An integrated supervision framework to safeguard the urban river water quality supported by ICT and models.

Models and information and communication technology (ICT) can assist in the effective supervision of urban receiving water bodies and drainage systems. Single model-based decision tools, e.g., water quality models and the pollution source identification (PSI) method, have been widely reported in this field. However, a systematic pathway for environmental decision support system (EDSS) construction by integrating advanced single techniques has rarely been reported, impeding engineering applications. This paper presents an integrated supervision framework (UrbanWQEWIS) involving monitoring-early warning-source identification-emergency disposal to safeguard the urban water quality, where the data, model, equipment and knowledge are smoothly and logically linked. The generic architecture, all-in-one equipment and three key model components are introduced. A pilot EDSS is developed and deployed in the Maozhou River, China, with the assistance of environmental Internet of Things (IoT) technology. These key model components are successfully validated via in situ monitoring data and dye tracing experiments. In particular, fluorescence fingerprint-based qualitative PSI and Bayesian-based quantitative PSI methods are effectively coupled, which can largely reduce system costs and enhance flexibility. The presented supervision framework delivers a state-of-the-art management tool in the digital water era. The proposed technical pathway of EDSS development provides a valuable reference for other regions.

Suitability evaluation of rural sewage treatment facilities in China considering lifcycle environmental impacts and regional differences.

Centralized vs. decentralized sewage treatment is one of the key issues in the planning of rural sewage treatment (RST) in China due to the country's diverse regional characteristics. There are very limited comprehensive evaluation models for selecting regionally suitable schemes and facilities, particularly for national or provisional scale planning. As a scenario-based multi-attribute decision-making (MADM) issue, this paper develops a novel RST suitability evaluation model by integrating the multi-attribute analytic hierarchy process (AHP) with the technique for order preference by similarity to an ideal solution (TOPSIS). The suitability evaluation model sets up 3 small-centralized and 4 decentralized RST facilities as candidates and includes 12 evaluation indicators that cover economic cost, life cycle environmental impacts, technical features and operations management. Eight generic scenarios are classified for Chinese rural areas based on differences in three major characteristic factors, i.e., population density (PD), the economic development level (EDL), and topographic slope (TS). The universal evaluation results show that a centralized sewage treatment scheme is more suitable for areas with a high PD/high EDL/low TS, while a decentralized scheme is more suitable for areas with a low PD/low EDL/high TS. Sensitivity analysis shows that in regions with a high PD/low EDL, the indicator weight of the construction investment cost in the model has a great influence on the facility suitability ranking. However, in regions with a high PD/high EDL, the ranking is the most sensitive to the indicator weights of the global warming potential and sewage treatment effect. Furthermore, as a spatial decision issue, an RST suitability map of Hunan Province in China is produced at the county level of resolution, and the map is generally consistent with our field knowledge of several counties in Hunan Province. The presented evaluation framework can be integrated into environmental decision support systems in the future to help local and central governments, water utilities, design institutes and other stakeholders scientifically plan RST projects.

Long-term trends and probability distributions of river water quality variables and their relationships with climate elasticity characteristics.

The statistical characteristics of surface water quality play fundamental roles in climate change-related studies. Climatic drivers will affect surface water quality, and such potential effects will vary between different regions and climate types. Here, we studied the long-term trends and probability distributions of water quality variables (including water temperature, pH, turbidity, DO, C-,N-,P-variables, etc.) and their relationship with climate elasticity, a non-parametric estimator of the sensitivity of the response of water quality to climate drivers, based on three typical watersheds: the Yukon, the Mekong, and the Murray. Significant decreasing trends were observed in the Yukon and Murray watersheds for the majority of water quality variables, except turbidity and filtered nitrate plus nitrite, whereas increasing trends were exhibited by most water quality variables in the Mekong watershed. Compared with the Yukon and Murray watersheds, the probability distributions of most water quality variables and their corresponding percentage change-based elasticity estimator samples are characterized by a heavy-tailed distribution in the Mekong watershed. The precipitation elasticity results are statistically meaningful in the Mekong and Murray watersheds, whereas temperature elasticity is significant in the Yukon watershed. The revealed characteristics of long-term trends and probability distributions pattern of basic water quality variables are helpful for water quality modeling. The findings suggested that the increasing trends, heavy-tailed probability distribution patterns, and the response of water quality to precipitation and temperature, especially in densely populated developing areas, can be modulated by restoration efforts, which will reduce the potential impacts of climatic and non-climatic factors.

