Drinking water management strategies for distribution networks: An integrated performance assessment framework.

Due to rapid population growth, urbanization, water contamination, and climate change, global water resources are under increasing pressure. Water utilities apply drinking water management strategies (DWMS) to ensure that water is safe for drinking. However, in recent years, due to increased inclination towards climate change, environmental emissions, and sustainable development goals; the environmental and economic performance of DWMSs is getting attention. An integrated framework combining life cycle thinking and water quality assessment techniques was developed in this study to evaluate the DWMSs' performance in terms of water quality, environment, and economics. Six DWMSs were assessed using the integrated framework as a case study. The environmental impacts in terms of human health, ecosystem, and resource use ranged from 1.46E-06 to 4.01E-06 DALY, 9.35E-10 to 3.80E-09 species.yr, and 0.0025-0.0071 USD-$, respectively. Pollution water index (PWI) and cost-benefit analysis (CBA) were used as decision-making techniques to assess the overall performance and suitability of DWMSs under given settings. The DWMSs with surface water as a source or ones providing relatively more degree of treatment have a relatively high PWI score (i.e., ≈0.31), reflective of high environmental impacts and water pollution compared to other alternatives. The CBA scores of selected alternatives ranged between 0.22 and 1.0. Furthermore, it was identified that DWMSs applied on relatively bigger water distribution systems can outweigh their costs (i.e., environmental and economic impacts). The proposed framework and approaches are flexible as they can incorporate different criteria in evaluating the performance and applicability of DWMSs.

Integrated probabilistic-fuzzy synthetic evaluation of drinking water quality in rural and remote communities.

An integrated probabilistic-fuzzy synthetic evaluation (PFSE) approach was developed for assessing drinking water quality in rural and remote communities (RRCs) through the lens of health risks and aesthetic impacts. The probabilistic health risk assessment can handle aleatory uncertainty raised by the variation of contaminant concentrations, and fuzzy synthetic evaluation (FSE) can address vagueness and ambiguity in human perception of risks and aesthetic impacts. The PFSE approach was applied to five RRCs in British Columbia, Canada where different drinking water quality issues, including high metal(loids) concentrations, the presence of coliforms, and poor aesthetics were reported. Cancer, non-cancer, and microbial risks assessed, as well as both quantitative and qualitative aesthetic impact assessment outcomes, were aggregated into synthetic water quality indices for water quality ranking. The probabilistic health risk assessment results revealed significant health risks for a community with relatively high arsenic concentrations (mean value = 7.0 µg/L) in the water supply. The microbial risks were also found significant (disability-adjusted life years >1 × 10-6) for all communities because of the presence of coliforms in the water. The FSE results indicated that the drinking water quality of five RRCs was associated with high aggregated impacts, which concurred with the "poor" water quality ratings according to the Canadian Water Quality Index. The water quality of the five RRCs was ranked based on the synthetic water quality evaluation indices. The PFSE approach can help decision-makers prioritize RRCs in effective resource allocation for addressing drinking water quality issues.

Developing an Integrated "Regression-QMRA method" to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning.

Worldwide Low Impact Developments (LIDs) are used for sustainable stormwater management; however, both the stormwater and LIDs carry microbial pathogens. The widespread development of LIDs is likely to increase human exposure to pathogens and risk of infection, leading to unexpected disease outbreaks in urban communities. The risk of infection from exposure to LIDs has been assessed via Quantitative Microbial Risk Assessment (QMRA) during the operation of these infrastructures; no effort is made to evaluate these risks during the planning phase of LID treatment train in urban communities. We developed a new integrated "Regression-QMRA method" by examining the relationship between pathogens' concentration and environmental variables. Applying of this methodology to a planned LID train shows that the predicted disease burden of diarrhea from Campylobacter is highest (i.e. 16.902 DALYs/1000 persons/yr) during landscape irrigation and playing on the LID train, followed by Giardia, Cryptosporidium, and Norovirus. These results illustrate that the risk of microbial infection can be predicted during the planning phase of LID treatment train. These predictions are of great value to municipalities and decision-makers to make informed decisions and ensure risk-based planning of stormwater systems before their development.

Energy efficiency in residential buildings amid COVID-19: A holistic comparative analysis between old and new normal occupancies.

