Research and new technologies

Stephanie Beaupark, Ngugi and University of Wollongong

Collaborative research in partnership with Australian Indigenous peoples creates an opportunity for the sharing and recognition of immense ecological and cultural knowledge that has resulted from more than 60,000 years of Indigenous custodianship of land and sea Country in Australia (Rose 1996, Rose 2000, Clarkson et al. 2017).

One successful example of knowledge sharing is demonstrated by research that came out of the Clean Air and Urban Landscapes (CAUL) hub (Paton-Walsh et al. 2019). This collaborative research investigated how Indigenous knowledge might inform the creation of a more suitable set of local ‘seasons’ for western Sydney. The research demonstrates that Indigenous ways of accumulating knowledge and adapting to the changing landscape are key to understanding and caring for Country, and in providing new framings for non-Indigenous (including western scientific) understanding of the climate (Roös 2014). A broader aim of this research was to improve understanding of the annual cycles associated with atmospheric pollutant concentrations within the region. Through the development of a set of ‘quasi seasons’, named Indigenous Knowledge Applied to Local Climatology (IKALC) seasons (Figure 49), the project was successful in using Indigenous concepts of weather and time of year for the western Sydney region to understand the times of year when meteorological conditions are most likely to result in poor air quality.

In cities, the key atmospheric pollutants responsible for poor air quality are fine particulate matter (PM_2.5), ozone, carbon monoxide (CO) and nitrogen oxides (NO_x). High atmospheric concentrations of these pollutants result in various health conditions (Barnett et al. 2006, Jerrett et al. 2009, Broome et al. 2015, Lelieveld et al. 2015, OEH 2017, OEH 2018, Hanigan et al. 2019b). In western Sydney, the meso-scale meteorology is affected by the topography of the landscape, resulting in the worst air pollution within the city. This can be due to phenomena such as cold air drainage from the mountains during colder times of the year, which traps air pollution close to the surface. This research explored the annual variability of air quality and highlighted how it is influenced by seasonal weather patterns such as temperature, rainfall and wind direction (Jiang et al. 2017, Paton-Walsh et al. 2017, Chambers et al. 2019). The allocation of the Eurocentric seasons of ‘summer’, ‘autumn’, ‘winter’ and ‘spring’ is an arbitrary division of the year and is not well aligned with synoptic-scale weather patterns of the Sydney Basin (Giblett 2012, Entwisle 2014). For example, the set annual dates of the ‘winter season’ start too late in the year to fully represent the coldest time of the year.

The set of IKALC seasons for western Sydney was developed by interviewing Indigenous Elders and knowledge holders of the area. The Darug people are the Indigenous language group located in the eastern Sydney area (Bursill et al. 2007). The research reflects perspectives of the individuals interviewed and is not intended as a complete representation of wider views of the Indigenous community of western Sydney. The project used the Indigenous perspectives and knowledge communicated throughout the study of what a ‘season’ is, and associated weather patterns, within an Indigenous cultural framework. This was combined with statistical analysis of local Bureau of Meteorology decadal-scale weather records to create the IKALC seasons.

The Indigenous co-authors communicated that precolonial Indigenous weather and seasonal knowledge are no longer applicable to the region and mostly have been lost in the past 200 years, when the landscape has changed dramatically. This resulted in a shift in focus to acknowledge that Indigenous knowledge has always been dynamic; cultural knowledge is an ongoing process of learning from the landscape, and being a strong custodian of Country requires ongoing refinement of contemporary Indigenous knowledge. These findings are complementary to western science, which constantly collects and interprets data to increase depth of environmental knowledge.

As recommended by Indigenous co-authors, the study was not restricted to western understanding and associated framings of seasonality or calendar months, resulting in a better representation of seasons for air quality. A key discovery was identification of the cold/still time of the year, occurring between 8 May and 27 July. In this period, concentrations of PM_2.5, CO and NO_x are at their maximum, and ozone is at a minimum in Chullora, Sydney. Compared with the Eurocentric seasons, this better captures the air quality in still, cold conditions from May to July, and the windy, cold conditions of August. Compared with other Australian seasonal calendars, the IKALC seasons also align with important botanical markers in the Sydney region, using qualitative evidence.

