A mass balance approach to urban water analysis using multi-resolution data

被引:5
|
作者
Hastie, Allisa G. [1 ]
Chini, Christopher M. [2 ]
Stillwell, Ashlynn S. [1 ]
机构
[1] Univ Illinois, Dept Civil & Environm Engn, 205 N Mathews Ave, Urbana, IL 61801 USA
[2] Air Force Inst Technol, Dept Syst Engn & Management, Wright Patterson AFB, OH USA
关键词
data quality; energy intensity; energy-water nexus; material flow analysis (MFA); non-revenue water; urban metabolism; ENERGY-REQUIREMENTS; BILLING DATA; DEMAND; MANAGEMENT; SYSTEMS; UTILITY;
D O I
10.1111/jiec.12995
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With a growing urban population and increasing climate uncertainty, it is necessary to quantitatively understand the flux of resources through cities, specifically energy and water resources. Many methods exist to analyze the urban energy-water nexus based on either physical characteristics of the system or using available data. While data-driven approaches can be valuable, they are often challenging to duplicate on a large scale due to data availability, or they do not allow for drawing specific, detailed conclusions. Our work seeks to remedy these challenges by using multi-resolution data and a mass balance approach to provide a reproducible and scalable method for analyzing water and embedded energy systems in cities with varying levels of infrastructure and technological advancements. Using a combination of utility-scale and meter-level data, we provide several quantitative performance gauges on a monthly and annual time scale. This process reveals notable seasonal variation in water demand, non-revenue water, and embedded energy, providing a holistic understanding of water and its embedded resources. Our work further confronts the challenges of integrating disparate data sets into a uniform format to allow for accurate analysis. These particular data, coupled with a mass balance methodology, facilitate the examination of an urban area from top-down and bottom-up perspectives, yielding opportunities to quantify additional performance metrics beyond a single approach.
引用
收藏
页码:213 / 224
页数:12
相关论文
共 50 条
  • [41] Multi-resolution image analysis using the quaternion wavelet transform
    Eduardo Bayro-Corrochano
    [J]. Numerical Algorithms, 2005, 39 : 35 - 55
  • [42] TARGET DETECTION USING MULTI-RESOLUTION ANALYSIS AND HOLDER CONSTANT
    Liu Wenyu Li Hua Zhu Guangxi (Dept. of Electron
    [J]. Journal of Electronics(China), 2001, (02) : 160 - 166
  • [43] Monitoring HVDC systems using wavelet multi-resolution analysis
    Gaouda, AM
    El-Saadany, EF
    Salama, MMA
    Sood, VK
    Chikhani, AY
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (04) : 662 - 670
  • [44] Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding
    Nsugbe, Ejay
    William Samuel, Oluwarotimi
    Asogbon, Mojisola Grace
    Li, Guanglin
    [J]. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2021, 6 (03) : 360 - 375
  • [45] Evaluation of Facial Paralysis Degrees Using Multi-Resolution Analysis
    Ngo, T. H.
    Chen, Y. W.
    Seo, M.
    Matsushiro, N.
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL APPLICATIONS (CISIA 2015), 2015, 18 : 648 - 651
  • [46] Deconstructing a polygenetic landscape using LiDAR and multi-resolution analysis
    Barrineau, Patrick
    Dobreva, Iliyana
    Bishop, Michael P.
    Houser, Chris
    [J]. GEOMORPHOLOGY, 2016, 258 : 51 - 57
  • [47] A novel multi-resolution representation for time series sensor data analysis
    Yupeng Hu
    Cun Ji
    Qingke Zhang
    Lin Chen
    Peng Zhan
    Xueqing Li
    [J]. Soft Computing, 2020, 24 : 10535 - 10560
  • [48] Spatial analysis and visualization of global data on multi-resolution hexagonal grids
    Stough, T.
    Cressie, N.
    Kang, E. L.
    Michalak, A. M.
    Sahr, K.
    [J]. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE, 2020, 3 (01) : 107 - 128
  • [49] Geometric Multi-resolution Analysis Based Classification for High Dimensional Data
    Tran, Dung N.
    Chin, Sang Peter
    [J]. CYBER SENSING 2014, 2014, 9097
  • [50] MULTI-RESOLUTION FRAMEWORK FOR SPITZOID NEOPLASM CLASSIFICATION USING HISTOLOGICAL DATA
    Del Amor, Rocio
    Javier Curieses, Francisco
    Launet, Laetitia
    Colomer, Adrian
    Moscardo, Anais
    Mosquera-Zamudio, Andres
    Monteagudo, Carlos
    Naranjo, Valery
    [J]. 2022 IEEE 14TH IMAGE, VIDEO, AND MULTIDIMENSIONAL SIGNAL PROCESSING WORKSHOP (IVMSP), 2022,