Investigating the effect of spatial patterns of artificial impervious surface on PM <sub>2.5</sub> at the intra-urban scale
Bibliographic record
Abstract
Artificial impervious surface, as the hallmark of urbanization, contributes to urban development but simultaneously leads to urban PM2.5 pollution. However, how artificial impervious surface affected PM2.5 at the intra-urban scale was rarely investigated. By using remote-sensing techniques to derive PM2.5 concentrations and dividing urban areas into two categories with different urban development situation (areas with drastic expansion of artificial impervious surface (DEA) and areas with slight or no changes of artificial impervious surface (SNCA)), we provided a new perspective for investigating the effects of spatial patterns of artificial impervious surface on PM2.5. First, based on multi-source predictors, a two-staged XGBoost model was adopted to derive PM2.5 concentrations. Then, after delineating DEA and SNCA based on the variations of artificial impervious surface and selecting indicators, the overall and local effects of spatial patterns of artificial impervious surface were respectively explored by Original Least Square regression (OLS) and Geographically Weighted Regression (GWR). The results demonstrated that, compared with in SNCA, population and economic development contributed to higher levels of PM2.5 concentrations in DEA. Additionally, in DEA, high PM2.5 concentrations tended to occur in areas with artificial impervious surfaces exhibiting high coverage, complex shape, and uncompact distribution. In SNCA, among all landscape spatial patterns, the effect of artificial impervious surface coverage was the strongest. Spatially, artificial impervious surface coverage and economic development had much more significant impacts on PM2.5 than other indicators. Among these two indicators, the exacerbating effect of artificial impervious surface coverage on PM2.5 pollution was more pronounced in SNCA, while economic development notably brought high PM2.5 concentrations in the suburban districts of DEA. In general, the framework proposed in this study advanced the understanding of the effects of artificial impervious surface on PM2.5 and the findings are valuable for the mitigation of PM2.5 pollution at the intra-urban scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".