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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. [ABSTRACT FROM AUTHOR]

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Long-term or residential services are designed to support older people who experience challenges to their physical and mental health. These services play an important role in the health and well-being of older adults who are more susceptible to problems such as malnutrition. Estimates of the significance of malnutrition require up-to-date prevalence data to inform government strategies and regulation, but these data are not currently available in Australia. The aim of this study was to collect malnutrition prevalence data on a large sample of people living in residential aged care facilities in Australia. A secondary aim was to examine the relationship between malnutrition and anthropometry (body mass index (BMI) and weight loss). This prevalence study utilised baseline data collected as part of a longitudinal study of malnutrition in 10 Residential Aged Care facilities across three states in Australia (New South Wales, South Australia, and Queensland). The malnutrition status of eligible residents was assessed by dietitians and trained student dietitians using the Subjective Global Assessment (SGA) with residents categorised into SGA-A = well nourished, SGA-B = mildly/moderately malnourished, and SGA-C = severely malnourished. Other data were extracted from the electronic record. Of the 833 listed residents, 711 residents were eligible and had sufficient data to be included in the analysis. Residents were predominantly female (63%) with a mean (SD) age of 84 (8.36) years and a mean (SD) BMI of 26.74 (6.59) kg/m2. A total of 40% of residents were categorised as malnourished with 34% (n = 241) categorised as SGA-B, and 6% (n = 42) SGA-C. Compared to the SGA, BMI and weight loss categorisation of malnutrition demonstrated low sensitivity and high specificity. These findings provide recent, valid data on malnutrition prevalence and highlight the limitations of current Australian practices that rely on anthropometric measures that under-detect malnutrition. There is an urgent need to implement a feasible aged care resident screening program to address the highly prevalent condition of malnutrition in Australia. [ABSTRACT FROM AUTHOR]

In January 2021, Australia initiated a national COVID-19 vaccine rollout strategy but faced setbacks, leading to negative press and media controversy, which may have diminished vaccine confidence. This study aimed to assess the factors influencing vaccine confidence in Australian adults (≥18 years of age) following the administration of a COVID-19 vaccine. Conducted at Blacktown Hospital, Sydney, a cross-sectional survey with 1053 respondents gauged vaccine confidence and influencing factors. The results showed overall high confidence (mean score 33/40). Trusted sources included the Australian Department of Health (77.8%), NSW Health (76.7%), and general practitioners (53.7%), while social media was distrusted (5.9%). The motivations for vaccination varied: university-educated individuals prioritised personal health (X2 = 17.81; p < 0.001), while religious and/or older respondents (≥50 years of age) emphasised community (X2 = 11.69; p < 0.001) and family protection (X2 = 17.314; p < 0.001). Multivariate logistic regression revealed use of the Australian Department of Health website as a trusted source of COVID-19 information as the strongest predictor of high confidence (>30; OR 1.43; p = 0.041), while exposure to fake news decreased confidence (OR 0.71; p = 0.025). The study underscores the importance of reliable health information sources in bolstering vaccine confidence and highlights the detrimental effects of misinformation. Promoting awareness of trustworthy health channels is crucial to combat vaccine hesitancy in Australia. [ABSTRACT FROM AUTHOR]

Climate change has resulted in a worldwide increase in intensity and frequency of extreme weather events including bushfires. Previous research has shown that communities often do not engage in disaster preparedness, even when sufficient education and resources are provided. With the projected increase in natural disasters, preparedness is paramount, and more research is needed to gain an understanding into what impacts community preparedness in the face of climate change. This study investigated one rural Australian community's preparedness for the 2019–2020 bushfires. Thirteen Australian adults who resided within a small rural community in New South Wales during the 2019–2020 bushfires participated in semi-structured interviews. Data were analysed using inductive thematic analysis. Participants reported being unprepared for the 2019–2020 bushfires and that the community has started to prepare for future bushfires. However, they also described a belief in 'climate cycles' rather than climate change, limiting engagement in preparedness for future hazards. Participants also reported that they did not talk about the 2019–2020 bushfires, although described experiencing residual anxiety. Recommendations included support needed for rural communities to help with future preparedness efforts and mental health symptoms. [ABSTRACT FROM AUTHOR]

