English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Abstract. The portable microAeth® MA200(MA200) is widely applied for measuring black carbon in human exposureprofiling and mobile air quality monitoring. Due to it being relatively new onthe market, the field lacks a refined assessment of the instrument'sperformance under various settings and data post-processing approaches. Thisstudy assessed the mobile real-time performance of the MA200 to determine asuitable noise reduction algorithm in an urban area, Augsburg, Germany.Noise reduction and negative value mitigation were explored via differentdata 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). Afternoise reduction, the treated data were evaluated and compared by (1) theamount of useful information attributed to retention of microenvironmentalcharacteristics, (2) the relative number of negative values remaining, (3) thereduction and retention of peak samples, and (4) the amount of useful signalretained after correction for local background conditions. Our resultsidentify CMA as a useful tool for isolating the central trends of raw blackcarbon concentration data in real time while reducing nonsensical negativevalues and the occurrence and magnitudes of peak samples that affect visualassessment of the data without substantially affecting bias. Correction forlocal background concentrations improved the CMA treatment by bringingnuanced microenvironmental changes into view. This analysis employsa number of different post-processing methods for black carbon data,providing comparative insights for researchers looking for black carbon datasmoothing approaches, specifically in a mobile monitoring framework and datacollected using the microAeth® series of Aethalometer.

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