Spatial Analysis of PM2.5 Data from Low-Cost Sensor Related to Economic Activities in Pekanbaru City
Bibliographic record
Abstract
Pekanbaru, a major economic hub in Sumatra, faces air quality challenges due to rapid urbanization and economic growth.PM2.5 pollution, driven by economic activities and recurring forest fires, poses significant health risks.While Indonesia's Air Pollutant Standard Index (ISPU) provides a regulatory framework, traditional air quality monitoring is limited by high costs, encouraging the use of low-cost portable sensors.This study utilizes such sensors to collect PM2.5 data from 18 locations over three days.Spatial analysis reveals stable PM2.5 patterns influenced by daily economic activities, with most areas classified as "Moderate," though some exhibit higher concentrations requiring attention.Spearman correlation analysis highlights strong links between PM2.5 levels and residential areas (r=0.75),farmland (r=0.73),healthcare centers (r=0.73),hotels (r=0.7), and commercial centers (r=0.64).Transportation also significantly impacts PM2.5, indicated by road length (r=0.65).In contrast, negative correlations with plantation areas (r=-0.63)emphasize the role of green spaces in mitigating pollution.Industrial areas (r=0.12) and terminals (r=0.33)have minimal influence, reflecting their localized nature.To reduce PM2.5 pollution, Pekanbaru should expand green spaces, regulate biomass burning, promote eco-friendly transport, and adopt balanced land-use planning.Public education is crucial to enhance air quality and protect public health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".