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The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5.

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long-term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the Wallops Flight Facility site via a bilateral approach of storm-scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud-process simulations through mixed-phase, light-rain, and deep-convective precipitation cases. The framework consists of creating a domain-shifted ensemble of large-scale forcing data sets, and configuring and performing cloud-process simulations with three different bulk microphysics schemes. Validation uses NASA S-band dual-POLarimetric radar observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble-based validation framework demonstrates that (a) no particular forcing or microphysics scheme outperforms the rest and (b) the skill scores of coarse- and fine-resolution ensemble simulations with different domain-shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse-resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations in support of aircraft and satellite instrument development as well as cloud-precipitation-convection parameterizations.

Microplastics are an emerging and persistent pollutant due to their threat to global ecological systems and human health. Recent studies showed that microplastics have infiltrated the remote Third Pole - the Tibetan Plateau. Here, we summarize the current evidence for microplastic pollution in the different environments (rivers/lakes, sediment, soil, ice/snow and atmosphere) of the Tibetan Plateau. We assess the spatial distribution, source, fate, and potential ecological effects of microplastics in this broad plateau. The integrated results show that microplastics were pervasive in biotic and abiotic components of the Tibetan Plateau, even at the global highest-altitude, Mt. Everest. Although the concentration of microplastics in the Tibetan Plateau was far below that found in the densely populated lowlands, it showed a higher concentration than that in the ocean system. Tourist populations are identified as a substantial source of anthropogenic plastic input rather than local residents due to the rapid development of the tourism industry. In the sparsely inhabited remote area of the Tibetan Plateau, long-range atmospheric transport facilitates allochthonous microplastic diffusion. Robust solar radiation in the Tibetan Plateau might enhanced production of secondary microplastics by weathering (UV-photooxidation) of abandoned plastic waste. A rough estimation showed that the microplastic export flux from melting glaciers was higher than that measured in most of the world's largest rivers, which affects local and downstream areas. Since the Tibetan Plateau is vital for Asian water supply and numerous endangered wildlife, the potential human and ecological risk of microplastics to these fragile ecosystems needs to be fully evaluated within the context of climate-change impacts.

Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway. We measured abundances of atmospheric-deposited microplastics of up to 105 items m-2 d-1 in dining/drinking venues, with 90% smaller than 100 µm and a dominance of amorphous fragments rather than fibers. Typical work-life scenarios projected an annual ingestion of 1.9 × 105 to 1.3 × 106 microplastics through atmospheric deposition on diet, with higher exposure rates for indoor versus outdoor dining/drinking settings. Ingestion of atmospheric-deposited microplastics through diet was similar in magnitude to presumed inhalation exposure, but 2-3 orders of magnitude greater than direct ingestion from food sources. Simple mitigation strategies (e.g., covering and rinsing dishware) can substantially reduce the exposure of atmospheric deposition microplastics through diet.

Currently, the industrial application of bioelectrochemical systems (BESs) that are incubated with natural electrochemically active microbes (EABs) is limited due to inefficient extracellular electron transfer (EET) by natural EABs. Notably, recent studies have identified several novel living biomaterials comprising highly efficient electron transfer systems allowing unparalleled proficiency of energy conversion. Introduction of these biomaterials into BESs could fundamentally increase their utilization for a wide range of applications. This review provides a comprehensive assessment of recent advancements in the design of living biomaterials that can be exploited to enhance bioelectrocatalytic performance. Further, modular configurations of abiotic and biotic components promise a powerful enhancement through integration of nano-based artificial mediators and synthetic biology. Herein, recent advancements in BESs are synthesized and assessed, including heterojunctions between conductive nanomaterials and EABs, in-situ hybrid self-assembly of EABs and nano-sized semiconductors, cytoprotection in biohybrids, synthetic biological modifications of EABs and electroactive biofilms. Since living biomaterials comprise a broad range of disciplines, such as molecular biology, electrochemistry and material sciences, full integration of technological advances applied in an interdisciplinary framework will greatly enhance/advance the utility and novelty of BESs. Overall, emerging fundamental knowledge concerning living biomaterials provides a powerful opportunity to markedly boost EET efficiency and facilitate the industrial application of BESs to meet global sustainability challenges/goals.

