Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles
Bibliographic record
Abstract
This study describes a significant upgrade to the particulate matter (PM) size disaggregation profile library used for preparing emissions files for the GEM-MACH (Global Environmental Multiscale-Modelling Air-quality and CHemistry) chemical transport model (CTM). This model uses a sectional (bin) approach to represent the PM size distribution, where one configuration employs 12 size bins to disaggregate PM2.5 and PM10 inventory emissions into the first 10 bins ranging from 0.01 to 10.24 μm in diameter. For the size disaggregation step, a small library of three generic PM size disaggregation profiles is currently applied for three broad source categories (area, mobile, and point). However, as might be expected, these generic profiles are not always representative: for example, emissions from two very different area sources ‒ paved road dust and residential wood combustion ‒ are disaggregated using the same generic size distribution profile. In order to improve the current small PM size disaggregation profile library, a comprehensive literature review was conducted: over 100 relevant publications were identified and PM size distribution profiles for 36 different emission source types were selected and compiled. These 36 source-type-specific PM size distribution profiles were then combined based on process type with corresponding PM speciation profiles to create a library of chemically speciated and size-resolved PM disaggregation profiles. This library can now be used by the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system for the 12-bin version of GEM-MACH to perform PM chemical speciation and size allocation in one step. The size-profile data collected and compiled in this study may also be used for emissions processing for other CTMs with a size-resolved representation of PM. Details of the compilation of the 36 PM size disaggregation profiles are discussed, and the differences in processed PM emissions based on the current and updated PM size disaggregation profile libraries are shown.Implications: A new and expanded particulate matter (PM) size disaggregation profile library covering 36 emission source types has been developed based on an extensive literature review. Its use can produce significant changes in the size allocation of bulk PM inventory emissions processed for input to size-resolved PM chemical transport models. Such models are used to predict atmospheric visibility and to simulate the interactions of aerosol particles with atmospheric radiation and with clouds. The use of more accurate, size-resolved primary PM emissions by these models should improve their predictive skill for atmospheric PM processes affecting air quality, meteorology, and climate.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".