Abstract

One of the main sources of air pollution in the Monterrey Metropolitan Area (MMA), Mexico, is vehicle exhaust. In this study, emission factors (EFs) for PM2.5, organic carbon (OC), elemental carbon (EC), trace metals (Na to Pb), cations (Na+, K+, NH4+) and anions (Cl−, NO3−, SO42−) from mobile sources operating under real-world conditions were determined using the Loma Larga Tunnel (LLT) as the experimental set-up. At each station, low-volume devices were deployed to collect 2.5 h-average PM2.5 samples. From the samples collected, PM2.5 EFs as well as chemical profiles were estimated. During the sampling periods conducted for this study, a fleet of 108,569 vehicles crossed the tunnel with average speeds that ranged from 43 km h−1 to 76 km h−1. 97% of the vehicle samples were gasoline-powered vehicles. Average emission rates of 22.8 ± 7.4 mg veh−1 km−1 and 187 ± 144 mg L−1 for PM2.5 were obtained. Contribution of dust resuspension to the total PM2.5 EFs was analyzed using silicon (Si) as a key marker and main component of fugitive dust. Based on this analysis, we estimated a contribution of dust resuspension of 20%–25%. Trace metals linked to brake-wear emissions indicate a clear distinction between down-slope and up-slope emission profiles. Even though PM2.5 EFs tended to be higher during downhill driving conditions, statistically we found no difference between these two driving modes. This could probably be caused by the cancellation of contributions between species that are preferentially emitted during downhill driving (more metal species and less OC and EC) and those emitted during uphill driving (more OC and EC, and less metals). Vehicular PM2.5 emissions were dominated mainly by OC and EC, species which represented 55.2 ± 2.8% and 16.3 ± 1.6% of the total dust-corrected emitted mass. The OC/EC ratio was 2.85 ± 0.79 and 1.19 ± 0.65 for heavy traffic and moderate traffic conditions, respectively. The results obtained match those of other tunnel studies and can be complementary to the results obtained by other techniques to derive a better emissions inventory for the MMA.

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