Currently, the industrial application of bioelectrochemical systems (BESs) that are incubated with natural electrochemically active microbes (EABs) is limited due to inefficient extracellular electron transfer (EET) by natural EABs. Notably, recent studies have identified several novel living biomaterials comprising highly efficient electron transfer systems allowing unparalleled proficiency of energy conversion. Introduction of these biomaterials into BESs could fundamentally increase their utilization for a wide range of applications. This review provides a comprehensive assessment of recent advancements in the design of living biomaterials that can be exploited to enhance bioelectrocatalytic performance. Further, modular configurations of abiotic and biotic components promise a powerful enhancement through integration of nano-based artificial mediators and synthetic biology. Herein, recent advancements in BESs are synthesized and assessed, including heterojunctions between conductive nanomaterials and EABs, in-situ hybrid self-assembly of EABs and nano-sized semiconductors, cytoprotection in biohybrids, synthetic biological modifications of EABs and electroactive biofilms. Since living biomaterials comprise a broad range of disciplines, such as molecular biology, electrochemistry and material sciences, full integration of technological advances applied in an interdisciplinary framework will greatly enhance/advance the utility and novelty of BESs. Overall, emerging fundamental knowledge concerning living biomaterials provides a powerful opportunity to markedly boost EET efficiency and facilitate the industrial application of BESs to meet global sustainability challenges/goals.

Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway. We measured abundances of atmospheric-deposited microplastics of up to 105 items m-2 d-1 in dining/drinking venues, with 90% smaller than 100 µm and a dominance of amorphous fragments rather than fibers. Typical work-life scenarios projected an annual ingestion of 1.9 × 105 to 1.3 × 106 microplastics through atmospheric deposition on diet, with higher exposure rates for indoor versus outdoor dining/drinking settings. Ingestion of atmospheric-deposited microplastics through diet was similar in magnitude to presumed inhalation exposure, but 2-3 orders of magnitude greater than direct ingestion from food sources. Simple mitigation strategies (e.g., covering and rinsing dishware) can substantially reduce the exposure of atmospheric deposition microplastics through diet.

Embedding-based retrieval (EBR) is a technique to use embeddings to represent query and document, and then convert the retrieval problem into a nearest neighbor search problem in the embedding space. Some previous works have mainly focused on representing the web page with a single embedding, but in real web search scenarios, it is difficult to represent all the information of a long and complex structured web page as a single embedding. To address this issue, we design a click feedback-aware web page summarization for multi-embedding-based retrieval (CPS-MEBR) framework which is able to generate multiple embeddings for web pages to match different potential queries. Specifically, we use the click data of users in search logs to train a summary model to extract those sentences in web pages that are frequently clicked by users, which are more likely to answer those potential queries. Meanwhile, we introduce sentence-level semantic interaction to design a multi-embedding-based retrieval (MEBR) model, which can generate multiple embeddings to deal with different potential queries by using frequently clicked sentences in web pages. Offline experiments show that it can perform high quality candidate retrieval compared to single-embedding-based retrieval (SEBR) model., Comment: Related authors disagree

Microplastics (MPs) are an environmental problem of growing concern. Aquatic sediments are considered as a final sink for MPs, but dredging can remobilize sedimentary MPs into both aquatic and terrestrial ecosystems. Although dredging is globally used for waterway deepening and ecological restoration, the environmental impacts of dredging on MP pollutants has not been previously assessed. In this study, Nile Red staining combined with micro-FTIR methods showed sediments containing high MP concentrations (6060-37610 n/kg·DW) from urban/suburban segments of a plain river network in Southeast China. The dredged sediments were stored in piles on farmlands, whereby MPs were subsequently dispersed to surrounding soils and surface waters while awaiting a permanent disposal option. MP concentrations in the soils surrounding the pile were higher in the dry season (wind dispersion), while MP concentrations in waters downstream of the piles were higher in the wet season (rainfall/runoff erosion). Whether dredge sediments are finally used to fertilize farmland, as fill material for coastal land reclamation or dumped into the ocean, MPs have the potential for remobilization into the environment causing concerns with aquatic food webs, agricultural production and human health. Therefore, disposal of dredge sediments containing MPs requires careful assessment to minimize potential environmental impacts.

