Abstract. 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.
Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles