Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Five-wheel framework for system-based monitoring of heavy metal pollution in rivers

Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.     

Catalytic degradation of toxic organic dye using an aluminum-doped Cu0.4Zn0.6-xAlxFe2O4 (x = 0.0, 0.2, 0.4, 0.6) spinel ferrite in a photo-Fenton system

Catalytic activity of spinel ferrite in breaking down toxic dye materials are promising due to their uniqueness. In this study, aluminum-doped copper zinc ferrite, Cu_0.4Zn_0.6-xAl_xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6), a catalyst for toxic dye degradation is synthesized through chemical co-precipitation route. The formation of the spinel ferrite catalyst is initially confirmed by Fourier transform infrared spectra, which shows the frequency of metal-oxygen bond vibration at 539 and 427 cm⁻¹ attributed to the tetrahedral and octahedral sites respectively. Higher intensity sharp peak of X-ray diffraction for (311) plane is the evidence for the phase purity and the formation of spinel ferrite. The crystallite size is found to decrease with the increase of Al³⁺ ion. The surface structure of the obtained particles is investigated using a scanning electron microscope. Analyses of the material's magnetic characteristics using a vibrating sample magnetometer (VSM) revealed that it is, in fact, a soft magnet, as evidenced by the loop of its hysteresis, which is narrow. The catalytic degradation of methylene blue dye under the mechanism of the photo-Fenton process is studied with the obtained spinel ferrites and the result is found to be as high as 96.5%. The process follows pseudo-second order kinetics and the Langmuir isotherm.     

Comparative Response of Indigenously Developed Bacterial Consortia on Progressive Degradation of Polyhydroxybutyrate Film Composites

The global demand of bioplastics has lead to an exponential increase in their production commercially. Hence, biodegradable nature needs to be evaluated in various ecosystems viz. air, water, soil and other environmental conditions to avoid the polymeric waste accumulation in the nature. In this paper, we investigated the progressive response of two indigenously developed bacterial consortia, i.e., consortium-I (C-I: Pseudomonas sp. strain Rb10, Pseudomonas sp. strain Rb11 and Bacillus sp. strain Rb18), and consortium-II (C-II: Lysinibacillus sp. strain Rb1, Pseudomonas sp. strain Rb13 and Pseudomonas sp. strain Rb19), against biodegradation behavior of polyhydroxybutyrate (PHB) film composites, under natural soil ecosystem (in net house). The biodegraded films recovered after 6 and 9 months of incubation were analyzed through Fourier transform infrared spectroscopy and scanning electron microscopy to determine the variations in chemical and morphological parameters (before and after incubation). Noticeable changes in the bond intensity, surface morphology and conductivity were found when PHB composites were treated with C-II. These changes were drastic in case of blends in comparison to copolymer. The potential isolates not only survived, but, also, there was a significant increase in bacterial diversity during whole period of incubation. To the best of our knowledge, it is the first report which described the biodegradation potential of Lysinibacillus sp. as a part of C-II with Pseudomonas sp. against PHB film composites.     

Production of cellulases from Aspergillus niger NS-2 in solid state fermentation on agricultural and kitchen waste residues

Various agricultural and kitchen waste residues were assessed for their ability to support the production of a complete cellulase system by Aspergillus niger NS-2 in solid state fermentation. Untreated as well as acid and base-pretreated substrates including corn cobs, carrot peelings, composite, grass, leaves, orange peelings, pineapple peelings, potato peelings, rice husk, sugarcane bagasse, saw dust, wheat bran, wheat straw, simply moistened with water, were found to be well suited for the organism's growth, producing good amounts of cellulases after 96 h without the supplementation of additional nutritional sources. Yields of cellulases were higher in alkali treated substrates as compared to acid treated and untreated substrates except in wheat bran. Of all the substrates tested, wheat bran appeared to be the best suited substrate producing appreciable yields of CMCase, FPase and β-glucosidase at the levels of 310, 17 and 33 U/g dry substrate respectively. An evaluation of various environmental parameters demonstrated that appreciable levels of cellulases could be produced over a wide range of temperatures (20-50 °C) and pH levels (3.0-8.0) with a 1:1.5 to 1:1.75 substrate to moisture ratio.     

