.org/0000-0001-6903-3722 1 ,
• Maitreyee Dutta 1 &
• Gonçalo Marques   ORCID: orcid.org/0000-0001-5834-6571 2  

Sustainable Environment Research volume  30 , Article number:  6 ( 2020 ) Cite this article

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Indoor air pollution (IAP) is a relevant area of concern for most developing countries as it has a direct impact on mortality and morbidity. Around 3 billion people throughout the world use coal and biomass (crop residues, wood, dung, and charcoal) as the primary source of domestic energy. Moreover, humans spend 80–90% of their routine time indoors, so indoor air quality (IAQ) leaves a direct impact on overall health and work efficiency. In this paper, the authors described the relationship between IAP exposure and associated risks. The main idea is to discuss the use of wireless technologies for the development of cyber-physical systems for real-time monitoring. Furthermore, it provides a critical review of microcontrollers used for system designing and challenges in the development of real-time monitoring systems. This paper also presents some new ideas and scopes in the field of IAQ monitoring for the researchers.

Introduction

With the ongoing improvements in quality of life, breathing environment has become an essential area of concern for researchers in the twenty-first century. Many studies confirm that indoor air is more deadly then outdoor air [ 1 ]. Nowadays, 90% of the rural households in the most developing countries and around 50% of the world’s population make use of unprocessed biomass for open fires and poorly functioning cooking stoves indoors. These deficient methods of cooking are responsible for indoor air pollution (IAP) and poor health of women as well as young children who are often exposed to such a polluted environment [ 2 ]. Biomass and coal smoke carry a wide range of harmful pollutants such as Particulate Matter (PM), Nitrogen Dioxide (NO 2 ), Carbon Monoxide (CO), Sulphur Oxides, polycyclic organic matter and formaldehyde [ 3 , 4 ]. Constant exposure to IAP due to the combustion of solid fuels is the common cause of several harmful diseases in developing countries. The list includes chronic obstructive pulmonary disease (COPD), otitis media, acute respiratory infections, tuberculosis, asthma, lung cancer, cancer of larynx and nasopharynx, low birth rate, perinatal conditions and severe eye diseases that can even cause blindness [ 5 , 6 ].

In the developed countries, the impact of modernization has brought a significant shift in indoor fire and heating systems from biomass fuels such as petroleum products and wood to electricity-based appliances. As per World Energy Outlook 2017 [ 7 ], even after several improvements in cooking measures, 1.3 billion people in developing Asia are expected to rely on biomass for cooking by the year 2030. As per current estimates, 2.8 million premature deaths are reported every year due to the use of coal and solid biomass for cooking [ 7 ]. The scenario becomes worse with the use of kerosene, candles and other harmful fuels for lighting [ 7 ]. Generally, the types of fuels being used for household needs can become cleaner and efficient only if people start moving up on the energy ladder. Note that, animal dung is the lowest level of this ladder, and the successive steps are built with crop residues, wood, charcoal, kerosene, gas, and electricity [ 8 ]. People throughout the world tend to move upward on this ladder as their socio-economic conditions allow them to improve their lifestyle, but reports reveal that poverty is the principal obstacle in using advanced and cleaner fuels. The slower development cycle in many parts of the world shows that biomass fuels will be utilized by poor households for decades ahead [ 9 ]. If we look at the stats provided by The Energy Progress Report 2019 [ 10 ], the global access to clean cooking was 58% in the year 2014, and it reached only 59% in the year 2016. The average growth rate was only 0.5% annually; unfortunately, it has been declining since the year 2010. With this annual rate of progress, it is not possible to meet the 2030 target of accessing cleaner fuels on universal level. In order to achieve the set goals, the annual growth rate must accelerate from 0.5 to 3% for the period 2016 to 2030. However, with the present stats, the chances are that almost 2.3 billion people worldwide will not have direct access to clean cooking in 2030. It means the health impacts of IAP will also persist; especially in the areas with inadequate ventilation arrangements [ 10 ].

Ventilation plays an essential role in the measurement of indoor air quality (IAQ). In case if proper ventilation arrangement is missing in building structures, the IAQ decreases and buildings become unhealthy to live. Studies reveal that IAP is observed as one of the major causes of increasing health issues associated with poor ventilation. As per a study conducted in few remote villages of Palpa district located in the western part of Nepal, the percentage deficit in ventilation is 80% as compared to the minimal rate suggested by the American Society of Heating, Refrigeration and Air Conditioning Engineers [ 11 ]. Another study report that poorly ventilation kitchens in Nepal have 100 times higher concentration of total suspended particles in comparison to the standard prescribed limit and it is due to excessive smoke generation in the premises [ 12 ]. Parajuli et al. [ 11 ] also monitored the impact of traditional cooking systems and improved cooking systems in the village houses. The estimated reduction of CO concentration and PM 2.5 concentration was 30 and 39% respectively, with the use of improved cooking systems as compared to traditional cooking systems. Generally, the occupational and educational stats along with housing conditions in urban areas are relatively better when compared to the rural areas. These conditions have a direct relationship with the choice of fuel for household needs and consequently have a significant impact on IAQ.

Reports reveal that poor IAQ is the second major factor for the higher mortality rate in India. It causes around 1.3 million deaths per year in the country. It is observed that out of 70% of the rural population in India [ 13 ], almost 80% of the people rely on biomass fuel to fulfill their household requirements [ 14 ]. The estimated number of people using harmful fuels for cooking in India is highlighted in Fig.  1 [ 15 ]. It means that the largest population of the country lacks access to cleaner and efficient sources of fuel for cooking needs. Kerosene and biomass cooking fuels are also the principal causes of stillbirth in developing countries. Studies reveal that around 12% of stillbirths can be easily prevented by using cleaner cooking fuel for the household needs in India. Similar studies conducted in other developing countries such as Bangladesh, Nepal, Kenya, and Peru show that IAP is causing severe health hazards. Hence it has become necessary to address the challenges, especially for indoor cooking in the rural sectors. Lack of knowledge and understanding of the benefits of cleaner cooking solutions is the principal cause of adverse health consequences. It is essential to design some efficient and affordable household cooking solutions over traditional stoves, and it can be done only after studying behavioral patterns of the low-income population in the country.

Stats about people using fuel for cooking in India [ 15 ]

The economic enhancements contribute to reducing IAP caused by various biomass fuels. However, the modern lifestyle is also leading to poor indoor environmental quality. With the improvement in the standard of living, most people are using indoor heating and cooling systems instead of natural ventilation systems [ 4 ]. This scenario has increased the cases of Sick Building Syndrome (SBS) somewhere around 30 to 200% [ 16 ]. Studies reveal that factors affecting indoor environment include the rate of air exchange, humidity, temperature, ventilation, air movement, biological pollutants, particle pollutants, and gaseous pollutants [ 17 ]. Buildings currently constructed are more airtight and make use of advanced insulation materials that help to reduce the loss of energy. However, the air conditioning systems and the latest building envelope also cause a reduction in the circulation of fresh air. Meanwhile, the increasing consumption of chemical products and synthetic materials in indoor environments has increased the presence of several Volatile Organic Compounds (VOC). It is one of the principal causes of hypersensitivity [ 18 ]. So, it is fair to say that we are still not safe from hazards associated with IAP.

To deal with the increased mortality and morbidity rate due to IAP, numerous researchers are developing indoor environmental quality monitoring systems. Most of the people spend 80 to 90% of their time indoors either at home or in the offices. Thus, it is necessary to take immediate steps to improve the quality of indoor air. The idea is to create some healthy solutions that can contribute to a comfortable living environment while reducing the chances of the occurrence of severe diseases. This paper puts some light in the direction of efforts made by early researchers to deal with the challenges associated with IAP.

The remaining parts of this review paper are organized below in three different sections, where section of “Indoor air quality and public health” describes the real-time cases of health impacts of IAP in developing countries along with the effect of various pollutants on public health. Section of “indoor air quality monitoring systems” presents an overview of some IAP monitoring systems developed in the past few years. The following section (Results/Discussion) provides a critical analysis of existing systems, along with the advantage and disadvantages of various technologies and sensor networks. Finally, the brief conclusion with future scopes of this study is given in the last section. This paper highlights the background of IAQ, primarily focusing on developing countries along with the potential ideas proposed for monitoring systems by different researchers. It is expected to guide future researchers to focus on new developments by considering the pros and cons of existing systems.

Indoor air quality and public health

Iaq and rural health.

Several studies have been reported in India regarding the harmful impacts of IAP. In a nationally representative case-control study published in the year 2010 [ 19 ], after adjusting all essential living conditions and demographic factors, excessive exposure to solid fuel increased the number of deaths among children in the age group of 1 to 4. It is because these infants are used to spend more time indoors with their mothers. The prevalence ratio presented in this study for girls was 1.33; 95% Confidence Interval (CI): 1.12–1.58 and for boys: 1.30; 95% CI: 1.08–1.56. Solid fuel was also reported as the most significant reason behind many cases of non-fatal pneumonia with a prevalence ratio of 1.94; 95% CI: 1.13–3.33 for girls and 1.54; 95% CI: 1.01–2.35 for boys [ 19 ].

Another case study [ 20 ] reveals that routine exposure to fuel other than liquid petroleum gas is directly linked to acute infections in the lower respiratory tract. The adjusted Odds Ratio = 4.73; 95% CI: 1.67–13.45, and these stats were obtained even after adjusting the rest of the risk factors. According to this study, out of the total number of children affected with acute lower respiratory tract infection; almost 24.8% were affected by pneumonia, 45.5% suffered from severe pneumonia whereas, the other 29.7% were observed to have a severe disease [ 20 ]. Furthermore, biomass fuel usage in India is also associated with prolonged nasal mucociliary clearance time. It was recorded to be 766 ± 378 s, whereas this time is reported to be 545 ± 216 s in the case of clean fuel users [ 21 ]. If we look at 2018 Environmental Performance Index Results, India ranked 177th among 180 countries; whereas, other developing countries like Nepal and Bangladesh ranked 176th and 179th respectively [ 22 ]. These stats reveal that some serious efforts are required to improve building health in most developing countries.

IAQ and potential pollutants

IAQ is determined by the concentration of several pollutants such as particle matter, primary, and secondary gaseous pollutants. Studies reveal that a higher number of PM in the urban indoor environment is observed to be of ultra-fine size. Typically, smaller than 0.1 μm, whereas the particles with a size larger than 0.1 μm are observed to be present in a short amount, somewhere below the 10% concentration level [ 23 , 24 ].

The list of primary gaseous pollutants includes radon, O 3 , Nitric oxides (NOx), Sulphur dioxide (SO 2 ), CO, Diatomic carbon, and VOCs. Within the past few years, the usage of chemical products in indoor environments has been increased drastically. These chemical materials generate several hazardous chemical pollutants under room temperature including VOCs. These compounds can cause several health issues with symptoms such as nausea, headache, dizziness, tiredness, nose, eye, and throat irritations [ 25 ]. Ground-level ozone is a colorless gas that acts as an integral component of the atmosphere and is the leading cause of several health diseases related to the respiratory system [ 25 ]. Common symptoms of CO poisoning include vomiting, nausea, weakness, dizziness, headache, and loss of consciousness. SO 2 is a highly reactive and colorless gas that plays an essential role in the atmosphere. It is harmful to human health and the patients that are already suffering from lung disease, older people, children, as well as those who face regular exposure to SO, are at higher risks of developing lung diseases and skin related problems. Nitrogen oxide is the leading cause of several infections associated with the respiratory system. Some of the most commonly observed symptoms of NO 2 toxicity include wheezing, coughing, bronchospasm, fever, diaphoresis, chest pain, dyspnea, headache, throat irritations, and pulmonary edema [ 26 ]. CO 2 is a by-product of combustion and is also produced by the metabolic process of living organisms. Several studies reveal that a moderate concentration of CO 2 in indoor air can cause fatigue and headaches, whereas higher levels lead to vomiting, dizziness, and nausea. Loss of concentration can also occur at too high levels of CO 2 [ 27 ].

Higher concentration of VOCs in buildings can irritate skin, throat, nose, and eyes. Medical health experts also report a broader set of illnesses due to VOCs, such as headaches, respiratory symptoms, fatigue and SBS [ 28 ].

The mixture of various pollutants present in the indoor air can cause a chain of chemical reactions, and it further generates secondary pollutants in the environment. Studies reveal that these secondary pollutants are more harmful when compared to the primary ones [ 29 , 30 ]. Indoor secondary pollutants (such as ozone, NO 2 , sulphur trioxide) are observed to cause significant discomfort and a harmful impact on human health. Moreover, they are challenging to measure and predict due to the complexities involved in their composition [ 27 ]. Volatile, non-volatile, and non-biological agents cause a harmful impact on indoor air while degrading the overall quality of the environment. The list of biological organisms includes dust mites, pollen, mildew, fungi, molds, bacteria, and many insects, animal dander, anthropoid, infectious agents, pollen, mycotoxins, infectious agents, and animal saliva. The dangerous combination of several indoor air allergens with specific outdoor allergens such as molds, grass pollen, animal allergens, cockroaches, and smoking cause risks of allergic sensitization, asthma and many other respiratory diseases [ 31 ]. The list of major indoor air pollutants, sources of emission, and associated medical health consequences is shown in Table  1 .

Indoor air quality monitoring (IAQM) systems

Currently, the increasing health issues due to IAP are an essential matter of discussion for researchers worldwide. Some professionals utilized advanced sensor networks and communication technologies to propose IAQ monitoring systems for the enhanced living environment. As researchers are actively working in this field to improve building health, it is difficult to review all existing and proposed IAQ monitoring systems in this paper. Nonetheless, this section includes studies based on the most prominent IAP parameters. As automatic alert systems are need of the hour in our busy schedules, we have preferably picked monitoring systems that propose online access to recorded environmental factors or generate SMS based alerts. Although several techniques have been invented for real-time monitoring, the preference to be reviewed was given to Wireless Sensor Network (WSN) and Internet of Things (IoT) based models due to their rising scope in the Industry 4.0 revolution.

Alhmiedat and Samara [ 32 ] developed a low-cost ZigBee sensor network architecture to monitor IAQ in real-time. It is possible to install four sensor nodes in the indoor environment and collect data for more than four weeks. The environmental data were further transferred for analysis via a ZigBee communication protocol. Authors of this paper analyzed CO 2 , benzene, NOx, and ammonia for IAQ assessment at the time of cooking in the kitchen, while other sensors collected desired input from the bedroom, living room and office area. It provides real-time monitoring of all factors contributing to indoor air; however, few developments to this system can be still made by reducing power consumption and improving the accuracy of monitored parameters.

Wu et al. [ 33 ] worked on mobile microscopy and machine learning methods to perform accurate quantification and impact analysis of PM. The authors demonstrated a cost-effective and portable PM imaging, quantification and sizing model named C-Air, and the results were displayed on a mobile-based app. A remote server was used for automated processing of essential digital holographic microscope images that ensues accurate PM measurements. This system was capable of providing valuable statistics about density distribution and particle size with the sizing accuracy of approximately 93%. C-Air can be customized to detect specific air particles such as mold and pollens. The performance of C-Air was tested for indoor as well as outdoor air environments.

Zampolli et al. [ 34 ] developed a low-cost model with an electronic nose based solid-state sensor array for monitoring IAQ. By using a combination of advanced pattern recognition techniques and optimized gas sensor array, researchers targeted the quantification of NOx, CO, along with VOCs and relative humidity (RH). The performance of the electronic nose was analyzed in real operating conditions where NO 2 concentrations at 20 ppb and CO at 5 ppm were monitored continuously for at least 45 d. This approach helped to identify the presence of individual pollutants along with the mixture of different contaminants in the test environment. This system was found feasible enough to detect NO 2 and CO levels in indoor air, and these results were further used to manage the appropriate usage of heating, ventilation, and air conditioning (HVAC) systems in the indoor environment without disturbing the air quality.

Kim et al. [ 35 ] focused on seven gases (CO 2 , VOCs, SO 2 , NOx, CO, PM, and ozone) to test IAQ in real-time. The experiments were conducted in three different settings: big church, medium size classroom, and small size living room to test the impact of different factors on IAQ. Researchers concluded that so many factors contribute to altering the quality of indoor air. Some of these are wind, location, airflow, the density of people and room size. However, it was found that gas sensors consume lots of power, so it is important to apply critical thinking for the selection of appropriate sensor nodes. Future researchers are also advised to work on environmental settings and sensor characteristics to ensure reliable calibration of the system to obtain accurate results.

Yu and Lin [ 36 ] constructed an intelligent wireless sensing and control system to deal with health issues caused by IAP. The system is made up of three different parts: 1) Data acquisition that helps in obtaining values about environmental indicators such as CO 2 concentration, RH, and temperature through polling mechanism; 2) Data analysis, responsible for collecting data and interfacing with the AutoRegressive Integrated Moving Average (ARIMA) prediction model to analyze air quality trends in the premises; and 3) Data feedback to trigger necessary actions based on fuzzy results. It may send a warning message or may control the speed of the fan automatically. Each sensor node in this hardware architecture is powered by the IEEE1451.4 standard, and the communication channel is established by ZigBee technology. The software architecture is precisely separated into three different sections where 1) Data monitoring agent creates a bridge between software and hardware, 2) Air quality analyzing agent takes care of air quality trends and triggers relevant actions for higher pollution levels; and 3) Application agent provides services for data display automatic control and alerts. The final ARIMA prediction model based IAQ monitoring system was deployed in the real-time environment at nine different areas of Taiwan. It included Environmental Protection Administration, university, and elementary schools. The performance of the system was further tested using two tests: Validation of the accuracy of the prediction model and validation of energy-saving performance. The system used to make useful decisions about ventilation equipment in the premises depending upon the threshold level of air quality parameters.

Pillai et al. [ 37 ] implemented a sensor network for IAQ monitoring using the Controller Area Network (CAN) interface. In order to run the experiment on a real-time basis, the sensors were distributed in a specific area, and a serial standard bus communication network was used for information exchange between them. CAN is a specially designed high integrity serial bus protocol that works on high speed by supporting information exchange rate between 20 kbit s − 1 to 1 Mbit s − 1 . Using CAN, researchers were able to develop a highly reliable, efficient, and economical communication link between display nodes and sensor nodes. The hardware tests provided highly accurate monitoring for IAQ with short processing time.

Abraham and Li [ 38 ] proposed a cost-effective WSN system for monitoring IAQ. The system was designed using low-cost micro gas sensors (CO, VOC, and CO 2 ) and use the Arduino microcontroller as the processing unit. The mesh network for this monitoring system was developed using the ZigBee module that promised low power, low cost and wireless solution for communication. Data calibration for micro gas sensor networks was further performed using Least-Square Method. It helped researchers to study the current status of IAQ while collecting valuable data for the long-term impacts of bad air quality on human health. The proposed system was also compared with standard GrayWolf System, and it was observed to be independent of humidity and temperature variations.

Cheng et al. [ 39 ] developed AirCloud that is a cloud-based air quality monitoring system designed to serve low cost personal and pervasive needs. The authors worked on two types of Internet-connected PM monitors (focused around PM 2.5 levels) that were named as mini air quality monitoring (AQM) and AQM. The monitoring process was based entirely upon the mechanical structures that were designed for maintaining optimal airflow. On the cloud side, the authors created an air quality analytics engine to learn and develop models of measured air quality with the help of sensors. This cloud-based engine helped in the calibration of mini AQMs and AQMs on a real-time basis while inferring PM 2.5 concentrations. This system provided relevant accuracies at lower cost ensuring dense coverage ability.

