Using snapshot measurements to identify high-emitting vehicles

This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022. <strong>Material for reproducing figures in the paper</strong> R script: plot.r Data for plot 2: <em>RS_Zurich_data.csv</em>: the sample RS data from Zurich. <em>algorithm_eu5d_final_iteration.rds</em>: the estimated average emission factor for each city fleet (outputs from the iterative algorithm) Data for plot 3:<em> </em> <em>Zurich_clean_identification.xlsx</em>: summary of the fraction of clean vehicles being identified by each potential RS threshold. <em>Zurich_high_emitter_identification.xlsx</em>: summary of the fraction of high-emitters being identified by each potential RS threshold. Data for plot 4: <em>validation_test_dataset.csv</em>: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions. <em>validation_algorithm_results.rds</em>: algorithm outputs when applied to the validation dataset. <strong>The iterative algorithm and sample data that can be used for demonstration</strong> Algorithm script: <em>iterative_algorithm.r</em> Sample RS data: <em>RS_Zurich_data.csv</em> Sample PEMS/Chassis test cycles: <em>sample_pems_chassis_cycles.csv</em>