Stringent lockdowns have been one of the defining features of the COVID-19 pandemic. Lockdowns have brought about drastic changes in living styles, including increased residential occupancy and telework practices predicted to last long. The variation in occupancy pattern and energy use needs to be assessed at the household level. Consequently, the new occupancy times will impact the performance of energy efficiency measures. To address these gaps, this work uses a real case study, a two-story residential building in the Okanagan Valley (British Columbia, Canada). Further, steady-state building energy simulations are performed on the HOT2000 tool to evaluate the resiliency of energy efficiency measures under a full lockdown. Three-year monitored energy data is analyzed to study the implications of COVID-19 lockdowns on HVAC and non-HVAC loads at a monthly temporal scale. The results show a marked change in energy use patterns and a higher increase in May 2020 compared to the previous two years. Calibrated energy models built on HOT2000 are then used to study the impacts of pre-COVID-19 (old normal occupancy) and post-COVID-19 (new normal occupancy) on energy upgrades performance. The simulations show that under higher occupancy times, the annual electricity use increased by 16.4%, while natural gas use decreased by 7.6%. The results indicate that overall residential buildings following pre-COVID-19 occupancy schedules had higher energy-saving potential than those with new normal occupancy. In addition, the variation in occupancy and stakeholder preferences directly impact the ranking of energy efficiency measures. Furthermore, this study identifies energy efficiency measures that provide flexibility for the decision-makers by identifying low-cost options feasible under a range of occupancy schedules.

Staged energy and water quality optimization for large water distribution systems.

Simultaneous optimization of energy and water quality in real-time large-sized water distribution systems is a daunting task for water suppliers. The complexity of energy optimization increases with a large number of pipes, scheduling of several pumps, and adjustments of tanks' water levels. Most of the simultaneous energy and water quality optimization approaches evaluate small (or hypothetical) networks or compromise water quality. In the proposed staged approach, Stage 1 uses a risk-based approach to optimally locate the chlorine boosters in a large distribution system based on residual chlorine failures and the associated consequences in different land uses of the service area. Integrating EPANET and CPLEX software, Stage 2 uses mixed integer goal programming for optimizing the day-ahead pump scheduling. The objective function minimizes the pumping energy cost as well as the undesirable deviations from goal constraints, such as expected water demand. Stage 3 evaluates the combined hydraulics and water quality performances at the network level. The implementation of the proposed approach on a real-time large-sized network of Al-Khobar City in Saudi Arabia, with 44 groundwater wells, 12 reservoirs, 2 storage tanks, 191 mains, 141 junctions, and 17 pumps, illustrated the practicality of the framework. Simulating the network with an optimal pumping schedule and chlorine boosters' locations shows a 40% improvement in water quality performance, desired hydraulics performance with optimal pump scheduling, and an average 20% energy cost reduction compared to the normal (unoptimized) base case scenario.

An integrated geospatial correlation analysis and human health risk assessment approach for investigating abandoned industrial sites.

An integrated geospatial correlation analysis (GCA)-human health risk assessment (HHRA) approach was developed to investigate abandoned industrial sites featured by heterogeneous contamination data. Critical areas of high health risk concerns can be prioritized for remediation using the integrated approach. An abandoned chemical complex site in Hubei, China was investigated as a case study. GCA and HHRA were performed using soil and groundwater sampling data collected in 2016 and 2019. Benzene, chlorobenzene, dichlorobenzenes, 2-nitrochlorobenzene, and α-hexachlorocyclohexane were determined to be critical contaminants in soil. The 2019 sampling data revealed new contaminated locations that were not found in the 2016 sampling campaign. High concentrations (89.81-386.55 mg/L) of vinyl chloride were also found in groundwater samples. Several critical location clusters of high concentrations of dichlorobenzenes, chlorobenzene, and α-hexachlorocyclohexane were found within the site according to the GCA outcomes. These contaminants could pose significant cancer and non-cancer risks to onsite workers. The critical areas were ranked according to cancer and non-cancer risks estimated by HHRA, respectively, for informed remediation planning. Among the critical contaminants, α-hexachlorocyclohexane, 2-nitrochlorobenzene, and 1,4-dichlorobenzene in soil, as well as vinyl chloride in groundwater, contributed a predominant part to the total health risk. The integrated approach can be used to assess the contamination of other similar abandoned industrial complex sites.

Predicting unregulated disinfection by-products in small water distribution networks: an empirical modelling framework.

