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Record W4372211804 · doi:10.5194/amt-16-2333-2023

Performance evaluation of portable dual-spot micro-aethalometers for source identification of black carbon aerosols: application to wildfire smoke and traffic emissions in the Pacific Northwest

2023· article· en· W4372211804 on OpenAlexafffundabout
Mrinmoy Chakraborty, Amanda Giang, Naomi Zimmerman

Bibliographic record

VenueAtmospheric measurement techniques · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAethalometerAerosolEnvironmental scienceCarbon blackAtmospheric sciencesParticulatesCombustionAbsorption (acoustics)SootSmokeMeteorologyEnvironmental chemistryChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract. Black carbon (BC) is a component of particulate matter, emitted from the incomplete combustion of carbonaceous fuels. The presence of BC in the atmosphere can disrupt the atmospheric radiation budget, and exposure to BC can adversely affect human health. Multi-wavelength light-absorption-based dual-spot aethalometers can be used to quantify the source and characteristics of BC from traffic or biomass-burning-based sources. However, aethalometer measurements are affected by artifacts such as aerosol loading and light scattering; hence, they often need correction to reduce measurement uncertainty. This work assesses the performance of the recently developed portable aethalometer (MA300, AethLabs). Due to their portability and ease of usage, MA300s can be suitable for mobile and personal exposure monitoring. Here, we evaluate BC concentration and source apportionment accuracy of three MA300 units relative to a widely used aethalometer, the AE33 (Magee Scientific). Synchronous field measurements were performed at a major traffic intersection during regular and wildfire-smoke-affected days in Vancouver, Canada. We find that MA300-reported BC mass concentrations were strongly correlated (Slope range between 0.73 and 1.01, with R2 = 0.9) compared to the reference instrument, yet there is visible instrumental variability in the normalized concentrations (5 %) across three units. The mean absolute error of MA300-reported BC concentrations ranged between 0.44–0.98 µg m−3, with the highest deviations observed in wildfire-smoke-affected polluted days. From the aerosol light absorption measurement perspective, MA300s tend to underestimate the absorption coefficients (babs) across the five wavelengths. UV channel light absorption results were subjected to the highest amount of noise and were found to be consistently underestimating in all the MA300 units, leading to systematic bias in source apportionment analysis. Absorption Ångström exponent values from the MA300 units were able to capture the variability of aerosol sources within a day, with a mean value of 1.15 during clean days and 1.46 during wildfire-smoke-affected days. We investigated the application of the latest non-linear aethalometer correction protocols in the MA300 and found that flow fluctuations enhanced noise across all channels, compared to onboard instrument correction. We also identify that the UV (λ = 370 nm) channel absorption measurements are most sensitive to instrumental artifacts during the wildfire-smoke-affected period. Hence, as an alternative to traditional UV and IR (λ = 880 nm)-based BC source apportionment methods, in this work, we tested the blue (λ = 470 nm) and IR wavelengths for BC source apportionment calculation. When the blue–IR-based source apportionment technique is adopted instead of the UV–IR, there is a 10 % (on average) decrease in the percentage difference of the apportioned components from the reference monitor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.261
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2023
Admission routes3
Has abstractyes

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