Predicting Indoor Concentrations of Black Carbon in Residential Environments.

Black carbon (BC) is a descriptive term that refers to light-absorbing particulate matter (PM) produced by incomplete combustion and is often used as a surrogate for traffic-related air pollution. Exposure to BC has been linked to adverse health effects. Penetration of ambient BC is typically the primary source of indoor BC in the developed world. Other sources of indoor BC include biomass and kerosene stoves, lit candles, and charring food during cooking. Home characteristics can influence the levels of indoor BC. As people spend most of their time indoors, human exposure to BC can be associated to a large extent with indoor environments. At the same time, due to the cost of environmental monitoring, it is often not feasible to directly measure BC inside multiple individual homes in large-scale population-based studies. Thus, a predictive model for indoor BC is needed to support risk assessment in public health. In this study, home characteristics and occupant activities that potentially modify indoor levels of BC were documented in 23 homes, and indoor and outdoor BC concentrations were measured twice. The homes were located in the Cincinnati-Kentucky-Indiana tristate region and measurements occurred from September 2015 through August 2017. A linear mixed-effect model was developed to predict BC concentration in residential environments. The measured outdoor BC concentrations and the documented home characteristics were utilized as predictors of indoor BC concentrations. After the model was developed, a leave-one-out cross-validation algorithm was deployed to assess the predictive accuracy of the output. The following home characteristics and occupant activities significantly modified the concentration of indoor BC: outdoor BC, lit candles and electrostatic or high efficiency particulate air (HEPA) filters in heating, ventilation and air conditioning (HVAC) systems. Predicted indoor BC concentrations explained 78% of the variability in the measured indoor BC concentrations. The data show that outdoor BC combined with home characteristics can be used to predict indoor BC levels with reasonable accuracy.

Effectiveness of a portable air cleaner in removing aerosol particles in homes close to highways.

Outdoor traffic-related airborne particles can infiltrate a building and adversely affect the indoor air quality. Limited information is available on the effectiveness of high efficiency particulate air (HEPA) filtration of traffic-related particles. Here, we investigated the effectiveness of portable HEPA air cleaners in reducing indoor concentrations of traffic-related and other aerosols, including black carbon (BC), PM2.5 , ultraviolet absorbing particulate matter (UVPM) (a marker of tobacco smoke), and fungal spores. This intervention study consisted of a placebo-controlled cross-over design, in which a HEPA cleaner and a placebo "dummy" were placed in homes for 4-weeks each, with 48-hour air sampling conducted prior to and during the end of each treatment period. The concentrations measured for BC, PM2.5 , UVPM, and fungal spores were significantly reduced following HEPA filtration, but not following the dummy period. The indoor fraction of BC/PM2.5 was significantly reduced due to the HEPA cleaner, indicating that black carbon was particularly impacted by HEPA filtration. This study demonstrates that HEPA air purification can result in a significant reduction of traffic-related and other aerosols in diverse residential settings.

Assessing the accuracy of commercially available gas sensors for the measurement of ambient ozone and nitrogen dioxide.

The objective of the National Institute for Occupational Safety and Health (NIOSH) accuracy criterion is to ensure that measurements from monitoring devices are within ±25% of the true concentration of the analyte with 95% certainty. To determine whether NO2 and O3 sensors meet this criterion, three commercially available units (Cairclip O3/NO2, Aeroqual NO2, and Aeroqual O3 sensors) were co-located three times with validated instruments (NOx chemiluminescence [NO2mon] and photometric O3 analyzers [O3mon]) at an outdoor monitoring station. As cofactors of sensor performance such as temperature (T) and relative humidity (RH) potentially influence the response of NO2 and O3 sensors, corrections for cofactors were made by using T, RH, and the sensor measurements to predict measurements made by NO2mon and O3mon during the first co-location period (training dataset). The developed models were tested in the merged data obtained from the second and third co-location periods (testing dataset). In the training and testing datasets, the mean NO2 as measured by NO2mon was 4.6 ppb (range = 0.4-35 ppb) and 9.4 ppb (range = 1-37 ppb), respectively. The mean O3 in the training and testing datasets as measured by O3mon was 38.8 ppb (range = 1-65 ppb) and 35.7 ppb (range = 1-61 ppb), respectively. None of the sensor measurements in the training dataset were within the NIOSH accuracy criterion (mean error ≥25%). After correcting for cofactors of sensor performance, the accuracy of the Cairclip O3/NO2 and the Aeroqual O3 sensors considerably improved when tested with the testing dataset (mean error = -1% and 14%, respectively). However, the Aeroqual NO2 sensor had an error that was not within ±25%. Raw measurements from the tested sensors may be unsuitable for assessing workers' exposure to NO2 and O3. Corrections for cofactors of Cairclip O3/NO2 and Aeroqual O3 sensor performance are required for more accurate occupational exposure assessment.

