Regional climate feedbacks in Central Chile and their effect on air quality episodes and meteorology

Urban Climate 10 (2014) 771–781

Contents lists available at ScienceDirect

Urban Climate journal homepage: www.elsevier.com/locate/uclim

Regional climate feedbacks in Central Chile and their effect on air quality episodes and meteorology Marcelo Mena-Carrasco a,b,⇑, Pablo Saide d, Rodrigo Delgado e, Pablo Hernandez c, Scott Spak d, Luisa Molina f, Gregory Carmichael d, Xiaoyan Jiang g a

Ministerio del Medio Ambiente, Santiago, Chile Centro FONDAP CIGIDEN, Santiago, Chile Center for Sustainability Research, Universidad Andres Bello, Santiago, Chile d Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, United States e Dirección Meteorologica de Chile, Santiago, Chile f Molina Center for Energy and the Environment, La Jolla, CA, United States g Washington State University, United States b c

a r t i c l e

i n f o

Article history: Received 1 February 2014 Revised 27 June 2014 Accepted 28 June 2014

Keywords: Regional climate Aerosol Andes Santiago Black carbon

a b s t r a c t Santiago, an emerging megacity of 7 million plus inhabitants has shown great improvement in its air quality reducing PM2.5 concentrations from 69 lg/m3 in 1989 to 24 lg/m3 in 2013 with a comprehensive air quality management strategy. An operational air quality forecasting model that has shown great potential in predicting air quality episodes is used to establish how the climate A1B scenario can impact the frequency of bad air days. In comparison to 2011, in 2050 extreme air quality episodes will be reduced in 20%. WRF-Chem is used to evaluate the effect of anthropogenic emissions on the regional climate including aerosol radiative feedbacks for October–November 2008. Anthropogenic emissions of sulfur and black carbon show different geographical patterns which result in local cooling (0.2–1 °C) in coastal Chile, due to large sources of SO2. Central Chile, where most of the population of the country lives, shows transportation of black carbon emissions into the Andes mountain range, resulting in local warming of 0.4 °C.

⇑ Corresponding author at: Ministerio del Medio Ambiente, Santiago, Chile. Tel.: +56 225735743. E-mail address: [email protected] (M. Mena-Carrasco). http://dx.doi.org/10.1016/j.uclim.2014.06.006 2212-0955/Ó 2014 Elsevier B.V. All rights reserved.

772

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

While global forcings may cause regional heating for 2050, reducing current black carbon emissions in Central Chile can reduce anthropogenic warming with immediate benefits to the regional climate, and simultaneously reducing local air pollution. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Rural urban migration is driven by many factors, including the search for better economic opportunities (Henderson, 2002). Cities are very efficient in delivering goods and services to the population (Gordon and Richardson, 1997), and densification thwarts increased emissions associated to sprawl (Glaeser and Kahn, 2010). But when population reaches a certain level, megacities can be very unsustainable in their demand for water resources and solid waste management (Niemczynowicz and Iwra, 1996). Megacities also cause a high density of population and emissions, which are ideal conditions for increased exposure to atmospheric pollutants (Parrish and Zhu, 2009), ultimately linked to (Dockery and Stone, 2007; Seaton et al., 1995; Miller et al., 2007), alongside with premature mortality (Samet et al., 2000; Pope et al., 2004). Megacities have been shown to have large regional impacts on air quality, increasing concentrations of particulate matter, ozone (O3), carbon monoxide (CO) and nitrogen oxides (NOx). Anthropogenic sources of pollution have been traced at long distances. Chinese megacity emissions have been detected in aircraft measurements in the northern Pacific, thousands of kilometers from their origin (Jacob et al., 2003). Mexico City’s emissions have been shown to increase regional concentrations of ozone by exporting large amounts of precursors almost 1000 km NE of the city, and aerosol sources were shown to decrease photochemical activity nearby, through the direct effects of aerosols scattering and absorbing sunlight (Le Quesne et al., 2009; Mena-Carrasco et al., 2009). Santiago’s anthropogenic black carbon, and ozone precursors have been detected some 3000 km northwest of the city, over the southeast Pacific Ocean (Mena-Carrasco et al., 2010; Yang et al., 2011; Saide et al., 2012a). Anthropogenic black carbon can deposit on glaciers and mountain snowpack, contributing to melting (Ramanathan and Carmichael, 2008). Central Chile’s Metropolitan Region, which includes Santiago, has a total population of 7,007,620, representing 40% of the nation’s population. The urban area is located entirely in a basin between two mountain ranges, the Cordillera de Los Andes to the east and the Cordillera de la Costa to the west, which limit horizontal dispersion. Successive air pollution attainment plans have successfully reduced annual average PM2.5 concentrations from 69 lg/m3 in 1989 to 24 lg/m3 in 2013 (Fig. 1). Among the most important measures contributing to this reduction were banning open burning during the winter months, establishing standards and permanent restrictions on passenger and fleet motor vehicle emissions, overhauling the public transportation system, and importing cleaner fuels for industrial processes. Also, diesel fuel in Santiago currently has a limit of 30 ppm sulfur, making it among the cleanest in the world, and supporting the deployment of clean diesel technology (Jhun et al., 2013). During the winter, PM2.5 and PM10 standards are routinely exceeded, and for those reasons there are bad air day measures in place to reduce emissions from industry and motor vehicles when an exceedance is predicted. Colder winters are associated to increased frequency of bad air days due to both lower dispersion conditions and increased emissions (Mena-Carrasco et al., 2012). Little has been done on regional climate modeling in Chile (Cortes et al., 2012), much less the effect of climate forcings on future air quality episodes, nor the effect of aerosols on regional meteorology. This study identifies the potential effects of current criteria pollutant emissions and future climate on air quality episodes in Santiago using a regional coupled chemistry-climate model, independently quantifying impacts of synoptic conditions that cause low dispersion conditions as well as the effect

