Pre-Feasibility Report of the ESI Water Working Group June ... · This report presents the...

Pre-Feasibility Report of the ESI Water Working GroupJune 2014

W A T E R W O R K I N G G R O U P | P R E - F E A S I B I L I T Y S T U D Y R E P O R T

CM I C E S I W W G | P R E - F E A S I B I L I T Y S T U D Y R E P O R T 2

Table of Contents

Table of Contents .................................................................................................................................. 2 List of Figures ........................................................................................................................................ 3 List of Tables ......................................................................................................................................... 3 1 Executive Summary ....................................................................................................................... 4

1.1 Overview .................................................................................................................................. 4 1.2 Remote, Real-Time Sensors for Water Quality Monitoring ........................................................ 4 1.3 National Database / Repository of Water Quality Information .................................................... 5 1.4 Project Selection and Risk Assessment .................................................................................... 5

2 Overview ........................................................................................................................................ 6 2.1 Background .............................................................................................................................. 6 2.2 Project Involvement .................................................................................................................. 6 2.3 Project Schedule ...................................................................................................................... 7 2.4 Prefeasibility Study Objectives .................................................................................................. 7

3 Problem Statements ...................................................................................................................... 8 3.1 Overview .................................................................................................................................. 8 3.2 Remote, Real-Time Sensors for Water Quality Monitoring ........................................................ 8 3.3 Database / Repository of Water Resources Information .......................................................... 11

4 Integrated Remote Sensors ......................................................................................................... 13 4.1 Overview ................................................................................................................................ 13 4.2 Platforms ................................................................................................................................ 13 4.3 Communication Options ......................................................................................................... 15 4.4 Sensor Devices ...................................................................................................................... 17 4.5 Data Storage .......................................................................................................................... 18 4.6 Power Options........................................................................................................................ 19 4.7 Notable Real-Time Monitoring Projects ................................................................................... 19 4.8 Gaps / Advancements in Research and Development ............................................................ 22 4.9 Partnership Opportunities ....................................................................................................... 28

5 Database / Repository of Water Resources Information ............................................................ 32 5.1 Database Examples ............................................................................................................... 32 5.2 Gap Analysis .......................................................................................................................... 38 5.3 Partnership Opportunities ....................................................................................................... 42

6 Project Selection Workshop ........................................................................................................ 45 6.1 Overview ................................................................................................................................ 45 6.2 Risk Assessment .................................................................................................................... 45 6.3 Assessment Criteria ............................................................................................................... 50 6.4 Conclusion / Recommendations ............................................................................................. 52

7 References ................................................................................................................................... 52



List of Figures

Figure 1 - Typical Terrestrial-based Monitoring Station .......................................................................... 14 Figure 2 – Libelium Waspmote Smart Sensor Platform (1) .................................................................... 14 Figure 3 - Libelium Waspmote Smart Sensor Platform (2) ..................................................................... 14 Figure 4 – Libelium Waspmote Smart Sensor Platform (3) .................................................................... 14 Figure 5 – EMM350 PISCES Pontoon Platform ..................................................................................... 15 Figure 6 - Real-time Water Quality Monitoring Network Buoy ................................................................ 15 Figure 7 - Real-time Water Quality Monitoring Buoy .............................................................................. 15 Figure 8 – Floating Node....................................................................................................................... 15 Figure 9 – Monitoring Buoy ................................................................................................................... 15 Figure 10 – YSI EcoMapper AUV .......................................................................................................... 23 Figure 11 – SHOAL Robotic Fish Prototype .......................................................................................... 23 Figure 12 – Michigan State University Miniaturized Robotic Fish for Water Quality Monitoring ............... 24 Figure 13 – Risk Assessment Components ........................................................................................... 46 Figure 14 – Likelihood-Consequence Risk Matrix .................................................................................. 47

List of Tables

Table 1 – Water Management Working Group Members ......................................................................... 7 Table 2 – Key Water Indicators ............................................................................................................. 10 Table 3 – Analytical requirements for metal mining effluent (MMER – Schedule 3)................................. 11 Table 4 – Additional MMER Water Quality Monitoring Requirements ..................................................... 11 Table 5 – Communication Options for Real-Time Water Quality Monitoring Stations .............................. 16 Table 6 – Existing Probes for Remote Real-Time Water Quality Monitoring ........................................... 17 Table 7 – Monitored Surface and Groundwater Parameters for NL Program .......................................... 20 Table 8 – Water Quality Parameters Measured by RAMP ...................................................................... 34 Table 9 – Risk Assessment Results for the Integrated water Sensor Project .......................................... 48 Table 10 – Risk Assessment Results for the Database / Repository Project ........................................... 49 Table 11 – Results of Assessment Criteria Exercise .............................................................................. 51



1 Executive Summary

1.1 Overview

This report presents the pre-feasibility work of the Water Working Group (WWG) – one of three working groups of the Environmental Stewardship Initiative (ESI) of the Canada Mining Innovation Council (CMIC).

The objective of the report is to determine the initial feasibility of two project concepts:

1. Remote, real-time sensors for water quality monitoring

2. National database / repository of water resources information

1.2 Remote, Real-Time Sensors for Water Quality Monitoring

Water monitoring is a critical environmental management practice for mine operators and is usually conducted by way of grab sampling. However, several challenges exist with this approach, including:

Costs Periodic sampling delays the detection of irregularities / non-conformance events Sampling errors associated with transport, sample storage, and cross-contamination Human error associated with lack of training and expertise Safety concerns with deploying personnel to remote sites

The development of remote, real-time sensors will help to overcome these challenges and will provide a more representative sample of water quality over time, as opposed to the ‘snapshot’ that grab sampling provides.

The main components of a remote, real-time water quality monitoring station are as follows:

Platform (e.g. terrestrial, buoy, autonomous underwater vehicle) Communication option (e.g cellular, satellite) Sensor devices Data storage Power supply (e.g. battery, solar)

A number of gaps were uncovered related to the deployment of remote, real-time water quality stations – especially pertaining to their deployment at mine sites – including, but not limited to:

Lack of sensor devices to detect constituents of concern Quality assurance / quality control (i.e. for compliance monitoring) Scalability Performance in cold / winter climates

Lastly, there are a number of groups across Canada that engage in the remote, real-time water quality

monitoring space – in terms of research, development, and innovation – that could partner with CMIC to help develop the project. Partnership development will be a key focus of ongoing project development work.



1.3 National Database / Repository of Water Quality Information

The development of the proposed water resources database is intended to enhance access to key water quality information. Access to the data will be useful in the context of baseline sampling and monitoring. For example, a key industry problem that has been identified in relation to water management is that the water quality data collected by lease holders or during the exploration phase is often not available to future lease holders, despite extensive prior sampling and monitoring taking place.

Additional benefits include:

Reduction in initial costs for project development, including shorter permitting times Amalgamation of diverse databases into a single, accessible format Establishment of a consistent reporting format Improvement in cumulative impact assessments as data will be available more widely for mining

projects Broad external application beyond the mining industry, including potential inter-industry

collaboration (i.e. forestry, oil sands)

Several examples of large-scale water quality databases / repositories were uncovered as part of the pre-feasibility research. Each database provides lessons learned and experiences -- in terms of platforms, infrastructure, and data management processes – that will likely prove useful for the development of the proposed database / repository.

However, some gaps were uncovered that need to be overcome to allow for successful development, including, but not limited to:

Lack of shared data in the mining industry Extent of industry participation Integrating existing data formats Quality assurance / quality control mechanisms

Several partnership opportunities exist that will help advance project goals, including private database companies, government organizations, universities, and non-governmental organizations. As mentioned, partnership development will be a key focus of ongoing project development work.

1.4 Project Selection and Risk Assessment

A project selection workshop was held to assess the two project concepts and determine which project(s) should be taken to the feasibility level. The workshop was structured around two main exercises:

1) Conducting a high-level risk assessment of the projects; and, 2) Screening the projects against assessment / selection criteria to allow for prioritisation.

Risks were uncovered for both projects that may prevent them from succeeding; however, management responses were formulated to help mitigate and / or eliminate those risks. Accordingly, it was determined that both projects could be taken to the feasibility level.



Therefore, the WWG recommends that the remote, real-time sensors for water quality monitoring project and the national database / repository of water resources information project be advanced by completing a feasibility study for each project.

2 Overview

2.1 Background

The Environmental Stewardship Initiative (ESI) of the Canada Mining Innovation Council (CMIC) is an industry-led consortium comprised of approximately 25 members from industry, academia, and government. Initiated in early 2012, the ESI’s mandate is to develop – through step-change technological innovation, project development, and multi-stakeholder collaboration – solutions to some of the myriad environmental, sustainability, and competiveness issues facing the Canadian mining industry.

The first phase of the ESI’s work was retaining Hatch Ltd1 to commission a scoping study outlining some potential areas where further environmental research and management innovation are required.2 Multiple multi-stakeholder surveys were also conducted in September 2013 to ascertain stakeholder needs and priorities in terms of research and innovation. Based on the results of the studies and ongoing collaboration through ESI workshops, the following concepts were selected for project development under the aegis of three ESI working groups:

Water Working Group (WWG) Remote, real-time sensors for surface water / groundwater monitoring; National database / repository of water resources information (i.e. physical,

chemical, and biological properties); Tailings Working Group (TWG)

Production of benign tailings (i.e. stabilization or removal of contaminants prior to placement);

In situ stabilization / remediation of contaminants within existing tailings; Closure Working Group (CWG)

Development of a standardized framework for closure criteria towards mine site relinquishment; and,

Passive systems for preventing / treating acid rock drainage (ARD).

2.2 Project Involvement

The pre-feasibility studies for each working group were prepared in collaboration with the working groups by an individual independent consultant of CMIC. In-kind contributions were provided by the members of the ESI and the respective working groups, including document review and input, meetings, and workshop participation. The composition of the water management working group is presented in Table 1 below.

1 As a member of CMIC, representatives of Hatch Ltd offered to volunteer their services to complete the study. 2 http://www.cmic-ccim.org/wp-content/uploads/2013/07/HatchScopingReport1.pdf



TABLE 1 – WATER MANAGEMENT WORKING GROUP MEMBERS

MEMBER ORGANIZATION

David Sanguinetti (Chair) BioteQ Environmental Technologies Inc.

Mark Thorpe (ESI Chair) Golden Star Resources

Philippa Huntsman-Mapila Natural Resources Canada / CanmetMining

Dennis Nazarenko LOOKNorth

Noelene Ahern Barrick Gold

Vida Ramin Prospectors and Developers Association of Canada

Mohammed Ali Hatch

In January 2014, CMIC submitted a funding proposal to LOOKNorth3 to support the development of the feasibility study work. The proposal is still in the final approval stages, with a decision expected by early June 2014. Subject to approval, it is anticipated that LOOKNorth will be a key strategic partner for further project development activities.

2.3 Project Schedule

The pre-feasibility work started in January 2014 and was conducted over the course of approximately four months. Ongoing meetings / workshops were conducted among ESI members (typically monthly) and among the individual working groups beginning in 2013. In-person workshops were conducted in early March 2014 to review draft pre-feasibility work towards the completion of the pre-feasibility work. Culminating workshops involving working group members were held in May 2014 to assess the results of the studies and to select projects for further project development. The pre-feasibility studies were finalized in early June 2014.

2.4 Prefeasibility Study Objectives

The objectives of the prefeasibility study are as follows:

Introduce the selected subjects; Identify associated challenges and risks; Determine optimal pathways for future activities; and, Select project(s) that will provide potential step changes in the mining business.

The selected projects will then undergo feasibility analyses. These analyses will allow the ESI to further prioritize its focus areas with the aim of developing a comprehensive development program directed by industry through CMIC.

Lastly, the studies will provide stakeholders with an understanding of the approaches taken to define the feasibility studies to aid in attracting stakeholder support and funding for the ESI’s ongoing project development activities.

3 LOOKNorth (Leading Operational Observations and Knowledge for the North) is a national Centre of Excellence for Commercialization and Research hosted by C-CORE.



3 Problem Statements

3.1 Overview

Water monitoring and analysis are necessary environmental management practices that are conducted throughout all stages of the mining life cycle, from exploration and development through to operations, closure, and post-closure. Water monitoring and analysis encompass myriad environmental aspects (e.g. baseline development, contaminant releases, acidic drainage, metal leaching) and are, therefore, central to the mining regulatory requirements at multiple jurisdictional levels.

