Assessment of background particulate matter concentrations in small cities and rural locations—Prince George, Canada
Bibliographic record
Abstract
UNLABELLED: This study investigates the development and application of a simple method to calculate annual and seasonal PM2.5 and PM10 background concentrations in small cities and rural areas. The Low Pollution Sectors and Conditions (LPSC) method is based on existing measured long-term data sets and is designed for locations where particulate matter (PM) monitors are only influenced by local anthropogenic emission sources from particular wind sectors. The LPSC method combines the analysis of measured hourly meteorological data, PM concentrations, and geographical emission source distributions. PM background levels emerge from measured data for specific wind conditions, where air parcel trajectories measured at a monitoring station are assumed to have passed over geographic sectors with negligible local emissions. Seasonal and annual background levels were estimated for two monitoring stations in Prince George, Canada, and the method was also applied to four other small cities (Burns Lake, Houston, Quesnel, Smithers) in northern British Columbia. The analysis showed reasonable background concentrations for both monitoring stations in Prince George, whereas annual PM10 background concentrations at two of the other locations and PM2.5 background concentrations at one other location were implausibly high. For those locations where the LPSC method was successful, annual background levels ranged between 1.8 +/- 0.1 microg/m3 and 2.5 +/- 0.1 microg/m3 for PM2.5 and between 6.3 +/- 0.3 microg/m3 and 8.5 +/- 0.3 microg/m3 for PM10. Precipitation effects and patterns of seasonal variability in the estimated background concentrations were detectable for all locations where the method was successful. Overall the method was dependent on the configuration of local geography and sources with respect to the monitoring location, and may fail at some locations and under some conditions. Where applicable, the LPSC method can provide a fast and cost-efficient way to estimate background PM concentrations for small cities in sparsely populated regions like northern British Columbia. IMPLICATIONS: In rural areas like northern British Columbia, particulate matter (PM) monitoring stations are usually located close to emission sources and residential areas in order to assess the PM impact on human health. Thus there is a lack of accurate PM background concentration data that represent PM ambient concentrations in the absence of local emissions. The background calculation method developed in this study uses observed meteorological data as well as local source emission locations and provides annual, seasonal and precipitation-related PM background concentrations that are comparable to literature values for four out of six monitoring stations.
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 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 | 0.001 | 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) | 0.000 | 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".