Smoke Exposure (pm2.5) (Postal Code metadata ) - 2 - Smoke Exposure (pm2.5) (Postal Code metadata ) - 2

The Canadian Optimized Statistical Smoke Model (CanOSSEM) was developed by the Environmental Health Services of the BC Centre for Disease Control and used to produce estimated concentrations of fine particulate across all populated regions of Canada. The estimates are optimized for wildfire smoke through use of multiple variables that are specific to this source. NOTE: Daily data indexed to postal codes will be available shortly. Un-indexed grid files are available on request to [email protected] is a random forest machine learning model that uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentration was 2.85 µg/m3 for the entire prediction set, with over 95% of the predictions lying within an absolute difference of 5 µg/m3 from the NAPS PM2.5 measurements. The model was evaluated using 10-fold cross-validation, leave-one-region-out and leave-one-year-out cross-validation.

Champ	Valeur
Dernière modification de la donnée	18 septembre 2023
Dernière modification de la métadonnée	9 octobre 2023
Créé	18 septembre 2023
Format	PDF
Licence	License not specified
Has views	False
Id	5f75a5db-5ffe-4a29-9481-fbe10f5994c8
Package id	c1480bc3-6a98-476a-8258-46da850086bc
Position	0
State	active

Smoke Exposure (pm2.5) (Postal Code metadata ) - 2

Informations additionnelles