As part of the above-mentioned scoping study, multi-stakeholder surveys, and ongoing ESI workshops, the WWG evaluated a variety of potential issues affecting water management to determine where further research, development, and innovation are required. In light of key drivers affecting water management strategies – including regulatory frameworks, operational controls, technical limitations, cost, and time – the following project concepts were selected for further project development activities:

Remote, real-time sensor devices for surface water / groundwater monitoring; and, National database / repository of water resources information.

Ultimately, it is anticipated that the above project concepts will help to enhance the effective management and stewardship of ever-important water resources.

3.2 Remote, Real-Time Sensors for Water Quality Monitoring

Central to the proposed remote sensors portion of the study is a review of existing technologies (e.g. sensors, platforms, transmission), as well as the water sensor technologies that are in the research and development stage. Relevant real-time, remote monitoring projects will also be analysed to determine potential applications in the mining industry. Mining companies that use similar technologies will be researched to highlight gaps and determine areas for further research. Research will also be conducted in relation to other groups and organizations that are involved in similar activities. This will be a key learning tool for the ESI WWG and will help to optimize the path forward for the feasibility study.

Additionally, a number of key questions on remote water quality monitoring were posed by the working group to guide the focus of the study:

What is the capability for event-driven data collection? What are the typical configuration scenarios for baseline, operational, and post-

closure data? What costs are associated with current technologies? What current and future requirements do regulators and mine operators have that

may affect the deployment of sensor-based water management strategies? What are the options for data retrieval (e.g. real-time telemetry, periodic

retrieval)? How can remote sensing (satellite, airborne) data complement in situ

measurement in various water management strategies? What is the main focus of other groups working in the remote sensing space?



Validation

The development of the proposed remote sensors and associated technologies will enhance the ability of industry to safely monitor key water characteristics. This will complement existing monitoring efforts and, in some instances, may be able to replace them.

Conventional water quality monitoring is mostly done by way of spot / grab sampling (i.e. collecting samples periodically) followed by laboratory analysis. Several challenges exist with this approach that remote, real-time monitoring helps to overcome. Chief among these is that periodic sampling delays the detection of irregularities / non-conformance events given the marked temporal variability of ambient surface waters (often rapidly in the case of discharges, leakages, and seepages) (O’Flynn et al. 2010). Consequently, remote, real-time monitoring stations are able to serve as crucial early warning systems and allow for earlier mitigative actions to be taken.

Real-time water quality monitoring is especially pertinent to remote and / or harsh locations that pose safety risks to monitoring personnel. Parsons (2011) illustrates this with the example of water quality monitoring in Nunavut. Specifically, all water quality sampling requires an air flight into the sites and all grab samples must be flown to the closest water analysis laboratory (Yellowknife). Typical costs for one round of sampling are as follows:

Flight $1,800 Hotel $400 Meals $200 Helicopter $10,000 Lab Samples $6,000 Miscellaneous (e.g. gear) $1,600 Total $20,000

Besides potential operational cost savings, sampling errors associated with transport, sample storage, and cross-contamination – as well as human error associated with lack of training and expertise – will likely be reduced. Moreover, remote, real-time sensors will likely provide a more representative sample of spatial and temporal water quality, as opposed to the ‘snapshot’ that grab sampling provides.

As a starting point, the tables below present some key water characteristics that the proposed sensors could detect. Table 2 outlines key water indicators that are central to water monitoring and management strategies, while Table 3 and Table 4 present a list of the deleterious substances as per the federal Metals Mining Effluent Regulations (MMER) of the federal Fisheries Act.

Crucially, it is desired that the sensors will be able to detect the MMER deleterious substances at or near the outlined method detection limits (MDL). Additionally, there are potential future industry and regulatory requirements that would be advantageous to incorporate into the monitoring devices to meet future industry needs. Notable potential examples include trace elements selenium and molybdenum (Hatch 2013: 24). Consideration will also be given to remote monitoring in Canada’s north, which poses particular challenges for water monitoring and analysis given its harsh climate and abundance of remote.

Once developed, the devices capable of such monitoring would provide a variety of benefits to the mining industry as well as to stakeholders at large including:

Early warning mechanisms are enhanced allowing for speedier response times; Reduced cost of field technicians and analysis;



Improved safety for field personnel, particularly in harsh climates and remote regions; Increased accuracy in environmental descriptions including baseline and operational

monitoring; Improved post-closure data collection, which is especially advantageous due to reduced

presence during the closure phase; Enhanced ability to monitor event-driven occurrences (e.g. storms); More rapid detection of potential abnormal events, allowing for preventative response

measures and reduced environmental impacts; Increased understanding of potential environmental impacts; and, Facilitates demonstration of regulatory compliance, thereby increasing regulatory and

stakeholder confidence.

TABLE 2 – KEY WATER INDICATORS

INDICATOR GROUP INDICATORS

Quantity

Areal Extent Water Level Snow water equivalent River / lake ice

Flow

Water body connectivity Surface flow rates Direction of flow Ground water capacity Recharge rates Ground / surface water connectivity

Physical Parameters

Algal blooms Temperature Dissolved solids Turbidity Chlorophyll Dissolved oxygen pH

Chemical Properties MMER Schedule 3 MMER Schedule 5, s. 4 MMER Schedule 5, s. 7



TABLE 3 – ANALYTICAL REQUIREMENTS FOR METAL MINING EFFLUENT (MMER – SCHEDULE 3)

DELETERIOUS SUBSTANCE / PH PRECISION ACCURACY METHOD DETECTION LIMIT (MDL)

Arsenic 10% 100 ± 10% 0.010 mg/L

Copper 10% 100 ± 10% 0.010 mg/L

Cyanide 10% 100 ± 10% 0.010 mg/L

Lead 10% 100 ± 10% 0.030 mg/L

Nickel 10% 100 ± 10% 0.020 mg/L

Zinc 10% 100 ± 10% 0.010 mg/L

TSS 15% 100 ± 15% 2.000 mg/L

Radium 226 10% 100 ± 10% 0.010 Bq/L pH 0.1 pH unit 0.1 pH unit N/A

TABLE 4 – ADDITIONAL MMER WATER QUALITY MONITORING REQUIREMENTS

MMER SCHEDULE 5, S. 4 MMER SCHEDULE 5, S. 7 SITE-SPECIFIC VARIABLES

Aluminum Aluminum Fluoride

Cadmium Cadmium Manganese

Iron Iron Uranium

Mercury Mercury Calcium

Molybdenum Molybdenum Chloride

Ammonia Ammonia Magnesium

Nitrate Nitrate Potassium

Hardness Hardness Sodium

Alkalinity Alkalinity Sulphate

Arsenic Thallium

Copper Total thiosalts

Lead Water depth

Nickel Optical depth Zinc Dissolved organic carbon

Radium 226 Total organic carbon

Cyanide Water flow

TSS

Dissolved oxygen content

Temperature

pH

Salinity

Selenium

Electrical Conductivity

3.3 Database / Repository of Water Resources Information

The second project proposed by the ESI WWG pertains to the development of a database / repository for water resources information. This portion of the study will examine relevant existing large data projects to help identify the following:

Specific challenges and opportunities associated with different project scopes; Ability to compare water supply and quality data across sites;



Potential business models; and, Possible partnership arrangements.

Some key considerations that the pre-feasibility study will help to answer include:

Who will the potential end-users be? What are the use cases for the data? Who will submit the data? What type of data will be submitted? Will users submit what they choose? What levels of access will be employed (i.e. single, tiered)? What is the proposed business model? What watersheds or geographical areas will be focused on? What community access / considerations can be incorporated? What are the privacy and liability issues related to data submission? What is the currency reliability and accuracy of the data submitted (QA / QC)? What methods have been employed to collect the data?

The development of the proposed database / repository is intended to enhance access to key water indicator information. Table 3 and Table 4 above provide a starting point for the type of indicator data that may be included in the database.

Access to the data will be useful in the context of baseline sampling and monitoring. For example, a key industry problem that has been identified in relation to water management is that the water data collected by the operator of a lease or during the exploration phase is often not available to future operators, despite extensive prior sampling and monitoring taking place. This data may have been collected over multiple years (often decades).

With the development of the water quality database, data loss can be minimized and duplication of efforts can be reduced, thus reducing overall costs for project proponents and / or current operators. Furthermore, the database will allow for a better understanding of historical water quality and quantity among industry, regulatory agencies, and stakeholders, which, in turn, will help to better understand current and future mine impacts. There are also benefits of shared data for proximal sites and the ability to preserve data as projects progress through multiple owners.

The proposed database also has the potential for involving communities and First Nations groups. These groups could potentially submit their own water quality information, which could help to improve transparency for the interested parties and could be a potential trust-building exercise among stakeholders. These aspects will be dependent on how the database is scoped and organized, including the potential – perhaps tiered – access mechanisms that will be employed.

Some additional potential benefits that the database will provide to industry and stakeholders include:

Reduction in initial costs for project development, including shorter permitting times; Amalgamation of diverse databases into a single, accessible format; Establishment of a consistent reporting format; Improvement in cumulative impact assessments; and, Broad external application beyond the mining industry, including potential inter-industry

collaboration (i.e. forestry, oil sands).



4 Integrated Remote Sensors

4.1 Overview

The following sections summarize existing technologies related to remote, real-time water quality monitoring, including a summary of available platforms, sensors, power sources, and transmission options (among others). There are gaps in current technologies and processes that will need to be addressed in order to suit industry requirements. Accordingly, a key focus of the study will be to outline some of these gaps.

4.2 Platforms

The platforms that support sensor devices can be divided into two general categories: mobile and stationary. Stationary platforms can be stationed on land, secured to a buoy, or situated on a pontoon. They may be standalone or they may consist of a centralized platform that serves as the focal point for communication between numerous mobile nodes. The centralized platform also usually contains the data logger for transmission of the data to the user. Conversely, mobile platforms are able to operate off-shore and across more than one point (e.g. autonomous surface vehicles (ASVs), autonomous underwater vehicles (AUVs), etc.).

Stationary platform technology and its integration are relatively mature. There are a multitude of real-time, water quality monitoring projects throughout Canada and the rest of the world that use these platforms, including remote sensor networks at mining projects in Canada (see Section 4.7.2). These platforms are typically bulky, although recent advancements in technology have reduced their size to some extent.

Figure 1 below shows a typical stationary platform on-land. Figure 2 shows a stationary platform with the use of a pontoon. Figure 3 shows a typical monitoring buoy. Figure 4 and Figure 5 show a recently released platform (released 27 February 2014) to demonstrate their reduced size.

Sensors secured to buoys are also relatively well-developed, although they too are bulky in nature. Figure 6 and Figure 7 show typical buoy platforms for real-time water quality monitoring.

Attached to many platforms are ‘auto-sampling’ components that allow for automatic grab sampling. These samples can be retrieved over time or, with developing technology, may be analyzed on the spot. This is an important emerging technique as grab sampling is still largely seen as the most reliable sample type (Anderson et al. 2010). Many platforms are also fitted with automatic weather stations, webcams, and devices to measure other, non-water environmental parameters such as air quality and greenhouse gas emissions.



FIGURE 1 - TYPICAL TERRESTRIAL-BASED MONITORING

STATION

FIGURE 2 – LIBELIUM WASPMOTE SMART SENSOR

PLATFORM (1)

FIGURE 3 - LIBELIUM WASPMOTE SMART SENSOR

PLATFORM (2)

FIGURE 4 – LIBELIUM WASPMOTE SMART SENSOR

PLATFORM (3)



FIGURE 5 – EMM350 PISCES PONTOON PLATFORM

FIGURE 6 - REAL-TIME WATER QUALITY MONITORING

NETWORK BUOY

FIGURE 7 - REAL-TIME WATER QUALITY MONITORING

BUOY

FIGURE 8 – FLOATING NODE

FIGURE 9 – MONITORING BUOY

4.3 Communication Options

There are a wide variety of off-the-shelf communication options that have been adapted to sensor networks. Preliminary research indicates that this component is relatively well-developed, with research

WATER W O R K I N G G R O U P | P R E - F E A S I B I L I T Y S T U D Y R E P O R T


and development activities taking place across industry, academia, and governments to drive further innovation.

Based on the conducted research, the following transmission options were uncovered:

TABLE 5 – COMMUNICATION OPTIONS FOR REAL-TIME WATER QUALITY MONITORING STATIONS

COMMUNICATION OPTION

2G

3G

3G / General Packet Radio Service (GPRS)

4G

802.15.4 (radio)

868MHz (radio)

900MHz (radio)

Bluetooth

C-Band antenna

Code Division Multiple Access (CDMA)

Econet

Ethernet Geostationary Operational Environmental Satellites (GOES)

GPS

GPRS

GSM cellular

Inmar Sat D+

Iridium modems

RS-232

Satellite transmission

WiFi

XBee

Zigbee

The selection of a particular transmission option will pivot on a variety of factors, including distance, cost, regulatory constraints (e.g. frequency limits) and security considerations. Other aspects of the stations (e.g. maintenance, calibration requirements, power considerations, etc.) will also influence the options to be selected.

