New insights from grey water footprint assessment: An industrial park level

Dong, Huijuan; Zhang, Lei; Geng, Yong; Peng, Li; Yu, Chenhui

doi:10.1016/j.jclepro.2020.124915

Cited by 21 publications

(12 citation statements)

References 26 publications

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“…In production systems where process water is one of the main inputs, especially in the textile and beverage sector, the money spent for water footprint and virtual water is vital. The management of the water used in production and the amount and quality of the wastewater generated are correlated with financial sustainability [22]. Likewise, the conscious consumer wants to know the water used and the wastewater generated throughout the entire supply chain of the product.…”

Section: Resultsmentioning

confidence: 99%

Smart water chain: Immutable, distributed and decentralized water transaction ledgers

Satilmisoglu

Keskin

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Blockchain is a transactional data storage system where data can be stored reliably without the need for a central database or trusted authority. The data can be anything like financial transactions, supply chain processes or medical records. It is similar to a classical database but uses a decentralized ledger and allowing each participant in the network to have their own copy of the ledger and be able to see all transactions. Data stored in the distributed ledger can only be read or written, not deleted or updated unlike traditional central database. Reliable data is essential for the water industry for information about the current status of any water system, to build trust between stakeholders at all scales, and for effective forecasting and future scenarios by reducing uncertainty. The aim of this study is to examine the potential of using blockchain-based algorithms (smart contracts, chain codes, decentralised identifiers etc.) for the water industry on the edge of digital transformation, and to propose water-related data processing system architectures for different water quantity-quality based scenarios.

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Section: Resultsmentioning

confidence: 99%

Smart water chain: Immutable, distributed and decentralized water transaction ledgers

Satilmisoglu

Keskin

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…GWF a is the amount of fresh water required to carry water pollutants caused by agricultural activities (e.g., livestock manure, livestock house cleaning, fertilizer, and pesticide use) [ 34 ]. According to previous studies [ 25 , 56 ], agricultural water pollution is mainly caused by nitrogen (N), including the usage of nitrogen fertilizers and pesticide spraying, etc. Accordingly, the estimation model is as follows:

where GWF a is the agricultural grey water footprint (m 3 ); α is the rate of nitrogen fertilizer entering the water (%), and the national average nitrogen fertilizer inflow rate of 7% is chosen for calculation, based on previous studies [ 34 , 48 ]; N is the annual amount of nitrogen applied to the crop (kg); C max is the standard concentration of pollutant water quality (kg/m 3 )—with reference to the Environmental Quality Standards for Surface Water of the People’s Republic of China [ 57 ], this paper adopts the 3rd level (of a total of 5 levels) as the minimum requirement for wastewater pollution control, with the maximum allowable nitrogen concentration of 1 mg/L—and C nat is the natural concentration of water (kg/m 3 ), which is the concentration of water pollution under natural conditions without any anthropogenic interference, and is usually assumed to be 0 [ 8 , 26 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

“…El-Marsafawy and Mohamed [ 24 ] estimated the agricultural VWF in Egypt to assess the general water consumption during the growth phase of crops. In addition, the concept of grey water footprint (GWF) has been proposed as an indicator of water pollution induced by pollutants such as nitrogen and phosphorus [ 25 , 26 ]. GWF evaluates the degree of water pollution by considering the amount of fresh water needed to dilute the water pollutants to meet certain water quality standards [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Regional Coordination Relationship between Water Utilization and Urbanization Based on Decoupling Analysis: A Case Study of the Beijing–Tianjin–Hebei Region

