A low-cost particle counter as a realtime fine-particle mass monitor

Aerosol Measurement Degradation in Low-Cost Particle Sensors Using Laboratory Calibration and Field Validation

Increasing concern over air pollution has led to the development of low-cost sensors suitable for wide-scale deployment and use by citizen scientists. This project investigated the AirU low-cost particle sensor using two methods: (1) a comparison of pre- and post-deployment calibration equations for 24 devices following use in a field study, and (2) an in-home comparison between 3 AirUs and a reference instrument, the GRIMM 1.109. While differences (and therefore some sensor degradation) were found in the pre- and post-calibration equation comparison, absolute value changes were small and unlikely to affect the quality of results. Comparison tests found that while the AirU did tend to underestimate minimum and overestimate maximum concentrations of particulate matter, ~88% of results fell within ±1 μg/m³ of the GRIMM. While these tests confirm that low-cost sensors such as the AirU do experience some sensor degradation over multiple months of use, they remain a valuable tool for exposure assessment studies. Further work is needed to examine AirU performance in different environments for a comprehensive survey of capability, as well as to determine the source of sensor degradation.

Assessment of the exposure to PM2.5 in different Lebanese microenvironments at different temporal scales

The weak potential of using the sole outdoor concentrations to represent personal exposure to PM_2.5 is confirmed by the literature; therefore, it is important to account for a person's movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM_2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R² = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM_2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon's annual PM_2.5 mean (24.2 µg⁄m³) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m³). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM_2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor-indoor-transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM_2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM_2.5 and to inspire similar studies to achieve the maximum efficiency.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM_2.5 and 1040 pseudo networks for NO₂. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM_2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO₂ less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure.

Visualization of Dust Generation in Outdoor Workplaces Using A Wearable Particle Monitor and Global Navigation Satellite System

We manufactured a wearable particle monitor (WPM), which is a simple and low-cost dust monitor. We aimed to evaluate the usefulness of the device by using it and location information of a Global Navigation Satellite System (GNSS) to measure dust generation in outdoor workplaces. We used nine WPMs and a particle counter KC-52 to measure in parallel the dust concentration diffusing standard particles in a dust exposure apparatus to evaluate the measurability of the WPM, and visualized dust generation in outdoor workplaces to evaluate its usability. We obtained location information using a GNSS in parallel with measuring with the WPM. The measured values of the WPM followed the measured values of the KC-52, with a strong correlation of the values between the KC-52 and each WPM. The discrepancy among devices tended to increase, however, because the measured values of the WPMs increased. For outdoor measurements, we could create a heat map of the relative values of dust generation by combining two data of the WPM and the GNSS. The methods of using the WPM could overview the conditions needed to produce dust emissions in dust-generating workplaces.

Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas

Although PM_2.5 measurements of low-cost particulate matter sensors (LCPMS) generally show moderate and strong correlations with those from research-grade air monitors, the data quality of LCPMS has not been fully assessed in urban environments with different road traffic conditions. We examined the linear relationships between PM_2.5 measurements taken by an LCPMS (Dylos DC1700) and two research grade monitors, a personal environmental monitor (PEM) and the GRIMM 11R, in three different urban environments, and compared the accuracy (slope) and bias of these environments. PM_2.5 measurements were carried out at three locations in Houston, Texas (Clinton Drive largely with diesel trucks, US-59 mostly with gasoline vehicles, and a residential home with no major sources of traffic emissions nearby). The slopes of the regressions of the PEM on Dylos and Grimm measurements varied by location (e.g., PEM/Dylos slope at Clinton Drive = 0.98 (R² = 0.77), at US-59 = 0.63 (R² = 0.42), and at the residence = 0.29 (R² = 0.31)). Although the regression slopes and coefficients differed across the three urban environments, the mean percent bias was not significantly different. Using the correct slope for LCPMS measurements is key for accurately estimating ambient PM_2.5 mass in urban environments.

Effects of aerosol particle size on the measurement of airborne PM2.5 with a low-cost particulate matter sensor (LCPMS) in a laboratory chamber

Previous validation studies found a good linear correlation between the low-cost particulate matter sensors (LCPMS) and other research grade particulate matter (PM) monitors. This study aimed to determine if different particle size bins of PM would affect the linear relationship and agreement between the Dylos DC1700 (LCPMS) particle count measurements (converted to PM2.5 mass concentrations) and the Grimm 11R (research grade instrument) mass concentration measurements. Three size groups of PM2.5 (mass median aerodynamic diameters (MMAD): < 1 µm, 1-2 µm, and > 2 µm) were generated inside a laboratory chamber, controlled for temperature and relative humidity, by dispersing sodium chloride crystals through a nebulizer. A linear regression comparing 1-min average PM2.5 particle counts from the Dylos DC1700 (Dylos) to the Grimm 11R (Grimm) mass concentrations was estimated by particle size group. The slope for the linear regression was found to increase as MMAD increased (< 1 µm, 0.75 (R2 = 0.95); 1-2 µm, 0.90 (R2 = 0.93); and > 2 µm, 1.03 (R2 = 0.94). The linear slopes were used to convert Dylos counts to mass concentration, and the agreement between converted Dylos mass and Grimm mass was estimated. The absolute relative error between converted Dylos mass and the Grimm mass was smaller in the < 1 µm group (16%) and 1-2 µm group (16%) compared to the > 2 µm group (32%). Therefore, the bias between converted Dylos mass and Grimm mass varied by size group. Future studies examining particle size bins over a wider range of coarse particles (> 2.5 µm) would provide useful information for accurately converting LCPMS counts to mass concentration.

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.

