Analysis of mobile monitoring data from the 1 microAeth ® MA 200 for measuring changes in black 2 carbon on the roadside in Augsburg 3

The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon in 22 human exposure profiling and mobile air quality monitoring. Due to its relatively new on the market, 23 the field lacks a refined assessment of the instruments performance under various settings and data 24 post-processing approaches. This study assessed the mobile real-time performance of the MA200 to 25 determine a suitable noise reduction algorithm in an urban area, Augsburg, Germany. Noise reduction 26 and negative value mitigation were explored via different data post-processing methods (i.e., local 27 polynomial regression (LPR), optimized noise reduction averaging (ONA), and centered moving 28 average (CMA)) under common sampling interval times (i.e., 5, 10, and 30 s).After noise reduction, 29 the treated-data were evaluated and compared by (1) the amount of useful information attributed to 30 retention of microenvironmental characteristics; (2) relative number of negative values remaining; (3) 31 reduction and retention of peak samples; and (4) the amount of useful signal retained after correction 32 for local background conditions. Our results identify CMA as a useful tool for isolating the central 33 trends of raw black carbon concentration data in real time while reducing non-sensical negative values 34 and the occurrence and magnitudes of peak samples that affect visual assessment of the data without 35 substantially affecting bias. Correction for local background concentrations improved the CMA 36 treatment by bringing nuanced microenvironmental changes into more visible. This analysis employs a 37 number of different post-processing methods for black carbon data, providing comparative insights for 38 2 researchers looking for black carbon data smoothing approaches, specifically in a mobile monitoring 39 framework and data collected using the microAeth® series of aethalometers. 40

Berkeley, USA) taken approximately 30 to 60 min between walks showed a good agreement (Pearson's 126 r =0.933) (Liu et al., 2021). In addition, it is worth noting that when the AE33 was used for monitoring 127 black carbon at the same time as the MA200, the AE33 was placed in a fixed station, while the MA200  Table 1. To demonstrate the 132 unit-to-unit comparability between the MA200 units, we performed intercomparisons at fixed 133 monitoring stations (Table S1) and during collocated mobile measurements (Fig. S2) Table 1).

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To account for the different land-use types of the microenvironments, a fixed walking route within the 151 centre of the city was determined. Wherever possible, the mobile measurements were carried out on the 152 right side of the road simulating people's common habits (driving and walking on the right side in 153 Germany

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To calculate the reduction of peak samples (RP), the number of peak samples was calculated before 238 and after post-processing data, and the difference value was obtained. Then the change in the number 8 of peak samples was divided by the total number of peak samples before post-processing data. After 240 noise reduction, we compared the reduction and the number of peak samples to further evaluate 241 post-processing methods. In short, if the reduction of peak samples is high, the treated data has a high 242 peak noise reduction without removing the numbers of peak samples. Therefore, the method with high 243 reduction of peak samples and retaining the number of peak samples after post-processing is 244 considered as the better method. The average eBC concentrations of raw, ONA-processed, LPR-processed, and CMA-processed data 264 (Measurements 1-10) monitored by all instruments were compared in this study (Table S2)  30.5 %, for 5 s, 10 s, and 30 s, respectively (Fig. 1a, Table 2, Fig S4a). Following this, the raw data 273 were processed using ONA, LPR, and CMA (Fig. 1b, 1c, and 1d).

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In the 5 s time base, the eBC values changed very rapidly (Fig. 1a), and the ONA processing of the data 275 resulted in only one value (which was negative) (Fig. 1b). Thus, the microenvironmental characteristics 276 of the eBC concentration were not reproduced. We found all ΔATN (ATNt(0)+Δt'-ATN0) data were 277 negative in the raw data collected at 5 s, which, according to the ONA method described above, 278 resulted in only a single value. In short, after the first measurement, the ΔATN threshold (which is 279 positive) for calculating the next value was never reached. The first value was likely a negative value 280 due to a combination of instrument noise, coincidence, and a low background concentration (i.e., low 281 baseline instrument signal), which is consistent with both the raw data measurements and the typical

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The ONA algorithm showed a strong tendency to remove negative values and, depending on the ΔATN 310 threshold employed by the user, can remove potentially meaningful low peaks. As a result, the 311 ONA-treated data may present bias that obscure nuanced microenvironmental trends (Fig. 1b).

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The processing of peak sample is a pivotal evaluation index for the measurement of time-averaged 330 roadside air quality. Passing vehicles, for example, may bias estimates of typical local concentrations 331 due to their contribution to the dataset of peak concentrations that may substantially related to 332 arithmetic averages. Therefore, after noise reduction, we compare the reduction and the retained  386 Therefore, to further compare the ONA and CMA algorithm, we also compared concentrations after 387 background correction (Fig. 3c and d). As shown in Figures 3c and d,