TSI DustTrak Research Articles

Variations in gaseous and particulate emissions from flaming and smoldering combustion of Douglas fir and lodgepole pine under different fuel moisture conditions

The effects of fuel moisture content (FMC) on emissions under both flaming and smoldering combustion conditions were studied for two different vegetative fuels, Douglas fir and lodgepole pine. A custom linear tube-heater apparatus was developed to produce steady-state emissions for two extreme FMC, recently live (FMC ∼ 50%) and fully dead and dry (FMC ∼ 3%). The concentrations of particulate matter and gaseous species measured using a TSI DustTrak and Fourier transform infrared spectroscopy (FTIR), respectively, showed a significant relationship with FMC for both flaming and smoldering combustion conditions. As expected, smoldering combustion showed larger production of CO, particulate matter, and unburned hydrocarbons when compared to flaming combustion. Unburnt hydrocarbons with higher emission factors produced during smoldering are CH4, C2H2, C2H6, C4H10, C4H8, C2H4, C3H6, CH2O2, C3H4O, CH2O, and CH3OH. This work also identified butane (C4H10) and hydrogen bromide (HBr) as gaseous species, which have not been commonly reported in literature. Acrolein (C3H4O) was also identified during combustion and it has previously reported only by ion detection method. These emissions were further investigated by performing a carbon balance of the carbon in the fuel vs. emitted carbon, and these were compared to literature values. Flaming-mode emission factors significantly correlated with FMC, while FMC had little influence on emission factors for smoldering combustion, when considering the dry mass of the burned fuel. These variations were not the same for each fuel species, suggesting that the type of fuel plays an important role in burning behavior and emissions, possibly due to the chemical makeup of moist and recently live fuels. The results were discussed and compared with previous measurements, where good agreement was observed. This study provides an important emissions dataset for modeling emissions from common wildland fuels under varying FMC and combustion conditions.Novelty and significanceThis research investigates the impact of fuel moisture content (FMC) on emissions during flaming and smoldering combustion of Douglas fir and lodgepole pine. A unique linear tube-heater apparatus was developed to maintain steady-state emissions for extreme FMC levels of recently live (FMC 50%) and fully dead/dry (FMC 3%). The study reveals a noteworthy correlation between FMC and concentrations of particulate matter and gaseous species. This finding provides valuable insights into combustion behavior. It also identifies butane (C4H10), acrolein (C3H4O), and hydrogen bromide (HBr), not commonly reported in literature. Additionally, the study highlights the varying influence of FMC on emission factors for flaming and smoldering combustion, indicating the pivotal role of fuel type in emissions. The dataset obtained serves as a valuable resource for modeling emissions from wildland fuels under diverse FMC and combustion conditions, advancing our understanding of fire behavior and environmental impacts.

The impact of vehicle parameters on road PM10 vehicle resuspended emissions: A case in South African low-income settlement