Low-cost sensor system for monitoring the oil mist concentration in a workshop

Metalworking fluids used in industrial workshops may present a major threat to the health of workers who have been exposed to a high oil mist concentration over a long period of time. Therefore, monitoring the temporal and spatial distribution of particulate matter concentration has great practical significance for the control of oil mist. Traditional particle monitors are generally cumbersome, expensive, and difficult to maintain, which to some extent restricts their extensive use in workshops. Recent years have witnessed tremendous developments in the area of low-cost sensors, which are of great help in obtaining high-density pollution data. In this paper, we evaluate the performance of an inexpensive laser sensor (A4-CG) during long-term oil mist monitoring in a machine shop for the first time. With the use of Lora technology, we developed an online oil mist monitoring network to access real-time concentration, temperature, and humidity information from distributed monitors. According to the results, the sensor data correlated well with measurements by the reference instrument (R² = 0.96), which means that the distributed sensor network can accurately detect the concentration level of oil mist in the workshop. In fact, most of the sensors demonstrated stable operation for up to half a year according to cluster analysis, while several sensors exhibited serious data drift. Furthermore, the results indicate that the peak oil mist concentration in most areas during production exceeded the value of 0.5 mg m^-3 recommended by NIOSH, and it was found that appropriately lowering the relative humidity can make sampling more accurate, while lowering the temperature can reduce the oil mist concentration in the workshop. Thus, measures to control oil mist such as generation and distribution of pollution sources should be on the agenda.

In-car particulate matter exposure across ten global cities

Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM_2.5; fine fraction) and ≤10 μm (PM_2.5-10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM_2.5 and PM₁₀ concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM_2.5 and PM₁₀ were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM_2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM_2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.

Theoretical Design of the Scattering-Based Sensor for Analysis of the Vehicle Tailpipe Emission

Measurement regulations demand, among other requirements, the reduction of particulate matter emissions from diesel engines. Considering this, the establishment of a new measurement instrument for periodic emission control and detection of the Diesel Particulate Filter (DPF) performance after the vehicle exhaust is necessary. To fulfil these requirements, this paper proposes the design of a new, simple, low-weight layout after the vehicle tailpipe. In order to check the operation condition of the proposed sensor, different factors such as the temperature (−10 to 50 ℃) humidity (60%), and flow rate of the sampled emission (laminar condition) are considered. The proposed layout uses an optical particle counter as a portable instrument for real-time detection of the particle concentration after exhaust of the internal combustion engine.

Personal exposure to fine particulate air pollutants impacts blood pressure and heart rate variability

Air pollution has increasingly been recognized as a major healthcare concern. Air pollution, particularly fine particulate matter (≤ 2.5 μm in aerodynamic diameter [PM_2.5]) has demonstrated an increase in adverse cardiovascular events. This study aimed to assess the cardiovascular response to personal exposure to different levels of PM_2.5. This prospective cohort study enrolled healthy volunteers aged ≥ 18 years with no cardiovascular disease. Study subjects carried personal exposure monitor of PM_2.5, digital thermo-hygrometer for temperature and humidity, 24-h blood pressure monitor, and continuous electrocardiogram monitor. Measurements were repeated twice with an interval of 6-12 months. Statistical models consisted of generalized estimation equations to various repeated measures of each subject. A total of 22 subjects were enrolled in this study between July 2018 and January 2019. Measurement was performed twice in all participants, and a total of 36 data were collected except for insufficient data collection. The mean age of the study population was 41.6 years, and 95% of the subjects were females. No study subjects had hypertension or other cardiovascular diseases. The average systolic blood pressure increased with higher PM_2.5 levels with marginal significance (0.22 mmHg [95% confidential intervals - 0.04 to 0.48 mmHg] per 10 μg/m³ of PM_2.5). All parameters for heart rate variability significantly decreased with a higher level of PM_2.5. In this study, we measured individual personal exposure to PM_2.5 by using a portable device. We found that 24-h exposure to high levels of PM_2.5 was associated with a significant decrease in heart rate variability, suggesting impaired autonomous nervous function.

Weakly Coupled Piezoelectric MEMS Resonators for Aerosol Sensing

This paper successfully demonstrates the potential of weakly coupled piezoelectric MEMS (Micro-Electro-Mechanical Systems) gravimetric sensors for the detection of ultra-fine particulates. As a proof-of-principle, the detection of diesel soot particles of 100 nanometres or less is demonstrated. A practical monitoring context also exists for diesel soot particles originating from combustion engines, as they are of serious health concern. The MEMS sensors employed in this work operate on the principle of vibration mode-localisation employing an amplitude ratio shift output metric for readout. Notably, gains are observed while comparing parametric sensitivities and the input referred stability for amplitude ratio and resonant frequency variations, demonstrating that the amplitude ratio output metric is particularly suitable for long-term measurements. The soot particle mass directly estimated using coupled MEMS resonators can be correlated to the mass, indirectly estimated using the condensation particle counter used as the reference instrument.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min , and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

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.

Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.

Assessment of Daily Personal PM2.5 Exposure Level According to Four Major Activities among Children

Particulate matters less than 2.5 micrometers in diameter (PM2.5), whose concentration has increased in Korea, has a considerable impact on health. From a risk management point of view, there has been interest in understanding the variations in real-time PM2.5 concentrations per activity in different microenvironments. We analyzed personal monitoring data collected from 15 children aged 6 to 11 years engaged in different activities such as commuting in a car, visiting a commercial building, attending an education institute, and resting inside home from October 2018 to March 2019. The fraction of daily mean exposure duration per activity was 72.7 ± 18.7% for resting inside home, 27.2 ± 14.4% for attending an education institute, and 11.5 ± 9.6% and 5.3 ± 5.9% for visiting a commercial building, commuting in a car, respectively. Daily median (interquartile range) PM2.5 exposure amount was 88.9 (55.9–159.7) μg in houses and that in education buildings was 43.3 (22.9–55.6) μg. Real-time PM2.5 exposure levels varied by person and time of day (p-value < 0.05). This study demonstrated that our real-time personal monitoring and data analysis methodologies were effective in detecting polluted microenvironments and provided a potential person-specific management strategy to reduce a person’s exposure level to PM2.5.

