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Many antibiotic resistance genes (ARGs) conferring resistance to a broad range of antibiotics have often been detected in aquatic environments such as untreated and treated wastewater, river and surface water. ARG proliferation in the aquatic environment could depend upon various factors such as geospatial variations, the type of aquatic body, and the type of wastewater (untreated or treated) discharged into these aquatic environments. Likewise, the strong interconnectivity of aquatic systems may accelerate the spread of ARGs through them. Hence a comparative and a holistic study of different aquatic environments is required to appropriately comprehend the problem of antibiotic resistance. Many studies approach this issue using molecular techniques such as metagenomic sequencing and metagenomic data analysis. Such analyses compare the broad spectrum of ARGs in water and wastewater samples, but these studies use comparisons which are limited to similarity/dissimilarity analyses. However, in such analyses, the discriminatory ARGs (associated ARGs driving such similarity/ dissimilarity measures) may not be identified. Consequentially, the reason which drives the dissimilarities among the samples would not be identified and the reason for antibiotic resistance proliferation may not be clearly understood. In this study, an effective methodology, using Extremely Randomized Trees (ET) Algorithm, was formulated and demonstrated to capture such ARG variations and identify discriminatory ARGs among environmentally derived metagenomes. In this study, data were grouped by: geographic location (to understand the spread of ARGs globally), untreated vs. treated wastewater (to see the effectiveness of WWTPs in removing ARGs), and different aquatic habitats (to understand the impact and spread within aquatic habitats). It was observed that there were certain ARGs which were specific to wastewater samples from certain locations suggesting that site-specific factors can have a certain effect in shaping ARG profiles. Comparing untreated and treated wastewater samples from different WWTPs revealed that biological treatments have a definite impact on shaping the ARG profile. While there were several ARGs which got removed after the treatment, there were some ARGs which showed an increase in relative abundance irrespective of location and treatment plant specific variables. On comparing different aquatic environments, the algorithm identified ARGs which were specific to certain environments. The algorithm captured certain ARGs which were specific to hospital discharges when compared with other aquatic environments. It was determined that the proposed method was efficient in identifying the discriminatory ARGs which could classify the samples according to their groups. Further, it was also effective in capturing low-level variations which generally get over-shadowed in the analysis due to highly abundant genes. The results of this study suggest that the proposed method is an effective method for comprehensive analyses and can provide valuable information to better understand antibiotic resistance.

There are over 40,000 chemical compounds registered for use in Australia; however, very few of these have been monitored in aquatic receiving environments. Furthermore, their toxicological effects on fish species in Australia are largely unknown. Studies on trace organic contaminants (TrOCs) and endocrine active compounds (EACs) in Australia have focused on wastewater effluents, and limited knowledge exists regarding the relative contribution of different potential sources of TrOCs and EACs to the aquatic environment (e.g. pesticide run off, animal farming operations, urban stormwater, industrial inputs). This study combined chemical, in vitro, in vivo and in situ techniques to determine the extent of endocrine disruption in fish living in 73 rivers across mainland Australia. These rivers were sampled quarterly for one year to determine the chemical concentrations of 56 compounds including 42 TrOCs and 14 EACs. Monitored chemicals include: 30 pharmaceuticals, 4 personal care products, 3 industrial compounds, 7 pesticides, 10 natural hormones and 2 synthetic hormones. At least one of the 56 compounds was detected in 99% (281 of 285) of grab river water samples. On average five compounds were detected per sample, with a median of four and a maximum of 21 compounds per sample. [...]

Compositional changes which occur during decomposition of vascular plant tissues have important implications for understanding the biogeochemical cycling of organic matter in aquatic environments. In the following study, long-term changes in the chemical and isotopic composition of six different vascular plant tissues were investigated in litter-bag decomposition experiments which lasted up to four years. Analysis of the major structural polymers and a residual uncharacterized fraction during decomposition revealed the following general stability series from most to least stable: uncharacterized material > lignin ≥ bulk tissue > carbohydrates > cutin. The relative increase in uncharacterized material was substantial in herbaceous tissues and resulted from decreases in initially large, recognizable carbohydrate and cutin fractions. The persistence of lignin through advanced stages of decomposition supported the general application of lignin as a biomarker. However, molecular-level analysis of unique lignin-derived oxidation products showed that the use of lignin as a biomarker can be complicated by the presence of relatively labile ester-bound phenols. To overcome this problem, a new parameter was introduced, Λ₆, which excluded ester-bound phenols and enhanced lignin biomarker potential. Although initially more abundant than lignin, carbohydrates were relatively labile and were largely depleted in highly decomposed tissues. Analysis of neutral sugars indicated that cellulose was the most labile structural carbohydrate. In herbaceous tissues, both glucose and xylose were initially abundant and decomposed more rapidly than other neutral sugars, leading to increased yields of minor sugars, and a general trend towards compositional uniformity of neutral sugars in highly decomposed material. Photooxidation was examined as a potentially important mechanism for selective removal of specific chemical components. Detritus exposed to direct sunlight contained much higher carbohydrate yields, similar levels of cutin acids, and a complete absence of lignin. Changes in stable carbon isotope compositions (ẟ¹³C) of tissues during decomposition reflected changes in chemical composition. Shifts in ẟ¹³C of up to 2‰ were observed in tissues that became enriched in lignin. Exogenous nitrogen comprised 25-69% of the nitrogen in highly decomposed detritus. Shifts in ẟ¹⁵N signatures caused by immobilization of exogenous nitrogen correlated with periods of extensive microbial activity

Hazard assessment of pesticides in aquatic environments requires accurate predictions of persistence and compartmentalization. A strategy for developing confidence in predictive fate models, such as the Exposure Analysis Modeling System (EXAMS) and the Simplified Lake and Stream Analysis model (SLSA), is to test the models using carefully chosen chemicals in semi-controlled and field situations. An objective of this approach would be to isolate the variability in a particular fate process and thereby assess the ability of an algorithm to model the process. For example, endothall, a relatively watersoluble aquatic herbicide, has essentially a sole fate process, biotransformation. Endothall was used to test the predictive capabilities of EXAMS and SLSA and to identify sources of variance in those predictions.