综合水质和水量诊断多核多层次城市群水代谢系统健康状况

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Ying Yang, Jing Wen, Meirong Su, Qionghong Chen
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引用次数: 0

摘要

随着水危机频率的增加,城市群(UAs)不得不仔细检查其水系统的健康状况。城市水系的健康与代谢过程密切相关。迄今为止,由于方法上的限制,还没有使用水量和水质来分析多核多层次UAs中的水系统。为了解决这一研究空白,我们开发了一个综合水质-水量模型,用于诊断可以处理嵌套多区域输入输出(MRIO)表的水代谢系统。我们将MRIO表进行耦合,建立了水量-水质综合代谢网络(IWMN)和水量代谢网络(QWMN)。我们利用粤港澳UA的数据对两个网络进行了测试,并利用生态网络分析对网络的活力、组织性、弹性和协作性四个方面进行了评估。我们发现,与QWMN相比,IWMN表现出较低的活力(内部循环10.4%)和以依赖为主导的组织(总贡献强度σ = -23)。政治驱动的差异形成了稳健性分布,而互惠倾向与复杂的剥削关系共存(52.4%),特别是在核心大城市香港,出现了58对新的竞争对。因此,我们建议优先优化高含水量产品的粤港澳贸易,以增强系统健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrating water quality and water quantity to diagnose the health of water metabolism systems in multi-core multi-level urban agglomerations

Integrating water quality and water quantity to diagnose the health of water metabolism systems in multi-core multi-level urban agglomerations
Urban agglomerations (UAs) are compelled to scrutinize the health of their water systems as the frequency of water crises increases. An urban water system’s health is closely related to metabolism processes. To date, water systems in multi-core multi-level UAs have not been analyzed using water quantity and water quality because of methodological constraints. To address this research gap, we developed an integrated water quality–water quantity model for diagnosing water metabolism systems that could process nested multi-region input-output (MRIO) tables. We coupled the MRIO tables and established two networks, an integrated water quantity–quality metabolism network (IWMN) and a water quantity metabolism network (QWMN). We tested the two networks with data from the Guangdong-Hong Kong-Macao UA and assessed four aspects of the networks’ health, namely vigor, organization, resilience, and collaboration, using ecological network analysis. We discovered that IWMN exhibited lower vigor (internal circulation 10.4%) and organization dominated by dependency (total contribution intensity σ = -23) compared to the QWMN. Polity-driven disparities shaped the robustness distribution, while a mutualism tendency coexisted with a complex exploitation relationship (52.4%), particularly in the core large-sized city of Hong Kong, where 58 new competitive pairs emerged. Thus, we recommend prioritizing Guangdong-Hong Kong-Macao trade optimization for high-water-content products to enhance system health.
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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