Kang and Hwang [ 40 ] introduced an air quality monitoring system to test the relevance of the Comprehensive Air Quality Index for accurate IAQ assessment. The authors also proposed a real-time Comprehensive Indoor Air Quality Indicator (CIAQI) system that can work effectively against all dynamic changes and is quite efficient in processing ability along with memory overhead. In order to develop the experimental setup for realistic indoor air environment monitoring, the authors used VOC, PM 10 , CO, temperature and humidity sensors. The authors also compared the proposed system performance with absolute concentration of all considered pollutants used for ambient air quality index (AQI) with Simple Moving Average scheme and observed that the proposed CIAQI system is more adaptive to real-time changes in the IAQ. Also, this system utilized small memory; therefore, it was considered as an economical solution for the IoT based air quality monitoring.

Bhattacharya et al. [ 41 ] developed a wireless system for monitoring IAQ by working on a few essential parameters such as humidity, temperature, gaseous pollutants, and PM. This system determines indoor environment health in terms of the AQI and at the same time gives real-time inputs to control HVAC systems. In order to serve the smart building applications, authors have also developed a toolkit that measures live air quality data in the form of graphs and numbers.

Ahn et al. [ 42 ] designed a microchip by utilizing six atmospheric sensors: VOCs, light quantity, humidity, temperature, fine dust, and CO 2 . The atmospheric changes were estimated using deep learning models. Performance of the proposed Gated Recurrent Network (GRU) model was also compared with other models such as Long Short-Term Memory (LSTM) networks and linear regression, where the proposed system presented better performance with higher accuracy of 85% over a variety of parameter settings.

Pitarma et al. [ 43 ] developed a low-cost IAQ monitoring unit using a WSN system in combination with microsensors, XBee modules, and Arduino. They worked on five major IAP parameters: luminosity, CO 2 , CO, humidity and air temperature while performing real-time monitoring on a web portal. The wireless communication network between sensors and gateway was established with the XBee module utilizing ZigBee networking protocol and IEEE802.15.4 radio standards. Sensors involved in real-time measurement were sensor SHT10 for RH and temperature; MQ7 for CO, T6615 sensor for CO 2 measurement and LDR5 mm for light detection. The web interface was designed using MySQL database and Personal Home Page (PHP). The prime goal to design this system was to help users get instant updates about exposure risks in the living environment.

Benammar et al. [ 44 ] designed an end to end IAQM system using WSN technology. It was focused around the measurement of RH, ambient temperature, Cl 2 , O 3 , NO 2 , SO 2 , CO, and CO 2 . The sensor nodes were made to communicate to the gateway via XBee PRO radio modules. The sensor nodes in this study include a set of calibrated sensor units, a data storage unit named Waspmote, and sensor interface board known as Gas Pro sensor board. The prime role of the gateway in this study was to process the IAQ data collected from target sites and perform reliable dissemination via a web server. This system was adapted to open source IoT web server platform, named Emoncms to ensure long-term storage as well as live monitoring of IAQM data. Seamless integration of smart mobile standards, WSN, and many other sensing technologies is performed to design the ultimate scalable smart system to monitor IAP. In order to meet the power requirements of the system, authors also designed separate battery units for the sensor network.

Saad et al. [ 45 ] created a system to monitor various environmental parameters that are directly related to air quality. They focused on RH, temperature, PM, and gaseous pollutants that have a direct impact on human health. A WSN was used to measure data from the target location, and it was transferred to the base station via the WSN node. A self-developed server program on the computer system used to access and process this data to analyze IAQ on a real-time basis.

Tiele et al. [ 46 ] focused on the design and development of a portable and low-cost indoor environment monitoring system. This study was performed on a few essential parameters of indoor air such as sound levels, illuminance, CO, CO 2 , VOCs, PM 10 , PM 2.5 , RH, and temperature. The experiments were conducted in both indoor work environments and outdoors. The authors defined an Indoor Environment Quality (IEQ) index to estimate the overall percentage of IEQ.

Moreno-Rangel et al. [ 47 ] presented a study to assess usability, accuracy, and the precision of low-cost IAQ monitor within a residential building. After analyzing the cost and complexity related issues associated with existing scientific solutions for IAQ monitoring, the authors proposed a reliable and low-cost system for households. They focused on a few essential parameters, such as PM 2.5 , CO 2 , VOCs, RH, and temperature. All sensors were calibrated before installation to ensure an adequate measurement. The collected data was analyzed using FOOBOT monitors based on the percentage of time the pollutant levels crossed the threshold levels set by World Health Organization. In order to enhance the accuracy of the measurement, authors in this study used IBM SPSS Statistics to perform statistical analysis.

Idrees et al. [ 48 ] closely observed the challenges associated with IAP and developed an Arduino based platform for real-time IAQ monitoring systems. They initiated steps toward the detailed investigation of factors such as computational complexity, infrastructure, issues, and procedures for efficient designing. The prototype for the proposed real-time IAQ monitoring system was designed using the IBM Watson IoT platform and Arduino board. The authors worked on eight parameters that have a considerable impact on human health in the building environment. The list includes RH, temperature, O 3 , SO 2 , NO 2 , CO, PM 2.5 , and PM 10 . The significant advantage of this system was its ability to reduce the computational burden of the sensing nodes by almost 70%, leading to longer battery life. In order to ensure higher accuracy for measurements, authors used standard calibration procedures on sensor networks, and a data transmission strategy was used to minimize the power consumption along with redundant network traffic. The three most essential layers of the proposed monitoring system were sensing layer, edge computing layer, and application layer. This model reported a reduction of 23% in the overall power consumption, and the performance was validated by setting the system in different environments.

Sivasankari et al. [ 49 ] proposed an IoT based system to monitor IAQ, and the analysis was performed using a Raspberry Pi model. The parameters included in this study were RH, temperature, NO 2 , CO, and concentrations of smoke. The measurements were done using MQ series sensors, Mics 2714 NO 2 sensor, LM-35, and DHT11 sensor. An analog to digital converter was also added to the system so that sensors can be directly interfaced with the Raspberry pi module via eight different channels. This system was used to generate an alarm for indicating a high concentration of emissions, such as a warning for the air pollution rate in the premises.

Arroyo et al. [ 50 ] presented an air quality measurement system made of a distributed sensor network and cloud-based WSN system. Low power ZigBee motes were used for collecting field data, and an optimized cloud computing system was implemented for processing, monitoring, storing, and visualizing received data. This laboratory study was based on the measurement of VOCs, including xylene, ethylbenzene, toluene, and benzene. Multilayer perceptron, principle component analysis, support vector machine, and backpropagation learning algorithm were used at the data processing stage.

This section summarizes the review of IAP monitoring systems that are proposed by early researchers from different countries in the past few years. The main idea is to discuss potential techniques, architectures, and configurations that are already used by researchers. Reliable and efficient monitoring systems can be used in urban as well as rural areas to monitor the IAP and its impact on residents. It is believed that instant alerts can guide people to make proper ventilation arrangements by opening windows or doors in the kitchen; such systems are more useful in homes having traditional cooking systems, and inadequate ventilation arrangements. The results and discussion section further provide a detailed analysis of these studies while covering the strengths, weaknesses of the existing IAQ monitoring systems along with future scopes to guide future researchers.

Results and discussion

Wsn based systems.

The trends in the development of the IAQ monitoring system reveal that most of the researchers in the past few years have worked on WSN based designs with ZigBee as the most reliable communication protocol. The ATmega microcontroller manages the real-time data collection; however, Raspberry Pi is another common choice for setting up a sensor network in the target environment. WSN is an Ad Hoc Network, where sensor networks consume immense energy while transmitting data in multiple hops. The time taken by sensors to send a signal to the monitoring unit was observed to be considerably high. In such situations, researchers needed to work on battery power management to improve overall system performance. However, only a few researchers, such as Yu and Lin [ 36 ] were successful in implementing energy-saving and cost-saving monitoring systems using WSN architecture. Trends reveal that most of the WSN based IAQ monitoring systems use web servers as data access platforms; it demands additional efforts to generate real-time alerts on user smartphones to prevent hazardous conditions. Table  2 highlights the summary of WSN based IAQ monitoring systems.

IoT based systems

Considering the battery life expectancy and reliable single-hop communication abilities, IoT monitoring systems are believed to be the most reliable solutions for IAQ measurement. With lower latencies and lesser power consumption, these systems also demand lesser efforts for maintenance. IoT based real-time monitoring systems are known as smart systems; consequently, most of the researchers and industrial manufacturers are more attracted to this architecture. Experts reveal that the IoT system can monitor a large number of parameters, even without compromising system performance. Studies carried by Idrees et al. [ 48 ] and Sivasankari et al. [ 49 ] gave a new edge to the IAQ monitoring systems with impactful IoT architecture design. However, very few researchers in the past few years have worked on prediction systems in the field of IAQ monitoring. Studies reveal that it is much easier to combine IoT monitoring systems to machine learning and deep learning networks to initiate reliable prediction decisions. It is a significant area of work for new age researchers. Table  3 presents a summary of IoT based IAQ monitoring systems.

