In our first studio exercise, we are tasked with evaluating a variety of environmental factors influencing our local atmospheric conditions (Light, Air Pressure, Humidity, Temperature). Humidity, which is a measure of the amount of water vapor in the air, serves as the subject of our analysis within the studio environment.
It can be measured in terms of Absolute Humidity, equal to the mass of water vapor/volume of air,
….or, as is more common, it can be measured as Relative Humidity, equal to the partial pressure of water vapor/saturated vapor pressure of water x 100%
Humidity has a considerable effect upon our comfort. We cool our bodies through the evaporation of moisture on our skin, a process which is limited when humidity is high. Because we perceive temperature through it’s rate of change, humid air thus makes us feel warmer. Humidity also has consequences on the built environment, because at large levels condensation forms on surfaces, leading to their corrosion and decay.
At a global scale, humidity is influenced by these factors:
- Temperature
- Solar radiation
- Altitude
- Wind exposure
- Cover and soil status
In the studio’s environment, there is an additional set of factors at work influencing the relative humidity:
- Room size
- Presence of windows, doors, and other openings
- Heating fixtures
- Lighting fixtures
- Other electronics
- People (1 person’s breathing alone produces 1/4 cup of water per hour)
In considering these variables, we decided to begin by determining areas around the room that would serve as a useful gauge of their influence. This data will be provided in two sets. The first will be provided by a series of immobile “static” sensors, placed at what we perceive to be key locations to receive input. These areas correspond with providers of humidity-influencing variables, such as an area of significant temperature change, or the presence of people. Accordingly, we selected the building’s immediate exterior, a window’s center, it’s sill, the radiator’s surface, a wall dividing the interior and exterior, a high human traffic area, the neighboring hall, and a sort of “control point” at the room’s center. These sensors will provide data over a variety of time scales, from minutes, to hours, days, and perhaps weeks.
The other data set will be provided by a mobile sensing unit, equipped with one humidity sensor, and set to traverse the room according to a set of rules that we prescribe beforehand. Though its form is as of yet unresolved, our early concepts have seen it represented as a small car that drives along cables spanning the room. This car, carrying with it the sensor, would travel the room’s length, returning a large assortment of humidity values across it’s path. By repeating this process, we would then create a gridded array of data points for the room’s entirety. The goal here is to produce a volume of data, measured across a relatively precise set of circumstances (in position and time).
This axonometric represents the gridded paths that might be taken by the mobile humidity sensing device
This is a plan view of the mobile sensing device's gridded path across the studio
In our following experiments, we hope to gain a better understanding of the type of data that we can expect to be returned. We plan to map a section of the studio with humidity data from our static sensors, and use that to derive a suitable form for our mobile sensing prototype. Ultimately, when we are able to combine all of our data sets, we hope to be able to attain conclusive information about the studio’s humidity levels, the manner in which they shift, and the degree to which other factors which influence this dynamic.
