AHP‑based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

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

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  Vol.:(0123456789)  1 3 Applied Water Science (2018) 8:72 https://doi.org/10.1007/s13201-018-0699-5   ORIGINAL ARTICLE AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya Polash Banerjee 1  · Mrinal Kanti Ghose 2  · Ratika Pradhan 3 Received: 2 November 2017 / Accepted: 4 April 2018 © The Author(s) 2018 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. Keywords  Analytic hierarchy process · Environmental impact assessment · Geographic information systems · Sensitivity analysis · Water pollution · Highway Introduction Highways are essential for the development and security of a region. However, understanding the detrimental effects of highway projects is pivotal for environmentally appro-priate 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 (Glas-son et al. 2005; Takyi 2012). Moreover, 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 easily interpretable EIA (Agrawal 2005).A highway is essentially a non-point source of water pollution. Construction and post-construction conditions of a highway generate pollutants, which degrade the water quality and affect the habitat of the nearby water bodies (Wu et al. 1998). Highway runoff contains relatively high Electronic supplementary material  The online version of this article (https ://doi.org/10.1007/s1320 1-018-0699-5) contains supplementary material, which is available to authorized users.  *  Polash Banerjee banerjee.polash@gmail.com Mrinal Kanti Ghose mkghose@cus.ac.in Ratika Pradhan ratika.p@smit.smu.edu.in 1  Department of Computer Science and Engineering, SMIT, Sikkim Manipal University, Majitar, Sikkim 737136, India 2  Department of Computer Applications, Sikkim University, Gangtok, Sikkim 737102, India 3  Department of Computer Applications, SMIT, Sikkim Manipal University, Majitar, Sikkim 737136, India   Applied Water Science (2018) 8:72  1 3  72 Page 2 of 17 concentration of pollutants as compared to the adjacent river (USEP 1996; Bingham et al. 2002; Gan et al. 2008). Statistical models suggest that traffic volume, rainfall char-acteristics, highway pavement type, and properties of pollut-ants and seasons are important determinants of road runoff composition (Aldheimer and Bennerstedt 2003; Forsyth et al. 2006; Granato 2013; Kayhanian et al. 2003; Kim et al. 2006; Li and Barrett 2008; Pagotto et al. 2000; Tong and Chen 2002; USEP 1996; Yannopoulos et al. 2004, 2013). These models mostly cater to highway projects of devel-oped countries. Applicability of these models in develop-ing countries remains largely unexplored. Depending upon the nature of the highway runoff study, traffic volume can be considered in two broad ways, namely, as average daily traffic and vehicles during storm. Average daily traffic is a good predictor of water pollutants like Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), and Zinc (Zn), while it poorly predicts the levels of lead, copper, and oil and grease (Thomson et al. 1997; Venner 2004). In con- trast, oil and grease has a significant relationship with the vehicles during storm rather than average daily traffic (Sten-strom et al. 1982; Venner 2004). Correlation studies of river water have shown COD is strongly correlated topercent Dis-solved Organic Carbon, Dissolved Oxygen, and Total Dis-solved Solids (TDS). Moreover, TSS is strongly associated with pH and TDS (Bhandari and Nayal 2008; Waziri and Ogugbuaja 2010).A wide variety of water quality indices are used in the impact assessment of a highway project. However, except for Water Quality Status Index (WQSI), all other water indices involve predetermined weight or the importance of water pollutants, which cannot be manipulated to see the impact of the change in water pollutant weight on the overall impact on water quality. (Karbassi et al. 2011; Mushtaq et al. 2015; Li et al. 2009; Yan et al. 2015). The weight of a pollutant can be determined using Multi-criteria Decision-Making (MCDM) methods like Delphi Method and Analytic Hier-archy Process (AHP) that use expert opinion-based criteria weighing (Mushtaq et al. 2015; Kumar and Alappat 2009). AHP decomposes the decision process into several levels of hierarchy. Based on a pairwise comparison of criteria for alternatives, a comparison matrix is made for the evalua-tion of criteria weights (Saaty 1980, 1990; Saaty and Vargas 1994). The data requirement for AHP is less data-intensive than classic statistical methods, which are based on histori-cal data (Arriaza and Nekhay 2008). Use of AHP-based weighing of pollutants helps in giving due importance to the characteristics and conditions of the study area, which may not be reflected in non-expert opinion-based criteria weighing (Karbassi et al. 2011).Limited literature is available on GIS-based water qual-ity impact assessment of highway projects (Agrawal 2005; Banerjee and Ghose 2016; Brown and Affum 2002). These studies do not provide a clear methodology to perform Spa-tial Water Quality Impact Assessment (SWQIA). In addi-tion, the reliability