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

From Crowdsourcing to Extreme Citizen Science: Participatory Research for Environmental Health

Environmental health issues are becoming more challenging, and addressing them requires new approaches to research design and decision-making processes. Participatory research approaches, in which researchers and communities are involved in all aspects of a research study, can improve study outcomes and foster greater data accessibility and utility as well as increase public transparency. Here we review varied concepts of participatory research, describe how it complements and overlaps with community engagement and environmental justice, examine its intersection with emerging environmental sensor technologies, and discuss the strengths and limitations of participatory research. Although participatory research includes methodological challenges, such as biases in data collection and data quality, it has been found to increase the relevance of research questions, result in better knowledge production, and impact health policies. Improved research partnerships among government agencies, academia, and communities can increase scientific rigor, build community capacity, and produce sustainable outcomes.

Evaluation of Performance of Inexpensive Laser Based PM2.5 Sensor Monitors for Typical Indoor and Outdoor Hotspots of South Korea

Inexpensive (<$300) real-time particulate matter monitors (IRMs), using laser as a light source, have been introduced for use with a Wi-Fi function enabling networking with a smartphone. However, the information of measurement error of these inexpensive but convenient IRMs are still limited. Using ESCORTAIR (ESCORT, Seoul, Korea) and PurpleAir (PA) (PurpleAir U.S.A.), we evaluated the performance of these two devices compared with the U.S. Environmental Protection Agency (EPA) Federal Equivalent Monitoring (FEM) devices, that is, GRIMM180 (GRIMM Aerosol, Germany) for the indoor measurement of pork panfrying or secondhand tobacco smoking (SHS) and Beta-ray attenuation monitor (BAM) (MetOne, Grants Pass, OR) for outdoor measurement at the national particulate matter (PM2.5) monitoring site near an urban traffic hotspot in Daejeon, South Korea, respectively. The PM2.5 concentrations measured by ESCORTAIR and PA were strongly correlated to FEM (r = 0.97 and 0.97 from indoor pan frying; 0.92 and 0.86 from indoor SHS; 0.85 and 0.88 from outdoor urban traffic hotspot). The two IRMs showed that PM2.5 mass concentrations were increased with increased outdoor relative humidity (RH) levels. However, after applying correction factors for RH, the Median (Interquartile range) of difference compared to FEM was (14.5 (6.1~23.5) %) for PA and 16.3 (8.5–28.0) % for ESCORTAIR, supporting their usage in the home or near urban hotspots.

Comparison of methods for converting Dylos particle number concentrations to PM2.5 mass concentrations

The aim of this study was to (a) develop a method for converting particle number concentrations (PNC) obtained by Dylos to PM2.5 mass concentrations, (b) compare this conversion with similar methods available in the literature, and (c) compare Dylos PM2.5 obtained using all available conversion methods with gravimetric samples. Data were collected in multiple residences in three European countries using the Dylos and an Aerodynamic Particle Sizer (APS, TSI) in the Netherlands or an optical particle counter (OPC, GRIMM) in Greece. Two statistical fitted curves were developed based on Dylos PNC and either an APS or an OPC particle mass concentrations (PMC). In addition, at the homes of 16 volunteers (UK and Netherlands), Dylos measurements were collected along with gravimetric samples. The Dylos PNC were transformed to PMC using all the fitted curves obtained during this study (and three found in the literature) and were compared with gravimetric samples. The method developed in the present study using an OPC showed the highest correlation (Pearson (R) = 0.63, Concordance (ρ_c ) = 0.61) with gravimetric data. The other methods resulted in an underestimation of PMC compared to gravimetric measurements (R = 0.65-0.55, ρ_c  = 0.51-0.24). In conclusion, estimation of PM2.5 concentrations using the Dylos is acceptable for indicative purposes.

Real-Time Monitoring of Spray Drift from Three Different Orchard Sprayers

In Washington State, half of all pesticide-related illnesses in agriculture result from drift, the off-target movement of pesticides. Of these, a significant proportion involve workers on another farm and orchard airblast applications. We compared the spray drift exposure reduction potential of two modern tower sprayers - directed air tower (DAT) and multi-headed fan tower (MFT), in relation to a traditional axial fan airblast (AFA) sprayer. We employed real-time particle monitors (Dylos DC1100) during a randomized control trial of orchard spray applications. Sections of a field were randomly sprayed by three alternating spray technologies - AFA, DAT and MFT - while monitors sampled particulate matter above and below the canopy at various downwind locations in a neighboring field. Geometric mean particle mass concentrations (PMC) outside the intended spray area were elevated during all applications at all of our sampling distances (16-74 m, 51-244 ft). After adjusting for wind speed and sampling height, the 75th percentile (95% confidence interval) PMC level was significantly greater during spray events than background levels by 105 (93, 120) μg/m³, 49 (45, 54) μg/m³ and 26 (22, 31) μg/m³ during AFA, DAT and MFT applications, respectively. Adjusted PMC levels were significantly different between all three sprayers. In this study, tower sprayers significantly reduced spray drift exposures in a neighboring orchard field when compared to the AFA sprayer, with the MFT sprayer producing the least drift; however these tower sprayers did do not fully eliminate drift.

Sources of Variability in Real-time Monitoring Data for Fine Particulate Matter: Comparability of Three Wearable Monitors in an Urban Setting

The increasing availability of portable air pollution monitoring devices has greatly enhanced the ability to measure personal exposures in real time. However, these devices vary considerably in their cost and specifications, and questions remain as to their reliability and practicality for use in epidemiological investigations. In this field study, three personal PM_2.5 exposure monitors (two nephelometers, one optical particle counter) were compared in an urban setting to assess their feasibility for use in future studies. In total, 3963 1-min measurements were collected over 12 days from locations of several types (e.g., above and below-ground subway stations, sidewalks next to urban traffic, outdoor construction sites) in the Washington, D.C. metropolitan area. Overall, we observed moderate-to-high agreement in pairwise comparisons of PM_2.5 concentrations between devices (R² range: 0.37 to 0.75). Bland-Altman plots showed that differences in device agreement varied over the range of mean concentrations. In linear mixed models adjusting for temperature and relative humidity, we saw significant interaction between device and location (p<0.05), suggesting that the relationship between devices was not constant in all locations. Our finding of heterogeneity in instrument comparability by location may have important implications in epidemiologic studies incorporating personal PM_2.5 measurements.

Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer

In Washington State, a majority of reported pesticide-related illnesses and application-related complaints involve drift. We employed real-time particle monitors (Dylos) during a series of experimental spray events investigating drift. Sections of an orchard block were randomly sprayed by an axial fan airblast sprayer, while monitors sampled particulate matter above and below the canopy at various downwind locations. We found elevated particle mass concentrations (PMC) at all distances (16-74 m). The 75th percentile PMC while spraying was significantly greater than the control periods by 107 (95% CI 94-121) μg/m³, after adjusting for sampler height and wind speed. The 75th percentile PMC below the canopy was significantly greater than above the canopy by 9.4 (95% CI 5.2-12) μg/m³, after adjusting for spraying and wind speed. In a restricted analysis of the spray events, the 75th percentile PMC significantly decreased by 2.6 (95% CI -3.2 to -1.7) μg/m³ for every additional meter away from the edge of the spray quadrant, after adjusting for canopy height and wind speed. Our results were consistent with a larger study that performed passive sampling during the same spray events, suggesting that real-time monitoring can be used as a screening tool for pesticide drift. Compared with traditional methods of drift sampling, real-time monitoring is overall an easily employed, affordable sampling technique, and it can provide minute-by-minute measurements that can be coupled with meteorological measurements to better understand how changes in wind speed and direction affect drift.

Emission Tax and Compensation Subsidy with Cross-Industry Pollution

This paper establishes a cross-industry pollution externality model. To explain a benevolent government, it may be possible to tax part of the welfare gains and use the revenue to compensate the affected polluted industry for the damage cost, thereby improving welfare. We show that the social welfare under emission tax with production subsidy is higher than the results of emission tax without production subsidy. The welfare of the polluted sector under emissions trading will be lower than the results of unbalanced budget environmental policy with subsidy. The welfare of the polluted labor union under lobby for compensation will be higher than the results of environmental policy with subsidy if the pollution damage and the weight on political contributions are sufficiently high.

Personal Exposure to PM2.5 Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS)

Traditional methods for measuring personal exposure to fine particulate matter (PM2.5) are cumbersome and lack spatiotemporal resolution; methods that are time-resolved are limited to a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable of resolving personal exposure by microenvironment. The AMAS is a wearable device that uses a GPS sensor algorithm in conjunction with a custom valve manifold to sample PM2.5 onto distinct filter channels to evaluate home, school, and other (e.g., outdoors, in transit, etc.) exposures. Pilot testing was conducted in Fresno, CA where 25 high-school participants ( n = 37 sampling events) wore an AMAS for 48-h periods in November 2016. Data from 20 (54%) of the 48-h samples collected by participants were deemed valid and the filters were analyzed for PM2.5 black carbon (BC) using light transmissometry and aerosol oxidative potential (OP) using the dithiothreitol (DTT) assay. The amount of inhaled PM2.5 was calculated for each microenvironment to evaluate the health risks associated with exposure. On average, the estimated amount of inhaled PM2.5 BC (μg day-1) and OP [(μM min-1) day-1] was greatest at home, owing to the proportion of time spent within that microenvironment. Validation of the AMAS demonstrated good relative precision (8.7% among collocated instruments) and a mean absolute error of 22% for BC and 33% for OP when compared to a traditional personal sampling instrument. This work demonstrates the feasibility of new technology designed to quantify personal exposure to PM2.5 species within distinct microenvironments.

Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ-Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

Laboratory evaluation of a low-cost, real-time, aerosol multi-sensor

Exposure to occupational aerosols are a known hazard in many industry sectors and can be a risk factor for several respiratory diseases. In this study, a laboratory evaluation of low-cost aerosol sensors, the Dylos DC1700 and a modified Dylos known as the Utah Modified Dylos Sensor (UMDS), was performed to assess the sensors' efficiency in sampling respirable and inhalable dust at high concentrations, which are most common in occupational settings. Dust concentrations were measured in a low-speed wind tunnel with 3 UMDSs, collocated with an aerosol spectrometer (Grimm 1.109) and gravimetric respirable and inhalable samplers. A total of 10 tests consisting of 5 different concentrations and 2 test aerosols, Arizona road dust and aluminum oxide, were conducted. For the Arizona road dust, total particle count was strongly related between the spectrometer and the UMDS with a coefficient of determination (R2) between 0.86-0.92. Particle count concentrations measured with the UMDS were converted to mass and also were related with gravimetrically collected inhalable and respirable dust. The UMDS small bin (i.e., all particles) compared to the inhalable sampler yielded an R2 of 0.86-0.92, and the large bin subtracted from the small bin (i.e., only the smallest particles) compared to the respirable sampler yielded an R2 of 0.93-0.997. Tests with the aluminum oxide demonstrated a substantially lower relationship across all comparisons. Furthermore, assessment of intra-instrument variability was consistent for all instruments, but inter-instrument variability indicated that each instrument requires its own calibration equation to yield accurate exposure estimates. Overall, it appears that the UMDS can be used as a low-cost tool to estimate respirable and inhalable concentrations found in many workplaces. Future studies will focus on deployment of a UMDS network in an occupational setting.

Interventions to encourage smoke-free homes in remote indigenous Australian communities: a study protocol to evaluate the effects of a community-inspired awareness-raising and motivational enhancement strategy

INTRODUCTION

Rates of secondhand smoke exposure are currently significantly higher among remote indigenous communities in the top end of Australia. By implementing a 'smoke-free home' rule, secondhand smoke exposure can be reduced. Smoke-free homes encourage quit attempts and improve the health of children. The prevalence of indigenous smoking rates in remote, discrete communities in Australia is elevated compared with their non-indigenous counterparts. The primary aim of this project is to examine the feasibility of conducting a health-driven intervention to encourage community members to make their homes a smoke-free zone.

