Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings

EAACI Guidelines on Environmental Science for Allergy and Asthma-Recommendations on the Impact of Indoor Air Pollutants on the Risk of New-Onset Asthma and on Asthma-Related Outcomes

The EAACI Guidelines used the GRADE approach to evaluate the impact of major indoor air pollutants (dampness and mould, cleaning agents, volatile organic compounds and pesticides) on the risk of new-onset asthma and on asthma-related outcomes. The guideline also acknowledges the synergies among indoor air pollutants and other components of the indoor exposome (allergens, viruses, endotoxins). Very low to low certainty of evidence was found for the association between exposure to indoor pollutants and increased risk of new-onset asthma and asthma worsening. Only for mould exposure there was moderate certainty of evidence for new-onset asthma. Due to the quality of evidence, conditional recommendations were formulated on the risk of exposure to all indoor pollutants. Recommendations are provided for prevention, patient care and mitigation in a framework supporting rational decisions for healthcare professionals and patients to individualize and improve asthma management. For policymakers and regulators this evidence-informed guideline supports setting legally binding standards and goals for indoor air quality at international, national and local levels. Asthma management counselled by the current EAACI guidelines can improve asthma-related outcomes but community and governmental measures for improved indoor air quality are needed to achieve significant impact.

Carbon Monoxide Poisoning: Diagnosis, Prognostic Factors, Treatment Strategies, and Future Perspectives

Carbon monoxide (CO) poisoning is a significant public health issue, with diagnosis often complicated by non-specific symptoms and limited access to specialised tools. Early detection is vital for preventing long-term complications. The review examines diagnostic challenges, prognostic factors, management strategies, and future advancements in CO poisoning. It highlights the limitations of current diagnostic techniques such as blood carboxyhaemoglobin levels and pulse CO-oximetry, while exploring emerging methods for rapid detection. Prognosis is influenced by exposure severity and delayed treatment, which increases the risk of neurological damage. Hyperbaric oxygen therapy (HBOT) remains the primary treatment but is not always accessible. Advances in portable CO-oximeters and biomarkers offer potential for improved early diagnosis and monitoring. Addressing resource limitations and refining treatment protocols are crucial for better patient outcomes. Future research should focus on personalised management strategies and the integration of modern technologies to enhance care.

Using low-cost sensors to assess common air pollution sources across multiple residences

The rapid development of low-cost sensors provides the opportunity to greatly advance the scope and extent of monitoring of indoor air pollution. In this study, calibrated particle matter (PM) sensors and a non-negative matrix factorisation (NMF) source apportionment technique are used to investigate PM concentrations and source contributions across three households in an urban residential area. The NMF is applied to combined data from all houses to generate source profiles that can be used to understand how PM source characteristics are similar or differ between different households in the same urban area. PM2.5 and PM10 concentrations in all three houses were greater, more variable, and significantly different to ambient concentrations recorded at a nearby ambient monitoring site. Concentrations were also significantly different between houses, with the World Health Organisation 24-h guideline limits for PM2.5 breached in one household. The applied methodology was highly successful at modelling concentrations for all the houses (R2 ≥ 0.983), finding that across the houses the I/O (indoor to outdoor sources ratio) was the lowest for PM1 (down to 0.08), and greatest for PM10 (up to 4.93). Whilst the sources could not be clearly distinguished further than being outdoors or indoors, the methodology provides clear insights to source variability within and between the monitored houses. These results highlight the importance of monitoring indoor air pollution to improve pollution exposure estimates, as whilst people may live in areas with acceptable ambient air quality, they can be exposed to unhealthy concentrations in their own homes. This method may be applied in future studies for extended periods to investigate the influence of source seasonality on PM concentrations or scaled up to investigate source variability across larger geographical areas.

A study of quantifying the influence of kitchen human activity on indoor air quality dynamics

Indoor air quality (IAQ) is increasingly recognised as one of the critical factors influencing human health, particularly given the amount of time people spend indoors. This study investigated the impact of real-life kitchen human activity (KHA) on IAQ. We used low-cost sensors to measure real-time concentrations of smoke, carbon monoxide (CO), and particulate matter (PM10 and PM2.5) in the kitchen of a household with three adults, analysing KHAs by dividing them into five categories. The fixed effect model was employed to analyse the data, explaining the impact of different KHAs on IAQ. The results showed that compared to other KHAs, using the gas stove had the greatest impact on IAQ, with average increases of 13% in smoke, 24.4% in CO, 9.8% in PM10, and 5.34% in PM2.5. The study also found that without windows and with insufficient ventilation, only using the range hood cannot effectively and obviously reduce PM levels. These findings highlight the need for comprehensive IAQ management strategies and further research. Despite its limitations, this study also validated the potential of low-cost sensors in IAQ monitoring.

Graphene-Based Fiber Materials for Gas Sensing Applications: State of the Art Review

The importance of gas sensors is apparent as the detection of gases and pollutants is crucial for environmental monitoring and human safety. Gas sensing devices also hold the potential for medical applications as health monitoring and disease diagnostic tools. Gas sensors fabricated from graphene-based fibers present a promising advancement in the field of sensing technology due to their enhanced sensitivity and selectivity. The diverse chemical and mechanical properties of graphene-based fibers-such as high surface area, flexibility, and structural stability-establish them as ideal gas-sensing materials. Most significantly, graphene fibers can be readily tuned to detect a wide range of gases, making them highly versatile in gas-sensing technologies. This review focuses on graphene-based composite fibers for gas sensors, with an emphasis on the preparation processes used to achieve these fibers and the gas sensing mechanisms involved in their sensors. Graphene fiber gas sensors are presented based on the chemical composition of their target gases, with detailed discussions on their sensitivity and performance. This review reveals that graphene-based fibers can be prepared through various methods and can be effectively integrated into gas-sensing devices for a diverse range of applications. By presenting an overview of developments in this field over the past decade, this review highlights the potential of graphene-based fiber sensors and their prospective integration into future technologies.

