Application of the low-cost sensing technology for indoor air quality monitoring: A review

In recent years, low-cost air pollution technologies have gained increasing interest and, have been studied widely by the scientific community. Thus, these new sensing technologies must provide reliable data with good precision and accuracy. Accordingly, this review aimed to evaluate and compare the low-cost sensing technology against other instruments used for comparison by various studies from the scientific literature to monitor indoor air quality in different indoor environments. After exclusions, a total of 42 studies divided into two subsections (11 laboratory studies and 31 field studies) were analysed considering their aim, location, study duration, sampling area, pollutant(s) evaluated, sensor/device and instrument used for comparison, performance indexes and main outcomes.& nbsp;The reviewed studies aimed to assess different low-cost sensors/devices to monitor indoor air quality against other instruments used for comparison. The vast majority of the studies took place in USA. The laboratory studies were mainly conducted in a controlled chamber, and field studies were performed in homes, offices, educational buildings, among others. In both cases, particulate matter was the most assessed pollutant, either with commercial devices (e.g.: Speck, Dylos, Foobot) or sensors (e.g. Sharp GP2Y1010AU0F). In general, based on statistical parameters, the air quality low-cost sensors/devices tested presented moderate correlations with the instruments used for comparison, revealing sufficient precision for monitoring air quality in indoor microenvironments, especially for qualitative analysis. Thus, low-cost sensing technology to monitor indoor air quality is encouraged, but not waiving the relevance of high quality instruments (mainly reference instruments).& nbsp;(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Impact of indoor air pollution in nursery and primary schools on childhood asthma

Poor indoor air quality in scholar environments have been frequently reported, but its impact on respiratory health in schoolchildren has not been sufficiently explored. Thus, this study aimed to evaluate the associations between children's exposure to indoor air pollution (IAP) in nursery and primary schools and childhood asthma. Multivariate models (independent and multipollutant) quantified the associations of children's exposure with asthma-related health outcomes: reported active wheezing, reported and diagnosed asthma, and lung function (reduced FEV1/FVC and reduced FEV1). A microenvironmental modelling approach estimated individual inhaled exposure to major indoor air pollutants (CO2, CO, formaldehyde, NO2, O-3, TVOC, PM2.5 and PM10) in nursery and primary schools from both urban and rural sites in northern Portugal. Questionnaires and medical tests (spirometry pre- and post-bronchodilator) were used to obtain information on health outcomes and to diagnose asthma following the newest international clinical guidelines. After testing children for aeroallergen sensitisation, multinornial models estimated the effect of exposure to particulate matter on asthma in sensitised individuals. The study population were 1530 children attending nursery and primary schools, respectively 648 pre-schoolers (3-5 years old) and 882 primary school children (6-10 years old). This study found no evidence of a significant association between IAP in nursery and primary schools and the prevalence of childhood asthma. However, reported active wheezing was associated with higher NO2, and reduced FEV1 was associated with higher O-3 and PM2.5, despite NO2 and O-3 in schools were always below the 200 mu g m(-3) threshold from WHO and National legislation, respectively. Moreover, sensitised children to common aeroallergens were more likely to have asthma during childhood when exposed to particulate matter in schools. These findings support the urgent need for mitigation measures to reduce IAP in schools, reducing its burden to children's health

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

Radon in Indoor Air: Towards Continuous Monitoring

Radon poses significant health risks. Thus, the continuous monitoring of radon concentrations in buildings' indoor air is relevant, particularly in schools. Low-cost sensors devices are emerging as promising technologies, although their reliability is still unknown. Therefore, this is the first study aiming to evaluate the performance of low-cost sensors devices for short-term continuous radon monitoring in the indoor air of nursery and primary school buildings. Five classrooms of different age groups (infants, pre-schoolers and primary school children) were selected from one nursery and one primary school in Porto (Portugal). Radon indoor concentrations were continuously monitored using one reference instrument (Radim 5B) and three commercially available low-cost sensors devices (Airthings Wave and RandonEye: RD200 and RD200P2) for short-term sampling (2-4 consecutive days) in each studied classroom. Radon concentrations were in accordance with the typical profiles found in other studies (higher on weekends and non-occupancy periods than on occupancy). Both RadonEye low-cost sensors devices presented similar profiles with Radim 5B and good performance indices (R-2 reaching 0.961), while the Airthings Wave behavior was quite different. These results seem to indicate that the RadonEye low-cost sensors devices studied can be used in short-term radon monitoring, being promising tools for actively reducing indoor radon concentrations

Can data reliability of low-cost sensor devices for indoor air particulate matter monitoring be improved?-An approach using machine learning

Poor indoor air quality has adverse health impacts. Children are considered a risk group, and they spend a significant time indoors at home and in schools. Air quality monitoring has traditionally been limited due to the cost and size of the monitoring stations. Recent advancements in low-cost sensors technology allow for economical, scalable and real-time monitoring, which is especially helpful in monitoring air quality in indoor environments, as they are prone to sudden peaks in pollutant concentrations. However, data reliability is still a considerable challenge to overcome in low-cost sensors technology. Thus, following a monitoring campaign in a nursery and primary school in Porto urban area, the present study analyzed the performance of three commercially available low-cost IoT devices for indoor air quality monitoring in real-world against a research-grade device used as a reference and developed regression models to improve their reliability. This paper also presents the developed on-field calibration models via machine learning technique using multiple linear regression, support vector regression, and gradient boosting regression algorithms and focuses on particulate matter (PM1, PM2.5, PM10) data collected by the devices. The performance evaluation results showed poor detection of particulates in classrooms by the low-cost devices compared to the reference. The on-field calibration algorithms showed a considerable improvement in all three devices' accuracy (reaching up to R2 > 0.9) for the light scattering technology based particulate matter sensors. The results also show the different performance of low-cost devices in the lunchroom compared to the classrooms of the same school building, indicating the need for calibration in different microenvironments