This study examined the role of intermittent illumination/dark conditions coupled with MnO2-ammendments to regulate the mobility of As and Fe in flooded arsenic-enriched soils. Addition of MnO2 particles with intermittent illumination led to a pronounced increase in the reductive-dissolution of Fe(III) and As(V) from flooded soils compared to a corresponding dark treatments. A higher MnO2 dosage (0.10 vs 0.02 g) demonstrated a greater effect. Over a 49-day incubation, maximum Fe concentrations mobilized from the flooded soils amended with 0.10 and 0.02 g MnO2 particles were 2.39 and 1.85-fold higher than for non-amended soils under dark conditions. The corresponding maximum amounts of mobilized As were at least 92 % and 65 % higher than for non-amended soils under dark conditions, respectively. Scavenging of excited holes by soil humic/fulvic compounds increased mineral photoelectron production and boosted Fe(III)/As(V) reduction in MnO2-amended, illuminated soils. Additionally, MnO2 amendments shifted soil microbial community structure by enriching metal-reducing bacteria (e.g., Anaeromyxobacter, Bacillus and Geobacter) and increasing c-type cytochrome production. This microbial diversity response to MnO2 amendment facilitated direct contact extracellular electron transfer processes, which further enhanced Fe/As reduction. Subsequently, the mobility of released Fe(II) and As(III) was partially attenuated by adsorption, oxidation, complexation and/or coprecipitation on active sites generated on MnO2 surfaces during MnO2 dissolution. These results illustrated the impact of a semiconducting MnO2 mineral in regulating the biogeochemical cycles of As/Fe in soil and demonstrated the potential for MnO2-based bioremediation strategies for arsenic-polluted soils.

This study examined the role of intermittent illumination/dark conditions coupled with MnO2-ammendments to regulate the mobility of As and Fe in flooded arsenic-enriched soils. Addition of MnO2 particles with intermittent illumination led to a pronounced increase in the reductive-dissolution of Fe(III) and As(V) from flooded soils compared to a corresponding dark treatments. A higher MnO2 dosage (0.10 vs 0.02 g) demonstrated a greater effect. Over a 49-day incubation, maximum Fe concentrations mobilized from the flooded soils amended with 0.10 and 0.02 g MnO2 particles were 2.39 and 1.85-fold higher than for non-amended soils under dark conditions. The corresponding maximum amounts of mobilized As were at least 92 % and 65 % higher than for non-amended soils under dark conditions, respectively. Scavenging of excited holes by soil humic/fulvic compounds increased mineral photoelectron production and boosted Fe(III)/As(V) reduction in MnO2-amended, illuminated soils. Additionally, MnO2 amendments shifted soil microbial community structure by enriching metal-reducing bacteria (e.g., Anaeromyxobacter, Bacillus and Geobacter) and increasing c-type cytochrome production. This microbial diversity response to MnO2 amendment facilitated direct contact extracellular electron transfer processes, which further enhanced Fe/As reduction. Subsequently, the mobility of released Fe(II) and As(III) was partially attenuated by adsorption, oxidation, complexation and/or coprecipitation on active sites generated on MnO2 surfaces during MnO2 dissolution. These results illustrated the impact of a semiconducting MnO2 mineral in regulating the biogeochemical cycles of As/Fe in soil and demonstrated the potential for MnO2-based bioremediation strategies for arsenic-polluted soils.

Microplastics (MPs) are an environmental problem of growing concern. Aquatic sediments are considered as a final sink for MPs, but dredging can remobilize sedimentary MPs into both aquatic and terrestrial ecosystems. Although dredging is globally used for waterway deepening and ecological restoration, the environmental impacts of dredging on MP pollutants has not been previously assessed. In this study, Nile Red staining combined with micro-FTIR methods showed sediments containing high MP concentrations (6060-37610 n/kg·DW) from urban/suburban segments of a plain river network in Southeast China. The dredged sediments were stored in piles on farmlands, whereby MPs were subsequently dispersed to surrounding soils and surface waters while awaiting a permanent disposal option. MP concentrations in the soils surrounding the pile were higher in the dry season (wind dispersion), while MP concentrations in waters downstream of the piles were higher in the wet season (rainfall/runoff erosion). Whether dredge sediments are finally used to fertilize farmland, as fill material for coastal land reclamation or dumped into the ocean, MPs have the potential for remobilization into the environment causing concerns with aquatic food webs, agricultural production and human health. Therefore, disposal of dredge sediments containing MPs requires careful assessment to minimize potential environmental impacts.