Integrating Real-Time Air Quality Monitoring, Ecological Momentary Assessment, and Spirometry to Evaluate Asthma Symptoms: Usability Study

BACKGROUND

Individuals are exposed to a variety of indoor residential toxins including volatile organic compounds and particulates. In adults with asthma, such exposures are associated with asthma symptoms, asthma exacerbations, and decreased lung function. However, data on these exposures and asthma-related outcomes are generally collected at different times and not in real time. The integration of multiple platforms to collect real-time data on environmental exposure, asthma symptoms, and lung function has rarely been explored.

OBJECTIVE

This paper describes how adults with asthma perceive the acceptability and usability of three integrated devices: (1) residential indoor air quality monitor, (2) ecological momentary assessment (EMA) surveys delivered via a smartphone app, and (3) home spirometry, over 14 days.

METHODS

Participants (N=40) with uncontrolled asthma were mailed the Awair Omni indoor air quality monitor, ZEPHYRx home spirometer, and detailed instructions required for the in-home monitoring. The air quality monitor, spirometer, and EMA app were set up and tested during a videoconference or phone orientation with a research team member. Midway through the 14-day data collection period, participants completed an interview about the acceptability of the study devices or apps, instructional materials provided, and the setup process. At the end of the 14-day data collection period, participants completed a modified System Usability Scale. A random sample of 20 participants also completed a phone interview regarding the acceptability of the study and the impact of the study on their asthma.

RESULTS

Participants ranged in age from 26 to 77 (mean 45, SD 13.5) years and were primarily female (n=36, 90%), White (n=26, 67%), college graduates (n=25, 66%), and residing in a single-family home (n=30, 75%). Most indicated that the air quality monitor (n=23, 58%), the EMA (n=20, 50%), and the spirometer (n=17, 43%) were easy to set up and use. Challenges with the EMA included repetitive surveys, surveys arriving during the night, and technical issues. While the home spirometer was identified as a plausible means to evaluate lung function in real time, the interpretation of the readings was unclear, and several participants reported side effects from home spirometer use. Overall, the acceptability of the study and the System Usability Scale scores were high.

CONCLUSIONS

The study devices were highly acceptable and usable. Participant feedback was instrumental in identifying technical challenges that should be addressed in future studies.

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

Carbon monoxide as an indicator of indoor air quality

Carbon monoxide is a priority pollutant that is suitable as an indicator for assessing indoor air quality. Monitoring should preferably be embedded in an intelligent network of different sensors.

Detection of atmospheric aerosols and terrestrial nanoparticles collected in a populous city in southern Brazil

The main objective of this study is to analyze hazardous elements in nanoparticles (NPs) (smaller than 100 nm) and ultrafine particles (smaller than 1 µm) in Porto Alegre City, southern Brazil using a self-made passive sampler and Sentinel-3B SYN satellite images in 32 collection points. The Aerosol Optical Thickness proportion (T550) identification was conducted using images of the Sentinel-3B SYN satellite at 634 points sampled in 2019, 2020, 2021, and 2022. Focused ion beam scanning electron microscopy analyses were performed to identify chemical elements present in NPs and ultrafine particles, followed by single-stage cascade impactor to be processed by high-resolution transmission electron microscopy. This process was coupled with energy-dispersive X-ray spectroscopy and later analysis via secondary ion mass spectrometry. Data was acquired from Sentinel-3B SYN images, normalized to a standard mean of 0.83 µg/mg, at moderate spatial resolution (260 m), and modeled in the Sentinel Application Platform (SNAP) software v.8.0. Statistical matrix data was generated in the JASP software (Jeffreys's Amazing Statistics Program) v.0.14.1.0 followed by a K-means cluster analysis. The results demonstrate the presence of between 1 and 100 nm particles of the following chemical elements: Si, Al, K, Mg, P, and Ti. Many people go through these areas daily and may inhale or absorb these elements that can harm human health. In the Sentinel-3B SYN satellite images, the sum of squares in cluster 6 is 168,265 and in cluster 7 a total of 21,583. The use of images from the Sentinel-3B SYN satellite to obtain T550 levels is of great importance as it reveals that atmospheric pollution can move through air currents contaminating large areas on a global scale.

Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges

With the development of IoT technology and low-cost indoor air quality (IAQ) sensors, the IoT-based IAQ monitoring platform has garnered significant research interest and demonstrated its potential in enhancing IAQ management. This study presents a comprehensive review of previous research on the development and application of IoT-based IAQ platforms in different built environments. It offers detailed insights into the design and implementation of recent IoT-based IAQ platforms. The findings indicate that the IoT-based IAQ platforms are able to provide reliable information for IAQ monitoring. To ensure quality control of the IoT-based IAQ platform, it is suggested to replace the sensors every 4-6 months for reliable monitoring. In another aspect, integrating data-driven technology into the platform is crucial for IAQ prediction and efficient control of ventilation systems, leveraging the wealth of data available from the IoT platform. According to recent studies that applied data-driven algorithms for IAQ management, it can be confirmed that the data-driven algorithms are able to prompt IAQ by providing either more information or a control strategy. However, it should be noted that only 9.1 % of the developed platforms integrated data-driven models for IAQ management. Based on our findings, current challenges and further opportunities are discussed. Future studies should focus on integrating data-driven algorithms into IoT-based IAQ platforms and developing digital twins that can be used for real building IAQ management. However, there is obvious tension between controlling ventilation for energy efficiency versus better air quality. It is important to make a balance between energy efficiency and better air quality according to the current situations of specific built environments. Also, the next generation of IoT-based IAQ platforms should include occupants in the loop to create a more occupant-centric IAQ management approach.

Comparing PM2.5, respirable dust, and total dust fractions using real-time and gravimetric samples in an exposure chamber study

Using an exposure chamber, we investigate the precision of the DustTrak DRX monitor by comparing its results to those obtained from taking traditional gravimetric samples of two stone minerals commonly used in asphalt and lactose powder. We also discuss the possibility of using real-time monitors such as DustTrak DRX for occupational exposure monitoring purposes. The results are based on 19 days of experiment, each day with measurements collected over 4 h. Compared to the gravimetric samples, the DustTrak DRX overestimated the PM_2.5 and respirable dust concentrations, while it underestimated the total dust concentration by a factor of nearly two. However, the ratios, being done for more than one material, between the DustTrak DRX and the gravimetric sample readings varied daily and across the different exposure materials. Real-time sensors have the potential to excel at identifying exposure sources, evaluating the measured control efficiency, visualizing variations in exposure to motivate workers, and contributing to the identification of measures to be implemented to reduce exposure. For total dust, a correction factor of at least two should be used to bring its readings up to those for the corresponding gravimetric samples. Also, if the DustTrak DRX is used in the initial profiling of occupational exposure, the exposure could be considered acceptable if the readings are well below the occupational exposure limit (OELs) after correction. If the DustTrak DRX readings, after correction, is close to, or above, the accepted exposure concentrations, more thorough approaches would be required to validate the exposure.

Intelligent operation, maintenance, and control system for public building: Towards infection risk mitigation and energy efficiency

During the post-COVID-19 era, it is important but challenging to synchronously mitigate the infection risk and optimize the energy savings in public buildings. While, ineffective control of ventilation and purification systems can result in increased energy consumption and cross-contamination. This paper is to develop intelligent operation, maintenance, and control systems by coupling intelligent ventilation and air purification systems (negative ion generators). Optimal deployment of sensors is determined by Fuzzy C-mean (FCM), based on which CO₂ concentration fields are rapidly predicted by combing the artificial neural network (ANN) and self-adaptive low-dimensional linear model (LLM). Negative oxygen ion and particle concentrations are simulated with different numbers of negative ion generators. Optimal ventilation rates and number of negative ion generators are decided. A visualization platform is established to display the effects of ventilation control, epidemic prevention, and pollutant removal. The rapid prediction error of LLM-based ANN for CO₂ concentration was below 10% compared with the simulation. Fast decision reduced CO₂ concentration below 1000 ppm, infection risk below 1.5%, and energy consumption by 27.4%. The largest removal efficiency was 81% when number of negative ion generators was 10. This work can promote intelligent operation, maintenance, and control systems considering infection prevention and energy sustainability.

Critical review on emerging health effects associated with the indoor air quality and its sustainable management

Indoor air quality (IAQ) is one of the fundamental elements affecting people's health and well-being. Currently, there is a lack of awareness among people about the quantification, identification, and possible health effects of IAQ. Airborne pollutants such as volatile organic compounds (VOCs), particulate matter (PM), sulfur dioxide (SO2), carbon monoxide (CO), nitrous oxide (NO), polycyclic aromatic hydrocarbons (PAHs) microbial spores, pollen, allergens, etc. primarily contribute to IAQ deterioration. This review discusses the sources of major indoor air pollutants, molecular toxicity mechanisms, and their effects on cardiovascular, ocular, neurological, women, and foetal health. Additionally, contemporary strategies and sustainable methods for regulating and reducing pollutant concentrations are emphasized, and current initiatives to address and enhance IAQ are explored, along with their unique advantages and potentials. Due to their longer exposure times and particular physical characteristics, women and children are more at risk for poor indoor air quality. By triggering many toxicity mechanisms, including oxidative stress, DNA methylation, epigenetic modifications, and gene activation, indoor air pollution can cause a range of health issues. Low birth weight, acute lower respiratory tract infections, Sick building syndromes (SBS), and early death are more prevalent in exposed residents. On the other hand, the main causes of incapacity and early mortality are lung cancer, chronic obstructive pulmonary disease, and cardiovascular disorders. It's crucial to acknowledge anticipated research needs and implemented efficient interventions and policies to lower health hazards.

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

IoT-based Edge Computing (IoTEC) for Improved Environmental Monitoring

This research considered several applications of a coupled Internet of Things sensor network with Edge Computing (IoTEC) for improved environmental monitoring. Two pilot applications, covering environmental monitoring of vapor intrusion and system performance of wastewater-based algae cultivation, were designed to compare data latency, energy consumption, and economic cost between the IoTEC approach and the conventional sensor monitoring method. The results show that the IoTEC monitoring approach, compared with conventional IoT sensor networks, could significantly reduce data latency by 13%, and the amount of data transmission decreased by an average of 50%. In addition, the IoTEC method can increase the duration of power supply by 130%. Collectively, these improvements could lead to a compelling cost reduction of 55% - 82% per year for monitoring vapor intrusion at five houses, with more houses leading to more significant savings. Additionally, our results demonstrate the feasibility of deploying machine learning tools at edge servers for more advanced data processing and analysis.

Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models

The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.

TVOC - Revisited

BACKGROUND

TVOC (total volatile organic compounds) has been used as a sum parameter in indoor air sciences for over 40 years. In the beginning, individual VOC concentrations determined by gas chromatography were simply added together. However, several methods for calculating TVOC have become established over time.

METHODS

To understand the manifold definitions of TVOC, one must trace the history of indoor air sciences and analytical chemistry. Therefore, in this work, the original approaches of TVOC are searched and explained. A detailed description of the measurement methods is followed by a critical evaluation of the various TVOC values and their possible applications. The aim is to give the reader a deeper understanding of TVOC in order to use this parameter correctly and to be able to better assess published results. In addition, related sum values such as TSVOC and TVVOC are also addressed.

RESULTS

A milestone was the analytical definition of VOCs and TVOC in 1997. A list of VOCs that should at least be considered when calculating TVOC was also provided. This list represented the status at that time, is no longer up-to-date and is being updated by a European working group as part of a harmonization process. However, there is still confusion about the exact definition and reasonable application of TVOC. The signals of other sum parameters, measured with photoacoustics, flame ionization, photoionization or electrochemical sensors, are also often given under the term TVOC.

CONCLUSIONS

It was recognized early that TVOC is not a toxicologically based parameter and is therefore only suitable for a limited number of screening purposes. Consequently, TVOC cannot be used in connection with health-related and odor-related issues. Nevertheless, such references are repeatedly made, which has led to controversial scientific discussions and even court decisions in Germany about the correct and improper use of TVOC.

A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness

This scoping review is focused on wearable devices for environmental monitoring. First, the main pollutants are presented, followed by sensing technologies that are used for the parameters of interest. Selected examples of wearables and portables are divided into commercially available and research-level projects. While many commercial products are in fact portable, there is an increasing interest in using a completely wearable technology. This allows us to correlate the pollution level to other personal information (performed activity, position, and respiratory parameters) and thus to estimate personal exposure to given pollutants. The fact that there are no univocal indices to estimate outdoor or indoor air quality is also an open problem. Finally, applications of wearables for environmental monitoring are discussed. Combining environmental monitoring with other devices would permit better choices of where to perform sports activities, especially in highly polluted areas, and provide detailed information on the living conditions of individuals.

Automatically Controlled Dust Generation System Using Arduino

A dust generator was developed to disperse and maintain a desired concentration of airborne dust in a controlled environment chamber to study poultry physiological response to sustained elevated levels of particulate matter. The goal was to maintain an indicated PM10 concentration of 50 µg/m³ of airborne dust in a 3.7 m × 4.3 m × 2.4 m (12 ft × 14 ft × 8 ft) controlled environment chamber. The chamber had a 1.5 m³/s (3200 cfm) filtered recirculation air handling system that regulated indoor temperature levels and a 0.06 m³/s (130 cfm) exhaust fan that exchanged indoor air for fresh outdoor air. Dry powdered red oak wood dust that passed through an 80-mesh screen cloth was used for the experiment. The dust generator metered dust from a rectangular feed hopper with a flat bottom belt to a 0.02 m³/s (46 cfm) centrifugal blower. A vibratory motor attached to the hopper ran only when the belt was operated to prevent bridging of powdered materials and to provide an even material feed rate. A laser particle counter was used to measure the concentration of airborne dust and provided feedback to an Arduino-based control system that operated the dust generator. The dust generator was operated using a duty cycle of one second on for every five seconds off to allow time for dispersed dust to mix with chamber air and reach the laser particle counter. The control system maintained an airborne PM10 dust concentration of 54.92 ± 6.42 µg/m³ in the controlled environment chamber during six weeks of continuous operation using red oak wood dust. An advantage of the automatically controlled dust generator was that it continued to operate to reach the setpoint concentration in response to changes in material flow due to humidity, partial blockages, and non-uniform composition of the material being dispersed. Challenges included dust being trapped by the recirculation filter and the exhaust fan removing airborne dust from the environmental chamber.

An Evaluation of Risk Ratios on Physical and Mental Health Correlations due to Increases in Ambient Nitrogen Oxide (NOx) Concentrations

Nitrogen oxides (NOx) are gaseous pollutants contributing to pollution in their primary form and are also involved in reactions forming ground-level ozone and fine particulate matter. Thus, NOx is of great interest for targeted pollution reduction because of this cascade effect. Primary emissions originate from fossil fuel combustion making NOx a common outdoor and indoor air pollutant. Numerous studies documenting the observed physical health impacts of NOx were reviewed and, where available, were summarized using risk ratios. More recently, the literature has shifted to focus on the mental health implications of NOx exposure, and a review of the current literature found five main categories of mental health-related conditions with respect to NOx exposure: common mental health disorders, sleep, anxiety, depression, and suicide. All the physical and mental health effects with available risk ratios were organized in order of increasing risk. Mental health concerns emerged as those most influenced by NOx exposure, with physical health impacts, such as asthma, only beginning to surface as the fourth highest risk. Mental health conditions occupied seven of the top ten highest risk health ailments. The results summarized in this narrative review show that there are clear positive correlations between NOx and negative physical and mental health manifestations, thus strengthening the argument in support of the reduction in ambient NOx levels.