773

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

Annual mean concentration of PM2.5 (ug/m3)

Time series of annual PM2.5 concentration of historical sites in Santiago Banning agricultural burns during winter Overhaul of 3000 gross polluting buses

70

Historical Sites Chilean Standard US EPA Standard WHO guideline recommendation

Permanent restriction on non catalytic vehicule use 60

Banning open chimneys Wood burning bans on bad air days Natural gas imports from Argentina

50

Natural gas import curtailments

LNG terminal in Quintero ends curtailment

Transantiago

40

30

20

10 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

year

Figure 1. Time series of annual PM2.5 concentrations of the 4 historical measurement sites in the MACAM network (La Florida, ´ higgins), 1989–2013. Las Condes, Pudahuel and Parque O

of anthropogenic aerosols from Central Chile on regional meteorology and resultant surface temperature. 2. Materials and methods 2.1. Air quality modeling and emissions inventories In this study we use two configurations of WRF-Chem (Grell et al., 2005), a fully coupled online model that carries out both chemical and meteorological calculations simultaneously. To evaluate the impact of global climate change on local air quality in megacity Santiago, we apply a high-resolution tracer model (2 km horizontal resolution, 39 vertical levels) as employed for operational forecasting of PM2.5 for Santiago, Chile and validated operationally by the Chilean Pollution Prevention Agency. A full chemistry model that incorporates all chemistry and secondary aerosol formation is used to evaluate regional climate feedbacks from anthropogenic sources of aerosol in Chile at 12 km resolution. 2.2. WRF-Chem CO/PM2.5 tracer model We use WRF-Chem version 3.1.1 to forecast PM2.5 and PM10 concentrations for Santiago, Chile. This model correlates CO and PM using CO as a passive tracer (Saide et al., 2011). The model has been evaluated for 2008, 2011 and 2012 (Mena et al., 2013) and is used operationally for air quality management in Santiago in the Chilean Weather Service (DMC). The model forecast output is not yet available to the public, while air quality observations are available in the National Air Quality Monitoring System (SINCA) (sinca.mma.gob.cl). The model features updated spatial distribution of CO emissions using high resolution LANDSCAN 2008 population densities at roughly 900 m (Dobson et al., 2000) and a temporal distribution based on urban traffic modeling (Corvalan et al., 2005). The model uses fixed chemical boundary conditions, and NCEP Final Analysis (FNL) and Global Forecast System (GFS, Environmental Modeling Center, 2003) for meteorological boundary conditions, which includes a one-day initialization with FNL and a four-day forecast using GFS. Future scenarios are modeled using the National Center for Atmospheric Research Community Climate System Model version 3 (CCSM3) (Collins et al., 2006) for the IPCC Special Report on Emission Scenarios (SRES) A1B scenario, which is based on continued regional global economic growth and increasing CO2 emissions. The CCSM3 forcing is applied to all domains in WRF-Chem every 6 h using a nested modeling domain that