This work is incomplete because it only includes weather observations. A future direction for the research is to create a comprehensive calendar for Sydney led by the local Indigenous community. This would include all aspects of the Indigenous climate framework (Figure 50). A more complete Indigenous cultural framing of the seasons would also demonstrate the interrelationship with biological indicators, land management and language, as known within local Indigenous knowledge systems (Woodward et al. 2009, Green et al. 2010, Woodward 2010, Nuggett et al. 2011, Prober et al. 2011). Documentation of this more holistic approach to seasonal understanding could create a resource for knowledge holders to pass on knowledge for future generations of the Darug people (Green et al. 2010, Woodward 2010, Prober et al. 2011). Further, exploration of current seasonal cycles may also improve seasonal definitions over extended periods (incorporating large-scale events such as the El Niño–Southern Oscillation) (Letnic et al. 2005, Zhou et al. 2009).

Using contemporary Indigenous knowledge of Sydney has enabled better understanding of the local climatology of the Sydney Basin. This approach is essential to understanding the climate of Australia, especially in cities. The methodology used to develop the IKALC seasons for western Sydney can be applied anywhere in the world to identify and predict the times of year when meteorological conditions are likely to result in poor air quality in a region. This can be informative for public policy on suitable emissions controls.

Land use regression (LUR) techniques have been developed to predict spatial variations in nitrogen dioxide (NO₂) as an indicator of traffic-related air pollution (Hanigan et al. 2017, Cowie et al. 2019). LUR has been shown to represent up to 70% of the spatial variability in NO₂ concentrations, and can capture peak roadside concentrations as well as background concentrations (Knibbs et al. 2016). The LUR technique has also been applied to predict particulate matter concentrations away from fixed air quality monitoring sites (Dirgawati et al. 2016).

The ability to accurately predict spatial variability in air pollution has been a game changer in epidemiology studies, where location and exposure can be connected to assess disease causation. Whereas it is clear that respiratory and cardiovascular diseases are associated with air pollution, even at low levels (Salimi et al. 2018, Vander Hoorn et al. 2019), other diseases such as Parkinson’s disease are not related to air pollution exposure (Salimi et al. 2020).

The Atmospheric Integrated Research facility for Boundaries and OXidative experiments (AIRBOX) is a purpose-built shipping container housing research-grade scientific instruments for making comprehensive atmospheric measurements. AIRBOX can be transported and deployed anywhere in Australia. It has also been installed on the Australian marine research vessels, the RSV Aurora Australis and RV Investigator, on their voyages to Antarctica (Figure 51).

AIRBOX can continuously sample the atmosphere using 9 instruments in tandem, analysing trace gases; aerosol size, mass and speciation; meteorological variables; boundary layer heights; and cloud profiles. Recently, AIRBOX has been measuring biogenic volatile organic compounds in the eucalypt forests between Sydney and Wollongong, aerosol composition at Garden Island in Western Australia, formation of aerosols and cloud condensation nuclei in the Southern Ocean (McFarquhar et al. 2021), and nutrient deposition via atmospheric aerosols to the Great Barrier Reef (Chen et al. 2019, Strzelec et al. 2020).

Fixed air quality monitoring stations have scientific instruments that are calibrated to recognise national standards; they are continuously maintained, and the data quality is checked. This requires a high level of effort and financial resourcing, which means that numbers of fixed monitoring stations are limited. They are located to be representative of the air quality experienced by a whole community, but sometimes high levels of pollution may be limited to a very localised areas, or a regional town where there is no fixed monitoring site.

There are many low-cost sensors on the market that purport to measure pollutant concentrations. However, sensors are not research-grade scientific instruments, and their factory calibrations can quickly drift once in use. Their performance can also be affected by ambient temperature and relative humidity. But sensors can indicate trends in air quality, and because of their low cost can be used to develop high-density measurement networks.

Importantly, low-cost sensors can be used by the public to monitor their personal exposure to air pollution. Known as ‘citizen science’, the ability to make air quality measurements empowers affected communities and can provide additional information to the air quality monitoring stations operated by states and territories.