METHODS AND ANALYSIS

This study uses mixed-methods exploratory evaluation design to obtain data from key informants and community householders to assess their willingness to implement a 'smoke-free' rule in their homes. Initial focus groups will provide guidance on intervention content and deliver evaluation procedures and community requirements. A rapid survey will be conducted to ascertain interest from community members in having the project team visit to discuss study objectives further and to have a particle meter (with consent) placed in the house. Focus groups recordings will be transcribed and analysed thematically. Rapid surveys will be analysed using frequency distributions and tabulations of responses.

ETHICS AND DISSEMINATION

The National Health and Medical Research Council guidelines on ethical research approaches to indigenous studies will be adhered to. The James Cook University Human Research Ethics Committee has provided ethics approval.

Randomized Trial to Reduce Air Particle Levels in Homes of Smokers and Children

INTRODUCTION

Exposure to fine particulate matter in the home from sources such as smoking, cooking, and cleaning may put residents, especially children, at risk for detrimental health effects. A randomized clinical trial was conducted from 2011 to 2016 to determine whether real-time feedback in the home plus brief coaching of parents or guardians could reduce fine particle levels in homes with smokers and children.

DESIGN

A randomized trial with two groups-intervention and control.

SETTING/PARTICIPANTS

A total of 298 participants from predominantly low-income households with an adult smoker and a child aged <14 years. Participants were recruited during 2012-2015 from multiple sources in San Diego, mainly Women, Infants and Children Program sites.

INTERVENTION

The multicomponent intervention consisted of continuous lights and brief sound alerts based on fine particle levels in real time and four brief coaching sessions using particle level graphs and motivational interviewing techniques. Motivational interviewing coaching focused on particle reduction to protect children and other occupants from elevated particle levels, especially from tobacco-related sources.

MAIN OUTCOME MEASURES

In-home air particle levels were measured by laser particle counters continuously in both study groups. The two outcomes were daily mean particle counts and percentage time with high particle concentrations (>15,000 particles/0.01 ft3). Linear mixed models were used to analyze the differential change in the outcomes over time by group, during 2016-2017.

RESULTS

Intervention homes had significantly larger reductions than controls in daily geometric mean particle concentrations (18.8% reduction vs 6.5% reduction, p<0.001). Intervention homes' average percentage time with high particle concentrations decreased 45.1% compared with a 4.2% increase among controls (difference between groups p<0.001).

CONCLUSIONS

Real-time feedback for air particle levels and brief coaching can reduce fine particle levels in homes with smokers and young children. Results set the stage for refining feedback and possible reinforcing consequences for not generating smoke-related particles.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.gov NCT01634334.

Ultrafine, fine, and black carbon particle concentrations in California child-care facilities

Although many U.S. children spend time in child care, little information exists on exposures to airborne particulate matter (PM) in this environment, even though PM may be associated with asthma and other respiratory illness, which is a key concern for young children. To address this data gap, we measured ultrafine particles (UFP), PM_2.5 , PM₁₀ , and black carbon in 40 California child-care facilities and examined associations with potential determinants. We also tested a low-cost optical particle measuring device (Dylos monitor). Median (interquartile range) concentrations for indoor UFP, gravimetric PM_2.5 , real-time PM_2.5 , gravimetric PM₁₀ , and black carbon over the course of a child-care day were 14 000 (11 000-29 000) particles/cm³ , 15 (9.6-21) μg/m³ , 15 (11-23) μg/m³ , 48 (33-73) μg/m³ , and 0.43 (0.25-0.65) ng/m³ , respectively. Indoor black carbon concentrations were inversely associated with air exchange rate (Spearman's rho = -.36) and positively associated with the sum of all Gaussian-adjusted traffic volume within a one-kilometer radius (Spearman's rho = .45) (P-values <.05). Finally, the Dylos may be a valid low-cost alternative to monitor PM levels indoors in future studies. Overall, results indicate the need for additional studies examining particle levels, potential health risks, and mitigation strategies in child-care facilities.

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM2.5 and PM10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R2 for converted hourly averaged Dylos mass measurements versus a PM2.5 BAM was 0.79 and that versus a PM10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R2 = 0.35-0.81).

IMPLICATIONS

The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors

There is concern regarding the heterogeneity of exposure to airborne particulate matter (PM) across urban areas leading to negatively biased health effects models. New, low-cost sensors now permit continuous and simultaneous measurements to be made in multiple locations. Measurements of ambient PM were made from October to April 2015-2016 and 2016-2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA. In general, there was moderate spatial inhomogeneity, as indicated by multiple pairwise measures including coefficient of divergence and signed rank tests of the value distributions. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the area, likely driven by a combination of meteorological conditions (wind speed, direction, and mixed layer heights) and the concentration of PM_2.5 being transported into the region. Although the accuracy of these PM sensors is limited, they are sufficiently precise relative to one another and to research grade instruments that they can be useful is assessing the spatial and temporal variations across an area and provide concentration estimates based on higher-quality central site monitoring data.

Small, Smart, Fast, and Cheap: Microchip-Based Sensors to Estimate Air Pollution Exposures in Rural Households

Over the last 20 years, the Kirk R. Smith research group at the University of California Berkeley—in collaboration with Electronically Monitored Ecosystems, Berkeley Air Monitoring Group, and other academic institutions—has developed a suite of relatively inexpensive, rugged, battery-operated, microchip-based devices to quantify parameters related to household air pollution. These devices include two generations of particle monitors; data-logging temperature sensors to assess time of use of household energy devices; a time-activity monitoring system using ultrasound; and a CO₂-based tracer-decay system to assess ventilation rates. Development of each system involved numerous iterations of custom hardware, software, and data processing and visualization routines along with both lab and field validation. The devices have been used in hundreds of studies globally and have greatly enhanced our understanding of heterogeneous household air pollution (HAP) concentrations and exposures and factors influencing them.

