Exposures to combustion sources near military operations in Iraq and Afghanistan using satellite observations
Bibliographic record
Abstract
BACKGROUND: U.S. military personnel deployed to Afghanistan and Iraq were stationed on bases impacted by airborne hazards including emissions from combustion sources. Due to limited environmental monitoring during military operations, exposure levels remain poorly characterized. OBJECTIVE: We used satellite observations to identify the locations and persistence of combustion sources on and near military bases in Afghanistan and Iraq from 2002 to 2012, the peak period of open-air combustion. METHODS: Daily fire detections from the Moderate Resolution Imaging Spectroradiometer (MODIS) were clustered using density-based methods to identify persistent burning within 5 km of bases. Validation was conducted using military imagery and Google Earth. A sensitivity analysis compared MODIS fire detections to those from the newer Visible Infrared Imaging Spectroradiometer (VIIRS) at a civilian burn pit in Djibouti. RESULTS: MODIS detected 285,810 fires in Iraq and 3702 in Afghanistan. Clustering identified 398 bases in Iraq and 122 in Afghanistan with burning nearby. In Iraq, persistent clusters were linked to oil and gas flares, while smaller clusters on bases in both countries were consistent with burn pits. MODIS and VIIRS both detected the Djibouti burn pit, but VIIRS recorded three times more fire detections, highlighting its sensitivity in detecting biomass and waste burning. IMPACT: This study is the first to use satellite fire detections to objectively map and quantify combustion sources at U.S. military bases in Iraq and Afghanistan during 2002-2012. By applying density-based clustering to MODIS data and validating with high-resolution imagery, we identified persistent burning patterns near and on bases. This approach overcomes the limitations of self-reported exposure data and provides a reproducible framework for assessing deployment-related combustion exposures. The findings highlight both the utility and limitations of MODIS and demonstrate the potential of satellite observations for Veteran health research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".