Other technologies

Some researchers also worked on architectures other than WSN and IoT, but few parameters reveal the low performance of such systems as compared to the potential of IoT systems for real-time monitoring. The most significant disadvantage of the C-Air platform presented by Wu et al. [ 33 ] was that this study was limited to PM levels only; but in the real world, IAQ is affected by many other pollutants as well. Zampolli et al. [ 34 ] tried working on multiple pollutants, but the study was limited to the simulation environment only; the practical implementation of such systems is the real challenge. Moreover, these researchers worked on low-cost sensors where calibration is a significant challenge, and it leads to a lack of performance for the overall design. Similar constraints were found with the approach followed by Pillai et al. [ 37 ], where the system was studied on breadboards in a controlled lab environment only. Cheng et al. [ 39 ] tried to implement a prediction model with CAN interface, but the study was again limited to PM levels only; the impact of other pollutants was not considered in this study. Moreno-Rangel et al. [ 47 ] presented a valuable study with FOOBOT monitors, and they considered multiple IAQ parameters for the real-time analysis, but the sensor calibration was again a relevant challenge to ensure desired performance. Table  4 presents a summary of IAQ monitoring systems based on architectures other than WSN and IoT.

Discussion and critical analysis

The primary requirement at present is to perform real-time monitoring of IAQ parameters and generate alerts to the building occupants to avoid hazardous conditions. The IoT approach has great potential in this direction to ensure lesser power consumption, negligible time delays, and has a better ability to interact with the physical world.

One of the prime concerns in the development of IAQ systems is the higher cost and massive power consumption of sensor nodes. If we consider the real-time applications of IAQ systems, the sensor units are usually installed in an industrial environment, inside homes, offices, and outdoor areas as well. However, in all these cases, the design of the sensor unit demands more focus on size, design cost, power consumption, communication protocol, and performance dependence on temperature and humidity variations. Sensor calibration is currently the main challenge in front of future researchers to ensure accurate real-time monitoring. Although Metal Oxide Semiconductor sensors are cheaper when compared to the optical and electromechanical sensors (some examples are TGS 2442 and TGS416), they work on the resistive heating; hence, consume loads of energy from limited battery unit of wireless motes. As a result, it reduces the overall lifetime of the network. A considerable solution to solve this problem is placing motes (a specific type of sensor node that can collect, process information and can communicate with other nodes in the network) in sleep mode when they are not working actively in the system. Some studies also reveal that a high-quality micro gas sensor can perform better in variable humidity and temperature conditions. One advanced solution to air quality monitoring is Mobile Sensing System for IAQ – personalized mobile sensing system that is gaining popularity due to the portable, energy-efficient and inexpensive design. Most of the researchers have used ZigBee to establish a communication network between sensor nodes and controller unit, but the prime disadvantages of ZigBee modules are short communication range and low network stability with high maintenance cost. The highly efficient IoT systems bring new scope to this field. By using IoT architecture and the Raspberry Pi microcontroller, which has in-built Wi-Fi communication features ensure fast data transfer. Note that the most used Arduino boards do not offer direct network connectivity. Therefore, users need to use additional modules for internet accessibility. One commonly used Wi-Fi module for Arduino boards is ESP8266 chip, but it needs an external converter for 5–3 logic shifting since most Arduino microcontrollers use 5 V operating voltage. Moreover, it leads to additional cost and energy consumption. Furthermore, Raspberry Pi 3 has more processing power than Arduino Uno as the clock speed for the former is 1.2 GHz, whereas later works on 16 MHz.

Several methods for real-time IAQ monitoring are available in the literature. Furthermore, the presented methods provide practical solutions to improve occupational health and contribute to enhanced living environments considering numerous technical challenges. However, few improvements in the system performance are still required to ensure a reliable solution. By using an IAQ monitoring system, the manager can understand the IAQ behavior of the environment and plan interventions to avoid unhealthy situations. Therefore, the development of enhanced IAQ monitoring systems will address critical health challenges in today’s world.

This section describes the weaknesses and strengths of the existing monitoring systems while describing the potential of available technologies and architectures. This in-depth review can guide new researchers to pick the most relevant topics for research in the future so that the quality of the living environment can be improved by inventing new methods and techniques.

Conclusions

In this review, the authors provide details about how various factors such as VOCs, PM 10 , PM 2.5 , CO, SO 2 , NO, O 3 , temperature, and RH affect IAQ. Furthermore, authors have highlighted the technical aspects of the studies performed by early researchers in this field. Trends reveal that most of the researchers till now have worked upon WSN and IoT architectures to study associated factors with IAQ and provide mobile computing software for data consulting.

Instead of working within a controlled laboratory environment or on simulation systems, researchers need to implement real-time IAQ monitoring systems in real scenarios. The development of prediction systems is another primary concern for future studies because it is easier to control the adverse impact of indoor air pollutants when we are aware of future happenings. Deep learning models such as LSTM and GRU can be utilized to design the prediction systems, and the instant alerts about variation in indoor pollutant levels above the threshold limit must be sent via SMS or email to the smartphones. Note that, LSTM is the enhanced strategy to traditional Recurrent Neural Network, whereas GRU is the further extension to LSTM with forget and update gates. These models work with parameterized functions that have a direct impact on ideal parameters of the data; hence lead to better prediction. Mobile app-based systems analysis is also an essential part of the design. This field has considerable scope for development, and future researchers need to work on in-depth design solutions by combining IoT and deep learning models to come up with cost-effective, accurate, and reliable IAQ management systems. However, the research should not be limited to the industrial environment and cities. Only slightly suitable systems must be designed for the village areas where people suffer more due to their excessive exposure to solid fuels. The development of such systems can lead to an incredible contribution to the medical health department as well.

The main areas of work for future researchers can be summarized as:

Developing an IAQ monitoring system that can work efficiently in real-time conditions, instead of simulated or laboratory-based environments.

Consider specific requirements of rural areas and design a cost-effective IAQ monitoring system to provide a safe solution for enhanced living environments.

Work on IAQ prediction systems so that appropriate preventive measures can be followed on time.

Designing a power-efficient and robust system for severe monitoring conditions in the urban as well as rural areas.

Developing more efficient systems that can generate instant alerts to the users via email and SMS whenever IAP crosses certain threshold levels.

Develop mobile app-based monitoring systems that can be operated by non-tech savvy people as well.

Developing quick alert systems with possible preventive measures like switch on/off air conditioner, open/close windows, and check gas leakage to guide people towards healthy solutions with a variety of specific pollutants in the living environment.

In conclusion, the monitoring solutions/architectures proposed to address the IAQ should incorporate artificial intelligence to predict unhealthy situations for the enhanced living environment and occupational health.

Availability of data and materials

No such sources of data or materials are used for this study.

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Acknowledgments

The authors wish to thank the National Institute of Technical Teachers’ Training and Research, Chandigarh, India, and Universidade da Beira Interior, Covilhã, Portugal, to provide the valuable resources to carry out this study.

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Saini, J., Dutta, M. & Marques, G. A comprehensive review on indoor air quality monitoring systems for enhanced public health. Sustain Environ Res 30 , 6 (2020). https://doi.org/10.1186/s42834-020-0047-y

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Indoor Air-Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems

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The research activity in the field of monitoring indoor quality systems has become an unavoidable challenge facing many researchers. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, we have observed that improving ventilation in a closed space can significantly reduce infection risk. In this paper, several researchers’ protocols and the methodology for monitoring indoor air-quality systems are presented. The majority of the reviewed works are aimed to reduce air pollution levels in the atmosphere. The partial access of sensed data, expensive and non-expansible of traditional air-quality monitoring systems, led to the adoption of IoT and WSN to create a genuine system capable of producing many high-quality services. Furthermore, ad hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper provides a brief but fully review of several researchers’ works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, this study highlights the need for developing systematic protocols for these systems and establishes smart air-quality monitoring systems capable of measuring pollutant concentrations in the air.

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AbdulWahhab, R.S., Jetly, K., Shakir, S. (2022). Indoor Air-Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. In: Husain, M.S., Adnan, M.H.B.M., Khan, M.Z., Shukla, S., Khan, F.U. (eds) Pervasive Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77746-3_12

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Indoor air quality in buildings: a comprehensive review on the factors influencing air pollution in residential and commercial structure.

1. Introduction

1.1. patterns of time spent indoors, 1.2. indoor pollution sources and health impacts, 1.3. purpose of study, 2. methodology, 3. iaq standards & assessment methods, 4. residential buildings and iaq assessment, 5. commercial buildings and iaq assessment, 6. conclusions and future scope, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Contaminants	Sources	Possible Consequences	Ref.
Allergens	Furry pets, dust mites	Asthma	[ , ]
Endotoxins	Presence of cats and dogs, contaminated humidifiers, storage of food waste, lower ventilation rate, increased amount of settled dust	Asthma, reduced lung function	[ , ]
Dampness and mold	Unattended plumbing leaks, leaks in building fabric, hidden food spills, standing water	Upper respiratory symptoms, cough, wheeze, and asthma	[ , ]
Smoke	Tobacco smoke	Premature mortality, lung cancer, coronary artery disease, childhood cough and wheeze, respiratory illness, infant death syndrome	[ , ]
Coal & biomass fuels combustion product	Cooking and heating	Combustion of solid fuels releases CO, N O, particulates, poly-cyclic hydrocarbons, which increases risk of lung cancer, childhood asthma	[ , ]
Carbon Monoxide (CO)	Vehicle exhaust from attached garages, gas stoves, furnaces, woodstoves, fireplaces & cigarettes	Headache, nausea, fatigue	[ , ]
Nitrogen dioxide (N O)	Combustion of fossil fuels e.g., gas or oil furnaces and stoves	Increased risk of respiratory symptoms	[ , ]
Pesticides	Contaminated soil, stored pesticide containers	Irritation to eye, nose, and throat, damage to central nervous system	[ , ]
Formaldehyde (HCHO)	Wood-based products assembled using urea-formaldehyde resins, cigarette smoke, paints, varnishes, floor finishes	Eye, nose, throat irritation, asthma, bronchitis, and possible carcinogen	[ , ]
Volatile Organic Compounds (VOC)	Cigarette smoke, recently dry-cleaned cloths, room deodorizers, paints, carpets	Asthma, bronchial hyper-reactivity	[ , , ]
Plastic Compounds	Polyvinyl chloride for flooring, plastic wall material	Bronchial obstruction, asthma, wheeze, cough, and phlegm	[ ]
Radon	Natural decay of uranium	Lung cancer, leukemia	[ , ]
Ultra-fine particles	Cooking, combustion activities	Serious impact on heart and lungs	[ , ]