Factors specific to mine sites will also determine the communications medium chosen. Given that mine site are typically located in remote locations, certain options may ultimately be precluded, such as cellular and internet options. Consequently, satellite communications will be a much preferred option. In fact, satellite transmission mechanisms are predicted to become more commonplace for all types of real-time water quality monitoring (Aquatic Informatics 2013). Many projects, including the Newfoundland and Labrador one outlined below, use Iridium modems, which are connected to the Iridium satellite constellation.

Because satellite technology is often precluded by cost considerations, especially for individual mine sites, miniaturized satellites are being developed. This includes ‘nanosatellites’, ‘picosatellites’, and ‘femtosatellites’ (i.e. chip satellites), which have wet masses between 1 kg and 10 kg., 0.1 kg and 1 kg., and 10 g to 100 g, respectively. The use of miniaturized satellites has applications to real-time monitoring, especially in remote areas that do not have access to telecommunications towers. Field tests have already



been conducted by the Southern Ontario Water Consortium (SOWC) at the Diavik diamond mine in the NWT and the Detour Lake gold mine in northern Ontario.

Lastly, given that the platforms function as early warning systems, e-mail, and SMS alerts are now commonplace to enable faster response times.

4.4 Sensor Devices

Central to a remote monitoring network for water quality are the attached sensor probes. The probes are the devices that detect and measure the water quality parameters, which are then relayed to the user via some communication medium.

The parameters for which sensor probes have been developed are as follows:

TABLE 6 – EXISTING PROBES FOR REMOTE REAL-TIME WATER QUALITY MONITORING

PARAMETER

Ammonium

Biochemical oxygen demand

Blue-green algae

BTX

Cadmium

Chemical oxygen demand (COD)

Chloride

Chlorophyll

Coloured dissolved organic matter (CDOM)

Conductivity

Depth Dissolved organic carbon (DOC)

Dissolved oxygen (DO)

Fluorescence

Fluoride

Free chlorine

Gage height

Hydrogen Sulfide

Lead

Nitrate

ORP

Oxidization reduction potential (ORP) (Redox)

Oxygen Saturation

Ozone

PAHs

pH

Phosphorous

Potassium

Pressure Rhodamine WT

Salinity

Silicate

Stream-flow



PARAMETER

Temperature

Total organic carbon (TOC)

Turbidity

UV254

Temperature, pH, dissolved oxygen, turbidity, and conductance have been the focus of most real-time,

remote monitoring projects given that the sensors are stable and require little calibration with time. They can be seen as a starting point for remote real-time monitoring, especially as early warning systems. This is because changes in one of or more of these parameters will indicate a likely water quality event, thus allowing for more timely responses.

As evidenced by the above (Table 6), many of the parameters that are central to the water quality monitoring programs of mine operators (i.e. regulatory requirements) are not commercially available. Although the measurement of the above parameters could assist mine operators as part of their monitoring strategies, and in some instances do so, the development of additional sensors would be highly beneficial to the industry. Section 4.8.2 explores this gap in greater detail.

4.5 Data Storage

Data loggers are the component of the monitoring platforms that collect data from the various sensors and then transmit the data to the user for external storage. These data loggers are similar to the ones that are used in portable field monitoring devices. However, real-time data loggers need to handle larger volumes of data and are able to transmit the data to a centralized location for remote access by data handlers. They are also able to store data in the event of a communications failure or in applications where communications to external sources are not included. Consequently, more sophisticated data platforms, which are often customized, are increasingly used to meet these challenges.

For example, many of the NL real-time water quality monitoring sites utilise a system call Vedas II, which is a self-contained data acquisition platform made by Valcom Industries of Guelph, ON. The platform has a modular design and has high accuracy, reliability, and rugged packaging, making it ideal for military, aviation, industrial, hydrological and meteorological applications (NL WRMD 2008).

Many projects use standard office software (e.g. Microsoft Excel) to store the received data. However, operators are trending towards using more complex relational, database management systems (RDBMS), which use structured query language (SQL). This is largely due to quality assurance and quality control concerns. For example, RDBMS allow for the automatic removal of spikes in data that are not physically possible, automate ongoing corrections that might be physically possible but are illogical to a particular monitoring location, and provide for the ability to log and audit corrections for manual confirmation. This helps to better establish rating curves, which in turn, help to ensure data consistency, comparability, and reliability. RDBMS also reduce the need for human resources – in terms of time, costs, and training – that are needed to quality-check vast amounts of data (Forsbloom 2013).

One of the most widely used data platforms is the Aquarius server developed by Aquatic Informatics. The server is a data management platform consisting of a web-based user interface, automated data import, and automatic data processing / quality control. The server is capable of managing networks of a vast scale (i.e. thousands of stations and hundreds of users) or small-scale operations (i.e. a small network of monitoring stations). The USGS uses the platform in a network consisting of thousands of monitoring



stations nationwide that are managed by approximately 3,000 users (Blanchard 2007). The Water Survey of Canada (WSC) also uses Aquarius for its real-time hydrometric data program, which monitors approximately 1,700 sites. A multitude of companies, including mining companies (e.g. Teck, DeBeers)4 and mining engineering / consulting firms (e.g. Knight Piésold, AMEC, Golder Associates), also use the platform for conventional data storage.

Recent advancements in this area include the use of open-source RDBMS platforms, such as MySQL, which allow for greater customization given the availability of the source code. Many platforms / devices are also utilizing cloud storage mechanisms to further economize costs and efficiently store the vast amounts of data collected.

4.6 Power Options

Some real-time monitoring platforms can be attached to traditional power supplies. However, because the platforms are generally placed in remote environments, they are typically fitted with batteries that are able to be recharged with the use of attached solar panels. This is the most common method of providing power supply to the platforms; most, if not all, of the platforms that were uncovered as part of this research were using solar power.

However, batteries still need to be replaced and solar panels may have limited functionality (e.g. in winter), in addition to their maintenance requirements. Thus, emerging technologies are trending toward self-powered systems. This includes the use of piezoelectric, wind energy modules as well as natural energy harvesting from ambient water movement. The potential use of microbial fuel cells to power biosensor devices is also the subject of some research (Shantaram 2005; Stein et al. 2012).

4.7 Notable Real-Time Monitoring Projects

Overview

There are a multitude of projects that use remote, real-time monitoring of water quality, including projects undertaken by governments, NGOs, and industry members (among others). The majority of these projects monitor the parameters outlined in Table 7, with some projects monitoring parameters beyond these. The remote monitoring projects are typically undertaken by governments and conservation authorities, and to a lesser extent industry members. However, there are some examples of the three working in partnership.

Newfoundland and Labrador

In Newfoundland and Labrador, a joint project between the provincial and federal government as well as several mining companies – namely Labrador Iron Mines, Vale, Nalcor, and the Iron Ore Company of Canada – was established in 2001 by the provincial Water Resources Management Division (WRMD) of the Department of Environment and Conservation (DEC). Each partner in the project has established remote, real-time monitoring points that measure the parameters outlined in Table 7. The data are transmitted in near real-time with results graphed and displayed to the DEC website for public access. The sites are located at various discharge points as well as tailings impoundment areas (Pugh 2012).

4 These two companies use the server for stream flow gaging.



The Newfoundland and Labrador DEC and WRMD could potentially be beneficial partners in future ESI WWG endeavours; at a minimum, the agencies and companies could provide valuable lessons learned from their nearly 15 years of experience.

TABLE 7 – MONITORED SURFACE AND GROUNDWATER PARAMETERS FOR NL PROGRAM5

SURFACE WATER PARAMETERS GROUNDWATER PARAMETERS

Temperature (°C) Temperature (°C)

pH (pH units) pH (pH units)

Turbidity (NTU) Redox (mV)

Specific Conductance (µS/cm) Specific Conductance (µS/cm)

Dissolved Oxygen (mg/L) Salinity (mg/L)

% Saturation (%) Depth for Surface (m)

Total Dissolved Solids (g/L)

Susquehanna River Basin Commission

The Susquehanna River Basin Commission conducts real-time water quality monitoring at the watershed level, namely the Susquehanna River, which flows through southern New York and northern Pennsylvania. The basic water quality parameters (i.e. temperature, pH, conductance, DO, and turbidity) are measured. However, the initiative is unique in that the program is tailored towards examining the potential impacts of the natural gas industry on important water resources. Specifically, the monitoring stations are located in areas where drilling for natural gas is most active. This will help to establish an enhanced baseline, in addition to aiding in determining any potential impacts on the part of industry. Funding for the initiative was / is provided by industry, state agencies, and NGOs.

Barrick Gold – Pascua Lama

At the Barrick Pascua-Lama site6, some 30 real-time water quality stations were installed to measure basic water quality parameters, including pH, turbidity, and flow rates. Given the limited parameters measured, the stations would like serve primarily as early warning systems to complement any comprehensive monitoring being undertaken.

Water Survey of Canada

The WSC monitors hydrometric data (i.e. water level and stream-flow) at over 1,700 sites across the country. The program is administered by the federal and provincial / territorial governments. Although the program involves measuring very limited data, lessons may be learned from how they manage data from the network given the program’s scale across the country. A wealth of expertise and experience also exists within the WSC.

United States Geological Service – WaterQualityWatch

As of 2007, the USGS WaterQualityWatch program consists of 1,300+ sites across all 50 states and overseas U.S. territories (e.g. Puerto Rico, Guam, etc.). The majority of the sites measure the basic

5 Real-Time Water Quality Monitoring Network in Newfound and Labrador, Env Dept Presentation 6 The project is currently suspended, but it nevertheless illustrates how the technology can be used.



parameters (i.e. temperature, pH, dissolved oxygen, conductance, and turbidity). A minority of sites also measure nitrate and chloride, although these are typically measured via surrogates and laboratory samples (Ziegler 2014: personal communication).

The USGS network is the biggest of its kind in the world. The organisation is predictably a leader in the field given the scale of the program (i.e. 1,700+ sites and 3,000+ employees) and its extensive knowledge / experience with real-time monitoring. This is especially true of data management (i.e. QA / QC) and SOPs, which can be evidenced by staff contributions to the various monitoring conferences throughout the world.

Credit Valley Conservation

Credit Valley Conservation (CVC) is a community-based environmental organization whose mandate is to protect, restore, and manage the natural resources of the Credit Valley Watershed. In 1999, the organisation commenced the Integrated Watershed Monitoring Program, which includes terrestrial (i.e. forest, wetland, and riparian ecosystems) and water quality monitoring.

Real-time water quality monitoring is now part of this program. The program makes use of 11 stations to collect the following water quality information:

Temperature (i.e. air and water); Dissolved oxygen; pH; Conductivity; Chloride; Turbidity; and, Water levels.

Alarms are automatically triggered if any of the water quality readings are recorded above a certain limit. A response is then coordinated with partnering agencies, including the respective municipality, the MoE for Ontario, and Environment Canada.

Canada-Alberta Oil Sands Joint Monitoring Initiative

Surface water quality monitoring is a primary component of this initiative. The program consists of comprehensive, conventional grab sampling and is complemented by a real-time water quality monitoring platform in the Slave River at Fitzgerald, AB. The platform consists of a pontoon fitted with a multi-probe sonde (i.e. temperature, pH, turbidity, DO, and specific conductance), satellite communication capabilities, and an on-board solar power management system using solar panels.

Solid Energy – New Zealand

Solid Energy’s use of similar technology for coal mine-impacted waters in New Zealand is instructive for enhanced environmental strategies. The company installed real-time monitoring equipment to remotely measure pH, which, in turn, enabled the company to automatically control a lime-dosing plant to raise the pH of a nearby stream to ecologically viable conditions.



Others

Other notable real-time water quality monitoring projects include:

LOBO Northwest Arm, Halifax; Fraser River Estuary; Deploy Ireland; Eyes on Bay Chesapeake; Our Lake – Central NY; San Joaquin River Real-Time Water Quality Monitoring Program; Lake Access; Sanibel-Captiva Conservation Foundation River, Estuary, and Coastal Observing Network; Integrated Ocean Observing System; CICEET Great Bay Real-Time Environmental Monitoring Network; and, Texas Commission on Environmental Quality.

