Air Quality Health Impact Evaluation via Severity Percentile and Variance
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Solution Overview
Problem
Current air quality evaluation methods, such as the AirNow air quality index (AQI), face challenges in accurately representing the health impact of air quality due to daily fluctuations, as average AQI values do not account for the frequency and severity of high AQI days, making it difficult to understand an area's general air quality and its susceptibility to unhealthy conditions.
Innovation Solution
A novel air quality evaluation method that calculates the severe air quality percentile (e.g., 90th percentile AQI) and variance of AQI values over a period, combined with clustering algorithms to categorize locations by air quality severity, generating an 'AreaAir index' that considers both severity and frequency of high AQI days, and visualizes the results for better understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If average AQI values are used to evaluate air quality, then the evaluation is simple and easy to compute, but it fails to accurately represent health impacts because it does not account for frequency and severity of high AQI days
Solution Approach 1:
The patent segments the air quality evaluation into multiple components: severe air quality percentile (capturing frequency of high AQI days) and variance (capturing severity and variability). This segmentation allows each component to address specific aspects of health impact that average AQI misses, while keeping the overall method computationally feasible through statistical aggregation of daily readings.
2Reliability
If severe air quality percentile and variance are calculated to account for frequency and severity, then health impact representation improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary data aggregation by calculating the severe air quality percentile and variance from daily AQI readings before final evaluation. This preliminary computation organizes the data structure in advance, allowing the final health impact assessment to simply combine these pre-computed statistics, thereby reducing real-time processing complexity while maintaining reliability.
3Loss of information
If clustering algorithms are applied to categorize locations, then understanding of air quality patterns improves, but the complexity of analysis and visualization increases
Solution Approach 1:
The patent applies clustering algorithms to group locations with similar air quality characteristics into distinct categories (e.g., high severity, moderate severity, low severity). This local quality approach assigns different analytical treatments to different location groups, allowing detailed pattern recognition within each cluster while presenting results through intuitive visualizations that highlight the most relevant patterns for each group.
Data Source
AI summary
In a computer-implemented method for evaluating air quality, air quality information is received for a plurality of locations for a plurality of days over a time period. For each location of the plurality of locations a severe air quality percentile for the air quality information for each location of the plurality of locations for the time period is determined and a variance of the air quality information for each location of the plurality of locations for the time period is determined. The plurality of locations is evaluated according to the severe air quality percentile for the air quality information and the variance of the air quality information for each location.