The development of modeling technology to adequately simulate water and pesticide movement within the rice paddy environment faces several challenges. These include: (1) adequately representing ponded conditions; (2) the collection/implementation of temporal/spatial pesticide application data at field scales; (3) the integration of various mixed-landuses simulation schemes. Currently available models do not fully consider these challenges and results may not be sufficiently accurate to represent fate and transport of rice pesticides at watershed scales. Therefore, in this study, an integrated simulation system, "RiceWQ-AnnAGNPS", was developed to fully address these challenges and is illustrated in a California watershed with rice farming practices. The integrated system successfully extends field level simulations to watershed scales while considering the impact of mixed landuses on downstream loadings. Moreover, the system maintains the application information at fine spatial scales and handles varying treated paddy areas via the "split and adjust" approach. The new system was evaluated by investigating the fate and transport of thiobencarb residues in the Colusa Basin, California as a case study. Thiobencarb concentrations in both water and sediment phases were accurately captured by the calibrated RiceWQ model at the edge of field. After spatial upscaling, the integrated system successfully reflected both the seasonal pattern of surface runoff and the timing of monthly thiobencarb loadings. Incorporating future enhancements can further improve model performance by including more detailed water drainage schedules and management practices, improving the accuracy of summer runoff estimations, and incorporating a more sophisticated in-stream process module. This integrated system provides a framework for evaluating rice pesticide impacts as part of a basin level management approach to improve water quality, which can be extended to other rice agrochemicals, or other

Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

Effective Jan. 1, 2018, the California Department of Pesticide Regulation enacted a regulation regarding the use of pesticides near public K-12 schools and licensed child day care centers, including a provision that bans specific types of applications, including air-blast and air-assist, during weekday school hours (6 a.m. to 6 p.m.) to provide an additional safety margin for pesticide exposure beyond those provided by other regulations. We considered the financial effect on almond growers in nine counties, accounting for four-fifths of total almond production in 2014, if they had been unable to complete a standard spring disease management program on any buffer zone acreage. Results indicated that total annual losses for those counties if such a regulation had been in effect would have been $8.7 million, with per-acre losses ranging from 22% to over 50% of total operating costs, depending on the county. However, using a methodology that took into account historical weather and soil hydrologic group data, we estimated average annual losses in the nine counties among almond growers would have been under $0.2 million because the regulation would have affected the number of sprays completed for relatively few acres in relatively few years.

Intimate coupling of microbial extracellular electron transfer (EET) and photoelectrochemical processes is an emerging research area with great potential to circumvent many disadvantages associated with traditional techniques that depend on independent microbial or photocatalysis treatment. Microbial EET processes involve microorganism oxidation of extracellular electron donors for respiration and synchronous reduction of extracellular electron acceptors to form an integrated respiratory chain. Coupled microbial EET-photoelectrochemical technologies greatly improve energy conversion efficiency providing both economic and environmental benefits. Among substitutes for semiconductor photocatalysts, cadmium sulfide nanoparticles (CdS NPs) possess several attractive properties. Specifically, CdS NPs have suitable electrical conductivity, large specific surface area, visible light-driven photocatalysis capability and robust biocompatibility, enabling them to promote hybrid microbial-photoelectrochemical processes. This review highlights recent advances in intimately coupled CdS NPs-microbial extracellular electron transfer systems and examines the mechanistic pathways involved in photoelectrochemical transformations. Finally, the prospects for emerging applications utilizing hybrid CdS NPs-based microbial-photoelectrochemical technologies are assessed. As such, this review provides a rigorous fundamental analysis of electron transport dynamics for hybrid CdS NPs-microbial photoelectrochemical processes and explores the applicability of engineered CdS NPs-biohybrids for future applications, such as in environmental remediation and clean-energy production.

The response of soil nitrous oxide (N2O) emission to manure application has been widely reported for laboratory experiments. However, the in-situ effects of manure application on soil N2O emission from field trials (i.e. real-world conditions) and related mechanisms are poorly understood at the global scale. Here, we performed a meta-analysis using 262 field observations from 44 publications to assess the in-situ effects of manure application on soil N2O emission and factors regulating N2O emission (e.g., agricultural practices, manure characteristics and initial soil properties). Our analysis found that manure application significantly increased soil N2O emission in field trials. The largest N2O emissions were observed in soils from warm temperate climates, planted with upland non-leguminous crops and using raw manure. Notably, water-filled pore space (WFPS) significantly affected N2O emission; soils with 50-90% WFPS had the highest N2O emissions. Initial soil properties (e.g. pH, texture and organic carbon (C)) were generally not significant for predicting N2O emission, possibly due to changes in soil properties induced by manure additions. Manures with carbon: nitrogen ratios (C:N) of 10-15 and C contents of 100-300 g C kg-1 produced the lowest N2O emission. The net N2O emission factor (1.13%) resulting from manure application was similar to additions of synthetic N fertilizer (1.25%) and crop residues (1.06%), suggesting that manure application resulted in a similar N2O emission to other soil amendments. Our analysis provides a scientific basis for manure management options to minimize N2O emissions from animal waste disposal on agricultural lands globally.

