In 2018, over 30,000 L of fluorine-free firefighting foam was used to extinguish an industrial warehouse fire of uncharacterized chemical and industrial waste. Contaminated firewater and runoff were discharged to an adjacent freshwater creek in Melbourne, Australia. In this study, we applied nontarget analysis using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS) to 15 surface water samples to investigate the presence of legacy, novel and emerging per-and polyfluoroalkyl substances (PFAS). We identified six novel and emerging fluorotelomer-based fluorosurfactants in the Australian environment for the first time, including: fluorotelomer sulfonamido betaines (FTABs or FTSA-PrB), fluorotelomer thioether amido sulfonic acids (FTSASs), and fluorotelomer sulfonyl amido sulfonic acids (FTSAS-So). Legacy PFAS including C₆–C₈ perfluoroalkyl sulfonic acids, C₄–C₁₀ perfluoroalkyl carboxylic acids, and perfluoro-4-ethylcyclohexanesulfonate were also detected in surface water. Of note, we report the first environmental detection of ethyl 2-ethenyl-2-fluoro-1-(trifluoromethyl) cyclopropane-1-carboxylate. Analysis of several Class B certified fluorine-free foam formulations allowed for use in Australia revealed that there was no detectable PFAS. Patterns in the homologue profiles of fluorotelomers detected in surface water are consistent with environments impacted by fluorinated aqueous film-forming foams. These results provide strong evidence that firewater runoff of stockpiled fluorinated firefighting foam was the dominant source of detectable PFAS to the surrounding environment.

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.

Antarctic trace element records could provide important insights into the impact of human activities on the environment over the past few centuries. In this study, we investigated the atmospheric concentrations of 14 representative heavy metals (Al, As, Cd, Co, Cu, Fe, K, Mg, Mn, Pb, Sb, Sr, Tl and V) from 174 samples collected in a 4-m snow pit at Dome Argus (Dome A) on the East Antarctic Plateau, covering the period from 1950 to 2016 A.D. We found great variability in the annual concentration of all metals. The crustal enrichment factors suggest that the concentrations of some heavy metals (Cd, Sb, Cu, As and Pb) were likely influenced by anthropogenic activities in recent decades. An analysis of source regions suggests that heavy metal pollution at Dome A was largely caused by human activities in Australia and South America (e.g. mining production, leaded gasoline). Based on the relationship between the trace elements fluxes and sea ice concentration (SIC), sea surface temperature (SST) and annual mean air temperature at 2 m above the ground (T₂ₘ), our analysis shows that deposition and transport of atmospheric aerosol at Dome A were influenced by circum-Antarctic atmospheric circulations.

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH₄ and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

An industrial warehouse illegally storing a large quantity of unknown chemical and industrial waste ignited in an urban area in Melbourne, Australia. The multiday fire required firefighters to use large amounts of fluorine-free foam that carried contaminated firewater runoff into an adjacent freshwater creek. In this study, the occurrence and fate of 42 per- and polyfluoroalkyl substances (PFASs) was determined from triplicate surface water samples (n = 45) from five locations (upstream, point-source, downstream; 8 km) over three sampling campaigns from 2018 to 2020. Out of the 42 target PFASs, perfluorocarboxylates (PFCAs: C4–C14), perfluoroalkane sulfonates (PFSAs: C4–C10), and perfluoroalkyl acid precursors (e.g. 6:2 fluorotelomer sulfonate (6:2 FTSA)) were ubiquitously detected in surface waters (concentration ranges: <0.7–3000 ng/L). A significant difference in ΣPFAS concentration was observed at the point-source (mean 5500 ng/L; 95% CI: 4800, 6300) relative to upstream sites (mean 100 ng/L; 95% CI: 90, 110; p ≤ 0.001). The point-source ΣPFAS concentration decreased from 5500 ± 1200 ng/L to 960 ± 42 ng/L (−83%) after two months and to 430 ± 15 ng/L (−98%) two years later. 6:2 FTSA and perfluorooctanesulfonate (PFOS) dominated in surface water, representing on average 31% and 20% of the ΣPFAS, respectively. Emerging PFASs including a cyclic perfluoroalkanesulfonate (PFECHS) and a C4 perfluoroalkane sulfonamide (FBSA) were repeatedly present in surface water (concentration ranges <0.3–77 ng/L). According to the updated Australian PFAS guidelines for ecological conservation, the water samples collected at the time of monitoring may have posed a short-term risk to aquatic organisms in regard to PFOS levels. These results illustrate that acute high dose exposure to PFASs can result from industrial fires at sites storing or stockpiling PFAS-based waste products. Continued monitoring will be crucial to evaluate potential long-term risk to wildlife in the region.