774

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

begins with a 18 km resolution spanning from 44 to 24 S and from 88 to 63 W (130  128 grid cells), scaling down to 6 km (6161) and then the 2 km resolution inner domain over Santiago (6173). Within WRF-Chem, the 2011 emissions rates are held constant for the 2050 simulations. 2.3. WRF-Chem full chemistry model We use a configuration of the WRF-Chem 3.3 model that was developed to study aerosol feedbacks in the semi-permanent stratocumulus deck during the VOCALS REx field campaign in coastal Chile and Peru (Bretherton et al., 2010, Wood et al., 2011). The model is applied to an extensive domain that includes most of continental Chile, spanning from Southern Peru to Puerto Montt in the South (12.8–41.5°S) and from the Pacific Ocean to the West, to portions of Argentina to the East (64–94°W). The model and configuration for this domain has undergone extensive evaluation of both chemical and meteorological parameters from surface, airborne, and satellite measurement platforms. The model has 75 vertical levels to reduce marine boundary layer and cloud base height biases found at lower resolutions (Wang et al., 2011). The Mellor-Yamada 2.5 scheme for PBL modeling was chosen, along with the Lin microphysics scheme. The Goddard shortwave radiation scheme was used to allow calculations that incorporate aerosol direct, indirect, and semi-direct climate interactions. No cumulus parameterizations were used as they were shown to overestimate liquid water path at mesoscale resolutions. Gaseous and aerosol species are simulated using the CBMZ gas phase chemical mechanism (Zaveri and Peters, 1999; Fast et al., 2006), and the 8-bin sectional MOSAIC aerosol module (Zaveri et al., 2008), and seawater DMS concentrations set to 2.8 nM according to the VOCA model specification.1 Updated emissions inventories were compiled from various sources including international databases from EDGAR (Olivier et al., 1994), pollution attainment plans, national mobile source emissions, and an updated large point source emissions inventory including smelters and power plants, as described elsewhere (Mena-Carrasco et al., 2012), for the full model domain, including all of continental Chile and portions of Peru, Bolivia, and Argentina. Biogenic emissions are modeled hourly using MEGAN (Guenther et al., 2006), while biomass burning emissions and fuel loadings are estimated based on Fire Information for Resource Management System MODIS fire counts (Davies et al., 2009), and dispersed hourly using a plume rise model (Freitas et al., 2006). Primary particulate matter emissions are speciated as 10% elemental carbon, 20% organic carbon, and 60% crustal aerosol. Chemical boundary conditions are provided by the NCAR MOZART model at a 6 h temporal resolution (Emmons et al., 2010), with a 6 day spin-up to reduce biases in initial conditions. Sea salt emissions are modeled online based on wind speed (Gong et al., 1997), and reduced by 50–90% to reduce high positive biases over coastal Chile (Saide et al., 2012). Windblown dust is not estimated due to poor model representation of crustal composition of Andean dust. Table 1 summarizes model parameters and inputs for this study. To estimate the role of climate forcings on the frequency of air quality episodes, we compare the number of air quality episodes calculated by the forecasting model for April–August 2011 and 2050. To estimate the anthropogenic impact of aerosol species on the radiative balance, and meteorological parameters, we run the model with all anthropogenic sources, including direct, indirect, and semidirect radiative effects in WRF-Chem, and compare to the model run including the same configuration but without anthropogenic emissions. 3. Results 3.1. WRF Tracer CO (PM2.5 forecasting) 3.1.1. Model evaluation The model was simulated for the April 1–September 1, 2011 and 2012 (Austral fall-winter) period, and compared to MACAM PM2.5 observations for all 11 sites of the network. But we focus on the site that concentrates the highest concentrations, Cerro Navia. The model is run with a 24 h spin-up, and 1

http://www.atmos.washington.edu/~mwyant/vocals/model/VOCA Model.

775

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781 Table 1 Summary of model scenarios for evaluating climate-air quality interactions. Model run

Period modeled

Model type

Aerosol effects

Meteorological boundary conditions

Grid

Horizontal resolution

Baseline AQ episodes Air pollution episodes Anthropogenic emissions

April–August 2011 April–August 2050 October 15– November 15, 2008 October 15– November 15, 2008

Tracer CO

No

FNL

61  73  39

2 km

Tracer CO

No

61  73  39

2 km

Full chemistry, MOSAIC aerosol module, 8 bins Without anthropogenic emissions.

