Towards Deep Learning for Air Quality Monitoring

Project Member(s): Ha, Q.

Funding or Partner Organisation: NSW Department of Planning, Industry and Environment
NSW Department of Planning, Industry and Environment

Start year: 2020

Summary: This project aims to develop a deep learning scheme for air quality data for prediction of some key air pollutants in NSW given different datasets from global meteorological data, meteorological models such as CCAM, WRF, chemical transport models such as CTMs, CMAQ, WRF-Chem. These data as well as those from various sources like air quality measurements from monitoring stations, remote sensing data and population data will be made available for the PhD student to prepare data sets for the training purpose. The results obtained will be used as preliminary results for ARS to improve forecast accuracy of some key pollutants like PM2.5, PM10, NO2 , SO2 , and O3 and to support joint research publications.

FOR Codes: Engineering, Environmental Sciences, Information And Computing Sciences, Environment, Pollution and contamination not elsewhere classified , Deep learning, Air pollution modelling and control, ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS, ENVIRONMENTAL MANAGEMENT

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.