Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 μg m-3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.

Assessment of Indoor Particulate Matter and Teacher's Perceived Indoor Climate in Government Schools of Bilaspur District, Chhattisgarh, India: A Cross-Sectional Study

Context

Indoor air pollution (IAP) affects the long-term health, cognitive growth, and academic performance of children. Since children spend most of their time at school, quantifying IAP in classrooms is an important parameter for air pollution.

Aim

To assess the average particulate matter (PM) levels inside and outside of classrooms along with their associated factors and teacher's perceived indoor climate.

Setting and Design

Cross-sectional survey in nine government-run schools.

Methods and Material

PM2.5 and PM10 were measured inside the classroom and outdoors simultaneously during summers, using an Atmos monitor for two consecutive days, along with several school and classroom characteristics. Perception about indoor air quality was captured from teachers (n = 15) using a validated questionnaire.

Statistical Analysis

Mean values of PM using mixed effect linear regression. Perceived indoor air quality is presented using percentages.

Results

Mean indoor PM2.5 and PM10 was 52.5 µg/m3 and 65 µg/m3. Indoor and outdoor PM levels were highly correlated, but the indoor-outdoor ratio of PM concentrations was more than 1. Teachers were mostly bothered by dust, dirt, and noise in the schools.

Conclusion

Indoor air quality was higher than World Health Organization (WHO) standards but within the national standards. Need further research to find the exact cause for higher indoor PM levels compared to outdoor PM levels.

Assessing residential PM2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors

Building conditions, outdoor climate, and human behavior influence residential concentrations of fine particulate matter (PM2.5). To study PM2.5 spatiotemporal variability in residences, we acquired paired indoor and outdoor PM2.5 measurements at 3,977 residences across the United States totaling >10,000 monitor-years of time-resolved data (10-min resolution) from the PurpleAir network. Time-series analysis and statistical modeling apportioned residential PM2.5 concentrations to outdoor sources (median residential contribution = 52% of total, coefficient of variation = 69%), episodic indoor emission events such as cooking (28%, CV = 210%) and persistent indoor sources (20%, CV = 112%). Residences in the temperate marine climate zone experienced higher infiltration factors, consistent with expectations for more time with open windows in milder climates. Likewise, for all climate zones, infiltration factors were highest in summer and lowest in winter, decreasing by approximately half in most climate zones. Large outdoor-indoor temperature differences were associated with lower infiltration factors, suggesting particle losses from active filtration occurred during heating and cooling. Absolute contributions from both outdoor and indoor sources increased during wildfire events. Infiltration factors decreased during periods of high outdoor PM2.5, such as during wildfires, reducing potential exposures from outdoor-origin particles but increasing potential exposures to indoor-origin particles. Time-of-day analysis reveals that episodic emission events are most frequent during mealtimes as well as on holidays (Thanksgiving and Christmas), indicating that cooking-related activities are a strong episodic emission source of indoor PM2.5 in monitored residences.

School and childcare facility air quality decision-makers' perspectives on using low-cost sensors for wildfire smoke response

BACKGROUND

During wildfire smoke episodes, school and childcare facility staff and those who support them rely upon air quality data to inform activity decisions. Where ambient regulatory monitor data is sparse, low-cost sensors can help inform local outdoor activity decisions, and provide indoor air quality data. However, there is no established protocol for air quality decision-makers to use sensor data for schools and childcare facilities. To develop practical, effective toolkits to guide the use of sensors in school and childcare settings, it is essential to understand the perspectives of the potential end-users of such toolkit materials.

METHODS

We conducted 15 semi-structured interviews with school, childcare, local health jurisdiction, air quality, and school district personnel regarding sensor use for wildfire smoke response. Interviews included sharing PM2.5 data collected at schools during wildfire smoke. Interviews were transcribed and transcripts were coded using a codebook developed both a priori and amended as additional themes emerged.

RESULTS

Three major themes were identified by organizing complementary codes together: (1) Low-cost sensors are useful despite data quality limitations, (2) Low-cost sensor data can inform decision-making to protect children in school and childcare settings, and (3) There are feasibility and public perception-related barriers to using low-cost sensors.

CONCLUSIONS

Interview responses provided practical implications for toolkit development, including demonstrating a need for toolkits that allow a variety of sensor preferences. In addition, participants expected to have a wide range of available time for monitoring, budget for sensors, and decision-making types. Finally, interview responses revealed a need for toolkits to address sensor uses outside of activity decisions, especially assessment of ventilation and filtration.

