Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

Airborne Exposure to Pollutants and Mental Health: A Review with Implications for United States Veterans

PURPOSE OF REVIEW

Inhalation of airborne pollutants in the natural and built environment is ubiquitous; yet, exposures are different across a lifespan and unique to individuals. Here, we reviewed the connections between mental health outcomes from airborne pollutant exposures, the biological inflammatory mechanisms, and provide future directions for researchers and policy makers. The current state of knowledge is discussed on associations between mental health outcomes and Clean Air Act criteria pollutants, traffic-related air pollutants, pesticides, heavy metals, jet fuel, and burn pits.

RECENT FINDINGS

Although associations between airborne pollutants and negative physical health outcomes have been a topic of previous investigations, work highlighting associations between exposures and psychological health is only starting to emerge. Research on criteria pollutants and mental health outcomes has the most robust results to date, followed by traffic-related air pollutants, and then pesticides. In contrast, scarce mental health research has been conducted on exposure to heavy metals, jet fuel, and burn pits. Specific cohorts of individuals, such as United States military members and in-turn, Veterans, often have unique histories of exposures, including service-related exposures to aircraft (e.g. jet fuels) and burn pits. Research focused on Veterans and other individuals with an increased likelihood of exposure and higher vulnerability to negative mental health outcomes is needed. Future research will facilitate knowledge aimed at both prevention and intervention to improve physical and mental health among military personnel, Veterans, and other at-risk individuals.

Practical considerations for using low-cost sensors to assess wildfire smoke exposure in school and childcare settings

BACKGROUND

More frequent and intense wildfires will increase concentrations of smoke in schools and childcare settings. Low-cost sensors can assess fine particulate matter (PM2.5) concentrations with high spatial and temporal resolution.

OBJECTIVE

We sought to optimize the use of sensors for decision-making in schools and childcare settings during wildfire smoke to reduce children's exposure to PM2.5.

METHODS

We measured PM2.5 concentrations indoors and outdoors at four schools in Washington State during wildfire smoke in 2020-2021 using low-cost sensors and gravimetric samplers. We randomly sampled 5-min segments of low-cost sensor data to create simulations of brief portable handheld measurements.

RESULTS

During wildfire smoke episodes (lasting 4-19 days), median hourly PM2.5 concentrations at different locations inside a single facility varied by up to 49.6 µg/m3 (maximum difference) during school hours. Median hourly indoor/outdoor ratios across schools ranged from 0.22 to 0.91. Within-school differences in concentrations indicated that it is important to collect measurements throughout a facility. Simulation results suggested that making handheld measurements more often and over multiple days better approximates indoor/outdoor ratios for wildfire smoke. During a period of unstable air quality, PM2.5 over the next hour indoors was more highly correlated with the last 10-min of data (mean R2 = 0.94) compared with the last 3-h (mean R2 = 0.60), indicating that higher temporal resolution data is most informative for decisions about near-term activities indoors.

IMPACT STATEMENT

As wildfires continue to increase in frequency and severity, staff at schools and childcare facilities are increasingly faced with decisions around youth activities, building use, and air filtration needs during wildfire smoke episodes. Staff are increasingly using low-cost sensors for localized outdoor and indoor PM2.5 measurements, but guidance in using and interpreting low-cost sensor data is lacking. This paper provides relevant information applicable for guidance in using low-cost sensors for wildfire smoke response.

Systematically quantifying the dynamic characteristics of PM2.5 in multiple indoor environments in a plateau city: Implication for internal contribution