Direct, indirect and semi-direct Direct, indirect and semi-direct

CCSM3 SRES A1B FNL

230  270  75

12 km

FNL

230  270  75

12 km

Baseline Full Chemistry

96 h of forecasting mode. We evaluate the last three days of forecasting, which represent model forecasts with a 24 h (1D), 48 h (2D) and 72 h (3D) window. Table 2 shows a summary of statistical parameters for 2011 and 2012. It is seen that model correlation coefficients with respect to observed values for Cerro Navia are somewhat similar. In 2011 forecasting with a longer window showed a lower correlation coefficient (decreasing from 0.7 to 0.59), while in 2012 model forecasting correlation coefficients were similar from 24 to 72 h forecasts. Also root mean square errors did not significantly change between 2011 and 2012, and between 24 and 72 h forecasts. When looking at contingency tables, and evaluating the model in its capacity to forecast specific thresholds (Good, under 50 lg/m3, Regular between 50 and 80 lg/m3, Alert between 80 and 110 lg/m3, and pre-emergency over 110 lg/m3) we see that for 2011 the model performed better in predicting air quality episodes, while in 2012 it was a two day forecasting that performed the best. 3.1.2. Effect of regional climate conditions on 2050 air quality episodes Modeled daily maximum 24 h mean PM2.5 concentrations are calculated for each day for April– August 2011 and 2050, and tabulated according to air quality episode categories. Analysis focuses on the Cerro Navia station, since it is the most sensitive indicator of urban population exposure, with both the highest number of exceedances and the most representative local residential population density. Table 3 shows that the model predicts a similar number of bad air days for 2011 and 2050, while total exceedances are reduced by 21%. There is also a 33% reduction in pre-emergency days, which represent extreme pollution events. Fig. 2 shows the mean effect of the climate scenario vs. the 2011 wintertime CO mean, which serves as a proxy for PM2.5. Central Santiago mean wintertime CO concentration for the present day (2011) peaks at 2.4 ppm in Cerro Navia, equivalent to 64 lg/m3 PM2.5, while 2050 mean CO concentrations for Table 2 Statistical evaluation of operational forecasting using WRF Tracer CO model, 2 km resolution, 2011– 2012, comparing model and observations for Cerro Navia station of the MACAM network. FB

MAE

RMSE

R (%)

% correct threshold

2011 1D 2D 3D

0.07 0.04 0.05

4.01 2.34 2.79

25.45 26.76 28.48

70 64 59

52 55 57

2012 1D 2D 3D

0.00 0.07 0.14

0.19 3.34 6.51

21.84 24.19 21.72

64 55 64

57 63 53

FB, fractional bias; MAE, mean absolute error; RMSE, root mean square error; 1D, one day forecast; 2D, two day forecast; 3D, three day forecast.

776

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781 Table 3 Comparison of the number of modeled PM2.5 air quality episodes (in days), April– August for 2011 and 2050. Santiago, Chile, using the WRF-Chem CO-Tracer Model, focusing on the Cerro Navia site. Category

2011

2050

Regular Alert Pre-emergency Emergency Total bad air days Total Exceedances,

50 29 12 0 41 91

33 31 8 0 39 72

Episodes based on modeled maximum 24 h PM2.5 means. Regular: 50–79 lg/m3, Alert: 80–109 lg/m3, pre-emergency 110–170 lg/m3, emergency: 170 lg/m3 and up. Bad air days (sum of alert, pre-emergency, emergency), Total exceedances: above 50 lg/m3.

Figure 2. Left: Mean wintertime CO concentrations (ppm) for base case (April–August 2011) Right: Mean wintertime CO concentrations (ppm) for 2050 Scenario (CCSM 3.0 A1B SRES scenario). Dot represents Cerro Navia station, wind barbs in knots.