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.

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.

Development of ASTM International D8405-Standard Test Method for Evaluating PM2.5 Sensors or Sensor Systems Used in Indoor Applications

Sensors and sensor systems for monitoring fine particles with aerodynamic diameters smaller than 2.5 µm can provide real-time feedback on indoor air quality and thus can help guide actions to manage indoor air pollutant concentrations. Standardized verification of the performance and accuracy of sensors and sensor systems is crucial for predicting the efficacy of such monitoring. A new ASTM International standard test method (ASTM D8405) was created for this need and is the most exacting laboratory protocol published to date for evaluating indoor air quality sensors and sensor systems measuring particles smaller than 2.5 µm in diameter. ASTM D8405 subjects sensors and sensor systems to five test phases: (1) an initial particle concentration ramp; (2) exposure to various temperature and humidity conditions; (3) exposure to interfering particles; (4) temperature cycling; and (5) a final particle concentration ramp to assess drift. This paper discusses the development of the standard test method, key aspects of the testing process, example evaluation results, and a comparison of this standard test method against peer evaluation protocols.

Spatial Variation of PM2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years

Spatial variation of indoor and outdoor PM_2.5 within three states for a five-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study (Wallace et al., 2022 Indoor Air 32:13105) investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4000 indoor PurpleAir monitors are included. Daily mean PM_2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids the use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name "PM_2.5_alt". This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM_2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM_2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM_2.5 is not homogeneous even at distances < 100 m. There is good agreement between PurpleAir outdoor monitors located <100 m apart and collocated Federal Equivalent Methods (FEM).

Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Indoor contribution to PM2 .5 exposure using all PurpleAir sites in Washington, Oregon, and California

Low-cost monitors have made it possible for the first time to measure indoor PM_2.5 concentrations over extended periods of time (months to years). Coupled with concurrent outdoor measurements, these indoor measurements can be divided into particles entering the building from outdoors and particles generated from indoor activities. Indoor-generated particles are not normally considered in epidemiological studies, but they can have health effects (e.g., passive smoking and high-temperature cooking). We employed The Random Component Superposition (RCS) regression model to estimate infiltration factors for up to 790 000 matched indoor and outdoor sites. The median infiltration factors for subgroups in the 3-state region ranged between 0.22 and 0.24, with an interquartile range (IQR) of 0.13-0.40. These infiltration factors allowed calculation of both the indoor-generated and outdoor-infiltrated PM_2.5 . Indoor-generated particles contributed, on average, 46%-52% of total indoor PM_2.5 concentrations. However, the site-specific fractional contribution of these indoor sources to total indoor PM_2.5 ranged from near-zero to nearly 100%. The influence of indoor-generated particles on potential exposures varied widely relative to outdoor concentrations. The greatest influence of indoor-generated particles occurred at low-to-moderate daily mean outdoor PM_2.5 levels around 6 μg/m³ and was negligible at outdoor concentrations >20 μg/m³ . Epidemiological studies incorporating only estimated exposures due to the particles of ambient origin may benefit from the newly available knowledge of long-term indoor-generated particle concentrations.

Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021

Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

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.

Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM_2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM_2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM_2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM_2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m³, compared to approximately 3–10 µg/m³ using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM_2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.

Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models

Air pollution significantly affects public health in many countries. In particular, indoor air quality can be equally, if not more, concerning than outdoor emissions of pollutant gases. However, monitoring the air quality in homes and apartments using chemical analyzers may be not affordable for households due to their high costs and logistical issues. Therefore, a new alternative is represented by low-cost air quality monitors (AQMs) based on low-cost gas sensors (LCSs), but scientific literature reports some limitations and issues concerning the quality of the measurements performed by these devices. It is proven that AQM performance is significantly affected by the calibration model used for calibrating LCSs in outdoor environments, but similar investigations in homes or apartments are quite rare. In this work, the assessment of an AQM based on electrochemical sensors for CO, NO2, and O3 has been performed through an experiment carried out in an apartment occupied by a family of four during their everyday life. The state-of-the-art of the LCS calibration is featured by the use of multivariate linear regression (MLR), random forest regression (RF), support vector machines (SVM), and artificial neural networks (ANN). In this study, we have conducted a comparison of these calibration models by using different sets of predictors through reference measurements to investigate possible differences in AQM performance. We have found a good agreement between measurements performed by AQM and data reported by the reference in the case of CO and NO2 calibrated using MLR (R2 = 0.918 for CO, and R2 = 0.890 for NO2), RF (R2 = 0.912 for CO, and R2 = 0.697 for NO2), and ANN (R2 = 0.924 for CO, and R2 = 0.809 for NO2).

