Data

Bushfire_specific_PM25_Aus_2001_2020_v1_3

Centre for Safe Air
Hanigan, Ivan ; Yuen, Cassandra ; Gopi, Karthik ; Borchers-Arriagada, Nicholas ; van Buskirk, Joseph ; Morgan, Geoffrey
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.17605/OSF.IO/WQK4T&rft.title=Bushfire_specific_PM25_Aus_2001_2020_v1_3&rft.identifier=https://doi.org/10.17605/OSF.IO/WQK4T&rft.publisher=Centre for Air pollution, energy and health Research&rft.description=This dataset contains bushfire specific daily PM2.5 predictions for Australia from 2001 to 2020 based on a random forest model. For more information and access see http://cardat.github.io/data_inventory/bushfire_specific_pm25_aus_2001_2020_v1_3.html. This is version 1.3 of the CAR Bushfire Smoke Exposure project, superseding v1.2. Additionally, a Seasonal-Trend decomposition using LOESS (STL) decomposition was calculated for years 2001-2019, using a seasonal window of 45. Due to the abnormal levels of PM2.5 in January 2020 from severe bushfires, the year 2020 was not included in the STL calculation. However, the decomposition was extrapolated to 2020 using 2019 seasonal and trend values. Flags for classifying days of high PM2.5 (e.g. dust event, bushfire event) were taken from v1.2. As the v1.2 prediction grid was slightly smaller than that of v1.3, an extrapolation was performed on the flags to cover coastal pixels. The creator acknowledges the Sydney Informatics Hub and the University of Sydney’s high performance computing cluster Artemis for providing the high performance computing resources that have contributed to the production of this dataset.&rft.creator=Hanigan, Ivan &rft.creator=Yuen, Cassandra &rft.creator=Gopi, Karthik &rft.creator=Borchers-Arriagada, Nicholas &rft.creator=van Buskirk, Joseph &rft.creator=Morgan, Geoffrey &rft.date=2023&rft.coverage=Australia&rft.coverage=northlimit=-9.1422; southlimit=-43.7405; westlimit=96.8169; eastLimit=167.998&rft_rights=v1.3 is available to Peter Gibson and anyone in CAR. Data available to others on request. Access to these data is mediated through the CARDAT team and requires permission from the Data Owner.&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

view details

v1.3 is available to Peter Gibson and anyone in CAR. Data available to others on request. Access to these data is mediated through the CARDAT team and requires permission from the Data Owner.

Access:

Restrictions apply

Brief description

This dataset contains bushfire specific daily PM2.5 predictions for Australia from 2001 to 2020 based on a random forest model. For more information and access see http://cardat.github.io/data_inventory/bushfire_specific_pm25_aus_2001_2020_v1_3.html. This is version 1.3 of the CAR Bushfire Smoke Exposure project, superseding v1.2. Additionally, a Seasonal-Trend decomposition using LOESS (STL) decomposition was calculated for years 2001-2019, using a seasonal window of 45. Due to the abnormal levels of PM2.5 in January 2020 from severe bushfires, the year 2020 was not included in the STL calculation. However, the decomposition was extrapolated to 2020 using 2019 seasonal and trend values. Flags for classifying days of high PM2.5 (e.g. dust event, bushfire event) were taken from v1.2. As the v1.2 prediction grid was slightly smaller than that of v1.3, an extrapolation was performed on the flags to cover coastal pixels. The creator acknowledges the Sydney Informatics Hub and the University of Sydney’s high performance computing cluster Artemis for providing the high performance computing resources that have contributed to the production of this dataset.

Data time period: 2001-01-01 to 2020-06-30

This dataset is part of a larger collection

Click to explore relationships graph

167.998,-9.1422 167.998,-43.7405 96.8169,-43.7405 96.8169,-9.1422 167.998,-9.1422

132.40745,-26.44135

text: Australia

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers