Articles | Volume 14, issue 7
https://doi.org/10.5194/amt-14-5139-2021
https://doi.org/10.5194/amt-14-5139-2021
Research article
 | 
28 Jul 2021
Research article |  | 28 Jul 2021

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

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

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Cited articles

AethLabs: MicroAeth® MA Series MA200, MA300, MA350 Operating Manual, available at: https://aethlabs.com/sites/all/content/microaeth/maX/MA200 MA300 MA350 Operating Manual Rev 03 Dec 2018.pdf (last access: 5 April 2021), 2018. 
Anenberg, S. C., Schwartz, J., Shindell, D., Amann, M., Faluvegi, G., Klimont, Z., Janssens-Maenhout, G., Pozzoli, L., Dingenen., R. V., Vignati, E., Emberson, L., Muller, N. Z., West, J. J., Williams, M., Demkine, M., Demkine, V., Hicks, W. K., Kuylenstierna, J., Raes, F., and Ramanathan, V.: Global air quality and health co-benefits of mitigating near-term climate change through methane and black carbon emission controls, Environ. Health Persp., 120, 831–839, https://doi.org/10.1289/ehp.1104301, 2012. 
Apte, J. S., Kirchstetter, T. W., Reich, A. H., Deshpande, S. J., Kaushik, G. C. A., Marshall, J, D., and Nazaroff, W. W.: Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India, Atmos. Environ., 45, 4470–4480, https://doi.org/10.1016/j.atmosenv.2011.05.028, 2011. 
Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Maeshall, J. D., Portier, C. J., Vermeulen, R. C. H., and Hameurg, S. P.: High-Resolution Air Pollution Mapping with Google Street View Cars. Exploiting Big Data, Environ. Sci. Technol., 12, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017. 
Brantley, H. L., Hagler, G. S. W., Kimbrough, E. S., Williams, R. W., Mukerjee, S., and Neas, L. M.: Mobile air monitoring data-processing strategies and effects on spatial air pollution trends, Atmos. Meas. Tech., 7, 2169–2183, https://doi.org/10.5194/amt-7-2169-2014, 2014. 
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Short summary
A monitoring campaign was conducted in Augsburg to determine a suitable noise reduction algorithm for the MA200 Aethalometer. Results showed that centred moving average (CMA) post-processing effectively removed spurious negative concentrations without major bias and reliably highlighted effects from local sources, effectively increasing spatio-temporal resolution in mobile measurements. Evaluation of each method on peak sample reduction and background correction further supports the reliability.