Room-level ventilation in schools and universities

Ventilation is of primary concern for maintaining healthy indoor air quality and reducing the spread of airborne infectious disease, including COVID-19. In addition to building-level guidelines, increased attention is being placed on room-level ventilation. However, for many universities and schools, ventilation data on a room-by-room basis are not available for classrooms and other key spaces. We present an overview of approaches for measuring ventilation along with their advantages and disadvantages. We also present data from recent case studies for a variety of institutions across the United States, with various building ages, types, locations, and climates, highlighting their commonalities and differences, and examples of the use of this data to support decision making.

Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 μg m-3 (6.1% relative standard deviation [RSD]) and 40.1 μg m-3 (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93-0.99) and good accuracy (mean absolute error (MAE) < 2 μg m-3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87-95) and good accuracy (MAE of 2.5-3.0 μg m-3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

A Study on the Behavior Patterns of Liquid Aerosols Using Disinfectant Chloromethylisothiazolinone/Methylisothiazolinone Solution

This study evaluates the behavioral characteristics of components (methylisothiazolinone (MIT) and chloromethylisothiazolinone (CMIT)) contained in disinfectant solutions when they convert to liquid aerosols. The analytical method for MIT and CMIT quantitation was established and optimized using sorbent tube/thermal desorber-gas chromatography-mass spectrometry system; their behavioral characteristics are discussed using the quantitative results of these aerosols under different liquid aerosol generation conditions. MIT and CMIT showed different behavioral characteristics depending on the aerosol mass concentration and sampling time (sampling volume). When the disinfectant solution was initially aerosolized, MIT and CMIT were primarily collected on glass filter (MIT = 91.8 ± 10.6% and CMIT = 90.6 ± 5.18%), although when the generation and filter sampling volumes of the aerosols increased to 30 L, the relative proportions collected on the filter decreased (MIT = 79.0 ± 12.0% and CMIT = 39.7 ± 8.35%). Although MIT and CMIT had relatively high vapor pressure, in liquid aerosolized state, they primarily accumulated on the filter and exhibited particulate behavior. Their relative proportions in the aerosol were different from those in disinfectant solution. In the aerosol with mass concentration of ≤5 mg m^-3, the relative proportion deviations of MIT and CMIT were large; when the mass concentration of the aerosol increased, their relative proportions constantly converged at a lower level than those in the disinfectant solution. Hence, it can be concluded that the behavioral characteristics and relative proportions need to be considered to perform the quantitative analysis of the liquid aerosols and evaluate various toxic effects using the quantitative data.

The Effects of Ventilation and Filtration on Indoor PM2.5 in Office Buildings in Four Countries

Fine particulate matter (PM_2.5) is an airborne pollutant associated with negative acute and chronic human health outcomes. Although the majority of PM_2.5 research has focused on outdoor exposures, people spend the majority of their time indoors, where PM_2.5 of outdoor origin can penetrate. In this work, we measured indoor PM_2.5 continuously for one year in 37 urban commercial offices with mechanical or mixed-mode ventilation in China, India, the United Kingdom, and the United States. We found that indoor PM_2.5 concentrations were generally higher when and where outdoor PM_2.5 was elevated. In India and China, mean workday indoor PM_2.5 levels exceeded the World Health Organization's 24-hour exposure guideline of 25 µg/m³ about 17% and 27% of the time, respectively. Our statistical models found evidence that the operation of mechanical ventilation systems could mitigate the intrusion of outdoor PM_2.5: during standard work hours, a 10 µg/m³ increase in outdoor PM_2.5 was associated with 19.9% increase in the expected concentration of indoor PM_2.5 (p<0.0001), compared to a larger 23.4% increase during non-work hours (p<0.0001). Finally, our models found that using filters with ratings of MERV 13-14 or MERV 15+ was associated with a 30.9% (95% CI: -55.0%, +6.2%) or 39.4% (95% CI: -62.0%, -3.4%) reduction of indoor PM_2.5, respectively, compared to filters with lower MERV 7-12 ratings. Our results demonstrate the potential efficacy of mechanical ventilation with efficient filtration as a public health strategy to protect workers from PM_2.5 exposure, particularly where outdoor levels of PM_2.5 are elevated.

Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York

OBJECTIVES

Few studies have comprehensively assessed multiple environmental exposures affecting children's health. This study applied machine-learning methods to evaluate how indoor environmental conditions at home and school contribute to asthma and allergy-related symptoms.

METHODS

We randomly selected 10 public schools representing different socioeconomic statuses in New York State (2017-2019) and distributed questionnaires to students to collect health status and home-and school-environmental exposures. Indoor air quality was measured at school, and ambient particle exposures (PM_2.5 and components) were measured using real-time personal monitors for 48 h. We used random forest model to identify the most important risk factors for asthma and allergy-related symptoms, and decision tree for visualizing the inter-relationships among the multiple risk factors with the health outcomes.

RESULTS

The top contributing factors identified for asthma were family rhinitis history (relative importance: 10.40%), plant pollen trigger (5.48%); bedroom carpet (3.58%); environmental tobacco smoke (ETS) trigger symptom (2.98%); and ETS exposure (2.56%). For allergy-related symptoms, plant pollen trigger (10.88%), higher paternal education (7.33%), bedroom carpet (5.28%), family rhinitis history (4.78%), and higher maternal education (4.25%) were the strongest contributing factors. Conversely, primary heating with hot water radiator was negatively (-6.86%) associated with asthma symptoms. Younger children (<9 years old) with family history of rhinitis and carpeting in the bedroom were the prominent combined risk factors for asthma. Children jointly exposed to pollen, solvents, and carpeting in their home tended to have greater risks of allergy-related symptoms, even without family history of rhinitis.

CONCLUSION

Family rhinitis history, bedroom carpet, and pollen triggers were the most important risk factors for both asthma and allergy-related symptoms. Our new findings included that hot-water radiator was related to reduced asthma symptoms, and the combination of young age, rhinitis history, and bedroom carpeting was related to increased asthma symptoms. Further studies are needed to confirm our findings.

Miniature Optical Particle Counter and Analyzer Involving a Fluidic-Optronic CMOS Chip Coupled with a Millimeter-Sized Glass Optical System

Our latest advances in the field of miniaturized optical PM sensors are presented. This sensor combines a hybrid fluidic-optronic CMOS (holed retina) that is able to record a specific irradiance pattern scattered by an illuminated particle (scattering signature), while enabling the circulation of particles toward the sensing area. The holed retina is optically coupled with a monolithic, millimeter-sized, refracto-reflective optical system. The latter notably performs an optical pre-processing of signatures, with a very wide field of view of scattering angles. This improves the sensitivity of the sensors, and simplifies image processing. We report the precise design methodology for such a sensor, as well as its fabrication and characterization using calibrated polystyrene beads. Finally, we discuss its ability to characterize particles and its potential for further miniaturization and integration.

Particulate matter emitted from ultrasonic humidifiers-Chemical composition and implication to indoor air

Household humidification is widely practiced to combat dry indoor air. While the benefits of household humidification are widely perceived, its implications to the indoor air have not been critically appraised. In particular, ultrasonic humidifiers are known to generate fine particulate matter (PM). In this study, we first conducted laboratory experiments to investigate the size, quantity, and chemical composition of PM generated by an ultrasonic humidifier. The mass of PM generated showed a correlation with the total alkalinity of charge water, suggesting that CaCO₃ is likely making a major contribution to PM. Ion chromatography analysis revealed a large amount of SO₄^2- in PM, representing a previously unrecognized indoor source. Preliminary results of organic compounds being present in humidifier PM are also presented. A whole-house experiment was further conducted at an actual residential house, with five low-cost sensors (AirBeam) monitoring PM in real time. Operation of a single ultrasonic humidifier resulted in PM_2.5 concentrations up to hundreds of μg m^-3 , and its influence extended across the entire household. The transport and loss of PM_2.5 depended on the rate of air circulation and ventilation. This study emphasizes the need to further investigate the impact of humidifier operation, both on human health and on the indoor atmospheric chemistry, for example, partitioning of acidic and basic compounds.

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

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

Factors Impacting Range Hood Use in California Houses and Low-Income Apartments

Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single family houses and 17 low-income apartments in California in which cooking activities, range hood use, and fine particulate matter (PM2.5) were monitored for one week per home. Range hoods were used for 36% of cooking events in houses and 28% in apartments. The frequency of hood use increased with cooking frequency across homes. In both houses and apartments, the likelihood of hood use during a cooking event increased with the duration of cooktop burner use, but not with the duration of oven use. Actual hood use rates were higher in the homes of participants who self-reported more frequent use in a pre-study survey, but actual use was far lower than self-reported frequency. Residents in single family houses used range hoods more often when cooking caused a discernible increase in PM2.5. In apartments, residents used their range hood more often only when high concentrations of PM2.5 were generated during cooking.