Disinfection is used to deactivate pathogens in drinking water. However, disinfectants react with natural organic matter present in water to form disinfection by-products (DBPs). While a few of these DBPs have been studied extensively and are regulated in many countries, new unregulated DBPs (UR-DBPs) have also recently been identified in drinking water. The UR-DBPs are considered to be more toxic than regulated DBPs (R-DBPs). To understand the occurrence of UR-DBPs in a water distribution network (WDN), this research presents an approach to predicting the behaviour of emerging UR-DBPs such as dichloroacetonitrile (DCAN), trichloropropanone (TCP), and trichloronitromethane (TCNM) in WDNs. Water quality data, generated by sampling and laboratory analysis of 12 small communities, was used to develop predictive models. A framework was also proposed alongside the predictive models to estimate the concentration of emerging UR-DBPs under limited water quality sampling information. Moreover, the relationship between emerging UR-DBP concentrations and their identified predictors was further observed and evaluated by developing contour profiles. DCAN and TCP predictive models have coefficient of determination (R2) > 85%, whereas for TCNM model, the R2 was > 65%. Water quality parameters including water temperature, turbidity, conductivity, and dissolved organic carbon concentrations were identified as key predictors. Similarly, trichloroacetic acid and bromodichloromethane were identified as key predictors among DBP families, to predict the occurrence of emerging UR-DBPs. Developed models and relationships between the UR-DBPs and predictors can help water utilities and regulators to manage the occurrence of UR-DBPs in small WDNs.

Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations.

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes.

Segmentation of COVID-19 pneumonia lesions: A deep learning approach.

Background: Lung CT scan has a pivotal role in diagnosis and monitoring of COVID-19 patients, and with growing number of affected individuals, the need for artificial intelligence (AI)-based systems for interpretation of CT images is emerging. In current investigation we introduce a new deep learning-based automatic segmentation model for localization of COVID-19 pulmonary lesions. Methods: A total of 2469 CT scan slices, containing 1402 manually segmented abnormal and 1067 normal slices form 55 COVID-19 patients and 41 healthy individuals, were used to train a deep convolutional neural network (CNN) model based on Detectron2, an open-source modular object detection library. A dataset, including 1224 CT slices of 18 COVID-19 patients and 9 healthy individuals, was used to test the model. Results: The accuracy, sensitivity, and specificity of the trained model in marking a single image slice with COVID-19 lesion were 0.954, 0.928, and 0.961, respectively. Considering a threshold of 0.4% for percentage of lung involvement, the model was capable of diagnosing the patients with COVID-19 pneumonia, with a sensitivity of 0.982% and a specificity of 88.5%. Furthermore, the mean Intersection over Union (IoU) index for the test dataset was 0.865. Conclusion: The deep learning-based automatic segmentation method provides an acceptable accuracy in delineation and localization of COVID-19 lesions, assisting the clinicians and researchers for quantification of abnormal findings in chest CT scans. Moreover, instance segmentation is capable of monitoring longitudinal changes of the lesions, which could be beneficial to patients' follow-up.

Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach.

Heavy metal(loids) in drinking water have long been a critical water quality concern. Chronic exposure to toxic heavy metals and metalloids (TMMs) through water ingestion can result in significant health risks to the public, while elevated concentrations of less toxic heavy metals (LTMs) can compromise the aesthetic value of water. An integrated probabilistic-fuzzy approach was developed to help water utilities assess water quality regarding heavy metal(loids) (WQHM). In probabilistic assessments, the probabilities of exceedance of health risk guidelines due to chronic exposure to TMMs and exceedance of aesthetic objectives due to elevated LTMs concentrations were quantified through Monte Carlo simulations. The probabilistic assessments can address the aleatory uncertainties due to random variations of health risk parameters. A fuzzy inference system, composed of fuzzy membership functions, operators, and rules, was used to facilitate interpreting WQHM based on the probabilities of guideline exceedance. Epistemic uncertainties due to vagueness and imprecision in linguistic variables used for describing health risks and aesthetic impacts can be reduced by fuzzy inferencing. The developed approach was applied to four water quality scenarios characterized by different combinations of TMMs and LTMs concentrations. Reasonable decisions were recommended for WQHM management under the four scenarios. The developed approach offers a useful tool for systematically assessing WQHM from a health risk mitigation perspective by addressing different types of uncertainties.

State of provincial regulations and guidelines to promote low impact development (LID) alternatives across Canada: Content analysis and comparative assessment.