Crucial time of emergency monitoring for reliable numerical pollution source identification.

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Catchment-scale life cycle impacts of green infrastructures and sensitivity to runoff coefficient with stormwater modelling.

Urban green infrastructure (GI) has been widely used in sponge city construction to manage hydrological processes. While studies on environmental benefits of GI from the perspective of whole life cycle assessment (LCA) have been reported in recent years, few have explored and compared the environmental performance of different GIs within a single catchment, which is directly linked to catchment-scale hydrological control. This study focuses on a Sponge City pilot project in Shenzhen, China, including three typical types of GI: permeable pavement, green roof, and sunken green space. By collecting hydrological data, land use, and life cycle inventory of GI and employing SWMM (Storm Water Management Model)-based stormwater modelling, we have revealed the environmental impacts at different stages of the life cycle of the GI scenario and three GIs through comparative and sensitivity analyses. Notably, we have disclosed, for the first time, the effect of the runoff coefficient in LCA. Our findings indicate that over the 30-year life cycle, the total environmental impact of the GI scenario is 24 % smaller than that of the hypothetical grey scenario. Permeable pavement exhibits the largest environmental impact per unit area, being 1.8 times and 7.6 times greater than that of the green roof and sunken green space, respectively. The operation stage of the three GIs significantly mitigates eutrophication and climate change. Furthermore, sensitivity analysis demonstrates that an increase in surface runoff undermines the environmental benefits of GIs. These results highlight the importance of embedding stormwater modelling into LCA, enabling catchment-scale integrated evaluation and equivalent assessment of different GIs within a single catchment whereby the influence of external factors such as climate change can be described, which aids in understanding the dynamic environmental performance of GIs. The proposed research framework and results are anticipated to provide valuable guidance for future GI construction and carbon-neutral policies.

The impact of environment on college students' entrepreneurial intention based on the mediating effect of personality traits.

Introduction: The entrepreneurship rate of Chinese college students is relatively low. This study investigates the environment factors that influence college students' entrepreneurial intention as mediated by personality traits. Methods: According to the entrepreneurial ecosystem theory, a hierarchical model of the environment factors was established which included three layers: personality traits as the micro system, family and education as the meso system, and social and policy support as the macro system. The structural equation model of the environment factors was constructed to reveal the significant influence path of various factors and the mediating role of personality traits. Data were collected from 436 undergraduate students in Henan Province, China. Results: Findings indicate that family, education, social, and policy factors have no significant direct influence on entrepreneurial intention. However, personality traits significantly influence entrepreneurial intention and mediate the effects of various factors on entrepreneurial intention. Discussion: Suggestions such as improving college students' entrepreneurial practice and promoting college students' proactive personalities are also put forward.

Road runoff as a significant nonpoint source of parabens and their metabolites in urban rivers.

Parabens are widely added to food, cosmetics, and medicines as preservatives and are typical contaminants of pharmaceuticals and personal care products (PPCPs). However, their fate and transport in urban watersheds remain largely unexplored. This study investigated the role of road runoff as a critical nonpoint source of parabens and their metabolites in urban rivers based on 73 multimedia (road runoff and dust in different urban land uses, wastewater, stormwater discharge and river water) samples collected from a highly urbanized drainage area. Seven parabens and five metabolites were detected in the road runoff, with mean concentrations of ∑parabens and ∑metabolites equal to 47.5 ng/L and 4710 ng/L, respectively. The concentrations in road runoff were comparable to those in treated wastewater and river water and showed a land use pattern of residential > industrial > commercial. A first flush effect of the contaminants was observed in a heavy rainfall event with an antecedent dry period. In general, the population-based and area-based emission intensities of ∑parabens and ∑metabolites in road runoff were one order of magnitude higher than those in wastewater effluent during the rainfall events. This study provides quantitative evidence that road runoff can be a major pollution source of parabens and their metabolites in rapidly growing cities during the wet season and calls for the integrated management of nonpoint sources to prevent urban river contamination by typical PPCPs.

Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring.

Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of 'robustness' measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH3-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

Improving urban drainage systems to mitigate PPCPs pollution in surface water: A watershed perspective.

