Air Quality Management Strategies in Large Cities: Effects of Changing the Vehicle Fleet Composition in Barcelona and Madrid Greater Areas (Spain) by Introducing Natural Gas Vehicles

摘要

Air quality modelling involves a strategy to manage air pollution in large cities, where air quality problems presently are mainly related to on-road traffic. Nowadays, one of the strategies to reduce emissions is based on the substitution of vehicles by introducing new technologies (e.g. cleaner fuels, hybrid vehicles, fuel cells, etc.). This work assesses the variation on air quality due to the substitution of specific vehicle fleets by natural gas vehicles in the two largest cities of Spain: Barcelona and Madrid. Six different scenarios are studied, focusing on the total or partial modification of public transportation vehicles (buses, taxis), freight vehicles and private vehicles. One scenario involving a combination of all of them is also studied. Under this perspective, the WRF/HERMES/CMAQ modelling system has been implemented and validated with a high resolution (1 km and 1 hour) in the area thanks to the calculation power of the MareNostrum super-computer of the Barcelona Supercomputing Center (94.21 TFlops peak). Daily average concentrations of NO_2, SO_2 and PM, both PM10 and PM2.5, and 8-hour average concentration for O_3 and 1-hour maximum concentrations for these species are estimated both in Barcelona and Madrid Greater Areas. All the scenarios studied involve a reduction in NO_2, SO_2 and PM concentrations. Most important changes in air quality are registered when the combined scenario is implemented. Ozone concentrations remain approximately in the same levels as in the base case scenario, except for some VOC-limited areas where the reduction of NOx involves a slight O_3 increase (under the 10%). A large reduction in PM concentration is observed for both cities when the 50% of commercial light vehicles is transformed. Results of the simulations for the combined scenario indicate that it is particularly effective in reducing PM10 (up to -43% in maximum hourly concentration at some points) and PM2.5 (up to -36%).