Atmospheric Environment 190 (2018) 195–208

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Atmospheric Environment

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Impact of residential combustion and transport emissions on air pollution in during winter T

∗ Andrea Mazzeoa, Nicolás Huneeusa,b, , César Ordoñeza, Andrea Orfanoz-Cheuquelafa, Laurent Menutc, Sylvan Maillerc, Myrto Valaric, Hugo Denier van der Gond, Laura Gallardoa,b, Ricardo Muñozb, Rodrigo Donosoe, Maurico Galleguillosa, Mauricio Ossesa,f, Sebastian Tolvettg a Center for Climate and Resilience Research (CR)2, FONDAP 1511009, Departamento de Geofísica, U. de Blanco Encalada, 2002, Santiago, Chile b Departamento de Geofísica, Faculdad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile c Laboratoire de Météorologie Dynamique, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université, Paris-Saclay, Sorbonne Universités, UPMC Univ Paris 06, Paris, France d Department of Climate, Air and Sustainability, TNO Utrecht, the Netherlands e Universidad de Santiago de Chile (USACH), Santiago, Chile f Universidad Técnica Federico Santa Maria, Santiago, Chile g Escuela de Mecánica, Universidad Tecnológica Metropolitana (UTEM), Santiago, Chile

ARTICLE INFO ABSTRACT

Keywords: Santiago (33.5°S, 70.5°W), the capital of Chile, is frequently affected by extreme air pollution events during Air quality wintertime deteriorating air quality (AQ) and thus affecting the health of its population. Intense residential PM2.5 heating and on-road transport emissions combined with poor circulation and vertical mixing are the main factors NOX responsible for these events. A modelling system composed of a chemistry-transport model (CHIMERE) and a Residential emissions meteorological model (WRF) was implemented to assess the AQ impacts of residential and transportation sources On-road emissions in the Santiago basin. A two-week period of July 2015 with various days with poor AQ was simulated focusing Mitigation policies on the impact on AQ with respect to fully inhalable particles (PM2.5) and nitrogen oxides (NOX). Three emission scenarios, within the range of targeted reductions of the decontamination plan of Santiago, were tested; namely 50% reduction of residential emission, 50% reduction of transport emissions and the combination of both. An additional scenario decreasing transport emissions in 10% was carried out to examine whether a linear de- pendence of surface concentrations on changes in emissions exists. The system was validated against surface and vertically resolved meteorological measurements. The model reproduces the daily surface concentration variability from the AQ monitoring network of Santiago. However, the model not fully captures the emissions variations inferred from the observations which may be due to missing sources such as resuspension of dust. Results show that, during the period studied, although both residential and transportation sources contribute to observed AQ levels in Santiago, reducing transport emissions is more effective in terms of reducing the number of days with pollution events than decreasing residential combustion. This difference in impact is largely due to the spatial distribution of the emission sources. While most of the residential combustion is emitted in the outskirts of the city, most of the transport emissions occur within the city, where most of the stations from AQ monitoring network of Santiago are located. As can be expected, the largest improvement of AQ in Santiago is achieved by the combined reduction of emissions in both sectors. Sensitivity analysis with 10% reduction in

transport emissions reveals a linear behavior between emissions and concentrations for NOX and approximate

linear behavior for PM2.5. The absence of secondary aerosols formation and dust resuspension in the current simulation could explain this deviation from linearity for fine particles. Nevertheless, it suggests that the results can be used for mitigation policies with emissions reductions below the 50% used in this study.

∗ Corresponding author. Center for Climate and Resilience Research (CR)2, FONDAP 1511009, Departamento de Geofísica, U. de Chile, Blanco Encalada, 2002, Santiago, Chile. E-mail address: [email protected] (N. Huneeus). https://doi.org/10.1016/j.atmosenv.2018.06.043 Received 16 December 2017; Received in revised form 20 June 2018; Accepted 28 June 2018 1352-2310/ © 2018 Elsevier Ltd. All rights reserved. A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

