PM 2.5 Response to Different Emissions Reductions Scenarios Over São Paulo State, Brazil. Taciana T. de A. Albuquerque a, J. Jason West b, Rita Yuri Ynoue c, Maria de Fátima Andrade c a Environmental Engineering Department/ Federal University of Espírito Santo: b Environmental Sciences & Engineering,/ University of North Carolina at Chapel Hill. c Atmospheric Sciences Department/ University of São Paulo. Growing levels of urbanization in developing countries have generally resulted in increasing air pollution due to higher activity in the transportation, energy, and industrial sectors, injuring the air pollution control programs (Emasp Report, 2011). In the last few years, several studies reported the relevance to establish a relationship between the emissions of precursors to the aerosol concentrations in a given area is a necessary first step for the design of PM 2.5 control strategy (West et al., 1999; San Martini et al., 2005; Pandis, 2004; Anenberg et al., 2011). The objective of this study was to evaluate the response of PM 2.5 concentrations to changes in precursor gases and primary particles emissions. The Models-3 Community Multiscale Air Quality Modeling System (CMAQ) was used to investigate the spatial and temporal variability of the efficacy of emissions control strategies in São Paulo State, Brazil. Meteorological fields were modeled using the Weather Research and Forecasting model WRFv3.1, for the 10-day period ( Aug, 2008) and after the SMOKE emissions model was applied to build a spatially and temporally resolved vehicular emissions inventory for a high resolution domain of 3-km (109 x 76 cells). The air quality simulations used measured concentrations as initial and boundary conditions. Aerosol processes and aqueous chemistry in CMAQ (AERO4) were used, as well as the Carbon Bond V gas phase mechanism. Seven different scenarios were simulated considering the current emission inventory, called base case, a future scenario considering a reduction of 50% of SO 2 emissions (Case2), a scenario considering no SO 2 emissions (Case 3), a reduction of 50% of SO 2, NO x and NH 3 emissions (Case 4), a scenario considering no sulfate (PSO4) and nitrate (PNO3) particles emissions (Case 5), another considering only excluding the PSO4 emissions (Case 6) and the last one considering no PNO3 emissions (Case 7). INTRODUCTION METODOLOGY Characteristics of the Metropolitan Area of São Paulo, Brazil Numerical Datas Lat=-23.6 o Lon= o Vehicles: > 7 million MASP = São Paulo city + 38 cities: MASP is located in the following geographical coordinates: 23.6 S and 46.7 W. It is almost 70 km distance from the ocean. MASP is located in the following geographical coordinates: 23.6 S and 46.7 W. It is almost 70 km distance from the ocean million inhabitants - 7,2 million vehicles significant industrial plants - Meteorological Model: Weather Research and Forecasting (WRF) version Met Data: GFS data (1˚× 1˚) - USGS – Global Land Cover - Air Quality Model: Community Multiscale Air Quality Model (CMAQ) version Vehicle Emission Inventory created by: Sparse Matrix Operator Kernel Emission (SMOKE) CMAQ WRF Smoke Model Inputs  Spatial distribution surrogate :  Earth’s city lights created with data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS)  Temporal distribution :  Same for the whole area  Light-duty fleet: Lents et al., 2004  Heavy-duty fleet: CETESB, 2008  Fleet distribution and activity : SPtrans (4) and CETESB, 2008  Emission Factors :  CO, NOx and PM 10 : Sanchez et al., 2009  VOC´s and SO 2 : CETESB, 2008  NH 3 : Fraser and Cass, 1998  Vehicular Density:  Each “city light intensity value” was equivalent to 24,8 vehicles.km -2. This figure shows the air quality and meteorological measurement stations from the local Environmental Agency CETESB used to validate this study. Shaded area represents the topography height (meters). Emission Database and Sensitivity Cases CasesEmissions Sensitivity Case-1Base Case Case-2Reducing 50% of the SO 2 emissions Case-3No SO 2 emission (SO 2 = 0) Case-4Reducing 50% of the SO 2, NH 3, No x emissions Case-5No emissions from PSO 4 and PNO 3 Case-6PSO 4 =0 Case-7PNO 3 =0 NUMERICAL RESULTS Difference of PM 2.5 average concentrations between the reduced scenarios and the Base Case PM 2.5 average concentrations: Base Case and reduced emissions scenarios Difference of PM2.5 average concentrations between Case 7 and the base case. Difference of PM2.5 average concentrations between Case 6 and the base case. Difference of PM2.5 average concentrations between Case 5 and the base case. Difference of PM2.5 average concentrations between Case 4 and the base case. Difference of PM2.5 average concentrations between Case 3 and the base case. Difference of PM2.5 average concentrations between Case 2 and the base case. Average of PM 2.5 concentrations for case 7 Average of PM 2.5 concentrations for case 6 Average of PM 2.5 concentrations for case 5 Average of PM 2.5 concentrations for case 4Average of PM 2.5 concentrations for case 3 Average of PM 2.5 concentrations for case 2Average of PM 2.5 concentrations for BC Base Case Evolution PM 2.5 Concentrations from Ipen, Ibirapuera, Congonhas, Pinheiros and Cerqueira César Stations. Arrow direction denotes increase or decrease of concentrations; arrow color denotes undesirable (red) or desirable (blue) response; Arrow size signifies magnitude of change; Small arrow signify possible small increase or decrease. Blank entry indicates negligible response. Differences between the Average of PM 2.5 Hourly Concentrations from Base Case and future scenarios: we will analyze the punctual decrease of PM 2.5 hourly concentration (at each MASP station) between the scenarios and the base case. At all stations, case 4 showed better results, decreasing a maximum PM 2.5 concentration of 12  g.m -3 on Aug 12, Reducing only primary particles (Case 5, Case 6 and Case 7), the results were not significant. Reductions on emissions precursors and their changes on the pollutant concentrations SUMMARY The main results showed that reductions only in SO 2 emissions are likely to be less effective than expected at reducing PM 2.5 concentrations at many locations of São Paulo State. Case 2 presented an average a decrease of 3  g/m 3 on PM 2.5 concentrations, but in some areas there were an increase of 1.2  g/m 3. Evaluating the ammonia gas availability between the base case and case 2, it was verified an increase of its concentrations in the south area of the grid, and the Nitric Acid showed a decrease of its concentrations. This result could indicate that nitric acid may was transferred to the aerosol phase through the reaction with ammonia gas, originating nitrate aerosol. Case 3 was irrelevant, showing only a decrease of 0.3  g/m3 in whole area. Case 4 showed the largest PM 2.5 reduction for entire domain, not showing an increase of the PM 2.5 concentration, in average. In case 5 at all stations was verified a decrease of PM 2.5 average concentrations. However, there are some places of the grid showing an increase of PM 2.5 concentrations, which varies from 0.3 to 1.2  g/m3, as also observed in case 2. Case 6 showed the same results that were observed on case 5. Case 7 did not show a significant result, presenting a small increase for the entire domain (0.3  g/m 3 ). This result may indicate that reductions in sulfate concentration may cause inorganic fine particle matter (PM 2.5 ) to respond nonlinearly, as nitric acid gas may transfer to the aerosol phase. The spatial and temporal distribution of concentration varies in the whole domain. In conclusion, the largest reduction in PM2.5 was obtained when occurred a reduction of 50% of SO 2, NO X and NH 3 emissions, considering the average at one point (surface stations) or the average over the entire domain. We suggest that the role of the secondary organic aerosols and of Black Carbon need to be considered when making policy decisions to control the PM 2.5 concentrations because together they represent around 70% of the PM 2.5 mass concentration in São Paulo, Brazil.