Sensitivity analysis of water quality for Delhi stretch of the River Yamuna, India

Abstract

Simulation models are used to aid the decision makers about water pollution control and management in river systems. However, uncertainty of model parameters affects the model predictions and hence the pollution control decision. Therefore, it often is necessary to identify the model parameters that significantly affect the model output uncertainty prior to or as a supplement to model application to water pollution control and planning problems. In this study, sensitivity analysis, as a tool for uncertainty analysis was carried out to assess the sensitivity of water quality to (a) model parameters (b) pollution abatement measures such as wastewater treatment, waste discharge and flow augmentation from upstream reservoir. In addition, sensitivity analysis for the "best practical solution" was carried out to help the decision makers in choosing an appropriate option. The Delhi stretch of the river Yamuna was considered as a case study. The QUAL2E model is used for water quality simulation. The results obtained indicate that parameters K(1) (deoxygenation constant) and K(3) (settling oxygen demand), which is the rate of biochemical decomposition of organic matter and rate of BOD removal by settling, respectively, are the most sensitive parameters for the considered river stretch. Different combinations of variations in K(1) and K(2) also revealed similar results for better understanding of inter-dependability of K(1) and K(2). Also, among the pollution abatement methods, the change (perturbation) in wastewater treatment level at primary, secondary, tertiary, and advanced has the greatest effect on the uncertainty of the simulated dissolved oxygen and biochemical oxygen demand concentrations.

Water quality modeling for urban reach of Yamuna river, India (1999–2009), using QUAL2Kw

“…QUAL2Kw model and its earlier versions like QUAL2e, QUAL2e-UNCAS, and QUAL2K have been applied to various watershed for simulating different parameters and generating various management scenarios (Oliveira et al 2011;Grabiç et al 2011;Lin et al 2010;Xiaobo et al 2008;Fan et al 2009;Kannel et al 2007;Gardner et al 2007;Azzellino et al 2006). The successful applications of QUAL series model on the same study area (Paliwal et al 2007;Parmar and Keshari 2011) implies that the model can be applied worldwide and can also provide basic knowledge for water quality assessment even with limited dataset. The Yamuna river in Delhi has high levels of pollution caused by the discharge of untreated or partially treated wastewater entering the river system directly or indirectly via wastewater drains.…”

“…It was observed that even after the application of both phase I and phase II, desired class of the river stretch was not met due to high pollution load resulting from population escalation and limitation in treatment facilities (CPCB 2007;. In past, various pollution assessment studies have been done to simulate the DO-BOD water quality of the river Yamuna (Delhi stretch) (Bhargava 1983(Bhargava , 1986Kazmi and Hansen 1997;Kazmi 2000;Kazmi and Agrawal 2005;Paliwal et al 2007;Sharma and Singh 2009;Parmar and Keshari 2011), but no modeling has been done to assess the impact of other critical parameters like pathogens or nitrogenous compounds on the river quality. The present study was undertaken to address this limitation.…”

Characteristics of the event mean concentration (EMCs) from rainfall runoff on mixed agricultural land use in the shoreline zone of the Yamuna River in Delhi, India

“…Over the years, several studies have been carried out to study the impact of point sources pollution on River Yamuna in Delhi (Bhargava 1983(Bhargava , 1986Kazmi and Hansen 1997;Kazmi and Agarwal 2005;Paliwal et al 2007;Sharma and Singh 2009;Parmar and Keshari 2011). Jamwal et al (2008) estimated the diffuse pollution in urban areas of Delhi for the first time and stated that rainfall runoff from the agricultural area directly enters the river and from urban area reaches the river via wastewater drains, adding more pollutant loading.…”

Mercury in the Soil of Two Contrasting Watersheds in the Eastern United States

“…Estimates of soil element stores have high uncertainty due to a variety of factors including the presence of rocks, variations in the depth and thickness of soil horizons, high spatial heterogeneity of chemical and physical variables, and difficulties in accurately measuring bulk density, among others [50]. These sources of uncertainty should be carefully considered when comparing estimates of soil element stores among various studies in which assumptions can vary widely.…”