A System Based on the Internet of Things for Real-Time Particle Monitoring in Buildings

Revolutionizing indoor air quality monitoring through IoT innovations: a comprehensive systematic review and bibliometric analysis

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers.

Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

Antibiotic resistance genes associated with size-segregated bioaerosols from wastewater treatment plants: A review

The antibiotic-resistant pollution in size-segregated bioaerosols from wastewater treatment plants (WWTPs) is of increasing concern due to its public health risks, but an elaborate review is still lacking. This work overviewed the profile, mobility, pathogenic hosts, source, and risks of antibiotic resistance genes (ARGs) in size-segregated bioaerosols from WWTPs. The dominant ARG type in size-segregated bioaerosols from WWTPs was multidrug resistance genes. Treatment units that equipped with mechanical facilities and aeration devices, such as grilles, grit chambers, biochemical reaction tanks, and sludge treatment units, were the primary sources of bioaerosol antibiotic resistome in WWTPs. Higher enrichment of antibiotic resistome in particulate matter with an aerodynamic diameter of <2.5 μm, was found along the upwind-downwind-WWTPs gradient. Only a small portion of ARGs in inhalable bioaerosols from WWTPs were flanked by mobile genetic elements. The pathogens with multiple drug resistance had been found in size-segregated bioaerosols from WWTPs. Different ARGs or antibiotic resistant bacteria have different aerosolization potential associated with bioaerosols from various treatment processes. The validation of pathogenic antibiotic resistance bacteria, deeper investigation of ARG mobility, emission mechanism of antibiotic resistome, and development of treatment technologies, should be systematically considered in future.

Particle-size stratification of airborne antibiotic resistant genes, mobile genetic elements, and bacterial pathogens within layer and broiler farms in Beijing, China

The particle-size distribution of antimicrobial resistant (AMR) elements is crucial in evaluating their environmental behavior and health risks, and exposure to the fecal microbiome via particle mass (PM) is an important route of transmission of AMR from livestock to humans. However, few studies have explored the association between air and fecal AMR in farm environments from the perspective of particle-size stratification. We collected feces and PMs of different sizes from layer and broiler farms, quantified antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and human pathogenic bacteria (HPB) using Droplet digital PCR (ddPCR), and analyzed the bacterial communities based on 16S rRNA sequencing. The particle-size distributions of 16S rRNA and AMR elements were similar and generally increased with larger particle sizes in chicken farms. In broiler farms, we observed a bimodal distribution with two peaks at 5.8-9.0 μm and 3.3-4.7 μm. The dominant airborne bacterial phyla were Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes. The dominant phyla in the feces were the same as those in the air, but the order of relative abundance varied. The particle-size distributions of specific bacterial genera differed between the animal-farm types. Overall, the degree of association between feces and different particulates increased with increasing particle size. The microbial communities in the coarse particles were similar to those in fecal samples. Escherichia coli, Staphylococcus spp., Campylobacter spp., and sul 2 (sulfonamide ARGs) tended to attach to small particles. We highlight the particle size-specific relationship between fecal and air microbes involving ARGs, MGEs, and HPB and provide valuable information for comprehensively assessing the transmission of fecal microorganisms through the airpath and its environmental and occupational health risks.

Assessment of house dust trace elements and human exposure in Ankara, Turkey

One of the impacts of the COVID-19 pandemic is leading people remain at homes longer than ever. Considering the elongation of the time people spend indoors, the potential health risks caused by contaminants including heavy metals in indoor environments have become even more critical. The purpose of this study was to evaluate the levels and sources of heavy metals in indoor dust, to assess the exposure to heavy metals via indoor dust, and to estimate the associated health risk. The highest median value was measured for Zn (263 μg g^-1), while the lowest median concentration value was observed for Cd (0.348 μg g^-1). The levels of elements measured in the current study were found to be within the ranges reported in the other parts of the world, mostly close to the lower end of the range. House characteristics such as proximity to the main street, presence of pets, number of occupants, and age of the building were the house characteristics influencing the observed higher concentrations of certain heavy metals in houses. Enrichment factor values range between 1.79 (Cr) and 20.4 (Zn) with an average EF value of 8.80 ± 6.80 representing that the targeted elements are enriched (EF>2) in indoor dust in Ankara. Positive matrix factorization results showed that the heavy metals in the house dust in the study area are mainly contributed from sources namely outdoor dust, carpets/furniture, solders, wall paint/coal combustion, and cigarette smoke. Carcinogenic and non-carcinogenic risk values from heavy metals did not exceed the safe limits recommended by EPA. The highest carcinogenic risk level was caused by Cr. The risk through ingestion was higher than inhalation, and the risk levels were higher for children than for adults.