Shen

Yao

2022

IJERPH

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Understanding the potential association between the urbanization process and regional water shortage/pollution is conducive to promoting the intensive utilization of local water resources. In this study, the water footprint model was used to estimate water utilization status in terms of both water quantity (virtual water footprint (VWF)) and water quality (grey water footprint (GWF)) in the Beijing–Tianjin–Hebei region (China) during 2004–2017. Their potential coordination relationship with the local urbanization process represented by the gross domestic product (GDP), population (POP), and built-up area (BA) was examined using the Tapio decoupling model. The results showed that from 2004 to 2017, (1) VWF in Beijing and Tianjin showed non-significant decreasing trends, with reductions of 1.08 × 109 and 1.56 × 109 m3, respectively, while that in Hebei showed a significant increasing trend, with an increase of 5.74 × 109 m3. This indicated a gradually increasing water demand in Hebei and decreasing demand in Beijing and Tianjin. In all three regions, the agricultural sector accounted for a relatively high proportion of VWF compared to other sectors. (2) GWF in Beijing, Tianjin, and Hebei all showed declining trends, with reductions of 2.19 × 1010, 2.32 × 1010, and 1.66 × 1011 m3, respectively, indicating considerable local water quality improvement. The domestic sector contributed as the main component of GWF in Beijing, while agriculture was the main contributor in Hebei. The major contributor in Tianjin transitioned from the domestic (before 2015) to the agricultural sector. (3) We found good coordination between VWF and GDP in all three regions, as their local economic development was no longer overly dependent on water consumption. However, the expansion of urban built-up area or population would bring about accelerated depletion of water resources. (4) GWF in the three provinces showed good coordination with GDP, POP, and BA in most years, implying that the development of urbanization no longer strongly caused the pollution of water resources. In sum, policymakers should focus on improving agricultural irrigation efficiency and residents’ awareness of water conservation, so as to gradually achieve sustainable water resource management in the BTH region.

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“…In recent years, footprint indicators have also been extensively used to study the transfer of resources and the impacts between regions by trade combined with top‐down input–output models (Santiago et al, 2020; Wang et al, 2016). Bottom‐up and top‐down approaches are widespread in footprint research, with the former being more suitable for agricultural products than for industrial products (Dong et al, 2021). Therefore, EF s have provided new perspectives for identifying and quantifying the impacts of production and consumption.…”

Section: Introductionmentioning

confidence: 99%

Assessment of environmental footprint using geospatial approach to ascertain the Sustainable Development Goal 2030s of India

Sharma,

Aarav,

Sharma

et al. 2023

Natural Resources Forum

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Utilization of natural resources has multiplied globally, resulting in serious environmental deterioration and impeding the achievement of the Sustainable Development Goals (SDGs). For the harmonious development of human nourishment and the balance of nature, it is vital to evaluate environmental segments' resource usage, transformation, and residue, referred to as ‘footprint,’ in order to highlight carrying capacity and sustainability. This analysis highlights the Environmental Footprint (EF) of India per state from 2010 to 2020 in terms of hectares per capita. This study evaluates India's biological, hydrological, energy, ecological, and pollution footprints, carrying capacity, environmental pressure, and environmental deficit using 17 distinct parameters categorized under the themes of biological resource, hydrological resource, energy resource, and pollution concentration. We proposed a reoriented methodology and EF concepts that determine India's footprint, carrying capacity, nature of sustainability, environmental pressure index, and its consequential links to the 2030 SDGs. As a result, the biological resources contributed to ~50% of the environmental footprint, while hydrological, energy, and pollutants made up the remaining. Between 2010 and 2020, Delhi, Uttar Pradesh, Bihar, and West Bengal had the highest EF, while Jammu and Kashmir and the north‐eastern provinces had the lowest. During the research period, the ecological deficit in India has increased overall. India impedes the 2030 SDGs; therefore, the study provides a picture of resource consumption, waste generation, economic growth, and societal changes, enabling academics and policymakers to redefine or document policy for a more sustainable future.

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New insights from grey water footprint assessment: An industrial park level

Cited by 21 publications

References 26 publications

Smart water chain: Immutable, distributed and decentralized water transaction ledgers

Smart water chain: Immutable, distributed and decentralized water transaction ledgers

Exploring the Regional Coordination Relationship between Water Utilization and Urbanization Based on Decoupling Analysis: A Case Study of the Beijing–Tianjin–Hebei Region

Assessment of environmental footprint using geospatial approach to ascertain the Sustainable Development Goal 2030s of India

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