Abstract
Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.
Highlights
Highways are essential for the development and security of a region
The aim of this study is to address the lack of appropriate understanding and methods to assess the spatiotemporal impact of highway construction on water quality in a developing country
The analysis showed that the ‘with project’ alternative had a higher priority of 0.727 as compared to the ‘without project’ alternative priority score of 0.273
Summary
Highways are essential for the development and security of a region. Understanding the detrimental effects of highway projects is pivotal for environmentally appropriate decision making. Environmental impact assessment (EIA) involves the assessment of impacts of a development project on the environmental attributes, including water resources (Barthwal 2012; Canter 1995). Conventional EIA can be time consuming, expensive, and subjective (Glasson et al 2005; Takyi 2012). Conventional EIA focuses mainly on the temporal aspect of the impacts and undermines the importance of their spatial distribution. Geographic information systems (GIS) can overcome these limitations and provide an unbiased and interpretable EIA (Agrawal 2005)
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