4.8 Gaps / Advancements in Research and Development

Innovative Platforms

As mentioned, conventional real-time monitoring platforms are bulky and difficult to install. The platforms are also susceptible to interference / malfunction due to extreme climates, weather events, and wildlife.

Thus, a variety of innovative platforms have been developed that are able to overcome some of the associated challenges. Devices using “plug-and-play” technology are becoming more commonplace. These devices have minimal installation and software requirements, which can be complex in conventional systems.

The Libelium Waspmote Smart Water Sensor is an example of this technology. It is also one of the first systems branded as an ‘Internet of Things’, whereby sensors and actuators that are embedded in physical objects are linked through wired and / or wireless networks, often using the same Internet Protocol (IP) that connects the Internet (McKinsey and Company 2010). The tools can sense external environmental parameters, communicate with other nodes, and respond to particular events. For example, in agriculture, remote sensors are used in conjunction with satellite imagery to monitor farm conditions and adjust the method with which each field is farmed – by adding additional fertilizer, for example (Chui et al. 2010). An example suited to the mining industry is the sensors detecting low pH values, which triggers an automatic dosing of a liming agent (see Section 4.7.9).

The development of AUVs is also a burgeoning discipline. In the past, AUVs for water quality monitoring have presented significant challenges to researchers and developers due to low communication bandwidth, large propagation delay, and high error probability (Cui et al. 2011: 1). However, advancements have been made, thus enabling the development of several innovative devices.

An example of this development is YSI’s AUV EcoMapper (see Figure 10). The device is capable of baseline monitoring, source water mapping, and event response (among others). Crucially, the AUV is equipped with a multi-parameter data sonde that is capable of measuring up to 10 water quality



parameters. However, there are application gaps that exist with the device that may preclude its suitability to the needs of mine operators, the most important of which is its limited, 8-12 hour run time.

FIGURE 10 – YSI ECOMAPPER AUV

Another example of an innovative development in the area of AUVs is the use of robotic fish. The ‘Shoal Group’, a public-private consortium formed the BMT Group, the University of Essex, the Tyndall National Institute, the University of Strathclyde, Thales Safare, and the Port Authority of Gijon (Spain), have developed prototypes of the fish for testing in harbour environments (see Figure 11). The devices incorporate biomimetic principles to mimic the movements of actual fish, thereby only marginally disturbing natural ecosystems. Individual devices may be used or multiple ones may be deployed as part of a larger network that communicate with one another through acoustic signals. The developers also foresee the ability of the fish to multi-task; for example, the devices could be able to monitor water quality while simultaneously tracking fish populations. The devices are also suitable for use in shallow water environments.

FIGURE 11 – SHOAL ROBOTIC FISH PROTOTYPE

The technology is still in the research and development stage, and challenges remain with the devices. Costs are inevitably high given that only prototypes have been developed. Battery life is also an obstacle, as the fish need to be recharged approximately every eight hours. Lastly, the size of the devices are relatively large; accordingly, developments are taking place to produce smaller, miniaturized devices.



An example of miniaturized development is Michigan State’s AquaSWARM (Small Wireless Autonomous Robots for Monitoring of Aquatic Environments) project (see Figure 12). The goal of the project is to “design and develop small, energy-efficient, autonomous underwater [fish] robots as sensor-rich platforms for dynamic, long-duration monitoring of aquatic environments”. The devices are built with fins built from electro-active polymers, which mimic real muscle tissue for biomimetic movements. Infrared sensors may also be used as eyes to allow the fish to avoid obstacles.

FIGURE 12 – MICHIGAN STATE UNIVERSITY MINIATURIZED ROBOTIC FISH FOR WATER QUALITY MONITORING

‘Lab-on-a-chip’ technology is another burgeoning area of research and technology development. Lab-on-a-chip devices are miniaturized, nano-platforms that offer the following potential benefits over conventional platforms / sensor devices:

Small volumes reduce the time taken to synthesize and analyse a product; Unique behaviour of liquids at the micro-scale allows greater control of molecular

concentrations and interactions; and, Reagent costs and the amount of chemical waste can be reduced (Daw et al. 2006).

Extensive research and development is ultimately needed to commercialize these devices for comprehensive water quality monitoring, although they represent a possible way forward in sensor development. A wealth of research, development, and commercialization may be tapped into given their success in the biomedicine and defense industries as well as smart computing in general.

Sensor Devices

A large gap in relation to applying real-time water quality monitoring to the mining industry is the limited parameters than can be measured with available sensors. Conventional real-time sensors – such as the ones outlined above – can be useful to mine operators and regulators, particularly as early warning systems. However, these basic parameters should be seen merely as a starting point for the development of the proposed project.

A variety of sensor probes exist that are able to take spot samples of most of the water quality constituents of concern to the mining industry. This includes myriad ion-selective electrodes that have been developed. However, the majority of these instruments currently have limited application to the



remote, real-time water quality monitoring of the sort that has been described herein. Very few ion-specific sensors are capable of measuring low levels of ion concentrations in ambient surface waters (Ziegler (USGS) 2014: personal communication). This challenge is exacerbated by calibration issues, namely that reference electrodes require a liquid injection and an electrolyte reservoir of constant composition (Bakker et al. 2009: 632). Thus, as Bakker et al. (2009: 632) conclude,

“Overall, current technology appears to be ill-suited for prolonged use of miniature sensing systems that are supposed to give reliable measuring results over prolonged periods of time on the order of days or even months. Rather, the devices are typically used to take spot samples in the field and / or laboratory [i.e. highly controlled settings].”

This reality was corroborated by communication with multiple suppliers of the various probes and electrodes. However, myriad engineering and design mechanisms used to overcome these challenges are currently the focus of much research. This includes the characterization of heavy metals by means of microscopy, profilometry, Rutherford backscattering spectroscopy (RBS), scanning electron microscopy (SEM), potentiometric measurements, and sensor array by polymeric sensor membranes (Spelthahn et al. 2012; Bakker et al. 2009). An example of a company that offers ISE for real-time applications is Austria’s s::can; the company offers ISEs for real-time use for ammonium and fluoride.

The use of biosensors for environmental monitoring is also becoming a burgeoning research topic. Biosensors are analytical devices that integrate a biological sensing element (e.g. an enzyme or antibody) with a physical (e.g. optical, mass, or electrochemical) transducer. The interaction between the target and the bio-recognition molecules is translated into a measurable electrical signal by exploiting light absorption, fluorescence, luminescence, reflectance, Raman scattering, and refractive index (Long et al. 2013a). Despite the advancements made in this space in recent years7, Long et al. (2013a: 13928) note that the use of biosensors in the field of environmental pollution control and early warning is still in its rudimentary stages.

Multiple processes can be used to compensate for the limited number of sensors available.

Automated grab sampling using automatic samplers (hereinafter, autosamplers) are increasingly being used for many parameters. Autosamplers are able to collect multiple samples (typically up to 24 before test bottles need to be restocked). To prevent sample degradation, the autosampler can be refrigerated or stocked with ice. Furthermore, autosamplers can be integrated with other devices, including flow meters, pH monitors, and water quality sondes. For example, a real-time sensor can detect threshold levels of a particular constituent, which, if surpassed, will automatically trigger a sample.

Furthermore, the conventional water quality parameters measured by most real-time networks are increasingly being used as surrogates to estimate and monitor real-time concentrations and loads of additional, non-measured water quality constituents (Christensen et al. 2002). Many models have been developed, most of which incorporate simple linear (ordinary least squares) regression analyses (Stone et al. 2013). As Stone et al. (2013: 1) note,

“Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints.”

7 A comprehensive overview is given by Long et al. (2013b).



Many of the models that have been developed have been shown to accurately predict a variety of parameters, including many major ions (Stone et al. 2013; Newfoundland and Labrador Water Resources Management Division 2013).

Notwithstanding these developments, additional advancements in sensor technology would be a large contribution to existing networks as well as the development of mining-specific networks. Thus, additional research tailored towards specific sensor probes, particularly those that measure the constituents of greatest concern to the mining industry as well as ones that are compact and scalable, is an avenue that could be pursued as part of future project development activities.

Long-term Deployment

Additional research and development is needed to enable the longer-term deployment of remote monitoring platforms. This includes actual deployment times as well as the frequency with which maintenance personnel must be deployed, which was one of the foremost challenges related to water quality monitoring outlined in the above problem statement.

Typically, bio-fouling (i.e. the accumulation of microorganisms, plants, algae, and / or animals on wetted surfaces) and sediment loads (i.e. solid matter carried by a flow stream) are the predominant factors that can overwhelm sensors and invalidate their data. Calibration drift is also a common problem (Clinton 2009). There are a variety of design features that help to overcome these challenges, although not all sensor technology has incorporated these developments. WETLabs is company that has done so through the following mechanisms:

Pump-controlled flow; Extensive copper cladding; Bleach Injection System (BLIS) to protect DO sensor and CTD flow path; Anti-foulant (AF) collar for conductivity cell; Light-blocked sample chambers; and, Bio-wiper to protect optical components.

These mechanisms allow for the water sample to take place inside the sensor device, thus preventing any external fouling (Sea-Bird Electronics 2012).8

Three other major factors may also preclude the long-term deployment of many systems, specifically sensor calibration challenges (i.e. requirement of controlled settings, filling probes with solution, etc.), power requirements, and harsh climates (see Section 4.8.7)

Scalability

Mine operators typically monitor water quality at many sites, Including sites mandated by regulatory agencies, baseline / reference points, and discharge points (among many others). Thus, a comprehensive, real-time water quality monitoring program would ideally have nodes at each of these points that are connected to a centralized network.

Technology could theoretically support a network of this sort, although inordinate costs would likely preclude such a scenario. Specifically, current platforms are too expensive and bulky to be implemented on a large, network-like scale. A typical real-time monitoring station merely measuring the basic

8 For additional information, see Dvorsky (2012), who provides an example of this challenge in practice.



parameters costs approximately US$20,000 to purchase and install, with costs of US$8,000 in annual maintenance (i.e. labor, equipment servicing, and data management) (Susquehanna River Basin Commission n.d.). Accordingly, real-time monitoring networks are only at a stage whereby they can complement existing monitoring programs.

It is envisaged that advancements in technology in the near future will allow for networks of a greater scale to be implemented by mine operators. The Libelium platform mentioned above is an example of this, as it is more compact and, according to the company website, will retail (in the coming months) at one-tenth the price of conventional systems. Miniaturized, low-cost, and recyclable devices, such as the emerging lab-on-a-chip devices described above, will further contribute to this.

Quality Assurance / Quality Control

Real-time monitoring platforms entail the collection of vast volumes of water quality data. Water resource managers are collecting, storing, managing, analyzing, and publishing more continuous hydrological data than ever before (Aquatic Informatics 2012). Some of this data will inevitably be inaccurate, given the inevitable sensor fouling and calibration drift that will take place. Consequently, quality assurance (QA) and quality control (QC) are important aspects of any real-time network. Furthermore, any real-time system will have, at its core, the goal of producing high quality, credible, and defensible data to support environmental management objectives. Stakeholder expectations are also evolving towards the availability of more and better quality data (Ibid).

One of the challenges involved in QA and QC is the fact that different sensor instruments often provide different values of the same parameter that is measured (NL WRMD 2011). The data may be close in scope, but it may not be entirely comparable (Ibid).

International standards are being developed by a variety organisations to promote streamlined QA / QC, including the USGS, World Meteorological Association (WMO), and the International Standards Organization (ISO).

Moreover, the Aquatic Sensor Workgroup (ASW), Methods and Data Comparability Board, which includes representatives from the resource management community, research institutions, and the private sector, was established to address gaps in information and practice surrounding water monitoring sensors used in the field. This includes both spot and continuous monitoring applications. Their work is split into two main categories: 1) the development of standard operating procedures (SOPs) for sensor calibration, deployment set-up, site selection, maintenance, and bio-fouling prevention; and 2) streamlining the practice of data analysis, data management, and metadata. These activities are ultimately intended to:

Standardize sensor protocols across agencies and groups; Streamline the validation of data, error calculations, and data correction, using standardized

software packages; and, Facilitate the reporting of all relevant data needed for data sharing (Katznelson & Sullivan

2012).

Development of Additional Surrogate / Estimation Models

As mentioned in Section 4.8.2, the measurements of parameters with established sensors (i.e. conductivity, turbidity, dissolved oxygen, etc.) can be used as surrogates to estimate the loads /



concentrations of other, non-measured parameters. Additional research in this area would be useful to the mining industry, especially in regards to mine-impacted matrices and according to mine site-specific conditions.

