Characterisation of the impact of open biomass burning on urban air quality in Brisbane, Australia

Open biomass burning from wildfires and the prescribed burning of forests and farmland is a frequent occurrence in South-East Queensland (SEQ), Australia. This work reports on data collected from 10-30 September 2011, which covers the days before (10-14 September), during (15-20 September) and after (21-30 September) a period of biomass burning in SEQ. The aim of this project was to comprehensively quantify the impact of the biomass burning on air quality in Brisbane, the capital city of Queensland. A multi-parameter field measurement campaign was conducted and ambient air quality data from 13 monitoring stations across SEQ were analysed. During the burning period, the average concentrations of all measured pollutants increased (from 20% to 430%) compared to the non-burning period (both before and after burning), except for total xylenes. The average concentration of O3, NO2, SO2, benzene, formaldehyde, PM10, PM2.5 and visibility-reducing particles reached their highest levels for the year, which were up to 10 times higher than annual average levels, while PM10, PM2.5 and SO2 concentrations exceeded the WHO 24-hour guidelines and O3 concentration exceeded the WHO maximum 8-hour average threshold during the burning period. Overall spatial variations showed that all measured pollutants, with the exception of O3, were closer to spatial homogeneity during the burning compared to the non-burning period. In addition to the above, elevated concentrations of three biomass burning organic tracers (levoglucosan, mannosan and galactosan), together with the amount of non-refractory organic particles (PM1) and the average value of f60 (attributed to levoglucosan), reinforce that elevated pollutant concentration levels were due to emissions from open biomass burning events, 70% of which were prescribed burning events. This study, which is the first and most comprehensive of its kind in Australia, provides quantitative evidence of the significant impact of open biomass burning events, especially prescribed burning, on urban air quality. The current results provide a solid platform for more detailed health and modelling investigations in the future.

Fine sediment resuspension dynamics in Moreton Bay

A comprehensive field study has been undertaken to investigate sediment resuspension dynamics in the Moreton Bay, a large semi-enclosed bay situated in South East Queensland, Australia. An instrumented tripod, which housed three current meters, three OBS sensors and one underwater video camera, was used to collect the field data on tides, currents, waves and suspended sediment concentrations at four sites (Sites 1, 2, 4, and 5) in the bay. Site I was located at the main entrance, Site 2 at the central bay in deep water, and Sites 4 and 5 at two small bays in shallow water. The bed sediment was fine sand (d(50) = 0. 2 mm) at Site 1, and cohesive sediment at the other three sites. Based on the collected field data, it is found that the dominant driving forces for sediment resuspension are a combination of ocean swell and tidal currents at Site 1, tidal currents at Site 2, and wind-waves at Sites 4 and 5. The critical bed shear stress for cohesive sediment resuspension is determined as 0. 079 Pa in unidirectional flow at Site 2, and 0. 076 Pa in wave-induced oscillatory flow at Site 5.

Pseudomonas aeruginosa exhibits frequent recombination, but only a limited association between genotype and ecological setting

Pseudomonas aeruginosa is an opportunistic pathogen and an important cause of infection, particularly amongst cystic fibrosis (CF) patients. While specific strains capable of patient-to-patient transmission are known, many infections appear to be caused by unique and unrelated strains. There is a need to understand the relationship between strains capable of colonising the CF lung and the broader set of P. aeruginosa isolates found in natural environments. Here we report the results of a multilocus sequence typing (MLST)-based study designed to understand the genetic diversity and population structure of an extensive regional sample of P. aeruginosa isolates from South East Queensland, Australia. The analysis is based on 501 P. aeruginosa isolates obtained from environmental, animal and human (CF and non-CF) sources with particular emphasis on isolates from the Lower Brisbane River and isolates from CF patients obtained from the same geographical region. Overall, MLST identified 274 different sequence types, of which 53 were shared between one or more ecological settings. Our analysis revealed a limited association between genotype and environment and evidence of frequent recombination. We also found that genetic diversity of P. aeruginosa in Queensland, Australia was indistinguishable from that of the global P. aeruginosa population. Several CF strains were encountered frequently in multiple ecological settings; however, the most frequently encountered CF strains were confined to CF patients. Overall, our data confirm a non-clonal epidemic structure and indicate that most CF strains are a random sample of the broader P. aeruginosa population. The increased abundance of some CF strains in different geographical regions is a likely product of chance colonisation events followed by adaptation to the CF lung and horizontal transmission among patients.