INDOOR VOC CONCENTRATIONS at NURSERY and PRIMARY SCHOOLS: IMPACT of COVID-19 PREVENTIVE MEASURES

The importance of evaluating indoor air pollutants, such as volatile organic compounds (VOC), became a topic of utmost interest, especially during COVID-19 pandemic due to the increasing cleaning and disinfection of hands, surfaces and spaces, the most common measures to prevent the spread of COVID-19. VOC presence in indoor environments can impair human health, particularly above threshold limit values. Thus, this study aimed to quantify the differences between VOC concentrations before and during COVID-19 pandemic in indoor air of one nursery and one primary school in Porto, Portugal. This study was carried out in early 2020 (before COVID-19 pandemic) and early 2021 (during COVID-19 pandemic) in two classrooms I07_A (nursery school) and S07_B (primary school). Both classrooms presented a similar school timetable between the two periods, but the personal and environmental hygiene throughout the day using alcohol-based sanitisers were hugely increased in 2021. Total VOC (TVOC) were monitored continuously with research-grade instruments for a minimum period of two consecutive weekdays. Two average periods were considered: (i) an average day period (hourly means of all weekdays); and (ii) an average occupancy period (hourly means during occupancy periods considering the school timetable). Descriptive statistical analysis, as well as normality (ShapiroWilk Test) and significance (Wilcoxon Signed Rank Test) tests were performed using the R software version 4.0.5. The statistical significance level considered was set to 0.05. TVOC concentrations exceeded the limit value in the Portuguese legislation (1,200 µg/m3) during occupancy period in both classrooms during COVID-19 pandemic, although they never exceeded before the pandemic. Moreover, a statistically significant increase (p-value < 0.05) on TVOC concentrations from 2020 to 2021 were observed in the two studied classrooms for both average day (mean difference: 647 µg/m3 and 1,170 µg/m3 for I07_A and S07_B, respectively) and average occupancy periods (mean difference: 521 µg/m3 and 2,730 µg/m3 for I07_A and S07_B, respectively). Therefore, it was possible to conclude that the continued use of alcohol-based products, as a result of COVID-19 prevention measures, could increase TVOC concentrations to unsafe levels in schools. (c) 2021 WIT Press

Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs

Urban air pollution is a global concern impairing citizens' health, thus monitoring is a pressing need for city managers. City-wide networks for air pollution monitoring based on low-cost sensors are promising to provide real-time data with detail and scale never before possible. However, they still present limitations preventing their ubiquitous use. Thus, this study aimed to perform a post-deployment validation and calibration based on two step methods for ozone low-cost sensor of a city-wide network for air pollution and meteorology monitoring using low-cost sensors focusing on the main challenges. Four of the 23 data collection units (DCUs) of the UrbanSense network installed in Porto city (Portugal) with low-cost sensors for particulate matter (PM), carbon monoxide (CO), ozone (O-3), and meteorological variables (temperature, relative humidity, luminosity, precipitation, and wind speed and direction) were evaluated. This study identified post-deployment challenges related to their validation and calibration. The preliminary validation showed that PM, CO and precipitation sensors recorded only unreliable data, and other sensors (wind speed and direction) very few data. A multi-step calibration strategy was implemented: inter-DCU calibration (1st step, for O-3, temperature and relative humidity) and calibration with a reference-grade instrument (2nd step, for O-3). In the 1st step, multivariate linear regression (MLR) resulted in models with better performance than non-linear models such as artificial neural networks (errors almost zero and R-2 > 0.80). In the 2nd step, the calibration models using non-linear machine learning boosting algorithms, namely Stochastic Gradient Boosting Regressor (both with the default and posttuning hyper-parameters), performed better than artificial neural networks and linear regression approaches. The calibrated O-3 data resulted in a marginal improvement from the raw data, with error values close to zero, with low predictability (R-2 similar to 0.32). The lessons learned with the present study evidenced the need to redesign the calibration strategy. Thus, a novel multi-step calibration strategy is proposed, based on two steps (pre and post-deployment calibration). When performed cyclically and continuously, this strategy reduces the need for reference instruments, while probably minimising data drifts over time. More experimental campaigns are needed to collect more data and further improve calibration models

The Effect of Fire Smoke Exposure on Firefighters' Lung Function: A Meta-Analysis

Firefighters are exposed to a range of harmful substances during firefighting. Exposure to fire smoke has been associated with a decrease in their lung function. However, the cause-effect relationship between those two factors is not yet demonstrated. This meta-analysis aimed to evaluate the potential associations between firefighters' occupational exposure and their lung function deterioration. Studies were identified from PubMed, Web of Science, Scopus and Science Direct databases (August 1990-March 2021). The studies were included when reporting the lung function values of Forced Expiratory Volume in 1 s (FEV1) or Forced Vital Capacity (FVC). The meta-analyses were performed using the generic inverse variance in R software with a random-effects model. Subgroup analysis was used to determine if the lung function was influenced by a potential study effect or by the participants' characteristics. A total of 5562 participants from 24 studies were included. No significant difference was found between firefighters' predicted FEV1 from wildland, 97.64% (95% CI: 91.45-103.82%; I-2 = 99%), and urban fires, 99.71% (95% CI: 96.75-102.67%; I-2 = 98%). Similar results were found for the predicted FVC. Nevertheless, the mean values of firefighters' predicted lung function varied significantly among studies, suggesting many confounders, such as trials' design, statistical methods, methodologies applied, firefighters' daily exposure and career length, hindering an appropriate comparison between the studies