sensative mold risk detection

Mold Risk Detection



According to the World Health Organization (WHO), 10%–50% of indoor environments in homes in, e.g., Europe, North America, Australia, India, and Japan are damp.

At least 45 million buildings in the United States have unhealthy mold levels. (Source)


Predict, detect, and act on environments favorable for mold growth.


By monitoring conditions, you can ventilate and heat as needed to address mold growth without wasting energy, resulting in significant energy cost savings.


Mold problems can occur in buildings throughout the year. Warm temperatures between 25 and 30°C (77 to 86°F) provide ideal conditions for mold growth. HVAC systems can contribute to this by providing moisture, food for mold spores (such as dust or dander), and warmth. However, you can prevent mold growth by addressing any of these three factors.

mold risk

Mold is typically detected by identifying musty odors, moisture sources like stagnant water or leaking pipes, and water-damaged building materials. However, these methods only catch mold that has already grown. An IoT solution can detect when the environment is conducive to mold growth, enabling you to prevent mold before it becomes a problem.

Mold remediation costs can be estimated at $2.50/ft2, including HEPA vacuum cleaning and washing with an antimicrobial twice. For a 2,000ft2 (185m2) home, this costs around $5,000.


Sensative’s Yggio DiMS platform and Strips MS +Comfort sensors for LoRaWAN.

Yggio has a decoder for STRIPS +Comfort, where we added the intelligence to warn of the risk of mold growth, which depends on temperature and relative humidity. You can use this information to control when to activate the ventilation and ensure it doesn’t run more than necessary. Additionally, you can receive alarms via SMS or e-mail if there is a risk of mold growth.

sensative mold risk detection data
sensative mold risk detection graph

drying period

Yggio presents this data in the form of graphs.

– The green curve represents the relative humidity directly taken from the sensor.

– The blue curve represents the temperature, which is also directly taken from the sensor.

– The purple curve indicates when we are in the mold risk area, which a smart algorithm in the decoder determines.

– The orange curve indicates the time when mold growth is most likely. The time indicated in the black legend box is when this occurs.

Under the black legend, you can see that the orange curve goes down to zero (high mold risk ends) only after a while after the purple curve (mold risk area left) does. This is the drying period required for the growing mold to die out, which prevents it from growing again when we re-enter the mold risk area. If high risk occurs for another 15 days, new mold spores will soon develop into mold growth, and the behavior will repeat itself.


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