Sreenivasulu Chadalavada

Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.

Optimisation approach for pollution source identification in groundwater: an overview

Groundwater pollution occurs from different anthropogenic sources like leakage from Underground Storage Tanks (USTs) and depositories, leakage from hazardous waste dump sites and soak pits. Remediation of these contaminated sites requires optimal decision-making system so that the remediation is done in a cost-effective and efficient manner. Identification of unknown pollution sources plays an important role in remediation and containment of contaminant plume in a hazardous site. This paper reviews different optimisation algorithms like classical, nonclassical such as Genetic Algorithm, Artificial Neural Network and Simulated Annealing and hybrid methods, which can be applied for optimal identification of unknown groundwater pollution sources.

Optimal Identification of Groundwater Pollution Sources Using Feedback Monitoring Information: A Case Study

A feedback-based methodology has been developed for identifying the unknown pollution sources in groundwater-contaminated aquifers. The methodology consists of models within an iterative feedback system, with the capacity of feeding back real-time measurements of pollutant concentrations for the sequential optimal designs and characterization of the contaminated aquifer study area. The resulting linked-simulation optimization model considers the delineation of the contaminant plume, optimally characterizing the site in terms of pollutant sources and the optimal monitoring network leading to the remediation and/or management of the contaminated aquifer. As part of the methodology, a simulation-optimization code was developed by linking a groundwater flow and transport model with an optimization code for the purpose of identifying the unknown pollution sources. The proposed methodology addresses the source identification process with very limited information available regarding the observed contamination data for the identification of unknown pollution sources. This methodology is applied to a chlorinated hydrocarbon contaminated site for the identification of unknown pollution sources. Information regarding the sources such as the magnitude, location and the duration of contamination activity were not known for the study area considered in this work except the information regarding the likely activities that led to its contamination. Developed methodology is applied to choose the optimal source locations from the identified potential locations. Depending on the availability of observed contaminant concentration values the domain for the methodology application is divided into three different management periods. The optimal source estimates obtained at the end of the third management period suggests that only one potential source location, S2, confirms to be the source and active. The qualitative assessment of the results also performed utilizing the contamination information obtained during the field investigations. The results demonstrate the practicability of the feedback-based methodology in identifying the unknown pollution sources in groundwater.

Application of infrared spectrum for rapid classification of dominant petroleum hydrocarbon fractions for contaminated site assessment

In this study, the infrared spectrum (4000-400 cm-1) was applied to identify and classify the different alkanes based on carbon chain length (Cn). It was found there were two bands coherent to the doublet at location 2954 and 2872 cm-1, respectively can be applied to identify the fraction of carbon chains. From C20 to C37, by the increase of the Cn, the intensities of the two bands were reduced as demonstrated. There were another two doublets existed at the region from 1480 to 1450 cm-1 and the region at 750 and 730 cm-1. It was observed the intensity of one coherent band at each of these regions was increased following the increase on the Cn. The bands center at 1462 and 730 cm-1 were increased from C20 to C37. The intensity ratio of the coherent bands can be applied to identify the Cn. Successfully identify four different petroleum products with different fractions of carbon chains in soil samples, is evidence the theory can be applied to investigate the fraction of carbon chains in soil. Coupling with handheld FTIR, it is possible to rapidly estimate the dominant fraction of Cn in soil in field.

Development of a modular vapor intrusion model with variably saturated and non-isothermal vadose zone

Human health risk assessment at hydrocarbon-contaminated sites requires a critical evaluation of the exposure pathways of volatile organic compounds including assessments of vapor exposure in indoor air. Although there are a number of vapor intrusion models (VIM) currently available, they rarely reproduce actual properties of soils in the vadose zone. At best, users of such models assume averaged parameters for the vadose zone based on information generated elsewhere. The objective of this study was to develop a one-dimensional steady-state VIM, indoorCARE™ model, that considers vertical spatial variations of the degree of saturation (or effective air-filled porosity) and temperature of the vadose zone. The indoorCARE™ model was developed using a quasi-analytical equation that (1) solves the coupled equations governing soil–water movement driven by pressure head and a soil heat transport module describing conduction of heat and (2) provides a VIM that accommodates various types of conceptual site model (CSM) scenarios. The indoorCARE™ model is applicable to both chlorinated hydrocarbon and petroleum hydrocarbon (PHC) contaminated sites. The model incorporates biodegradations of PHCs at a range of CSM scenarios. The results demonstrate that predictions of indoor vapor concentrations made with the indoorCARE™ model are close to those of the J&E and BioVapor models under homogeneous vadose zone conditions. The newly developed model under heterogeneous vadose zone conditions demonstrated improved predictions of indoor vapor concentrations. The research study presented a more accurate and more realistic way to evaluate potential human health risks associated with the soil-vapor-to-indoor-air pathways.