Parabens are preservatives widely used in pharmaceutical and personal care products (PPCPs). This study investigated urban water pollution by parabens from a watershed perspective. Water and sediment samples were collected from one of the most polluted urban streams in China. Six parabens and five paraben metabolites were frequently detected in the samples, whereas the overall pollution level was intermediate according to a global comparison. The spatial distributions of the chemical concentrations along the river are influenced by multiple factors, and WWTPs appear to be a major factor. In general, the target pollutants were detected at higher concentrations in the dry season than in the wet season, but extraordinary concentration peaks in water were observed downstream of wastewater treatment plants (WWTPs), indicating a dominant contribution from combined sewage overflows (CSOs) during rainfall events. In a representative WWTP-influenced reach, CSOs account for its 97.3% of ∑parabens input and 96.9% of ∑metabolites input in a typical rainfall event. Converting the existing combined sewer systems to separate stormwater drainage systems could reduce the inputs of ∑parabens and ∑metabolites by 86.9-84.5%, respectively. This study highlights the role of urban drainage systems in preventing surface water pollution by PPCPs. CAPSULE: Urban drainage systems play a critical role in controlling pollution by parabens and their metabolites in urban surface water.

[Construction and application of fluorescence labeled multiplex typing system for 3 new miniSTR loci].

OBJECTIVE: To establish a miniSTR multiplex set including three STR loci unlinked from the CODIS loci: D1S1676, D6S1274 and D17S1299, to generate amplified fragment less than 115 bp in size and to study the genotype of degraded DNA samples. METHODS: After amplification with different fluorescence labeled primers, the amplified products from 100 unrelated individual and 2 highly degraded specimens were analyzed by 310 Genetic Analyzer. RESULTS: Three miniSTR loci were determined by fluorescence-labeled multiplex-PCR technique. Each locus was successfully genotyped in all 100 samples. In D1S1676, D6S1274 and D17S1299 loci, 9, 9, 7 alleles and 27, 23, 18 genotypes were observed respectively. The distribution of genotype for three miniSTR loci in Chengdu Han population was in accordance with Hardy-Weinberg equilibrium. The combined exclusion probability and the combined discrimination power of the three STR loci in Chengdu Han population were 0.9991 and 0.9160 respectively. CONCLUSION: This miniSTR multiplex set could be used in individual identification and paternity test. It also provides a new method in the analysis of degraded DNA sample.

Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies.

It is critical for surface water management systems to provide early warnings of abrupt, large variations in water quality, which likely indicate the occurrence of spill incidents. In this study, a combined approach integrating a wavelet artificial neural network (wavelet-ANN) model and high-frequency surrogate measurements is proposed as a method of water quality anomaly detection and warning provision. High-frequency time series of major water quality indexes (TN, TP, COD, etc.) were produced via a regression-based surrogate model. After wavelet decomposition and denoising, a low-frequency signal was imported into a back-propagation neural network for one-step prediction to identify the major features of water quality variations. The precisely trained site-specific wavelet-ANN outputs the time series of residual errors. A warning is triggered when the actual residual error exceeds a given threshold, i.e., baseline pattern, estimated based on long-term water quality variations. A case study based on the monitoring program applied to the Potomac River Basin in Virginia, USA, was conducted. The integrated approach successfully identified two anomaly events of TP variations at a 15-minute scale from high-frequency online sensors. A storm event and point source inputs likely accounted for these events. The results show that the wavelet-ANN model is slightly more accurate than the ANN for high-frequency surface water quality prediction, and it meets the requirements of anomaly detection. Analyses of the performance at different stations and over different periods illustrated the stability of the proposed method. By combining monitoring instruments and surrogate measures, the presented approach can support timely anomaly identification and be applied to urban aquatic environments for watershed management.

Emergency material allocation with time-varying supply-demand based on dynamic optimization method for river chemical spills.

Aiming to minimize the damage caused by river chemical spills, efficient emergency material allocation is critical for an actual emergency rescue decision-making in a quick response. In this study, an emergency material allocation framework based on time-varying supply-demand constraint is developed to allocate emergency material, minimize the emergency response time, and satisfy the dynamic emergency material requirements in post-accident phases dealing with river chemical spills. In this study, the theoretically critical emergency response time is firstly obtained for the emergency material allocation system to select a series of appropriate emergency material warehouses as potential supportive centers. Then, an enumeration method is applied to identify the practically critical emergency response time, the optimum emergency material allocation and replenishment scheme. Finally, the developed framework is applied to a computational experiment based on south-to-north water transfer project in China. The results illustrate that the proposed methodology is a simple and flexible tool for appropriately allocating emergency material to satisfy time-dynamic demands during emergency decision-making. Therefore, the decision-makers can identify an appropriate emergency material allocation scheme in a balance between time-effective and cost-effective objectives under the different emergency pollution conditions.

Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory.

Field monitoring strategy is critical for disaster preparedness and watershed emergency environmental management. However, development of such is also highly challenging. Despite the efforts and progress thus far, no definitive guidelines or solutions are available worldwide for quantitatively designing a monitoring network in response to river chemical spill incidents, except general rules based on administrative divisions or arbitrary interpolation on routine monitoring sections. To address this gap, a novel framework for spatial-temporal network design was proposed in this study. The framework combines contaminant transport modelling with discrete entropy theory and spectral analysis. The water quality model was applied to forecast the spatio-temporal distribution of contaminant after spills and then corresponding information transfer indexes (ITIs) and Fourier approximation periodic functions were estimated as critical measures for setting sampling locations and times. The results indicate that the framework can produce scientific preparedness plans of emergency monitoring based on scenario analysis of spill risks as well as rapid design as soon as the incident happened but not prepared. The framework was applied to a hypothetical spill case based on tracer experiment and a real nitrobenzene spill incident case to demonstrate its suitability and effectiveness. The newly-designed temporal-spatial monitoring network captured major pollution information at relatively low costs. It showed obvious benefits for follow-up early-warning and treatment as well as for aftermath recovery and assessment. The underlying drivers of ITIs as well as the limitations and uncertainty of the approach were analyzed based on the case studies. Comparison with existing monitoring network design approaches, management implications, and generalized applicability were also discussed.

Impact of early biliary complications in liver transplantation in the presence or absence of a T-tube: a Chinese transplant centre experience.

BACKGROUND: Biliary complications continue to be an important determinant of the recipient's survival rate after orthotopic liver transplantation (OLT). The objective of this study was to evaluate the incidence of early biliary complications in OLT in the presence or absence of a T-tube. METHODS: This retrospective study, based on inpatient data, focused on the relationship between T-tube placement and early biliary complications of 84 patients after OLT, from November 2002 to June 2005. Patients were divided into two groups based on whether or not a T-tube was used following bile duct reconstruction: T-tube group (group I, n = 33); non-T-tube group (group II, n = 51). RESULTS: 45.2% of OLT recipients had a malignant neoplasm. There were no significant differences in the demographic characteristics or operation data between the two groups. Overall, early biliary tract complications developed in 19.0% (16/84) of patients. The rate of early biliary complications was 30.3% (10/33) and 11.8% (6/51) in groups I II, respectively (p = 0.035). Biliary complications which were directly caused by T-tube placement occurred in 12.1% (4/33) of patients in group I. Overall, the percentage of malignant neoplasms, chronic viral cirrhosis, fulminant liver failure and other primary disease recipients with early biliary complications were 6.2%, 37.5%, 43.8% and 12.5%, respectively. CONCLUSION: This study suggests that the use of a T-tube in Chinese patients undergoing OLT causes a higher incidence of early biliary complications. Most of the early biliary complications occurred in chronic viral cirrhosis and fulminant liver failure recipients.

Engineering risk assessment for emergency disposal projects of sudden water pollution incidents.

Without an engineering risk assessment for emergency disposal in response to sudden water pollution incidents, responders are prone to be challenged during emergency decision making. To address this gap, the concept and framework of emergency disposal engineering risks are reported in this paper. The proposed risk index system covers three stages consistent with the progress of an emergency disposal project. Fuzzy fault tree analysis (FFTA), a logical and diagrammatic method, was developed to evaluate the potential failure during the process of emergency disposal. The probability of basic events and their combination, which caused the failure of an emergency disposal project, were calculated based on the case of an emergency disposal project of an aniline pollution incident in the Zhuozhang River, Changzhi, China, in 2014. The critical events that can cause the occurrence of a top event (TE) were identified according to their contribution. Finally, advices on how to take measures using limited resources to prevent the failure of a TE are given according to the quantified results of risk magnitude. The proposed approach could be a potential useful safeguard for the implementation of an emergency disposal project during the process of emergency response.

Emergency material allocation and scheduling for the application to chemical contingency spills under multiple scenarios.