About 70% of the roads in low-income settlements are unpaved and close (≤15m) to residents, thus a major source of ambientand indoor PM10 concentrations. International studies have suggested that decreasing vehicle speed and managing vehicle type onpaved or unpaved roads can reduce vehicle dust emissions. These mitigation strategies should be examined before being adoptedand gazetted into South African air quality management plans. This study aimed to characterise roads and traffic, emphasisingdetermining the impact of vehicle type on PM10 emissions. GIS was used to determine the proportion of paved and unpaved roads. Atraffic counter was used to monitor vehicles and to determine traffic composition, diurnal cycles, and average speed per road type.Field campaigns (summer; 6 days; 15 hrs per day) were carried out in Bokamoso to monitor PM10 concentration usinga TSI DustTrak DRX® real-time optical aerosol counter. The Box Model method quantifies vehicle PM10 emission factors for heavy-duty, medium-duty, and motor vehicles. About 0.88 km of road within Bokamoso is paved with a daily traffic volume of >2000, and 3.6 km is unpaved with >250 daily traffic volume. Paved road heavy-duty vehicle non-exhaust PM10 emissions reported a positive medium to strong coefficient of determination to vehicle speed increase with a coefficient of determination of 0.59. Even though there is a positive coefficient of determination between heavy-duty vehicle non-exhaust PM10 emission factors and speed on unpaved roads, it is weak (0.2) due to low and less variable vehicle speed. Motor vehicle paved and unpaved road non-exhaust emission factors showed no significant coefficient of determination with increased vehicle speed. Paved road non-exhaust emission factors were not significantly variable with vehicle type. They ranged between an average of 0.15-0.18 g/km/h, with motor vehicles reporting an average of 0.18 g/km/h while heavy-duty vehicles reported an average of 0.15 g/km/h. On the contrary, unpaved road non-exhaust traffic PM10 emissions ranged between 0.16-0.3 g/km/h. The emission factors presented may be used to model vehicle traffic emissions, improve on-road vehicle dust emissions impact assessment and as a guide when deciding on local applicable road dust mitigation strategies

Air Quality Analysis of Indoor Working Environments from Different Industries and Regions of Punjab

Background: Air pollution is a major health hazard and one of most crucial environmental aspects which cause mass mortality of population. Workers in different factories often directly exposed to particulate matter which can initiate respiratory related problems which may steer to incidence of cancer. Materials and Methods: Various particulate matter (PM) fractions and gaseous emissions were monitored in indoor environments of different industries using DRX Aerosol Monitor. Parallel monitoring of PM2.5 was performed in outdoor air by using TSI DUSTTRAK. Besides, analyses of indoor environment of different industrial units for volatile organic carbon (VOC’s), temperature, relative humidity, gaseous emissions (CO, CH4, O3, and NO2) was done by using Aeroqual. Results: The levels of particulate matter (PM) of all fractions and other criteria pollutant gases were significantly higher in all manufacturing units compared with recommended permissible WHO limits. Furthermore, PM2.5 levels were significantly higher in outdoor environment as compared to indoor workplaces at all monitored sites. Conclusion: There is a dire need to consider these high levels of PM and other gaseous emissions and take some serious steps to control this ever-increasing problem.

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

Impact of COVID-19 Restriction Measures on Ambient PM10 Concentrations during Universiti Putra Malaysia Convocation

The outbreak of COVID-19 resulted in many significant restricted social and economic activities. The pandemic provided unprecedented natural experiment in which the study was specifically conducted to investigate the impact on particulate matters with aerodynamic diameter less than 10 µm (PM10) resulting from the restriction measures taken by the university management to manage COVID-19 during convocation. PM10 and meteorological parameters (ambient temperature, relative humidity and wind speed) were monitored using TSI Dusttrak aerosol monitor and Kestrel 4500, respectively. Traffic volume was also considered as the significant source of air pollutants due to higher number of vehicles during the event. Two sets of data were collected during peak hours (7.30-9.30 a.m., 12.30-14.30 p.m. and 16.30-18.30 p.m.) on normal working days (before the outbreak in 2018) and during the convocation (after the outbreak in 2022) at two monitoring points along the major road of Persiaran Universiti 1. The results showed that ambient PM10 concentrations were remarkably reduced by ~65-84%, especially during afternoon peak hours. Parking space allocated far from the main hall area contributed to lower PM10 concentrations. From the parking space, graduands were required to either walk with guidance or take the provided university buses to go to the main hall. Pearson correlation analysis showed that PM10 concentrations have significant relationship with the meteorological parameters (p<0.05). However, in terms of traffic emission, it does not necessarily influence solely by the vehicles. From the observation, walking activities by the graduands, students, visitors, etc. around the monitoring areas also contributed to the dust uplifting, hence influencing the level of PM10. The study elucidated the influence of COVID-19 restriction measures on PM10 concentrations which can be a steppingstone towards a better management in reducing the pollutant in the future convocation events.

Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification

In this study fine particulate matter (PM2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R2 values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

Optimizing Parameters for Smoke Evacuation.

Current literature lacks recommendations regarding the ideal organization of the smoke evacuation system to minimize inhalation of surgical smoke. This study determines optimal parameters of the smoke evacuation system with respect to the surgical field. This study was conducted in an outpatient surgical facility at a tertiary care center. After 30 seconds of continuous electrocautery of tissue, particulate measurements were recorded using the TSI DustTrak Aerosol Monitor 8520 (>2.5 μm particles) and the TSI P-Trak Ultrafine Particle Counter 8525 (<1 μm particles) while changing the angle and distance of the smoke evacuation system. Particulate matter measurements were lower when suction angle was at 45° than at 90°. For both small-sized and large-sized particles, the lowest particulate matter was recorded when the evacuator was maintained at a 45° angle, 2 to 4 inches away from the cauterizing procedure. Particulate matters dramatically increased after increasing the distance of the smoke evacuator beyond 8 inches from the procedural site. In an effort to reduce smoke inhalation, the authors recommend that smoke evacuation should be placed at a 45° suction angle, no further than 8 inches away from the surgical site, while preserving the surgeon's field of vision.

Comparison of Direct-reading and Gravimetric Methods of Particle Measurement in a Science Building, Silpakorn University

This study aimed to develop relationships between particulate matter (PM) concentrations obtained from a direct-reading instrument to those from a gravimetric method. TSI DustTrak II Aerosol Monitors (Model 8530), a direct-reading instrument for PM10 and PM2.5 measurement, together with personal air pumps connected to a Sensidyne cyclone and a SKC Personal Environmental Monitor (PEM) for gravimetric PM10 and PM2.5 measurements respectively were deployed in the Faculty of Science building, Silpakorn University, Nakhon Pathom, Thailand. Comparison of the results from each instrument indicated that PM10 and PM2.5 concentrations obtained from the TSI DustTrak were higher. The linear relationship from ordinary least squares (OLS) regression between PM10 data determined by TSI DustTrak (x) and Sensidyne cyclone (y ̂) was significant (R2=0.92) and could be represented as y ̂ = 0.272x. For PM2.5, the relationship between concentrations determined by TSI DustTrak (x) and SKC PEM (y ̂) was also significant (R2=0.92) and represented by y ̂ = 4.848√x. Validation of both equations was undertaken by comparing predicted values from these relationships against the actual concentrations found by gravimetric analysis, with R2=1.0 and 0.92 for PM10 and PM2.5, respectively. It is suggested that these site-specific OLS regression equations can provide fast and convenient estimation of concentrations derived by gravimetric analysis from direct-reading TSI DustTrak monitor data.

Performance Evaluation of Real-time DustTrak Monitors for Outdoor Particulate Mass Measurements in a Desert Environment

Using the filter-based gravimetric technique as the reference method, this study aimed to evaluate the performance of the TSI DustTrak DRX® aerosol monitor, which offers real-time measurements, and establish suitable factors for bias correction in real-world operating conditions. Overall, the DustTrak demonstrated high precision in tracking the PM2.5 and PM10 mass concentrations; however, the measurements significantly deviated from those obtained via the reference method, showing an overestimation by a factor of ~2 for the fine fraction and an underestimation by ~20% for the coarse one. Applying a correction factor based on a constant proportion produced an acceptable level of accuracy, but utilizing one adjusted for humidity achieved even better results. These factors can also be used to enhance the accuracy of DustTrak measurements under comparable conditions, i.e., for aerosol with similar properties in similar environments. Additionally, the proposed approaches can serve as a model for field calibration and measurement data correction in other studies employing various types of real-time optical monitors.

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.

