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

Xiansheng Liu,Xun Zhang,L Drew Hill,Andrew H A White,Ralf Zimmermann,Gert Jakobi,Jürgen Schnelle-Kreis,Brigitte Schloter-Hai,Hadiatullah Hadiatullah,Jan Bendl

doi:10.5194/amt-14-5139-2021

Abstract

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.

Highlights

Black carbon particulate matter with size ranging from 0.01 to 1 μm (Zhou et al, 2020) is a pollutant comprised of a range of carbonaceous materials produced by the incomplete combustion of fossil fuel and biomass containing carbon (Goldberg, 1985), and it is suspected of exerting a significant impact on health
Noise reduction and negative value mitigation were explored via different data postprocessing 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)
The results show that the three post-processing methods accounted of approximately 1 % bias from the average of raw concentrations

Summary

Introduction

X. Liu et al.: Analysis of mobile monitoring data from the microAeth® MA200. Mobile monitoring has been widely applied for the collection of real-time air quality measurements to assess local air quality and air pollutant exposures (Liu et al, 2020, 2021). This method can improve the spatio-temporal resolution of measurement data in the urban environment, and it enables the collection of data such as the traffic-related air pollutant concentrations (Liu et al, 2019). A noise reduction method that appears to better facilitate background estimation and correction (as described below calculated from noise-reduced data via a defined background estimation and evaluation approach) is assessed to select a better post-processing method

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Conclusion