The Tech Between Us Podcast: Environmental Sensors for Clean Air with Ronan Cooney

Jessica Miley

11 Dec, 2023

The Tech Between Us Podcast: Environmental Sensors for Clean Air with Ronan Cooney

Discover more about the crucial topic of indoor air quality.

artificial intelligence

- environmental monitoring

- IoT

- sensors

A critical public health issue

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.

Equipping customers with valuable data

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.

Sensor fusion meets complex demands

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.

Listen to part two of the discussion here.

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Ocean research benefits from new wave measurement sensor

based on Xsens MTi-3

Remco Sikkema

fromMovella

24 Mar, 2022

environmental monitoring

Measurement of the significant wave height, period and direction of waves in open water, and analysis of these measurements over short or long stretches of time provide information which is crucial to many people and organizations. Most obviously, it helps skippers of vessels such as fishing boats as well as operators of civilian and military shipping fleets to monitor and predict the conditions in which they sail.

But wave measurement has many other uses such as scientific research, to learn about ocean behavior, extreme weather events, global warming and more. Wave measurement is also used for weather forecasting, and in the construction of permanent infrastructure such as wind turbines or temporary installations such as fish farms.

For decades wave measurement was a low-technology, expensive process which required the deployment of large, special-purpose buoys. The status quo was disrupted when Seaview Systems, based in Dexter, Michigan in the US, introduced a wholly new approach. Its SVS-603HR Wave Sensor is based on an MTi-3 motion sensor from Xsens, and runs a suite of sophisticated algorithms developed by the computer scientists at Seaview Systems.

Much smaller, cheaper, and lower-power than the old technology – but just as accurate – the SVS-603HR can easily be installed on almost any type of new or existing buoy, or in an autonomous surface vehicle (ASV) or autonomous underwater vehicle (AUV).

So what part does Xsens MEMS motion-sensor technology play in measuring waves?

A typical deployment of a small buoy equipped with the SVS-603HR wave sensor

Photo credits: Ed Verhamme, LimnoTech and Great Lakes Outreach Media

A new generation of technology for oceanography

The modern era of wave measurement began in 1968 with the introduction of the Waverider® buoy manufactured by Datawell of The Netherlands. This type of wave measurement device relies on simple elect

Image: A satellite image of the Mississippi River Delta with land shown in bright green and water shown in dark blue. Credit: NASA Earth Observatory image by Michael Taylor and Adam Voiland, using Landsat data from the U.S. Geological Survey

Satellites Help Scientists Track Dramatic Wetlands Loss in Louisiana

New research uses NASA satellite observations and advanced computing to chronicle wetlands lost (and found) around the globe.

Jane Lee

fromNASA

11 Nov, 2022

environmental monitoring

- satellites

This article was discussed in our Next Byte podcast.

The full article will continue below.

From Lake Pontchartrain to the Texas border, Louisiana has lost enough wetlands since the mid-1950s to cover the entire state of Rhode Island. Using a first-of-its kind model, NASA-funded researchers quantified those wetlands losses at nearly 21 square miles (54 square kilometers) per year since the early 1980s.

In the new study, scientists used the NASA/USGS Landsat satellite record to track shoreline changes across Louisiana from 1984 to 2020. Some of those wetlands were submerged by rising seas; others were disrupted by oil and gas infrastructure and hurricanes. But the primary driver of losses was coastal and river engineering, which can have positive or negative effects depending on how it is implemented.

Centimeter by centimeter, wetlands are built by the slow accumulation – accretion – of mineral sediment and organic material carried by rivers and streams. Accretion makes new soil and counters erosion, the sinking of land, and the rise of sea level.

Human intervention and engineering often hold back or divert the flow of sediments that naturally accrete to build and replenish wetlands. For instance, reinforced levees and thousands of miles of canals and excavated banks have isolated many wetlands from the Mississippi River and the network of streams that course through its delta like veins and capillaries. In a few cases, engineering projects have added sediment to delta areas and built new land.

The researchers mapped land change in coastal Louisiana from 1984 to 2020. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss – more than 180 square miles (466 square kilometers). Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences