U.S. Urban-Scale Intake Fraction of Motor Vehicle Emissions: Trends During 1950–2000
Bibliographic record
Abstract
SS4-12 Introduction: The intake fraction depends on factors such as the size of the exposed population, proximity between people and emissions, and pollutants’ environmental persistence. Previous work (Marshall et al. Atmos Environ. 2005;39:1363) suggests that for nonreactive motor vehicle emissions, urban-scale intake fraction is proportional to linear population density (LPD). LPD is the population of an urban area divided by the square root of urban land area (LPD = P/A0.5). An LPD value of 20 people per meter, for example, would mean that a 1-m wide strip of land fully traversing an urban area would contain 20 people. LPD accounts for urban-scale population and proximity, but not pollutants’ persistence. To a first approximation, long-term changes in urban-scale intake fraction of nonreactive vehicle emissions are expected to be proportional to changes in LPD. Methods: This investigation analyzed a panel dataset of LPD values by decade (1950–2000) for U.S. Census-designated Urban Areas. A Census block (or block group) is defined as “urban” if it has a population density greater than 1000 mile−2 and is surrounded by census blocks with density greater than 500 mile−2. An “urban area” is a contiguous group of urban Census blocks with a combined population of at least 50,000 people. Results: For U.S. urban areas during 1950–2000, on average, population increased by a factor of approximately 2 and land area increased by a factor of approximately 7, yielding a factor of approximately 3 decline in urban population density (“sprawl”). In specific urban areas, LPD may increase or decrease during the time considered. Surprisingly, however, the distribution of LPD values—including mean, median, and shape of the distribution—remained approximately constant. (During 1950–2000, mean LPD declined only 9%, from 24 m−1 to 22 m−1, and population-weighted LPD declined 26%, from 41 m−1 to 30 m−1.) This finding indicates that intake fraction of urban vehicle emissions may go up or down over time in specific urban areas, but to a first approximation, the distribution of values is expected to be approximately constant during 1950–2000. Discussion: These findings have important implications for exposure assessors and for urban planners. They suggest that the average exposure impact of each kilogram of urban vehicle emissions has remained roughly constant over time. They also suggest a scaling rule for how urban land area expanded (1950–2000) in response to population growth: on average, each new urban resident occupied roughly twice the land area of existing residents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".