1. Introduction For the former, this will be achieved by progressive passage of public and private vehicles to more modern “EURO” engine versions whereas During fall and winter period, extreme events of air pollution fre- for the latter, a ban on the use of wood for combustion in the entire quently affect the city of Santiago, capital of Chile (33.5°S, 70.5°W, metropolitan region and the progressive transition to other types of 600 m a.s.l.), with strong impact on public health (Franck et al., 2014; fuels (e.g. electricity, natural gas) is envisaged as well as the replace- Mullins and Bharadwaj, 2015). Although the number of these extreme ment of current stoves by more efficient and cleaner ones. events and average concentration levels decreased over the last decades The paper is organized as follows: Section 2 describes the main (Barraza et al., 2017; Saide et al., 2016; Gallardo et al., 2018), such features of the modelling system, the different observations used to events still occur and they are a matter of public concern. These events evaluate its performance and gives a short description of the air quality consist of high levels of particulate matter (PM10 and PM2.5) reaching conditions observed during the simulated period. In Section 3 we pre- 3 3 hourly concentrations of 400–500 μg/m of PM10 and 200–300 μg/m sent the validation of the modelling system, the dispersions patterns of PM2.5. within the Santiago basin and the assessment of the impact of reducing The composition of particulate matter in Santiago has been ana- residential combustion and/or road transport emissions. Finally, the lyzed by Carbone et al. (2013) showing the predominance of organics main conclusions are presented in Section 4. (59%), while nitrate (14%) and ammonium (12%) dominate the non- organic compounds. Barraza et al. (2017) have quantified the con- 2. Methodology tribution of emission sectors through a source attribution technique suggesting that in 2014, on an annual base, particle composition can be In this work we use the Weather Research and Forecasts Model traced back to motor vehicles (37%), industrial sources (19%), copper (WRF), version 3.7.1 (Skamarock et al., 2008), with the CTM CHIMERE, smelters (14%), wood burning (12%), coastal sources (10%), and urban version 2014b (Menut et al., 2013), to simulate the winter period from dust (3%). During the winter period, PM contribution from re- 2.5 15th to 28th of July 2015. The period has been chosen on one hand sidential combustion increases up to an average of 30% of the total because of the high concentrations of PM observed during these days amount of PM . 2.5 2.5 and, on the other hand, because of vertical atmospheric measurements A large contribution to PM emissions is represented by residential available for those days due to a campaign conducted in Santiago. A list heating and road transportation, estimated to emit respectively up to of the configurations adopted for each model is provided in Table 1. 1800 and 2700 tons/year of PM in the year 2012 (USACH, 2014). 2.5 The evaluation of the modeling system is done by assessing first the These emissions are not evenly distributed throughout the year, espe- meteorological model (Section 2.2) and then the CTM (Section 2.3). cially the residential combustion emissions occur mostly during the Both models are evaluated against available surface and profile ob- winter season from May to September (Mena-Carrasco et al., 2012). servations. Model performance is assessed through standard statistics: Air pollution in Santiago is strongly influenced by the complex to- mean bias, root mean square error (RMSE) and Pearson correlation pography surrounding it. The southern Andes (4500 m a.s.l.) in the east, coefficient (R). Normalized versions of the mean bias and RMSE (MNB the coastal range (1500 m a.s.l.) in the west and transversal mountain and NRMSE, respectively) are used to evaluate WRF's performance to chains in the north and in the south surround the basin contributing to reproduce surface and profile observations (Monteiro et al., 2007; the occurrence of poor ventilation conditions. These topographic con- Vautard et al., 2007; Borrego et al., 2008). Finally, different emissions ditions are further strengthened by the influence of the Pacific sub- scenarios for Santiago are tested with the modelling system (Section tropical high pressure system leading to poor ventilation and limited 2.4). vertical mixing (Muñoz and Alcafuz, 2012). Moreover, thermal inver- sions are often intensified by recurrent sub-synoptic weather patterns known as coastal lows and described by Rutllant and Garreaud (1995) 2.1. Observations and Garreaud et al. (2002). These conditions prevent the dispersion of pollutants (Garreaud and Rutllant, 2003) leading to accumulation of In order to assess the models ability to simulate the dispersion of ff gases (CO, NOX and VOCs) and aerosols (PM10 and PM2.5)(Villalobos pollutants in Santiago, we used observations from two di erent net- et al., 2015; Gramsch et al., 2006, 2014; Gallardo et al., 2002; Saide works (Fig. 1). et al., 2011). Surface meteorological parameters (temperature, relative humidity Since 2000, several numerical models have been applied with dif- at 2 m, wind direction and speed at 2 m and 10 m) from the Chilean ferent approaches in Chile to study air quality. The first studies were weather service (DMC, from Spanish: Direccion Meteorolgica de Chile - focused on the relationship between emissions of CO, NOX,SOX and www.meteochile.gob.cl/) are used. This network consists of eight PM10 and their impact on Santiago's air quality (Jorquera, 2002a; b; Gallardo et al., 2002; Olivares et al., 2002; Schmitz, 2005). More recent Table 1 Main configuration options applied in the meteorological model (WRF) and works have focused on forecasting PM2.5 concentrations in Santiago using CO as a tracer (Saide et al., 2011, 2016) while others have made chemical transport model (CHIMERE) from the modelling system used in this work. predictions of hourly average concentrations of PM2.5 in downtown Santiago based on statistical approaches (Perez et al., 2000; Pérez and Process type Parameters References Gramsch, 2016). WRF A natural next step is to use numerical models simulating simulta- Initial and Boundaries GFS final analysis - Wu et al. (2002) neously both particulate matter and gaseous pollutants in order to have Conditions FNLs a complete description of their formation and dispersion patterns in the PBL Parametrization MYNN2 Nenes et al. (1998) Santiago basin. Land Use GEPLU Zhao et al. (2016) The aim of this work is to set-up a chemistry and transport model Radiation Scheme CAM Collins et al. (2004) CHIMERE (CTM) for the Santiago region and to assess the impact of potential Chemistry SAPRC07-A Carter (2010) reduction of residential combustion and/or transport emissions of PM2.5 Photolysis Rate Constants TUV Madronich et al. (1998) and NOX on Santiago's air quality. These emissions scenarios, explained Gas/aerosol partition ISORROPIA Nenes et al. (1998) in Section 2.4, are within the range of the emissions reduction targeted Horizontal and Vertical Van Leer van Leer (1979) in the current decontamination plan for Santiago from the Ministry of Transport Initial and Boundaries LMDz-INCA Hauglustaine et al. the Environment (MMAplan, 2014). In particular, the plan provides for Conditions (2014) significant emission reductions in the transport and residential sector.

