Groundwater exploitation in India has increased rapidly over the last 50 years as reflected by the growth of the number of groundwater abstraction structures (from 3.9 million in 1951 to 18.5 million in 1990) and shallow tube wells (from 3000 in 1951 to 8.5 million in 1990) (Muralidharan, 1998; Singh & Singh, 2002).Today groundwater is the source for more than 85 % of India’s rural domestic water requirements, 50 % of urban water and more than 50 % of irrigation demand. The increase in demand in the last 50 years has led to declining water tables in many parts of the country. For example, 15% of the assessment units (Blocks/Mandals/Talukas) have groundwater extraction in excess of the net annual recharge (Central Ground Water Board, 2007). According to Rodell et al. (2009), the extent of groundwater depletion between 2002 and 2008 was 109 km3, which is about half the capacity of India’s total surface-water reservoirs.

This report presents practical field aspects gained during two years of monitoring with state-of-the-art spectrometers and ion-selective sensors, combining (i) continuous measurements of the quality and flow rates of combined sewer overflows (CSO) with (ii) continuous measurements of water quality parameters within the urban stretch of the River Spree. It describes the set-up and the implementation of the monitoring and evaluates the outcomes and experiences towards “lessons learnt”. The challenge of CSO monitoring is their event-based and highly dynamic nature during rain events. Applied online sensors allow dynamic measurements of CSO and water quality impacts for a wide range of parameters. However, the success of online monitoring campaigns depends highly on three main considerations. Firstly, the representativity of the measurement station. The location of the probe must be representative of the concentration over the entire cross section of the sewer or the river. Further criteria have to be considered for the selection of the monitoring sites (e.g. easy access to the probes for maintenance) (chapter 2). Secondly, the quality of the raw measurements. External conditions can influence the quality of measurements and lead to wrong values or outliers. – To avoid drifts, probes need to be cleaned and checked regularly. We found that monitoring stations must be visited at least once a week for functional check-ups. During the two years of monitoring, the maintenance methodology have been continously improved to ensure the best measurement conditions (chapter 3). – But even under state-of-the-art operation of the probes, some values can be affected by errors and lead to misinterpretation. Thus, a validation step is required to detect wrong values and separate them from valid values. Given the large amount of data, an Access-based tool has been developed to support semi-automatic validation of monitoring data (chapter 4). Lastly, the calibration of raw measuments and the determination of uncertainties is critical. Online probes were not able to provide accurate measurements without being calibrated to local conditions with parallel laboratory measurements (online probe refers in this document to spectrometer and ISE-Probe). A Monte-Carlo method was adapted to perform regressions between raw measurement and lab values, which allows considering both uncertainties of sensor and lab chain. For instance, total uncertainty of the UV/VIS probe was between 15 and 30% for chemical oxygen demand (COD), accounting for errors from sensor, laboratory and field (representativity of site). The uncertainties in concentration and flow measurements lead to an uncertainty in CSO COD load between 20 and 70%, depending on the average concentration and flow of the event (chapter 5). In order to gain grab samples and provide high quality calibration, an automatic sampler has been installed at the sewer monitoring. However, for operational purposes, a sewer operator will expect to gain quality online data without the effort and costs of sampling each CSO. In order to estimate the optimal sampling effort, we investigated how many events (or how many lab measurements) are necessary for calibration depending on aimed at uncertainty. From a set of 12 sampled CSO events, we simulate all possible random combinations of events and calculated each time the resulting measurement uncertainty (chapter 5.5). Results shown in Figure A indicate that at least 7 random events need to be sampled to calibrate the probe reducing uncertainties of COD measurement under 30%. It has to be noted that the concentration range of the grab samples has a high influence on the quality of the calibration. A similar analysis considering only events with high lab variations (range > 500 mg/l) showed that then only 4 events must be sampled to reduce uncertainty under 30%. Considering these results, we recommend parallel short sampling campaigns with autosamplers (grab sampling) for application of spectrometers for CSO monitoring. If the lab measurements cover the entire range of water quality variations, a minimum of 3-4 rain events should be sampled to build an accurate calibration function with acceptable uncertainty. If sampled concentration range is exceeded by later measurements, new sampling campaigns should be planned. Since both sensor and autosampling results were available, CSO COD loads have been calculated using both spectrometer and lab values (chapter 6). Results indicate that load calculated with lab samples are within the error range of the loads calculated with spectrometer values. However, the frequency of grab sampling should be less than 10 minutes, to match concentration peaks and quick quality variations in our case. For the purpose of CSO load calculation, autosampler-based monitoring remains a cost-effective alternative to online probes. For a dynamic description of CSO (pollutant sources, mass/flow balance, etc.), autosampler-based data are limited by the minimal sample frequency and the sampling capacity. Investment and effort of online monitoring can overcome these limitations. For river monitoring, online probes enable measuring water quality variations with an acceptable uncertainty, if the probes are properly calibrated. Here, autosamplers are clearly limited by their sampling capacity as the impacts are spread on several days in the case of the River Spree. Since no autosampler was available during the two monitoring years no clear correlation could be established for the spectrometer parameters (TSS, COD, BOD). As the manual approach often fails to catch CSO impacts, an autosampler has been purchased for the last monitoring year in 2012. For NH4 + measurement, the ISE probe has been successfully calibrated performing monthly NH4 measurements in a bucket of river water spiked with ammonium standard solution to reach values in the range expected during CSO (1-2 mg/l).

This report concludes the first phase of the project “OptiWells”, which focuses on the optimization of drinking water well field operation with respect to energy efficiency. The purpose of this document is to provide sound answers to questions that utilities and well field operators are facing. Thus, it is built as a thematically organized sequence of main questions and answers rather than an extensive manuscript-like report. In total, 13 questions are addressed in detail, while 3 main “unanswered” questions and issues are detailed at the end of this report. The focus of this report is identical to the project’s focus: it addresses energy efficiency issues within the well field system. Thus, the main area of focus of the project lies in the interactions between the groundwater, the well, the pump and raw water pipe system. Drinking water treatment, as well as water distribution is not included in this study. This document, in combination with the other project deliverables, shall provide an overview of the potential optimizations for drinking water well fields. It shall yield both answers about saving potentials in general, and give some concrete examples from a French well field. By doing so, it shall assist the identification of solutions for an energyefficient groundwater abstraction, and provide a basis for a sound, practical methodology for well field energy audits and assessments.

Chennai is the largest city in South India located in the eastern coastal plains. Water supply to the Chennai city is met by reservoirs and by groundwater. Most of the groundwater is pumped to the city from the well fields located in the Araniyar and Korttalaiyar River (A-K River) catchment north of Chennai.

In the 2nd phase of the project (OXIRED 2), trials at lab and technical scale were conducted to validate the results for trace organic and DOC removal from OXIRED 1 and to gain a more reliable knowledge about oxidation by-product formation for surface water from Berlin. To assess the stability of the process, a pilot unit was operated at Lake Tegel. Moreover the effect of oxidation + MAR on toxicological parameters was investigated (s. D 1.1). To prepare a field study three sites in Germany were evaluated regarding their suitability including parameters such as aquifer depth and composition, source water quality and possibility of authorization (s. D 2.1). The results were that none of the sites (Hobrechtsfelde, Braunschweig WWTP or artificial recharge site in Görlitz) was identified as suitable. The current state-of-the-art for influencing the redox zonation in the subsurface was reviewed (D 3.1) and the options to assess the quantity, composition and activity of the microbial population in the soil samples were summarized (D 2.2). To investigate the dynamic of redox processes, short term column tests were conducted (D 3.2). On the basis of these results reactive flow and transport modelling was carried out (D 3.2 and 3.3). The aim of this report is to give a summary of the main results from OXIRED 2 and to identify promising opportunities for further experiments and transfer to field scale.

The project OXIRED 2 started in January 2010 as a continuation of OXIRED 1. The project is guided by KompetenzZentrum Wasser Berlin (project leader Dr. G. Grützmacher); it is sponsored by Berliner Wasserbetriebe (BWB) and VEOLIA Eau. WP3 (Redox Control and Optimization at AR Ponds) consists of two main parts: (I) Laboratory column experiments with special emphasis on sediment characteristics (by TUB) and (II) Numerical modeling of the results of the TUB column experiments (by UIT). The present report belongs to Part II of WP3. In Berlin, around 70 % of abstracted groundwater originates from riverbank filtration and artificial recharge (AR). During percolation and subsurface passage the quality of the infiltrated water improves due to physical filtration, sorption and biodegradation. Biodegradation is a major driver for redox zonation and so it is highly influenced by redox conditions, too. The main purpose of WP3 is to investigate these processes in column experiments including its numerical simulation.

There is a significant potential for optimizing pump systems currently in use in groundwater wells. This potential lies in: (i) the improvement in pump technology, which can yield up to ~5% more efficiency, (ii) the improvement in motor technology, which can yield up to ~3% more efficiency, with further improvements if innovations from aboveground motors are adapted, (iii) the improvement in performance adaptability, which can be very efficient in some cases (~10-50%), but also counterproductive if not adapted to current situation (0% or even efficiency loss), and sometimes not very flexible (impeller trimming); (iv) the improvement of the system maintenance and management which may yield up to ~20% more efficiency, and which, in general, has a shorter payback time than performance adaptability options.The improvement of equipments may induce only moderate additional costs if it is done at the time of scheduled new investments, after amortization of the equipment formerly in use. Unfortunately, these expected savings are influenced by uncertainties, which can be of the same order of magnitude as the savings themselves. For instance, the determination of the optimal operation point of a pump bears uncertainties between 1% and 4% and grows with pump rotation speed (Gülich 2010). Other considerable saving potentials lie within cleaning, maintenance and smart wellfield operation with short to moderate payback times (Table 6). These potentials are however very site-specific, and difficult to estimate on a general basis. Best practices for a “smart” pumping shall include choosing equipment that fits the actual requirements of the system, operating the pumps nearest of their Best Efficiency Point, and operating the motors in an energy-efficient load range. The most obvious energy savings are those associated with improvements in the efficiency of the motor and of the pump (Shiels 1998). Such gains are often worth the added capital expenditure – although often having moderate to long payback times. However, as underlined by (Kaya, Yagmur et al. 2008), that pumps have high efficiency alone is not enough for a pump system to work in maximum efficiency. An improvement of pump technology will yield, even optimistically seen, an efficiency improvement of up to 10%, which is the potential “theoretical limit” (EC 2003). For further improvements, it is necessary to consider solutions that go beyond the pump system, since maximizing efficiency depends not only on a good pump design, but also on a good system design. Even the most efficient pump in a system that has been wrongly designed is going to be inefficient. Moreover, an efficient pump in an inefficient well is pointless. Hence, a global approach of the groundwater abstraction system is required. The optimization potentials highly depend on the site characteristics themselves, on the local demand (what distribution of the demand? what load profile?), and on the operation and maintenance history (e.g., what is the cleaning frequency of the pipes, if any?). Finally, one should not forget the primary objective of water abstraction, which is satisfying a given water demand, thus, the safety of drinking water production prevails over energy efficiency.