Parameters	CAS	WHO [ ]	Singapore [ ]	NIOSH [ ]	Canada [ ]	China [ ]	UK [ ]	Australia [ ]	US EPA [ ]
Benzene (C H )	71-43-2	No safe level of exposure can be recommended	-	-	-	90 ug/m [1 h avg.]	-	-	-
Carbon Di-oxide (CO )	124-38-9	100 mg/m (15 min) 35 mg/m (1 h) 10 mg/m (8 h) 7 mg/m (24 h)	1000 ppm (8 h avg.)	5000 ppm (8 h avg) 30,000 ppm (15 min)	≤6300 mg/m (≤3500 ppm)	1000 ppm (daily avg.)	15,000 ppm (15 min avg.) 5000 ppm (5 min avg.)	30,000 ppm (15 min avg.)	800 ppm
Carbon mono-oxide (CO)	630-08-0	86 ppm (15 min avg.) 51 ppm (30 min avg.) 25 ppm (1-h avg.) 8.6 ppm (8-h avg.)	10 mg/m (9 ppm) (8 h avg.)	35 ppm (8 h avg.)	≤11 ppm (8 h avg) ≤25 ppm (1 h avg.)	5.0 mg/m (daily avg.)	11.6 mg/m (8 h avg.)	9 ppm (10,000 μg/m ) (8 h avg.)	35 ppm (1 h) 9 ppm (8 h)
Formaldehyde	50-00-0	mg/m (30 min) 0.2 mg/m (long term)	0.1 ppm (120 μg/m ) (8 h avg.)	0.016 ppm 0.1 ppm (15 min)	120 µg/m	0.12 mg/m (1 h avg.)	2 ppm (15 min avg.) (2500 μg/m )	2500 μg/m (15 min avg.)	920 μg/m (8 h)
Naphthalene	91-20-3	0.01 mg/m (annual avg.)	-	-	-	-	-	-	-
Nitrogen dioxide	10102-44-0	200 μg/m (1 h) 40 μg/m (annual avg.)	-	1 ppm (15 min)	≤100 µg/m ≤480 µg/m (1 h)	0.10 mg/m (daily avg.)	200 μg/m (1 h) 40 μg/m (1 year)	-	0.053 ppm
Polycyclic aromatic hydrocarbons	83-32-9	No threshold can be determined	-	-	-	-	-	-	-
Trichloroethylene	79-01-6	4.3 × 10 μg/m (unit risk)	-	-	-	-	-	-	-
Tetrachloroethylene	127-18-4	0.25 mg/m (annual avg.)	-	-	-	-	-	-	-
Ozone	10028-15-6	-	0.05 ppm (8 h avg.) (0.100 mg/m )	0.1 ppm	≤240 µg/m (1 h)	0.1 mg/m (1 h avg.)	100 μg/m (8 h)	0.1 ppm (1 h) 0.08 ppm (4 h)	0.12 ppm (1 h) 0.08 ppm (8 h)
Sulfur dioxide (SO )	7446-09-5	-	-	2 ppm (8 h avg.) 5 ppm (15 min)	≤50 µg/m ≤1000 µg/m (5 min)	0.15 mg/m (daily avg.)	-	0.25 ppm (10 min) 0.2 ppm (1 h)	0.5 ppm (3 h) 0.14 ppm (24 h) 0.03 ppm (1 year)
Relative Humidity (RH)	-	-	<70%	-	30–80%—summer; 30–55%—winter	-	-	-	-
Radon (Rn)	10043-92-2	-	-	-	800 Bq/m (1 yr avg.)	-	-	-	-
PM	-	25 μg/m (24 h avg.) 10 μg/m (annual avg.)	-	-	≤40 µg/m ≤100 µg/m (1 h)	-	-	-	65 μg/m (24 h)
PM	-	50 μg/m (24 h) 20 μg/m (1 year)	150 μg/m (in office)	-	-	0.15 mg/m (24 h)	-	90 μg/m (1 h avg.)	150 μg/m (24 h) 50 μg/m (1 year)

Sampling Item	Sampling Methods/Tools	Sampling Duration/Cautions	Ref.
CO , RH, temperature	Q-Trak monitor (TSI Inc.): Nondispersive infrared analyzer	Sampling duration: 7 days, 10 min (min) average	[ , , , , ]
	Integrated data loggers (Hobo HO-8)	Sampling in every 5 min	[ ]
	Indoor air quality meter (IAQ-CALC model 7545)	NA	[ ]
CO	Electrochemical sensor (Draeger Pac III) FIM CO- Tester Tx for exhaled air	Sampling duration: 7 days, 5 min average	[ ]
NO	Passive samplers (Palmes tubes) containing triethanolamine absorbent and analyzed by a spectrophotometer	NA	[ , ]
PM	Dust-Trak air monitor (Model 8520, TSI Inc.), Light scattering	Sampling rate: 1.7 L/min, 1-min interval	[ ]
	Pumped gravimetric method	Sampling duration: 24 h	[ ]
	Model 2100 Mini- Partisol air sampler (Ruprecht & Patashnick Co.) coupled to a ChemPass model 3400	37 mm diameter membrane (2 µm porosity) was used to collect particulate matters	[ ]
	GRIMM environmental dust monitor, light scattering technology	Sampling rate: 1.2 L/min, for 2 weeks (suitable for PM2.5 and PM1 also)	[ ]
	Minivol portable air sampler (Airmetrics, PAS 201) with pall flex membrane filter (47 mm)	Filter conditioned in dry air for 48 h, sampling duration 5–7 h	[ ]
PM	PTFE filters (37-mm diameter, 2-μm porosity)	Sampling rate: 1.8 L/min using a personal impactor, duration: 5 p.m. to 8 a.m. on weekdays and 24 h on weekends. Passive samplers and PM filters were stored in a freezer to keep them cool and avoid sunlight exposure	[ ]
	Low volume sampling pump (model 224-PCXR8) with PEM impactor	Every 5 min intervals	[ , ]
Airborne bacteria	Burkard single stage impactor (Burkard Manufacturing Co. Ltd.) with an agar plate, followed by colony counting	Sampling rate: 10 mL/min for 9 min, incubated at 35 °C in an oven for 2 days	[ ]
HCHO	SKC formaldehyde monitoring kit: Colorimetric method	Sample should be refrigerated and protected from sunlight and immediately sent to the air laboratory for analysis within 1 h	[ ]
	Sample collection: Portable pump (Flec-FL. 1001 or Sibata) with 2,4-DNPH cartridge connected with ozone scrubber. Analysis: two stage thermo desorption followed by gas chromatography/mass spectroscopy	30 min ventilation of housing unit followed by 5 h of sealing. Samples were taken after that, 30 min each.	[ ]
	Radial diffusive samplers filled with 2,4-dinitrophenylhydrazine (2,4-DNPH)-coated Florisil (Radiello code 165), analyzed by liquid chromatography with detection by UV absorption	Sampling duration: 2 weeks	[ , ]
	Diffusion sampler SKC UMEx100 based on chemosorbtion on 2,4-dintrophenyl htydrazine, analyzed by liquid chromatography	Sampling duration: 1 week	[ ]
	Air pull through 2,4-dinitrohydrazine (DNPH) coated silica gel cartridge (Supeleo LPDNPH S10)	Sampling rate: 0.2 L/min for 40 min	[ , ]
VOC	Mass flow controllers (Model No. FC4104CV-G, Autoflow lnc.) trapped by Nutech Cryogenic Concentrator (Model 3550A), analyzed by Hewlett Packard Gas Chromatography (GC) (Model HP6890) using TO-14 method	Sampling rate: 0.011 L/min for 8-h	[ ]
	Diffusive samplers	Exposure period of three days to two weeks	[ ]
	Radial diffusive sampling onto carbograph 4 adsorbents (Radiello code 145), analyzed by gas chromatography-mass spectrometry	Sampling duration: 7 days	[ , ]
	Passive sampling (diffusion principle) with organic vapor monitors	Middle of the room, height: 1.5 to 2 m	[ ]
	Thermal desorption tube, analyzed by gas chromatograph/mass selective detector (GC/MSD)	Sampling rate: 0.07∼0.1 L/min	[ , ]
	Proton transfer reaction mass spectrometer (PTR-MS)	Sampling duration: Less than 5 min	[ ]
	Tenax-TA tubes, analyzed by gas-chromatography with flame ionization detection (Varian, model 3700) & modified thermal desorption	Sampling rate: 20 mL/min for 40 min	[ , ]
	Air pumped through a charcoal filter (Anasorb 747)	Sampling rate: 250 mL/min for 4 h	[ ]
	Air collected on adsorbent tubes and analyzed by gas chromatography-mass spectrometry	Sampling rate: 100 mL/min for 100 min	[ ]
	Organic vapor sampler, adsorbed on activated charcoal column, analyzed by gas chromatography-mass spectrometry	Sampling duration: 8 h	[ ]
TBC	RCS sampler (Biotest air samplers) following centrifugal impaction principle	Sampling rate: 40 L/min for 4 min	[ ]
Rn	CR-393 alpha track diffusion radon gas detectors	Sampling duration: 3 months	[ ]
	Alpha Guard Professional Radon Monitor	Sampling duration: 1 week	[ ]
	Passive measurements of Radon volumic activity by accumulating alpha radiation on 12 m cellulose nitrate film (Kodalpha dosimeter)	Sampling duration: 2 months	[ ]
	Passive dosimeters (Kodalpha LR 115 detectors)	Sampling duration: 2 months, only in heating season	[ ]
Gamma	Gamma radiometer of the Geiger-Muller type (Saphymo 6150 AD6)	Sampling duration: 3–4 h	[ ]
Total Suspended Particulates & respirable suspended particulates (TSPs & RSPs)	PVC filters (pore size 0.45 μm, diameter 37 mm, SKC, USA)	Sampling rate: 2.5 L/min	[ ]
Lead (Pb)	Airborne lead: mixed cellulose ester filter (pore size 0.8 μm, diameter 37 mm), analyzed with a Varian GTA100 model graphite furnace mounted on a Varian SpectrAA-880 model atomic absorption spectrophotometer based on NIOSH method 7105 Surface lead: collected with wet tissues based on NIOSH method 9100	Sampling rate: 4 L/min	[ ]
Ammonia (NH )	Kitagawa precision gas detector tubes	NA	[ ]
Airborne asbestos	Open-faced mixed cellulose ester filter (37 mm diameter and 0.8 μm pore size)	Sampling rate: 2.5 L/min	[ ]
Airborne micro-organism	25 mm nucleopore filter	Pore size 0.4 nm, sampling rate 2 L/min for 4 h	[ ]
Mold & bacteria	CAMNEA method	Sampling rate: 4 h outside the window	[ ]
Bacterial aerosols	Swirling liquid impingers	Sampling rate: 12.5 L/min	[ ]

