Affirmative action programs: an organizational justice perspective

Affirmative Action Programs (AAPs) are becoming increasingly common in the workplace. Typically their effectiveness has been assessed by the number of women and minorities hired and promoted. A neglected, but important measure of effectiveness is how AAPs are perceived by employees. When employees perceive that AAPs violate notions of fairness, negative attitudes and behaviors may occur and lead to decreased organizational performance. A model of perceived AAP fairness is presented using an organizational justice perspective. An initial (partial) test of the model is conducted and avenues for future research are discussed.     

Capacity assessment to secure COVID-19 waste treatment in Vietnam

Journal of Material Cycles and Waste Management - The accelerated generation of COVID-19 waste under the Delta-fuelled outbreak placed a sudden burden on waste disposal in Vietnam. To secure the...     

Face mask and medical waste generation in the City of Baguio,Philippines: its current management and GHG footprint

Journal of Material Cycles and Waste Management - The daily use of facemask to prevent virus transmission increases the negative effect on the environment because of improper waste disposal. Due to...     

港口规划环境影响评价指标体系的研究

介绍了规划环境影响评价的发展及应用,根据规划环境影响评价及港口规划的特点,对港口规划环境影响评价的内容、技术路线和指标体系的建立进行了研究,并提出港口规划的环境目标和评价指标,为我国港口规划环境影响评价技术的完善提供借鉴。     

Changes in the Distribution of Broadleaf Tree Species in the Central Part of the Southern Urals since the 1970s

Russian Journal of Ecology - Changes in the distribution of broadleaf tree species—Tilia cordata Mill., Quercus robur L., Acer platanoides L., and Ulmus glabra Huds.—in the central part...     

Effect of Anthropogenic Factors on the Ability of Narrow-skulled Voles (Lasiopodomys gregalis) to Adapt to Captive Conditions

Russian Journal of Ecology - To test the hypothesis that animals from habitats exposed to high anthropogenic pressure are more successful in adapting to captivity, immature narrow-skulled voles...     

Dynamics of Tree and Shrub Vegetation in the Eastern Sayan Mountain Tundra

Russian Journal of Ecology - Climate change entails shifts in the ranges of woody plants along both latitudinal and altitudinal gradients in the boreal forest biome. In this study,...     

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.     

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.