Pollutant Emission Record Quality Scoring for Reliable Source Estimation
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Solution Overview
Problem
Current methods for evaluating the quality of pollutant emission data are subjective, inconsistent, and economically costly, lacking a systematic workflow for automating the decision process.
Innovation Solution
A method for automated record quality determination that combines atmospheric and concentration data metrics to produce a quantitative metric for ranking and filtering data records, optimizing data processing and reducing power consumption through adaptive averaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated record quality determination is implemented, then productivity and consistency of data evaluation are improved, but device complexity and implementation costs increase
Solution Approach 1:
The automated quality determination system segments the evaluation process into distinct modules: data collection from sensors, atmospheric condition assessment, concentration signal quality assessment, and combined quality metric generation. Each module handles a specific aspect of quality evaluation, making the complex system manageable and maintainable while achieving high productivity through systematic automated processing
Solution Approach 2:
The patent introduces an intermediary automated quality determination system that mediates between raw sensor data and source term estimation processing. This intermediary layer objectively evaluates data quality based on predefined criteria and automatically filters or flags records, eliminating subjective expert judgment while maintaining high evaluation throughput
2Measurement precision
If expert judgment is used for data quality assessment, then measurement precision and reliability are improved, but loss of time and economic costs increase
Solution Approach 1:
The system performs preliminary automated quality assessment of data records before they undergo source term estimation. By pre-evaluating atmospheric conditions and concentration signal quality using objective criteria, the system prepares data in advance, ensuring high assessment accuracy while eliminating the time-consuming manual review process that would otherwise be required for each record
3Reliability
If comprehensive data quality criteria are applied, then reliability of source term estimation is improved, but device complexity and processing requirements increase
Solution Approach 1:
Comprehensive quality criteria are segmented into two independent assessment components: atmospheric condition quality (evaluating wind stability, atmospheric stability class) and concentration signal quality (evaluating sensor performance, signal strength, plume interception). This segmentation allows each criterion to be evaluated independently with appropriate complexity, while together they provide comprehensive reliability assurance for source term estimation
Solution Approach 2:
The system changes parameters by transforming qualitative expert judgment criteria into quantitative measurable parameters. Atmospheric conditions are assessed using measurable parameters like wind speed variability and atmospheric stability class, while concentration signal quality uses parameters like signal-to-noise ratio and detection threshold compliance. This parameter transformation maintains comprehensive evaluation rigor while reducing subjective complexity
Data Source
AI summary
Embodiments presented provide for a method of evaluation for data that is inconsistent or that changes over time. These embodiments involve the processing of pollutants in a stream such that accuracy is maintained during the processing for high quality evaluation. The method includes collecting atmospheric data for an area, collecting concentration data for pollutant emissions, processing the atmospheric data to determine a quality of the atmospheric data, processing the concentration data for pollutant emissions to determine a concentration data quality, producing a combined quality metric from the processed atmospheric data and the processed concentration data, and determining if the combined quality metric is of a high or low level.