Investigation Location	Sample Number	Study Area	Indoor Material	Ventilation	Parameters Examined
Hong Kong (2002), [ ]	6	Living room, Kitchen	Plastering wall, wallpaper, tile/wood/vinyl floor	Natural ventilation with air conditioning	CO , HCHO, PM , Bacteria, C H , C H CH , C H CH CH , C H (CH ) , CHCl , CH Cl
Australia (2002), [ ]	27 (ED) * & 4 (NB) *	Living room, bedroom	NA	NA	VOC, HCHO
Singapore (2004), [ ]	3	Bedroom	NA	Natural ventilation with air conditioning	CO , RH, particulate profile, bacteria, fungi, temperature
England & Wales (2005), [ ]	37	Living room, kitchen, other rooms	timber framed construction, traditional brick/block frame, cavity wall insulation	mechanical extract ventilation and passive stack ventilators	NO , CO, HCHO, VOC, RH particulates, temperature
Ottawa, Canada (2005), [ ]	75	Living room and family room	NA	NA	37 VOCs
China (2007), [ ]	6	Living room, Kitchen	NA	NA	PM
France (2008), [ , ]	567	Rooms, attached or integrated garages and outside the dwellings	NA	NA	CO, VOC, particles, Rn, dog, cat and dust mite allergens, radon and gamma radiation
India (2008), [ ]	5	Kitchen, bedroom	NA	Natural Ventilation	particulate matter (RSPM), CO , CO, SO , and NO
Korea (2009), [ ]	158	Living room, kitchen, master room, other room	Wall & ceiling: Silk/Balpo, floor: PVC/wood, furniture: MDF	NA	HCHO, VOC, C H , C H CH , C H CH CH , (CH ) C H , C H Cl , C H CH=CH
China & Japan (2009), [ ]	57 (Jp) & 14 (Ch)	Living room, kitchen, bedroom	Wallpaper (Japan); paint (China)	NA	VOC (C H , C H CH , C H CH CH , (CH ) C H , C H (CH )
Italy (2011), [ ]	60	Living room	NA	NA	PM, NO , CO, O
Ireland & Scotland (2011), [ ]	100	Living room	NA	NA	PM , CO, CO , NO
Germany (2013), [ ]	2246	Living or child’s room	NA	NA	60 VOC’s
UAE (2014), [ ]	628	Family room	NA	Sealed AC	CO, HCHO, H S, NO , SO , PM , PM
United States (2015), [ ]	17	NA	Hardwood floors, carpets	Natural ventilation with air conditioning	CO , CO, RH, temperature, particulate matter, VOC, HCHO
United States (2015), [ ]	86	Living room and kitchen	Low VOC carpet, flooring, carpet pad, zero VOC paint	HVAC system	PM, HCHO, VOC
France (2017), [ ]	567	Bedroom and living room	NA	Mechanical ventilation	CO , RH, VOCs, HCHO, PM , PM
France (2018), [ ]	72	Living room, master bedroom	Lightweight/masonry facades, timber frame, thermal insulation	Mechanical or hybrid ventilation	CO , CO, RH, NO , VOCs, HCHO, Rn, airborne particles, temperature
Macedonia (2017), [ ]	25	Living room	NA	NA	Temperature, RH, TVOC, PM
Northern Ireland (2019), [ ]	5	Main living area, bedroom	Timber & Masonry	Balanced mechanical heat recovery ventilation or demand-controlled ventilation systems	Rn
Paraguay (2019), [ ]	80	Kitchen	NA	NA	PM , CO
Finland & Lithuania (2019), [ ]	45	Living room	NA	Natural and mechanical ventilation	CO, NO , VOCs, Rn, microbial content
California, USA (2020), [ ]	23	Bedroom, living room, kitchen, dinning area	NA	Mechanical ventilation	CO , NO , HCHO, PM
California, USA (2020), [ ]	70	Bedroom, living room	NA	Mechanical ventilation	CO , NO , HCHO, PM , NOx, RH, temperature

Investigation Location	Sample Number	Seasonal Variation	Indoor Material	Ventilation	Parameters Examined
Australia (2003), [ ]	20 office, 4 schools, 1 hospital & 1 old home	NA	NA	NA	VOC
Korea (2007), [ ]	55 schools, 30 std/class	Summer, autumn, winter	Pressed wood desks, chairs, furnishings	Mainly naturally ventilated	CO, CO , PM , TBC, TVOCs, HCHO
Korea (2011), [ ]	17 pre-schools (71 classrooms)	Late spring and summer	Concrete, floor covered with linoleum/wood, no carpet	Naturally ventilated	TSPs, RSPs, lead, asbestos, TVOCs, HCHO, and CO
Greece (2007), [ ]	3 (office)	Spring	glazed windows. Painted gypsum board wall, plastic tiles, no carpet	Natural ventilation	PM
Greece (2008), [ ]	1 (school)	Summer, fall, and winter	NA	Natural ventilation	PM , O , CO
Antwerp, Belgium (2008), [ ]	27 (primary school)	Winter and early summer	NA	Natural ventilation	PM , K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Pb, Al, Si, S, Cl, NO2, SO , O , and C H , C H CH , C H CH CH , and (CH ) C H
Hong Kong (2008), [ ]	82 (office)	NA	NA	mechanically ventilated and air-conditioned	Airborne fungi count
Beijing (2009), [ ]	2 (office)	Spring and early summer	NA	Mechanical ventilation	RH, HCHO, VOCs, NH , CO , mold and bacteria
Michigan, USA (2007), [ ]	64 (school)	Spring and early summer	Carpet	Mechanical ventilation	Ventilation rates, VOCs and bioaerosols, CO , RH, and temperature
California, USA (2012), [ ]	37 (office & others)	NA	NA	Rooftop heating, ventilation, and air conditioning units	Black carbon, PM , PM , PM
Colorado Boulder, USA (2016), [ ]	1 (university)	Spring	Latex paint in wall	Dedicated air handling unit	VOC
USA (2016), [ ]	14	All seasons	NA	2 Mechanical ventilation & 2 natural ventilation	CO, CO , HCHO, NO , O , PM
Chennai, India (2012), [ ]	1 (school)	Winter & summer	NA	Natural ventilation	PM , PM , PM , CO, HCHO, bioaerosols
Delhi, India (2017), [ ]	3 (2 office & 1 EB*)	June-July	Concrete flooring	Air condition	CO , PM , VOC
Dubai & Fujairah, UAE (2014), [ ]	16 (elementary school)	Summer & winter	NA	NA	TVOC, CO , O , CO, particle concentration
Gliwice, Poland (2015), [ ]	2 (Nursery school)	Winter	NA	Stack ventilation and airing	VOC, PM, bacterial and fungal bioaerosol, CO
Netherland, (2015), [ ]	17 (Primary school)	Winter	NA	Naturally ventilated	Endotoxin, b(1,3)-glucans, PM , PM , NO
Italy (2016), [ ]	7 school (16 Classrooms)	Winter & spring	Single/double glazed Al/Fe window	Manual airing	CO , particulate concentration, Rn
Qatar (2017), [ ]	1 (Office Building)	Summer	NA	HVAC	PM , PM
Qatar (2017), [ ]	16 (urban schools)	Winter	Floor: vinyl or ceramic tile	Mechanically ventilated	temperature, RH, CO, CO and particulate matters (PM and PM )
Turkey (2018), [ ]	4 (university classrooms)	Winter & summer	Desk & table: MDF veneered compressed chipboards, Door: woodwork	Natural ventilation	Temperature, RH, CO , Rn, PM , PM , PM , PM , and PM
Europe (2016), [ ]	37 (office)	Winter & summer	NA	Mostly mechanical ventilation	VOC, HCHO, O , NO , PM
Europe, (2019), [ ]	37 office (140 office room)	Winter & summer	Synthetic floor covering, dispersion or emulsion wall paint, furniture: wood and derivatives (45%) or metal (31%), ceiling: synthetic	Mostly mechanical ventilation	HCHO, VOC, PM , O , NO , temperature, RH
Sweden (2019), [ ]	4 (preschool)	All seasons	Low emitting materials	Heat recovery ventilation & heat recovery with DCV	Temperature, RH, particle-size distribution, CO , NO , HCHO and TVOC
Sweden (2019), [ ]	7 school (145 classrooms)	Summer & winter	NA	Mechanical ventilation with DCV and centralized air handling units	Temperature, CO
Southern Italy (2019), [ ]	12 (lower secondary schools)	Summer & winter	NA	Natural ventilation	Temperature, RH, CO , NO , PM , biological pollutants in indoor dust (endotoxins and Der p 1)

