Metadata driven 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 networked sensors to determine the risk of airborne viral infection transmission in indoor spaces, particularly effective for SARS-COV-2 and other coronaviruses, which is crucial for reducing transmission risks in buildings and public spaces.
Innovation Solution
A computer-implemented system that continuously interacts with networked air quality sensors to calculate an Airborne Virus Infection Risk Score (CAIRS) by analyzing parameters like CO2, particulate matter, and humidity, and provides actionable recommendations to improve air quality and reduce infection risk through HVAC system control and air filtration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time measurements from multiple networked air quality sensors are analyzed to calculate airborne virus infection risk scores, then the ability to detect and assess viral transmission risk is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the monitoring network into autonomous sensor nodes that independently collect and pre-process air quality data (CO2, PM2.5, PM10, humidity, temperature). Each sensor segment operates independently but contributes to the overall risk assessment, allowing the system to scale without proportionally increasing central processing complexity.
Solution Approach 2:
The air quality sensors are designed to measure multiple parameters simultaneously (CO2 concentration, particulate matter, humidity, temperature) using a single integrated platform. This multi-functionality reduces the need for separate specialized devices and simplifies the overall system architecture while maintaining comprehensive monitoring capabilities.
2Reliability
If continuous monitoring of multiple air quality parameters is implemented, then the detection capability for viral transmission risks is improved, but the energy consumption and operational costs increase
Solution Approach 1:
The system implements periodic sampling of air quality parameters at optimized intervals rather than truly continuous monitoring. The sampling frequency is dynamically adjusted based on occupancy detection and risk levels, reducing energy consumption during low-risk periods while maintaining reliable detection capability when needed.
Solution Approach 2:
The sensor network includes autonomous nodes that self-manage their operational parameters, including adaptive sampling rates and local data buffering. Each sensor unit independently optimizes its energy usage based on environmental conditions and occupancy patterns, reducing the need for centralized energy management overhead.
3Object-affected harmful factors
If the system provides real-time risk scores and actionable recommendations to occupants and managers, then the ability to reduce viral transmission risk is improved, but the complexity of data communication and control systems increases
Solution Approach 1:
The system implements closed-loop feedback by continuously monitoring air quality parameters, calculating risk scores, and automatically adjusting HVAC controls to maintain safe conditions. The feedback mechanism prioritizes critical control actions and uses standardized communication protocols to minimize system complexity while ensuring effective risk mitigation.
Solution Approach 2:
A centralized cloud-based platform serves as an intermediary that receives data from distributed sensors, performs complex risk calculations, and translates results into actionable recommendations for occupants and managers. This intermediary architecture offloads computational complexity from local devices and standardizes communication interfaces.
4Object-affected harmful factors
If the system integrates with HVAC systems and air treatment appliances to automatically improve air quality, then the effectiveness of risk reduction is improved, but the device complexity and integration requirements increase
Solution Approach 1:
The system combines air quality sensing, risk calculation, and HVAC control functions into an integrated platform. By merging these previously separate systems into a unified architecture with standardized communication interfaces, the overall integration complexity is reduced while maintaining the ability to automatically adjust ventilation and air treatment based on real-time risk assessment.
Data Source
AI summary
A computer implemented system and process of analyzing real-time measurements of a plurality of 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.