Genotoxic Effects of Exposure to Urban Traffic Related Air Pollutants on Children in Klang Valley, Malaysia

Traffic-related air pollutants (TRAPs) are currently increasing due to the increment of vehicle numbers in Malaysia. The emission of pollutants from these vehicles have genotoxic properties that can potentially induce genetic damage in human. In this study, micronuclei assay is used to determine the potential genotoxic exposure by assessing the presence of micronuclei frequency (MN) in buccal cells. The specific objective of this study is to determine the association between TRAPs and frequency of MN among school children in Klang Valley. A comparative cross-sectional study was conducted among primary school children (9�11 years old) in high-density traffic area (n = 94, Kuala Lumpur) and low-density traffic area (n = 94, Hulu Langat). A questionnaire was distributed to the parents to obtain respiratory symptoms information. Buccal cells were analyzed to determine the frequency of micronuclei. Air quality assessment was carried out in a total of 6 schools (consisted of exposed and comparative groups) by using TSI DustTrak DRX Aerosol Monitor 8534 for measurement of PM2.5 and PM10, LaMotte�s Model BD Air Sampling Pump for measurement of Nitrogen dioxide (NO2) and Sulphur dioxide (SO2), ppbRAE 3000 for total Volatile Organic Compound (TVOC) and TSI Q-TRAK 7565 for measurement of Carbon dioxide (CO2) and Carbon monoxide (CO). The concentrations of PM10, PM2.5, NO2, SO2, TVOC and CO at exposed schools were significantly higher (p < 0.005). compared to those in comparative schools. All pollutants were significantly associated (p < 0.001) with respiratory symptom phlegm and MN frequency. Additionally, the MN frequency in the exposed group was significantly higher (p < 0.001) than in the comparative group. All in all, this study demonstrated that air pollutants, especially NO2 and CO, have significantly influenced the MN frequency among children in primary schools. This study suggested that exposure to TRAPs among Malaysian school children has increased the risk for respiratory complications with the formation of MN. © The Author(s).

A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R2 values higher than 0.97, root mean square error less than 1.0 μg/m3, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs.

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Abstract. Poultry layer houses are a significant source of particulate matter (PM) emissions, which potentially affect worker and animal health. Particulate matter characteristics, such as concentration and size distribution inside layer houses, are critical information for assessment of the potential health risks and development of effective PM mitigation technologies. However, this information and its spatial and seasonal variations are lacking for typical layer facilities. In this study, two TSI DustTrak monitors (DRX 8533) and an Aerodynamic Particle Sizer (APS 3321) were used to measure PM mass concentrations and number-weighted particle size distributions in two typical manure-belt poultry layer houses in Ohio in three seasons: summer, autumn, and winter. Bimodal particle size distributions were consistently observed. The average count median diameters (mean ±SD) were 1.68 ±0.25, 2.16 ±0.31, and 1.87 ±0.07 µm in summer, autumn, and winter, respectively. The average geometric standard deviations of particle size were 2.16 ±0.23, 2.16 ±0.18, and 1.74 ±0.17 in the three seasons, respectively. The average mass concentrations were 67.4 ±54.9, 289.9 ±216.2, and 428.1 ±269.9 µg m-3 for PM2.5; 73.6 ±59.5, 314.6 ±228.9, and 480.8 ±306.5 µg m-3 for PM4; and 118.8 ±99.6, 532.5 ±353.0, and 686.2 ±417.7 µg m-3 for PM10 in the three seasons, respectively. Both statistically significant (p &amp;lt; 0.05) and practically significant (difference of means &amp;gt;20% of smaller value) seasonal variations were observed. Spatial variations were only practically significant for autumn mass concentrations, likely due to external dust infiltration from nearby agricultural activities. The OSHA-mandated permissible exposure limit for respirable PM was not exceeded in any season. Keywords: Air quality, Particulate matter, Poultry housing, Seasonal variation, Spatial variation.

