Lego Air Quality Sensor

To present the background you may use the accompanying slide show:

https://docs.google.com/presentation/d/1sc_QHbBamYe9fkiuvdFguANOjlmAPU9huqi7g6odd8M/edit?usp=sharing 

Air Quality Background:

The World Health Organization estimates that in 2012 approximately 3.7 million people died as a result of ambient air pollution. Among commonly monitored air pollutants, fine particulate matter (PM2.5), particles with diameters smaller than 2.5 microns, has the greatest adverse health effects. Elevated PM2.5 levels are a particularly important issue in northern Utah where PM2.5 levels can exceed national ambient air quality standards for periods ranging from a few days to weeks, particularly in the winter.  These episodes of poor air quality create significant health and quality-of-life consequences for the region’s citizens, including increased incidence of asthma, juvenile arthritis, and mortality.

Government agencies, such as the Utah Division of Air Quality (DAQ), and citizens rely on air-quality data from sparsely distributed monitoring stations for planning purposes and for communicating air quality. These stations are equipped with high-quality, costly instruments that meet federal monitoring requirements. However, sparsely distributed stations may not accurately represent the pollutant gradients within a city. In Salt Lake City differences in elevation, land use and other factors result in daily average PM2.5 concentrations at the neighborhood-level that may not be well represented by the nearest state monitoring station. In addition to sparse spatial distribution, the government monitoring stations have limited temporal resolution. For example, only two stations in Salt Lake County provide hourly PM2.5 levels. This gap in temporal resolution is particularly important in light of studies suggesting that even short-term increases in pollutant levels increase the incidence and severity of asthma and cardiac events.

Networks of low-cost, air-quality sensors can help bridge these spatial and temporal gaps and provide key information to air-quality managers, health-care providers, and the community at large to better understand air quality and minimize exposure risks. However, many of these low-cost sensors lack independently gathered calibration data, quality assurance procedures, or descriptions of when the sensors may provide inaccurate readings. Presenting unreliable or uncertain information from sensor networks can cause either unnecessary public concern or complacency about pollution levels and the associated health risks.

Two of the project goals are to leave each classroom with a low-cost, air-quality sensor and to have your class help determine:

•  How well outdoor air-quality measurements represent local air quality in and outside your school.
• How well the sensors perform over time.

Electronics Background:

Only very basic understanding of electronics is useful but not absolutely necessary.

Arduino Board: All the needed connections on the arduino board are highlighted in the figure in the Materials section of this module.

White LED: The LED light connects to 3.3 V to produce a white light in the sensor’s light source. Note that LEDs are directional; most of the time the shorter wire on the LED should go to ground and the longer wire to the higher voltage, but not necessarily. If the LED does not turn on you may try switching the ground to the other wire. The LED can easily fit in lego blocks meant to hold axles, or a solid block can be drilled to fit the two wire leads and the light can be glued to the surface.

Fan: The fan should have a red and black wire. The black wire should attach to ground and the red to 5V supply (Use Vin when powered by a USB cord). Have the students wire up the fan and take note of which direction it blows air so that they install it correctly. It is likely better to pull air through the sensor than to blow air into it; the air will move more smoothly through the sensor. Care should be taken to keep fingers away from the spinning fan.

Photoresistor Sensor: A photoresistor is a component that resists the flow of electrical current dependent upon how much light is shining on it. Because the Arduino boards can only measure voltage, we must use the photoresistor in a simple voltage divider circuit and measure the voltage between the photoresistor and another resistor. We want the voltage signal to increase when more light hits the photoresistor and the photoresistor we are using decreases resistance with more light. As such, our voltage divider should have the photoresistor nearest to the 5V power supply and a standard static resistor nearest to ground. See the circuit diagram for the sensing circuit in the Materials section.

During our experiments our brand of photoresistor ranged from 5,760 to 4,183 ᘯ, from clear air to air filled with mist. To assure we have sufficient voltage response we chose a static resistor of 10 kᘯ.  We pre-solder and secure this voltage divider to a lego block prior to starting this teaching module, but a more in-depth procedure could have the students create their own sensing circuit.

Note that the black wire from the sensing circuit should go to Pin 2 on the arduino (which the code sets to ground, 0V)). The red wire should go to Pin 3, which the code sets to 5V. For our arduino code to respond properly, the third wire should go to the analog in pin, A5.

Indicator Light: The NeoPixel light strip we are using (https://www.adafruit.com/products/1426) has eight lights but it is very simple to use and hook up. The black wire should go to the arduino ground, and the red to the arduino 5V. The third wire must be plugged into the digital pin number 6 in order for the script to properly operate the lights. Once connected and calibrated (as described in the methods section) the lights should go from green to red and increase in number as they detect more particulates in the air.

Light Scattering Background:

The sensors built by the students (and many research sensors) work by measuring scattered light. As light travels through clean air, which is free of particulate pollution, it travels roughly in a straight path. Almost none of the light could make, say, a 90° turn. However, if particulate matter filled the air, light may be reflected off the particles in a random direction. You can readily see such scattered light if, for example, you shine a laser through a cloud of smoke; the result will appear as a line of scattered light traveling through the cloud, whereas with clean air you may not ever detect a laser was passing through the space (Such a demonstration with a laser pointer and fog machine can be a good introductory addition to this module; you could also use a blue laser and tonic water compared to regular water as the tonic water’s quinine will scatter the laser light).

While this light is scattered randomly, some of the light will be scattered towards our sensor and detected. The more particulate matter in the air, the more likely it is that a photon of light will find its way to the sensor (a photoresistor in the instance of this module), and therefore we can quantify the amount of particulate pollution in the air.

Alternatively one could attempt to detect the amount of light lost after passing through the polluted air, by placing the sensor directly across from the light source. In this case more light would hit the sensor in clean air than in polluted air. If students attempt this method they are not likely to be successful due to the fact that the sensor will likely be saturated with light and insensitive to the loss of a very small percentage of its total intensity. Scattered light is a prefered indicator of particulate matter because 1. such sensors are typically more sensitive to the range of light intensity between zero and minimal light than between two large intensities of light; and 2. scattered light can be more indicative of particle size.

As such, we would suggest a possible working design for such a sensor may look like the following illustration (though we would recommend you do not share this with students; results are more memorable if they are allowed to explore their design options and then explain their performance):

The sensor is 90° off from the light path, and somewhat setback and a fair distance from the light to avoid direct light hitting the sensor.  The air path makes several turns to avoid light pollution from the outside. However, several designs may be successful and students should be encouraged to exercise their creativity.