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Air quality special statement spring–summer 2019–20: focus areas

The air quality special statement spring–summer 2019–20 has 2 focus areas – monitoring and analysis and modelling and forecasting.

 

Monitoring and analysis

Our analysis of smoke monitoring data investigated how our air quality monitoring methods and instruments were affected by the extreme levels of pollution during the NSW bushfire period in spring summer 2019–20. Episode analyses focused on the extent and intensity of high pollution events across our monitoring networks.

Key points

  • A review of the impact of smoke from bushfires and hazard reduction burns on air quality in Sydney and surrounding regions over the past 20 years found that the spring-summer bushfires of 2019–20 was the worst event on record in terms of the intensity and duration of smoke impacts on visibility and fine particle levels.
  • The community-initiated Blue Mountains Air Watch project found that the highest particle levels during the 12-month study period, from June 2019 to May 2020, were recorded at Katoomba and Lithgow during the bushfires period in spring–summer 2019–20.
  • Our instrument performance testing found that lightweight, solar powered indicative particle monitors performed well as indicators of near real time smoke levels, compared to standard compliance particle monitors.
  • A review of particle concentrations observed across the NSW Rural Air Quality Monitoring Network and modelling of particle pathways was undertaken for an event in early January. It found that south-easterly winds transported smoke from extensive fires in south-east Australia, across rural inland New South Wales, over distances of greater than 1000 kilometres.

Case studies

Modelling and forecasting

Our smoke modelling and forecasting work brings together air quality measurements, air emissions inventories, air quality modelling and tools for data analysis, to classify the air quality over a given airshed. This allows us to predict more realistically the dynamic behaviour of smoke and dust plumes in near-real time. When air pollution levels are predicted to exceed health standards, we provide timely alerts to the public and emergency response agencies.

Key points

  • We adapt and apply a wide variety of modelling tools to estimate the relationship between pollution sources and their effects on ambient air quality and health. This allows us to generate fit-for-purpose responses for managing air pollution in the context of New South Wales.
  • Inputs for air quality models are updated continually to accommodate the varying environmental conditions in New South Wales. This involves continuous improvement in emissions inventories and meteorological inputs, as well as advances in modelling air pollutant behaviours - such as chemical transformation, transport and deposition.
  • Our air quality modelling capability supports the NSW air quality forecasting operations, policy development and research activities. This allows us to assess the potential health impacts and the resulting cost implications associated with pollution incidents or selected scenarios, facilitating decision making in air quality and public health management, such as emissions reduction strategies and air pollution emergency management.
  • Data generated in our air quality monitoring and modelling activities provide a valuable resource for evidence-based decision making in air quality management and assessment. We work with science partners to develop appropriate methods and tools to facilitate application of our data for different end user needs.

More detail

An infographic showing the NSW DPIE Air Quality Forecasting System

The NSW Air Quality Forecasting System is based on a suite of state-of-the-art models. For example: (1) Trajectory and dispersion modelling is used to forecast the likely path that air pollution plumes may follow. The model HYSLPIT in NSW (Hybrid Single-Particle Lagrangian Integrated Trajectory model) has been tailored for New South Wales to track particle pollution pathways. It combines observed air quality measurements and meteorological forecasts. (2) Numerical and statistical models use mathematical techniques to simulate the physical and chemical processes in the atmosphere that affect air pollutants as they disperse and react. These modelling tools work together to generate predictions of varying levels of air pollutants across the study area.

An infographic showing the modular emissions modelling system

Our modular emissions modelling system (MEMS) combines various modules or components for estimating emissions of pollutants, from a variety of anthropogenic and natural sources, including dust storms and fires. Scripts and tools are continually improved to further process the emissions into the format needed for air quality modelling. MEMS’ improved smoke and dust emissions modelling components generate estimates of the variable emissions of particles during bushfires and dust storms. This allows air quality forecasting to model the dynamic behaviour of smoke and dust plumes more realistically and in near-real time.