TON

Test Your Knowledge

Quiz: The Nose Knows: Understanding the Threshold Odor Number (TON) in Water Treatment

Instructions: Choose the best answer for each question.

1. What does the Threshold Odor Number (TON) represent? a) The concentration of odor-causing substances in water. b) The intensity of an odor in water. c) The dilution factor at which an odor becomes detectable. d) The number of odor-causing compounds in water.

2. How is the TON typically determined? a) Using a chemical analysis of the water sample. b) Measuring the intensity of the odor using a specialized instrument. c) Observing the reaction of fish to the water sample. d) Conducting a sensory test with trained panelists.

3. Which of the following is NOT a factor influencing the TON? a) Temperature b) pH c) Water pressure d) Presence of microorganisms

4. A TON of 20 indicates: a) A strong odor that needs to be addressed. b) No detectable odor in the water sample. c) A weak odor that is barely perceptible. d) The water is safe for drinking.

5. Why is the TON an important metric for water treatment facilities? a) It helps determine the effectiveness of water treatment processes. b) It helps identify potential sources of contamination. c) It helps monitor the overall water quality. d) All of the above.

Exercise:

Scenario:

A water treatment facility is monitoring the odor of its treated water. They have been consistently observing a TON of 5 for the past month. However, this week, the TON has risen to 15.

Task:

Explain the potential reasons for the increase in the TON. What steps should the water treatment facility take to address this issue?

Techniques

The Nose Knows: Understanding the Threshold Odor Number (TON) in Water Treatment

Chapter 1: Techniques for Determining TON

The primary method for determining the Threshold Odor Number (TON) is the sensory test, also known as the olfactometry test. This relies on the human sense of smell, employing trained panelists to assess the odor intensity of diluted water samples. The procedure typically involves:

1. Sample Preparation: The water sample is serially diluted with odor-free water using a standardized dilution procedure (e.g., 1:1, 1:2, 1:4, etc.). The dilutions are often performed in glass flasks or specialized odor-free containers.

2. Panel Selection and Training: A panel of at least five trained individuals with a proven ability to detect and differentiate odors are selected. Training ensures consistency in odor perception and reduces inter-panelist variability. Training usually involves practice sessions with known odor concentrations.

3. Odor Assessment: Panelists smell each diluted sample and indicate whether or not they detect an odor. This is often done using a forced-choice method (e.g., comparing the sample to an odor-free control) to reduce bias.

4. TON Calculation: The TON is calculated as the reciprocal of the highest dilution factor at which at least 50% of the panelists detect an odor. For example, if the odor is detectable at a 1:10 dilution but not at 1:20, the TON is 10.

Limitations of the Sensory Test: Subjectivity is a key limitation. Panelist sensitivity varies, and factors like fatigue, illness, and even recent exposure to other scents can influence results. The method is also time-consuming and requires trained personnel.

Chapter 2: Models for Predicting TON

Predictive models for TON are less common than the sensory test due to the complexity of odor perception and the many variables influencing it. However, some approaches are being explored:

• Statistical Models: These models attempt to correlate TON with measurable water quality parameters like chemical concentrations (e.g., geosmin, 2-methylisoborneol), temperature, and pH. Multiple linear regression and other statistical techniques can be employed. The accuracy of these models heavily depends on the quality and quantity of data used to build them.

• Machine Learning Models: More advanced machine learning algorithms, such as neural networks and support vector machines, are increasingly being used to analyze complex datasets and predict TON based on various water quality indicators. These models have the potential to handle non-linear relationships and improve prediction accuracy.

• Chemosensory Models: These models attempt to link chemical composition of the water with the perceived odor, based on understanding of odorant-receptor interactions. These models are still under development and face significant challenges due to the vast complexity of odorant interactions and the human olfactory system.

Chapter 3: Software for TON Analysis

Specialized software is not widely available for TON calculation itself, as the calculation is relatively straightforward. However, software is used in various supporting roles:

• Data Management: Spreadsheets (e.g., Microsoft Excel, Google Sheets) and dedicated laboratory information management systems (LIMS) are used to manage and organize data from sensory tests. This includes recording panelists' responses, dilution factors, and calculated TON values.

• Statistical Analysis: Statistical software packages (e.g., R, SPSS, SAS) are used for analyzing sensory test data, performing statistical tests, and building predictive models.

• Data Visualization: Software such as GraphPad Prism or similar tools can create graphs and charts to visualize TON data over time, helping to track trends and identify potential problems.

Chapter 4: Best Practices for TON Determination and Management

Accurate and reliable TON determination requires adherence to best practices:

• Standardized Procedures: Use established methods like Standard Methods for the Examination of Water and Wastewater for consistent sample preparation, dilution, and sensory evaluation.

• Trained Panelists: Employ a panel of trained and qualified odor assessors, regularly assessing their sensitivity and retraining them as needed.

• Controlled Environment: Conduct tests in a controlled environment to minimize the influence of extraneous odors and distractions.

• Regular Calibration: Periodically check the sensitivity and accuracy of the testing process using reference odorants with known concentrations.

• Data Management: Maintain detailed records of all samples, dilutions, panelists’ responses, and calculated TON values.

• Preventive Measures: Implement strategies to control odor-causing substances in the water treatment process, aiming to reduce the TON before it reaches problematic levels.

Chapter 5: Case Studies of TON Applications

Case studies demonstrate the practical application of TON in water treatment:

• Reservoir Management: Monitoring TON in a reservoir can help detect algal blooms or other events causing odor problems, enabling timely interventions to prevent widespread contamination.

• Wastewater Treatment Plant Optimization: Tracking TON throughout a wastewater treatment plant can identify bottlenecks or inefficiencies in odor removal processes, leading to improvements in treatment efficiency and odor control.

• Drinking Water Quality Monitoring: Regular TON monitoring ensures compliance with drinking water standards and helps protect public health by detecting and addressing potential odor issues before they become widespread.

• Industrial Discharge Monitoring: Monitoring TON in industrial discharges ensures that effluent meets regulatory requirements and minimizes environmental impact related to odor.

These examples highlight the importance of TON as a key indicator of water quality and the effectiveness of treatment processes in managing unpleasant odors.