In the wastewater infrastructure design process, the systematic collection of data and information is the critical link between preparation and analysis. As outlined in the competency standard “Design Wastewater Collection and Treatment Infrastructure” (Unit Code: CON/OS/CET/CR/09/6A), data and information is collected based on tools prepared—a principle that ensures the data gathering phase is executed efficiently, consistently, and in alignment with the specific information requirements identified during tool preparation. When data and information is collected based on tools prepared, the integrity of the entire design process is maintained .
This comprehensive guide explores how data and information is collected based on tools prepared, examining the types of data collected, field execution methods, quality assurance procedures, and practical considerations for effective data collection in wastewater infrastructure projects.
1. Understanding the Data Collection Process
1.1 The Role of Prepared Tools
The tools prepared during the earlier phase directly determine the efficiency, accuracy, and completeness of data collection. When data and information is collected based on tools prepared, the process follows a structured workflow:
| Stage | Activity | Purpose |
|---|---|---|
| Deployment | Install and configure monitoring equipment | Establish data collection points |
| Data Acquisition | Execute field measurements and sample collection | Gather raw data |
| Data Validation | Perform field checks and quality assurance | Ensure data integrity |
| Data Storage | Record and secure collected data | Enable analysis |
Water companies and public authorities require “the accurate, high-resolution data needed to plan infrastructure, optimise treatment capacity, and meet regulatory requirements,” making proper collection essential .
1.2 Information Hierarchy in Infrastructure Design
Data collection must be executed according to the hierarchy of information needs:
| Data Collection Phase | Primary Purpose | Information Required |
|---|---|---|
| Preparedness | Establish baseline understanding | Baseline indicators, risk analysis, capacity mapping |
| Needs Assessment | Understand severity, scale, priorities | Primary assessment data, population figures, WASH insecurity analysis |
| Strategic Planning | Define targets and response priorities | Population in need, severity categorization |
| Implementation | Track operational delivery | Activity reporting, people targeted and reached |
| Quality Assessment | Assess effectiveness and accountability | Post-distribution monitoring, feedback data |
2. Types of Data Collected
2.1 Primary Data Collection
When data and information is collected based on tools prepared, the data collection process follows the specific tools and methods identified during preparation. Field surveys and onsite observations are primary methods for collecting original data .
Field Measurements and Observations:
The study employed both primary and secondary data sources :
| Data Category | Description | Sources |
|---|---|---|
| Topographic Information | Elevation profiles, slope gradients, drainage characteristics | City Council records; Satellite Imagery |
| Sanitation Infrastructure | Type, distribution, condition of existing facilities | Utility Reports; Field Surveys |
| Demographic Data | Population density, household size, urban growth patterns | National Statistics Bureaus |
| Wastewater Quality | BOD₅, COD, TSS, nitrogen, phosphorus levels | Field sampling; laboratory analysis |
| Soil and Land Use | Soil classification, infiltration capacity, land-use zoning | Ministry records; Field Observations |
| Climatic Conditions | Rainfall patterns, temperature, evapotranspiration | Meteorological Authorities |
| Stakeholder Perspectives | Community perceptions, local knowledge, challenges | Interviews; Community Meetings |
2.2 Secondary Data Collection
Secondary data is derived from institutional records, technical reports, and peer-reviewed literature :
- Institutional Records: Utility reports, government databases
- Technical Reports: Engineering studies, regulatory filings
- Geospatial Data: GIS, satellite imagery, maps
- Peer-Reviewed Literature: Academic studies, research papers
3. Field Execution: Implementing Data Collection
3.1 Feasibility Assessment and Scoping
Before deployment, a detailed assessment is conducted to understand project goals, whether it is asset planning, compliance monitoring, growth forecasting, or event-based catchment assessment. This stage includes consideration of rainfall dependency, seasonal variability, flow balance needs, and access requirements .
When data and information is collected based on tools prepared, the feasibility assessment ensures the selected tools are appropriate for site conditions.
3.2 Equipment Deployment
MCERTS-certified equipment is deployed to collect high-accuracy flow and quality data across multiple points :
- Ultrasonic or Doppler flow meters for flow velocity and depth measurement
- Level sensors for water level monitoring
- Rain gauges for precipitation measurement
- Refrigerated auto-samplers for 24-hour composite sampling
Equipment is installed with robust mounting, battery/solar options, and remote telemetry where needed, ensuring minimal disruption and maximum data integrity .
Real-Time Data Collection Example:
Flow monitoring for sanitary sewer systems has evolved significantly. In recent years, wastewater engineers benefit from digital and wireless communication outputs from flow meters, integration with SCADA systems, and other IoT automation to perform short and long-term monitoring. This constant data collection allows users to establish a control point and then compare data from inclement weather events and determine outliers for further investigation .
3.3 Flow Monitoring Execution
When data and information is collected based on tools prepared, both dry and wet weather flows are monitored using time- or event-based strategies :
| Flow Type | Monitoring Strategy | Data Collected |
|---|---|---|
| Dry Weather Flow (DWF) | Continuous monitoring | Diurnal patterns, baseline flow |
| Wet Weather Flow | Event-based monitoring | Storm responses, peak flows |
| Storm Overflow Volumes | Event monitoring | Overflow frequency, volume |
| Infiltration Contributions | Continuous monitoring | I&I quantification |
Monitoring durations can range from a few weeks to several months depending on regulatory drivers or planning needs .