People generally spend most of their time indoors, making a comprehensive evaluation of air pollution characteristics in various indoor microenvironments of great significance for accurate exposure estimation. In this study, field measurements were conducted in Kunming City, Southwest China, using real-time PM2.5 sensors to characterize indoor PM2.5 in ten different microenvironments including three restaurants, four public places, and three household settings. Results showed that the daily average PM2.5 concentrations in restaurants, public spaces, and households were 78.4 ± 24.3, 20.1 ± 6.6, and 18.0 ± 4.3 µg/m3, respectively. The highest levels of indoor PM2.5 in restaurants were owing to strong internal emissions from cooking activities. Dynamic changes showed that indoor PM2.5 levels increased during business time in restaurants and public places, and cooking time in residential kitchens. Compared with public places, restaurants generally exhibit more rapid increases in indoor PM2.5 due to cooking activities, which can elevate indoor PM2.5 to high levels (5.1 times higher than the baseline) in a short time. Furthermore, indoor PM2.5 in restaurants were dominated by internal emissions, while outdoor penetration contributed mostly to indoor PM2.5 in public places and household settings. Results from this study revealed large variations in indoor PM2.5 in different microenvironments, and suggested site-specific measures for indoor PM2.5 pollution alleviation.

Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors

Low-cost air quality monitors are growing in popularity among both researchers and community members to understand variability in pollutant concentrations. Several studies have produced calibration approaches for these sensors for ambient air. These calibrations have been shown to depend primarily on relative humidity, particle size distribution, and particle composition, which may be different in indoor environments. However, despite the fact that most people spend the majority of their time indoors, little is known about the accuracy of commonly used devices indoors. This stems from the fact that calibration data for sensors operating in indoor environments are rare. In this study, we sought to evaluate the accuracy of the raw data from PurpleAir fine particulate matter monitors and for published calibration approaches that vary in complexity, ranging from simply applying linear corrections to those requiring co-locating a filter sample for correction with a gravimetric concentration during a baseline visit. Our data includes PurpleAir devices that were co-located in each home with a gravimetric sample for 1-week periods (265 samples from 151 homes). Weekly-averaged gravimetric concentrations ranged between the limit of detection (3 μg/m3) and 330 μg/m3. We found a strong correlation between the PurpleAir monitor and the gravimetric concentration (R>0.91) using internal calibrations provided by the manufacturer. However, the PurpleAir data substantially overestimated indoor concentrations compared to the gravimetric concentration (mean bias error ≥ 23.6 μg/m3 using internal calibrations provided by the manufacturer). Calibrations based on ambient air data maintained high correlations (R ≥ 0.92) and substantially reduced bias (e.g. mean bias error = 10.1 μg/m3 using a US-wide calibration approach). Using a gravimetric sample from a baseline visit to calibrate data for later visits led to an improvement over the internal calibrations, but performed worse than the simpler calibration approaches based on ambient air pollution data. Furthermore, calibrations based on ambient air pollution data performed best when weekly-averaged concentrations did not exceed 30 μg/m3, likely because the majority of the data used to train these models were below this concentration.

Adequacy of stationary measurements as proxies for residential personal exposure to gaseous and particle air pollutants

People are exposed to myriad of airborne pollutants in their homes. Owing to diverse potential sources of air pollution and human activity patterns, accurate assessment of residential exposures is complex. In this study, we explored the relationship between personal and stationary air pollutant measurements in residences of 37 participants working from home during the heating season. Stationary environmental monitors (SEMs) were located in the bedroom, living room or home office and personal exposure monitors (PEMs) were worn by the participants. SEMs and PEMs included both real-time sensors and passive samplers. During three consecutive weekdays, continuous data were obtained for particle number concentration (size range 0.3-10 μm), carbon dioxide (CO2), and total volatile organic compounds (TVOC), while passive samplers collected integrated measures of 36 volatile organic compounds (VOCs) and semi volatile organic compounds (SVOCs). The personal cloud effect was detected in >80% of the participants for CO2 and >50% participants for PM10. Multiple linear regression analysis showed that a single CO2 monitor placed in the bedroom efficiently represented personal exposure to CO2 (R2 = 0.90) and moderately so for PM10 (R2 = 0.55). Adding a second or third sensor in a residence did not lead to improved exposure estimates for CO2, with only 6-9% improvement for particles. Selecting data from SEMs when participants were in the same room improved personal exposure estimates by 33% for CO2 and 5% for particles. Out of 36 detected VOCs and SVOCs, 13 had at least 50% higher concentrations in personal versus stationary samples. Findings from this study aid improved understanding of the complex dynamics of gaseous and particle pollutants and their sources in residences, and could support the development of refined procedures for residential air quality monitoring and inhalation exposure assessment.

