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.