Source Apportionment and Health Risk Assessment of PM2.5-bound Elements in Windsor, Ontario, Canada
Notice bibliographique
Résumé
Windsor, Ontario, Canada frequently experiences poor air quality due to local emissions and transboundary pollution inputs. This study investigates (1) the ambient concentration levels of PM2.5 mass and PM2.5-bound elements, (2) major sources of PM2.5-bound elements and source contributions, (3) sensitivity of Positive Matrix Factorization (PMF) modeling to input concentration data, (4) human health risks from inhalation exposure to PM2.5-bound elements, (5) major contributors to elemental concentrations and health risks, and (6) temporal variability of concentrations, source contributions, and health risks. Hourly concentrations of PM2.5 mass, black carbon (BC), brown carbons (BrCs), and 24 PM2.5-bound elements were continuously monitored at the Windsor West station during April 2021 ─ April 2023. USEPA’s PMF model was utilized to identify sources and quantify their contributions. USEPA’s health risk assessment approach was used to estimate total and individual lifetime cancer risks (CRs) and chronic hazard quotients (HQs) due to inhalation exposure to six and eleven elements, respectively. The two-year average PM2.5 mass concentration was 9.2 μg/m³, slightly exceeding the Canadian Ambient Air Quality Standards of 8.8 μg/m³. The total elemental concentration was 1.4 μg/m³, which accounted for 15% of PM2.5 concentration. Five PM2.5-bound element sources were resolved by the PMF modeling, (1) coal/heavy oil burning (33% of total elemental concentrations), (2) vehicular exhaust (28%), (3) metal processing (20%), (4) crustal dust (16%), and (5) vehicle tire and brake wear (3%). The three traffic-related sources (i.e., vehicular exhaust, crustal dust, and vehicle tire and brake wear) and two industrial sources (i.e., coal/heavy oil burning and metal processing) contributed nearly equally (47% vs. 53%) to total PM2.5-bound element concentration. The sensitivity analysis of the PMF modeling yielded: (1) Treatment of concentrations below the method detection limits (MDLs), i.e., leaving as is vs. replacing with ½ MDLs had negligible effects on source identification, source contribution estimations, and model performance. (2) The modeling results are not sensitive to excluding BrCs concentrations, because they are strongly correlated with BC concentrations, which were already included in the PMF modeling. (3) Conducting PMF separately for episodic events is beneficial for identifying unique sources associated with these events and improving model performance. Both the total CR (4.1×10⁻⁵) and total HQ (0.82) remained below the USEPA acceptable thresholds of 10⁻⁴ and 1, respectively. Among the five sources identified by PMF modeling, metal processing was the largest contributor to total CR (52%) and total HQ (60%), followed by coal/heavy oil combustion (19% and 16%) and vehicular exhaust (19% and 12%), and the remaining two sources, crustal dust and vehicle tire and brake wear (10% and 12%). The seasonal PM2.5 concentrations were highest in Winter, followed by Summer, Spring, and Fall. The seasonal variability of total CR and HQ was small. The hour-of-day PM2.5 showed higher concentration in the early morning and lower in the afternoon. Neither the seasonal nor the diurnal trends of most elements are similar to that of the PM2.5 mass, calling for the monitoring of PM2.5-bound elements. Among the 24 PM2.5-bound elements, the top five elements (S, Si, Fe, K, and Ca, from most to least abundant) combined contributed 95% of total elemental concentrations. However, the elements contributing most to concentrations are not the largest contributors to health risks due to different toxicities among the elements. Specifically, Cd was the largest contributor to both total cancer risk (62%) and total hazard quotient (73%) due to its high inhalation unit risk and low reference concentration, while Cd only contributed 0.5% of total elemental concentrations. Metal processing as a source contributed over half of total CR and total HQ but only 20% of elemental concentrations. Emission control measures should consider major contributors to ambient concentrations and those to health risks.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».