On-Road Remote Sensing of Automobile Emissions in the La Brea Area: Year 3, October 2003
Bibliographic record
Abstract
The University of Denver conducted a five-day remote sensing study in the La Brea, California area in October of 2003. The remote sensor used in this study measures the ratios of CO, HC, and NO to CO2 in motor vehicle exhaust. From these ratios, we calculate the percent concentrations of CO, CO2, HC and NO in the exhaust that would be observed by a tailpipe probe, corrected for water and any excess oxygen not involved in combustion. Mass emissions per mass or volume of fuel can also be determined. The system used in this study was configured to determine the speed and acceleration of the vehicle, and was accompanied by a video system to record the license plate of the vehicle.\nFive days of fieldwork, October 27-31, 2003, were conducted as vehicles entered I-10 eastbound frontage road from La Brea Blvd. in west Lost Angles basin. A database was compiled containing 25,847 records. Of these records, the State of California provided make and model year information on 20,191 which contained valid measurements for at least CO and CO2, and most contained valid measurements for HC and NO as well. The database, as well as others compiled by the University of Denver, can be found at www.feat.biochem.du.edu.\nThe mean percent CO, HC, and NO were determined to be 0.34%, 0.012%, and 0.032%, respectively. The emissions measurements in this study exhibit a gamma distribution, with the dirtiest 10% of the measurements responsible for 72.2%, 60.3%, and 59.3% of the CO, HC, and NO emissions, respectively. The HC readings contain a 35 ppm offset, which has been used to reduce all of the measured HC values for comparisons.\nVehicle emissions as a function of vehicle specific power revealed that NO emissions show a flat dependence on specific power when speed and acceleration are measured after emissions. This is quite possibly a result of increased CO emissions in the same VSP range. HC emissions show a negative dependence on specific power – the expected trend. CO emissions show a positive dependence on specific power in the range from 5 to 30 kW/tonne.\nUsing vehicle specific power, the emissions from the vehicle fleet measured in 2003 were adjusted to match the vehicle driving patterns of the fleet measured in 1999. After doing so, it was seen that the emissions measured in the current year are lower than those measured during 1999. Model year adjustments gave equivocal results.\nA new analysis looked at vehicle emission levels as a function of the type of transmission the vehicle uses. It suggests that when comparing emissions between E-23 sites one may need to consider transmission type in addition to age and vsp. Since, even after age adjustments are made, manual transmission equipped vehicles at La Brea had more than twice the average CO, 40% higher HC and 20% higher NO emissions.\nAn analysis of high emitting vehicles showed that there is considerable overlap of CO and HC high emitters, for instance 3.7% of the measurements contribute 36% of the total CO and 35% of the total HC. The noise levels in the CO, HC and NO measurement channels were determined to be within acceptable limits that were minimal when compared to the standard error of the mean of the measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".