Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.

Polymer leachates emulate naturally derived fluorescent dissolved organic matter: Understanding and managing sample container interferences.

Fluorescence spectroscopy has become a fundamental tool for the qualitative and quantitative fingerprinting of dissolved organic matter. Due to the inherent sensitivity of the technique, a strict sampling protocol should be followed to ensure sample integrity. A literature survey conducted as part of this research determined that 27% of fluorescence sampling has been conducted in polymeric containers, while 52% did not report. Given the potential for fluorescence leachates to arise from plastics commonly used in sampling bottles, a systematic laboratory investigation was undertaken to assess the likelihood of leachate contamination and consequent interferences. It was observed that characteristic fluorescent dissolved organic matter (FDOM) leachates from standard polypropylene sampling containers were produced at environmentally relevant peaks, Peak T (λEx/λEm: 250/349 nm) and B (λEx/λEm: 250/306 nm), commonly attributed to tryptophan-like and tyrosine-like molecular origins. Leachate fluorescence and concentration generally increased with elevated storage temperatures (>4 °C), sample acidification, container steam sterilisation and in new containers, with variability across different manufactured batches. For example, at ambient storage temperatures, the highest observed leachate intensity could contribute an error equivalent to as much as 98% (Peak T) and 2062% (Peak B) for highly treated water or 28% (Peak T) and 398% (Peak B) for surface water. For leachates formed under typical conditions, i.e., 3-day fridge storage, this reduced to 9% (Peak T) and 15% (Peak B) or 3% (Peak T/B) for the same water samples. In addition, PP was found to be typically unsuitable for DOC measurements, except under strict conditions (well-aged containers in short term cold storage). Consequently, we demonstrate the need for container material reporting, refrigerated storage, steam sterilisation avoidance, and the importance of glass usage for low FDOM samples. Future research should investigate the potential for polymer-based pollution as a potential origin of environmentally sampled FDOM.

Expert views of climate change adaptation in least developed Asia.

Drawing primarily from original data collected from more than 100 semi-structured research interviews, this study discusses the benefits of four climate change adaptation projects being implemented in Bangladesh, Bhutan, Cambodia, and the Maldives. The article begins by explaining its research methods and selecting a sample of Global Environment Facility-Least Developed Country Fund projects being implemented in Asia to analyze. It then describes ongoing adaptation efforts in each of these four countries. It finds that projects enhance infrastructural resilience by building relevant, robust, and flexible technologies. They build institutional resilience by creating strong, permanent, legitimate organizations in place to respond to climate change issues. They promote community resilience by enhancing local ownership, building capacity, and creating networks that help ordinary people learn and adapt to climate change. We find that all four of our case studies couple adaptive improvements in technology and infrastructure with those in governance and community welfare, underscoring the holistic or systemic aspect of resilience. Our study also demonstrates the salience of a functions-based approach to resilience and adaptive capacity rather than an asset-based one.

Suppression of the barley uroporphyrinogen III synthase gene by a Ds activation tagging element generates developmental photosensitivity.

Chlorophyll production involves the synthesis of photoreactive intermediates that, when in excess, are toxic due to the production of reactive oxygen species (ROS). A novel, activation-tagged barley (Hordeum vulgare) mutant is described that results from antisense suppression of a uroporphyrinogen III synthase (Uros) gene, the product of which catalyzes the sixth step in the synthesis of chlorophyll and heme. In homozygous mutant plants, uroporphyrin(ogen) I accumulates by spontaneous cyclization of hydroxyl methylbilane, the substrate of Uros. Accumulation of this tetrapyrrole intermediate results in photosensitive cell death due to the production of ROS. The efficiency of Uros gene suppression is developmentally regulated, being most effective in mature seedling leaves compared with newly emergent leaves. Reduced transcript accumulation of a number of nuclear-encoded photosynthesis genes occurs in the mutant, even under 3% light conditions, consistent with a retrograde plastid-nuclear signaling mechanism arising from Uros gene suppression. A similar set of nuclear genes was repressed in wild-type barley following treatment with a singlet oxygen-generating herbicide, but not by a superoxide generating herbicide, suggesting that the retrograde signaling apparent in the mutant is specific to singlet oxygen.