Public engagement with air quality data: using health behaviour change theory to support exposure-minimising behaviours

Exposure to air pollution prematurely kills 7 million people globally every year. Policy measures designed to reduce emissions of pollutants, improve ambient air and consequently reduce health impacts, can be effective, but are generally slow to generate change. Individual actions can therefore supplement policy measures and more immediately reduce people's exposure to air pollution. Air quality indices (AQI) are used globally (though not universally) to translate complex air quality data into a single unitless metric, which can be paired with advice to encourage behaviour change. Here we explore, with reference to health behaviour theories, why these are frequently insufficient to instigate individual change. We examine the health behaviour theoretical steps linking air quality data with reduced air pollution exposure and (consequently) improved public health, arguing that a combination of more 'personalised' air quality data and greater public engagement with these data will together better support individual action. Based on this, we present a novel framework, which, when used to shape air quality interventions, has the potential to yield more effective and sustainable interventions to reduce individual exposures and thus reduce the global public health burden of air pollution.

Indoor air pollution from the household combustion of coal: Tempo-spatial distribution of gaseous pollutants and semi-quantification of source contribution

Coal is a widely used solid fuel for cooking and heating activities in rural households, whose incomplete combustion in inefficient household stoves releases a range of gaseous pollutants. To evaluate the impact of coal combustion on indoor air quality, this study comprehensively investigated the indoor air pollution of typical gaseous pollutants, including formaldehyde (HCHO), carbon dioxide (CO₂), carbon monoxide (CO), total volatile organic compounds (TVOC), and methane (CH₄), during coal combustion process in rural households using online monitoring with high tempo-spatial resolution. The indoor concentrations of gaseous pollutants were considerably elevated during the coal combustion period, with the indoor concentrations being significantly higher than those in courtyard air. The levels of several gaseous pollutants (CO₂, CO, TVOC, and CH₄) in indoor air were much higher during the flaming phase than the de-volatilization and smoldering phases, while HCHO peaked in the de-volatilization phase. The gaseous pollutant concentrations mostly decreased from the room ceiling to the ground level, while their horizontal distribution was relatively uniform within the room. It was estimated that coal combustion accounted for about 71 %, 92 %, 63 %, 59 %, and 21 % of total exposure to indoor CO₂, CO, TVOC, CH₄, and HCHO, respectively. Improved stove combined with clean fuel could effectively lower the concentrations of CO₂, CO, TVOC, and CH₄ in indoor air and reduce the contributions of coal combustion to these gaseous pollutants by about 21-68 %. These findings help better understand the indoor air pollution resulting from residential coal combustion and could guide the development of intervention programs to improve indoor air quality in rural households of northern China.

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.

Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors

Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM_2.5 and PM₁₀ (3.9 ± 6.8 ug/m³ and 9.6 ± 12.7 μg/m³ respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m³ and 4.2 ± 6.9 μg/m³ respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM₁ concentration (1.6 ± 1.9 μg/m³), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM₁ was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM_2.5, and up to 50 % of the PM₁₀, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

Identification of Factors Determining Household PM2.5 Variations at Regional Scale and Their Implications for Pollution Mitigation

Indoor PM_2.5, particulate matter no more than 2.5 μm in aerodynamic equivalent diameter, has very high spatiotemporal variabilities; and exploring the key factors influencing the variabilities is critical for purifying air and protecting human health. Here, we conducted a longer-term field monitoring campaign using low-cost sensors and evaluated inter- and intra-household PM_2.5 variations in rural areas where energy or stove stacking is common. Household PM_2.5 varied largely across different homes but also within households. Using generalized linear models and dominance analysis, we estimated that outdoor PM_2.5 explained 19% of the intrahousehold variation in indoor daily PM_2.5, whereas factors like the outdoor temperature and indoor-outdoor temperature difference that was associated with energy use directly or indirectly, explained 26% of the temporal variation. Inter-household variation was lower than intrahousehold variation. The inter-household variation was strongly associated with distinct internal sources, with energy-use-associated factors explaining 35% of the variation. The statistical source apportionment model estimated that solid fuel burning for heating contributed an average of 31%-55% of PM_2.5 annually, whereas the contribution of sources originating from the outdoors was ≤10%. By replacing raw biomass or coal with biomass pellets in gasifier burners for heating, indoor PM_2.5 could be significantly reduced and indoor temperature substantially increased, providing thermal comforts in addition to improved air quality.