Probability inundation map for a scenario combining storm surge and sea-level rise

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Seagrass time-series analysis products, in Moreton Bay, Australia, with links to GeoTIFFs

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (~200 km**2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

Habitat Maps derived from Point Lookout Ecological Surveys (PLEA) of the Shag Rock, Manta Ray Bommie and Flat Rock dive sites at Point Lookout, North Stradbroke Island, Australia, in 2014, in ArcGIS (shapefile) format

In 2014, UniDive (The University of Queensland Underwater Club) conducted an ecological assessment of the Point Lookout Dive sites for comparison with similar surveys conducted in 2001 - the PLEA project. Involvement in the project was voluntary. Members of UniDive who were marine experts conducted training for other club members who had no, or limited, experience in identifying marine organisms and mapping habitats. Since the 2001 detailed baseline study, no similar seasonal survey has been conducted. The 2014 data is particularly important given that numerous changes have taken place in relation to the management of, and potential impacts on, these reef sites. In 2009, Moreton Bay Marine Park was re-zoned, and Flat Rock was converted to a marine national park zone (Green zone) with no fishing or anchoring. In 2012, four permanent moorings were installed at Flat Rock. Additionally, the entire area was exposed to the potential effects of the 2011 and 2013 Queensland floods, including flood plumes which carried large quantities of sediment into Moreton Bay and surrounding waters. The population of South East Queensland has increased from 2.49 million in 2001 to 3.18 million in 2011 (BITRE, 2013). This rapidly expanding coastal population has increased the frequency and intensity of both commercial and recreational activities around Point Lookout dive sites (EPA 2008). Habitats were mapped using a combination of towed GPS photo transects, aerial photography and expert knowledge. This data provides georeferenced information regarding the major features of each of the Point Lookout Dive Sites.

Ecological assessment data of the marine flora and fauna of Point Lookout in 2014 (PLEA)

In 2014, UniDive (The University of Queensland Underwater Club) conducted an ecological assessment of the Point Lookout Dive sites for comparison with similar surveys conducted in 2001. Involvement in the project was voluntary. Members of UniDive who were marine experts conducted training for other club members who had no, or limited, experience in identifying marine organisms and mapping habitats. Since the 2001 detailed baseline study, no similar seasonal survey has been conducted. The 2014 data is particularly important given that numerous changes have taken place in relation to the management of, and potential impacts on, these reef sites. In 2009, Moreton Bay Marine Park was re-zoned, and Flat Rock was converted to a marine national park zone (Green zone) with no fishing or anchoring. In 2012, four permanent moorings were installed at Flat Rock. Additionally, the entire area was exposed to the potential effects of the 2011 and 2013 Queensland floods, including flood plumes which carried large quantities of sediment into Moreton Bay and surrounding waters. The population of South East Queensland has increased from 2.49 million in 2001 to 3.18 million in 2011 (BITRE, 2013). This rapidly expanding coastal population has increased the frequency and intensity of both commercial and recreational activities around Point Lookout dive sites (EPA 2008). Methodology used for the PLEA project was based on the 2001 survey protocols, Reef Check Australia protocols and Coral Watch methods. This hybrid methodology was used to monitor substrate and benthos, invertebrates, fish, and reef health impacts. Additional analyses were conducted with georeferenced photo transects. The PLEA marine surveys were conducted over six weekends in 2014 totaling 535 dives and 376 hours underwater. Two training weekends (February and March) were attended by 44 divers, whilst biological surveys were conducted on seasonal weekends (February, May, July and October). Three reefs were surveyed, with two semi-permanent transects at Flat Rock, two at Shag Rock, and one at Manta Ray Bommie. Each transect was sampled once every survey weekend, with the transect tapes deployed at a depth of 10 m below chart datum. Fish populations were assessed using a visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape), 5 m high and 20 m in length. Fish families and species were chosen that are commonly targeted by recreational or commercial fishers, or targeted by aquarium collectors, and that were easily identified by their body shape. Rare or otherwise unusual species were also recorded. Target invertebrate populations were assessed using visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The diver surveying invertebrates conducted a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Target impacts were assessed using a visual census along the 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The transect was surveyed via a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Substrate surveys were conducted using the point sampling method, enabling percentage cover of substrate types and benthic organisms to be calculated. The substrate or benthos under the transect line was identified at 0.5m intervals, with a 5m gap between each of the three 20m segments. Categories recorded included various growth forms of hard and soft coral, key species/growth forms of algae, other living organisms (i.e. sponges), recently killed coral, and, non-living substrate types (i.e. bare rock, sand, rubble, silt/clay).

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.