1. Air Pollutant Prediction Using Precipitation
Patrick Chang
Henry M. Gunn High School
JSM 2019
July 30, 2019

2. Goals
The purpose of predicting pollutant levels is to make sure certain pollutants do not reach high enough levels to do harm to people.
The pollutants that are being referenced in this presentation are Carbon Monoxide (CO), Lead (Pb), Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulfur Dioxide (SO2).
The goal is to find major factors that can significantly affect pollutant levels and use those factors to predict the pollutant levels on the next day.
My first attempt was to use precipitation to predict pollutant levels since more precipitation seems to be correlated to a general decrease in air pollutant levels.
However, as there are many other factors that affect the amount of air pollutants, precipitation is not directly causing the drop in air pollutants. Will it be possible to predict the pollutant level by precipitation? If not, what other factors should be considered?
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3. Methods
Pollutant
Precipitation
Wind
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4. Results (Linear Regression Bivariate Fit)
NO2 , Rsquare = 0.011
Lead, Rsquare =
CO, Rsquare =
Pollutant
Ozone, Rsquare =
PM10, Rsquare = 0.032
SO2 , Rsquare = 0.022
Precipitation
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5. Results (Multiple Linear Regression)CO
NO2 CO
Lead
NO2
Ozone
PM10
SO2
R Square
0.396
0.122
0.277
0.11
0.29
0.202
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6. Results (Neural Networks)CO CO
Lead
NO2
Ozone
PM10
SO2
Neural Networks RMS
0.257
0.0025
9.34
0.0095
7.53
0.75
Linear Regression RMS
0.607
0.0028
21.13
0.014
13.96
1.05
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7. Conclusion
It is not simple to predict air pollutant levels of the next day as there are many unpredictable factors, such as wildfires or human activities.
Using precipitation to predict pollutant levels proves to be insufficient as precipitation is not very correlated to pollutant levels, unlike what I may have thought, and rain can be very infrequent in certain areas.
There are also more factors that can be measured, which may also have a major impact on pollutant levels.
Wind speed has more of an impact to pollutant levels than precipitation, so it is more suitable for pollutant prediction.
In this study, Neural networks usually predict pollutant levels more effectively than linear regression. One possible reason is that Neural networks are more flexible for modeling different situations.
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