Harvard researchers designed an air quality sensor inspired by dogs' noses

The new sensor system mimics biological olfactory processes in mammals, significantly improving the accuracy of air quality measurements from 38% to 98%, enabling real-time monitoring.

Existing sensors often fail to identify specific volatile organic compounds (VOCs), offering only total VOC readings without differentiating between harmful and benign substances.

Harvard researchers’ innovation integrates machine learning with active sniffing techniques modeled after mammals, which allows the sensor system to gather detailed air quality data.

Current air quality testing methods are mostly passive and inaccurate, requiring lab analysis while the innovative sensor promises a low-cost solution with real-time results.

Read at Fast Company