peak at 1.9 ppm (51 lg/m3 PM2.5), roughly 20% less, assuming that emissions do not change under 2050 conditions, and that the relationship between CO and PM2.5 is also unaltered. Wind directions and magnitudes are not changed significantly. These results are consistent with the effect on air quality episodes, but probably more important as this wintertime mean concentration is more representative of chronic exposure to pollution CO and PM2.5 in Santiago are highly correlated (roughly R = 0.9) and even higher in high pollution episodes, as is shown in Saide et al. (2011). Fig. 3 shows the mean modeled temperature for 2011 compared to 2050 (CCSM3.0 A1B SRES Scenario) for the April–August wintertime modeling period. The 2050 average temperature in the city of 13 °C represents an increase of 5 °C over the present day mean temperature of 8 °C. This higher mean temperature reduces the severity of thermal inversions and increases vertical diffusion from surface pollution sources. The 2050 simulation also shows a shift in wind speeds, reducing stagnation near Cerro Navia, shifting it to other locations in Santiago. As land cover is held constant, the local urban heat island and temperature differential from the surrounding mountains is unchanged. These calculations quantitatively show the effect of future climate on dispersion, and its effect on air quality. There are limited dynamical studies downscaling climate models for Santiago, Chile. Most analysis is carried out from coarser global climate models. However one statistical downscaling model

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

777

Figure 3. Mean wintertime modeled temperature. Left: WRF-Chem Tracer CO April–August 2011 model using FNL boundary conditions. Right: WRF-Chem Tracer CO April–August 2050 model using CCSM 3.0 A1B SRES climate scenario.

comparing A2 and B1 scenarios for 2050 (using the NASA GISS model) shows a wintertime increase of maximum temperature of 5 degrees, while minimum temperatures are increased in 2 degrees (Cortes 2012). Dynamical downscaling of climate models for Santiago has not occurred before, and while this version of WRF does explicitly account for urban heating processes, high resolution land use compared to coarser land use in climate models may explain larger warming. 3.2. Anthropogenic aerosol radiative forcing effects on regional meteorology The full chemistry regional model was run for the VOCALS-Rex period (October 15–November 15, 2008 (Wood et al., 2011). The model was evaluated extensively and compared to surface sites, aircraft measurements, and satellite observations (Yang et al., 2011; Saide et al., 2012a,b), showing that it represented chemical and meteorological parameters relevant to radiative feedback modeling, such as secondary aerosol formation (sulfate), cloud water content, cloud base and top height, wind speed, temperature, aerosol number concentrations, and cloud effective radius. Model and satellite comparisons showed excellent representation of how anthropogenic aerosol affected the Chilean marine stratocumulus, making cloud droplets smaller (Saide et al., 2012b). Here, we explicitly calculate how anthropogenic aerosols impact regional skin temperature. Fig. 4 shows two images: The image of the left side shows the mean modeled surface skin temperature for the VOCALS period using the full aerosol feedback calculations and the image of the right side shows the difference on surface temperature caused by the effect of these feedbacks. Coastal cooling is found near large sources of sulfur from power plants and smelters, with a mean temperature decrease of 1 °C. Large portions of Chile undergo net cooling of up to 0.4 °C for coastal locations and 0.2–0.3 °C inland. It also shows a net warming from 0.2 to 0.4 °C near central Chile where Santiago and other population centers are located. Inland warming is highest (0.4 °C) over the Andes Mountains Range. The model simultaneously estimates aerosol feedback effects, so it is difficult to assess if is due mostly to direct or indirect effects. Direct heating can be attributed to black carbon emissions in central Chile, mostly from Santiago which can be transported towards the Andes mountain range (Fig. 5), where glaciers that provide water for Central Chile are located (Le Quesne et al., 2009). Previous research has shown that anthropogenic sources of aerosol affect cloud properties, decreasing cloud effective radius, liquid water path, which alters the energy budget (Yang et al., 2011; Saide et al., 2012a,b) and may lead to brighter clouds that reflect more, causing offshore and coastal cooling (Twohy et al., 2013) due to the Twomey effect (Twomey 1974). Also large sources of sulfur (smelters

778

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

Figure 4. Left: modeled October 15–November 15, 2008 surface skin temperature for full WRF-Chem model. Right: Modeled anthropogenic aerosol effects on surface temperature.

Figure 5. Mean modeled total black carbon Left: 12 km total domain. Right: Zoomed in Central Chile.

