The influence of regional wind patterns on air quality during forest fires near Sydney, Australia

Storey, Michael A.; Price, Owen F.; Fox-Hughes, Paul

doi:10.1016/j.scitotenv.2023.167335

Cited by 5 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is evidence for plume lofting: radar analysis has shown plume top height to increase with the area of individual fires [13]. Secondly, there are occasions where very large regional fire areas were burning under wind patterns that shift smoke away from Sydney: e.g., easterlies blowing smoke inland (where PM probably increased at inland stations) (Figure 6b) or, in fires north of Sydney, westerlies blowing smoke directly out to sea (Figure 6a) [31]. Such scenarios may result in more pollution in other populated areas not captured in our analysis (we only used PM 2.5 in Sydney), such as Wollongong, Newcastle and Bathurst.…”

Section: Discussionmentioning

confidence: 99%

Comparing the Effects of Wildfire and Hazard Reduction Burning Area on Air Quality in Sydney

Storey,

Price

2023

Atmosphere

Self Cite

View full text Add to dashboard Cite

Landscape fires emit smoke that contains particulate matter (PM) that can be harmful to human health. Prescribed fires or hazard reduction burns (HRBs) and wildfires can substantially reduce air quality in populated areas. While HRBs reduce the size and PM output of future wildfires, they also produce PM. There is a critical question of whether conducting HRBs adds to or reduces the total PM (prescribed + wildfire PM) exposure of populations, which has important community health implications. We modelled mean 24 h PM2.5 in Sydney from fire type, 24 h active regional fire area and weather predictors. We compared HRB and wildfire area effects via an interaction between fire area and fire type, and we found that a non-linear effect of fire area was most appropriate. We conducted a trade-off analysis by using the model to predict increased HRB area scenarios and calculated the number of HRB and wildfire days with ‘Fair’ or worse air quality under each scenario. Regional HRBs and wildfire areas had similar effects on PM2.5 in Sydney. Increasing regional HRB area produced substantial increases in HRB exceedance days (>12.5 µgm−3) in Sydney but only a small reduction in wildfire exceedance days. Our results indicate that small fires in the region have a higher per-hectare impact on PM levels in Sydney, and consequently, increasing regional HRB area would result in more poor air quality days overall (HRB + wildfire days) in Sydney.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparing the Effects of Wildfire and Hazard Reduction Burning Area on Air Quality in Sydney

Storey,

Price

2023

Atmosphere

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, in the BOAS region, wildfires were influenced by forest fires in Siberia (Ponomarev et al, 2022), where the principal fire type was woody savanna/shrubs (31%). Regarding AUST, in January 2020, a significant forest fire event occurred (Storey et al, 2023), resulting in peak emission of 4.48 Tg. The primary fire types were temperate forest (24%) and savanna grassland (18%).…”

Section: Temporal Variations In Obb Carbon Emissionsmentioning

confidence: 99%

Global Emissions Inventory from Open Biomass Burning (GEIOBB): Utilizing Fengyun–3D global fire spot monitoring data

Liu,

Chen,

Shi

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Open biomass burning (OBB) significantly affects regional and global air quality, climate change, and human health. It is susceptible to fire types, including forests, shrublands, grasslands, peatlands, and croplands burning. Global high–resolution satellites can detect active fires, enabling a more accurate estimation of these emissions. In this study, we developed a global high–resolution (1×1 km) daily emission inventory associated with OBB emissions using the Chinese Fengyun–3D satellite’s global fire spot monitoring data, satellite and observational biomass data, vegetation index–derived spatiotemporal variable combustion efficiencies, and land–type–based emission factors. The average annual OBB emissions for 2020–2022 were 2,586.88 Tg C, 8841.45 Tg CO2, 382.96 Tg CO, 15.83 Tg CH4, 18.42 Tg NOX, 4.07 Tg SO2, 18.68 Tg OC, 3.77 Tg BC, 5.24 Tg NH3, 15.85 Tg NO2, 42.46 Tg PM2.5 and 56.03 Tg PM10. Specifically, taking carbon emissions as an example, the average annual OBB for 2020–2022 were 72.71 (BONA), 165.7 (TENA), 34.1 (CEAM), 42.9 (NHSA), 520.5 (Southern Hemisphere South America; SHSA), 13 (EURO), 8.4 (MIDE), 394.3 (Northern Hemisphere Africa; NHAF), 847 (Southern Hemisphere Africa; SHAF), 167.4 (BOAS), 27.9 (CEAS), 197.3 (Southeast Asia; SEAS), 13.2 (EQAS), and 82.4 (AUST) Tg. SHAF was identified as the region with the largest emissions. Notably, savanna grassland accounted for the lion’s share of total emissions, contributing to 46 %, followed by woody savanna/shrubs at 33 %. Moreover, notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August. This surge in carbon emissions is chiefly attributed to fires in the savanna grasslands, woody savanna/shrubs, and tropical forests of SHAF, SHSA, and NHAF. Fires in savanna grasslands were predominant in the NHAF, contributing to 77 % of emissions during January–April, whereas in the SEAS, woody savanna/shrubs (52 %) and tropical forests (23 %) were the primary sources. Our comprehensive high–resolution inventory of OBB emissions provides valuable insights for enhancing the accuracy of air quality modelling, atmospheric transport and biogeochemical cycle studies. The GEIOBB dataset can be downloaded at http://figshare.com with the following identifier DOI: https://doi.org/10.6084/m9.figshare.24793623 (Liu et al., 2023).

show abstract

“…Drawing upon this finding, the author recommended that more sophisticated machine learning (ML) techniques such as the Kohonen self-organising map (SOM) method (Kohonen, 2001) [31] can be useful for assessing the impact of weather and climatic conditions on local air quality in the region. The SOM method can be applied for both data classification (structure discovery) and data visualisation (interpretation of results), as was demonstrated in studies for other regions [15,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part II: Classification of Synoptic Circulation Types and Local Meteorological Patterns and Their Relation to Elevated Air Pollution in Spring and Summer

Jiang,

Riley,

Azzi

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

show abstract

The influence of regional wind patterns on air quality during forest fires near Sydney, Australia

Cited by 5 publications

References 34 publications

Comparing the Effects of Wildfire and Hazard Reduction Burning Area on Air Quality in Sydney

Comparing the Effects of Wildfire and Hazard Reduction Burning Area on Air Quality in Sydney

Global Emissions Inventory from Open Biomass Burning (GEIOBB): Utilizing Fengyun–3D global fire spot monitoring data

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part II: Classification of Synoptic Circulation Types and Local Meteorological Patterns and Their Relation to Elevated Air Pollution in Spring and Summer

Contact Info

Product

Resources

About