We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.
In the present study, we examined how sensitivity to oil changes in combination with environmental stressors in Fundulus grandis embryos. We exposed embryos (<24 h post fertilization) to a range of high-energy water accommodated fraction (HEWAF) concentrations (0-50 parts per billion [ppb] total polycyclic aromatic hydrocarbons [PAHs]) made from Macondo crude oil in conjunction with various environmental conditions (temperature: 20 and 30 °C; salinity: 3, 7, and 30 practical salinity units [PSU]; and dissolved oxygen: 2 and 6 mg/L). Endpoints included mortality, hatching rates, and expression of cytochrome p450 1a and 1c (cyp1a, cyp1c) in hatched larvae. There was 100% mortality for all fish under the 2 parts per million (ppm) dissolved oxygen regimes. For the 6 mg/L dissolved oxygen treatments, mortality and median lethal time (LT50) were generally higher in the 30 °C treatments versus the 20 °C treatments. Oil increased mortality in fish exposed to the highest concentration in the 20-3-6 (°C-PSU-mg/L), 25-7-6, and 30-30-6 conditions. Hatching was driven by environmental conditions, with oil exposure having a significant impact on hatching in only the 25-7-6 and 30-30-6 groups at the greatest HEWAF exposure. Expression of cyp1a was up-regulated in most treatment groups versus the controls, with cyp1c expression exhibiting a similar pattern. These data suggest interactive effects among temperature, salinity, and PAHs, highlighting a need to further assess the effects of oil exposure under various environmental conditions. Environ Toxicol Chem 2018;37:1916-1925. © 2018 SETAC.
In this paper, we used complex network analysis approaches to investigate topological coevolution over a century for three different urban infrastructure networks. We applied network analyses to a unique time-stamped network data set of an Alpine case study, representing the historical development of the town and its infrastructure over the past 108 years. The analyzed infrastructure includes the water distribution network (WDN), the urban drainage network (UDN), and the road network (RN). We use the dual representation of the network by using the Hierarchical Intersection Continuity Negotiation (HICN) approach, with pipes or roads as nodes and their intersections as edges. The functional topologies of the networks are analyzed based on the dual graphs, providing insights beyond a conventional graph (primal mapping) analysis. We observe that the RN, WDN, and UDN all exhibit heavy tailed node degree distributions [P(k)] with high dispersion around the mean. In 50 percent of the investigated networks, P(k) can be approximated with truncated [Pareto] power-law functions, as they are known for scale-free networks. Structural differences between the three evolving network types resulting from different functionalities and system states are reflected in the P(k) and other complex network metrics. Small-world tendencies are identified by comparing the networks with their random and regular lattice network equivalents. Furthermore, we show the remapping of the dual network characteristics to the spatial map and the identification of criticalities among different network types through co-location analysis and discuss possibilities for further applications.
The objective of this research is to evaluate whether complex dynamics of urban drainage networks (UDNs) can be expressed in terms of their structure, i.e. topological characteristics. The present study focuses on the application of topological measures for describing the transport and collection functions of UDNs, using eight subnetworks of the Dresden sewer network as study cases. All UDNs are considered as weighted directed graphs, where edge weights correspond to structural and hydraulic pipe characteristics which affect flow. Transport functions are evaluated in terms of travel time distributions (TTDs), under the hypothesis that frequency distributions of Single Destination Shortest Paths (SDSP) of nodes to the outlet had similar shapes than TTDs. Assessment of this hypothesis is done based on two-sample Kolmogorov-Smirnov tests and comparisons of statistical moments. Collection analysis, i.e. determination of flow paths, is done based on two approaches: (1) using Edge Betweenness Centrality (EBC), and (2) based on the number of SDSP going through an edge connecting a node to the outlet, referred as Paths. Hydrodynamic simulation results are used to validate the outcomes of graph analysis with actual flow behaviors. Results indicate that given an appropriate edge weighting factor, in this case Residence Time, SDSP has the potential to be used as an indicator for flow transport in UDNs. Moreover, both EBC and Paths values were highly correlated to average flows. The first approach, however, proved to be inadequate for estimating flows near the outlet but appropriate for identifying different paths in meshed systems, while the second approach lead to better results in branched networks. Further studies regarding the influence of UDNs layout are needed.
This paper was published online on 9 March 2017 with an error in Eq. (1b) and surrounding text and in the caption to Fig. 6. The caption of Fig. 6 should read as "WDN subnets along a gradient of breaking points between the power laws of trunk and tail (k break outliers from panel (d) in Fig. 5): (a) DZ11: k break = 5, p(k) trunk = 0.40k −2.49 and p(k) tail = 0.09 k −1.47 , N = 2425 (dual-mapped nodes); (b) DZ10: k break = 8, p(k) trunk = 1.46k −2.80 and p(k) tail = 0.26k −1.88 , N = 3179; (c) DZ01: k break = 10, p(k) trunk = 1.17k −2.37 and p(k) tail = 0.138k −1.686 , N = 1497; (d) DZ32: in this subnet power-law distributions of trunk and tail converge as the breaking point between the two power laws increases (k break = 20), and p(k) = 1.07k −2.27 (N = 2271)." On page 2, left-hand column, the text above Eqs. (1a) and (1b) should read as "The node degree distributions (NDD) for both types of water networks can be approximated by a Pareto power-law distribution [Eq. (1a); large, mature networks], p(k) = ak −γ , (1a) for k 2, or a double Pareto power-law distribution [Eq. (1b); small, immature networks], described by a two-piece function in the form p(k) trunk = ak −γ trunk , p(k) tail = bk −γ tail , (1b) where k 2 for p(k) trunk and k k break for p(k) tail. The exponent, γ [Eq. (1a)] and γ trunk [Eq. (1b)], for both WDNs and SSNs converges above a threshold of network size, measured as dual-mapped nodes N > 10 2 ." The paper has been corrected as of 25 February 2019. The caption and text are incorrect in the printed version of the journal.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.