Filtration is one of the strategies that can be implemented to mitigate the public health challenge due to elevated concentrations of particles in underground railway stations (URS). During a particle (PM2.5 and PM10) monitoring campaign performed in a Paris URS over more than 5 years, two filtration systems were tested. One of these systems employs positive ionization (Experiment 1), the second one is based on water filtration (Experiment 2). The present study focuses on evaluating the performance of these experiments, using two distinct methods: the daily amplitude coefficient (DAC), and a generalized additive model (GAM). The DAC method requires only particulate matter measurements, while the GAM method necessitates additional measurements (indoor CO2 concentration, temperature and humidity, outdoor pollutant concentration) as it analyses the nonlinear relationships between all these factors. The results show that both filtration technologies are more effective at reducing PM2.5 concentrations than PM10, but Experiment 2 was less efficient than Experiment 1. Specifically, DAC analysis leads to a reduction of 17.8 ± 3.2 % for PM10 and 25.9 ± 6.6 % for PM2.5 for Experiment 1, compared to only 0.4 ± 0.1 % for PM10 and 3.7 ± 1.0 % for PM2.5 for Experiment 2. GAM analysis gives similar results, with a reduction of 13.1 ± 0.8 % for PM10 and 26.0 ± 0.8 % for PM2.5 for Experiment 1, and for Experiment 2 a reduction of 1.3 ± 2.0 % for PM10 and 4.6 ± 1.3 % for PM2.5. Yet, because of differences in the filtration setups, such as the position of the modules and the maximal air flow capacity, and of unspecified internal characteristics of the systems, no conclusion can be drawn as to whether one of the filtration techniques is more efficient than the other. The two analysis methodologies lead to similar results and allow to fully and rigorously exploit long time series. The DAC method can be applied using only PM measurements, while the GAM method requires additional measurements, but at the same time provides valuable insights about the effects of each parameter considered.
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