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Fig. 1. Location of the eleven stations from the air quality monitoring network of Santiago (red dots): Cerrillos (CRR), (CRN), El Bosque (ELB), Independencia (IDP), La Florida (LFD), (LCD), (PDH), (PTO), O'Higgins (OHG), (QLC) and (TLG). In addition the location of the five stations from the Chilean weather service (blue dots) is also included: Lo Pinto (LPT), (PRD), San Pablo (SPB), El Paico (EPC), San Jos (SJS). Finally, location of the Ceilometer at the DGF (yellow dot) and teth- ered balloon and radiosonde at DMC site (green dot) are also indicated. (For interpretation of the refer- ences to colour in this figure legend, the reader is referred to the Web version of this article.)

stations in Santiago as well as the surrounding areas of the city. Five of uses it for regional forecasts analysis 5 days ahead and by weather them, the closest to the city, have been used in the present work. agencies all over the world with thousands of users (Powers et al., The Ministry of Environment (MMA, from Spanish: Ministerio del 2017). Medio Ambiente) operates and maintains an air quality network of The general set-up of WRF follows the choices described in Saide eleven stations in Santiago measuring continuously the surface con- et al. (2016) for the Planetary Boundary Layer (PBL) scheme and initial centrations of various air pollutants as well as standard meteorological and boundary conditions summarized in Table 1. The MYNN2 parameters (http://sinca.mma.gob.cl). This network provides hourly (Nakanishi and Niino, 2004) PBL scheme has been applied with 24 concentrations of sulfur dioxide (SO2), nitrogen oxides (NO, NO2 and levels below 1000 m and 22 up to the highest level at 50 hPa. Initial and NOX), carbon monoxide (CO), ozone (O3), and particulate matter PM10 boundary conditions adopted in this work are from GFS final analysis and PM2.5 (Gramsch et al., 2006). Surface meteorological data are also (Wu et al., 2002). Also, in order to obtain a more realistic snow cover used in this study with 2 m temperature and relative humidity, 10 m for the Santiago basin in winter, a spin-up simulation of 30 days has wind speed and direction. been performed. In addition to measurements from both networks, vertical mea- A high-resolution (30 m) land use map has been developed by the surements of temperature, relative humidity and wind speed at down- Geomatics and landscape ecology Laboratory (GEP) of the University of town Santiago from a monitoring campaign from the 18th to the 26th of Chile based on Landsat Imagery from 2014 (Zhao et al., 2016)(hereafter July are also used in the present study (Huneeus, 2017). During this GEPLU). This map has been aggregated to the model resolution and campaign a tethered balloon was launched up to an altitude of 1000 m adapted to the MODIS classification of 20 levels of land use categories and a.g.l every 3 h and a radiosonde was also launched daily at 5:00 p.m. used in all the simulations performed by WRF. Sensitivity tests were (local time) at the DMC site (Fig. 1). conducted assessing the performance for different PBL schemes, surface Finally, ceilometer (Vaisala CL31) data providing the vertical dis- model and longwave radiation scheme (not shown). Based on the results, tribution of aerosols are also used in this study. This instrument has the Rapid Radiative Transfer Model (RRTM) scheme (Mlawer et al., 1997) been operating continuously since March 2007 on the roof of the used in Saide et al. (2016) was replaced with the CAM (Collins et al., Department of Geophysics of the University of Chile (site DGF - Fig. 1) 2004) longwave radiation scheme. A summary of the parameters adopted and provides mean profiles of range-corrected attenuated backscatter for WRF configuration is found in Table 1. every 4–8 s with vertical resolution of 20 m (Muñoz and Undurraga, A system of three nested-domains was implemented in WRF with a 2010). resolution of 18 km (hereafter STGO1), 6 km (hereafter STGO2) and 2 km (hereafter STGO3) with the inner domain centered on the city of Santiago (Fig. 2). The size of STGO1 has been chosen to resolve the 2.2. Meteorological model WRF regional meteorology while STGO2 and STGO3 have been chosen in order to represent and the Metropolitan Region of San- WRF is a numerical weather prediction system designed for atmo- tiago (MR), respectively. The inner domain (STGO3) includes the spheric simulations (Skamarock et al., 2008). It is used for research and complex topography of the basin including the Andean Cordillera to the for multiple operational applications simulating across scales from tens east and the Coastal Range to the west, as well as neighboring cities of of meters to thousands of kilometers. The model has been developed Valparaiso on the coast (33.0°S, 71.6°W) and Rancagua to the south with the collaboration of the National Oceanic and Atmospheric Ad- (34.2°S, 70.7°W). ministration of U.S. (NOAA - http://www.noaa.gov/) that currently