The use of personal and indoor air pollution monitors in reproductive epidemiology studies

BACKGROUND

Personal and indoor air pollution monitors represent two ways to assess acute air pollution exposures; however, few reproductive epidemiology studies have incorporated these tools.

OBJECTIVE

To provide an overview of the unique challenges and opportunities that arise when measuring acute exposure to air pollution in two ongoing reproductive epidemiology studies.

METHODS

The Air Pollution, In Vitro Fertilization (IVF), and Reproductive Outcomes (AIR) Study recruits women undergoing IVF to wear a personal particulate matter (PM) air pollution monitor (AirBeam2©) for the 72-hour period following the start of controlled ovarian stimulation. The Reproductive Effects of Chemicals and Air Pollutants (RECAP) Study recruits men across the United States to place an air pollution monitor (emmET) in their home for 3 months, use a smartphone application, and provide a semen sample. We highlight the key issues identified in implementing exposure assessment for both studies.

RESULTS

The main advantages of using the AirBeam2© personal monitor are as follows: (a) the low cost, (b) the ability to collect multiple size fractions of PM data every second, (c) the portability, (d) its capability to track GPS location, and (e) the ability for the participant to observe their real-time exposure information. The limited battery life, incompatibility with iOS-based smartphones, and frequent connection issues that arise between the AirBeam2© and smartphone are the main disadvantages. The main advantages of the emmET are the ability to measure multiple air pollutants at a high level of accuracy, collect data for a long period of time without burdening the participant, and ship monitors to participants around the country without the need for in-person set-up by trained technicians; however, the monitor only measures the indoor home environment.

CONCLUSIONS

Novel methods can be utilised to characterise short-term air pollution exposure in reproductive epidemiology studies and represent an exciting area for future research.

Indoor air quality in California homes with code-required mechanical ventilation

Data were collected in 70 detached houses built in 2011-2017 in compliance with the mechanical ventilation requirements of California's building energy efficiency standards. Each home was monitored for a 1-week period with windows closed and the central mechanical ventilation system operating. Pollutant measurements included time-resolved fine particulate matter (PM_2.5 ) indoors and outdoors and formaldehyde and carbon dioxide (CO₂ ) indoors. Time-integrated measurements were made for formaldehyde, NO₂ , and nitrogen oxides (NO_X ) indoors and outdoors. Operation of the cooktop, range hood, and other exhaust fans was continuously recorded during the monitoring period. Onetime diagnostic measurements included mechanical airflows and envelope and duct system air leakage. All homes met or were very close to meeting the ventilation requirements. On average, the dwelling unit ventilation fan moved 50% more airflow than the minimum requirement. Pollutant concentrations were similar to or lower than those reported in a 2006-2007 study of California new homes built in 2002-2005. Mean and median indoor concentrations were lower by 44% and 38% for formaldehyde and 44% and 54% for PM_2.5 . Ventilation fans were operating in only 26% of homes when first visited, and the control switches in many homes did not have informative labels as required by building standards.

