Titolo | Methodology for a preliminary assessment of water use sustainability in industries at sub-basin level |
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Tipo di pubblicazione | Articolo su Rivista peer-reviewed |
Anno di Pubblicazione | 2023 |
Autori | Sabia, Gianpaolo, Mattioli D., Langone Michela, and Petta Luigi |
Rivista | Journal of Environmental Management |
Volume | 343 |
Paginazione | 118163 |
Data di pubblicazione | Jan-10-2023 |
ISSN | 03014797 |
Parole chiave | basin management, decision making, drinking water, environmental assessment, Environmental assessment methods, environmental management, Frame data, groundwater resource, Indicator system sub-basin scale, Indicators systems, Information management, Irrigation, Italy, Land use, seasonal variation, Sub-basin scale, Subbasins, Sustainability, Sustainable development, Time frame, Time-frame data, Water conservation, water management, water planning, water supply, Water sustainability, water use efficiency, Waters managements |
Abstract | The sustainability of industrial production, especially for highly water-demanding processes, is strictly related to water resource availability and to the dynamic interactions between natural and anthropogenic requirements over the spatial and temporal scales. The increase in industrial water demand raises the need to assess the related environmental sustainability, facing the occurrence of global and local water stress issues. The identification of reliable methodologies, based on simple indices and able to consider the impact on local water basins, may play a basilar role in water sustainability diagnosis and decision-making processes for water management and land use planning. The present work focalized on the definition of a methodology based on the calculation of indicators and indices in the view of providing a synthetic, simple, and site-specific assessment tool for industrial water cycle sustainability. The methodology was built starting from geo-referenced data on water availability and sectorial uses derived for Italian sub-basins. According to the data monthly time scale, the proposed indices allowed for an industrial water-related impacts assessment, able to take into account the seasonal variability of local resources. Three industrial factories, located in northern (SB1, SB2) and central (SB3) Italian sub-basins, were selected as case studies (CS1, CS2, CS3) to validate the methodology. The companies were directly involved and asked to provide some input data. The methodology is based on the calculation of three synthetic indexes: the Withdrawal and Consumption water Stress Index (WCSI) allowed for deriving a synthetic water stress level assessment at the sub-basin scale, also considering the spatial and temporal variations; the industrial water use sustainability assessment was achieved by calculating the Overall Factory-to-Basin Impact (OFBI) and the Internal Water Reuse (IWR) indices, which allowed a preliminary evaluation of the factories' impacts on the sub-basin water status, considering the related water uses and the overall pressures on the reference territorial context. The WCSI values highlighted significant differences between the northern sub-basins, characterised by limited water stress (WCSISB1 = 0.221; WCSISB2 = 0.047), and the central ones, more subjected to high stress (WCSISB3 = 0.413). The case studies CS1 and CS3 showed to exert a more significant impact on the local water resource (OFBICS1 = 0.18%; OFBICS2 = 0.192%) with respect to CS2 (OFBI = 0.002%), whereas the IWR index revealed the different company's attitude in implementing water reuse practices (IWRCS1 = 40%; IWRCS1 = 27%; IWRCS1 = 99%). The proposed methodology and the indices may also contribute to assessing the effectiveness of river basin management actions to pursue sustainable development goals. © 2023 |
Note | cited By 0 |
URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160320312&doi=10.1016%2fj.jenvman.2023.118163&partnerID=40&md5=f527fbb17378c344e57039c2f6fd41de |
DOI | 10.1016/j.jenvman.2023.118163 |
Titolo breve | Journal of Environmental Management |
Citation Key | Sabia2023 |