Furthermore, as Rounds (2012) notes, one of the biggest gaps to be filled in relation to real-time water quality monitoring is “the need for tools to forecast future conditions and extend data spatially”. Temporal measurement should also be included in this assessment. Taken together, these developments will allow for greater progress towards a more representative understanding of water quality, rather than the mere snapshot that grab sampling provides.

The ‘living laboratory’ at Ramsey Lake in Sudbury is an example of a research project that would be beneficial in this respect (see Section 4.9.6). As the project participants note, the lake has been affected by “100+ years of extreme industrial acid and metal-laden emission impact”. Thus, continuous long-term monitoring will allow for an increased understanding of groundwater, surface water, and contaminant interactions. In turn, this will allow for the development of more dynamic and predictive models.

Making good use of data (i.e. adding value) will be a growing trend alongside developments in ‘big data’, especially as it pertains to environmental monitoring (Rounds 2012). In other words, the vast amounts of raw data that are collected must be turned into useful information that can be acted upon if need be.

Performance in Winter Climates

Additional research is needed into the performance of the platforms / devices in winter climates, namely ones that are subject to cold, harsh, and often extreme weather conditions. This will form a key component of the upcoming feasibility work.

Issues that are particularly exacerbated in winter climates include:

Sensor functionality at low / sub-optimal temperatures; Physical harm to platforms from environmental factors (i.e. ice, snow, wildlife, etc.) Reliable power (e.g. solar power has limited function with high snowfall); and, Accessibility for routine maintenance.

These challenges were corroborated by representatives / practitioners working on the NL real-time water quality monitoring program. The challenges are often so acute that many stations are shut down during the winter months.

4.9 Partnership Opportunities

Newfoundland and Labrador Program

The NL participants in the NL program are potential partners for the development of the proposed project. The NL program is one of the few examples of mining companies in Canada that are utilising remote, real-time monitoring. The NL government has significant experience in the space, and can be seen as one of the leaders in the area, as evidenced by the bi-annual real-time monitoring conference that they host, which attracts practitioners from throughout the world.

Synergies may also be present with LOOKNorth, given that they are located in St. John’s and have an ongoing partnership with the NL government (a CMIC member).



Southern Ontario Water Consortium

The SOWC was formed in 2011 as a consortium of eight Ontario universities. It is supported by CAD$19.58M in funding from FedDev Ontario and CAD$8.9M from the Government of Ontario through the Ministry of Research and Innovation’s Ontario Research Fund. IBM has also provided “industry-leading hardware and software, along with expert personnel support”, which is valued at over CAD$28M. The SOWC is a platform for research, development, and demonstration and water technologies and services with activities divided into six clusters:

Watershed management; Wastewater management; Ecotoxicology; Drinking water treatment technologies; Development of new analytical techniques; and, Sensor development.

Southern Ontario Smart Computing Innovation Platform

SOSCIP is a research consortium similar to the SOWC with 7 Ontario universities participating. The group is benefiting from approximately CAD$20M, CAD$15M, and CAD$175M from the federal government, Ontario government, and IBM, respectively.

The program will enable the creation of the ‘IBM Canada Research and Development Centre’. SOSCIP has 5 project portfolios, including a water portfolio, which consists of four subprojects, three of which are related to real-time water monitoring. These are entitled:

“Real-Time, Remote Sensor, Watershed Data Analysis” (conducted by David Rudolph, University of Waterloo);

“Water Quality Monitoring”9 (conducted by Jamal Deen, McMaster University); and, “Real-time Drinking Water Management & Monitoring” (conducted by Mohamed Ibnahla,

Queens University).

WaterTAP

WaterTAP (Technology Acceleration Project) is technology hub based in Ontario whose mission is to “accelerate Ontario’s water technology success”. They do so by connecting early commercial stage Ontario companies with resources for product development, recognizing and sharing the success of Ontario water utilities, and introducing water entrepreneurs, businesses, investors, and advisors worldwide to the Ontario water community. The organisation also advocates on behalf of the water industry to foster innovation-friendly water management policies at the government level. The organisation is partially funded by the Government of Ontario.

9 This project aims to create a “low-cost, easy-to-use, real-time sensor system for water quality monitoring, including biological and chemical contamination detection”.



Global Institute for Water Security

The Institute was launched at the University of Saskatchewan in 2011 and is comprised of over 70 faculty and government scientists as well as 50 students and post-doctoral fellows as part of interdisciplinary teams. It is co-located with Environment Canada’s National Hydrology Research Centre.

One of the Institute’s main research themes is the ‘Sustainable Development of Natural Resources’. As part of this theme, the Institute is developing a real-time monitoring network in northern Saskatchewan. The network is still in the development stage with many of its components yet to be worked out. Advanced analytical facilities, namely the Canadian Light Source and the Toxicology Centre of the University of Saskatchewan will support the theme’s projects.

Mirarco / Symbioticware / Laurentian University

Mirarco is not-for-profit applied research firm based out of Laurentian University in Sudbury that is focused on innovation in the global natural resources industry. Mirarco coordinates purpose-driven, applied research teams (including post-secondary students, professors, etc.) and the use of testing, analytical and visualisation facilities. The organisation also helps coordinate funding subsidy programs and tax credits.

The organisation has undertaken a variety of innovative work related to environmental systems monitoring, including water monitoring. A notable project is the development of the ‘SymBot’ device in conjunction with Symbioticware10, who provide “ruggedized hardware and software solutions for real-time gathering, transmitting, and analyzing equipment [as well as] sensor data for improved safety, productivity, and asset utilization”. The device is paired with a remote underwater sampling unit to gather water quality parameters (i.e. temperature, pH, electrical conductivity, salinity, dissolved oxygen, and oxidation / reduction potential) of the dominant Sudbury watershed – Ramsey Lake – in near real-time. The platform also has a meteorological station that collects additional environmental data, such as wind speed / direction, air temperature, humidity, and solar radiation.

As outlined in Section 4.8.6, the project is notable given that Ramsey Lake is a “living laboratory” for the study of the effects of “100+ years of extreme industrial acid and metal-laden emission impact” (Miarco website). Accordingly, continuous, long-term monitoring of the lake will allow for an increased understanding of groundwater, surface water, and contaminant interactions, which in turn, will allow for the development of enhanced dynamic and predictive models.

LOOKNorth

LOOKNorth would be a beneficial partner for further project development activities, given their experience with remote sensing technologies and the wealth of research and development that they have conducted – and are currently undertaking – in conjunction with C-CORE, Memorial University, the NL government, and industry (among others). Furthermore, there may be synergies with the NL real-time monitoring program, given LOOKNorth’s partners as well as their location in St. John’s.

10 Symbioticware has partnered with organisations like CMIC in the past, namely the Centre for Excellence in Mining Innovation (CEMI).



IBM

CMIC has opened discussions with IBM regarding potential partnership opportunities. These talks have been at a high-level and IBM has expressed strong interest in potential projects to engage the mining industry. Other potential partners (i.e. SOWC, SOSCIP) are already partnering with IBM, namely through IBM’s contribution of hardware and infrastructure to the programs.

Universities

There are numerous universities throughout Canada that are developing real-time components and integrated systems. It may be beneficial to tap into existing networks / consortiums (i.e. SOWC, SOSCIP) rather than establish a wholly different network. However, there are a multitude of research groups across the country that are specializing in related fields, including multiple Canada Research Chairs. For example, there are 30+ microfluidics research groups across the country alone.

RealTech

RealTech is an innovative, water quality technology company based in Toronto that develops both portable and real-time continuous water quality analyzers. One of their most innovative developments is their continuous ‘spectrum analyzers’. The devices are being developed in conjunction with Mekorot, Israel’s national water company, which is world-renowned for its expertise in water technology and water management.

Canadian Water Network

The Canadian Water Network (CWN) is a Centre of Excellence that focuses on coordinating multidisciplinary research related to water management and connecting experts and “water managers”. The group has not yet undertaken research / development related to mining, although they plan to do so as part of future project development activities.

High-level discussions have taken place between CMIC and the CWN to explore potential partnership opportunities. A partnership that aims to bring together the different players in the remote, real-time water quality monitoring space is a possible avenue for future development.

Real-Time Water Quality Monitoring Equipment

The following is a list of some major companies that supply various real-time water quality monitoring equipment, including platforms, sensor devices, transmission equipment, etc.:

YSI; Hach Hydromet (Hydrolab); Cole Parmer; In Situ Inc.; Global Water; Campbell Scientific; Pasco Scientific (Avya Educational); and, Primodal (partners with SOWC).



5 Database / Repository of Water Resources Information

5.1 Database Examples

Overview

There are a variety of platforms that exist that are aligned with the preliminary scope of the proposed ESI WWG database. These include comprehensive water quality databases, repositories, and portals that are set up and run by a variety of organizations, including mining industry participants, regulatory agencies, and international organizations. The data platforms range from simple flat-file databases using Excel spreadsheets and Access tables to more complex relational databases (e.g. structured query language (SQL) databases) that employ a greater logical structure in the way that data are stored.

One of the objectives of the pre-feasibility study was to discern the various types of industry databases that could potentially serve as a model for the proposed database, or, at a minimum, to help glean useful insights in the form of lessons learned. However, few examples of mining industry-shared data were uncovered. Nevertheless, this by no means precludes the development of the proposed database, as valuable information and lessons learned from these initiatives can still prove useful. Moreover, this reinforces the novelty / innovativeness of the proposed database.

Newfoundland and Labrador Water Resources Portal

The initial version of the NL Water Resources Portal (WRP) was launched in 2004 to facilitate the sharing of drinking water quality information across provincial departments. The current version, which is available online to the public, was launched in 2010.

The primary water resources data that are available in the portal include:

Drinking water quality data and treatment profiles; Protection areas for ground and surface water supplies; Boil water advisories; Ambient water quality data, station profiles, and watersheds; Real-time water quality data; Hydrometric station data and station profiles; Climate station profiles; Dam and sewage outfall locations; and Mapping applications.

The program was developed in conjunction with Natural Resources Canada (NRCan) and Environment Canada, mostly through the GeoConnections program (see Section 5.3.3). A local GIS software developer / provider – Tamarack Geographic Technologies – was commissioned to provide custom GIS services for the project.

In 2010, several additional data streams were highlighted for future incorporation into the portal, including the following (Khan & Tucker 2010):

1 in 20 year and 1 in 100 year flood risk mapping; Remote sensing imagery and derived products; Environmental permits;



Operator training and education resources; Flood events documentation; Wetlands inventory; Water and wastewater distribution network; and, Diverted watersheds.

The portal does not contain other data that have been collected by industry.

Regional Aquatics Monitoring Program

The Regional Aquatics Monitoring Program (RAMP) is a joint environmental monitoring program that assesses the health of the rivers and lakes in the oil sands region of northeastern Alberta. There are approximately 20 program members, including the major oil sands companies, First Nations groups, and federal, provincial, and municipal government agencies (i.e. Alberta Environment, Environment Canada, the Department of Fisheries and Oceans Canada, and the Regional Municipality of Wood Buffalo). The initiative is primarily industry-funded.

According to the program’s terms of reference (2006: Section 2.0), RAMP’s mandate “is to determine, evaluate and communicate the state of the aquatic environment and any changes that may result from cumulative resource development within the Regional Municipality of Wood Buffalo”. To achieve this mandate, RAMP has nine primary objectives:

Monitor aquatic environment in the oil sands area to detect and assess cumulative effects and regional trends;

Collect baseline data to characterize variability in the oil sands area; Collect and compare data against which predictions contained in environmental impact

assessments (EIA’s) can be assessed; Collect data that satisfies the monitoring required by regulatory approvals of oil sand

developments; Collect data that satisfies the monitoring requirements of company-specific community

agreements with associated funding; Recognize and incorporate traditional knowledge into monitoring and assessment

activities; Communicate monitoring and assessment activities and results to RAMP members,

communities in the Regional Municipality of Wood Buffalo, regulatory agencies and other interested parties;

Review and adjust the program to incorporate monitoring results, technological advances, community concerns and new or changed approval conditions; and,

Conduct a periodic peer review of the program’s objectives against its results, and to recommend adjustments necessary for the program’s success (Ibid: Section 3.0).

The collected data are stored in a central database, which is organized around six clusters, namely: acid sensitive lakes, benthic invertebrates, climate and hydrology, fish populations, sediment quality, and water quality.