Using low-cost sensors to assess real-time comfort and air quality patterns in indoor households

People spend most of their time in indoor environments without knowing about the air quality in these spaces. In this study, indoor low-cost sensors were used (for 5 months) to assess the comfort and air quality patterns in two indoor households. To strengthen the robustness of the considered approach and build confidence in the obtained comfort and indoor air quality (IAQ) levels, the sensor measurements were also compared against information from reference monitoring equipment; in which, high correlation coefficients were obtained (> 0.85) and also low errors (on average 22%). The IAQ results were strongly influenced by the residents' activity and behaviour, the outdoor weather conditions, and indoor/outdoor air pollution sources. Overall, the recommended values of temperature and relative humidity for the occupant's comfort in indoor environments were not fulfilled. The highest particulate matter (PM) levels were recorded at the weekend (on average +14% higher), while maximum CO₂ and CO levels were obtained on the weekdays (on average +9% higher). PM daily profiles followed the outdoor concentrations with the maximum levels at the end of the night and the lowest values in the early morning/mid-afternoon. The highest and lowest CO₂ concentrations were registered in the early morning (< 1536 ppm) and mid-afternoon (< 627 ppm), respectively, while the CO daily profiles showed a high impact of outdoor emissions, with the minimum concentrations up to 0.81 mg m^-3 (at 10 a.m. or 6 p.m.), and a maximum concentration of 1.87 mg m^-3 (at 10 p.m.). Real-time comfort conditions and IAQ levels are a powerful approach to providing fast decisions to minimise human exposure and prevent negative health impacts.

The Effect of Kitchen Ventilation Modification on Independent and Combined Associations of Cooking Fuel Type and Cooking Duration with Suicidal Ideation: A Cross-Sectional Study

Although household air pollution (HAP) is associated with an increased risk of mental disorders, evidence remains scarce for the relationship between HAP and suicidal ideation. A total of 21,381 qualified participants were enrolled on the Henan Rural Cohort Study. HAP information including cooking fuel type, cooking duration and kitchen ventilation was collected by questionnaires. Suicidal ideation was evaluated by item nine of the Patient Health Questionnaire-9 (PHQ-9). Independent and combined associations of cooking fuel type and cooking duration with suicidal ideation were explored by logistic regression models. Analyses were conducted in different kitchen ventilation groups to detect the potential effect modification. The adjusted odds ratio (OR) and 95% confidence interval (95% CI) of solid fuel users versus clean fuel users for suicidal ideation was 1.37 (1.16, 1.62), and the risk of suicidal ideation increased by 15% (95% CI: 5%, 26%) for each additional hour of the cooking duration. Participants cooking with solid fuel for long durations were related to the highest risk of suicidal ideation (OR (95% CI): 1.51 (1.22, 1.87)). However, all these associations were not observed in those cooking with mechanical ventilation. Mechanical ventilation ameliorated relationships between solid fuel use and long-duration cooking with suicidal ideation.

A predictive model of indoor PM2.5 considering occupancy level in a hospital outpatient hall

