Urban Air Pollution Data Collection, Mapping, and Prediction Using Mobile Sensors Installed on Courier Trucks
Bibliographic record
Abstract
Particulate matter with a diameter of 2.5 microns or less (PM 2.5 ) has significant impacts on human health, making it essential to understand its spatial and temporal variations. This study focuses on developing land use regression (LUR) models and improving their performance in predicting PM 2.5 concentrations in an urban setting. In this study, air quality data were collected using a sensor on a courier truck in downtown Toronto. Extreme gradient boosting (XGBoost), a machine learning algorithm, was employed to address limitations in traditional linear regression based LUR models, incorporating predictors such as land use, meteorology, and emissions to build robust models. A total of 27 models were trained, with varying road segment lengths, predictors, and outlier treatment thresholds. Three models tested the impact of road segment length on model predictions. Eight models examined the effect of removing outliers with different thresholds, revealing that appropriate thresholds improve accuracy. Ten models assessed the addition of emission and traffic data, which did not enhance performance, likely because of overlapping effects with other predictors. In six models, time-variant predictors such as time of day, month, humidity, wind speed, temperature, and pollutant concentrations from stationary stations were included. Adding these predictors significantly improved model performance, highlighting the complex relationships in LUR models for PM 2.5 predictions and offering valuable insights for air quality assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".