Using snapshot measurements to identify high-emitting vehicles

This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022.

Material for reproducing figures in the paper

• R script: plot.r
• Data for plot 2:
  • RS_Zurich_data.csv: the sample RS data from Zurich.
  • algorithm_eu5d_final_iteration.rds: the estimated average emission factor for each city fleet (outputs from the iterative algorithm)
• Data for plot 3:       
  • Zurich_clean_identification.xlsx: summary of the fraction of clean vehicles being identified by each potential RS threshold.        
  • Zurich_high_emitter_identification.xlsx: summary of the fraction of high-emitters being identified by each potential RS threshold.
• Data for plot 4:
  • validation_test_dataset.csv: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions.
  • validation_algorithm_results.rds: algorithm outputs when applied to the validation dataset.

The iterative algorithm and sample data that can be used for demonstration

• Algorithm script: iterative_algorithm.r
• Sample RS data: RS_Zurich_data.csv
• Sample PEMS/Chassis test cycles: sample_pems_chassis_cycles.csv