The water quality portion of the database is populated with information from samples collected at stations on rivers, stream, and lakes throughout the RAMP study area. RAMP typically collects at least three years of baseline data at stations of interest before development activities commence. This allows for current water quality to be compared to historical conditions, which enables the determination of the



effect oil sands development is having on the water resources. Table 8 presents a summary of the parameters that are measured as part of the program.11

TABLE 8 – WATER QUALITY PARAMETERS MEASURED BY RAMP

WATER QUALITY PARAMETER

Conductivity

Dissolved phosphorous

Ions (e.g. sodium, chloride, calcium, magnesium, sulphate)

Naphthenic acids

Nitrate / nitrite

pH

Total alkalinity

Total and dissolved aluminum

Total arsenic

Total boron

Total dissolved solids (TDS)

Total mercury

Total molybdenum Total nitrogen

Total strontium

Total suspended solids (TSS)

The data are usually collected four times a year (i.e. during each season), although additional monitoring takes place in the fall due to amenable weather / flow conditions. Moreover, the earliest data available are from 1997 and it appears that there is a 2-year lag on online data submission. Data are available online for public consumption and reports can be prepared instantaneously and displayed on-screen or as a downloadable Excel file.

The experiences operators have had with the program will likely offer lessons learned, particularly in terms of multi-stakeholder collaboration, data infrastructure, and the corresponding challenges that were experienced. The RAMP program also demonstrates that data submission, storage, and dissemination need not be a vast undertaking requiring complex software and other infrastructure, given that the platform that is used is relatively basic (i.e. a flat-file type query platform).

Canada-Alberta Oil Sands Environmental Monitoring Information Portal

The Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring is an initiative undertaken by the federal and provincial governments as well as their respective regulatory agencies. The program aims to enhance the understanding of the cumulative effects of the oil sands, including characterization of the state of the environment in the oil sands region, environmental change, and potential future effects.

The program includes an environmental monitoring information portal, which includes maps of the monitoring region, details of the monitoring sites, the most up-to-date data collected by the program’s scientists in the field, and scientific analysis and interpretation of the data and results. Water quality data are available, including data on:

11 For a full list of analyte availability, see http://www.ramp-alberta.org/data/Water/WaterAnalyteAvailability.aspx

http://www.ramp-alberta.org/data/Water/WaterAnalyteAvailability.aspx



Nutrients; Metals (dissolved and total); PAHs; and, Major ions.

Historical baseline data are available, as are ongoing monitoring results. This includes the use of bio-monitoring sampling reaches / stations to collect benthic invertebrate samples, algal biomass, water chemistry, and habitat characteristics (i.e. substrate composition, depth, and water velocity). Spatially, the data are limited to the larger Athabasca, Peace, and Slave Rivers, as opposed to the specific monitoring points that comprise operator water quality monitoring programs.

The data are publically available and can be accessed online as downloadable Excel files. As the sampling frequency increases and additional data are made available for public access, an integrated, open-management program will be created to store the data. This program is slated to be operational by 2015.

Water Survey of Canada National Data Water Archive

The WSC maintains two main databases as part of the National Water Data Archive, namely HYDEX and HYDAT. The former is a relational database that collects inventory information on the various monitoring stations that form part of the WSC network. The database contains information about the stations themselves (i.e. location, equipment, and type of data).

HYDAT is a relational database that contains computed data for the 2,900+ monitoring stations in the WSC network as well as 5,100+ discounted sites across Canada. There is evidently a vast amount of data contained within the database, although the range of data is not nearly as comprehensive as the data collected by mine operators as part of their water quality monitoring programs. Specifically, the data included in HYDAT include daily and monthly means of flow, water levels, and sediment concentrations (for sediment sites). Peaks and extremes are also recorded for some sites.

The data are managed using the Aquarius software platform, are publically available, and can be downloaded as a Microsoft Access database file.

USGS National Water Information System

The USGS National Water Information System (NWIS) was established in 2001 to facilitate the sharing of water quality data within the USGS network, to USGS cooperators, and to the general public. The data are collected from 1.5 million sites throughout the US as well as overseas territorial sites (e.g. Puerto Rico, Guam, etc.).

Data are collected in conjunction with local Water Science Centers, which in turn collect data from the municipalities, conservation authorities, and tribes (amongst others) that operate monitoring stations / programs. Relevant data includes site characteristics, time-series data for gage height, stream-flow, groundwater level, precipitation, and physical and chemical properties of water. The online database provides access to most the data in the NWIS that are collected throughout the US. The data are updated regularly, typically upon receipt at Water Science Centers.

Several output options are available:

Graphs of current stream-flow conditions, water levels, and water quality;



Tabular output in HTML and ASCII tab-delimited files; and, Summary lists for selected sites that can be used as a basis for reselection to acquire

refined results (USGS 2011).

The database does not contain water quality metrics collected by industry. However, the database may still serve as a useful guide for the development of ESI WWG’s proposed database due to the scale of the program, the standardized reporting format that has been created to facilitate the sharing of the data, and the considerable experience and expertise the USGS has in the area.

United Nations Environment Programme GEMStat

The United Nations Environment Programme (UNEP) manages the Global Environment Monitoring System (GEMS), which includes the Water Programme as one of its main components. The programme houses the GEMStat database, which stores various data provided by government and non-governmental agencies. The global network spans over 100 countries and 3,000+ stations. Information is available on a wide variety of data, including12:

Hydrologic and sampling variables (i.e. instantaneous discharge); Major ions; Metal content; Microbiology (i.e. faecal streptococci, faecal coliform bacteria, and chlorophyll A); Organic matter and organic contaminants; and, Physical / chemical characteristics.

Crucially, Environment Canada is the GEMS Water Programme’s primary sponsor. EC houses the overall programme as well as the GEMStat database. Accordingly, EC could be a valuable partner for ESI WWG (see Section 5.3.3).

US EPA STORET Data Warehouse

The STORET data warehouse is the US EPA’s repository for water quality, biological, and physical data. Data are submitted in a standard format by a variety of groups, including federal and state regulatory agencies, Tribal groups, local governments, academic groups / researchers, conservation authorities, and volunteer monitoring organizations.

The following are the primary data that are stored in the database:

300,000+ taxa with full hierarchy for benthic macro-invertebrates, fish species, and periphyton;

Thousands of chemical parameters (e.g. DO, pH, salinity, conductivity, etc.); Physical characteristics (e.g. measurements of substrate, stream canopy, and habitat); Field sampling and monitoring methods (quantitative and qualitative); and, Graphics and text documents (e.g. jpegs and pdfs).

The repository is powered by Oracle software. Personal Oracle is used for workstations, whereas Oracle Server is used for server platforms. The STORET Legacy Data Center is a parallel database containing historical water quality data, spanning approximately 100 years (1900 – 1998).

12 All of the data listed is of course not available for every site.



WVDEP Division of Mining and Reclamation

The West Virginia Department of Environmental Protection (WVDEP) Division of Mining and Reclamation has undertaken to streamline all of the state’s five water quality monitoring databases onto a single database. The incorporated data are collected for regulatory and permitting purposes at the federal and state level, including modern data and “permit legacy data” (Schaer 2008). The primary databases housing this data are (Ibid):

National Pollution Discharge Elimination System (NPDES); Surface Mine Control Reclamation Act (SMRCA) permit data; Watershed data; Abandoned mine land program data; and, Other NPDES sampling (i.e. total maximum daily load, anti-degradation).

The program was developed in conjunction with West Virginia University and the Natural Resource Analysis Center. The program offers insight as to how data may be amalgamated into an accessible format, what tools can be used to do so, and what challenges arise as part of the process.

The database utilizes an Oracle database to store the data and it is cross-checked using third-party software for compliance. Furthermore, the data are migrated into the database using EQuIS, an environmental data management and GIS decision support software package developed by EarthSoft.

One of the most problematic aspects of developing the database was the actual integration of the diverse datasets. As Schaer (Ibid: 2) notes, “collecting over 2,000,000 water quality and quantity samples on various […] water quality databases was the easy part. Now [database owners] must come to grips with […] how to integrate all this data from many varied sources”. It is anticipated that integration will also be a challenging aspect of the development of an ESI WWG database (see Section 5.2.3).

Geoscience Databases

A multitude of geoscience databases exist, which can be public (i.e. set up and run by government agencies), private, and, increasingly, open-source. These databases could potentially serve as models for the development of the proposed ESI WWG database.

These databases would be particularly useful if the proposed database is to include comprehensive information and data related to water quality monitoring, rather than simply facilitating the sharing of flat files. Ideally, the proposed database will evolve into a comprehensive platform similar in nature to these databases, with wide-ranging information on water resource management, including, but not limited to: water quality data, watershed mapping, information resources, bibliographic information, etc. However, this will hinge on the results of the scoping exercise that will be developed as part of ongoing project development work.

Mining Observatory Data Control Center

One of the few examples of intra-industry data exchange in the mining industry is the Mining Observatory and Data Control Centre (MODCC), which is a recent joint project between the Sudbury Neutrino Observatory (SNOLAB) and CEMI. CMIC is involved in the project as a partner, as are mining software and consulting services firms Mira Geoscience and Objectivity (a drilling optimization company). To help start the centre, the Northern Ontario Heritage Fund Corporation contributed CAD$0.75M.



The facility, which is not yet operational, will house mining and exploration data that will be shared with other companies as well as researchers. A particular focus will be on mining and exploration equipment fitted with sensors, whose data will allow researchers and developers to optimize performance. Ultimately, the centre will aim to contribute to developing the “next generation of tools and protocols to mine at deeper levels” (Migneault 2013).

One of the biggest concerns highlighted by the project participants is the sharing of data with the wider public. To alleviate this concern, tiered access will be employed to ensure that only approved partners can access the data. This will be a similarly critical database component for ESI WWG to scope out, given the potential sensitive nature of water quality data. Furthermore, companies will likely need to pay to access certain types of data (Ibid).

Although this initiative does not involve the sharing of water data, it is anticipated that many lessons learned can be uncovered given that it is but one of a few examples of data being shared across the mining industry.

5.2 Gap Analysis

Shared Data in the Mining Industry

Few examples of data being shared among mining operators were uncovered as part of the pre-feasibility research. This includes all types of data, including water quality data. No examples of companies sharing water quality data in a centralized database were found throughout the major mining jurisdictions in the world (i.e. Canada, the US, Australia, and South Africa, etc.). The closest example was the RAMP database mentioned above; however, this database was populated by water quality data that was conducted supplementary to existing water monitoring. Therefore, there is a data confidentiality hurdle to be overcome for the implementation of the database.

As mentioned, the closest analogue to the proposed integrated water database is geoscience databases. However, these databases developed out of the regulatory requirement that companies report the assessment work, performed in a given year, to meet expenditure requirements to maintain their claims in good standing. Furthermore, these databases typically include the results of geoscience data collected by NRCan as well as provincial and territorial geological surveys. There are no similar requirements for water quality data and the geological survey counterpart, the WSC, does not collect data on nearly as wide a scale as they do for geological resources. Water quality data have to be submitted to regulatory agencies as part of impact assessments and ongoing reporting; however, not all of this information is public and water quality monitoring by mining operators usually goes far above and beyond these requirements.

The fact that little data are currently being shared need not be seen as a hindrance to the development of the database; rather, it could be seen as a useful opportunity to promote intra-industry cooperation and data exchange. In fact, the sharing of data, knowledge, and expertise is often regarded as one of the new strategies that mining companies will need to take to become more competitive in the future (IBM White Paper 2009).

Industry Participation

The value of the proposed database / repository / portal will – to a large degree – pivot on the amount of comprehensive data that is contained within it. Therefore, critical to deciding the feasibility of the



project will be a determination as to the willingness of potential participants to submit comprehensive data. Potential participants include mine operators, the various regulatory agencies responsible for collecting water quality data, First Nations groups, and NGOs (e.g. conservation authorities).

This readiness to share should be gaged as part of the feasibility study work. Specific components of this work could include:

Data various stakeholders would be willing to provide; Nature and scope of the data that has been submitted to regulatory agencies; Conditions under which participants would be willing to provide data and those under

which they would not; Additional information beyond water quality data (e.g. watershed mapping, additional

geospatial data, traditional knowledge, etc.); The conditions under which the data can be used and applied (e.g. regulatory purposes,

early warnings, etc.); Clarity of definitions being used (e.g. what constitutes a compliance point, discharge

point, etc.); and, Accepted data collection methodologies and consistent QA / QC procedures.

Integration of Existing Databases

As noted in Section 5.1.9, an anticipated challenge related to the development of the proposed ESI WWG database will be the integration of data into an accessible format. The database examples outlined above will help to glean useful insights into this, given that data from multiple databases – sometimes hundreds as in the case of GEMStat – were integrated into one system. For the ESI WWG database, data could potentially be submitted from 10+ provincial / territorial jurisdictions as well as any water quality data that is required to be collected at the federal level (i.e. MMER data reporting). To discern the scale of the challenge, additional research should be undertaken as part of the feasibility study to determine how water quality data are collected and reported in the major mining jurisdictions across the country. The equivalency of the data can then be assessed, which, in turn, will help determine any potential data conversion and corresponding data ingestion challenges.

Notwithstanding any challenges, the development of the database may help to promote the adoption of a standardized reporting format, which was highlighted as one of the goals of water quality monitoring that the database will ideally help achieve.

An example of a standardized framework through which data are exchanged onto one platform is the US EPA’s Water Quality Exchange (WQX). The WQX defines a standard set of data elements that must be captured in a data submission file before it can be stored in the STORET Data Warehouse. Furthermore, the WQX uses a standard set of internet protocols that define how data submission is made to the EPA.

Quality Assurance / Quality Control

As with any large-scale data collection and corresponding storage activities, QA and QC are critical. As Marino (2004) notes, “a well-defined quality control and quality assurance […] program is an essential foundation for any data project”. This is especially true if operators / companies will be relying on particular data as part of their environmental management strategies, such as baseline establishment, impact assessments, and the corresponding environmental management actions that are undertaken.



Ingesting data into a database, particularly when the data come from diverse sources, presents particular challenges for QA and QC. However, these challenges can be managed with specialized software and tools. For example, within the WVDEP program, third-party software was used to match historical and baseline data that were being ingested from the various sources to help ensure data consistency, validity, and defensibility. An example of such software is ESRI’s ArcGIS Data Reviewer. Rather than employ a resource-intensive, manual process, the software allows for an automated QA / QC program.

Ideally, the proposed database will be continue to be populated with data once developed. This further reinforces the need for robust QA / QC mechanisms. Mechanisms must also be put in place to ensure that the organizations that submit data follow accepted QC / QC standards, which will likely include appropriate internal measures, laboratory accreditation, international standards (e.g. ISO / IEC 17025), and any federal / provincial regulatory requirements.

Accordingly, the development of a quality management plan will be a critical component of the construction of the proposed database. The standardized reporting format described above should help to simplify this process, as will a requirement that accepted, robust QA / QC measures be followed. A data disclaimer will also likely be necessary for users to accept before using the database.

Scoping Aspects

One of the key requirements for future project development activities will be a determination as to the scope of the proposed database. This is because the project has the potential to balloon into an unworkable one given the myriad possibilities at hand. Accordingly, the project should be scaled progressively beginning with a pilot stage, perhaps merely as a document repository.

The following sections summarize some the main scoping aspects for future consideration.

Type of Platform

The proposed database could take a variety of forms, ranging from a simple flat-file document repository, to a relational queried database incorporating SQL and XML language, to a fully functional, comprehensive water resources portal. The latter platform could have a database as a component, in addition to a variety of other modules. Examples of the first, second, and third type of platform are, respectively: the RAMP program; the USGS, GEMStat, and WSC databases; and, the Environmental Monitoring Portal of the Joint Canada-Alberta Oil Sands Monitoring program as well as the STORET data warehouse.

A portal could serve multiple purposes to help benefit the water quality monitoring programs of mine operators. For example, the portal could share resources on evolving best practices, tools for data management, analysis, and use, and strategies for overall water management. This could help to enhance comprehensive environmental management strategies as well as overall business sustainability in terms of reduced costs and increased efficiencies.

Types of Data

The types of data that could be included in the database are also virtually endless. This will likely pivot on the specific type of platform, although, as mentioned, this will likely evolve over time. This will also hinge on what types of data participants are willing to provide.

Specific types of data could include the following:



Complete data from a comprehensive water quality program of a mine operator; Data that was submitted for regulatory purposes / permitting (i.e. baseline EIA, project

EIS, post-closure monitoring, etc.); Historical data retained by regulatory agencies; Information from other water quality databases; Geospatial information; Topographic information; Maps; International data (i.e. Canadian companies operating overseas); Community data collection; Volunteer data collection; and Traditional ecological knowledge.

As is important with any database, the specific metadata that will be required alongside actual data submission will be critical. This includes information on when, where (e.g. latitude, longitude, jurisdiction, watershed, etc.), how (i.e. grab sample, real-time measurement, computed values, etc.), and what (i.e. water, sediment, fish tissue, etc.) was monitored. The QA / QC mechanisms employed could also be a type of metadata that could be submitted. Defining the initial dataset to be included in the database will allow the initial work to be simplified while consideration should be given to expansion to other datasets once the fundamental architecture is operating.

Data Contributors

Who will submit data is also an important scoping consideration, with mine operators of course being the most important contributors. Other contributors could include:

Regulatory agencies; First Nations groups; Community groups; Volunteer groups; University programs; Conservation authorities; and, Existing data providers.

End Users

There are multiple potential scenarios in terms of access by various groups, including companies, communities, the general public, regulatory agencies, and researchers (among many others). The database could be tiered to provide different levels of access. The tiered approach to data access will hinge on a variety of factors, including any corresponding risks, fee mechanisms, and the willingness of data contributors to share their data with particular groups. Consequently, data contributors could perhaps stipulate the level of access required to obtain their data.

Host

Potential hosts include CMIC itself, a university institution (e.g. a water science centre), a government agency (e.g. EC), or a standalone organization. There are myriad factors to consider for each potential host. Ideally, the organisation that hosts the database will have significant expertise and experience



processing, storing, and managing data to minimize risks related to data ingestion, quality assurance / quality control, and security. Long-term organizational stability will also be a key factor.

Business Model

The associated business model will likely be done at a late stage, perhaps following the feasibility study. Issues will include funding mechanisms, fee mechanisms, and the hosting mechanisms outlined above. However, many of these questions will be relevant to the other scoping requirements. Thus, it is important to be cognizant of such issues at all stages of project development.

5.3 Partnership Opportunities

Database Companies

Given the potential complexity of constructing and organizing a water quality database, it would be appropriate to engage the products and services of database companies so as to leverage their expertise and experience. Prominent examples include Oracle and IBM. Oracle software and database infrastructure are some of the most widely used across various industries, including in relation to environmental management. Furthermore, as mentioned in Section 4.9.8, CMIC has opened high-level discussions with IBM about potential partnerships given IBM’s growing interest in aligning some of their business activities with mining industry participants.

There are several companies and firms that specialize in environmental databases, which are tailored to processing, storing, and managing complex environmental data. Examples of these include the EQuIS software developed by EarthSoft and the Aquarius suite of software solutions outlined in the previous section. These systems are already used for a variety of comprehensive water quality databases (i.e. USGS NRTWQ, WSC). In fact, EQuIS is likely the most widely used environmental data management software throughout the world.

Many platforms combine the use of robust data management software / hardware (i.e. Oracle) with tailored environmental data management components. Integration with GIS platforms can also be enabled, which can help to create meaningful graphical and tabular output (Swain & Erkkila 1998).

Lastly, open source database software could be used (e.g. MySQL). Using this type of platform could enable greater customizability in terms of the design and function of the database given that the source code is openly available.

Southern Ontario Smart Computing Platform

SOSCIP houses a wide variety of powerful computing platforms that could be tapped into by CMIC through collaboration and / or a partnership. The organization’s High Performance Computing (HPC) Platform consists of three distinct and unique systems, including:

An IBM Blue Gene / Q, which is Canada’s fastest supercomputer; An advanced cloud and analytic platform; and, A state-of-the-art agile computing environment.

The second system above presents a unique opportunity, given its relevance to database platforms. From the SOSCIP website, some of the products that can be used with the SOSCIP cloud in relation to databases are:



DB2 - Integrates data from diverse sources, large, structured storage with flexible access, compression, time/series;

BigInsights - Programmatic framework for “big data” workloads (HADOOP/MapReduce); SPSS Statistics and Modeler - Data mining and predictive analytics; SPSS C&DS and Decision Management - Predictive workflow deployment; and, Cognos Business Intelligence - Reporting, dashboards and visualizations on web and

mobile.

Environment Canada / Natural Resources Canada

As per the NRCan webpage, “GeoConnections is a national partnership initiative led by [NRCan] to facilitate access to and use of geospatial information in Canada through the development, integration, and use of the Canadian Geospatial Data Infrastructure (CGDI).” The building blocks of the CDGI are: architecture (i.e. data, services, applications, and users); standards and specifications (i.e. Open Geospatial Consortium and the International Organization for Standardization (ISO)); and, technology solutions (i.e. Atlas of Canada, GeoGratis, RésEau, etc.) (NRCan 2008: 7-9).

The NL water resources portal (see Section 5.1.2) was developed in partnership with GeoConnections. Thus, it would be worthwhile to initiate communication to determine any potential collaboration / synergies between the two initiatives.

Furthermore, since its inception in 1978, the overall GEMS Water Programme and its GEMStat database have been hosted and finically supported by Environment Canada. This is done in conjunction with the Canada Centre for Inland Waters (CCIW) in Burlington, ON, which hosts staff from EC’s Water Science and Technology Directorate, as well as members of the Department of Fisheries and Oceans. The CCIW is the largest freshwater research facility in Canada, and houses state-of-the-art research facilities, such as a world-class ecotoxicological wetlab. Given that the database is one of the largest, most comprehensive, and most collaborative water quality data management initiatives, it is worthwhile exploring the possibility of partnering with the agency. Several potential synergies are also present with the real-time monitoring component of the study.

Water Survey of Canada

The WSC is the national authority responsible for the collection, interpretation, and dissemination of standardized water resource data and information. They could be a beneficial partner in developing the proposed database given their extensive expertise and experience collecting, processing, and managing vast amounts of data. This includes their experience throughout the world providing consulting services on various projects.

The activities of the WSC includes managing a monitoring network of 2,500+ sites for which it collects basic hydrometric data as well as historical data from 5,500+ non-active sites. The agency also consists of 250+ hydrologists and field technicians whose individual expertise could prove useful to CMIC’s endeavours.

There are potential synergies with the integrated remote sensor network program being proposed by CMIC. Specifically, the WSC has extensive experience collecting real-time water quality data from the above-mentioned sites.



Taken together, these attributes give the organization an advantage in standardization mechanisms, QA / QC, and the storage of water quality data on a large scale (among many others).

Newfoundland and Labrador Department of Environment and Conservation

The WRMD of the NL DEC could be potential collaborators, given their breadth of experience with myriad water management initiatives. This includes the proposed integrated remote network for quality as well as the aforementioned water resources portal.

The water resources portal – alongside the real-time monitoring network – set up and run by the WRMD is one of the closest parallels to the proposed CMIC database. The expertise and experience that was embedded in the WRMD as a result of the development of the database would be valuable to CMIC’s endeavours.

Universities

Collaboration with universities may prove useful given their existing resources and networks. Much innovation occurs in the university setting in relation to GIS applications and customizable platforms. Collaboration could be in the form of existing networks (i.e. SOWC) or it could be through separate partnerships.

A potential opportunity is foreseen given the breadth of data that could be available in the database. Specifically, the data could be very beneficial to researchers in terms of research related to mine-impacted waters over time and given varying site-specific conditions. This includes potential benefits in the field of genomics. These benefits could potentially provide much impetus for commitments from universities.

As with the real-time monitoring component, detailed analysis into potential partnerships will form part of ongoing project development activities, including as the feasibility components progress.

SOWC

Potential synergies exist between the SOWC’s initiatives and the proposed ESI WWG database, in addition to the proposed integrated remote water quality network. The SOWC houses significant data and information management infrastructure, most notably through the investments made by IBM in conjunction with the facilities within the various participating universities.

Furthermore, one of the central functions of the SOWC is to serve as a platform for research development and demonstration. Accordingly, a potential partnership could involve the piloting of the proposed ESI WWG database with SOWC infrastructure. This will help to determine / test the technical components of the database so as to reduce the risk of failure associated with an initial roll out of the database on a large scale.

As mentioned, CMIC has opened discussions with the SOWC and discussions regarding specific partnerships will commence once the initial feasibility component of ESI WWG’s project development activities is completed.

Geoscience Database Platforms

A potential partnership with geoscience information / data service providers could be undertaken to help launch the database. This could take the form of a standalone initiative that incorporates some of



the software / infrastructure that is currently used. Alternatively, water quality information could be integrated into existing platforms, perhaps as an add-on. Additional research should be undertaken to determine which platforms are currently used by mining industry participants (i.e. operators, service providers, consulting firms, etc.) to determine optimal potential synergies.

The history of the development of the geological survey databases would provide guidance on the development of a water quality database.

