Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 2: Analysis of the biomass burning contribution and the modern carbon fraction
Bibliographic record
Abstract
Abstract. Submicron aerosol was analyzed during the MILAGRO field campaign in March 2006 at the T0 urban supersite in Mexico City with a High-Resolution Aerosol Mass Spectrometer (AMS) and complementary instrumentation. Positive Matrix Factorization (PMF) of high resolution AMS spectra identified a biomass burning OA (BBOA) component, which includes several large plumes that appear to be from forest fires within the region. Here, we show that the AMS BBOA concentration at T0 correlates with fire counts in the vicinity of Mexico City and that most of the BBOA variability is captured when the FLEXPART model is used for the dispersion of fire emissions as estimated from satellite fire counts. The resulting FLEXPART fire impact index correlates well with the observed BBOA, CH3CN, levoglucosan, and potassium, indicating that wildfires in the region surrounding Mexico City are the dominant source of BBOA at T0 during MILAGRO. The impact of distant BB sources such as the Yucatan is small during this period. All fire tracers are correlated, with BBOA and levoglucosan showing little background, acetonitrile having a well-known tropospheric background of ~100–150 ppt, and PM2.5 potassium having a background of ~160 ng m−3 (two-thirds of its average concentration), which does not appear to be related to BB sources. We define two high fire periods based on satellite fire counts and predicted fire impacts. We then compare these periods with a low fire period when the impact of regional fires is about a factor of 5 smaller. Fire tracers are very elevated in the high fire periods whereas tracers of urban pollution do not change between these periods. Dust is also elevated during the high BB period but this appears to be coincidental due to the drier conditions and not driven by direct dust emission from the fires. The AMS oxygenated OA factor (OOA, mostly secondary OA or SOA) does not show an increase during the fire periods or a correlation with fire counts, FLEXPART-predicted fire impacts or fire tracers, indicating that it is dominated by urban and/or regional sources and not by the fires near the MCMA. A new 14C aerosol dataset is presented. Both this new and a previously published dataset of 14C analysis suggest a similar BBOA contribution as the AMS and chemical mass balance (CMB), resulting in 15% higher modern carbon during the high vs. low regional fire periods. The new dataset has ~15% more fossil carbon on average than the previously published one, and possible reasons for this discrepancy are discussed. During the low regional fire period, 37% of organic carbon (OC) and 30% total carbon (TC) are from modern sources, suggesting the importance of urban and regional modern carbon sources other than the fires, such as food cooking and regional biogenic SOA. Overall, the fires from the region near the MCMA are estimated to contribute 15–23% of the OA and 7–9% of the fine PM at T0 during MILAGRO, and 2–3% of the fine PM as an annual average. The 2006 MCMA emissions inventory contains a substantially lower impact of the forest fire emissions, although a fraction of these emissions occur just outside of the MCMA inventory area. The ambient BBOA/ΔCH3CN ratio is much higher in the afternoon when the wildfires are most intense than during the rest of the day, which may explain some disagreements between BB impacts from afternoon aircraft flights and those from 24-h ground measurements. Finally, we show that there are large differences in the contributions of the different OA components to the surface concentrations vs. the integrated column amounts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".