Estimation of daily NO2 with explainable machine learning model in China, 2007-202
Bibliographic record
Abstract
Surface nitrogen dioxide (NO2) is an effective indicator of anthropogenic combustion and is associated with regional burden of disease. Though satellite-borne column NO2 is widely used to acquire surface concentration through the integration of sophisticated models, long-term and full-coverage estimation is hindered by the incomplete retrieval of satellite data. Moreover, the mechanical relationship between surface and tropospheric NO2 is often ignored in the context of machine learning (ML) approach. Here we develop a gap-filling method to obtain full-coverage column NO2 by fusing satellite data from different sources. The surface NO2 is then estimated during 2007-2020 in China using the XGBoost model, with daily out-of-sample cross-validation (CV) R2 of 0.75 and root-mean-square error (RMSE) of 9.11 µg/m3. The back-extrapolation performance is verified through by-year CV (daily R2 = 0.60 and RMSE = 11.46 µg/m3) and external estimations in Taiwan before 2013 (daily R2 = 0.69 and RMSE = 8.59 µg/m3). We explore the variable impacts in three hotspots of eastern China through SHAP (Shapley additive explanation) values. We find the driving contributions of column NO2 to the variation of ground pollution during 2007-2020 (average SHAP = 5.09 µg/m3 compared with the baseline concentration of 33.39 µg/m3). The estimated effect is also compared with ordinary least squares (OLS) model to provide a straightforward understanding. The related health burden is further calculated by using the annual NO2. We demonstrate the employment of explainable ML model is beneficial for comprehend the coupled relationship in surface NO2 change.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".