# Presentation: 184406

## Analysis of Aerosol Particle Concentration Using MFRSR 

- Goddard Institute For Space Studies
- The City College Of New York, Department of Electrical Engineering
- Xavier Estevez

## What are aerosols?

- Air consists of molecules of N2, O2, CO2, and various other gases
- Aerosols are fine solid or liquid particles suspended in a gas
- Some examples of atmospheric aerosols are smoke, sulfates, volcanic ash, pollen, mold spores

## Remote Sensing

- Is the observation of some attribute of a subject by means that do not involve direct contact with that subject
- In other words, “look don’t touch”
- A familiar remote sensing system is that of your eyes and brain
- Examples of remote sensing: weather radar, satellite imagery, climbing a mountain and looking at things, LIDAR, seismometers, telescopes, radio telescopes, x-rays, MRI.  The applications are almost endless.

## Remote Sensing of Aerosols

- In order to determine the concentration of aerosols in the atmosphere, we use optical remote sensing.
- Aerosol particles reflect light.  We can detect these particles by measuring the loss of intensity of light as it passes through an aerosol-bearing medium
- Different wavelengths of light can detect different particle sizes.
- Simply put, short wavelength light detects smaller particles, and long wavelength light detects larger particles

- Long wavelength light

- Short wavelength light

## Apparatus

-  Multi-Filter Rotating Shadowband Radiometer
- Multi-Filter
- Senses several different wavelengths of light
- Rotating Shadowband
- Has a motorized arm thatperiodically covers the sensor
- Radiometer
- Measures intensity of solar radiation

- http://www.yesinc.com/products/data/mfr7/index.html

## What Does It Tell Us?

- The moving shadowband allows one instrument to collect direct and diffuse intensity readings
- Data analysis tells us how much light is reflected by the atmosphere
- Variations in this amount are related to concentration of aerosol particles

## Methods

- Control Unit /
- Data Acquisition System

- Data Acquisition System (DAS) controls the MFR, stores data in internal memory
- Laptop is connected to the DAS to download  the data
- Data files are analyzed using various software  tools

- RS-232

- Laptop

- MFR

## Program Flow Chart (simplified)

- File Splitter

- Raw input data
- (23 columns,
- with outliers)

- Separate files for
-  each morning
- and afternoon
- (7 columns, stripped
- of outliers)

- Day Processor

- 2-Dimensional
- Data Array Object
- Values converted to
- Secant and log

- Line Fitter

- Results
- List of optical depths
- For each of 5
- different wavelength
- channels

## Beer’s Law

*The deeper the glass, the darker the brew,*
*The less the amount of light that gets through*

- Ig = I0 e–m
- Loge Ig = Loge I0 – m

- The intensity of the light that reaches the earth’s surface is decreased by two factors– the length of its path through the atmosphere, and the optical properties of the atmosphere
- The relationship can be modeled as a linear equation.
- The slope of this line is equal to the total optical depth (how effectively the atmosphere blocks light)

## Langley Regression Analysis

- As the sun moves across the sky, sunlight must pass through varying amounts of air
- The light’s path is shortest at noon, and longest at sunrise and sunset
- Beer’s law tells us that there is a direct relationship between path length and light intensity– light that passes through a path twice as long is affected twice as much.
- We assume that the optical depth of the atmosphere remains constant over a half-day period, and can therefore determine optical depth by plotting light intensity against path length (the secant of the solar zenith angle).

## Data Filtering

- The optical depth for the time period in this graph is equal to the slope of the red line.
- The red line was not drawn mathematically, it “just looks right”
- This technique is not statistically valid, we have to use a linear regression equation to draw the trend line
- That regression applied to this data set would yield a line with a less severe slope and a lower y-intercept, due to the disproportionate effect of outlying points.

- Secant of solar zenith angle vs. log e Solar radiation intensity (W/m2/nm)
- 415 nm, afternoon of 22-June-2004

## Linear Regression

- Linear regression is a technique used to plot a straight line from a 2-dimensional collection of plotted data points
- This allows one to model real-world data theoretically
- The line produced will pass as closely as possible to as many of the data points as possible
- The equation which returns the slope of the best-fit line is as follows:

## Results

- The final product of my research is a list of optical depths for approximately 70 days, and the Java application that I used to calculate these values.
- I do not see any discernible patterns in these optical depths.  They do not appear to conform to any linear or periodic functions as far as I can tell.

## Discussion

- One potential source of error is the fact that due to cloudy or overcast conditions, some days did not yield any acceptable data-points, or yielded too few data-points to obtain any statistically valid trend
- Another error source is the fact that even the best data-cleaning algorithm cannot determine with absolute certainty which readings are invalid.
- This does not confirm or deny the validity of the results.  Further evaluation of the data is needed in order to determine the value

## References

- “Atmospheric Aerosols: What are they, and why are they so important?” _[http://oea.larc.nasa.gov/PAIS/Aerosols.html](http://oea.larc.nasa.gov/PAIS/Aerosols.html)_
- “Linear Regression”http://www.math.csusb.edu/faculty/stanton/probstat/regression.html
- “Excel Tutorial On Linear Regression”http://phoenix.phys.clemson.edu/tutorials/excel/regression.html
- “Langley Method”http://www.optics.arizona.edu/rsg/menu_items/resources/equip/langley.htm
**“**MFR-7 MULTI-FILTER ROTATING SHADOW BAND RADIOMETER”http://www.yesinc.com/products/data/mfr7/index.html