The approach of constrained spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high spatiotemporal resolution (currently mainly for NO2 and NOx, applied in California). It is based on our published paper in 2017 (Li, L., Lurmann, F., Habre, R., Urman, R., Rappaport, E., Ritz, B., Chen, J., Gilliland, F., Wu, J., (2017) <doi:10.1021/acs.est.7b01864>). Specifically, it includes the following functionalities: 1) Extracting meteorological parameters from the US continential grids dataset on the server; 2) Extracting Thiessen's polygon id for spatial effect models using the models trained on the server; 3) Extracting the yearly means based on the configuration of Thiesseon polygons based on the data on the server; 4) Parallel predictions using python to call the functions on the server side ; 5) Constrained optimization to adjust the estimated concentrations;
|Maintainer||Lianfa Li <[email protected]>|
|Package repository||View on GitHub|
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