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has been adopted for the inner domains (STGO3 and STGO2). This approach permits to provide boundary conditions for the inner domains as calculated by their respective parent domain (STGO3 two-way- nested with STGO2 and STGO2 with STGO1) at every time step.

2.3. Atmospheric chemistry model CHIMERE

CHIMERE, version 2014b, is an eulerian off-line three-dimensional CTM able to reproduce the gas-phase chemistry, aerosols formation, transport and deposition (Menut et al., 2013). The model has been used in several research applications and inter-comparison studies on ozone

and inhalable particles (PM10) from the continental scale (Zyryanov et al., 2012; Bessagnet et al., 2016), to the urban scale (Vautard et al., 2007; Van Loon et al., 2007). Furthermore, it is used for pollution event analysis, scenario studies (Markakis et al., 2015), operational forecasts as well as for impact studies of pollution on health (Valari and Menut, 2010) and vegetation (Anav et al., 2011). The configuration adopted in this work for CHIMERE uses initial and boundary conditions from a global three-dimensional chemistry- climate model (LMDz-INCA, Hauglustaine et al.(2014)), both for gas- eous and for aerosols species (Table 1). No relevant sources are up-wind and outside of the larger domain (Saide et al., 2011) and therefore no major impact is expected on the model performance to simulate con- centration from the choice of the boundary conditions. The complete chemical mechanism applied in all simulations is SAPRC07-A (Carter, 2010). The mechanism permits the description of more than 275 re- actions of 85 different species. For the particle/gases partitioning of semi-volatile inorganic species, the thermodynamic equilibrium model ISORROPIA (Nenes et al., 1998) is used. Finally, horizontal and vertical diffusion schemes are calculated using the Van Leer scheme (van Leer, 1979). A three day spin-up period has been applied in order to favour the stabilization of the model to the initial and boundary conditions. The domains chosen for CHIMERE are the intermediate domains used for WRF: STGO2 nested with STGO3.We underline that the size of the largest domain, STGO2, was defined to include the possible recircula- tion processes at regional and local level. A vertical resolution of 20 pressure levels is applied with a top pressure layer at 300 hPa (ap- proximately 9000 m) and 11 vertical levels in the first 2000 m. The thickness of the layers increases exponentially from the first level at 30 m up to 200 m after which a constant thickness is considered.

2.4. Emissions

The anthropogenic emission inventory used in this work is a com- bination of a local inventory for the Metropolitan Region (hereafter USACH; de Santiago USACH (2014)) and the global inventory HTAPv2.2 (Janssens-Maenhout et al., 2015) elsewhere. HTAPv2.2 is a global inventory with a resolution of 0.1 × 0.1°

providing yearly and monthly fluxes of gases (CO, NO, NOX,SOX,NH3, NMVOCs) and aerosols (BC, OC, PM2.5 and PM10) for the years 2008 and 2010. In this work we use the estimated emissions for the year 2010 (Janssens-Maenhout et al., 2015). USACH is a high resolution (1 × 1 km) emission inventory, pro-

