Exposure to ambient black carbon (BC) is associated with adverse health effects. Black carbon levels display large spatial and temporal variability in many settings, such as cities and rural households where fossil fuel and biomass, respectively, are commonly burned for transportation, heat and cooking. This paper addresses the optimization of the miniaturized personal BC monitor, the microAeth® for use in epidemiology studies. To address false positive and negative peaks in real time BC concentrations resulting from changes in temperature and humidity, an inlet with a diffusion drier was developed. In addition, we developed data cleaning algorithms to address occasional false positive and negative fluctuations in BC readings related to physical vibration, due in part to both dirt accumulations in the optical inserts and degraded components. These methods were successfully used to process real-time BC data generated from a cohort of 9-10 year old children (N= 54) in NYC, who wore 1 or 2 microAeth units for six 24hr time periods. Two hour and daily BC averages after data cleaning were consistent with averaged raw data (slopes near 1 with R =0.99, p<0.001; R= 0.95, p<0.001, respectively), strongly suggesting that the false positive and negative excursions balance each other out when averaged for at least 2 hrs. Data cleaning of identified suspect events allows more confidence in the interpretation of the real-time personal monitoring data generated in environmental exposure studies, with mean percent difference <10% for 19 duplicate deployments.
Keywords: black carbon; data cleaning algorithms; exposure assessment; personal monitor; relative humidity; soot; vibration.