Performance evaluation of low-cost air quality sensors: A review

The monitoring of air quality compliance requires the use of Federal Reference Methods (FRM)/Federal Equivalent Methods (FEM); nevertheless, the validity and reliability of low-cost sensors deserve attention due to their affordability and accessibility. This review examines the methodologies of previous studies to characterise the performance of low-cost air quality sensors and to identify the influential factors in sensor evaluation experiments. The data on four statistical measures (Correlation of Determination, r²; Root Mean Square Error, RMSE; Mean Normalised Bias, MNB; and Coefficient of Variation, CV) and details about five methodological factors in experimental design (environmental setting, reference instrument, regression model, pollutant attribute, and sensor original equipment manufacturer (OEM) specification) were extracted from a total of 112 primary articles for a detailed analysis. The results of the analysis suggested that low-cost air quality sensors exhibited improved r² and RMSE in the experiments with stable environmental settings, in the comparison against non-designated reference instruments, or in the analysis where advanced regression models were used to adjust the sensor readings. However, the pollutant attribute and sensor OEM specification had inconclusive effects on r² and RMSE due to contradictory results and lack of sufficient data. MNB and CV, two measures that US EPA recommends to determine the suitable application tier of air quality sensors, varied significantly among published experiments due to the discrepancy in experimental design. The outcomes of this work could provide direction to researchers regarding sensor evaluation experiments and guide practitioners to effectively select and deploy low-cost sensors for air quality monitoring.

Time and spatially resolved tracking of the air quality in local public transport

As an indoor environment, public transport is subject to special conditions with many passengers in a comparatively small space. Therefore, both an efficient control of the climatic parameters and a good air exchange are necessary to avoid transmission and spread of respiratory diseases. However, in such a dynamic system it is practically impossible to determine pathogenic substances with the necessary temporal and spatial resolution, but easy-to-measure parameters allow the air quality to be assessed in a passenger compartment. Carbon dioxide has already proven to be a useful indicator, especially in environments with a high occupancy of people. Airborne particulate matter can also be an important aspect for assessing the air quality in an indoor space. Consequently, the time courses of temperature, relative humidity, carbon dioxide and particulate matter (PM₁₀) were tracked and evaluated in local public transport buses, trams and trains in the Brunswick/Hanover region. In all measurements, the climatic conditions were comfortable for the passengers. Carbon dioxide was strongly correlated with occupancy and has proven to be the most informative parameter. The PM₁₀ concentration, however, often correlated with the dynamics of people when getting on and off, but not with the occupancy. Sensors, equipped with integrated GPS, were installed in the passenger cabins and were found to be useful for recording location-related effects such as stops. The results of this study show that the online recording of simple parameters is a valuable tool for assessing air quality as a function of time, location and number of people. When the occupancy is high, a low carbon dioxide level indicates good ventilation, which automatically reduces the risk of infection. It is therefore recommended to take more advantage of low-cost sensors as a control for air conditioning systems in passenger cabins and for evaluations of the dynamics in public transport.

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters

With increasing global concerns regarding indoor air quality (IAQ) and air pollution, concerns about regularly replacing ventilation devices, particularly high-efficiency particulate air (HEPA) filters, have increased. However, users cannot easily determine when to replace filters. This paper proposes models to estimate the dust loading levels of HEPA filters for an energy-recovery ventilation system that performs air purification. The models utilize filter pressure drops, the revolutions per minute (RPM) of supply fans, and rated airflow modes as variables for regression equations. The obtained results demonstrated that the filter dust loading level could be estimated once the filter pressure drops and RPM, and voltage for the rated airflow were input in the models, with a root mean square error of 5.1–12.9%. Despite current methods using fewer experimental datasets than the proposed models, our findings indicate that these models could be efficiently used in the development of filter replacement alarms to help users decide when to replace their filters.

Challenges of a Healthy Built Environment: Air Pollution in Construction Industry

Air pollution is a global concern, especially in cities and urban areas, and has many implications for human health and for the environment. In common with other industrial sectors, the construction industry emits air pollutants. In scientific literature, the contribution the construction industry makes to air pollution is underexposed. This systematic literature review (SLR) paper gives an overview of the current literature regarding air pollution within the construction industry. Air pollution is discussed focusing mainly on three levels: (i) buildings and their building life cycle stages, (ii) construction processes and components, and (iii) building material and interior. The final sample of the SLR comprises 161 scientific articles addressing different aspects of the construction industry. The results show that most articles address the use stage of a building. Particulate matter in different sizes is the most frequently examined air pollutant within the SLR. Moreover, about a third of the articles refer to indoor air pollution, which shows the relevance of the topic. The construction industry can help to develop a healthier built environment and support the achievement of cleaner air within various life cycle stages, e.g., with optimized construction processes and healthier materials. International agreements and policies such as the Sustainable Development Goals (SDGs) can support the sustainable development of the construction industry.

Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review

In the last years, the issue of exposure assessment of airborne pollutants has been on the rise, both in the environmental and occupational fields. Increasingly severe national and international air quality standards, indoor air guidance values, and exposure limit values have been developed to protect the health of the general population and workers; this issue required a significant and continuous improvement in monitoring technologies to allow the execution of proper exposure assessment studies. One of the most interesting aspects in this field is the development of the "next-generation" of airborne pollutants monitors and sensors (NGMS). The principal aim of this review is to analyze and characterize the state of the art and of NGMS and their practical applications in exposure assessment studies. A systematic review of the literature was performed analyzing outcomes from three different databases (Scopus, PubMed, Isi Web of Knowledge); a total of 67 scientific papers were analyzed. The reviewing process was conducting systematically with the aim to extrapolate information about the specifications, technologies, and applicability of NGMSs in both environmental and occupational exposure assessment. The principal results of this review show that the use of NGMSs is becoming increasingly common in the scientific community for both environmental and occupational exposure assessment. The available studies outlined that NGMSs cannot be used as reference instrumentation in air monitoring for regulatory purposes, but at the same time, they can be easily adapted to more specific applications, improving exposure assessment studies in terms of spatiotemporal resolution, wearability, and adaptability to different types of projects and applications. Nevertheless, improvements needed to further enhance NGMSs performances and allow their wider use in the field of exposure assessment are also discussed.