Centre for Excellence in Mining Innovation

CEMI and CMIC have collaborated on projects in the past, most notably the Footprints exploration project. As outlined in Section 5.1.11, a component of this initiative is the Mining Observatory Data Control Centre, which consists of a shared industry database, albeit one of a different nature than water quality data (i.e. exploration, drilling, and mining data). Nevertheless, the collaboration could serve as the foundation for an additional partnership.

6 Project Selection Workshop

6.1 Overview

A project selection workshop was held on 13 May 2014 in Vancouver that included the working group members, the ESI Chair, and the consultant that helped prepare the pre-feasibility studies.

The primary objectives of the workshop were to assess the initial feasibility of the proposed projects and to provide a recommendation as to which project(s) should be taken to the feasibility level. To achieve these objectives, the workshop was structured around two main exercises: 1) conducting a risk assessment of the projects; and, 2) screening the projects against assessment criteria to allow for prioritization. Feasibility preparation and planning also took place as part of the workshop.

6.2 Risk Assessment

Overview

A high-level risk management exercise developed by Shepherd (1997) was used to identify, assess / analyze, and plan for potential risks associated with the projects. An overview of these components are presented in Figure 13.



FIGURE 13 – RISK ASSESSMENT COMPONENTS

The identified risks were assessed according to the likelihood of the risk and the magnitude of the associated consequences. The risks were then placed on a likelihood-consequence matrix (presented in Figure 14), which enabled the group to determine the scenarios that presented a high risk to impeding project success. If a risk placed on the ‘high’ end of the matrix, a management response would be required to control, reduce, or eliminate the risk. If a management response was not available, the project would be deemed infeasible. The results of the risk assessments for the projects are presented in Table 9 and Table 10 below.



FIGURE 14 – LIKELIHOOD-CONSEQUENCE RISK MATRIX

W A T E R W O R K I N G G R O U P P R E - F E A S I B I L I T Y S T U D Y R E P O R T


TABLE 9 – RISK ASSESSMENT RESULTS FOR THE INTEGRATED WATER SENSOR PROJECT

RISK LIKELIHOOD CONSEQUENCE RANKING COMMENTS

Unable to obtain MMER sensitivity within 3 years

2 4 Medium

There is a low likelihood that the sensors will be unable to detect substances at MMER sensitivity levels within three years. Probes exist that can take spot samples of many MMER substances, although engineering challenges (e.g. calibration) typically preclude their use in remote, real-time projects. If such challenges cannot be overcome, the consequences would be unacceptable given that MMER sensitivity is required for most monitoring purposes. However, the sensors could still be useful, albeit in a more limited context (e.g. as early warning systems). A more detailed risk assessment related to this area will be conducted as part of the feasibility-level work.

Unable to define sensitivities 2 3 Low There is a low likelihood that the project will be unable to define key sensitivities for the various water quality indicators. Detection limits are relatively well-defined as part of existing water quality frameworks (e.g. MMER), although they may vary across jurisdictions and over time. To mitigate the risk, sensitivities will be chosen that allow for monitoring at the majority of sites.

Unable to define key parameters

1 3 Low

It is unlikely that the project will be unable to define the key parameters that the sensors may monitor. The key parameters of primary importance to the mining industry are already well-defined (i.e. MMER) and are generally similar for most hard rock mines. If key parameters cannot be defined, the consequences will be moderate as the project can still succeed with the development of sensors that can detect a portion of the key water quality parameters.

Unable to achieve cost needs 3 4 Medium

It is likely that the costs associated with sensor development and deployment will not be feasible. This includes the current cost of deployment and the costs associated with sensor / network development. The consequences of this would be unacceptable, although many workarounds are available. To mitigate the risk, a business model will be developed that will devise various funding arrangements (e.g. funding slices, royalty agreements, selling the technology, etc.). Securing funds to allow for further development will also be a key focus of ongoing project work.

Unable to adapt to changing parameter levels

1 4 Low

It is unlikely that the technology will not be able to adapt to changing parameter levels. These levels change periodically (e.g. the MMER are reviewed and modified every 10 years) and more precise detection capabilities are developed as technology progresses. Although unlikely, if the appropriate adaptation cannot be achieved, the consequences would be unacceptable given that the levels would need to be achieved for comprehensive monitoring purposes.

Unable to achieve cold climate reliability

3 3 Medium It is likely that the sensors will be unreliable in cold climates. Specifically, ice formation, ice scouring, and sub-optimal temperatures typically preclude their use during the winter months. The consequences of this are moderate as the sensors will likely need to be removed and re-installed periodically. Notwithstanding this, the sensors could still provide valuable data collection during times with more optimal weather conditions.

No regulator acceptance of data

2 2 Low

There is a low likelihood that regulators would not accept the data that would be collected by the sensors. QA and QC mechanisms would be used to ensure data reliability and errors would be corrected before any submission. Real-time monitoring may also provide a more representative analysis of water quality as compared with traditional grab sampling as the latter provides merely a ‘snapshot’ of water quality, whereas real-time monitoring covers a greater temporal range.



TABLE 10 – RISK ASSESSMENT RESULTS FOR THE DATABASE / REPOSITORY OF WATER RESOURCES INFORMATION PROJECT

RISK LIKELIHOOD CONSEQUENCE RANKING COMMENTS

Unable to determine ownership

2 5 Medium There is a low likelihood that the ESI will be unable to find a willing owner for the database especially if it is commercialized. Without establishing an owner / operator for the database, the project will be unable to be implemented. Determining a potential owner will be a key component of the feasibility-level work.

No stakeholder acceptance of database

1 5 Medium It is not likely that stakeholders would reject the development of the database. The database will facilitate the availability of data, thereby strengthening existing water quality monitoring and management. In turn, it is anticipated that the database will increase stakeholder confidence in water quality management.

Unacceptable QA / QC for water quality data

4 4 High

It is likely that the data that are provided will have errors, irregularities, and / or inconsistencies due to the amount of data that will populate the database and differing QA / QC procedures used by operators (among other factors). The consequences of this are unacceptable given that the data would not be able to be relied upon. To mitigate the risk, a robust QA / QC control process will need to be established. Grab sampling may also be required to verify some data. Learning from the experience of existing databases / networks will be a key component of the feasibility-level work.

Unable to access data to populate database

2 4 Medium There is a low likelihood that the ESI will not be able to access data to populate the database. A large amount of data will be available from regulatory agencies (i.e. data submitted as part of EIA, EIS, etc.) that will be requested, likely through an Access to Information request. Determining the extent to which operators and participating companies will share their data will be a core component of the feasibility-level work.

Ongoing liability for data unacceptable

3 2 Medium The database may subject participating companies to ongoing liability or claims of negatively affecting water quality. The risk can be managed by allowing participating companies to select the data that is shared (e.g. end-of-pipe, baseline, etc.). Tiered access mechanisms could be used to ensure that data is used appropriately. Disclaimers will also be developed.

Unable to manage excess data 1 5 Medium

The database has the potential to incorporate a vast amount of data. It is not likely that the data will be unmanageable; some of the existing databases outlined in Section 5.1 are larger and have developed the technical ability to store and manage the data appropriately. Thus, it is anticipated that the risk can be managed with few difficulties. Learning from the experience of existing databases will help to achieve an appropriate management tool for managing the data.

Incorrect platform / architecture selection

2 3 Low There is a low likelihood that the ESI will select unsuitable architecture to store the shared data. There are existing platforms that are able to store vast amounts of data, including ones that are tailored to storing water quality parameters (e.g. the Aquarius platform used by the USGS).

Unable to select pilot areas 1 2 Low It is anticipated that selecting the pilot areas to test the database will be relatively straightforward. This will allow the project to be staged and will enable the project participants to demonstrate the feasibility of the project and resolve any initial challenges. A pilot project will also help to uncover any initial, unforeseen risks.

Unable to sustain project / fulfill mandate

2 4 Medium If the project cannot be sustained and / or the project’s mandate cannot be fulfilled, the risk would be unacceptable and the project would fail. Selecting a business model that incorporates long-term stability (e.g. a stable and long-lasting organization, continual funding arrangements, etc.) will be critical in this respect.



6.3 Assessment Criteria

The project selection workshop was organized such that, following the risk assessments, the projects would be screened against selection criteria, compared against each other, and prioritized based on the results. The following assessment criteria were formulated and ranked in order of importance:

Reduces stakeholder and operations risk;

Affects all mining projects in Canada (legacy and current);

Reduces health and safety risks;

Saves time and money;

Promotes stakeholder buy-in and project sustainability;

Likelihood exists that the concept would succeed if CMIC was not involved;

Facilitates a step-change improvement in the mining business;

Encourages applications beyond mining;

Profitability of the concept; and,

Options exist for a quick win (e.g. possibly applications from other industries, availability of a

staged approach to project development).

The results of the assessment criteria exercise are presented in Table 11 below. Project 1 refers to the

database / repository of water resources information. Project 2 refers to the remote sensors for water

quality project.



TABLE 11 – RESULTS OF ASSESSMENT CRITERIA EXERCISE

ASSESSMENT CRITERIA RESULTS (PROJECT 1, PROJECT 2)*

COMMENTS

Reduces stakeholder and operations risk

,

The database will facilitate access to water quality data and will allow for a better understanding of the impacts of mining activities on water bodies. This will allow for the enhanced management of water quality, which will reduce stakeholder and operations risk (e.g. environmental liabilities). The real-time, remote sensor devices will also reduce risk by providing a more representative sample of water quality as compared to grab sampling. The devices can also serve as early warning devices, which will enable operators to implement mitigation efforts more promptly.

Affects all mining projects in Canada , The database will house data from all types of mines throughout Canada. Nothing precludes the remote, real-time sensors from being deployed at a particular type of mine.

Reduces health and safety risks — , The database does not provide any direct enhancements to health and safety. The remote, real-time sensors will directly reduce health and safety risks given that such monitoring will – to some degree – reduce the frequency of field sampling, which is often dangerous especially in remote environments.

Saves time and money , The database will save time and money by avoiding duplication of efforts in water quality sampling. The sensors will save time and money by reducing the frequency of field sampling and costly laboratory analysis.

Stakeholder buy-in and project sustainability

, Initial stakeholder buy-in for both projects has been strong. Given the benefits that will accrue to stakeholders, it is anticipated that buy-in will increase with greater awareness.

Step-change improvements exist , The database project will be structured such that a staged approach will be taken, beginning with a pilot / demonstration project that will progressively incorporate more data from additional sites. The sensor project could facilitate a step-change improvement by developing monitoring devices for a range of additional water quality parameters towards the development of sensors for all of the parameters that are key to the mining industry.

Applicability beyond mining industry , Data could be included in the database that was provided by a participant from outside the mining industry. Similarly, the data could be used by a variety of users (e.g. other industries, researchers, communities, etc.). Nothing precludes the sensors from being used by other industries.

Project would not be implemented if CMIC not involved

, X The database would likely not be developed without the initiative of an organization like CMIC that serves as a focal point for industry. The sensors would likely be developed given the number of participants in the space and the potential for profitability as a result of further development.

Potential for profitability — , The database has some potential for profitability as it could be commercialized with fees charged for access. The sensors have greater potential for profitability given the proprietary nature of the development and their broad application to water quality monitoring efforts across myriad industries.

Potential for quick wins exists , X The database has the potential to achieve quick wins as the project will be staged and then scaled up appropriately. Requesting data that was submitted as part of the regulatory process could be a first step towards populating the database. The development of the sensors will likely take place over a longer timeframe given the technological components of the project.

* Meets criteria X Does not meet criteria — Neutral



6.4 Conclusion / Recommendations

The initial risk assessments indicated that there are a number of risks associated with the projects and that some of the risks were of a high magnitude. However, no risks were identified that would preclude the projects from succeeding at the current stage. For high-magnitude risks, management actions were formulated to reduce, eliminate, and / or control the risk. Thus, it was determined that both projects could be taken to the feasibility level.

The assessment criteria exercise enabled the group to prioritize the development of the projects. The results indicated that both projects met the majority of the criteria, although the database / repository of water resources information project did so to a greater degree. Accordingly, the database project was prioritized.

Given this prioritization, the team made the recommendation to conduct a feasibility study for the database project from June to December 2014. The group also recommended to conduct a feasibility study for the remote, real-time sensors project. It is planned that the study will begin in September 2014.

The main components of the feasibility studies will be drawn from the major gaps that were outlined as part of the pre-feasibility study, the risks that were identified, and the assessment criteria that were formulated. Ongoing risk tracking will take place as part of the studies and additional risk assessments will be conducted to identify and evaluate any new, previously-unforeseen risks. Lastly, ongoing partnership development will continue to be a key focus of the group.

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