Building low-carbon and disaster-resilient communities: integrating climate mitigation and adaptation into the assessment of self-help housing design

Housing is not only one of the major sources of carbon dioxide (CO₂) emissions, but it has been increasingly vulnerable to climate-induced disasters, particularly those sheltering the urban poor. Both mitigation and adaptation measures are therefore required for the design and construction of low-income housing to encourage low carbon development and improve resilience to disasters. As self-help housing—the common type of housing for the urban poor in developing countries—is usually built by ordinary people with participatory approach, it is crucial to develop a low-carbon and disaster-resilient assessment tool for assisting them in making informed decisions during planning process. The objective of this article is to propose a new approach for developing a low-carbon and disaster-resilient assessment tool for self-help housing (LoDAT-SH) by combining opinions from experts and community residents to assign weights, identify indicators, and establish benchmarks with the aim to develop a simple, relevant, and practical tool for non-expert users like self-help residents. The application of the proposed methodology to a case study of a developing country, Thailand, shows the ability of LoDAT-SH, which contains 45 indicators in the four categories of low carbon development, disaster resilience, community participation, and financial consideration, in enabling self-help residents to assess the performance of their housing design, identify potential measures to create a low-carbon and disaster-resilient housing, and prioritize such actions. To support the creation of a low-carbon and disaster-resilient housing as the mitigation and adaptation strategy for urban development at the global level, the study suggests that the methodology of LoDAT-SH should be replicated to develop a more comprehensive assessment tool applicable for the use in self-help housing design in other developing countries, which will house about 900 million of the urban poor by 2020.     

Assessment of inherent vulnerability of forests at landscape level: a case study from Western Ghats in India

Assessment of vulnerability is an important step in building long-term resilience in the forestry sector. The objective of this paper is to present a methodological approach to assess inherent vulnerability of forests at landscape level. The approach involves use of vulnerability indicators, the pairwise comparison method, and geographic information system (GIS) tools. We apply this approach to assess the inherent vulnerability of forests of the Western Ghats Karnataka (WGK) landscape, which is a part of the Western Ghats biodiversity hotspot in India. Four vulnerability indicators, namely biological richness, disturbance index, canopy cover, and slope, are selected. We find that forests in 30, 36, 19, and 15 % grid points in this region show low, medium, high, and very high inherent vulnerability, respectively. The forest showing high and very high inherent vulnerability are mostly dry deciduous forests and plantations located largely on the eastern side of the landscape. We also find that canopy cover is one of the key indicators that determine the inherent vulnerability of forests, and natural forests are inherently less vulnerable than man-made plantations. Spatial assessment of inherent vulnerability of forests at landscape level is particularly useful for developing strategies to build resilience to current stressors and climate change in future.     

Daytime light intensity affects seasonal timing via changes in the nocturnal melatonin levels

Daytime light intensity can affect the photoperiodic regulation of the reproductive cycle in birds. The actual way by which light intensity information is transduced is, however, unknown. We postulate that transduction of the light intensity information is mediated by changes in the pattern of melatonin secretion. This study, therefore, investigated the effects of high and low daytime light intensities on the daily melatonin rhythm of Afro-tropical stonechats (Saxicola torquata axillaris) in which seasonal changes in daytime light intensity act as a zeitgeber of the circannual rhythms controlling annual reproduction and molt. Stonechats were subjected to light conditions simulated as closely as possible to native conditions near the equator. Photoperiod was held constant at 12.25 h of light and 11.75 h of darkness per day. At intervals of 2.5 to 3.5 weeks, daytime light intensity was changed from bright (12,000 lux at one and 2,000 lux at the other perch) to dim (1,600 lux at one and 250 lux at the other perch) and back to the original bright light. Daily plasma melatonin profiles showed that they were linked with changes in daytime light intensity: Nighttime peak and total nocturnal levels were altered when transitions between light conditions were made, and these changes were significant when light intensity was changed from dim to bright. We suggest that daytime light intensity could affect seasonal timing via changes in melatonin profiles. Professor Dr. E. Gwinner died on 07 September 2004.