Researchers at Queensland’s University of Technology (QUT) evaluated the performance of low-cost sensors for a range of purposes. The research found that these sensors were unsuitable for measuring low particle concentrations in clean conditions, and poor when there is a lot of moisture in the air (high relative humidity) (Jayaratne et al. 2018). One of QUT’s recommendations was that individual sensors are calibrated according to the intended source category of particles to measured, because differences were found in the performance of the sensor when exposed to concrete dust or combustion-derived particles in the laboratory (Jayaratne et al. 2020).

Examples of applications of low-cost sensor networks are provided below.

Investigating nuisance and improving understanding

Wynnum (Queensland) is located close to the Brisbane port, where coal is transported by train from mines in southern Queensland and loaded onto ships for export. Residents were concerned about large volumes of black dust in the air, which deposited around their homes.

For 1 year commencing in December 2018, Queensland’s Department of Science and the Environment helped residents to determine whether the coal trains were the cause of the dust at Wynnum (Queensland Department of Environment and Science 2020). Residents used particulate matter (PM) sensors and collected dust for analysis by wiping down surfaces. Residents were also encouraged to keep a record of other activities, such as lawnmowing, and evidence of smoke that could affect their measurements.

PM levels were measured to be well below the National Environment Protection Measures standards across the year, although did increase with prescribed burns and bushfire smoke. The composition of dust collected from the surface wipes showed high concentrations of soil or rock particles (52%) and rubber (20%), both as a result of vehicles resuspending road dust, and tyre and brake dust. Microscopic analysis for coal particles determined that less than 1% of dust collected at Wynnum homes was from coal. Residents found learning about the relationships between wind speed and direction and fluctuations in their sensor readings very interesting, as they could relate the peaks to specific sources.

Increasing spatial coverage

During the 2018 Commonwealth Games on the Gold Coast, Queensland, Knowing our Ambient Local Air (KOALA) sensors built by QUT provided real-time evidence of the very clean conditions at 10 locations where athletes were competing. KOALAs were also deployed in the Blue Mountains and Lithgow, New South Wales, in May 2019 to capture the current air quality and study how it might be improved in future (NSW EPA 2020).

The University of Wollongong built and deployed a network of 20 sensors around the city of Liverpool, New South Wales, to determine spatial gradients in air quality (Forehead et al. 2020). The study showed elevated PM concentrations at busy roadsides, which were an order of magnitude more than measurements made by the fixed air quality monitoring station located 1.35 km away. Pedestrians are advised to consider walking routes a street back from busy main roads to reduce their exposure.

The Latrobe Valley Information Network placed 45 sensors around this industrialised region of Victoria (Figure 52). The Latrobe Valley is home to several power stations and the location of the Hazelwood mine fire in 2014. Air quality continues to be a concern for residents. The sensors measure PM and meteorological variables such as temperature and wind speed. The aim of the network is to provide an early indication of air quality problems to residents so that they can take action (shutting doors and windows, and sheltering inside).

Engaging students

Encouraging students’ interest in air quality is a welcome addition to Australian curriculums. The Schools Weather and Air Quality (SWAQ) project was instigated by the University of New South Wales to study how increased urbanisation affected local pollution in 7 schools around the suburbs of Sydney. SWAQ equipped the schools with air quality sensors to gather local data. Students were involved in designing how the data are visualised on the SWAQ data portal (Figure 53) and will analyse the data in curriculum-aligned classroom activities.

Smoke observation gadgets (SMOGs) were designed by CSIRO to be simple and robust enough for students to build themselves and deploy at home. In regional Victoria, students are measuring the local impacts of smoke from planned burning. They discovered that particle concentrations were high during burn-offs, and that high smoke levels and elevated humidity worsened their asthma symptoms. Students kept a diary of home activities such as lighting fires or barbecuing, and could see the impacts on the SMOGs immediately from the traffic-light display indicating the particle concentrations (Figure 54). Students enjoyed building the SMOG kits and taking ownership of the data analyses.