A Mass-Conservative Temporal Second Order and Spatial Fourth Order Characteristic Finite Volume Method for Atmospheric Pollution Advection Diffusion Problems
Bibliographic record
Abstract
In this paper, a mass-conservative temporal second order and spatial fourth order characteristic finite volume method (MC-T2S4-CFVM) for solving atmospheric pollution advection diffusion problems is developed. While the characteristics tracking is applied to treat the advection term, we use conservative interpolation to treat the advective integrals over the irregular tracking volume cells at the previous time level. A temporal second order discretization by averaging along the characteristics is proposed for the diffusion term, where the diffusion fluxes are approximated by high order spatial discrete operators that provide continuity of the discrete fluxes across the edges of volume cells and tracking volume cells. The developed characteristic finite volume method is mass conservative and has fourth order accuracy in space and second order accuracy in time. Numerical tests of Gaussian pulse moving verify the temporal and spatial accuracies of MC-T2S4-CFVM as well as the mass-conservative property. The proposed scheme has much better accuracy over the classical characteristic schemes. Computational results of transporting a square of concentration 1 show the high accuracy of MC-T2S4-CFVM in preserving mass and volume. Atmospheric pollution simulations are further carried out by using the MC-T2S4-CFVM. Test results of local emission simulation exhibit the big impact of East Asian monsoon winds on the transport of local emission. The predicted results of PM$_{2.5}$ concentrations in the realistic simulation are consistent with observed data in three metropolitan municipalities and the capitals of seven provinces in China. Investigating through the time series of wind vectors and PM$_{2.5}$ concentration, the influence of wind transport has been numerically observed on the air quality of the cities and provinces. The developed high order mass-conservative characteristic method can be used to solve the large scale atmospheric pollution problems in real-world applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".