Spatial Calibration & Mapping of Particulate Matter (SCMPM) is an open source and efficient calibration and mapping approach based on real-time spatial model that calibrates measurements from low-cost sensor in an environment with high relative humidity. The model provides spatial calibration of low cost PM2.5 sensors using the regulatory air quality monitoring stations.
- The "Low-cost air box" for low-cost PM 2.5 observations are available at https://pm25.lass-net.org/
- The "Automatic Monitoring Instrument" for PM2.5 established by the Taiwanese Environmental Protection Agency (TWEPA) is available at https://data.epa.gov.tw/en
Step 1: - run mainStep1.m provides the non-spatial calibration using Linear Regression (LR) & spatial calibration using GWR
Step 2: - run mainStep2.m Plot GWR or LR result; 1: plot GWR maps; 0: plot LR maps
Input: - (1) Data/Newcase/1/airbox.csv; (2) Data/Newcase/1/EPA.csv; (3) Data/TWN_shp/gadm36_TWN_shp
All the codes are exclusively written for MATLAB environment.
The main script and data are written & provided by:
Geosmart Lab
Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan (R.O.C)
To support the use of this code and the data please cite the following publication:
Chu, H. J., Ali, M. Z., & He, Y. C. (2020). Spatial calibration and PM2.5 mapping of low-cost air quality sensors. Scientific reports, 10(1), 1-11. https://doi.org/10.1038/s41598-020-79064-w