The hospital outpatient hall is more complex and sensitive than other indoor places because of its high density, flow of patients, and risk of infection. The prediction of indoor pollutants, such as PM_2.5, is a critical health risk factor and an important topic in the study of indoor air quality. Numerous black-box models have been built to predict PM_2.5, which are prone to overfitting and low precision in long sequence time prediction due to their limited weighting calculation and factors considered In this study, subject-object weighting incorporates a long sequence time-series model that considers occupancy (SO-LSTS) to predict PM_2.5 concentrations in a hospital outpatient hall. First, the occupancy level was obtained using image recognition technology. Second, both the subjective (improved AHP) and objective (entropy weight) information were coupled by a distance function and then integrated into the LSTS model. Finally, the model performance was compared to six traditional models and the impact on the output length and hyper-parameter confirmation was assessed. The results demonstrate that the occupancy factor can improve the model performance by 54 %, and the model accuracy is improved by 89 % compared to the traditional Informer method. Our study considers real-time environmental and occupancy levels, which can compensate for the difficulty of interpreting the black-box model and identifying an accurate and resource-efficient proactive control model for hospital environmental management compared to conventional approaches.

Characterization of the vertical variation in indoor PM2.5 in an urban apartment in China

Indoor air pollution has aroused increasing concerns due to its significant adverse health impacts. Indoor PM_2.5 exposure assessments often rely on PM_2.5 concentration measured at a single height, which overlooks the vertical variation of PM_2.5 concentrations accompanied by various indoor activities. In this study, we characterize the vertical profile of PM_2.5 concentration by monitoring PM_2.5 concentration at eight different heights in the kitchen and the bedroom, respectively, using low-cost sensors with high temporal resolution. The localized enhancement of PM_2.5 concentration in elevated heights in the kitchen during cooking was observed on clean and polluted days, showing dominating contribution from cooking activities. The source contribution from cooking and outdoor penetration was semi-quantified using regression models. Stratified source contribution from cooking activities was evident in the kitchen during the cooking period. The contribution in elevated heights (above 170 cm) almost tripled the contrition in bottom layers (below 140 cm). In contrast, little vertical variation was observed during other times of the day in the kitchen or the bedroom. The exposure level calculated using the multi-height measurement in this study is consistently higher than the exposure level estimated from the single-height (at 110 cm) measurement. A more significant discrepancy existed for the cookers (17.8%) than the non-cookers (13.5%). By profiling the vertical gradient of PM_2.5 concentration, we show the necessity to conduct multi-height measurements or proper breathing-height measurements to obtain unbiased concentration information for source apportionment and exposure assessment. In particular, the multi-height measuring scheme will be crucial to inform household cooking emission regulations.

Pesticide screening and health risk assessment of residential dust in a rural region of the North China Plain

Pesticides that have accumulated in arable soil could be easily transported by wind erosion, thereby potentially threating air quality and human health in surrounding areas. The risks this poses to farmers exposed to pesticide-associated dust is still unknown, especially in rural areas of China. In this study, we screened pesticide residues in dust (indoor and outdoor) collected from the homes and yards of pesticide sprayers (21 participants) and farm workers (14 participants) living in Quzhou County located in the North China Plain to assess health risks by exposed to pesticide-contaminated dust. The results showed that multiple pesticide residues were detected in the dust samples and more than 90% of the samples contained over 10 pesticide residues. The maximum detected number of residues was 23, out of the 25 pesticides currently used in the farming area. There was a wide range of pesticide concentrations with the geometric mean values measuring between 0.03 and 0.86 mg kg^-1. More residues and higher concentrations of pesticides were detected in indoor dust compared to outdoor dust. Over the monitoring period, the pesticide application has not caused significant pesticide accumulation in dust. The measured concentrations of carbendazim, dimethomorph, dimethomorph and pendimethalin paired indoor-outdoor dust samples were significantly correlated (p < 0.05). The health risks were assessed using the hazard index (HI) and highest HI was found for children under indoor exposure (HI = 0.82). In addition, based on the survey and statistics, pesticide preparation in the home was significantly correlated with the pesticide indoor exposure level. Therefore, farmers should take measures, such as preparing pesticides outside of the house or in the open fields with protection, in order to avoid the exposure risk of pesticides associated with dust.

Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centers

Daycare centers (DCCs) are where infants and toddlers (0-4 years old) spend the most time besides their homes. Given their higher susceptibility to the effects of air pollutants, as compared to older children and adults, indoor air quality (IAQ) is regarded as an essential parameter to monitor in DCCs. Recent advances in IAQ monitoring technologies have enabled the deployment of low-cost air quality monitors (LCMs) and single sensors (LCSs) to continuously monitor various indoor environments, and their performance testing should also be performed in the intended indoor applications. To our knowledge, there is no study evaluating the application of LCMs/LCSs in DCCs scenarios yet. Therefore, this study is aimed to assess the response of five types of LCMs (previously not tested) and five LCSs to typical DCCs emission activities in detecting multiple IAQ parameters, i.e., particulate matter, carbon dioxide, total volatile organic compounds, temperature, and relative humidity. These LCMs/LCSs were compared to outcomes from research-grade instruments (RGIs). All the experiments were performed in a climate chamber, where three kinds of typical activities (background; arts-and-crafts; cleaning; [in a total of 32 events]) were simulated by recruited subjects at two typical indoor climatic conditions (cool and dry [20 ± 1 °C & 40 ± 10%], warm and humid [26 ± 1 °C & 70 ± 5%]). Results showed that tested LCMs had the ability to capture DCCs activities by simultaneously monitoring multiple IAQ parameters, and LCMs/LCSs revealed a strong correlation with RGIs in most events (R2 values from 0.7 to 1), but, for some events, the magnitude of responses varied widely. Sensirion SCD41, an emerging CO2 sensor built on the photoacoustic sensing principle, had a more accurate performance than all tested NDIR-based CO2 sensors/monitors. In general, the study implies that the selection of LCMs/LCSs for a specific application of interest should be based on emission characteristics and space conditions.

A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings

Particulate matter with an aerodynamic diameter less than 2.5μm (PM_2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM_2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM_2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM_2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM_2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM_2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM_2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.

Insights into the Profile of the Human Expiratory Microbiota and Its Associations with Indoor Microbiotas

Microorganisms residing in the human respiratory tract can be exhaled, and they constitute a part of environmental microbiotas. However, the expiratory microbiota community and its associations with environmental microbiotas remain poorly understood. Here, expiratory bacteria and fungi and the corresponding microbiotas from the living environments were characterized by DNA amplicon sequencing of residents' exhaled breath condensate (EBC) and environmental samples collected from 14 residences in Nanjing, China. The microbiotas of EBC samples, with a substantial heterogeneity, were found to be as diverse as those of skin, floor dust, and airborne microbiotas. Model fitting results demonstrated the role of stochastic processes in the assembly of the expiratory microbiota. Using a fast expectation-maximization algorithm, microbial community analysis revealed that expiratory microbiotas were differentially associated with other types of microbiotas in a type-dependent and residence-specific manner. Importantly, the expiratory bacteria showed a composition similarity with airborne bacteria in the bathroom and kitchen environments with an average of 12.60%, while the expiratory fungi showed a 53.99% composition similarity with the floor dust fungi. These differential patterns indicate different relationships between expiratory microbiotas and the airborne microbiotas and floor dust microbiotas. The results here illustrated for the first time the associations between expiratory microbiotas and indoor microbiotas, showing a potential microbial exchange between the respiratory tract and indoor environment. Thus, improved hygiene and ventilation practices can be implemented to optimize the indoor microbial exposome, especially in indoor bathrooms and kitchens.

The effect of COVID-19 restrictions on particulate matter on different modes of transport in China

Since the COVID-19 pandemic, ventilation on transport has been improved to control the aerosol transmission. We utilized portable monitors to measure real-time concentrations of PM₁₀, PM_2.5, PM_1.0 and black carbon (BC) on six modes of transport and estimate personal exposures under the epidemic prevention. The mean concentrations of PM₁₀, PM_2.5, PM_1.0 and BC measured on transport were 18.8 ± 19.4, 16.6 ± 16.5, 12.2 ± 10.8 and 4.1 ± 6.9 μg/m³, respectively. It reduced PM levels on subway to apply the full fresh air mode rather than partial recirculation mode. Airplane had the lowest concentrations and the highest decay rates, implying the most efficient ventilation and filtration. PM were higher on intra-city transport than inter-city, and significantly increased on arrival at stations. BC and BC/PM ratios were higher on road transport than rail transport, indicating the contribution of exhaust emissions. The ventilation mode to exchange air with the outside and the positive association between concentrations and decay rates on high-speed train suggested filtration efficiency should be improved simultaneously with enhancing ventilation. Wearing facemasks on transport further protects passengers against PM exposure, which reduced personal exposure concentrations on four modes of transport lower than 10 μg/m³, the World Health Organization guideline.