Evidence about the adverse effects of methamphetamine (METH) on invertebrates is scarce. Hence, C. elegans, a representative invertebrate model, was exposed to METH at environmental levels to estimate chronic and transgenerational toxicity. The results of chronic exposure were integrated into an underlying toxicity framework of METH in invertebrates (e.g., benthos) at environmentally relevant concentrations. The induction of cellular oxidative damage-induced apoptosis and fluctuation of ecologically important traits (i.e., feeding and locomotion) might be attributed by the activation of the longevity regulating pathway regulated by DAF-16/FOXO, and detoxification by CYP family enzymes. The adverse effects to the organism level included impaired viability and decreased fecundity. The results from transgenerational exposure elucidated the cumulative METH-induced damage in invertebrates. Finally, a new risk assessment method named toxicity indicator sensitivity distribution (TISD) analysis was proposed by combining multiple toxicity indicator test data (ECₓ) to derive the hazardous concentration for 10% indicators (C₁₀) of one species. The risk quotient (RQ) values calculated by measured environmental concentrations and C₁₀ in southern China, southeastern Australia, and the western US crossed the alarm line (RQ = 5), suggesting a need for long-term monitoring.

Detailed, high resolution time-series observations were performed to investigate sources, diel cycling, natural attenuation, and loadings of dissolved trace metals/metalloids in a subtropical headwater stream draining intensive horticulture in Australia. A transect of ∼3 km away from the source (farms) showed >75% reduction in concentration and loads of most trace elements. Mercury and arsenic had elevated loads downstream relative to other elements. Hourly time-series sampling revealed elevated creek discharge at night, accompanied by elevated nickel, selenium, copper, and mercury loads. Inputs from groundwater or treated sewage used for irrigation within the catchment are likely sources. Groundwater bore and treated sewage samples were highly contaminated with either zinc, copper, or mercury. Comparisons of daily and hourly samples indicated common sampling strategies can underestimate horticultural contaminant loadings. Load estimates for mercury and copper derived from hourly samples were 1.6- to 7- fold greater than loads from daily sample data collected over 79 days with varying rainfall. These high contaminant concentrations and loads are of concern to food products receiving irrigation and protected waterbodies downstream.

This study critically evaluated the native pygmy mussel (Xenostrobus securis) as a biomonitor of the key metal contaminants in the highly urbanised Sydney Estuary, south-eastern Australia. Five metals (Cd, Cr, Cu, Pb and Zn) were identified as key contaminants, based on their enrichment factors (EFs) in the whole soft tissue of X. securis at 24 sampling sites, relative to baseline values from near-pristine reference sites in the adjacent Hawkesbury Estuary. Inverse relationships established between mussel size (dry tissue weight) and tissue concentrations of each metal were used to reduce variance (by 4-fold) among individuals; gender and reproductive status had no significant (p > 0.05) effect on tissue metal concentrations in X. securis. Metal concentrations in three environmental matrices – filtered (<0.2 μm) surface water (operationally defined as the dissolved/colloidal phase), suspended particulate matter (SPM; >0.2 μm) and surface sediment (<2 mm particle size), which are most relevant to a suspension-feeding estuarine bivalve, were also determined at each sampling site. For each of the five metals, highly significant (p < 0.01) positive linear regressions were established between metal EFs for mussel tissue and each environmental matrix. Metals in surface sediment and SPM explained 80–91% and 81–90%, respectively, of the variability in metal concentrations in mussel tissue, with filtered surface water explaining 74–86%. Cumulative mussel tissue EFs of all five metals, when regressed against each environmental matrix, showed that surface sediment concentrations explained 93% of their variability between sites, SPM 94% and filtered surface water 87–90%. Hence, X. securis very closely reflects the metal concentrations in its aquatic environment. The study provides a quality-assured benchmark of key metal contamination in the Sydney Estuary, and an appropriate methodology that may be used to discern any changes in metal contaminant status using X. securis.

Excess exposure to fluoride causes substantive health burden in humans and livestock globally. However, few studies have assessed the distribution and controls of variability of ambient background concentrations of fluoride in soil. Ambient background concentrations of fluoride in soil were collated for Greater Melbourne, Greater Geelong, Ballarat and Mitchell in Victoria, Australia (n = 1005). Correlation analysis and machine learning techniques were used to identify environmental and anthropogenic influences of fluoride variability in soil. Sub-soils (>0.3 m deep), in some areas overlying siltstone and sandstone, and to a lesser extent, overlying basalt, were naturally enriched with fluoride at concentrations above ecological thresholds for grazing animals. Soil fluoride enrichment was predominantly influenced by parent material (mineralogy), precipitation (illuviation), leaching during palaeoclimates and marine inputs. Industrial air pollution did not significantly influence ambient background concentrations of fluoride at a regional scale. However, agricultural practices (potentially the use of phosphate fertilisers) were indicated to have resulted in added fluoride to surface soils overlying sediments. Geospatial variables alone were not sufficient to accurately model ambient background soil fluoride concentrations. A multiple regression model based on soil chemistry and parent material was shown to accurately predict ambient background fluoride concentrations in soils and support assessment of fluoride enrichment in the environment.