3.4 Load Monitoring and Composite Sampling
To calculate pollutant loading, auto-samplers collect 24-hour composite samples representative of flow variations :
- Samples collected automatically at pre-set intervals
- Composite samples represent average conditions over 24 hours
- Refrigerated storage preserves sample integrity
Composite samples are analyzed by UKAS-accredited laboratories for :
- BOD (Biological Oxygen Demand)
- COD (Chemical Oxygen Demand)
- Ammonia
- Total Suspended Solids (TSS)
- Total Phosphorus and Nitrogen
- pH, conductivity, temperature
- Site-specific or consented parameters
3.5 Field Data Acquisition Examples
Example: Industrial Wastewater Data Collection
Based on agreements between factory owners and project teams, field measurements and sampling are conducted at industrial facilities. The field measurements and sampling started on a biweekly basis, with some exceptions (e.g., monthly sampling in some industrial areas). Wastewater sampling was stopped during certain periods, resumed later, and completed within the scheduled timeframe .
Example: Water Quality Data Acquisition
Data was acquired using specialized equipment including :
- Thermometer
- pH Meter
- EC Meter
- DO Meter
- Filtration Assembly
- Analytical Balance
- Oven
- Imhoff Cone
- Hach UV-Vis Spectrophotometer
- BOD Measurement System
- BOD Incubator
Example: Greywater Data Collection
In a rural household study, samples were collected once weekly from each treatment section :
- Sample containers: Sterile 500 ml glass bottles (microbiological analysis) and 1000 ml plastic bottles (physico-chemical analyses)
- Field measurements: pH, electrical conductivity, temperature, dissolved oxygen using portable meters
- Laboratory analysis: BOD₅, COD, suspended solids, nitrate, ammonium, orthophosphate per Standard Methods
- Microbiological analysis: Fecal coliforms, E. coli, enterococci using spread plate method
4. Innovative Data Collection Technologies
4.1 Intelligent Water Infrastructure Technology
The Water Infrastructure Modernization Act of 2025 defines intelligent water infrastructure technology as technologies that rely on :
- The use of real-time monitoring, management, analytics, and data collection tools
- Embedded intelligence and predictive maintenance capabilities
- Real-time remote sensors that provide continuous monitoring of water quality
- Artificial intelligence and other intelligent optimization tools
Key Technology Categories:
4.2 CCTV and AI-Powered Inspection
In Edmonton, the CCTV inspection program has evolved to include automated defect classification based on PACP (Pipeline Assessment Certification Program) standards. An AI-powered video analytics system processes the footage, frame by frame, to identify and categorize potential defects such as cracks, root intrusions, and other defects .
When data and information is collected based on tools prepared, AI-enhanced tools enable:
- Automated defect classification: Reduces human fatigue and variation
- Faster processing: Identifies critical defects quickly
- Standardized reporting: Consistent assessment across projects
4.3 Digital Twins and Operational Analytics
Autodesk Info360 Insight is a cloud-based digital twin solution for operational analytics, incident management, and compliance reporting. It enables :
- Automated data collection, cleansing, and consolidation
- Selection of desired sensors mapped onto a prebuilt digital workspace
- Accelerated start-up time and collaborative expertise application
- Quick identification of affected customers during incidents
- Automated calculation and formatting tools for regulatory agencies
4.4 Free-Floating Sensor Systems
ADS FlowSIGHT by Subterra is a programmatic monitoring solution that uses untethered, free-floating Scouts in critical sewers to collect vital data. The data is then screened for critical defects and inventory reconciliation that are reported back to the asset owner. Repeat deployments are compared to measure change and assist in more informed decision-making .
How it works:
- Deploy & Collect: ADS deploys untethered, free-floating Scouts
- Screen & Report: Data is screened for critical defects
- Compare & Prioritize: Repeat deployments are compared to measure change
5. Data Quality Assurance and Validation
5.1 Field Quality Assurance
When data and information is collected based on tools prepared, quality assurance procedures are implemented during collection:
Sensor Drift Checks:
Calibration Verification:
Cross-Verification:
5.2 Laboratory Quality Assurance
All analysis follows standard methods and quality assurance procedures, with full traceability and chain-of-custody documentation provided. This ensures results are reliable, repeatable, and meet the requirements of regulatory bodies and investment cases .
Laboratory Quality Requirements:
- UKAS-accredited laboratories
- Standard methods (Clesceri et al., 1999)
- Quality assurance procedures
- Full traceability
- Chain-of-custody documentation
5.3 Data Validation
Detailed data validation ensures the integrity of results, including :
- Sensor drift checks
- Cross-verification of flow and rainfall data
- Statistical assessment of anomalies
- QA procedures aligned to MCERTS and WRc guidance
Regulatory Readiness:
The goal is to ensure the data is regulator-ready and robust for planning purposes .
6. Regulatory Reporting and Documentation
6.1 Data Documentation
Data is provided in structured reports aligned to regulatory expectations and water company templates :
- Summary of monitoring approach
- Flow/load data tables
- Observations and anomalies
- Flow balance and performance analysis
- Recommendations for further action
6.2 Chain of Custody
When data and information is collected based on tools prepared, proper chain of custody is maintained :
- Sample collection documentation
- Transport and storage records
- Laboratory receipt confirmation
- Analysis results traceability
7. Conclusion
The collection of data and information based on prepared tools is a systematic process that translates planning into action. When data and information is collected based on tools prepared, the data gathering phase is executed efficiently, consistently, and in alignment with the specific information requirements.
Key takeaways for engineering practice:
- Follow the prepared tools—deploy equipment according to the established plan
- Execute field measurements systematically—use standard methods and protocols
- Implement quality assurance—perform field checks and validation
- Document thoroughly—maintain chain of custody and data records
- Integrate innovative technologies—AI, digital twins, and sensors enhance data collection
By executing data collection based on prepared tools, engineers can gather the critical data needed to design wastewater infrastructure that is safe, compliant, and sustainable.