One Year Evaluation of Three Low-Cost PM2.5 Monitors

The availability of low-cost monitors marketed for use in homes has increased rapidly over the past few years due to the advancement of sensing technologies, increased awareness of urban pollution, and the rise of citizen science. The user-friendly packages can make them appealing for use in research grade indoor exposure assessments, but a rigorous scientific evaluation has not been conducted for many monitors on the open market, which leads to uncertainty about the validity of the data. Furthermore, many previous sensor studies were conducted for a relatively short period of time, which may not capture the changes this type of instrument may exhibit over time (known as sensor aging). We evaluated three monitors (AirVisual Pro, Speck, and AirThinx) in an occupied, non-smoking residence over a 12-month period in order to assess the sensors, the built-in calibrations, and the need for additional data to achieve high accuracy for long deployments. Two units of each type of monitor were evaluated in order to assess the precision between units, and a personal DataRAM (pDR-1200) with a filter was placed in the home for about 20% of the sampling period (e.g., about a week each month) to evaluate the accuracy over time. The average PM_2.5 mass concentration from the periods of colocation with the pDR were 5.31 μg/m³ for the gravimetric-corrected pDR (hereafter pDR-corrected), 5.11 and 5.03 μg/m³ for the AirVisual Pro units, 13.58 and 22.68 μg/m³ for the Speck units, and 7.56 and 7.57 μg/m³ for the AirThinx units. The AirVisual Pros exhibited the best accuracy compared to the filter at about 86%, which was slightly better than the nephelometric component of the pDR compared to the filter weight (84%). The accuracies of the Speck (-174 and -405%) and AirThinx (42 and 40%) monitors were much lower. When the 1-minute averaged PM_2.5 mass concentrations were categorized by air quality index (AQI), the pDR-corrected matched the AirVisual Pro, Speck, and AirThinx bins about 97, 40, and 87% of the time, respectively. The Pearson correlation coefficients (R²) between the unit pairs and the pDR were 0.90/0.90, 0.50/0.27, and 0.92/0.93 for the AirVisual Pro, Speck, and AirThinx units, respectively. The R² between units of the same type were 0.99, 0.17, and 1.00 for the AirVisual Pro, Speck, and AirThinx, respectively. All of the monitors could achieve better accuracy by adding filter corrections and post-processing to correct for known biases in addition to the manufacturer's correction routine. Monthly calibrations yielded the highest accuracies, but nearly as high of accuracies could be achieved with only one or two calibrations for the Air Visual Pro and the AirThinx for many applications. In general, this type of new low-cost monitor shows exciting potential for use in scientific research. However, only one of the three monitors exhibited high accuracy (within 20% of the true mass concentration) without any post processing or additional measurements, so an evaluation of each monitor is essential before the data can be used to confidently evaluate residential exposures.

Assessing Impact of Household Intervention on Indoor Air Quality and Health of Children with Asthma in the US-Mexico Border: A Pilot Study

Few studies have investigated household interventions to enhance indoor air quality (IAQ) and health outcomes in relatively low-income communities. This study aims to examine the impact of the combined intervention with asthma education and air purifier on IAQ and health outcomes in the US-Mexico border area. An intervention study conducted in McAllen, Texas, between June and November 2019 included 16 households having children with asthma. The particulate matter (PM2.5) levels were monitored in the bedroom, kitchen, and living room to measure the IAQ for 7 days before and after the intervention, respectively. Multiple surveys were applied to evaluate changes in children's health outcomes. The mean PM2.5 levels in each place were significantly improved. Overall, they significantly decreased by 1.91 μg/m3 on average (p < 0.05). All surveys showed better health outcomes; particularly, quality of life for children was significantly improved (p < 0.05). This pilot study suggests that the combined household intervention might improve IAQ in households and health outcomes for children with asthma and reduce health disparities in low-income communities. Future large-scale studies are needed to verify the effectiveness of this household intervention to improve IAQ and asthma management.

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44-0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire.

Indoor Particulate Matter during HOMEChem: Concentrations, Size Distributions, and Exposures

It is important to improve our understanding of exposure to particulate matter (PM) in residences because of associated health risks. The HOMEChem campaign was conducted to investigate indoor chemistry in a manufactured test house during prescribed everyday activities, such as cooking, cleaning, and opening doors and windows. This paper focuses on measured size distributions of PM (0.001-20 μm), along with estimated exposures and respiratory-tract deposition. Number concentrations were highest for sub-10 nm particles during cooking using a propane-fueled stovetop. During some cooking activities, calculated PM_2.5 mass concentrations (assuming a density of 1 g cm^-3) exceeded 250 μg m^-3, and exposure during the postcooking decay phase exceeded that of the cooking period itself. The modeled PM respiratory deposition for an adult residing in the test house kitchen for 12 h varied from 7 μg on a day with no indoor activities to 68 μg during a simulated day (including breakfast, lunch, and dinner preparation interspersed by cleaning activities) and rose to 149 μg during a simulated Thanksgiving day.

Wildfire and prescribed burning impacts on air quality in the United States

Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM _2.5 ) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research.

IMPLICATIONS

In the recent decade the area of wildfires in the United States has increased dramatically and the resulting smoke has exposed millions of people to unhealthy air quality. In this critical review we examine the key factors and impacts from fires including natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry and human health.

