Software

  Sensor Evaluation Toolkit (SET)

Please find the R package under this link. The main file is the sensorCompareWithQi.R for data with wind info (thus, for each measurement the speed and direction).
If your data does not have wind info the main file is sensorCompareWithQiNoWind.R.

The zip file contains both sample data with and without wind info. The main files contain detailed info how to run the files.

Please cite the following paper in any future publication using this packageB. Fishbain, U. Lerner, T. Cole-Hunter, N. Castell, O. Popoola, D.M. Broday, T. Martinez Iñiguez, M. Nieuwenhuijsen, M. Jovasevic- Stojanovic, D. Topalovic, R.L. Jones, K. Galea, Y. Etzion, F. Kizel, Y.N. Golumbic, A .Baram-Tsabari, J.A. Robinson, D. Kocman, M. Horvat, V. Svecova, A. Arpaci and A. Bartonova, “An Evaluation Tool Kit of Air Quality Micro-Sensing Units”, Science of the Total Environment, 575(1):639-648, 2017.

Introduction to Sensor Evaluation Toolkit


Spectral Methods for Imputation of Missing Air Quality Data

Please find the Matlab package under this link. For installation unzip the file into your Matlab working directory. Once the file is unzipped, you should start with AllInOne.m file. The imputation methods’ code can be found under the Methods directory in the unzipped directory.

The example is run on a long SO2 (sulfur dioxide) sequence when the data is omitted both in random an in chunks (see S. Moshenberg, U. Lerner and B. Fishbain, “Spectral Methods for Imputation of Missing Air Quality Data”, Environmental Systems Research, 4(26):1-13, 2015. for more details). The data file is a Matlab’s mat file (SO2Sequnce.mat) located under the main working directory.

Please cite the following paper in any future publication using this packageS. Moshenberg, U. Lerner and B. Fishbain, “Spectral Methods for Imputation of Missing Air Quality Data”, Environmental Systems Research, 4(26):1-13, 2015.