Fine particles in homes of predominantly low-income families with children and smokers: Key physical and behavioral determinants to inform indoor-air-quality interventions

Children are at risk for adverse health outcomes from occupant-controllable indoor airborne contaminants in their homes. Data are needed to design residential interventions for reducing low-income children's pollutant exposure. Using customized air quality monitors, we continuously measured fine particle counts (0.5 to 2.5 microns) over a week in living areas of predominantly low-income households in San Diego, California, with at least one child (under age 14) and at least one cigarette smoker. We performed retrospective interviews on home characteristics, and particle source and ventilation activities occurring during the week of monitoring. We explored the relationship between weekly mean particle counts and interview responses using graphical visualization and multivariable linear regression (base sample n = 262; complete cases n = 193). We found associations of higher weekly mean particle counts with reports of indoor smoking of cigarettes or marijuana, as well as with frying food, using candles or incense, and house cleaning. Lower particle levels were associated with larger homes. We did not observe an association between lower mean particle counts and reports of opening windows, using kitchen exhaust fans, or other ventilation activities. Our findings about sources of fine airborne particles and their mitigation can inform future studies that investigate more effective feedback on residential indoor-air-quality and better strategies for reducing occupant exposures.

Exposure of in-pram babies to airborne particles during morning drop-in and afternoon pick-up of school children

In-pram babies are more susceptible to air pollution effects, yet studies assessing their exposure are limited. We measured size-resolved particle mass (PMC; 0.25-32 μm) and number (PNC; 0.2-1 μm) concentrations on a 2.7 km route. The instruments were placed inside a baby pram. The route passed through 4 traffic intersections (TIs) and a bus stand. A total of ∼87 km road length was covered through 64 trips, made during school drop-in (morning) and pick-up (afternoon) hours. The objectives were to assess PMC and PNC exposure to in-pram babies at different route segments, understand their physicochemical characteristics and exposure differences between in-pram babies and adults carrying them. Over 5-fold variability (14.1-78.2 μg m^-3) was observed in PMCs. Small-sized particles, including ultrafine particles, were always higher by 66% (PM₁), 29% (PM_2.5) and 31% (PNC) during the morning than afternoon. Coarse particles (PM_2.5-10) showed an opposite trend with 70% higher concentration during afternoon than morning. TIs emerged as pollution hotspots for all the particle types. For example, PM_2.5, PM_2.5-10 and PNCs during the morning (afternoon) at TIs were 7 (10)%, 19 (10)% and 68 (62)% higher, respectively, compared with the rest of the route. Bus stand was also a section of enhanced exposure to PNC and PM_2.5, although not so much for PM_2.5-10. EDX analyses revealed Cl, Na and Fe as dominant elements. Road salt might be a source of NaCl due to de-icing during the measurements while Fe contributed by non-exhaust emissions from brake abrasion. The respiratory deposition rates imitated the trend of PMC, with higher doses of coarse and fine particles during the afternoon and morning runs, respectively. Special protection measures during conveyance of in-pram babies, especially at pollution hotspots such as traffic intersections and bus stands, could help to limit their exposure.

How Sensors Might Help Define the External Exposome

The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.

Feasibility of using low-cost portable particle monitors for measurement of fine and coarse particulate matter in urban ambient air

Exposure to ambient particulate matter (PM) is known as a significant risk factor for mortality and morbidity due to cardiorespiratory causes. Owing to increased interest in assessing personal and community exposures to PM, we evaluated the feasibility of employing a low-cost portable direct-reading instrument for measurement of ambient air PM exposure. A Dylos DC 1700 PM sensor was collocated with a Grimm 11-R in an urban residential area of Houston Texas. The 1-min averages of particle number concentrations for sizes between 0.5 and 2.5 µm (small size) and sizes larger than 2.5 µm (large size) from a DC 1700 were compared with the 1-min averages of PM_2.5 (aerodynamic size less than 2.5 µm) and coarse PM (aerodynamic size between 2.5 and 10 µm) concentrations from a Grimm 11-R. We used a linear regression equation to convert DC 1700 number concentrations to mass concentrations, utilizing measurements from the Grimm 11-R. The estimated average DC 1700 PM_2.5 concentration (13.2 ± 13.7 µg/m³) was similar to the average measured Grimm 11-R PM_2.5 concentration (11.3 ± 15.1 µg/m³). The overall correlation (r²) for PM_2.5 between the DC 1700 and Grimm 11-R was 0.778. The estimated average coarse PM concentration from the DC 1700 (5.6 ± 12.1 µg/m³) was also similar to that measured with the Grimm 11-R (4.8 ± 16.5 µg/m³) with an r² of 0.481. The effects of relative humidity and particle size on the association between the DC 1700 and the Grimm 11-R results were also examined. The calculated PM mass concentrations from the DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.

IMPLICATIONS

The performance of a low-cost particulate matter (PM) sensor was evaluated in an urban residential area. Both PM_2.5 and coarse PM (PM_10-2.5) mass concentrations were estimated using a DC1700 PM sensor. The calculated PM mass concentrations from the number concentrations of DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.

An Experiment with Air Purifiers in Delhi during Winter 2015-2016

Particulate pollution has important consequences for human health, and is an issue of global concern. Outdoor air pollution has become a cause for alarm in India in particular because recent data suggest that ambient pollution levels in Indian cities are some of the highest in the world. We study the number of particles between 0.5μm and 2.5μm indoors while using affordable air purifiers in the highly polluted city of Delhi. Though substantial reductions in indoor number concentrations are observed during air purifier use, indoor air quality while using an air purifier is frequently worse than in cities with moderate pollution, and often worse than levels observed even in polluted cities. When outdoor pollution levels are higher, on average, indoor pollution levels while using an air purifier are also higher. Moreover, the ratio of indoor air quality during air purifier use to two comparison measures of air quality without an air purifier are also positively correlated with outdoor pollution levels, suggesting that as ambient air quality worsens there are diminishing returns to improvements in indoor air quality during air purifier use. The findings of this study indicate that although the most affordable air purifiers currently available are associated with significant improvements in the indoor environment, they are not a replacement for public action in regions like Delhi. Although private solutions may serve as a stopgap, reducing ambient air pollution must be a public health and policy priority in any region where air pollution is as high as Delhi's during the winter.

Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, 0.25 to 0.76, and 0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results - some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM_2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO₂ sensor (multiple correlation coefficient R² _adj-orig = 0.57, R² _adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.

Evaluation of a Low-Cost Aerosol Sensor to Assess Dust Concentrations in a Swine Building

Exposure to dust is a known occupational hazard in the swine industry, although efforts to measure exposures are labor intensive and costly. In this study, we evaluated a Dylos DC1100 as a low-cost (~$200) alternative to assess respirable dust concentrations in a swine building in winter. Dust concentrations were measured with collocated monitors (Dylos DC1100; an aerosol photometer, the pDR-1200; and a respirable sampler analyzed gravimetrically) placed in two locations within a swine farrowing building in winter for 18-24-h periods. The particle number concentrations measured with the DC1100 were converted to mass concentration using two methods: Physical Property Method and Regression Method. Raw number concentrations from the DC1100 were highly correlated to mass concentrations measured with the pDR-1200 with a coefficient of determination (R (2)) of 0.85, indicating that the two monitors respond similarly to respirable dust in this environment. Both methods of converting DC1100 number concentrations to mass concentrations yielded strong linear relationships relative to that measured with the pDR-1200 (Physical Property Method: slope = 1.03, R (2) = 0.72; Regression Method: slope = 0.72, R (2) = 0.73) and relative to that measured gravimetrically (Physical Property Method: slope = 1.08, R (2) = 0.64; Regression Method: slope = 0.75, R (2) = 0.62). The DC1100 can be used as a reasonable indicator of respirable mass concentrations within a CAFO and may have broader applicability to other agricultural and industrial settings.

Low-Cost Air Quality Monitoring Methods to Assess Compliance With Smoke-Free Regulations: A Multi-Center Study in Six Low- and Middle-Income Countries

INTRODUCTION

Many low- and middle-income countries (LMICs) have enacted legislation banning smoking in public places, yet enforcement remains challenging. The aim of this study was to assess the feasibility of using a validated low-cost methodology (the Dylos DC1700) to provide objective evidence of smoke-free (SF) law compliance in hospitality venues in urban LMIC settings, where outdoor air pollution levels are generally high.

METHODS

Teams measured indoor fine particulate matter (PM2.5) concentrations and systematically observed smoking behavior and SF signage in a convenience sample of hospitality venues (bars, restaurants, cafes, and hotels) covered by existing SF legislation in Mexico, Pakistan, Indonesia, Chad, Bangladesh, and India. Outdoor air PM2.5 was also measured on each sampling day.

RESULTS

Data were collected from 626 venues. Smoking was observed during almost one-third of visits with substantial differences between countries-from 5% in India to 72% in Chad. After excluding venues where other combustion sources were observed, secondhand smoke (SHS) derived PM2.5 was calculated by subtracting outdoor ambient PM2.5 concentrations from indoor measurements and was, on average, 34 µg/m(3) in venues with observed smoking-compared to an average value of 0 µg/m(3) in venues where smoking was not observed (P < .001). In over one-quarter of venues where smoking was observed the difference between indoor and outdoor PM2.5 concentrations exceeded 64 µg/m(3).

CONCLUSIONS

This study suggests that low-cost air quality monitoring is a viable method for improving knowledge about environmental SHS and can provide indicative data on compliance with local and national SF legislation in hospitality venues in LMICs.

IMPLICATIONS

Air quality monitoring can provide objective scientific data on SHS and air quality levels in venues to assess the effectiveness of SF laws and identify required improvements. Equipment costs and high outdoor air pollution levels have hitherto limited application in LMICs. This study tested the feasibility of using a validated low-cost methodology in hospitality venues in six LMIC urban settings and suggests this is a viable method for improving knowledge about SHS exposure and can provide indicative data on compliance with SF legislation.

Inter-comparison of Low-cost Sensors for Measuring the Mass Concentration of Occupational Aerosols

Low-cost sensors are effective for measuring the mass concentration of ambient aerosols and secondhand smoke in homes, but their use at concentrations relevant to occupational settings has not been demonstrated. We measured the concentrations of four aerosols (salt, Arizona road dust, welding fume, and diesel exhaust) with three types of low-cost sensors (a DC1700 from Dylos and two commodity sensors from Sharp), an aerosol photometer, and reference instruments at concentrations up to 6500 μg/m³. Raw output was used to assess sensor precision and develop equations to compute mass concentrations. EPA and NIOSH protocols were used to assess the mass concentrations estimated with low-cost sensors compared to reference instruments. The detection efficiency of the DC1700 ranged from 0.04% at 0.1 μm to 108% at 5 μm, as expected, although misclassification of fine and coarse particles was observed. The raw output of the DC1700 had higher precision (lower coefficient of variation, CV = 7.4%) than that of the two sharp devices (CV = 25% and 17%), a finding attributed to differences in manufacturer calibration. Aerosol type strongly influenced sensor response, indicating the need for on-site calibration to convert sensor output to mass concentration. Once calibrated, however, the mass concentration estimated with low-cost sensors was highly correlated with that of reference instruments (R²=0.99). These results suggest that the DC1700 and Sharp sensors are useful in estimating aerosol mass concentration for aerosols at concentrations relevant to the workplace.