Examining the functional range of commercially available low-cost airborne particle sensors and consequences for monitoring of indoor air quality in residences

Low-cost airborne particle sensors are gaining attention for monitoring human exposure to indoor particulate matter. This study aimed to establish the concentrations at which these commercially available sensors can be expected to report accurate concentrations. We exposed five types of commercial integrated devices and three types of "bare" low-cost particle sensors to a range of concentrations generated by three different sources. We propose definitions of upper and lower bounds of functional range based on the relationship between a given sensor's output and that of a reference instrument during a laboratory experiment. Experiments show that the lower bound can range from approximately 3 to 15 μg/m³ . At greater concentrations, sensor output deviates from linearity at approximately 300-3000 μg/m³ . We also conducted a simulation campaign to analyze the effect of this limitation on functional range on the accuracy of exposure readings given by these devices. We estimate that the upper bound results in minimal inaccuracy in exposure quantification, and the lower bound can result in as much as a 50% error in approximately 10% of US homes.

Use of IoT sensing and occupant surveys for determining the resilience of buildings to forest fire generated PM2.5

Wildfires and associated emissions of particulate matter pose significant environmental and health concerns. In this study we propose tools to evaluate building resilience to extreme episodes of outdoor particulate matter using a combination of indoor and outdoor IoT measurements, coupled with survey-based information of occupants' perception and behaviour. We demonstrated the application of the tools on two buildings with different modes of ventilation during the Chico Camp fire event. We characterized the resilience of the buildings on different temporal and spatial scales using the well-established I/O ratio and a newly proposed E-index that evaluates indoor concentration in the context of adopted 24-hour exposure thresholds. Indoor PM2.5 concentration during the entire Chico Camp Fire event was 21 μg/m3 for 4th Street (Mechanically Ventilated) and 36 μg/m3 for Wurster Hall (Naturally Ventilated). The cumulative median I/O ratio during the fire event was 0.27 for 4th Street and 0.67 for Wurster Hall. Overall E-index for 4th Street was 0.82, suggesting that the whole building was resilient to outdoor air pollution while overall E-index was 1.69 for Wurster Hall suggesting that interventions are necessary. The survey revealed that occupant perception of workplace air quality aligns with measured PM2.5 in the two buildings. The results also highlight that a large portion of occupants wore face masks, even though the PM2.5 concentration was below WHO threshold level. The results of our study demonstrate the utility of the proposed IoT-enabled and survey tools to assess the degree of protection from air pollution of outdoor origin for a single building or across a portfolio of buildings. The proposed survey tool also provides direct links between the PM2.5 levels and occupants' perception and behavior.

Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things

The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.

Design and evaluation of a portable PM2.5 monitor featuring a low-cost sensor in line with an active filter sampler

Fine particulate air pollution (PM2.5) is a health hazard with numerous indoor and outdoor sources. Versatile monitors are needed to characterize PM2.5 sources, concentrations, and exposures in a range of locations and applications. Whereas low-cost light-scattering PM sensors provide real-time measurements with limited accuracy, gravimetric samples provide more accurate, albeit time-integrated, measurements. When used together, low-cost sensor data can be corrected to gravimetric samples. Here we describe the development of a portable PM2.5 monitor that features a low-cost sensor in line with an active filter sampler. Laboratory tests were conducted to determine (1) the accuracy and precision of PM2.5 concentrations derived from the filter sample and (2) correction factors for the low-cost sensor response to ammonium sulfate, Arizona road dust, urban particulate matter, and match smoke. Filter samples collected at 0.25 and 1.0 L min-1 had mean biases of -10% and -4%, relative to a tapered element oscillating microbalance, and a relative standard deviation (RSD) that ranged from 1% to 17%. The low-cost sensor correction factor varied with the test aerosol, sample flow rate, and between individual monitors. Gravimetric correction reduced the bias and RSD of ∼1 hour average concentrations measured by low-cost sensors in three collocated monitors. A week-long field experiment was also conducted to investigate how the monitor could be used to learn about sources of residential air pollution. Field data were used to identify: (1) pollution events resulting from cooking and use of a wood furnace and (2) variations in the number of air changes per hour inside the residence.

A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California

Ambient particulate matter (PM) pollution is a major environmental health risk in urban areas. Dense networks of low-cost air quality sensors are emerging to characterize the spatially heterogeneous concentrations that are typical of urban settings, but are not adequately captured using traditional regulatory monitors at central sites. In this study, we present the 100×100 BC Network, a 100-day deployment of low-cost black carbon (BC) sensors across 100 locations in West Oakland, California. This 15 km² community is surrounded by freeways and affected by emissions associated with local port and industrial activities. We assess the reliability of the sensor hardware and data collection systems, and identify modes of failure to both quantify and qualify network performance. We illustrate how dynamic, local emission sources build upon background BC concentrations. BC concentrations varied sharply over short distances (∼100 m) and timespans (∼1 hour), depending on surrounding land use, traffic patterns, and downwind distance from pollution sources. Strong BC concentration fluctuations were periodically observed over the diurnal and weekly cycles, reflecting the impact of localized traffic emissions and industrial facilities in the neighborhood. Overall, the results demonstrate how distributed sensor networks can reveal the complex spatiotemporal dynamics of combustion-related air pollution within urban neighborhoods.