Indoor particulate matter and lung function in children.

People generally spend more time indoors than outdoors resulting in a higher proportion of exposure to particulate matter (PM) occurring indoors. Consequently, indoor PM levels, in contrast to outdoor PM levels, may have a stronger relationship with lung function. To test this hypothesis, indoor and outdoor PM2.5 and fungal spore data were simultaneously collected from the homes of forty-four asthmatic children aged 10-16 years. An optical absorption technique was utilized on the collected PM2.5 mass to obtain concentrations of black carbon (BC) and ultraviolet light absorbing particulate matter, (UVPM; a marker of light absorbing PM2.5 emitted from smoldering organics). Enrolled children completed spirometry after environmental measurements were made. Given the high correlation between PM2.5, BC, and UVPM, principal component analysis was used to obtain uncorrelated summaries of the measured PM. Separate linear mixed-effect models were developed to estimate the association between principal components of the PM variables and spirometry values, as well as the uncorrelated original PM variables and spirometry values. A one-unit increase in the first principal component variable representing indoor PM (predominantly composed of UVPM and PM2.5) was associated with 4.1% decrease (99% CI = -6.9, -1.4) in FEV1/FVC ratio. 11.3 µg/m3 increase in indoor UVPM was associated with 6.4% and 14.7% decrease (99% CI = -10.4, -2.4 and 99% CI = -26.3, -2.9, respectively) in percent predicted FEV1/FVC ratio and FEF25-75 respectively. Additionally, 17.7 µg/m3 increase in indoor PM2.5 was associated with 6.1% and 12.9% decrease (99% CI = -10.2, -1.9 and 99% CI = -24.9, -1.0, respectively) in percent predicted FEV1/FVC ratio and FEF25-75, respectively. Outdoor PM, indoor BC, and indoor fungal spores were not significantly associated with lung function. The results indicate that indoor PM is more strongly associated with lung function in children with asthma as compared with outdoor PM.

Combining sensor-based measurement and modeling of PM2.5 and black carbon in assessing exposure to indoor aerosols.

Accurate, cost-effective methods are needed for rapid assessment of traffic-related air pollution (TRAP). Typically, real-time data of particulate matter (PM) from portable sensors have been adjusted using data from reference methods such as gravimetric measurement to improve accuracy. The objective of this study was to create a correction factor or linear regression model for the real-time measurements of the RTI's Micro Personal Exposure Monitor (MicroPEM™) and AethLab's microAeth® black carbon (AE51) sensor to generate accurate real-time data for PM2.5 (PM2.5RT) and black carbon (BCRT) in Cincinnati metropolitan homes. The two sensors and an SKC PM2.5 Personal Modular impactor were collocated in 44 indoor sampling events for 2 days in residences near major roadways. The reference filter-based analyses conducted by a laboratory included particle mass (SKC PM2.5 and MicroPEM™ PM2.5) and black carbon (SKC BC); these methods are more accurate than real-time sensors but are also more cumbersome and costly. For PM2.5, the average correction factor, a ratio of gravimetric to real-time, for the MicroPEM™ PM2.5 and SKC PM2.5 utilizing the PM2.5RT and was 0.94 and 0.83, respectively, with a coefficient of variation (CV) of 84% and 52%, respectively; the corresponding linear regression model had a CV of 54% and 25%. For BC, the average correction factor utilizing the BCRT and SKC BC was 0.74 with a CV of 36% with the associated linear regression model producing a CV of 56%. The results from this study will help ensure that the real-time exposure monitors are capable of detecting an estimated PM2.5 after an appropriate statistical model is applied.