of these models is arguable as they lack appropriate model validation criteria. Moreover, none of them have discussed the importance of individual water pol-lutants on the overall value of a spatial water quality score or index. Spatial analysis of water quality usually involves interpolation of individual water pollutants in the study area, followed by combining their thematic maps using appropri-ate water quality index (Gajendra 2011; Yan et al. 2015; Zhou et al. 2007). Studies show that kriging is an effective interpolation method (Fallahzadeh et al. 2016; Ostovari et al. 2012; Sadat-Noori et al. 2014). Spatial Explicit Sensitivity Analysis (SESA) is progres-sively becoming an essential component of spatial model validation. It is the measurement of variation in the model outputs explained by the variation in the model inputs (Chen et al. 2010, 2011; Crosetto et al. 2000; Feizizadeh et al. 2014; Lilburne and Tarantola 2009; Qi et al. 2013; Xu and Zhang 2013). The outputs of a robust spatial model show marginal perturbations to changes in the model inputs. How-ever, the computational cost associated with SESA is a major constraint for its inclusion in spatial analysis.The aim of this study is to address the lack of appropri-ate understanding and methods to assess the spatiotempo-ral impact of highway construction on water quality in a developing country. The SWQIA model performs a project-specific spatiotemporal assessment of traffic-induced water pollution in East Sikkim. With further validation, in terms of a wider study area and comprehensive water quality parameters, it can be used in developing countries to assess impacts of highway construction on water quality. It acts as a geovisualization and temporal extrapolation tool for traffic-induced water pollution. Moreover, SWQIA model can capture people’s perception of the project impacts. The results of the model are encouraging and show that it is a robust model with good prediction capability. Materials and methods Study area The study area stretches from Rangpo (27 ° 10 ′ 31.26 ″  N, 88 ° 31 ′ 44.43 ″ E, Elevation 300 m) to Ranipool (27 ° 17 ′ 28.74 ″  N, 88 ° 35 ′ 31.11 ″ E, Elevation 847 m) in the East district of Sikkim, a stretch of 27 km along the route of NH 10 high-way. It is the main route which connects Sikkim with the rest of India. In 2008–09, the broadening of NH 10 had com-menced to promote defence and economic growth in Sik-kim. The highway has been broadened from its present width of 7–12 m. This broadening of the highway will cause an increase in traffic volume. The project stretches from Sevok  Applied Water Science (2018) 8:72  1 3 Page 3 of 17 72 in West Bengal to North Sikkim. However, the road cor-ridor chosen for the study is relatively much smaller than the actual stretch of the highway because of its relatively homogenous geography. A drainage area of 147 km 2  was delineated to include all the micro-catchments providing runoff to the highway/rivers (Machado et al. 2017; Siqueira et al. 2017). Furthermore, the project impact area of 7.4 km 2  was demarcated by merging 50 m buffers around the rivers and the highway. The rationale of considering the project impact area was based on the accessibility of the river water/ road runoff by the wildlife and humans living near the river/ highway (Antunes et al. 2001; Geneletti 2004). The study area has steep elevations, which is predominated by subtrop-ical vegetation, interspersed with small human habitations, traditional farming areas, and towns like Rangpo, Singtam, and Ranipool. The highway closely follows river Teesta and Rani Khola (Fig. 1). It is also worth noting that Sikkim has high biodiversity and it is home to a large number of endemic species (Arrawatia and Tambe 2011). Moreover, it has a unique culture which gives high value to its natural resources. Therefore, unabated water pollution can severely affect the ecological and cultural sanctity of this area. Data collection Based on the changes in Annual Average Daily Traffic (AADT) and landuse & landcover (LULC), three time frames were considered for the study, viz. the year 2004 as pre-project scenario, 2014 as project implementation scenario, and 2039 as post-project scenario (Fig. 3). The changes considered from ‘pre-project’ to ‘project imple-mentation’ scenarios included changes in AADT and LULC. While only change in AADT was considered for ‘post-pro- ject year’ scenario. AADT for ‘post-project year’ scenario was calculated based on annual growth rates for traffic, pro-vided by Border Roads Organization. LULC in ‘pre-project’ and ‘project implementation’ scenarios was estimated using satellite images, whereas such an estimation was not pos-sible for ‘post-project’ scenario. Five water pollutants were considered for the study (Table 1) mainly based on the abil-ity of the empirical model to predict their concentration in the road runoff, and second, on the availability of a com-plete data set of historical water quality of the rivers near the highway. Keeping in view of the ecological and cultural sensitivity of the local water bodies, drinking water quality standards of US