Method and system for airborne viral infection risk and air quality analysis from networked air quality sensors
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Solution Overview
Problem
Current air quality monitoring systems lack an autonomous computer-implemented method to analyze real-time measurements from air quality sensors to determine the risk of airborne viral infection transmission in enclosed spaces, such as buildings, which is critical for reducing the risk of viral infections like SARS-CoV-2.
Innovation Solution
A computer-implemented system and method that continuously interacts with air quality sensors to calculate an airborne virus infection risk score by analyzing parameters like CO2, PM2.5, PM10, and humidity, and communicates with HVAC systems or air treatment appliances to adjust ventilation, filtration, and humidity levels to minimize infection risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time air quality monitoring is implemented using networked sensors, then the ability to detect airborne viral infection risk is improved, but the system complexity and cost increase
Solution Approach 1:
The system divides the monitoring function into multiple distributed air quality sensors placed throughout the building, each independently measuring local parameters. This segmentation allows comprehensive coverage without requiring a single complex centralized system, resolving the contradiction between detection precision and system complexity.
Solution Approach 2:
The air quality sensors perform multiple functions: measuring CO2, PM2.5, PM10, humidity, and temperature simultaneously. This multi-functionality enables comprehensive viral infection risk assessment using a single sensor platform, improving detection capability without proportionally increasing system complexity.
2Speed
If continuous real-time analysis of air quality parameters is performed, then the responsiveness to viral transmission risk is improved, but the energy consumption and processing requirements increase
Solution Approach 1:
The system performs periodic analysis of air quality parameters at scheduled intervals rather than continuous real-time processing. This periodic action maintains responsiveness to risk changes while significantly reducing energy consumption and computational processing requirements compared to continuous analysis.
Solution Approach 2:
The system automatically analyzes sensor data and generates risk assessments without requiring manual intervention or continuous high-power processing. The autonomous operation reduces energy consumption while maintaining rapid responsiveness to changing air quality conditions.
3Reliability
If the system autonomously controls HVAC and air treatment systems, then the effectiveness of reducing viral transmission risk is improved, but the control system complexity increases
Solution Approach 1:
The system continuously monitors air quality parameters and automatically adjusts HVAC and air treatment systems based on real-time risk assessments. This feedback loop ensures reliable viral transmission risk reduction while using standardized control protocols that manage system complexity.
Solution Approach 2:
The system integrates control of multiple air treatment devices (HVAC systems, air purifiers, humidifiers) into a single unified control platform. This merging approach improves overall effectiveness by coordinating all air treatment functions while simplifying the control architecture compared to managing each device separately.
4Measurement precision
If comprehensive air quality parameters (CO2, PM2.5, PM10, humidity) are monitored, then the accuracy of infection risk assessment is improved, but the cost and device complexity increase
Solution Approach 1:
The air quality sensors are designed to measure multiple parameters (CO2, PM2.5, PM10, humidity, temperature) simultaneously using integrated sensor arrays. This multi-functionality provides comprehensive infection risk assessment data without requiring separate sensors for each parameter, thus avoiding proportional increases in sensor quantity and cost.
Solution Approach 2:
The system combines multiple sensing functions into single integrated air quality monitoring stations. By merging CO2 sensing, particulate matter detection, and humidity measurement into unified devices, the system achieves comprehensive monitoring accuracy while minimizing the total number of individual sensor components required.
Data Source
AI summary
A computer implemented system and process of analyzing real-time measurements of a one or more air quality sensors to provide an calculated estimate of airborne virus infection and air quality evaluation from current air quality measurements, advise those at risk, advise responsible parties of recommended actions to take, and in some embodiments take direct action in communication of instructions to air filtration and treatment equipment and HVAC systems to improve outside air flow, increase filtration, treat contaminated air and reduce humidity and reduce the risk of airborne virus transmission. Sensor data, calculated airborne infection risk, air quality, warnings and reports are created and distributed to network connected devices.