Ozone Layer and Gas Emission Analysis - 2021

Summary

• Discuss whether the ozone layer protection over the past decades is adequate by fitting the ARIMA model and building R shiny dashboard.

Team

• Songlin Xie, Tianyi Jiang, Jianghong Zhou, Rushi Lin.

Background

The Clean Air Status and Trends Network (CASTNET) is a program managed and operated by the U.S. Environmental Protection Agency (EPA). It is a long-term atmospheric monitoring program with over 90 sites located throughout the United States and Canada. 

Current problems: 

• The emission of harmful gas dramatically increased due to human activities. 

• Exposures of sulfur and nitrogen species are harmful to the ozone layer. 

Objective

• Characterizing the trend of Sulfur Dioxide and Nitrogen Trioxide concentration levels.

• Fitting ARIMA model to predict future gas concentration level.

• Build R shiny interactive dashboard to visualize the gas concentration level. 

Data

Raw data dimension: 129167 x 16.

Cleaned data dimension: 124739 x 7.

• "SITE_ID": Site identification code.

• "YEAR": calendar year of measurement.

• "WEEK": week of a year (defined by the first Tuesday-to-Tuesday week of the year).

• "DATEON" and "DATEOFF": the date and time when the sample collection began and ended.

• "SO2_CONC": mean ambient sulfur dioxide (SO2) concentration in ug/m^3.

• "NO3_CONC": mean ambient nitrate (NO3) concentration in ug/m^3.

ARIMA Model

The final model is ARIMA(1,1,2)(0,1,1)[52]. 

For demonstration purpose, the example data is from the Salamonie Reservoir Site in Indiana:

• Kernel smoothing to visualize the trends. 

• Blue lines indicate some seasonality. 

• Red lines indicate a constant mean for nitrate concentration and a downward trend for Sulfur dioxide concentration. 

Autocorrelation plot and partial autocorrelation plot:

• The sulfur plot on the left has periodic fluctuations and decays very slowly, proving that this data has seasonal effects. 

• The nitrate plot on the right is purely periodic fluctuations. 

• The fluctuating period is 52 lags. 52 weeks is equal to one year, so this is a yearly seasonal effect. 

Use the sulfur dioxide concentration as an example of how we build the model:

• Observe his data has a decreasing trend

• Take the first difference to detrend the data. 

Scale the series with reciprocal squared roots:

• The variance is not completely constant, but if you look at the scale of the y-axis, this series has been much improved. 

• From the ACF plot here, we can see that there is a rough repetition in every 52 lags, so we take the 52nd difference. 

• Although the plot is not perfectly showing the seasonal effect, in actual practice, it is common not to have a perfect ACF.

• The ACF cuts off after lag 1, while PACF tails off.

• It indicates a Seasonal ARMA model with 1st order MA. 

With Seasonal ARMA(0,1)[52] model, use BIC to select p and q values:

• p = 1.

• q = 2.

The final model becomes ARIMA(1,1,2)(0,1,1)[52]:

• All p-values are small, so all the coefficients are significant.

Forecast future 52 weeks SO2 concentration with ARIMA(1,1,2)(0,1,1)[52]:

• Visually, it perform good in capturing the seasonality.

R shiny Dashboard

The final Rshiny Dashboard is shown below. We can observe information of:

• SO2 and NO3 weekly concentration level in a geographical map.

• Time-series plot with future forecast at a given site.