City-wide measurement of outdoor PM2.5 and black carbon to support evidence-based environmental policy in Dhaka, Bangladesh
Bibliographic record
Abstract
Existing literature reported high concentrations of ambient particulates, specifically during the dry months, severely affecting street and construction site workers, elderly, and school-aged children in Dhaka city, Bangladesh. The available air quality data from only three continuous air monitoring stations is inadequate for any evidence-based study regarding public health outcomes. Significant knowledge gaps exist due to air monitoring networks being limited to low-cost optical sensors, key suburban areas, slum areas, and industrial areas remaining uncovered and little information about sources contributing to air pollution. Therefore, this ongoing air monitoring study aimed to conduct a city-wide measurement campaign of fine particulate matter (PM2.5) and black carbon (BC), investigate source contributors to air pollution using source apportionment analysis, and produce spatiotemporal land use regression (LUR) models to estimate PM2.5 and BC concentrations across the city. The study team has recently conducted two seasonal (two months each) air monitoring campaigns from systematically designed eight fixed sites and sixty-one rotating sites considering the major land use classes across the city domain. Sites covered different land use types such as commercial, residential, industrial, suburban, major roads, green space, and brick kilns. The significant challenges during the air monitoring campaign included high road traffic from religious congress, political protests, and waterlogging from sudden intense rainfall. Weather conditions such as high heat and heavy rain affected the functioning and performance of equipment. Besides, exposure of the field team to dengue outbreaks, particularly during the wet season, had to be dealt with. Preliminary results showed that the unadjusted concentration of PM2.5 from the Zefan sensors was substantially higher (often exceeded the WHO 24-hour standard) in the dry season compared to the wet season across the different monitoring sites. Concentrations were also higher during nighttime compared to daytime in both seasons, and this difference was much more pronounced in the dry season. The fixed site in a significant industrial area, Shyampur, showed the highest concentrations compared to the other sites during both seasons, with a dry season average of approximately 290 ug/m3 and a wet season average of about 175 ug/m3. The real-time PM2.5 data will be further validated with filter-based gravimetric measurements for quality assurance. Filters are currently being analyzed for mass, black carbon, and chemical composition in a geochemistry laboratory. Incorporating source apportionment analysis and land use-based regression models of the datasets will support source identification. This will help to improve air pollution mitigation policies and implementation plans for reducing pollution while targeting its sources. The ultimate findings of this research will be conducive to assessing public health outcomes by incorporating socio-economic, demographic, and health data with the air quality data from this study, which is much needed in formulating an improved public health policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".