Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

• Published in: Atmospheric measurement techniques 2021-07, Vol.14 (7), p.5139-5151
• Main Authors: Liu, Xiansheng, Hadiatullah, Hadiatullah, Zhang, Xun, Hill, L. Drew, White, Andrew H. A., Schnelle-Kreis, Jürgen, Bendl, Jan, Jakobi, Gert, Schloter-Hai, Brigitte, Zimmermann, Ralf
• Format: Article
• Language: English
• Subjects: Aerosols; Air monitoring; Air quality; Algorithms; Black carbon; Carbon; Carbon content; Data; Data smoothing; Methods; Mitigation; Noise; Noise reduction; Pollutants; Polynomials; Post-production processing; Real time; Retention; Roadsides; Urban areas
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-14-5139-2021

The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon in human exposure profiling and mobile air quality monitoring. Due to it being relatively new on the market, the field lacks a refined assessment of the instrument's performance under various settings and data post-processing approaches. This study assessed the mobile real-time performance of the MA200 to determine a suitable noise reduction algorithm in an urban area, Augsburg, Germany. Noise reduction and negative value mitigation were explored via different data post-processing methods (i.e., local polynomial regression (LPR), optimized noise reduction averaging (ONA), and centred moving average (CMA)) under common sampling interval times (i.e., 5, 10, and 30 s). After noise reduction, the treated data were evaluated and compared by (1) the amount of useful information attributed to retention of microenvironmental characteristics, (2) the relative number of negative values remaining, (3) the reduction and retention of peak samples, and (4) the amount of useful signal retained after correction for local background conditions. Our results identify CMA as a useful tool for isolating the central trends of raw black carbon concentration data in real time while reducing nonsensical negative values and the occurrence and magnitudes of peak samples that affect visual assessment of the data without substantially affecting bias. Correction for local background concentrations improved the CMA treatment by bringing nuanced microenvironmental changes into view. This analysis employs a number of different post-processing methods for black carbon data, providing comparative insights for researchers looking for black carbon data smoothing approaches, specifically in a mobile monitoring framework and data collected using the microAeth® series of Aethalometer.