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

779

Figure 6. Mean modeled total sulfate.

and power plants), which are frequently located on the coast, form sulfate, which reflects radiation (Fig. 6). Observed aerosol concentrations during VOCALS also showed that offshore anthropogenic aerosol was largely sulfate, with much smaller black carbon concentrations. Most smelters and power plants are located along the coast, and prevailing synoptic conditions make continental outflow efficient. Black carbon emissions, on the other hand, tend to be transported toward the Andes, near the source regions. 4. Conclusion An operationally validated air quality forecast model is used to compare air quality episodes in 2011 with 2050 using a climate model for meteorological boundary conditions. The modeled A1B climate scenario for 2050 shows a 5 °C increase in average wintertime temperatures in Santiago, Chile over the present day, and also causing a shift in stagnant horizontal dispersion from Northwest to Southeast Santiago. The result of these conditions causes a 20% reduction in wintertime means of PM2.5, and 21% of 24 h PM2.5 exceedances, and a 33% reduction of extreme air quality episodes. This analysis does not consider the probable and potentially significant reduction in heating-related

780

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

emissions, which are proportional to heating degree days. Without any changes to local land cover, this analysis neglects any increases in urban heat island and urban chimney effects that might also contribute to reduced stagnation. Thus, this simulation represents a conservative estimate of the changes in local temperature, wind speed, and their impacts on megacity air quality. Present-day (2011) anthropogenic aerosol radiative feedbacks are responsible for increases in inland surface temperature (0.2–0.4 °C) over Santiago and populous Central Chile, and a slight increase in sea surface temperature off the coast of Chile, with local temperature reductions (0.4–1 °C) near large sources of sulfur, particularly coastal copper smelters. Black carbon emitted in Santiago and Central Chile is transported over the Andes Mountains. The change in atmospheric radiative forcing alone causes a surface warming over the Andes of up to 0.4 °C, which can speed melting of glacial sources of water for Central Chile, and represents an additional source of warming for the Andes due to black carbon deposition and increase in snowmelt. These results find that Santiago megacity and the Andes of Central Chile are likely to warm much more than other parts of the region and the globe, with an impact an order of magnitude stronger than those due to local climate forcing by Chilean emissions of absorbing aerosols. Results suggest that reducing urban black carbon emissions can have a direct benefit in mitigating regional and megacity warming and glacial melting in Chile as global temperatures rise, alongside reducing local particulate matter and its associated health effects. Acknowledgment This research was funded by CONICYT/Fondap/15110017 and EPA STAR R835037. References Bretherton, C.S., Wood, R., George, R.C., Leon, D., Allen, G., Zheng, X., 2010. Southeast Pacific stratocumulus clouds, precipitation and boundary layer structure sampled along 20 degrees S during VOCALS-REx. Atmos. Chem. Phys. 10 (21), 10639–10654. Collins, W.D., Bitz, C.M., Blackmon, M.L., Bonan, G.B., Bretherton, C.S., Carton, J.A., Chang, P., Doney, S.C., Hack, J.J., Henderson, T.B., Kiehl, J.T., Large, W.G., McKenna, D.S., Santer, B.D., Smith, R.D., 2006. The Community Climate System Model version 3 (CCSM3). J. Clim. 19 (11), 2122–2143. Cortes, G., Schaller, S., Rojas, M., Garcia, L., Descalzi, A., Vargas, L., McPhee, J., 2012. Assessment of the current climate and expected climate changes in the Metropolitan Region of Santiago de Chile. Helmholtz Centre for Environmental Research UFZ-Report 03/2012. . Corvalan, R.A., Osses, M., Urrutia, C.M., Gonzalez, P.A., 2005. Estimating traffic emissions using demographic and socio-economic variables in 18 Chilean urban areas. Popul. Environ. 27 (1), 63–87. Davies, D.K., Ilavajhala, S., Wong, M.M., Justice, C.O., 2009. Fire information for resource management system: archiving and distributing MODIS active fire data. IEEE Trans. Geosci. Remote Sens. 47 (1), 72–79. Dobson, J.E., Bright, E.A., Coleman, P.R., Durfee, R.C., Worley, B.A., 2000. LandScan: a global population database for estimating populations at risk. Photogr. Eng. Remote Sensing 66 (7), 849–857. Dockery, D.W., Stone, P.H., 2007. Cardiovascular risks from fine particulate air pollution. N. Engl. J. Med. 356 (5), 511–513. Emmons, L.K., Walters, S., Hess, P.G., Lamarque, J.F., Pfister, G.G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laeipple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S.L., Kloster, S., 2010. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67. doi: 10.5194/gmd-3-43-2010. Environmental Modeling Center: The GFS atmospheric model, NCEP Office Note, 442, 2003. Fast, J.D., Gustafson, W.I., Easter, R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., Grell, G.A., Peckham, S.E., 2006. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorologychemistry-aerosol model. J. Geophys. Res. Atmos. 111(D21). Freitas, S.R., Longo, K.M., Andreae, M.O., 2006. Impact of including the plume rise of vegetation fires in numerical simulations of associated atmospheric pollutants. Geophys. Res. Lett. 33 (17). Glaeser, E.L., Kahn, M.E., 2010. The greenness of cities: carbon dioxide emissions and urban development. J. Urban Econ. 67 (3), 404–418. Gong, S.L., Barrie, L.A., Blanchet, J.P., 1997. Modeling sea-salt aerosols in the atmosphere 1. Model development. J. Geophys. Res. D: Atmos. 102 (3), 3805–3818. Gordon, P., Richardson, H.W., 1997. Are compact cities a desirable planning goal? J. Am Plan. Assoc. 63 (1), 95–106. Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., Eder, B., 2005. Fully coupled ‘‘online’’ chemistry within the WRF model. Atmos. Environ. 39 (37), 6957–6975. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6 (11), 3181–3210. Henderson, V., 2002. Urbanization in developing countries. World Bank Res. Obs. 17 (1), 89–112. Jacob, D.J., Crawford, J.H., Kleb, M.M., Connors, V.S., Bendura, R.J., Raper, J.L., Sachse, G.W., Gille, J.C., Emmons, L., Heald, C.L., 2003. Transport and chemical evolution over the Pacific (TRACE-P) aircraft mission: design, execution, and first results. J. Geophys. Res. D: Atmos. 108 (20).

