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

https://docs.google.com/presentation/d/1BcF_PTui–5KUw_jwGCTYwLSYKQLMLqkZ4vViSR6Xnc/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. The primary concern is that the wires are connected properly.

Arduino Board: All the needed connections on the arduino board are highlighted in the figure in the Materials section of this module. This could be explained to be, basically, a mini computer. It takes in data from the AQ sensor, manipulates it to get particulate concentrations, and then puts that data out for the user in the form of LCD characters, and/or a file on the SD Card, or it can also send that data to a computer to be plotted in real-time.

AQ Sensor: This device uses scattering of laser light (discussed below) to measure particulate counts. These work by measuring scattered light. The interior of one of these sensors is depicted in the following figure.

Some models have air coming in from a different area, but they all pull air through a dark chamber which has a light detector and a laser shining through it.

As laser 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 and hit the sensor. However, if particulate matter filled the air, light may be reflected off the particles in random directions. 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; the quinine in the tonic will scatter the 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, and therefore we can quantify the amount of particulate pollution in the air.

LCD Screen: This device is only meant to show real-time data. It has 5V and ground wires to power the device and two other wires used to send data to be displayed.

MicroSD Card Reader/Writer: This device also has a host of wires which power the device and facilitate communication with the arduino. The Arduino will create a file on the SD card named PM_Data.csv.  It’s called a CSV file (comma separated value), and it can be opened in a program like Excel or most any other program for data analysis. If the file already exists, the board will write new headers in the existing file and add the new data to the end of the old data (you may need to delete this file periodically to avoid filling up the card). Note that each time the sensor system restarts it will give a new random RunID and use that ID at the header of each new data set in the CSV file. The code does this because the arduino has no way of giving the actual time or date (it keeps resetting its clock at every restart). The user should match the RunID with a known start time and date.

Possible Research Questions Addressable by Students:

The research questions which student come up with themselves may be quite creative and much better, but here are a couple to get them started:

• Fireplaces: How much does wood vs gas matter? How far from a wood fire fireplace can the particulate effect be detected?
• Clothes dryers: Can particulates be detected in the laundry room when the dryer is on vs off? Can it be detected out by the dryer vent? If a dryer vent is clogged a lot of particulate matter can enter the house.
• Candles or incense: Assure all fire is handled in the safest manner by an adult. Are these a significant health risk? Which is worse and by how much? If my parent lights a candle in one room, what does that mean for particulate matter in other rooms?
• Aerosols (hairspray, cleaners): Do these generate significant particulates in the room where they are used? Are these particles different than, say, particles from a fire? Note: don’t spray directly around the sensor or it will coat the detector and it won’t work very well…
• Cleaning/dusting: Gather evidence that doing chores may be hazardous to your health…
• Cooking. Does what’s cooked matter? How it’s cooked?
• Soldering: What risks are there in soldering electronic components? How much does ventilation matter?
• Powders: Do kids playing in a sandbox generate a lot of particulates? Using flour? Mixing cement?
• Industrial sources: What particulate concentrations are there near a gravel pit or incinerator or power plant? How about a restaurant? Would type of restaurants matter (wood fired pizza place vs a sandwich shop)?
• Weather effects: How does wind affect readings from sources of particulates? Rain? Humidity?
• Elevation: If I take my sensor on a hike on a day with poor air quality, can I see evidence of the inversion layer and how quickly that transition happens? Send your sensor up on a tethered weather balloon…. You’ll need to use a phone app to also track elevation.
• Air quality events: What are the effects of fireworks used on local holidays? What about a forest fire a couple states away?
• Indoor vs outdoor: What is the relationship between indoor air quality with outdoor air quality? Does it matter what season it is? Does the age of the home matter?
• Motor vehicles: What is the effect of living near a busy street verses being in an isolated neighborhood? What is the effect of busses and parents dropping off and picking up kids at school and how far does it range?
• And many many others…