New Methods for Personal Exposure Monitoring for Airborne Particles

Airborne particles have been associated with a range of adverse cardiopulmonary outcomes, which has driven its monitoring at stationary central sites throughout the world. Individual exposures, however, can differ substantially from concentrations measured at central sites due to spatial variability across a region and sources unique to the individual, such as cooking or cleaning in homes, traffic emissions during commutes, and widely varying sources encountered at work. Personal monitoring with small, battery-powered instruments enables the measurement of an individual's exposure as they go about their daily activities. Personal monitoring can substantially reduce exposure misclassification and improve the power to detect relationships between particulate pollution and adverse health outcomes. By partitioning exposures to known locations and sources, it may be possible to account for variable toxicity of different sources. This review outlines recent advances in the field of personal exposure assessment for particulate pollution. Advances in battery technology have improved the feasibility of 24-h monitoring, providing the ability to more completely attribute exposures to microenvironment (e.g., work, home, commute). New metrics to evaluate the relationship between particulate matter and health are also being considered, including particle number concentration, particle composition measures, and particle oxidative load. Such metrics provide opportunities to develop more precise associations between airborne particles and health and may provide opportunities for more effective regulations.

On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter

Respirable particulate matter present in outdoor and indoor environments is a health hazard. The particle concentrations can quickly change, with steep gradients on short temporal and spatial scales, and their chemical composition and physical properties vary considerably. Existing networks of aerosol particle measurements consist of limited number of monitoring stations, and mostly aim at assessment of compliance with air quality legislation regulating mass of particles of varying sizes. These networks can now be supplemented using small portable devices with low-cost sensors for assessment of particle mass that may provide higher temporal and spatial resolution if we understand the capabilities and characteristics of the data they provide. This paper overviews typical currently available devices and their characteristics. In addition it is presented original results of measurement and modelling in the aim of one low-cost PM monitor validation.

Outdoor wood furnaces create significant indoor particulate pollution in neighboring homes

CONTEXT

The use of outdoor wood furnaces (OWFs) is common in many parts of the United States. Little published information exists on the concentrations of outdoor and indoor fine particulates found near OWFs.

OBJECTIVE

To compare PM2.5 (cts) and PM0.5 (cts) particle concentrations inside four Connecticut homes located 30.5-259 m from OWFs, and inside six Connecticut control homes located more than 2 km from the nearest OWF.

MATERIALS AND METHODS

PM2.5 (cts) and PM0.5 (cts) measurements were made with a Dylos light-scattering particulate counter.

RESULTS

Mean PM2.5 (cts) concentrations were 4.21 times as great in the four OWF exposed homes than the six control homes (0.302 × 10(6) counts/m(3) versus 0.0718 counts × 10(6)/m(3) p < 0.001). The mean PM2.5 (cts) concentrations inside the four OWF exposed homes roughly corresponds to a mass PM2.5 of 37 µg/m(3), which is above the US EPA 24-h PM2.5 limit of 35 µg/m(3). Mean PM0.5 (cts) concentrations were 3.44 times as great in the four OWF exposed homes than in the six control homes (0.657 versus 0.191 × 10(6)/m(3) p < 0.001). Mean PM2.5 (cts) and PM0.5 (cts) concentrations were significantly higher in the house 259 m from an OWF as compared with the mean of the six control homes.

CONCLUSION

Existing regulations, such as the present Connecticut law requiring a 61 meter distance between an OWF and neighboring homes, are not adequate to protect the health of neighboring residents.

Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments

Adverse health effects from exposure to air pollution are a global challenge and of widespread concern. Recent high ambient concentration episodes of air pollutants in European cities highlighted the dynamic nature of human exposure and the gaps in data and knowledge about exposure patterns. In order to support health impact assessment it is essential to develop a better understanding of individual exposure pathways in people's everyday lives by taking account of all environments in which people spend time. Here we describe the development, validation and results of an exposure method applied in a study conducted in Scotland. A low-cost particle counter based on light-scattering technology - the Dylos 1700 was used. Its performance was validated in comparison with equivalent instruments (TEOM-FDMS) at two national monitoring network sites (R(2)=0.9 at a rural background site, R(2)=0.7 at an urban background site). This validation also provided two functions to convert measured PNCs into calculated particle mass concentrations for direct comparison of concentrations with equivalent monitoring instruments and air quality limit values. This study also used contextual and time-based activity data to define six microenvironments (MEs) to assess everyday exposure of individuals to short-term PM2.5 concentrations. The Dylos was combined with a GPS receiver to track movement and exposure of individuals across the MEs. Seventeen volunteers collected 35 profiles. Profiles may have a different overall duration and structure with respect to times spent in different MEs and activities undertaken. Results indicate that due to the substantial variability across and between MEs, it is essential to measure near-complete exposure pathways to allow for a comprehensive assessment of the exposure risk a person encounters on a daily basis. Taking into account the information gained through personal exposure measurements, this work demonstrates the added value of data generated by the application of low-cost monitors.

Promoting smoke-free homes: a novel behavioral intervention using real-time audio-visual feedback on airborne particle levels

Interventions are needed to protect the health of children who live with smokers. We pilot-tested a real-time intervention for promoting behavior change in homes that reduces second hand tobacco smoke (SHS) levels. The intervention uses a monitor and feedback system to provide immediate auditory and visual signals triggered at defined thresholds of fine particle concentration. Dynamic graphs of real-time particle levels are also shown on a computer screen. We experimentally evaluated the system, field-tested it in homes with smokers, and conducted focus groups to obtain general opinions. Laboratory tests of the monitor demonstrated SHS sensitivity, stability, precision equivalent to at least 1 µg/m(3), and low noise. A linear relationship (R(2) = 0.98) was observed between the monitor and average SHS mass concentrations up to 150 µg/m(3). Focus groups and interviews with intervention participants showed in-home use to be acceptable and feasible. The intervention was evaluated in 3 homes with combined baseline and intervention periods lasting 9 to 15 full days. Two families modified their behavior by opening windows or doors, smoking outdoors, or smoking less. We observed evidence of lower SHS levels in these homes. The remaining household voiced reluctance to changing their smoking activity and did not exhibit lower SHS levels in main smoking areas or clear behavior change; however, family members expressed receptivity to smoking outdoors. This study established the feasibility of the real-time intervention, laying the groundwork for controlled trials with larger sample sizes. Visual and auditory cues may prompt family members to take immediate action to reduce SHS levels. Dynamic graphs of SHS levels may help families make decisions about specific mitigation approaches.