Effective Jan. 1, 2018, the California Department of Pesticide Regulation enacted a regulation regarding the use of pesticides near public K-12 schools and licensed child day care centers, including a provision that bans specific types of applications, including air-blast and air-assist, during weekday school hours (6 a.m. to 6 p.m.) to provide an additional safety margin for pesticide exposure beyond those provided by other regulations. We considered the financial effect on almond growers in nine counties, accounting for four-fifths of total almond production in 2014, if they had been unable to complete a standard spring disease management program on any buffer zone acreage. Results indicated that total annual losses for those counties if such a regulation had been in effect would have been $8.7 million, with per-acre losses ranging from 22% to over 50% of total operating costs, depending on the county. However, using a methodology that took into account historical weather and soil hydrologic group data, we estimated average annual losses in the nine counties among almond growers would have been under $0.2 million because the regulation would have affected the number of sprays completed for relatively few acres in relatively few years.

The response of soil nitrous oxide (N2O) emission to manure application has been widely reported for laboratory experiments. However, the in-situ effects of manure application on soil N2O emission from field trials (i.e. real-world conditions) and related mechanisms are poorly understood at the global scale. Here, we performed a meta-analysis using 262 field observations from 44 publications to assess the in-situ effects of manure application on soil N2O emission and factors regulating N2O emission (e.g., agricultural practices, manure characteristics and initial soil properties). Our analysis found that manure application significantly increased soil N2O emission in field trials. The largest N2O emissions were observed in soils from warm temperate climates, planted with upland non-leguminous crops and using raw manure. Notably, water-filled pore space (WFPS) significantly affected N2O emission; soils with 50-90% WFPS had the highest N2O emissions. Initial soil properties (e.g. pH, texture and organic carbon (C)) were generally not significant for predicting N2O emission, possibly due to changes in soil properties induced by manure additions. Manures with carbon: nitrogen ratios (C:N) of 10-15 and C contents of 100-300 g C kg-1 produced the lowest N2O emission. The net N2O emission factor (1.13%) resulting from manure application was similar to additions of synthetic N fertilizer (1.25%) and crop residues (1.06%), suggesting that manure application resulted in a similar N2O emission to other soil amendments. Our analysis provides a scientific basis for manure management options to minimize N2O emissions from animal waste disposal on agricultural lands globally.

Intimate coupling of microbial extracellular electron transfer (EET) and photoelectrochemical processes is an emerging research area with great potential to circumvent many disadvantages associated with traditional techniques that depend on independent microbial or photocatalysis treatment. Microbial EET processes involve microorganism oxidation of extracellular electron donors for respiration and synchronous reduction of extracellular electron acceptors to form an integrated respiratory chain. Coupled microbial EET-photoelectrochemical technologies greatly improve energy conversion efficiency providing both economic and environmental benefits. Among substitutes for semiconductor photocatalysts, cadmium sulfide nanoparticles (CdS NPs) possess several attractive properties. Specifically, CdS NPs have suitable electrical conductivity, large specific surface area, visible light-driven photocatalysis capability and robust biocompatibility, enabling them to promote hybrid microbial-photoelectrochemical processes. This review highlights recent advances in intimately coupled CdS NPs-microbial extracellular electron transfer systems and examines the mechanistic pathways involved in photoelectrochemical transformations. Finally, the prospects for emerging applications utilizing hybrid CdS NPs-based microbial-photoelectrochemical technologies are assessed. As such, this review provides a rigorous fundamental analysis of electron transport dynamics for hybrid CdS NPs-microbial photoelectrochemical processes and explores the applicability of engineered CdS NPs-biohybrids for future applications, such as in environmental remediation and clean-energy production.

Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

Assessing heavy metal pollution in river sediments and identifying the key factors contributing to metal pollution are critical components for devising river environmental protection and remediation strategies to protect human and ecological health. This is especially important in urban areas where metals from a wide range of sources contribute to sediment pollution. In this study, the metal enrichment factor (EF) was used to measure the watershed distribution of Cu, Zn, Pb and Cd in sediments in the Wen-Rui Tang urban river system in Wenzhou, Eastern China. The Geographical Detector Method (GDM) was specifically evaluated for its ability to analyze spatial relationships between metal EFs and their anthropogenic and natural control factors, including densities of industry (DI), livestock (DL), service industries (DS), population (DP), and roads (DR), along with agricultural area (AG), sediment total organic carbon (TOC), and soil types (ST). Results showed that the watershed was highly contaminated by all metals with an EF trend of Cd ≫ Zn > Cu > Pb. The spatial distribution of EFs demonstrated high contamination of all metals in the southwestern region of the watershed where industrial activities were concentrated, and higher Cu and Zn concentrations in the northeastern region having a high density of livestock production. GDM results identified DI as the dominant determinant for all metals, while TOC and ST were determined to have a moderate secondary influence for Zn, Pb and Cd. Additionally, GDM revealed several additive and nonlinear interactions between anthropogenic and natural factors influencing metal concentrations. Compared to other correlation, multiple linear regression and geographically weighted regression, GDM demonstrated distinct advantages of being able to assess both categorical and continuous variables and determine both single and multiple factor interactions. These attributes provide a more comprehensive understanding of metal spatial di

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, ErCd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale.

Assessing heavy metal pollution in river sediments and identifying the key factors contributing to metal pollution are critical components for devising river environmental protection and remediation strategies to protect human and ecological health. This is especially important in urban areas where metals from a wide range of sources contribute to sediment pollution. In this study, the metal enrichment factor (EF) was used to measure the watershed distribution of Cu, Zn, Pb and Cd in sediments in the Wen-Rui Tang urban river system in Wenzhou, Eastern China. The Geographical Detector Method (GDM) was specifically evaluated for its ability to analyze spatial relationships between metal EFs and their anthropogenic and natural control factors, including densities of industry (DI), livestock (DL), service industries (DS), population (DP), and roads (DR), along with agricultural area (AG), sediment total organic carbon (TOC), and soil types (ST). Results showed that the watershed was highly contaminated by all metals with an EF trend of Cd ≫ Zn > Cu > Pb. The spatial distribution of EFs demonstrated high contamination of all metals in the southwestern region of the watershed where industrial activities were concentrated, and higher Cu and Zn concentrations in the northeastern region having a high density of livestock production. GDM results identified DI as the dominant determinant for all metals, while TOC and ST were determined to have a moderate secondary influence for Zn, Pb and Cd. Additionally, GDM revealed several additive and nonlinear interactions between anthropogenic and natural factors influencing metal concentrations. Compared to other correlation, multiple linear regression and geographically weighted regression, GDM demonstrated distinct advantages of being able to assess both categorical and continuous variables and determine both single and multiple factor interactions. These attributes provide a more comprehensive understanding of metal spatial di

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, ErCd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale.

This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions., Comment: Accepted by EMNLP 2019

Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets., Comment: Accepted by EMNLP 2019

Source water nutrient management to prevent eutrophication requires critical strategies to reduce watershed phosphorus (P) loadings. Shanxi Drinking-Water Source Area (SDWSA) in eastern China experienced severe water quality deterioration before 2010, but showed considerable improvement following application of several watershed management actions to reduce P. This paper assessed the changes in total phosphorus (TP) concentrations and fluxes at the SDWSA outlet relative to watershed anthropogenic P sources during 2005⁻2016. Overall anthropogenic P inputs decreased by 21.5% over the study period. Domestic sewage, livestock, and fertilizer accounted for (mean ± SD) 18.4 ± 0.6%, 30.1 ± 1.9%, and 51.5 ± 1.5% of total anthropogenic P inputs during 2005⁻2010, compared to 24.3 ± 2.7%, 8.8 ± 10.7%, and 66.9 ± 8.0% for the 2011⁻2016 period, respectively. Annual average TP concentrations in SDWSA decreased from 0.041 ± 0.019 mg/L in 2009 to 0.025 ± 0.013 mg/L in 2016, a total decrease of 38.2%. Annual P flux exported from SDWSA decreased from 0.46 ± 0.04 kg P/(ha·a) in 2010 to 0.25 ± 0.02 kg P/(ha·a) in 2016, a decrease of 44.9%. The success in reducing TP concentrations was mainly due to the development of domestic sewage/refuse collection/treatment and improved livestock management. These P management practices have prevented harmful algal blooms, providing for safe drinking water.

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80-97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI < 1); however, nine sites showed CR values above the acceptable level of 10-4 for potential cancer risk from arsenic in the early 2000s. Uncertainty analysis revealed an exposure risk for As at all sites because some CR values exceeded the 10-4 level of concern; levels of Cd near an old industrial area also exceeded the Cd exposure standard (2.6% of CR values > 10-4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R2 values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies.

