Investigating evidential reasoning for the interpretation of microbial water quality in a distribution network

Total coliforms are used as indicators for evaluating microbial water quality in distribution networks. However, total coliform provides only a weak “evidence” of possible fecal contamination because pathogens are subset of total coliform and therefore their presence in drinking water is not necessarily associated with fecal contamination. Heterotrophic plate counts are also commonly used to evaluate microbial water quality in the distribution networks, but they cover even a wider range of organisms. As a result, both of these indicators can provide incomplete and highly uncertain bodies of evidence when used individually. In this paper, it is shown that combing these two sources of information by an appropriate data fusion technique can provide improved insight into microbial water quality within distribution networks. Approximate reasoning methods like fuzzy logic and probabilistic reasoning are commonly used for data fusion where knowledge is uncertain (i.e., ambiguous, incomplete, and/or vague). Traditional probabilistic frameworks like Bayesian analysis, reasons through conditioning based on prior probabilities (which are hardly ever available). The Dempster–Shafer (DS) theory generalizes the Bayesian analysis without requiring prior probabilities. The DS theory can efficiently deal with the difficulties related to the interpretation of overall water quality where the redundancy of information is routinely observed and the credibility of available data continuously changes. In this paper, the DS rule of combination and its modifications including Yager’s modified rule, Dubois–Prade disjunctive rule and Dezert–Smarandache rule are described using an example of microbial water quality in a distribution network.     

Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP)

Environmental risk management is an integral part of risk analyses. The selection of different mitigating or preventive alternatives often involve competing and conflicting criteria, which requires sophisticated multi-criteria decision-making (MCDM) methods. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which integrates subjective and personal preferences in performing analyses. AHP works on a premise that decision-making of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. However, AHP involves human subjectivity, which introduces vagueness type uncertainty and necessitates the use of decision-making under uncertainty. In this paper, vagueness type uncertainty is considered using fuzzy-based techniques. The traditional AHP is modified to fuzzy AHP using fuzzy arithmetic operations. The concept of risk attitude and associated confidence of a decision maker on the estimates of pairwise comparisons are also discussed. The methodology of the proposed technique is built on a hypothetical example and its efficacy is demonstrated through an application dealing with the selection of drilling fluid/mud for offshore oil and gas operations.     

Life cycle greenhouse gas footprint of shale gas: a probabilistic approach

With the increase in natural gas (NG) production in recent years, primarily from shale gas, some sources, including the US Environmental Protection Agency (EPA), have suggested that upstream methane emissions are increasing. Much of the recent controversy has centered on emissions during well drilling, testing, and completion even though emissions downstream of the wellhead are also of concern. The study critically assessed the current state of knowledge about the life cycle GHG footprint of NG, analyzed the assumptions, data and analysis methodologies used in the existing literature. This study comprehensively analyzed the emission of methane from different stage of the life of well for conventional and unconventional NG using the EPA’s revised 2011 estimates as well as other existing literature and publicly available government data. The study proposed a probabilistic model to estimate the range of total GHG footprint of NG with varying probabilities. Through the bottom up approach starting from the well construction to the delivery of NG to the small user and using Monte Carlo simulation, the study identified the critical sources of fugitive emissions from the NG. As expected, emissions from well completion and periodic emissions (e.g. liquid unloading in the case of onshore conventional wells and workovers in the case of unconventional wells) are significant contributors to the overall GHG footprint of NG, and possess large opportunity for reduction. Finally the application of probabilistic model is demonstrated through a case study using the data from the Montney and Horn River shale gas basins in the Northern British Columbia to estimate the range of total GHG footprint of shale gas with varying probabilities. The study found that the GHG footprint of Montney and Horn River wells are much smaller than that of Barnett shale (which is representative of US shale gas) due to strict flaring regulations followed in BC. The study also undercuts the outcome of Howarth et al. (Clim Chang Lett 106:679–690, 2011), which states that the GHG footprint of shale gas is at least 20 % greater than coal.     

An estimation of the height system bias parameter <Emphasis Type="Italic">N</Emphasis><Subscript>0</Subscript> using least squares collocation from observed gravity and GPS-levelling data

This paper deals with the analysis of gravity anomaly and precise levelling in conjunction with GPS-Levelling data for the computation of a gravimetric geoid and an estimate of the height system bias parameter N_o for the vertical datum in Pakistan by means of least squares collocation technique. The long term objective is to obtain a regional geoid (or quasi-geoid) modeling using a combination of local data with a high degree and order Earth gravity model (EGM) and to determine a bias (if there is one) with respect to a global mean sea surface. An application of collocation with the optimal covariance parameters has facilitated to achieve gravimetric height anomalies in a global geocentric datum. Residual terrain modeling (RTM) technique has been used in combination with the EGM96 for the reduction and smoothing of the gravity data. A value for the bias parameter N_o has been estimated with reference to the local GPS-Levelling datum that appears to be 0.705 m with 0.07 m mean square error. The gravimetric height anomalies were compared with height anomalies obtained from GPS-Levelling stations using least square collocation with and without bias adjustment. The bias adjustment minimizes the difference between the gravimetric height anomalies with respect to residual GPS-Levelling data and the standard deviation of the differences drops from 35 cm to 2.6 cm. The results of this study suggest that N_o adjustment may be a good alternative for the fitting of the final gravimetric geoid as is generally done when using FFT methods.     

Recovering the large gaps in Landsat 7 SLC-off imagery using weighted multiple linear regression (WMLR)

Since 2003, the permanent failure of the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor has seriously limited the scientific applications and usability of ETM+ data. While a number of methods have been conducted to fill the regular un-scanned locations in ETM+ SLC-off images, only a few researches have been developed to recover the large gap areas in such images. In this study, an innovative gap filling method has been introduced to reconstruct the large gap locations in SLC-off images via multi-temporal auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regression (MLR) model in two successive steps. In the first step, almost half the gap locations have been recovered using the MLR model, then in the second step a weighted multiple linear regression (WMLR) algorithm is proposed to recover the remaining missing values. The simulated and actual case studies show that the proposed approach may provide a powerful tool for recovering the large gaps in SLC-off images, especially when there is a long time interval between the auxiliary fill images and the target SLC-off image.     

Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area,Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library (pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan. The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data to support the spatial delineation of outcropping rock sequences in stratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basis to further analyze the lithological spot in numerous regions in the Hindu Kush.     

Optimization of chlorination boosters in drinking water distribution network for Al-Khobar City in Saudi Arabia

Water quality management in distribution networks is directly related to spatial distribution of chlorine boosters and its dosages. Water chlorination is essential to reduce the effects of bacterial and other microbiological contaminants. A higher dosage of chlorine generates harmful by-products in addition to changes in drinking water’s taste and odor. The optimization of chlorine dosage is necessary to decrease the microbial contaminants that affect water quality. Once the chlorine threshold is determined for microbial contaminant, it will help decision makers suggest optimal values. These decisions can rely on the estimated water quality index (WQI). WQI is an index to evaluate water quality and can be linked to adequate residual chlorine with optimal booster dosage, numbers, and locations in water distribution network (WDN). The city of Al-Khobar, Saudi Arabia’s WDN was selected to validate the application of this study. Based on geographic location, the city Al-Khobar water network was divided into five zones. The initial temporal and spatial analysis pointed out poor water quality zones. EPANET, a modeling and simulating software, was applied to evaluate the WQI. Those EPANET results were then integrated with an optimization model. The optimization model suggested new chlorine booster locations to improve water quality in the city of Al-Khobar water distribution network.