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This repository implements Inverse Distance Weighting (IDW) interpolation in R to create prediction surfaces for NO₂, PM₂.₅, and O₃ concentrations.

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SammyGIS/Air-Quality-Prediction-Analysis-in-R

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IDW Interpolation Prediction Analysis in R

Using Inverse Distance Weighting (IDW) interpolation to create prediction surfaces in R. IDW method was used to predict distribution of N02, PM_25 and O3 of Air Quality data collected from sensors across USA.

Data Source

The EPA provides many prepared datasets. Air quality data for year 2000 was used in this analysis In this exercise, the Annual Summary Data for will be used – Concentrations by Monitor: https://aqs.epa.gov/aqsweb/airdata/download_files.html#Annual

Study Area

The Analysis will cover Five States of "South Carolina", "North Carolina", "Georgia", "Kentucky" and "Tennessee" image

Datasets

  • Nitrogen dioxide (NO2)
  • Ozone (03)
  • PM2.5 Local conditions

Data Preparation

Our research is limited to three air quality parameters: ozone (O3), nitrogen (NO3), and PM2:5 local condition. The data has 55 fields, however only Latitude, Longitude, Datum, Parameter Name, Arithmetic Mean, and State Name are relevant for this analysis.

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This repository implements Inverse Distance Weighting (IDW) interpolation in R to create prediction surfaces for NO₂, PM₂.₅, and O₃ concentrations.

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