Source water nutrient management to prevent eutrophication requires critical strategies to reduce watershed phosphorus (P) loadings. Shanxi Drinking-Water Source Area (SDWSA) in eastern China experienced severe water quality deterioration before 2010, but showed considerable improvement following application of several watershed management actions to reduce P. This paper assessed the changes in total phosphorus (TP) concentrations and fluxes at the SDWSA outlet relative to watershed anthropogenic P sources during 2005⁻2016. Overall anthropogenic P inputs decreased by 21.5% over the study period. Domestic sewage, livestock, and fertilizer accounted for (mean ± SD) 18.4 ± 0.6%, 30.1 ± 1.9%, and 51.5 ± 1.5% of total anthropogenic P inputs during 2005⁻2010, compared to 24.3 ± 2.7%, 8.8 ± 10.7%, and 66.9 ± 8.0% for the 2011⁻2016 period, respectively. Annual average TP concentrations in SDWSA decreased from 0.041 ± 0.019 mg/L in 2009 to 0.025 ± 0.013 mg/L in 2016, a total decrease of 38.2%. Annual P flux exported from SDWSA decreased from 0.46 ± 0.04 kg P/(ha·a) in 2010 to 0.25 ± 0.02 kg P/(ha·a) in 2016, a decrease of 44.9%. The success in reducing TP concentrations was mainly due to the development of domestic sewage/refuse collection/treatment and improved livestock management. These P management practices have prevented harmful algal blooms, providing for safe drinking water.

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80-97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI < 1); however, nine sites showed CR values above the acceptable level of 10-4 for potential cancer risk from arsenic in the early 2000s. Uncertainty analysis revealed an exposure risk for As at all sites because some CR values exceeded the 10-4 level of concern; levels of Cd near an old industrial area also exceeded the Cd exposure standard (2.6% of CR values > 10-4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R2 values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies.

In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time., Comment: EMNLP 2018

Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions. Our RAMN integrates together the deep semantic representations, the shallow lexical mismatching information and the initial rank produced by an external search engine. For the first time, we propose attention autoencoders to generate semantic representations of questions. In addition, we employ lexical mismatching to capture surface matching between two questions, which is derived from the importance of each word in a question. We conduct experiments on the open CQA datasets of SemEval-2016 and SemEval-2017. The experimental results show that our unsupervised model obtains comparable performance with the state-of-the-art supervised methods in SemEval-2016 Task 3, and outperforms the best system in SemEval-2017 Task 3 by a wide margin.

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (CODMn) from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records.

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (CODMn) from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records.

Minghua Zhang, Mitao Huang, Pihong Zhang, Pengfei Liang, Licheng Ren, Jizhang Zeng, Jie Zhou, Xiong Liu, Tinghong Xie, Xiaoyuan Huang Department of Burns Reconstruction Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China Objective: Electrical injuries to the fingers account for the majority of total severe burns that occur each year. While several types of flaps have been used in covering finger defects, all have limitations or disadvantages. The purpose of this study was to introduce our clinical experiences of using the lateral tarsal artery (LTA) flap to successfully restore fingers after electrical injury.Patients and methods: From 2005 to 2012, 10 patients with 14 severe electrical burns to their fingers, including six thumbs and four index and four middle fingers, were treated with LTA flap. The wound size ranged from 2.0×3.0 cm to 3.5×5.0 cm. The flap with free tendon graft was used to repair the tendon defect in four cases, free nerve graft was used to repair the feeling defect in two cases, and the flap with nerve was used to repair the feeling defect in two cases. All the patients were followed up for 3 months to 2 years.Results: All skin flaps adhered successfully and there were no complications. All patients were satisfied with the esthetic appearance and functional outcome of the finger reconstruction.Conclusion: LTA flap is a reliable method to restore fingers after severe electrical injuries. Keywords: electrical injuries to fingers, lateral tarsal artery flap

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimi

In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.

In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimi

Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOE began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.