Submicron aerosols share potential pathogens and antibiotic resistomes with wastewater or sludge

Generation of size-segregated aerosols is an important eco-environmental problem in wastewater treatment plants (WWTPs), but the characteristics of potential pathogens and antibiotic resistomes in submicron aerosols (PM_1.0) were almost unknown. Here, 16S rRNA gene amplification and shotgun metagenome sequencing were respectively used to study the profiles of potential pathogens and antibiotic resistance genes (ARGs) in PM_1.0 from a full-scale WWTP. Acinetobacter and sul1 were respectively the predominant potential pathogens and ARG subtypes in PM_1.0 from aeration process. A total of 9 potential pathogens and 147 ARG subtypes, were shared among WWTP-PM_1.0, wastewater/sludge, and ambient air. Significant differences of potential pathogens or ARGs were found between WWTP-PM_1.0 and wastewater/sludge, however, wastewater/sludge had more crucial source contribution than the ambient air. Moreover, 13 potential pathogens and 40 ARG subtypes were easily aerosolized in PM_1.0 from at least one of the treatment units. ARGs were mainly harbored by Proteobacteria, and multidrug resistance genes were the most ARG type carried by potential pathogens. Taken together, this study indicates the prevalence of potential pathogens, ARGs, and ARG-carrying potential pathogens in WWTP-PM_1.0, which highlights the potential risk of PM_1.0 in spreading potential pathogens and antibiotic resistomes into the air environments.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Development of IoT-Based Particulate Matter Monitoring System for Construction Sites

Particulate matters (PMs) generated on construction sites can pose serious health risks to field workers and residents living near construction sites. PMs are generated in a wide range of locations; therefore, they must be managed in real time at various locations within construction sites for practical management of the PMs. However, no such systems exist currently. Therefore, this study aims to develop a system that can manage PMs in real time at multiple locations in a construction site using the Internet of Things technology. Accordingly, measuring instrument, network, and program services were developed as system components, while considering the characteristics of construction sites, and the construction site PM monitoring system was developed by integrating these components. Finally, performance certification and field application tests were performed to verify the developed system. The construction site PM monitoring system (CPMS) achieved grade 1 for reproducibility, relative precision, and data acquisition rate, and grade 2 for accuracy and coefficient of determination. Thus, it received a performance certification of grade 2, in total. In particular, regarding accuracy, which is a shortcoming of the light-scattering method and represents the accuracy of measurements, the CPMS was found to have an accuracy of 74.2%.

Characteristics of size-segregated aerosols emitted from an aerobic moving bed biofilm reactor at a full-scale wastewater treatment plant

Aerosol emissions from wastewater treatment plants (WWTPs) have been associated with health reverberation but studies about characteristics of size-segregated aerosol particulate matter (PM) are scarce. In this study, the measurement of particulate number size distribution in the range of < 10 µm, and the collection of PM_10-2.5, PM_2.5-1.0 and PM_1.0, were conducted from an aerobic moving bed biofilm reactor (MBBR) at a full-scale WWTP. MBBR aerosols showed a unimodal number size distribution, with the majority of particles (>94%) in the ultrafine size range (<100 nm). For toxic metal(loid)s or potential pathogens, significant differences were found within MBBR aerosols (PM_10-2.5, PM_2.5-1.0, and PM_1.0), and also between MBBR aerosols and wastewater. Both wastewater and ambient air had important source contributions for MBBR aerosols. The compositions of toxic metal(loid)s in PM_1.0, and the populations of potential bacterial or fungal pathogens in PM_10-2.5 and PM_2.5-1.0, were dominated by that from wastewater. Compared to PM_10-2.5 and PM_2.5-1.0, PM_1.0 had the highest aerosolization potential for the toxic metal(loid)s of As, Cd, Co, Cr, Li, Mn, Ni, U, and Zn, and the genera of Acinetobacter, Pseudomonas and Fusarium. Due to the size-segregated specialty, targeted measures should be employed to reduce the health risks. CAPSULE: The compositions of toxic metal(loid)s in PM_1.0, and the populations of potential pathogens in PM_10-2.5 and PM_2.5-1.0, were dominated by that from wastewater.