Ultrafine and Fine Particulate Matter Levels over Resurfacing Operations at Skating Arenas

Background: Ice arenas are a unique public indoor space where there is extensive use of combustion engines combined with an exercising population. This pilot study seeks to identify ultrafine particle (UFP) levels and fine particulate matter (PM2.5) in indoor ice arenas across resurfacing operations. Ice making is a time-consuming and expensive process. In order to preserve the ice and ensure a smooth surface, arena operators resurface ice based on usage. Ice resurfacing involves driving a resurfacing machine over the surface of the ice as it simultaneously scrapes off a thin layer of ice and fills scratches with water, resulting in a smooth and even ice surface. Pollutants can be trapped near the ice from stagnant air flow due to a combination of boards and glass surrounding the ice and a thermal inversion which keeps colder air near the ice surface.Methods: UFP and PM2.5 measurements were continually collected using a TSI P-Trak and a TSI Dusttrak DRX. Samples were run indoors before, during and for approximately 20 minutes after one resurfacing event to capture changes in particle levels throughout the event and outdoor background measurements were taken. Sampling was conducted on two separate days in 12 arenas, once in the morning and once in the afternoon. Arenas were selected that differed in their ventilation characteristics and resurfacing equipment (7-propane, 3-natural gas, 1-gasoline, 1 electric).Results: Mean resurfacing time was 9.1 minutes (range 6-16) and was performed hourly during high arena usage. Peaks in UFP were typically observed 10 minutes post resurfacing with a mean increase in peak UFP from pre- to post- resurfacing across arenas of 65% (range 13-400%). However, PM2.5 levels were not elevated following resurfacing operations.Conclusion: The poster will provide results on changes in UFP levels by fuel type of resurfacer.

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors.

A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full “heating season” (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m3, RMSE of 4.056 and 6.084 µg/m3, and R2 of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

Indoor air quality analysis of residential buildings in northeast China based on field measurements and longtime monitoring

Outdoor air contamination was frequently observed and focused in northeast China in winter when severe cold days are common and understanding dynamic characteristics of indoor air quality in this area is essential for occupants' ventilation strategies. Herein, using spectrophotometric method, GC-MS (Gas Chromatography-Mass Spectrometer) method, TSI DustTrak particle tester and Telaire 7001 CO2 tester, we investigated the concentration change of formaldehyde (HCHO), volatile organic compounds (VOCs), PM2.5 and CO2 of 21 houses for 4 seasons under 2 conditions (airtight and natural ventilation). Moreover, 6 houses were selected to install convenient sensors for longtime monitoring. Outdoor temperature, concentration of PM2.5, residents' opening window behavior, indoor infiltration rate, temperature, relative humidity and the furniture surface area were also recorded for comparison and correlation analysis. According to the detected results, worst indoor conditions were revealed that average concentrations of HCHO (autumn), TVOC (summer), PM2.5 (winter) and CO2 (winter) were 0.094 mg/m3, 0.924 mg/m3, 0.073 mg/m3, and 883 ppm, respectively. Indoor concentration of PM2.5, TVOC and CO2 exceeded the standard limit and they changed with seasonal characteristics. The observed 4 factors have different effects on indoor air quality. Based on this research, it is recommended that outdoor condition be considered while opening and closing window in winter and ventilation be strengthened in summer.

