According to the U.S. Environmental Protection Agency, most Americans spend about 90% of their time indoors, a setting where pollutant levels are often 2 to 5 times higher than outdoors. This heightened concentration of pollutants indoors is a concern, especially since traditional indicators of poor air quality, like smoke or strong smells, are less apparent inside buildings.
In this recent episode of the Mouser Electronics podcast "The Tech Between Us," host Raymond Yin soy sits down with Ronan Cooney, Head of Product at Ambisense. The two take a deep dive into the engineering aspect of indoor air quality and environmental sensors.
The episode delves into the significance of monitoring key components of indoor air, such as CO2 levels, Volatile Organic Compounds (VOCs), and particulates. This monitoring is essential for maintaining a healthy indoor environment.
A major highlight of the episode is gaining an understanding of the role of Ambisense in this field. Ambisense focuses on equipping customers with valuable data, empowering them to make informed decisions and take appropriate actions based on the insights provided. This approach underscores the importance of data-driven solutions in managing indoor air quality.
The podcast also sheds light on the advancements in sensor technology. Sensor manufacturers are working towards reducing the costs of these devices while ensuring they maintain high performance. The use of Non-Dispersive Infrared (NDIR) technology for CO2 sensors is now considered a basic standard in the industry, marking a significant step forward in sensor technology.
Another key aspect discussed is the integration of the right sensors and IoT sensor nodes. This integration is not just about collecting data but also about interpreting it effectively to improve indoor environments. The use of sensor fusion and algorithms plays a pivotal role here, as it allows for the integration of data from various environmental sensors into a cohesive and comprehensive picture for the end-users.
Understanding the correlation between different environmental factors is also crucial. This understanding helps in identifying and addressing various indoor air quality issues more effectively. The fusion of different sensors is vital in gaining a thorough understanding of the dynamics within a space, including factors like occupancy and particulate levels.
The use of artificial intelligence (AI) and machine learning in managing sensor data is a prominent theme in this episode. Cooney explains how AI-driven anomaly detection can help identify unusual patterns and issues in air quality, aiding in effective decision-making. The development of AI technology is hailed as a game-changer across industries, with its potential being actively explored to refine data interpretation and enhance user experiences.
The episode concludes with a focus on the growing awareness and responsibility towards providing healthy and productive spaces. With the advancements in technology, it is no longer acceptable to be unaware of indoor air quality issues. The technology available today makes it possible to be more proactive in ensuring the health and well-being of individuals in indoor environments.
This podcast episode serves as a valuable resource for anyone interested in the field of indoor air quality, offering insights into the latest trends and technologies in sensor and data analysis.