In the emergency management relevant to chemical contingency spills, efficiency emergency rescue can be deeply influenced by a reasonable assignment of the available emergency materials to the related risk sources. In this study, an emergency material scheduling model (EMSM) with time-effective and cost-effective objectives is developed to coordinate both allocation and scheduling of the emergency materials. Meanwhile, an improved genetic algorithm (IGA) which includes a revision operation for EMSM is proposed to identify the emergency material scheduling schemes. Then, scenario analysis is used to evaluate optimal emergency rescue scheme under different emergency pollution conditions associated with different threat degrees based on analytic hierarchy process (AHP) method. The whole framework is then applied to a computational experiment based on south-to-north water transfer project in China. The results demonstrate that the developed method not only could guarantee the implementation of the emergency rescue to satisfy the requirements of chemical contingency spills but also help decision makers identify appropriate emergency material scheduling schemes in a balance between time-effective and cost-effective objectives.

A two-stage optimization model for emergency material reserve layout planning under uncertainty in response to environmental accidents.

In the emergency management relevant to pollution accidents, efficiency emergency rescues can be deeply influenced by a reasonable assignment of the available emergency materials to the related risk sources. In this study, a two-stage optimization framework is developed for emergency material reserve layout planning under uncertainty to identify material warehouse locations and emergency material reserve schemes in pre-accident phase coping with potential environmental accidents. This framework is based on an integration of Hierarchical clustering analysis - improved center of gravity (HCA-ICG) model and material warehouse location - emergency material allocation (MWL-EMA) model. First, decision alternatives are generated using HCA-ICG to identify newly-built emergency material warehouses for risk sources which cannot be satisfied by existing ones with a time-effective manner. Second, emergency material reserve planning is obtained using MWL-EMA to make emergency materials be prepared in advance with a cost-effective manner. The optimization framework is then applied to emergency management system planning in Jiangsu province, China. The results demonstrate that the developed framework not only could facilitate material warehouse selection but also effectively provide emergency material for emergency operations in a quick response.

Screening of pollution control and clean-up materials for river chemical spills using the multiple case-based reasoning method with a difference-driven revision strategy.

In-depth filtering of emergency disposal technology (EDT) and materials has been required in the process of environmental pollution emergency disposal. However, an urgent problem that must be solved is how to quickly and accurately select the most appropriate materials for treating a pollution event from the existing spill control and clean-up materials (SCCM). To meet this need, the following objectives were addressed in this study. First, the material base and a case base for environment pollution emergency disposal were established to build a foundation and provide material for SCCM screening. Second, the multiple case-based reasoning model method with a difference-driven revision strategy (DDRS-MCBR) was applied to improve the original dual case-based reasoning model method system, and screening and decision-making was performed for SCCM using this model. Third, an actual environmental pollution accident from 2012 was used as a case study to verify the material base, case base, and screening model. The results demonstrated that the DDRS-MCBR method was fast, efficient, and practical. The DDRS-MCBR method changes the passive situation in which the choice of SCCM screening depends only on the subjective experience of the decision maker and offers a new approach to screening SCCM.

High-capacity adsorption of aniline using surface modification of lignocellulose-biomass jute fibers.

Pyromellitic dianhydride (PMDA) modified jute fiber (MJF) were prepared with microwave treatment to generate a biosorbent for aniline removal. The characterization of the biosorbent was investigated by SEM, BET and FT-IR analysis to discuss the adsorption mechanism. The studies of various factors influencing the adsorption behavior indicated that the optimum dosage for aniline adsorption was 3g/L, the maximum adsorption capacity was observed at pH 7.0 and the adsorption process is spontaneous and endothermic. The aniline adsorption follows the pseudo second order kinetic model and Langmuir isotherm model. Moreover, the biosorbent could be regenerated through the desorption of aniline by using 0.5M HCl solution, and the adsorption capacity after regeneration is even higher than that of virgin MJF. All these results prove MJF is a promising adsorbent for aniline removal in wastewater.

Evaluation and selection of emergency treatment technology based on dynamic fuzzy GRA method for chemical contingency spills.

A robust scheme to address emergency pollution accident is becoming more and more important with the rise of the frequency and intensity of the emergency pollution accidents. Therefore, it is crucial to select an appropriate technology in an emergency response to chemical spills. In this study, an evaluation framework based on dynamic fuzzy GRA method has been developed to make forward optimum scheme for the selection of emergency treatment technology. Dynamic analysis and linguistic terms are used to evaluate alternatives to improve efficiency of emergency treatment procedures by addressing the vagueness and ambiguity in decision making. The method was then applied in a case study to evaluate emergency arsenic treatment technology and demonstrate its applicability and feasibility in emergency arsenic pollution under two scenarios associated with different arsenic levels. Therefore, not only the results can be used for selecting emergency treatment technology, but also help decision-makers identify desired decisions for contaminant mitigation with a quick response and cost-effective manner.