M. Mena-Carrasco et al. / Urban Climate 10 (2014) 771–781

781

Jhun, I., Oyola, P., Moreno, F., Castillo, M.A., Koutrakis, P., 2013. PM2.5 mass and species trends in Santiago, Chile, 1998 to 2010: The impact of fuel-related interventions and fuel sales. J. Air Waste Manage. Assoc. 63 (2), 161–169. Le Quesne, C., Acuña, C., Boninsegna, J.A., Rivera, A., Barichivich, J., 2009. Long-term glacier variations in the Central Andes of Argentina and Chile, inferred from historical records and tree-ring reconstructed precipitation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 281 (3–4), 334–344. Mena, M., Delgado, R., Hernandez, P., Saide, P., Cienfuegos, R., Pinochet, P.I., Molina, LT., Carmichael, G.R., 2013. Developing a multi hazard air quality forecasting model for Santiago, Chile. Presented at 2013 Meeting of the Americas, AGU, Can Cun, Mexico, May 14–17, A32A–05. Mena-Carrasco, M., Carmichael, G.R., Campbell, J.E., Zimmerman, D., Tang, Y., Adhikary, B., D’Allura, A., Molina, L.T., Zavala, M., Garcia, A., Flocke, F., Campos, T., Weinheimer, A.J., Shetter, R., Apel, E., Montzka, D.D., Knapp, D.J., Zheng, W., 2009. Assessing the regional impacts of Mexico City emissions on air quality and chemistry. Atmos. Chem. Phys. 9 (11), 3731–3743. Mena-Carrasco, M., Carmichael, G.R., Carmichael, Spak, S., Molina, L.T., Saide, P., 2010. Characterizing anthropogenic sources of pollution and their influence on regional air quality and meteorology during the VOCALS-REX experiment. Presented at 2010 Fall Meeting, AGU, San Francisco, Calif., 13–17 Dec., A51A–0059. Mena-Carrasco, M., Oliva, E., Saide, P., Spak, S.N., de la Maza, C.B., Osses, M., Tolvett, S.N., Campbell, J.E., Tsao, T.E.C.-C., Molina, L.T., 2012. Estimating the health benefits from natural gas use in transport and heating in Santiago Chile. Sci. Total Environ. 429, 257–265. Miller, K.A., Siscovick, D.S., Sheppard, L., Shepherd, K., Sullivan, J.H., Anderson, G.L., Kaufman, J.D., 2007. Long-term exposure to air pollution and incidence of cardiovascular events in women. N. Engl. J. Med. 356 (5), 447–458. Niemczynowicz, J., Iwra, M., 1996. Megacities from a water perspective. Water Int. 21 (4), 198–205. Olivier, J.G.J., Bouwman, A.F., Vandermaas, C.W.M., Berdowski, J.J.M., 1994. Emission database for global atmospheric research (Edgar). Environ. Monit. Assess. 31 (1–2), 93–106. Parrish, D.D., Zhu, T., 2009. Clean air for megacities. Science 326 (5953), 674–675. Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and longterm exposure to particulate air pollution – epidemiological evidence of general pathophysiological pathways of disease. Circulation 109 (1), 71–77. Ramanathan, V., Carmichael, G., 2008. Global and regional climate changes due to black carbon. Nat. Geosci. 1 (4), 221–227. Saide, P., Spak, S.N., Carmichael, G.R., Mena-Carrasc, M.A., Yang, Q., 2012a. Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx. Atmos. Chem. Phys. 12 (6), 3045–3064. Saide, P.E., Carmichael, G.R., Spak, S.N., Minnis, P., Ayers, J.K., 2012b. Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number. Proc. Natl. Acad. Sci. USA 109 (30), 11939–11943. Saide, P.E., Carmichael, G.R., Spak, S., Gallardo, L., Osses, A., Mena-Carrasco, M.A., 2011. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model. Atmos. Environ. 45 (16), 2769–2780. Saide, P.E., Spak, S.N., Carmichael, G.R., Mena-Carrasco, M.A., Yang, Q., Howell, S., Leon, D.C., Snider, J.R., Bandy, A.R., Collett, J.L., Benedict, K.B., de Szoeke, S.P., Hawkins, L.N., Allen, G., Crawford, I., Crosier, J., Springston, S.R., 2012. Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx. Atmos. Chem. Phys. 12 (6), 3045– 3064. Samet, J.M., Dominici, F., Curriero, F.C., Coursac, I., Zeger, S.L., 2000. Fine particulate air pollution and mortality in 20 US Cities, 1987–1994. N. Engl. J. Med. 343 (24), 1742–1749. Seaton, A., Macnee, W., Donaldson, K., Godden, D., 1995. Particulate air-pollution and acute health-effects. Lancet 345 (8943), 176–178. Twohy, C.H., Anderson, J.R., Toohey, D.W., Andrejczuk, M., Adams, A., Lytle, M., George, R.C., Wood, R., Saide, P., Spak, S., Zuidema, P., Leon, D., 2013. Impacts of aerosol particles on the microphysical and radiative properties of stratocumulus clouds over the southeast Pacific Ocean. Atmos. Chem. Phys. 13 (5), 2541–2562. Twomey, S., 1974. Pollution and the planetary albedo. Atmos. Environ. (1967) 8 (12), 1251–1256. Wang, S., O’Neill, L.W., Jiang, Q., De Szoeke, S.P., Hong, X., Jin, H., Thompson, W.T., Zheng, X., 2011. A regional real-time forecast of marine boundary layers during VOCALS-REx. Atmos. Chem. Phys. 11 (2), 421–437. Wood, R., Mechoso, C.R., Bretherton, C.S., Weller, R.A., Huebert, B., Straneo, F., Albrecht, B.A., Coe, H., Allen, G., Vaughan, G., Daum, P., Fairall, C., Chand, D., Klenner, L.G., Garreaud, R., Grados, C., Covert, D.S., Bates, T.S., Krejci, R., Russell, L.M., de Szoeke, S., Brewer, A., Yuter, S.E., Springston, S.R., Chaigneau, A., Toniazzo, T., Minnis, P., Palikonda, R., Abel, S.J., Brown, W.O.J., Williams, S., Fochesatto, J., Brioude, J., Bower, K.N., 2011. The VAMOS ocean-cloud-atmosphere-land study regional experiment (VOCALS-REx): goals, platforms, and field operations. Atmos. Chem. Phys. 11 (2), 627–654. Yang, Q., Gustafson, W.I., Fast, J.D., Wang, H., Easter, R.C., Morrison, H., Lee, Y.N., Chapman, E.G., Spak, S.N., Mena-Carrasco, M.A., 2011. Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem. Atmos. Chem. Phys. 11 (23), 11951–11975. Zaveri, R.A., Peters, L.K., 1999. A new lumped structure photochemical mechanism for large-scale applications. J. Geophys. Res. Atmos. 104 (D23), 30387–30415. Zaveri, R.A., Easter, R.C., Fast, J.D., Peters, L.K., 2008. Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. 113, D13204.