The use of Low Impact Development (LID) alternatives requires the establishment of appropriate regulations and guidelines on acceptable practices and developing consensus among stakeholders, thus assuring the rights of all water-users and for conflict resolution. This content analysis aims to examine whether stormwater regulations and guidelines have addressed the use of LID alternatives in urban settings and compares the current state of regulations in the context of Canadian provinces and territories., A list of eight core criteria relevant to the implementation of LID has been identified and an ordinal scale ranging from 1 to 6 is proposed to track the progress towards LID-friendly regulations in each province. Furthermore, based on comparative assessment, Canadian provinces are categorized into three groups: 'highly, moderately, and slightly LID-friendly' to project a broad view of the current state of regulations required to promote LID alternatives. . Results show that LID has become the mainstream technology for stormwater management in Alberta, British Columbia, Ontario, and Quebec, which are categorized as 'highly LID-friendly' provinces. The provinces where LID alternatives have gained considerable acceptance are categorized as 'moderately LID-friendly', which include Manitoba, Newfoundland and Labrador, Nova Scotia, Prince Edward Island, and Saskatchewan. Lastly, the province of New Brunswick is categorized as 'slightly LID-friendly', because of very limited use of LID alternatives in the stormwater management regulations. These findings of this content analysis can be of significant value to strengthen provincial/territorial regulations and extend the benefits of LID in stormwater quality management and sustainable water management.

Unregulated disinfection By-products in drinking water in Quebec: A meta analysis.

Disinfection by-products (DBPs) are formed primarily by the reaction of natural organic matter and disinfectants. DBPs that are not regulated are referred to as unregulated DBPs (U-DBPs) and they are in majority in total DBPs. U-DBPs can be more toxic than regulated DBPs. U-DBPs such as haloacetonitriles (HANs), haloacetonitriles (HKs) and halonitromethanes (HNMs) are widely present in drinking water supplies in different regions of the world. This study investigated the occurrence of U-DBPs and their variability in drinking water in the Province of Quebec (Canada), using the water quality database of 40 municipal water systems generated by our research group. The concentrations of HANs, HKs, and their compounds, including chloropicrin (CPK), were highly variable in different water systems in Quebec. The concentration range of these U-DBPs is in line with drinking water concentration ranges in different regions of the world. Factors such as system size, water source, season, pH, total organic carbon content, free residual chlorine and disinfectant types cause significant variations in the concentrations of HANs, HKs and their constituent compounds, including CPK, in drinking water in Quebec. This information is valuable for decision making concerning source water selection, water distribution planning, water treatment plant design including disinfection, and overall drinking water quality management related to U-DBPs. Moreover, U-DBPs and regulated DBPs are strongly correlated, although the degree of correlation can vary with water source, system size and season, indicating that regulated DBPs can be used as surrogates of U-DBPs.

3D Printing-Based Integrated Water Quality Sensing System.

The online and accurate monitoring of drinking water supply networks is critically in demand to rapidly detect the accidental or deliberate contamination of drinking water. At present, miniaturized water quality monitoring sensors developed in the laboratories are usually tested under ambient pressure and steady-state flow conditions; however, in Water Distribution Systems (WDS), both the pressure and the flowrate fluctuate. In this paper, an interface is designed and fabricated using additive manufacturing or 3D printing technology-material extrusion (Trade Name: fused deposition modeling, FDM) and material jetting-to provide a conduit for miniaturized sensors for continuous online water quality monitoring. The interface is designed to meet two main criteria: low pressure at the inlet of the sensors and a low flowrate to minimize the water bled (i.e., leakage), despite varying pressure from WDS. To meet the above criteria, a two-dimensional computational fluid dynamics model was used to optimize the geometry of the channel. The 3D printed interface, with the embedded miniaturized pH and conductivity sensors, was then tested at different temperatures and flowrates. The results show that the response of the pH sensor is independent of the flowrate and temperature. As for the conductivity sensor, the flowrate and temperature affect only the readings at a very low conductivity (4 µS/cm) and high flowrates (30 mL/min), and a very high conductivity (460 µS/cm), respectively.

Selecting Sustainability Indicators for Small to Medium Sized Urban Water Systems Using Fuzzy-ELECTRE.

Urban water systems (UWSs) are challenged by the sustainability perspective. Certain limitations of the sustainability of centralized UWSs and decentralized household level wastewater treatments can be overcome by managing UWSs at an intermediate scale, referred to as small to medium sized UWSs (SMUWSs). SMUWSs are different from large UWSs, mainly in terms of smaller infrastructure, data limitation, smaller service area, and institutional limitations. Moreover, sustainability assessment systems to evaluate the sustainability of an entire UWS are very limited and confined only to large UWSs. This research addressed the gap and has developed a set of 38 applied sustainability performance indicators (SPIs) by using fuzzy-Elimination and Choice Translating Reality (ELECTRE) I outranking method to assess the sustainability of SMUWSs. The developed set of SPIs can be applied to existing and new SMUWSs and also provides a flexibility to include additional SPIs in the future based on the same selection criteria.

