Spatiotemporal analysis of criteria air pollutants and volatile organic compounds from a moving vehicle
Bibliographic record
Abstract
This thesis describes the on-road analysis of criteria air pollutants (CAPs) and volatile organic compounds (VOCs) from a moving vehicle. CAPs and VOCs have numerous direct and indirect effects on the environment and public health and are generated from a variety of point and diffuse sources. The concentration of these pollutants can vary on the scale of metres and seconds due to variable emission rates of sources, meteorology, and the topography of an area. CAPs are conventionally measured on a spatial scale of tens of kilometres and one hour or longer time resolution, which limits the understanding of their impact and leaving many communities lacking information regarding their air quality. VOCs are not measured as frequently as CAPs, owing to the difficulty, challenges, and cost associated with sampling. The Mobile Mass Spectrometry Lab (MMSL) was developed to collect high geospatial (15 – 1,500 m) and temporal (1 – 10 s) resolution measurements of CAPs (O3, NOx, PM2.5), CO2, CH4, and VOCs. CAPs and greenhouse gases were monitored using standard analyzers, while VOCs were measured using a proton-transfer reaction time-of-flight mass spectrometer (PTR-MS). PTR-MS is a real-time, direct, in situ technique that can monitor VOCs in the ambient atmosphere without sample collection. The PTR-MS monitored up to mass-to-charge 330 with a sample integration time of 1 or 10 seconds and had detection limits into the low- to mid-ppt. PTR-MS is a soft ionization technique that is selective to all compounds with a proton affinity less than water, which excludes the atmospheric matrix and includes most VOCs. The measurements provided by the PTR-MS provided a rich dataset for which to develop workflow and processing methods alongside sampling strategies for the collection of high geospatial and temporal VOC data. The first on-road deployment of the MMSL was performed across the Regional District of Nanaimo and the Alberni-Clayoquot Regional District in British Columbia, Canada, from July iv 2018 – April 2019 to monitor the geospatial and temporal variation in the concentration of CAPs and VOCs. VOCs detected in the areas include hydrocarbons like toluene, C2-benzenes, and terpenes, organic acids like acetic acid, oxygenated compounds like acetone and acetaldehyde, and reduced sulfur compounds like methanethiol and dimethyl sulfide. While observed concentrations of VOCs were mostly below detection limits, concentration excursions upwards of 2,200 ppb for C2-benzenes (reported as ethylbenzene) for instance, were observed across the various communities and industries that comprise central Vancouver Island. VOCs like monoterpenes, were observed near the wood industries up to 229 ppb. Combustion related VOCs, like toluene and C2-benzenes, were often observed on major transportation corridors and was found to vary significantly between seasons, with winter measurements often exceeding those made in the summer. Reduced sulfur compounds, common components of nuisance odours, were measured around a few industries like waste management and wood industries. The second on-road deployment of the MMSL focused on the analysis of VOCs in the community around a wastewater treatment plant (WWTP) to identify the source of odours in the area. VOCs were also monitored in the odour control process of the WWTP to identify the VOCs being emitted, how much were emitted, and where potential deficiencies were in the process in a unique study. Median emission rates at the facility for methanethiol, dimethyl sulfide, and dimethyl disulfide were determined to be 100, 19, and 21 kg yr-1, respectively. VOC monitoring in the community encompassed the WWTP and the other major industries in the area, including agricultural land, a composting facility, and a marina. The highest measurements of odorous reduced sulfur compounds were observed around the WWTP, upwards of 36 ppb for methanethiol. Unsupervised multivariate analysis was performed to identify groups of VOCs present and their potential sources. Three groups were identified, one of which was related to reduced sulfur compounds. This group was observed around the WWTP, indicating that the WWTP was the likely source of malodours in the community.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".