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Mannan, M.; Al-Ghamdi, S.G. Indoor Air Quality in Buildings: A Comprehensive Review on the Factors Influencing Air Pollution in Residential and Commercial Structure. Int. J. Environ. Res. Public Health 2021 , 18 , 3276. https://doi.org/10.3390/ijerph18063276

Mannan M, Al-Ghamdi SG. Indoor Air Quality in Buildings: A Comprehensive Review on the Factors Influencing Air Pollution in Residential and Commercial Structure. International Journal of Environmental Research and Public Health . 2021; 18(6):3276. https://doi.org/10.3390/ijerph18063276

Mannan, Mehzabeen, and Sami G. Al-Ghamdi. 2021. "Indoor Air Quality in Buildings: A Comprehensive Review on the Factors Influencing Air Pollution in Residential and Commercial Structure" International Journal of Environmental Research and Public Health 18, no. 6: 3276. https://doi.org/10.3390/ijerph18063276

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Air quality in Chelyabinsk

Air quality index (aqi) and pm2.5 air pollution in chelyabinsk.

Last update at 10:00, Sep 26 (Local time)

57.5K people follow this city

Chelyabinsk Air Quality Map

Real-time chelyabinsk air pollution map.

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What is the pollen count in Chelyabinsk today?

What is the current weather in Chelyabinsk?

	Index	Low
Tree pollen	None
Grass pollen	Low
Weed pollen	Low

Weather	Clear sky
Temperature	7°C
Humidity	70%
Wind	7.2 km/h
Pressure	1030 mbar

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10:00, Sep 26 (Local time)

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Real-time chelyabinsk air quality ranking.

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What is the current air quality in Chelyabinsk?

#	station
1

Air pollution level	Air quality index	Main pollutant
Good	11 US AQI	PM2.5

Pollutants		Concentration
PM2.5		2

PM2.5 concentration in Chelyabinsk air currently meets the WHO annual air quality guideline value

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Chelyabinsk air quality index (AQI) forecast

Monday, Sep 23

Day	Pollution level	Weather	Temperature	Wind
AQI US		AQI US		AQI US		AQI US		AQI US		AQI US		AQI US		AQI US		AQI US		AQI US

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AIR QUALITY ANALYSIS AND STATISTICS FOR Chelyabinsk

What is the air quality index of chelyabinsk.

Chelyabinsk is a city and the administrative centre of Chelyabinsk Oblast, Russia. According to a 2010 census, the population was estimated as 1.1 million people, which put it as the 7 th largest city in Russia (by population). The city is to the east of the Ural Mountains and sits on the banks of the Miass River which is regarded as the boundary between Europe and Asia.

At the beginning of 2021, Chelyabinsk was experiencing “Good” quality air with a US AQI reading of just 21. The measured concentration of PM2.5 was 5.1 µg/m³.

With levels such as these windows can be opened to let in the fresh air and all types of outdoor activity can be enjoyed.

What are the main sources of air pollution in Chelyabinsk?

The level of air pollution in Chelyabinsk is formed under the influence of emissions from large metallurgical enterprises, energy enterprises, as well as vehicle emissions, the number of which is increasing every year.

On 6 th January, there was also a strong burning smell in the city and visibility was significantly reduced. Smoke and the smell of burning spread to the city with a weak south wind, but without receding. Possible sources of pollution were suspected these are fires at a landfill on the southern edge of the city, as well as emissions from oil refineries, the legality of which is being checked.

What is the pollution level in Chelyabinsk?

Calm weather for Chelyabinsk most often means that the city will be covered with smog. Emissions from factories and exhaust fumes from cars and trucks do not dissipate but envelop houses. As the locals joke, on such days the city air can not only be seen but also touched. People say that it’s impossible to walk outside because of developing a sore throat and suffering from stinging eyes. The windows in the house cannot be opened and those families with young children invest in an air purifier as a counter-measure.

Is air pollution in Chelyabinsk getting better or worse?

Due to the rise in the dollar exchange rate, it has become much more profitable to export Chelyabinsk metal for export. Because of this, production increased and consequently, there were more emissions. Unfortunately, the owners decided to extract excess profits over a short period of time and not invest in filtration systems.

To solve this problem, eco-activists proposed to install filters, move the most harmful industries outside the city (as they did in other large Russian cities, such as Yekaterinburg, Kazan and Moscow), introduce a moratorium on cutting down green zones and increase the number of stationary posts that measure air pollution, up to 20, and enable online monitoring of factory emissions.

Even though the emissions were recorded, the authorities were under no obligation to reveal the figures to the public.

A group of environmentalists decided to create a system that records the dynamics of changes in the concentration of harmful substances and the plume of their dispersion. They decided to measure suspended particles with a size of 2.5 micrometres (PM2.5): if their content in the air jumped sharply, this indirectly indicates an emergency situation. After realising what type of device they needed, the activists created it themselves: they chose suitable sensors, tested them and then ordered a whole series.

Simple Chelyabinsk citizens who want to know what they breathe helped to finance this. Through a website, the townspeople are able to order a measuring device, which the activists will give them at the cost price of 3,000 roubles. Having received the device, it is enough to plug it into an outlet, hang it from the window and connect it to the Internet. After that, it will automatically start sending information about air pollution to the server, and it will be displayed on the map in real-ime.

What can be done to improve the air quality in Chelyabinsk?

The topic of air pollution was discussed at an environmental forum back in 2017 but none of the experts could say what was happening to the air quality such as how much emissions come from large factories, and how much comes from small enterprises and vehicles.

Because of the huge demand for individual monitoring devices, the initiative turned out to be in great demand: residents are now actively sharing information and discussing the measurement results. Thanks to the monitoring system, activists managed to find out that the main emissions occur in portions, at night and on weekends. When they began to investigate it further, it turned out that the posts of Roshydromet (The Russian Federal Service for Hydrometeorology and Environmental Monitoring) were not working at that time. As a result, they managed to ensure that the governor allocated funds for the operation of posts around the clock and daily. At first, they did not notice us, then they fought with us, and now we have won and the authorities are using our system.

This system which was instigated by the local environmentalist group does not allow hiding emission data, has pushed government agencies to fulfil their responsibilities and inform people about air pollution. For the creation of a monitoring system in Chelyabinsk, they promised to allocate about 300 million roubles from the funds of pollutants and from the federal and regional budgets.

What are the effects of breathing Chelyabinsk’s poor quality air?

Small particles of PM2.5 and PM10 and various other harmful compounds, mainly from combustion processes, are transported into the indoor air from outside. They also arise from many internal sources, such as smoking. Exposure to small particles increases premature mortality, cardiovascular disease, lung cancer and possibly asthma.

A wide variety of gaseous volatile organic compounds (VOCs) can be present in indoor air. These compounds evaporate into the indoor air, especially from building materials. Emissions may increase due to wetting of the materials. The main health effects of VOCs in residential environments are related to transient irritation and respiratory symptoms.

Industrial mineral fibres, such as glass fibres (glass wool) and rock fibres (rock wool), can enter the indoor air. Mineral wool fibres are used in building thermal insulation materials, acoustic panels and sound insulation such as ventilation duct silencers. Glass and rock wool can cause skin, eye and respiratory tract irritation.

Chelyabinsk air quality data attribution

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Indoor Air Quality and Health

In the last few decades, Indoor Air Quality (IAQ) has received increasing attention from the international scientific community, political institutions, and environmental governances for improving the comfort, health, and wellbeing of building occupants. Several studies on this topic have shown both qualitative and quantitative IAQ variations through the years, underlining an increase in pollutants and their levels. To this aim, IAQ-related standards and regulations, policies for non-industrial buildings, and monitoring plans have been developed in several countries. It has been estimated that people spend about 90% of their time in both private and public indoor environments, such as homes, gyms, schools, work places, transportation vehicles, etc.; thus, IAQ has a significant impact on health and quality of life in general. For many people, the health risks from exposure to indoor air pollution may be greater than those related to outdoor pollution. In particular, poor indoor air quality can be harmful to vulnerable groups such as children, young adults, the elderly, or those suffering chronic respiratory and/or cardiovascular diseases.

Indoor environments represent a mix of outdoor pollutants prevalently associated with vehicular traffic and industrial activities, which can enter by infiltrations and/or through natural and mechanical ventilation systems, as well as indoor contaminants, which originate inside the building, from combustion sources (such as burning fuels, coal, and wood; tobacco products; and candles), emissions from building materials and furnishings, central heating and cooling systems, humidification devices, moisture processes, electronic equipment, products for household cleaning, pets, and the behavior of building occupants (i.e., smoking, painting, etc.).

IAQ can be affected by various chemicals, including gases (i.e., carbon monoxide, ozone, radon), volatile organic compounds (VOCs), particulate matter (PM) and fibers, organic and inorganic contaminants, and biological particles such as bacteria, fungi, and pollen. The large number of variables that impact IAQ inevitably leads to a wide range of studies and scientific papers published in journals from many kinds of scientific subjects (e.g., chemistry, medicine, environmental sciences, etc.). To further underline the importance of IAQ studies, the present special issue was published. It includes 22 contributions by some of the main experts in the field of indoor air pollution in public and private buildings and related health concerns.

