Efficient preparation of greener N-doped carbon nanotube composites for water treatment by the microwave polyol method

N-doped carbon nanotubes have unique structures and strong interactions with metal nanoparticles due to the presence of nitrogen. There is actually a need for nanoparticles to treat water, without leaching of toxic metals. Here, we synthesized nanocomposites by deposition of Ag and Fe nanoparticles on N-doped carbon nanotubes with a surface area of 52 m²/g and 2 % N content to form nanocomposites. Transmission electron microscopy (TEM) of the nanocomposites revealed that the best dispersion of the deposited nanoparticles was achieved by the microwave-assisted polyol method. The Ag and Fe nanoparticles were indeed monodispersed and uniformly distributed on the surface of the N-doped carbon nanotubes. Deposition could be achieved in 5 min. The wet impregnation and deposition–precipitation methods gave composites with agglomerated nanoparticles. We observed that leaching of Fe and Ag into water was also influenced by the preparation method. No leaching of nanoparticles was observed when the composites were prepared by the microwave polyol method. This synthesis is therefore efficient with less energy and time. The strong metal/N-doped carbon nanotube interactions render these composites suitable for use in water purification.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.     

Male response to an aggressive visual signal, the wing wave display, in swamp sparrows

Signaling often involves complex suites of behaviors that incorporate different sensory modalities. Whatever modality is used to establish that a signal functions in communication researchers must demonstrate that receivers respond to it. The territory defense response of male swamp sparrows involves a variety of behaviors that includes both vocal and visual displays. One of these, the “wing wave” display, is a distinctive movement that predicts physical attack. Here, we use robotic taxidermic mounts paired with song to test the hypothesis that wing waving is a signal and, specifically, that male receivers respond to wing waving as a signal of aggressive intent. As predicted, subjects responded more aggressively to the mount during wing waving trials than during stationary trials. A second experiment demonstrated that this effect cannot be attributed simply to increased attention to movement. Less expectedly, subjects did not alter their own display behavior in response to wing waving as compared to a static mount. We conclude that the wing wave display in the context of singing is a signal that functions in male–male aggressive communication. Questions remain, including whether wing waving functions as a signal in the absence of singing and whether wing waving and song are redundant signals or communicate different information.     

Practitioner Insights into Weed Management on California’s Rangelands and Natural Areas

Working rangelands and natural areas span diverse ecosystems and face both ecological and economic threats from weed invasion. Restoration practitioners and land managers hold a voluminous cache of place-based weed management experience and knowledge that has largely been untapped by the research community. We surveyed 260 California rangeland managers and restoration practitioners to investigate invasive and weedy species of concern, land management goals, perceived effectiveness of existing practices (i.e., prescribed fire, grazing, herbicide use, and seeding), and barriers to practice implementation. Respondents identified 196 problematic plants, with yellow starthistle (Centaurea solstitialis L.) and medusahead (Elymus caput-medusae L.) most commonly listed. Reported adoption and effectiveness of weed management practices varied regionally, but the most highly rated practice in general was herbicide use; however, respondents identified considerable challenges including nontarget effects, cost, and public perception. Livestock forage production was the most commonly reported management goals (64% of respondents), and 25% of respondents were interested in additional information on using grazing to manage invasive and weedy species; however, 19% of respondents who had used grazing for weed management did not perceive it to be an effective tool. Across management practices, we also found common barriers to implementation, including operational barriers (e.g., permitting, water availability), potential adverse impacts, actual effectiveness, and public perception. Land manager and practitioner identified commonalities of primary weeds, management goals, perceived practice effectiveness, and implementation barriers across diverse bioregions highlight major needs that could be immediately addressed through management–science partnerships across the state’s expansive rangelands and natural areas.     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

Backwater Confluences of the Ohio River: Organic and Inorganic Fingerprints Explain Sediment Dynamics in Wetlands and Marinas

In the Ohio River (OR), backwater confluence sedimentation dynamics are understudied, however, these river features are expected to be influential on the system’s ecological and economic function when integrated along the river’s length. In the following paper, we test the efficacy of organic and inorganic tracers for sediment fingerprinting in backwater confluences; we use fingerprinting results to evidence sediment dynamics controlling deposition patterns in confluences used for wetland and marina functions; and we quantify the spatial extent of tributary drainages with wetland and marina features in OR confluences. Both organic and inorganic tracers statistically differentiate sediment from stream and river end‐members. Carbon and nitrogen stable isotopes produce greater uncertainty in fingerprinting results than inorganic elemental tracers. Uncertainty analysis of the nonconservative tracer term in the organic matter fingerprinting application estimates an apparent enrichment of the carbon stable isotopes during instream residence, and the nonconservativeness is quantified with a statistical approach unique to the fingerprinting literature. Wetland and marina features in OR confluences impact 42% and 11% of tributary drainage areas, respectively. Sediment dynamics show wetland and marina confluences experience deposition from river backwaters with longitudinally linear and nonlinear patterns, respectively, from sediment sources.     

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

A statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.     

Sanitation workers at the frontline: work and vulnerability in response to COVID-19

ABSTRACT

The absence of a vaccine and effective treatment for COVID-19 has created public panic and burdened the health systems in most countries. Along with health workers’, sanitation personnel are also working at the frontlines in the war against the disease by keeping cities clean. Sanitation workers are engaged in Drudgery, Dangerous, Dirty and Dehumanising work that makes them vulnerable for developing the chronic respiratory diseases due to the exposure of various hazardous materials and toxic gases that are emitted from the solid waste. The sanitation workers working on a contractual basis are excluded from the labour policies and welfare programs who are playing a vital role in fighting the pandemic. Women sanitation workers are even more vulnerable because most of them are non-literate, poor in financial management and under-represented in the sanitation employee’s union. The local and state governments should protect and safeguard sanitation workers by providing them with adequate protective equipment, ensure payment of paying adequate salaries and provide them with health insurance.