Showing papers by "Guohe Huang published in 2018"

Insights into the Toxicity of Triclosan to Green Microalga Chlorococcum sp. Using Synchrotron-Based Fourier Transform Infrared Spectromicroscopy: Biophysiological Analyses and Roles of Environmental Factors.

Abstract: This study investigated the toxicity of triclosan to the green microalga Chlorococcum sp. under multiple environmental stressors. The interactions between triclosan and environmental stressors were explored through full two-way factorial, synchrotron-based Fourier transform infrared spectromicroscopy and principal component analyses. Phosphorus concentration, pH * phosphorus concentration, and temperature * pH * NaCl concentration were the most statistically significant factors under triclosan exposure. The variation of those factors would have a huge impact on biophysiological performances. It is interesting to find Chlorococcum sp. may become more resistant against triclosan in phosphorus-enriched environment. Besides, particular significant factors from multiple environmental stressors showed the impacts of triclosan on the corresponding response of Chlorococcum sp. owing to the specific structure and performance of biomolecular components. Moreover, two high-order interactions of temperature * pH * NaCl concentration and temperature * pH * NaCl concentration * phosphorus concentration had more contributions than others at the subcellular level, which could be attributed to the interactive complexity of biomolecular components. Due to cellular self-regulation mechanism and short exposure time, the biophysiological changes of Chlorococcum sp. were undramatic. These findings can help reveal the interactive complexity among triclosan and multiple environmental stressors. It is suggested that multiple environmental stressors should be considered during ecological risk assessment and management of emerging pollutants.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation

Abstract: Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the preand post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical preand post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions. Plain Language Summary Data assimilation techniques are recognized as a promising tool for probabilistic hydrologic predictions. And the preand post-processing of assimilation experiments play a crucial role in advancing our understanding of the nonlinear dynamic behavior of hydrologic prediction systems. This paper presents a unified computational framework that enables a systematic integration of data assimilation using the ensemble Kalman filter (EnKF) as well as statistical preand post-processing techniques, strengthening our capability in providing probabilistic streamflow predictions. Both synthetic and real data assimilation experiments are conducted to demonstrate applicability of the proposed computational framework in the Guadalupe River basin, Texas. Results verify that the preand post-processing of assimilation experiments provide meaningful insights into the potential interactions among the EnKF error parameters and those among hydrologic model parameters. In addition, the Gaussian anamorphosis establishes a seamless bridge between data assimilation and uncertainty quantification. Therefore, such a unified computational framework has significant potential for performing robust hydrologic forecasting.

The evolution of Resources Conservation and Recycling over the past 30 years: A bibliometric overview

Abstract: The aim of this study is to investigate the publication characteristics and development of Resources Conservation and Recycling (RCR) during its past 30 years. Through bibliometric analysis, this paper identified the most prolific authors/institutions/countries and the most cited articles, and tracked the dynamic evolution of hot topics. Besides, VOS viewer software was applied to visualize the collaboration network, journal co-citation network, and keywords co-occurrence network. The study revealed a positive trend in literature production of RCR. The most productive institution, is University of Utrecht in Netherlands, in terms of both total publication and total citations. Keywords frequency and keywords co-occurrence network analysis showed that the most prolific themes are corresponded to the basic aims and scope of the journal. The mainstream research in RCR focuses on recycling, waste management, sustainability, and environmental impact. Life cycle assessment, material flow analysis, and substance flow analysis are popular methods in recent years. Moreover, the emerging hot topics may attract great interest in future, including “food waste”, “carbon footprint”, “resource efficiency”, “circular economy”, “waste of electric and electronic”, “packaging waste”, and “China”. Knowing the objective bibliometric characteristics and the research topic evolution can serve as a useful reference for future studies, which may be of interest to the general audience.

Ceramic Water Filters: A Point-of-Use Water Treatment Technology to Remove Bacteria from Drinking Water in Longhai City, Fujian Province, China

Abstract: While provision of safe drinking water is considered a basic human right, there are major challenges in the developing world for its provision. The ability to deliver safe water using a cost-appropriate technology is a major aspect of the problem. One of the technologies that has the potential to contribute significantly is the ceramic water filter (CWF); however, as shown herein, there are significant differences between performance of CWFs in the laboratory and in field applications. The CWFs employed in this study (field and laboratory) have a pore fraction of 21.0 - 22.4% and an average maximum pore diameter of 5.7 - 15.2 I¼m. Field studies were completed in Longhai City, China, a rural community in southeastern China with red earth, high precipitation and intensive human/ domestic activities. During field trials, CWFs demonstrated an average removal efficiency of 94.7%, with values ranging from 75 - 100%, whereas in laboratory studies, average removal efficiency was determined to be 99.5%, with values ranging from 97.7 - 99.9%. Differences between the lab and field removal efficiencies are attributed to contamination of the filter element and receptacle by villagers during field utilization and cleaning. Effective technology transfer to the end-user is required to achieve the bacterial removal efficiency attainable by the technology itself.

A coupled dynamical-copula downscaling approach for temperature projections over the Canadian Prairies

Abstract: In this study, a coupled dynamical-copula downscaling approach was developed through integrating the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and the copula method. This approach helps to reflect detailed features at local scales based on dynamical downscaling, while also effectively simulating the interactions between large-scale atmospheric variables (predictors) and local surface variables (predictands). The performance of the proposed approach in reproducing historical climatology of the Canadian Prairies was evaluated through comparison with observations. Future climate projections generated by the developed approach were analyzed over three time slices (i.e., the 2030s, 2050s, and 2080s) to help understand the plausible changes in temperature over the Canadian Prairies in response to global warming. The results showed that there would be an apparent increasing pattern over the Canadian Prairies. The projections of future temperature over three time slices can provide decision makers with valuable information for climate change impacts assessment over the Canadian Prairies.