Public Health Service (USPH 1962) were considered, except pH, where Bureau of Indian Standards (BIS 2012) standard was considered for the present study. Various inputs for SWQIA model were prepared, as men-tioned in Table 2. AHP model A structured questionnaire on pairwise comparison of water pollutants and project alternatives was administered to a panel of experts, based on a numerical scale having values ranging from 1 to 9 as suggested by Saaty (2000) (Table 3). The expert choice software was used for the preparation of comparison Fig. 1 Study area   Applied Water Science (2018) 8:72  1 3  72 Page 4 of 17     T   a    b    l   e    1    D  e  s  c  r   i  p   t   i  o  n  o   f  w  a   t  e  r  q  u  a   l   i   t  y  p  a  r  a  m  e   t  e  r  s   a     D  o  a  m  e   k  p  o  r  e   t  a   l .   (   2   0   1   6   )   N  a  m  e   M  e  a  n   i  n  g   R  e   l  a   t   i  o  n  w   i   t   h  r  o  a   d  r  u  n  o   ff   a 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Applied Water Science (2018) 8:72  1 3 Page 5 of 17 72 matrix and calculation of the weight of the pollutants. Two project alternatives were considered for the AHP model, viz. ‘with project’ and ‘without project’, for comparison of the impacts. The ‘with project’ alternative assumed that the high-way had been broadened and traffic volume had increased, while ‘without project’ alternative assumed no change in the highway width and the traffic volume remains unchanged. In AHP, the elements of the comparison matrix, a ij  >  0  , express the expert’s evaluation of the preference of the i th criterion in relation with the  j th. It is worth noting that ij  =  whenever i  =  j  and a ij  =  1 ∕ a  ji  for i  ≠  j  . The total number of pairwise comparisons by expert is n ( n  −  1 )∕ 2  , where ‘ n ’ is the total number of criteria under consideration. The eigenvector, w,  matching the maximum eigenvalue,  max  , of the comparison matrix is the preferred solution of the AHP model, that isor(1)  =   max  . (2) ⎛⎜⎜⎝ a 11  ⋯  a 1 n ⋮ ⋱ ⋮ a n 1  ⋯  a nn ⎞⎟⎟⎠⎛⎜⎜⎝ w 1 ⋮ w n ⎞⎟⎟⎠ =   max ⎛⎜⎜⎝ w 1 ⋮ w n ⎞⎟⎟⎠ , where A  is the comparison matrix. The elements of w  must fulfill the condition, ∑ i = 1 w i  =  1  , and under ideal condition,  max  =  n  . The reliability of the AHP model is assessed by consistency ratio, CR  =  CI ∕ RI  , where Consistency Index, CI  = (  max  −  n )∕( n  −  1 )  , and Random Consistency Index, RI, that is obtained by a large number of simulation runs. It varies upon the order of the comparison matrix (Saaty 2000; Taha 2010). An inconsistency value not more than 0.1 is acceptable for an AHP model. Modelling of seasonal peak storm runoff  Rainfall occurs almost the entire year in Sikkim (IMD 2014). However, there is a substantial drop in rainfall in the non-monsoon months, which is from November to March. While April–October gets a relatively high proportion of annual average rainfall (Rahman et al. 2012). Thereby, the non-monsoon months were considered as antecedent dry period and the highest daily rainfall was considered as maxi-mum intensity rainfall. The drainage area and micro-catch-ments feeding the road runoff/rivers in the study area were Table 2 Data types, source, and processing method for SWQIA model a  Courtesy: MoRTH, Traffic data of NH 31A (nearest town, Singtam) collected on July 2004 and December 2004 by Ministry of Road Trans-port and Highways, Govt. of India. Downloaded from http://morth .nic.in/write readd ata/subli nkima ges/sikki m2987 77224 7.htm accessed on 18/05/2014. BRO, Traffic data of NH 31A (nearest town, Rangpo) collected from 29/06/2012 to 06/07/2012 by Border Roads Organization, Govt. of India. The AADT was projected for 2014 and 2039 based on annual growth rates (in percent) for traffic estimated by BRO b  LISS III accessed from http://bhuva n.nrsc.gov.in/data/downl oad/index .php under Resourcesat-I satellite image on 18/12/2014 Data typeData sourceData processingAADT a BRO, MoRTHDirect inputDEMbhuvan.nrsc.gov.inDirect inputLULC b Sikkim State Remote Sensing Applications CentreSupervised image classificationMeteorological parametersRahman et al. (2012)Direct inputSlope PercentDEM from bhuvan.nrsc.gov.inArcGIS Spatial AnalystSoil TextureCISMHE (2008b)Georeferencing, vectorization, rasterizationWater quality profile of Teesta and Rani Khola rivers for year 2004CISMHE (2008a)Direct inputWater quality profile of Teesta and Rani Khola rivers for year 2014Bhutia (2015), Gurung et al. (2015)Direct input Table 3 Importance scale used in AHPScale of importanceDescription1Both decision elements are equally important3First element is slightly more influenced than the second5First element is stronger than the second7First element is significantly stronger than the second9First element is extremely significant than second2,4,6,8Judgement values between equally, slightly, strongly, very strongly and extremelyReciprocalsWhen the i th criterion is compared to the  j th criterion, a ij  , then 1 ∕ a ij  is the judge-ment value when the  j th criterion is compared with the i th, i.e., a ij  =  1 ∕ a ij .
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