Household PM2.5 pollution in rural Chinese homes: Levels, dynamic characteristics and seasonal variations

Humans generally spend most of their time indoors, and fine particulate matter (PM_2.5) in indoor air can have seriously adverse effects on human health due to the long exposure time. This study conducted field measurements to explore seasonal variations of PM_2.5 concentrations in household air by revisiting the same rural homes in southern China and factors influencing indoor PM_2.5 concentrations were explored mainly by one-way ANOVA. The PM_2.5 concentrations of outdoor, kitchen and living room air were 38.9 ± 12.2, 47.1 ± 20.3 and 50.8 ± 24.1 μg/m³ in summer, respectively, which were 2.3 to 2.9 times lower than those in winter (p < 0.05). The lower indoor PM_2.5 pollution in summer was attributed to the transition to clean household energy and better ventilation. Fuel type can significantly affect PM_2.5 concentrations in the kitchen, with greater PM_2.5 pollution associated with wood combustion than electricity. Our study firstly found mosquito coil emission was an important contributor to PM_2.5 in the living room of rural households, which should be investigated further. Dynamic variations of PM_2.5 suggested that cooking, heating and mosquito coil emission can rapidly increase indoor PM_2.5 concentrations (up to one order of magnitude higher than baseline values), as well as the indoor/outdoor PM_2.5 ratios. This study had the first insight of seasonal differences of household PM_2.5 in the same rural homes using real-time monitors, confirming the different patterns and characteristics of household PM_2.5 pollution in different seasons.

Hysteresis effects of meteorological variation-induced algal blooms: A case study based on satellite-observed data from Dianchi Lake, China (1988-2020)

As one of three top-priority eutrophic lakes in China, Dianchi Lake has received national attention due to its severe eutrophication in recent decades. Meteorological factors are the main factors driving the formation and persistence of algae blooms. In addition, meteorological variation-induced algal blooms usually have a hysteresis effect. However, there have been few quantitative studies on this hysteresis effect. In the present study, Landsat images were used to extract the dynamic characteristics of changes in algal blooms in Dianchi Lake from 1988 to 2020. The hysteresis effect of meteorological factors driving algal blooms was studied by employing the modified lag-correlation method. The results showed that the algal blooms in Dianchi Lake were most severe between 1998 and 2008. During the periods of algal blooms, the values of air temperature (AT) and precipitation (PP) were significantly higher, while those wind velocity (WV) and sunshine duration (SSD) were obviously lower, than the corresponding annual mean values. AT and PP were significantly positively correlated with algal bloom factors in both the formation and persistence stages of algal blooms, while SSD and WV both promoted their regression, but these effects were less significant in the persistence period than in the formation period. Moreover, rainfall led to a decrease in SSD and WV, indirectly contributing to algal blooms. Furthermore, AT, PP and SSD are the main factors impacting the duration of persistent blooms. The time periods during which each meteorological factor was most influential were as follows: 1) AT - 25-30 days before the maximum bloom. 2) PP - within the first 10 days before the maximum bloom. 3) Both SSD and WV - 15-20 days before the maximum bloom. The results of this study support the prediction of algal blooms in Dianchi Lake.