Student satisfaction from the influence of the built environment, price fairness and teaching care: a study at a community-supported university

Purpose

This study aims to test and analyze factors that may influence the satisfaction of university students, specifically, from the built environment, price fairness and teaching care.

Design/methodology/approach

A survey was conducted with 250 students from a university in southern Brazil. The data were analyzed by structural equation modeling.

Findings

The main results proved that the built environment, price fairness and teaching care have a significant and positive influence on the satisfaction of university students.

Practical implications

The factors presented explanation power for student satisfaction, which shows that they are essential aspects and, therefore, must be observed by universities to satisfy their students.

Originality/value

The study identifies the impact of factors such as the environment, price and teaching service on student satisfaction.

Predictive control for indoor environment based on thermal adaptation

Previous studies show that the indoor environment quality (IEQ) of buildings directly affects human health and comfort. This study aims to predict the change of indoor parameters at the next moment under the influences of the current indoor climate and outdoor climate and control the IEQ parameters based on the human thermal adaption in advance. We combine the simulation and the mathematical method to establish the office building model with air-conditioning and lighting systems and construct the bilinear model of the IEQ parameters and control variables. Unknown parameters are identified using the experimental method. Model predictive control (MPC) based on human thermal comfort is discussed by considering human thermal adaptation, and the neutral temperature is calculated through the dynamic relationship between outdoor and indoor temperatures. Results show that the temperature setpoint is adjusted in accordance with human adaptability, and the air-conditioning, fan, and lighting systems are controlled via MPC. The usage time of air-conditioning and light is reduced, and thus, energy is saved.

Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review

The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO₂ and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.

Real-time measurements of PM2.5 and ozone to assess the effectiveness of residential indoor air filtration in Shanghai homes

Portable air cleaners are increasingly used in polluted areas in an attempt to reduce human exposure; however, there has been limited work characterizing their effectiveness at reducing exposure. With this in mind, we recruited forty-three children with asthma from suburban Shanghai and deployed air cleaners (with HEPA and activated carbon filters) in their bedrooms. During both 2-week filtration and non-filtration periods, low-cost PM_2.5 and O₃ air monitors were used to measure pollutants indoors, outdoors, and for personal exposure. Indoor PM_2.5 concentrations were reduced substantially with the use of air cleaners, from 34 ± 17 to 10 ± 8 µg/m³ , with roughly 80% of indoor PM_2.5 estimated to come from outdoor sources. Personal exposure to PM_2.5 was reduced from 40 ± 17 to 25 ± 14 µg/m³ . The more modest reductions in personal exposure and high contribution of outdoor PM_2.5 to indoor concentrations highlight the need to reduce outdoor PM_2.5 and/or to clean indoor air in multiple locations. Indoor O₃ concentrations were generally low (mean = 8±4 ppb), and no significant difference was seen by filtration status. The concentrations of pollutants and the air cleaner effectiveness were highly variable over time and across homes, highlighting the usefulness of real-time air monitors for understanding individual exposure reduction strategies.

A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies

The World Health Organization declared COVID-19 a global pandemic on the 11th of March 2020, but the world is still reeling from its aftermath. Originating from China, cases quickly spread across the globe, prompting the implementation of stringent measures by world governments in efforts to isolate cases and limit the transmission rate of the virus. These measures have however shattered the core sustaining pillars of the modern world economies as global trade and cooperation succumbed to nationalist focus and competition for scarce supplies. Against this backdrop, this paper presents a critical review of the catalogue of negative and positive impacts of the pandemic and proffers perspectives on how it can be leveraged to steer towards a better, more resilient low-carbon economy. The paper diagnosed the danger of relying on pandemic-driven benefits to achieving sustainable development goals and emphasizes a need for a decisive, fundamental structural change to the dynamics of how we live. It argues for a rethink of the present global economic growth model, shaped by a linear economy system and sustained by profiteering and energy-gulping manufacturing processes, in favour of a more sustainable model recalibrated on circular economy (CE) framework. Building on evidence in support of CE as a vehicle for balancing the complex equation of accomplishing profit with minimal environmental harms, the paper outlines concrete sector-specific recommendations on CE-related solutions as a catalyst for the global economic growth and development in a resilient post-COVID-19 world.