Low-Cost Air Quality Sensing towards Smart Homes

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

Stochastic risk assessment of urban soils contaminated by heavy metals in Kazakhstan

Kazakhstan's growing economy has been posing a threat to the natural environment in the country. The study aimed to investigate the status of soil contamination by five heavy metals (Pb, Cd, Cu, Zn, and Cr) in Kazakhstan during 2010-2018 as well as its impact on the population. Data for the analysis were collected from the governmental monitoring agency, which has reported the concentrations of five heavy metals in the Kazakhstan soil each year. Preliminary screening suggested the four most contaminated cities (Balkhash, Ust-Kamenogorsk, Ridder, and Shymkent). Mean soil concentrations in these cities varied between 251 and 442 mg/kg for Pb, 5-9 mg/kg for Cd, 8-138 mg/kg for Cu, 87-178 mg/kg for Zn, and 2-5 mg/kg for Cr. Analysis of geo-accumulation index pointed out the possible anthropogenic origin of the contamination by Pb and Cd in these cities (mean I_geo was 3.81 for Pb and 3.45 for Cd). Further probabilistic risk assessment for these cities demonstrated that mean hazard indices for children fluctuated between 1 and 2 in two cities (Shymkent and Balkhash), whereas cancer risks for both age groups stayed in the range of 1 × 10^-6 and 5 × 10^-6, indicating that soil remediation is urgently required for the health of the citizens and environments. Both ~60-90% of adults and ~30-60% of children cases exceeded the threshold of carcinogenic assessment (1 × 10^-6), suggesting that a large portion of the population in these cities could be affected by heavy metals in soil. The study provides background understanding for decision making on remediation actions and environmental policy and hazardous waste management in Kazakhstan.

Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

Development of low-cost indoor air quality monitoring devices: Recent advancements

The use of low-cost sensor technology to monitor air pollution has made remarkable strides in the last decade. The development of low-cost devices to monitor air quality in indoor environments can be used to understand the behaviour of indoor air pollutants and potentially impact on the reduction of related health impacts. These user-friendly devices are portable, require low-maintenance, and can enable near real-time, continuous monitoring. They can also contribute to citizen science projects and community-driven science. However, low-cost sensors have often been associated with design compromises that hamper data reliability. Moreover, with the rapidly increasing number of studies, projects, and grey literature based on low-cost sensors, information got scattered. Intending to identify and review scientifically validated literature on this topic, this study critically summarizes the recent research pertinent to the development of indoor air quality monitoring devices using low-cost sensors. The method employed for this review was a thorough search of three scientific databases, namely: ScienceDirect, IEEE, and Scopus. A total of 891 titles published since 2012 were found and scanned for relevance. Finally, 41 research articles consisting of 35 unique device development projects were reviewed with a particular emphasis on device development: calibration and performance of sensors, the processor used, data storage and communication, and the availability of real-time remote access of sensor data. The most prominent finding of the study showed a lack of studies consisting of sensor performance as only 16 out of 35 projects performed calibration/validation of sensors. An even fewer number of studies conducted these tests with a reference instrument. Hence, a need for more studies with calibration, credible validation, and standardization of sensor performance and assessment is recommended for subsequent research.

Cost-effectiveness of the common agricultural policy and environmental policy in country districts: Spatial spillovers of pollution, bio-uniformity and green schemes in Poland

A significant part of the common agricultural policy (CAP) focuses on implementing environmentally friendly practices, which have been evaluated in many studies. However, these analyses do not usually consider spatial spillovers that may concern pollution and biodiversity, as well as participation in policy schemes. Most studies evaluate national environmental policies at the macroeconomic level, focusing on cities. However, the majority of natural resources are in rural districts, and environmental policy is mainly implemented at the local level, where most of the budgets for environmental protection are decided. Thus, in this paper, our first objective is to assess the cost-effectiveness of Poland's environmental policy schemes, combining local expenditures at the county level with the CAP's green schemes. Additionally, we investigate the spatial (neighbourhood) effects of environment quality and the policy, as well as their mutual interactions. First, the environmental quality at the county level is proxied by the composite environmental quality index (CEQI); second, the Spatial Durbin Model (SDM) with endogenous covariates is estimated. Third, the interactions among the policy's spatial effects are explored using the structural equation model (SEM). We find that the CAP's green component not only positively affects the environmental quality in the long term but also generates positive spillovers that have been hitherto underestimated.

A Scoping Review of Technological Approaches to Environmental Monitoring

Indoor environment quality (IEQ) can negatively affect occupant health and wellbeing. Air quality, as well as thermal, visual and auditory conditions, can determine how comfortable occupants feel within buildings. Some can be measured objectively, but many are assessed by interpreting qualitative responses. Continuous monitoring by passive sensors may be useful to identify links between environmental and physiological changes. Few studies localise measurements to an occupant level perhaps due to many environmental monitoring solutions being large and expensive. Traditional models for occupant comfort analysis often exacerbate this by not differentiating between individual building occupants. This scoping review aims to understand IEQ and explore approaches as to how it is measured with various sensing technologies, identifying trends for monitoring occupant health and wellbeing. Twenty-seven studies were reviewed, and more than 60 state-of-the-art and low-cost IEQ sensors identified. Studies were found to focus on the home or workplace, but not both. This review also found how wearable technology could be used to augment IEQ measurements, creating personalised approaches to health and wellbeing. Opportunities exist to make individuals the primary unit of analysis. Future research should explore holistic personalised approaches to health monitoring in buildings that analyse the individual as they move between environments.