Field Comparison of TSI DustTrak versus TEOM in Two Poultry Houses

Abstract. The TSI DustTrak monitor has been used for particulate matter (PM) monitoring at various animal facilities. The instrument determines PM concentrations based on the principle of light scattering. Several assumptions (e.g., particle size, refractive index, and density) are imposed during the calibration process; however, they may not apply to PM emanating from agricultural settings. In this study, PM10 monitoring was conducted at a broiler house and a layer breeding house with four collocated instruments: three DustTrak monitors and one tapered element oscillating microbalance (TEOM). Being a federal equivalent method (FEM) for PM10 monitoring, TEOM was selected here as a transfer standard for assessing the field performance of DustTrak. Results revealed a good linearity between DustTrak and TEOM PM10 readings (R2 =0.92 and 0.85 in the broiler and layer breeding houses, respectively). However, DustTrak significantly underestimated PM10 concentrations in both houses. To correct for the monitoring bias by DustTrak, an average correction factor was derived from correlation analysis that characterized the ratio of DustTrak’s PM10 response to TEOM’s. The factor was calculated as 0.267 for the broiler house and 0.244 for the layer breeding house. Mie scattering simulation was performed to further verify the derived correction factors. A factor of 0.204 was estimated from the simulation, and it accorded well with experimental results. A dependence of the correction factor on PM10 concentration was noted in both poultry houses, indicating the feasibility of developing a concentration-dependent correction factor for future monitoring efforts. Such a relationship could also be explained by Mie scattering. This study is expected to facilitate a better understanding of the limitations and perspectives of the TSI DustTrak and other light scattering PM monitors for agricultural air quality research. Keywords: DustTrak, Mass concentration, Mie scattering, PM10, Poultry, TEOM.

Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors

Recently, portable monitors have been increasingly used to quantify air pollutant concentrations at high spatiotemporal resolution. A sampling campaign was conducted to measure the fine particulate matter (PM2.5) and carbon monoxide (CO) exposure concentrations in transport microenvironments (TMEs) in Hong Kong in January and June 2015 using TSI DustTrak and Q-Trak portable monitors. The objectives were to: (1) calibrate DustTrak and Q-Trak; (2) evaluate variability between seasons and microenvironments; (3) estimate indoor/outdoor relationships; and (4) determine minimum sample size. Calibration equations, obtained through side-by-side measurement against stationary reference methods in winter and summer, were applied to correct the measured PM2.5 data set. In general, PM2.5 concentrations in all TMEs were significantly higher in winter than in summer. The mean PM2.5 concentration in winter was lower for underground sections of the Mass Transit Railway (MTR) metro system (31 μg/m3) than for other TMEs, whereas in summer TMEs had mean PM2.5 concentrations in the range of 10–15 μg/m3, with above-ground MTR train as an exception, at 23 μg/m3. PM2.5 concentrations measured in TMEs were strongly correlated with nearby air quality monitoring stations (AQMSs) measurements in winter, but in summer there was little correlation. The minimum sample size estimates varied more among TMEs in summer versus winter because of the differences in PM2.5 concentration distributions related to changes in ambient PM2.5 concentrations and ventilation practices. This study provides a feasible protocol on the calibration and application of portable monitors in TME air quality measurement and develops a method for estimating minimum sample size.

Performance calibration of low-cost and portable particular matter (PM) sensors

In this study, we calibrated the performance of low-cost and portable PM sensors under the condition of steady-state particle mass concentration by challenging them with lab-generated particles in different size distributions and compositions. Tested PM sensors included four low-cost optical sensors, i.e., Sharp, Shinyei, Samyoung and Oneair, TSI DustTrak and Personal Dust Monitor (PDM). A scientific Tapered Element Oscillating Microbalance (TEOM; Thermal Scientific Model 1405) was applied as the reference. Same as what concluded in previous studies, our calibration data show that the readouts of all tested PM sensors had linear relationships with the particle mass concentration when the size distribution and composition of test particles were kept unchanged. However, the linear calibration lines for four low-cost PM sensors and DustTrak are different when the size distribution (i.e., mean size and geometrical standard deviation) and composition of particles were varied. The PDM output in general agreed well with that of reference TEOM for particles with mass concentration higher than 300μg/m3. The PDM, however, lost its sensitivity for measuring particles in the mass concentration less than 300μg/m3. Our study also demonstrated that the calibration of low-cost PM sensors should be performed under the condition of steady particle mass concentration instead of under the condition of transient particle concentration, even at very slow transient situation.