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015-2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO₂, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015-2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.

Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose

Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO2, CO, PM10, NO2 temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of "low cost air quality monitors" (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.

Indoor Air Quality Assessment Using a CO2 Monitoring System Based on Internet of Things

Indoor air quality (IAQ) parameters are not only directly related to occupational health but also have a significant impact on quality of life as people typically spend more than 90% of their time in indoor environments. Although IAQ is not usually monitored, it must be perceived as a relevant issue to follow up for the inhabitants' well-being and comfort for enhanced living environments and occupational health. Carbon dioxide (CO₂) has a substantial influence on public health and can be used as an essential index of IAQ. CO₂ levels over 1000 ppm, indicates an indoor air potential problem. Monitoring CO₂ concentration in real-time is essential to detect IAQ issues to quickly intervene in the building. The continuous technological advances in several areas such as Ambient Assisted Living and the Internet of Things (IoT) make it possible to build smart objects with significant capabilities for sensing and connecting. This paper presents the iAirCO₂ system, a solution for CO₂ real-time monitoring based on IoT architecture. The iAirCO₂ is composed of a hardware prototype for ambient data collection and a Web and smartphone software for data consulting. In future, it is planned that these data can be accessed by doctors in order to support medical diagnostics. Compared to other solutions, the iAirCO₂ is based on open-source technologies, providing a total Wi-Fi system, with several advantages such as its modularity, scalability, low-cost, and easy installation. The results reveal that the system can generate a viable IAQ appraisal, allowing to anticipate technical interventions that contribute to a healthier living environment.

An Internet of Things-Based Environmental Quality Management System to Supervise the Indoor Laboratory Conditions

Indoor air quality (IAQ) is not only a determinant of occupational health but also influences all indoor human behaviours. In most university establishments, laboratories are also used as classrooms. On one hand, indoor environment quality (IEQ) conditions supervision in laboratories is relevant for experimental activities. On the other hand, it is also crucial to provide a healthy and productive workplace for learning activities. The proliferation of cost-effective sensors and microcontrollers along with the Internet of Things (IoT) architectures enhancements, enables the development of automatic solutions to supervise the Laboratory Environmental Conditions (LEC). This paper aims to present a real-time IEQ-laboratory data collection system-based IoT architecture named iAQ Plus (iAQ+). The iAQ+ incorporates an integrated Web management system along with a smartphone application to provide a historical analysis of the LEC. The iAQ+ collects IAQ index, temperature, relative humidity and barometric pressure. The results obtained are promising, representing a meaningful contribution for IEQ supervision solutions based on IoT. iAQ+ supports push notifications to alert people in a timely way for enhanced living environments and occupational health, as well as a work mode feature, so the user can configure setpoints for laboratory mode and schoolroom mode. Using the iAQ+, it is possible to provide an integrated management of data information of the spatio-temporal variations of LEC parameters which are particularly significant not only for enhanced living environments but also for laboratory experiments.

Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective

By igniting in the coal seam and injecting gas agent, underground coal gasification (UCG) causes coal to undergo thermochemical reactions in situ and, thus, to be gasified into syngas for power generation, hydrogen production, and storage. Compared with traditional mining technology, UCG has the potential sustainable advantages in energy, environment, and the economy. The paper reviewed the development of UCG projects around the world and points out that UCG faces difficulties in the field of monitoring and control in UCG. It is expounded for the current research status of monitoring and control in UCG, and clarified that monitoring and control in UCG is not perfect, remaining in the stage of exploration. To improve the problem of low coal gasification rate and gas production, and then to make full use of the potential sustainable advantages, the paper offers a perception platform of a UCG monitoring system based on the Internet-of-Things (IoT) and an optimal control model for UCG based on deep learning, and has an outlook on breakthrough directions of the key technologies related to the package structure design for moisture-proof and thermal insulation, antenna design, the strategy for energy management optimization, feature extraction and classification design for the network model, network structure design, network learning augmentation, and the control of the network model, respectively.