NOx removal efficiency of urban photocatalytic pavements at pilot scale

Photocatalytic technology implemented in construction materials is a promising solution to contribute to alleviate air quality issues found in big cities. Photocatalysis has been proved able to mineralise most harmful contaminants. However, important problems associated with monitoring the efficiency of these solutions under real conditions still remain, including the lack of affordable analytical tools to measure NO_x concentrations with enough accuracy. In this work, two pilot scale demonstration platforms were built at two different locations to assess the photocatalytic NO_X removal efficiency of ten selected materials exposed outdoors for AQmesh low-cost sensor PODs were used to measure ground-level to measure NO and NO₂ concentrations during nearly one year. The pollutant removal efficiency of the materials was then calculated based on a comparison with simultaneously concentration measurements carried-out on reference, non-active materials. It was found that the NO₂ removal efficiency presented large variations across the seasons, with maxima during the warmer months, while NO efficiencies were comparatively steadier. Statistical analysis delivered evidence that the efficiencies significantly depend on different meteorological variables (irradiance and relative humidity) besides NO, NO₂ ambient concentrations. Lower efficiencies were observed for higher concentration levels and vice versa. The influence of water vapour could be related to two different effects: a short-term contribution by the instantaneous air humidity and a long-term component associated with the hygroscopic state of the material. The contribution of wind to the pollutant removal efficiencies was principally related to the humidity of air masses moving above the location and to the advection of pollutants from specific emission sources.

Review of the characteristics and possible health effects of particles emitted from laser printing devices

Many studies have shown that the use of laser printing devices (LPDs) contributes to the release of particles into the indoor environment. However, after more than two decades of research, the physicochemical properties of LPD-emitted particles and the possible health effects from exposure to particles are still heavily debated. We therefore carried out a critical review of the published studies around emissions and health effects of LPD-emitted particles, aiming at elucidating the nature of these particles and their potential health risks. Realizing the varying methodologies of the studies, a classification of the reviewed studies is adopted, resulting in three categories of emission studies (chamber experiment, office/room measurement, and photocopy shop measurement), and three types of health studies (in vitro/animal studies, human studies in the real world, and human studies in controlled settings). The strengths and limitations of each type of study are discussed in-depth, which in turn helps to understand the cause of divergent results. Overall, LPD-emitted particles are mainly condensed or secondary-formed semi-volatile organic compounds (SVOCs), while solid toner particles account for a very small fraction. The health risk from exposure to LPD-emitted particles is small compared with the health risk from exposure to ambient particles.

Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality

Indoor air pollution (IAP) is a serious threat to human health, causing millions of deaths each year. A plethora of pollutants can result in IAP; therefore, it is very important to identify their main sources and concentrations and to devise strategies for the control and enhancement of indoor air quality (IAQ). Herein, we provide a critical review and evaluation of the major sources of major pollutant emissions, their health effects, and issues related to IAP-based illnesses, including sick building syndrome (SBS) and building-related illness (BRI). In addition, the strategies and approaches for control and reduction of pollutant concentrations are pointed out, and the recent trends in efforts to resolve and improve IAQ, with their respective advantages and potentials, are summarized. It is predicted that the development of novel materials for sensors, IAQ-monitoring systems, and smart homes is a promising strategy for control and enhancement of IAQ in the future.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region

In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile-Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman's rank-order correlations (rs = 0.64-0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.

Particulate Matter Measurement Indoors: A Review of Metrics, Sensors, Needs, and Applications

Many populations spend ∼90% of their time indoors, with household particulate matter being linked to millions of premature deaths worldwide. Particulate matter is currently measured using particle mass, particle number, and particle size distribution metrics, with other metrics, such as particle surface area, likely to be of increasing importance in the future. Particulate mass is measured using gravimetric methods, tapered element oscillating microbalances, and beta attenuation instruments and is best suited to use in compliance monitoring, trend analysis, and high spatial resolution measurements. Particle number concentration is measured by condensation particle counters, optical particle counters, and diffusion chargers. Particle number measurements are best suited to source characterization, trend analysis and ultrafine particle investigations. Particle size distributions are measured by gravimetric impactors, scanning mobility particle sizers, aerodynamic particle sizers, and fast mobility particle sizers. Particle size distribution measurements are most useful in source characterization and particulate matter property investigations, but most measurement options remain expensive and intrusive. However, we are on the cusp of a revolution in indoor air quality monitoring and management. Low-cost sensors have potential to facilitate personalized information about indoor air quality (IAQ), allowing citizens to reduce exposures to PM indoors and to resolve potential dichotomies between promoting healthy IAQ and energy efficient buildings. Indeed, the low cost will put this simple technology in the hands of citizens who wish to monitor their own IAQ in the home or workplace, to inform lifestyle decisions. Low-cost sensor networks also look promising as the solution to measuring spatial distributions of PM indoors, however, there are important sensor/data quality, technological, and ethical barriers to address with this technology. An improved understanding of epidemiology is essential to identify which metrics correlate most with health effects, allowing indoor specific PM standards to be developed and to inform the future of experimental applications.

Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control

This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.