In particular, an indoor air sampling was monitored by Orecchio et al. [ 1 ] to determine 181 VOCs emitted from several sources (fuels, traffic, landfills, coffee roasting, a street-food laboratory, building work, indoor use of incense and candles, a dental laboratory, etc.) located in Palermo (Italy) by using canister auto-samplers and the gas chromatography-mass spectrometry technique for VOC analysis.

Concerning indoor air in residential houses, the study of Vilčeková et al. [ 2 ] attempted to provide more information about the IAQ of 25 houses in several cities of the Formal Yugoslav Republic of Macedonia. Air pollutants measured included humidity, total VOCs, PM, and sound pressure. The authors found interesting dependences between characteristics of buildings (year of construction, year of renovation, smoke, and heating system) and chemical-physical measurements (temperature, relative humidity, TVOC, PM 2.5 , and PM 10 ) using statistical approaches (i.e., R software, Van der Waerden test).

The influence of particle size on human indoor exposure to airborne halogenated flame retardants (HFRs), released from consumer products, was investigated by La Guardia et al. [ 3 ]. Their findings demonstrated that the larger, inhalable air particulates carried the bulk (>92%) of HFRs and indicated that contributions and the bioavailability of respirable and inhalable airborne particles should both be considered in future risk assessment studies.

IAQ in enclosed environments was also studied by Chen et al. [ 4 ] who investigated the occurrence and levels of chemicals (including humidity, temperature, carbon monoxide, carbon dioxide, formaldehyde, TVOCs, ozone, PM 10 and PM 2.5 , and microbial agent concentrations (i.e., bacteria and fungi) in North Taiwan underground subway stations).

Moreover, various studies have been conducted on the health risks of dampness and mold in houses, but few studies have been performed in workplaces and schools. The paper of Lanthier-Veilleux et al. [ 5 ] is an examination of the independent contribution of residential dampness or mold (i.e., visible mold, mold odor, dampness, or water leaks) to asthma, allergic rhinitis, and respiratory infections among students at the Université de Sherbrooke (Quebec, QC, Canada); while the work of Szulc et al. [ 6 ] evaluated the microbiological contamination at a plant biomass processing thermal power station located in Poland.

Among the factors that influence the estimation of human exposure to indoor air pollution, the pattern of human behavior and activity play a fundamental role. Odeh and Hussein [ 7 ] evaluated, for the first time, the human activity pattern of 285 subjects (17–63 years old) residents in Amman (Jordan) in order to use the outcomes in future human exposure studies.

Environmental tobacco smoke (ETS) is also considered a key contributor to indoor air pollution and public health. In comparison to the large body research on toxicological substances of ETS and concentrations of indoor ETS-dependent PM, less attention has been paid on the correlation between the odor concentration and the chemical composition of ETS. The odor concentrations of field ETS, second-hand smoke (SHS), and third-hand smoke (THS) in prepared samples were determined by Noguchi et al. [ 8 ] using the triangle-odor-bag method, while the chemical compositions of the same samples were determined by proton transfer mass spectrometry. Results of this study evidenced that the main contributing components to the odor of the field ETS samples (acetaldehyde, acetonitrile, acetic acid, and other unknown components with a mass-to-charge ratio ( m / z ) of 39 and 43) were different from those found in SHS and THS samples.

A potential threat to IAQ in indoor environments can be related to the contribution of outdoor pollutants concentrations and rates of infiltration, which affect the concentrations to which people are exposed indoors. Scheepers et al. [ 9 ] investigated the concentrations of volatile organic compounds (VOCs), acrolein, formaldehyde, nitrogen dioxide (NO 2 ), respirable particulate matter (PM 4.0 and PM 2.5 ), and their respective benz(a)pyrene contents over a period of two weeks in indoor and outdoor locations at a university hospital, found that chemical IAQ was primarily driven by known indoor sources and activities, and did not show evidence of significant contributions of known outdoor local sources to any of the IAQ parameters measured.

In particular, the ventilation rate (VR) is a fundamental parameter affecting the IAQ and the energy consumption of buildings. The manuscript of Batterman [ 10 ] reviews the use of CO 2 as a “natural” tracer gas for estimating VRs in school classrooms, and provides details and guidance for the steady-state, build-up, decay, and transient mass balance methods. The CO 2 tracer approach was also used by Matthews et al. [ 11 ] within a large university building in Manchester to estimate air-exchange rates. The same authors presented an innovative approach based on the use of perfluorocarbon tracers to trace the amount of outdoor material penetrating into the university building and the flow of material within the building itself.

Minimizing indoor air pollutants is paramount to high performance schools, due to the potentially detrimental effects that VOCs, particulate matter including allergens and molds, and combustion gases may have on the health and wellbeing of students. In addition to their capacity to trigger asthma or allergy attacks, some of these pollutants are notorious for causing flu-like symptoms, headaches, nausea, and irritation of the eyes, nose, and throat. Moreover, a recent research suggests that a school’s physical environment also can play a major role in academic performance. However, newer designs, construction practices, and building materials for “green” buildings and the use of “environmentally friendly” products have the promise of lowering chemical exposure. Zhong et al. [ 12 ] determined VOC concentrations and IAQ parameters in 144 classrooms in 37 conventional and high performance elementary schools in the USA, and found that aromatics, alkanes, and terpenes were the most detected VOCs, whose concentrations did not show significant differences between the two kinds of schools.

This special issue also presents the relationships and potential conflicts between IAQ and passive houses and/or other highly energy-efficient buildings, focusing the attention on the influence of ventilation systems. Wallner et al. [ 13 ] investigated, between 2010 and 2012, whether occupants of two types of buildings (mechanical vs. natural ventilation) experience different health, wellbeing, and housing satisfaction outcomes, as well as whether associations with indoor air quality existed. The study evidenced that inhabitants of energy-efficient, mechanically ventilated homes rated the quality of indoor air and climate significantly higher and, independently of the type of ventilation, associations between vegetative symptoms (dizziness, nausea, headaches) and formaldehyde concentrations as well as between CO 2 levels and perceived stale air were observed.

More topics covered in this special issue are related to the IAQ in healthcare facilities together with the air cleanliness in operating theatres, which are fundamental aspects for preserving the health of both the patient and the medical staff. Numerous monitoring campaigns were performed by Romano et al. [ 14 ] to determine ultrafine particle concentrations in operating theatres equipped with upward displacement ventilation or with a downward unidirectional airflow system. The results demonstrated that the use of electrosurgical tools generate an increase of particle concentration in the surgical area as well as within the entire operating theatre area, strongly related to the surgical ventilation, ventilation principle, and electrosurgical tools used. Cipolla et al. [ 15 ] monitored the VOCs concentrations (including hydrocarbons, alcohols, and terpenes) using passive diffusive samplers in two different anatomical pathology wards in the same hospital, evidencing a different VOC contamination due to the structural difference of the buildings and different organizational systems.

Another theme that emerges from the studies presented in this special issue is the household air pollution (HAP) from the combustion of biomass fuels, including wood, agricultural residues, animal dung, coal, and charcoal, in open fires or traditional stoves. Such inefficient cooking and heating practices are still commonly used in developing countries and release many air pollutants, such as carbon monoxide, oxygenated organics, free radicals, and PM, in particular PM 2.5 , which may be linked to several health complications, including low birth weight, cardiovascular disease, tuberculosis, cataracts, and other respiratory complications.

The study of Kurti et al. [ 16 ] determined whether HAP exposure was associated with reduced lung function and respiratory and non-respiratory symptoms in Belizean adults and children, demonstrating that adults experienced greater respiratory and non-respiratory symptoms; whereas the research conducted by Medgyesi et al. [ 17 ] investigated the effects of exposure to biomass fuel cookstove emissions on women in rural Bangladesh, associated with acute elevated PM 2.5 concentrations, and evidencing a decrease in pulmonary function. Novel evidence that using cleaner fuels such as liquefied petroleum gas (LPG) with respect to dirty fuels like wood/straw for domestic cooking is associated with a significant lower probability of chronic or acute diseases was demonstrated by Nie et al. [ 18 ], in their study on women in rural China. These findings support literature data showing that inefficient biomass burning stoves may cause high levels of HAP and threaten long-term health diseases. To reduce HAP in developing countries, clean cooking programs and strategic governmental policies should be adopted, taking into consideration the main factors influencing adoption beyond health, such as cost, taste, fears, and cultural traditions, as evidenced in the study of Hollada et al. [ 19 ] assessing the attitudes, preferences, and beliefs about traditional versus liquefied petroleum gas (LPG) stoves in primary cooks and their families in rural Puno, Peru.

Residential exposure to radon is strictly associated with lung cancer risk; thus, radon monitoring in households located in areas classified by United States–Environmental Protection Agency (US-EPA) as zones with high potential radon exposure is essential to safeguard the health of residents. Stauber et al. [ 20 ] presented a pilot study to monitor radon levels in 201 households located in Dekalb county (GA, USA), and found that radon exceeded EPA moderate risk levels in 18% of households and high risks in 4% of the homes tested, suggesting that a more extensive radon screening is needed in the entire county.

Taking into account the increasing IAQ concerns and complaints, it becomes important to develop a practical diagnostic tool for proper IAQ management. The study of Wong et al. [ 21 ], conducted in Hong Kong, proposes a stepwise IAQ screening protocol to facilitate cost-effective IAQ management among building owners and managers and to identify both lower and higher risk groups for unsatisfactory IAQ. Furthermore, the study of Marques and Pitarma [ 22 ] led to the development of an IAQ system through web access and mobile applications to monitor the IAQ of different building rooms in real time.

As seen, the contributions to this special issue cover a large area of IAQ-related studies, and it is expected that more deep research will be stimulated and conducted as a result of this special issue.

Acknowledgments

We would like to thank all the authors and reviewers that made this special issue possible.

Conflicts of Interest

The authors declare no conflicts of interest.