Columnar Aerosol Optical Property Characterization and Aerosol Typing Based on Ground-Based Observations in a Rural Site in the Central Yangtze River Delta Region

Accurate and updated aerosol optical properties (AOPs) are of vital importance to climatology and environment-related studies for assessing the radiative impact of natural and anthropogenic aerosols. We comprehensively studied the columnar AOP observations between January 2019 and July 2020 from a ground-based remote sensing instrument located at a rural site operated by Central China Comprehensive Experimental Sites in the center of the Yangtze River Delta (YRD) region. In order to further study the aerosol type, two threshold-based aerosol classification methods were used to investigate the potential categories of aerosol particles under different aerosol loadings. Based on AOP observation and classification results, the potential relationships between the above-mentioned results and meteorological factors (i.e., humidity) and long-range transportation processes were analyzed. According to the results, obvious variation in aerosol optical depth (AOD) during the daytime, as well as throughout the year, was revealed. Investigation into AOD, single-scattering albedo (SSA), and absorption aerosol optical depth (AAOD) revealed the dominance of fine-mode aerosols with low absorptivity. According to the results of the two aerosol classification methods, the dominant aerosol types were continental (accounting for 43.9%, method A) and non-absorbing aerosols (62.5%, method B). Longer term columnar AOP observations using remote sensing alongside other techniques in the rural areas in East China are still needed for accurate parameterization in the future.

Quantify individual variation of real-time PM2.5 exposure in urban Chinese homes based on a novel method

Fine particulate matter (PM_2.5 ) concentrations show high variations in different microenvironments indoors, which has considerable impact on risk management. However, the real-time variations of PM_2.5 exposure associated with per activity/microenvironment and intra-variation among family members remain undefined. In this study, real-time monitors were used to collect real-time PM_2.5 data in different microenvironments in 32 households in urban community of China. Peak concentrations of PM_2.5 were found in kitchen. The parallel levels of PM_2.5 household indoor and outdoor indicated the benefit of clean energies use. To validly assess the health risk of individuals, we proposed a novel method to estimate the real-time exposure of all residents and firstly investigate the intra-variation of PM_2.5 exposure among family members. The member who is responsible for cooking in the family had the maximum PM_2.5 exposure. The ratios among intraindividual variations demonstrated children usually had lower exposure compared to the adults as they stayed more time in lower polluted microenvironments such as living room and bedroom. The exposure intensity in living room was above 1.0 for most residents, indicating it is warranted to alleviate the air pollution in living room. This study firstly focused on the intra differences of PM_2.5 exposure among family members and provided a new insight for indoor air pollution management. The results suggested when adopting measures to reduce exposure, the microenvironments pattern of each member should be taken into consideration. Future work is welcomed to move another big step on this issue to protect the human health.

Contributions of internal emissions to peaks and incremental indoor PM2.5 in rural coal use households

Indoor air quality is critically important to the human as people spend most time indoors. Indoor PM_2.5 is related to the outdoor levels, but more directly influenced by internal sources. Severe household air pollution from solid fuel use has been recognized as one major risk for human health especailly in rural area, however, the issue is significantly overlooked in most national air quality controls and intervention policies. Here, by using low-cost sensors, indoor PM_2.5 in rural homes burning coals was monitored for ~4 months and analyzed for its temporal dynamics, distributions, relationship with outdoor PM_2.5, and quantitative contributions of internal sources. A bimodal distribution of indoor PM_2.5 was identified and the bimodal characteristic was more significant at the finer time resolution. The bimodal distribution maxima were corresponding to the emissions from strong internal sources and the influence of outdoor PM_2.5, respectively. Indoor PM_2.5 was found to be correlated with the outdoor PM_2.5, even though indoor coal combustion for heating was thought to be predominant source of indoor PM_2.5. The indoor-outdoor relationship differed significantly between the heating and non-heating seasons. Impacts of typical indoor sources like cooking, heating associated with coal use, and smoking were quantitatively analyzed based on the highly time-resolved PM_2.5. Estimated contribution of outdoor PM_2.5 to the indoor PM_2.5 was ~48% during the non-heating period, but decreased to about 32% during the heating period. The contribution of indoor heating burning coals comprised up to 47% of the indoor PM_2.5 during the heating period, while the other indoor sources contributed to ~20%. The study, based on a relatively long-term timely resolved PM_2.5 data from a large number of rural households, provided informative results on temporal dynamics of indoor PM_2.5 and quantitative contributions of internal sources, promoting scientific understanding on sources and impacts of household air pollution.

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.