Kitchen Area Air Quality Measurements in Northern Ghana: Evaluating the Performance of a Low-Cost Particulate Sensor within a Household Energy Study

Household air pollution from the combustion of solid fuels is a leading global health and human rights concern, affecting billions every day. Instrumentation to assess potential solutions to this problem faces challenges—especially related to cost. A low-cost ($159) particulate matter tool called the Household Air Pollution Exposure (HAPEx) Nano was evaluated in the field as part of the Prices, Peers, and Perceptions cookstove study in northern Ghana. Measurements of temperature, relative humidity, absolute humidity, and carbon dioxide and carbon monoxide concentrations made at 1-min temporal resolution were integrated with 1-min particulate matter less than 2.5 microns in diameter (PM2.5) measurements from the HAPEx, within 62 kitchens, across urban and rural households and four seasons totaling 71 48-h deployments. Gravimetric filter sampling was undertaken to ground-truth and evaluate the low-cost measurements. HAPEx baseline drift and relative humidity corrections were investigated and evaluated using signals from paired HAPEx, finding significant improvements. Resulting particle coefficients and integrated gravimetric PM2.5 concentrations were modeled to explore drivers of variability; urban/rural, season, kitchen characteristics, and dust (a major PM2.5 mass constituent) were significant predictors. The high correlation (R2 = 0.79) between 48-h mean HAPEx readings and gravimetric PM2.5 mass (including other covariates) indicates that the HAPEx can be a useful tool in household energy studies.

Indoor Sources of Air Pollutants

People spend an average of 90% of their time in indoor environments. There is a long list of indoor sources that can contribute to increased pollutant concentrations, some of them related to human activities (e.g. people's movement, cooking, cleaning, smoking), but also to surface chemistry reactions with human skin and building and furniture surfaces. The result of all these emissions is a heterogeneous cocktail of pollutants with varying degrees of toxicity, which makes indoor air quality a complex system. Good characterization of the sources that affect indoor air pollution levels is of major importance for quantifying (and reducing) the associated health risks. This chapter reviews some of the more significant indoor sources that can be found in the most common non-occupational indoor environments.

Enhanced selectivity of target gas molecules through a minimal array of gas sensors based on nanoparticle-decorated SWCNTs

Layers of CNTs decorated with metal and metal–oxide nanoparticles can be used to develop highly selective gas sensor arrays.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Performance of low-cost monitors to assess household air pollution

Exposure to household air pollution is a leading cause of morbidity and mortality globally. However, due to the lack of validated low-cost monitors with long-lasting batteries in indoor environments, most epidemiologic studies use self-reported data or short-term household air pollution assessments as proxies of long-term exposure. We evaluated the performance of three low-cost monitors measuring fine particulate matter (PM_2.5) and carbon monoxide (CO) in a wood-combustion experiment conducted in one household of Spain for 5 days (including the co-location of 2 units of HAPEX and 3 units of TZOA-R for PM_2.5 and 3 units of EL-USB-CO for CO; a total of 40 unit-days). We used Spearman correlation (ρ) and Concordance Correlation Coefficient (CCC) to assess accuracy of low-cost monitors versus equivalent research-grade devices. We also conducted a field study in India for 1 week (including HAPEX in 3 households and EL-USB-CO in 4 households; a total of 49 unit-days). Correlation and agreement at 5-min were moderate-high for one unit of HAPEX (ρ = 0.73 / CCC = 0.59), for one unit of TZOA-R (ρ = 0.89 / CCC = 0.62) and for three units of EL-USB-CO (ρ = 0.82-0.89 / CCC = 0.66-0.91) in Spain, although the failure or malfunction rate among low-cost units was high in both settings (60% of unit-days in Spain and 43% in India). Low-cost monitors tested here are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiologic studies. More field validation is needed to assess evolving sensors and monitors with application to health studies.

End-user perspective of low-cost sensors for outdoor air pollution monitoring

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.

The Room-Temperature Chemiresistive Properties of Potassium Titanate Whiskers versus Organic Vapors

The development of portable gas-sensing units implies a special care of their power efficiency, which is often approached by operation at room temperature. This issue primarily appeals to a choice of suitable materials whose functional properties are sensitive toward gas vapors at these conditions. While the gas sensitivity is nowadays advanced by employing the materials at nano-dimensional domain, the room temperature operation might be targeted via the application of layered solid-state electrolytes, like titanates. Here, we report gas-sensitive properties of potassium titanate whiskers, which are placed over a multielectrode chip by drop casting from suspension to yield a matrix mono-layer of varied density. The material synthesis conditions are straightforward both to get stable single-crystalline quasi-one-dimensional whiskers with a great extent of potassium replacement and to favor the increase of specific surface area of the structures. The whisker layer is found to be sensitive towards volatile organic compounds (ethanol, isopropanol, acetone) in the mixture with air at room temperature. The vapor identification is obtained via processing the vector signal generated by sensor array of the multielectrode chip with the help of pattern recognition algorithms.

Factors Shaping the Human Exposome in the Built Environment: Opportunities for Engineering Control

The "exposome" is a term describing the summation of one's lifetime exposure to microbes and chemicals. Such exposures are now recognized as major drivers of human health and disease. Because humans spend ∼90% of their time indoors, the built environment exposome merits particular attention. Herein we utilize an engineering perspective to advance understanding of the factors that shape the built environment exposome and its influence on human wellness and disease, while simultaneously informing development of a framework for intentionally controlling the exposome to protect public health. Historically, engineers have been focused on controlling chemical and physical contaminants and on eradicating microbes; however, there is a growing awareness of the role of "beneficial" microbes. Here we consider the potential to selectively control the materials and chemistry of the built environment to positively influence the microbial and chemical components of the indoor exposome. Finally, we discuss research gaps that must be addressed to enable intentional engineering design, including the need to define a "healthy" built environment exposome and how to control it.