The impacts of decision uncertainty on municipal solid waste management.

Municipal solid waste treatment options are not necessarily pragmatic if the stakeholders in the system don't mutually agree on their shares of liabilities. Stakeholders will select an option if their benefits are maximized and costs are minimized. A decision support framework is required to assess various waste treatment options and predict the optimal decision, considering multiple criteria and conflicting preferences of multiple stakeholders. Because of the inherent complexity, uncertainty is unavoidable and should be acknowledged to enhance the reliability in the decision-making process. Uncertainties in the cost and benefit estimates, and stakeholders' ability in verbalizing their preferences and their knowledge about each other's priorities can impact the outcome of such environmental management problem. In this study, uncertainty assessment methods such as sensitivity analysis, fuzzy Analytical Hierarchy Process, and Bayesian games have been explored. A case study in Vancouver (BC, Canada) has been used as a proof of concept.

Drinking Water Management and Governance in Canada: An Innovative Plan-Do-Check-Act (PDCA) Framework for a Safe Drinking Water Supply.

Drinking water management in Canada is complex, with a decentralized, three-tiered governance structure responsible for safe drinking water throughout the country. The current approach has been described as fragmented, leading to governance gaps, duplication of efforts, and an absence of accountability and enforcement. Although there have been no major waterborne disease outbreaks in Canada since 2001, a lack of performance improvement, especially in small drinking water systems, is evident. The World Health Organization water safety plan approach for drinking water management represents an alternative preventative management framework to the current conventional, reactive drinking water management strategies. This approach has seen successful implementation throughout the world and has the potential to address many of the issues with drinking water management in Canada. This paper presents a review and strengths-weaknesses-opportunities-threats analysis of drinking water management and governance in Canada at the federal, provincial/territorial, and municipal levels. Based on this analysis, a modified water safety plan (defined as the plan-do-check-act (PDCA)-WSP framework) is proposed, established from water safety plan recommendations and the principles of PDCA for continuous performance improvement. This proposed framework is designed to strengthen current drinking water management in Canada and is designed to fit within and incorporate the existing governance structure.

Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking framework.

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework (R WQI framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R WQI framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R WQI framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events.

Risk-based framework for optimizing residual chlorine in large water distribution systems.

Managing residual chlorine in large water distribution systems (WDS) to minimize human health risk is a daunting task. In this research, a novel risk-based framework is developed and implemented in a distribution network spanning over 64 km2 for supplying water to the city of Al-Khobar (Saudi Arabia) through 473-km-long water mains. The framework integrates the planning of linear assets (i.e., pipes) and placement of booster stations to optimize residual chlorine in the WDS. Failure mode and effect analysis are integrated with the fuzzy set theory to perform risk analysis. A vulnerability regarding the probability of failure of pipes is estimated from historical records of water main breaks. The consequence regarding residual chlorine availability has been associated with the exposed population depending on the land use characteristics (i.e., defined through zoning). EPANET simulations have been conducted to predict residual chlorine at each node of the network. A water quality index is used to assess the effectiveness of chlorine practice. Scenario analysis is also performed to evaluate the impact of changing locations and number of booster stations, and rehabilitation and/or replacement of vulnerable water mains. The results revealed that the proposed methodology could facilitate the utility managers to optimize residual chlorine effectively in large WDS.

Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index.

Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index's usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.

A new closeness centrality measure via effective distance in complex networks.

Closeness centrality (CC) measure, as a well-known global measure, is widely applied in many complex networks. However, the classical CC presents many problems for flow networks since these networks are directed and weighted. To address these issues, we propose an effective distance based closeness centrality (EDCC), which uses effective distance to replace conventional geographic distance and binary distance obtained by Dijkstra's shortest path algorithm. The proposed EDCC considers not only the global structure of the network but also the local information of nodes. And it can be well applied in directed or undirected, weighted or unweighted networks. Susceptible-Infected model is utilized to evaluate the performance by using the spreading rate and the number of infected nodes. Numerical examples simulated on four real networks are given to show the effectiveness of the proposed EDCC.