Water quality degradation is often a severe consequence of rapid economic expansion in developing countries. Methods to assess spatial-temporal patterns and trends in water quality are essential for guiding adaptive management efforts aimed at water quality remediation. Temporal and spatial patterns of surface water quality were investigated for 54 monitoring sites in the Wen-Rui Tang River watershed of eastern China to identify such patterns in water quality occurring across a rural-suburban-urban interface. Twenty physical and chemical water quality parameters were analyzed in surface waters collected once every 4-8 weeks from 2000 to 2010. Temporal and spatial variations among water quality parameters were assessed between seasons (wet/dry) and among major land use zones (urban/suburban/rural). Factor analysis was used to identify parameters that were important in assessing seasonal and spatial variations in water quality. Results revealed that parameters related to organic pollutants (dissolved oxygen (DO), chemical oxygen demand (manganese) (COD(Mn)), and 5-day biochemical oxygen demand (BOD₅)), nutrients (ammonia nitrogen (NH₄ ⁺-N), total nitrogen (TN), total phosphorus (TP)), and salt concentration (electrical conductivity (EC)) were the most important parameters contributing to water quality variation. Collectively, they explained 70.9 % of the total variance. A trend study using the seasonal Kendall test revealed reductions in COD(Mn), BOD₅, NH₄ ⁺-N, petrol, V-phen, and EC concentrations over the 11-year study period. Cluster analysis was employed to evaluate variation among 14 sampling sites representative of dominant land use categories and indicated three, three, and four clusters based on organic, nutrient, and salt water quality characteristics, respectively. Factors that are typically responsible for water quality degradation (including population, topography, and land use) showed no strong correlation with water quality trends implying considerable p

Water quality degradation is often a severe consequence of rapid economic expansion in developing countries. Methods to assess spatial-temporal patterns and trends in water quality are essential for guiding adaptive management efforts aimed at water quality remediation. Temporal and spatial patterns of surface water quality were investigated for 54 monitoring sites in the Wen-Rui Tang River watershed of eastern China to identify such patterns in water quality occurring across a rural-suburban-urban interface. Twenty physical and chemical water quality parameters were analyzed in surface waters collected once every 4-8 weeks from 2000 to 2010. Temporal and spatial variations among water quality parameters were assessed between seasons (wet/dry) and among major land use zones (urban/suburban/rural). Factor analysis was used to identify parameters that were important in assessing seasonal and spatial variations in water quality. Results revealed that parameters related to organic pollutants (dissolved oxygen (DO), chemical oxygen demand (manganese) (COD(Mn)), and 5-day biochemical oxygen demand (BOD₅)), nutrients (ammonia nitrogen (NH₄ ⁺-N), total nitrogen (TN), total phosphorus (TP)), and salt concentration (electrical conductivity (EC)) were the most important parameters contributing to water quality variation. Collectively, they explained 70.9 % of the total variance. A trend study using the seasonal Kendall test revealed reductions in COD(Mn), BOD₅, NH₄ ⁺-N, petrol, V-phen, and EC concentrations over the 11-year study period. Cluster analysis was employed to evaluate variation among 14 sampling sites representative of dominant land use categories and indicated three, three, and four clusters based on organic, nutrient, and salt water quality characteristics, respectively. Factors that are typically responsible for water quality degradation (including population, topography, and land use) showed no strong correlation with water quality trends implying considerable p

Room temperature ionic liquids (RTILs) are emerging as a new class of 'green' solvents for use in a wide range of chemical processes. RTILs can sensitize or quench fluorescence of organic chemicals and their interactions still remain unclear, especially for weakly fluorescent chemicals. Herein, we report the effects of six RTILs on the fluorescence behavior of bisphenol A (BPA). The fluorescence intensities (FIs) of BPA in a RTIL-acetonitrile system were significantly quenched compared to acetonitrile. The quenching effect was stronger for [CnMIM]BF4 than [CnMIM]PF6. A decreasing trend of fluorescence lifetime (FL) of BPA was observed for [C6MIM]PF6 (4.26 to 3.86 ns) and [C14MIM]PF6 (4.15 to 3.78 ns) with increasing RTIL concentrations in the range of 1-10 mM. The quenching mode was consistent with a static quenching mechanism based on the consistency of FL and FI results. The emission bands of BPA and RTILs did not interfere with each other when RTILs were used as the solvent. The investigated RTILs enhanced the FIs of strongly fluorescing chemicals (12.0 ~ 19.0-fold for norfloxacin and 6.1 ~ 8.5-fold for dansyl chloride), but quenched those of weakly fluorescing chemicals (BPA). These results demonstrate that RTILs have different fluorescent effects on organic chemicals with different fluorophores. The interactions between RTILs and BPA result from many factors in addition to viscosity, such as solvent electrostatic dielectric constant, refractive index, density, polarization and molecular interaction. These results provide a theoretical foundation for application of RTILs in the analysis of weakly fluorescing chemical.