Department of Environment and Natural Resources

Rosa damascena essential oil (EO) of Kelaat M’gouna region was investigated for volatile composition, antioxidant activity, and antibacterial activity in this study. The EO yield was 0.05%, and gas chromatography-tandem mass spectrometry (GC/MS-MS) identified 57 compounds that represented over 99.95% of the total EO composition. Antioxidant activity was determined by DPPH, ABTS, and FRAP assays, showing good free radical scavenging activity as evidenced by an IC50 value of 454.68±10 μg/mL. Antibacterial activity was assessed through agar diffusion test, measurement of inhibition zone, and testing of minimum bactericidal concentration (MBC) and minimum inhibitory concentration (MIC). Gram-negative bacteria were less sensitive, with an inhibition zone of 9.83 to 11.67 mm, while Gram-positive bacteria were more sensitive, with an inhibition zone of 15.67 to 15.83 mm. The oil was found to possess antibacterial activity against Staphylococcus aureus , Micrococcus luteus , and Bacillus subtilis with MBC values of 400, 600, and 1000 μg/mL, and MIC values of 65, 62.5, and 125 μg/mL, respectively. These findings indicate the potential of Rosa damascena EO as a natural antimicrobial and antioxidant agent for application in the cosmetic and food industries. The results further suggest that incorporation of this oil with antibiotics could reduce the amount needed to treat nosocomial infections, perhaps limiting toxicity and treatment cost. Further studies are needed to comprehensively determine its therapeutic application.

In this study, front-face fluorescence was explored as a rapid, nondestructive and innovative alternative approach for detecting paraffin adulteration in beeswax. The study was performed on a raw unfiltered Lebanese beeswax and a Spanish filtered sample. Eight series of beeswax-paraffin mixtures were prepared using six different beeswax types and two are mixed samples, with paraffin added in concentrations ranging from 0% to 55% by weight. Fluorescence excitation-emission matrices were acquired in triplicate and preprocessed prior to chemometric decomposition and regression. Parallel factor decomposition revealed six significant components. The excitation and emission profiles, as well as their intensities, obtained from PARAFAC decomposition were studied. The effects caused by filtering level, different beeswax origins, and the addition of paraffin were all detected. The origin of the autofluorescence of beeswax was also examined. Partial least squares regression models were then applied to predict paraffin concentration. The model based on raw samples only yielded the best performance with an R of 96.3% and a satisfactory prediction error (RMSEV = 4.916). The model applied to both the mixed and raw samples and that applied to all samples also gave satisfactory correlations (R = 95.2 and 95.5%, respectively). This work demonstrates that FFFS has strong potential to offer rapid detection of paraffin adulteration and can be applied to raw beeswax. This method can also be used as a quality control tool for monitoring wax processing.

The National Partnership for Climate Projections (NPCP) was established as a collaborative effort of the Australian climate projections community to develop a consistent approach to deliver future climate information. As bias correction of climate model outputs is important for many applications, a NPCP bias correction intercomparison project was initiated. The first phase of the intercomparison aimed to support the production of national-scale climate projections by the Australian Climate Service. It focused on five methods – Equidistant Cumulative Density Function matching (ECDFm), Quantile Matching for Extremes (QME), Quantile Delta Change (QDC), N-Dimensional Multi-Variate Bias Correction (MBCn) and Multivariate Recursive Nesting Bias Correction (MRNBC) – and applied them to daily timescale Coordinated Regional Climate Downscaling Experiment (CORDEX) data produced by NPCP partner organisations. Each method was assessed over a calibration period and also via cross-validation on several metrics relating to the temperature and precipitation climatology, variability, distribution, extremes and trends. The best-performing bias correction methods were QME and MRNBC. The ECDFm method also performed well on most metrics, but under certain circumstances it could dramatically increase the model bias. The QDC method is a delta change method (i.e. it perturbs the observations rather than correcting model data) and compared very favourably to the four bias correction methods. The QME, MRNBC and QDC methods were subsequently used by the Australian Climate Service to produce climate projections datasets for Australia.

Avian paramyxovirus 4 (APMV4) is detected sporadically in wild birds and poultry, globally. While predominantly detected in waterfowl, the natural reservoir and ecology of APMV4 remains unclear. Herein we report historical and contemporary detections of APMV4 in Australia, and through sequencing demonstrate the likely long-term presence in a diversity of Australian wild bird species. This is evidenced by contemporary Australian genome sequences being more similar to historical Australian genomes rather than contemporary Asian genomes and that these genomes form a single lineage. This suggests that APMV4 has been circulating on the continent for decades. While APMV4 has demonstrable, albeit sporadic, impact on poultry globally, there has never been a report of this virus causing disease in poultry in Australia, suggesting it likely continues to pose a low risk for the poultry industry. As wild birds serve as natural reservoirs for numerous viruses of potential concern to poultry, dedicated surveillance is critical for revealing the risk profile of wild bird viruses, such as APMV4, to poultry.

Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements. Recent Earth observation foundation models provide globally consistent geospatial representations derived from diverse multimodal datasets, offering a potential pathway toward scalable biomass monitoring. Here, we evaluate Google Satellite Embeddings (GSE), generated by the AplphaEarth Foundation Model, for regional-scale AGB estimation across diverse temperate forest ecosystems in the northeastern United States. We integrated annual GSE observations, airborne LiDAR, and continuous forest inventory measurements from the Northeastern Forest Inventory Network (NEFIN) within a machine-learning framework. Combined LiDAR-GSE models achieved an R^2 of 0.79 for AGB estimation. Capitalizing on annual GSE observations expanded the training dataset by more than tenfold through temporal growth adjustment, increasing predictive performance to R^2 = 0.82 while reducing model bias by over 70