Simple Summary: The perceptions and practices of sheep farmers with regards to their knowledge of sheep health management on their farms can have a significant impact on the overall health, welfare, and production of their animals and on the economics of their farming enterprise. District veterinary services play an important role in providing technical services and support to the farmers. Survey studies were conducted to understand and compare the practices and perceptions of sheep famers and district veterinarians (DVs) in NSW, Australia, to sheep health and its management. The findings illustrated differences between the farmer and DV perception of sheep health and diseases of greatest concern and provided valuable insights into their different observations and practices. For example, sheep producers were most concerned with parasites whereas for DVs sheep husbandry advice was most concerning. However, both groups ranked nutrition advice as the second most concerning area related to sheep health. Of particular interest were the differences in the communication methods that they prefer which, if more closely aligned, may enable more effective communication. The findings of these studies provide some guidance and recommendations for improving the management of the health, welfare, and overall economic value of sheep farming in NSW. The study objectives were to understand the practices and perceptions of sheep farmers and district veterinarians (DVs) towards sheep health management and, the impact of disease at the farm level in addition to the availability, accessibility, and use of veterinary services. Data were collected using question-based surveys, distributed online and in-person to sheep farmers (45 respondents) and DVs (25 respondents). Most farmers were male, ≥51 years old, who placed a high priority on the health and welfare of their sheep. For disease prevention most farmers vaccinated their sheep (91%) and 86.7% had a farm biosecurity plan, although its components and their application varied, e.g., the isolation of new or sick sheep. Fencing costs were most frequently identified (70.5% respondents) as a financial concern for sheep farmers. Their most common sources of information about disease control and prevention were DVs (66.7%), private veterinarians (60.0%), the internet (42.9%), rural suppliers (35.7%), and farmers/neighbours (28.6%). Fifty-eight percent of farmers reported a long distance from veterinarian services. Farmers preferred to receive information via email (77.8%), whereas 56% of DVs preferred to share information via phone call. This discrepancy presents an opportunity to align these mechanisms more closely for effective dissemination of information and increased producer engagement. [ABSTRACT FROM AUTHOR]

Dust storms and wildfires occur frequently in south-eastern Australia. Their effects on the ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon, but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans. Carbon dioxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance cycle due to dust storms and wildfires is not well known. Recent studies on the carbon emission of the 2019–2020 summer wildfires in eastern Australia indicated that this megafire event emitted approximately 715 million tonnes of CO2 (195 Tg C) into the atmosphere from burned forest areas. This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Tasman Sea due to the February 2019 dust storm event and the 2019–2020 Black Summer wildfires. Central Australia and western New South Wales were the sources of the dust storm emission (11 to 16 February 2019), and the Black Summer wildfires occurred along the coast of New South Wales and Victoria (from early November 2019 to early January 2020). The WRF-Chem model is used for dust storm simulation with the AFWA (Air Force Weather Agency of the US) dust emission version of the GOCART model, and the WRF-Chem model is used for wildfire simulation with FINN (Fire Emission Inventory from NCAR) emission data. The results show the similarities and differences in the deposition of particulate matter, phytoplankton growth and carbon reabsorption patterns in the Tasman Sea from these events. A higher rate of deposition of PM2.5 on the ocean surface corresponds to a higher rate of phytoplankton growth. Using the WRF-Chem model, during the 5-day dust storm event in February 2019, approximately ~1230 tons of total dust was predicted to have been deposited in the Tasman Sea, while ~132,000 tons of PM10 was deposited in the early stage of the wildfires from 1 to 8 November 2019. [ABSTRACT FROM AUTHOR]