Variation in gravimetric correction factors for nephelometer-derived estimates of personal exposure to PM2.5

Many portable monitors for quantifying mass concentrations of particulate matter air pollution rely on aerosol light scattering as the measurement method; however, the relationship between scattered light (what is measured) and aerosol mass concentration (the metric of interest) is a complex function of the refractive index, size distribution, and shape of the particles. In this study, we compared 33-h personal PM_2.5 concentrations measured simultaneously using nephelometry (personal DataRAM pDR-1200) and gravimetric filter sampling for working adults (44 participants, 249 samples). Nephelometer- and filter-derived 33-h average PM_2.5 concentrations were correlated (Spearman's ρ = 0.77); however, the nephelometer-derived concentration was within 20% of the filter-derived concentration for only 13% of samples. The nephelometer/filter ratio, which is used to correct light-scattering measurements to a gravimetric sample, had a median value of 0.52 and varied by over a factor of three (10th percentile = 0.35, 90^th percentile = 1.1). When 33-h samples with >50% of 10-s average nephelometer readings below the nephelometer limit of detection were removed from the dataset during sensitivity analyses, the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration increased to 25%. We also evaluated how much the accuracy of nephelometer-derived concentrations improved after applying: (1) a median correction factor derived from a subset of 44 gravimetric samples, (2) participant-specific correction factors derived from one same from each subject, and (3) correction factors predicted using linear models based on other variables recorded during the study. Each approach independently increased the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration to approximately 45%. These results illustrate the challenges with using light scattering (without correction to a concurrent gravimetric sample) to estimate personal exposure to PM_2.5 mass among mobile adults exposed to low daily average concentrations (median = 8 μg m^-3 in this study).

Investigating measurements of fine particle (PM2.5 ) emissions from the cooking of meals and mitigating exposure using a cooker hood

There is growing awareness that indoor exposure to particulate matter with diameter ≤ 2.5 μm (PM_2.5 ) is associated with an increased risk of adverse health effects. Cooking is a key indoor source of PM_2.5 and an activity conducted daily in most homes. Population scale models can predict occupant exposures to PM_2.5 , but these predictions are sensitive to the emission rates used. Reported emission rates are highly variable and are typically for the cooking of single ingredients and not full meals. Accordingly, there is a need to assess PM_2.5 emissions from the cooking of complete meals. Mean PM_2.5 emission rates and source strengths were measured for four complete meals. Temporal PM_2.5 concentrations and particle size distributions were recorded using an optical particle counter (OPC), and gravimetric sampling was used to determine calibration factors. Mean emission rates and source strengths varied between 0.54-3.7 mg/min and 15-68 mg, respectively, with 95% confidence. Using a cooker hood (apparent capture efficiency > 90%) and frying in non-stick pans were found to significantly reduce emissions. OPC calibration factors varied between 1.5 and 5.0 showing that a single value cannot be used for all meals and that gravimetric sampling is necessary when measuring PM_2.5 concentrations in kitchens.

Outlier detection and gap filling methodologies for low-cost air quality measurements

Air pollution is a major environmental health problem around the world, which needs to be monitored. In recent years, a new generation of low-cost air pollution sensors has emerged. Poor or unknown data quality, resulting from the intrinsic properties of the sensor as well as the lack of a consensus on data processing methodologies for these sensors, has, among other factors, prevented widespread adoption of these sensors. To contribute to the creation of this consensus, we reviewed the available methodologies for quality control, outlier detection and gap filling and applied two outlier detection methodologies and five gap filling methodologies to a case study (consisting of an 11-month long air quality data set from a low-cost sensor). We showed that erroneous data can be detected in a fully automated way, and that point and contextual outlier detection methodologies can be applied to low-cost air pollution data and yield meaningful results. The linear interpolation showed the best performance for gap filling for low-cost air pollution sensors. In conclusion, data cleaning procedures are important, and the presented methods can form part of a generalised data processing methodology for low-cost air pollution sensors.