viding yearly total emissions for gases (CO, NO, NO2,SO2,CO2,NH3, Fig. 2. Domains used for WRF and CHIMERE simulations with resolutions of NMVOCs) and aerosols (PM2.5 and PM10) for the year 2012. We note 18 × 18 km referred to as STGO1 in the text (above), 6 × 6km reffered to as that PM10 emissions for the transport sector are calculated as 20% STGO2 (middle) and STGO3 corresponding to 2 × 2 km (below). The red higher than PM emissions while resuspension of dust is not included squared area (lower figure) shows the city domain of Santiago used for sce- 2.5 in the estimate (USACH, 2014). This inventory was used to design and narios analysis described in Section 3.3. (For interpretation of the references to fi colour in this figure legend, the reader is referred to the Web version of this de ne the current decontamination plan of PM2.5 for the Metropolitan article.) Region. Its use as emission baseline within Santiago allows to explore the impact of different mitigation measures proposed in the deconta- mination plan. The absence of an official and publicly available emis- Finally, the NCEP-FNL Operational Model Global Tropospheric sion inventory outside the Metropolitan region justifies the use of Analysis data (FNL, 2000) have been used for lateral boundary condi- HTAPv2.2 for outside this region. tions for the coarse domain STGO1 while a two-way-nesting approach HTAPv2.2 allocates almost twice as much PM2.5 emissions to the

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Fig. 3. Total NOX (left) and PM2.5 (right) emissions for July 2015 for all sectors based on USACH (blue) and HTAP (red) inventories. The latter is calculated on the same area corresponding to USACH inventory. Emissions outside of Santiago (including cities of Valparaiso and Rancagua) provided by HTAP in- ventory are also included (HTAP-Regional, yellow). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

metropolitan area than USACH whereas for NOX, USACH estimates larger emissions than HTAPv2.2 (Fig. 3). Large emissions in the HTAPv2.2 inventory are present outside of the metropolitan area mostly due to the presence of two cities (Valparaiso and Rancagua) with associated port and industrial activities, both outside of the USACH domain. Both inventories have been aggregated onto the same spatial grids of 6 × 6 and 2 × 2 km before being merged.The final product used in this study (hereafter HTAP-USACH) combines the original and more detailed emissions for the Metropolitan Region of Santiago from USACH with the HTAPv2.2 emissions for the neighbouring urban and natural areas outside of the USACH domain. Fig. 4. Spatial distribution of residential PM2.5 emissions (top) and transport

Biogenic emissions used in this work are provided directly in NOX emissions (bottom) for July 2015 from USACH inventory for domain with CHIMERE by the global Model of Emissions of Gases and Aerosols from 2 × 2 km resolution (STGO3). Nature (MEGAN) (Guenther et al., 2006). No fires occurred during the simulated period and therefore no emissions from biomass burning are Table 2 taken into account. PM2.5 and NOX total emissions for July 2015 from the combined HTAP and In this study we focus on the impact of residential and transport USACH inventory (HTAP-USACH) (first column) and for different emissions emissions of NOX and PM2.5 (Fig. 4) on air quality in Santiago. To do so, scenarios considered in this study; namely 50% emission reduction in re- different emission scenarios are tested to assess the impact of given sidential combustion (HRES, third column), 50% reduction in transport emis- mitigation options. The selected scenarios are within the range of tar- sions (HTRA, fourth column) and 50% emission reductions in both sectors geted reductions in emissions of the current decontamination plan of (HRT, fifth column). Monthly totals are calculated on the city domain are Santiago (MMAplan, 2014). Three scenarios are examined: reduction of outlined by the red square in Fig. 2. 50% of the residential emissions (hereafter HRES), reduction of 50% of (tons/mon) HTAP-USACH HRES HTRA HRT road transport emissions (hereafter HTRA) and reduction of both emission sectors by 50% (hereafter HRT). Moreover, to test the sensi- PM2.5 1106 901 957 752 tivity of the model we also run a scenario with a reduction of 10% of NOX 4196 4070 2578 2453 road transport emissions (hereafter 10PCTRA). The reduction of 50% of residential combustion emissions results in the north-west (230 μg/m3), El Bosque in the south-west (240 μg/m3), an average decrease of 19% in PM total emissions while reducing 2.5 Puente Alto in the south-east (200 μg/m3) and outside Santiago, transport activity by half, results in a decrease of 14% (Table 2). Re- Talagante in the south-west (180 μg/m3)(Fig. 5). duction of residential emissions has limited impact on reduction of total Highest daily levels were present in the north and northwest side of NOX (3%) in contrast to transport emissions reductions that generates the city (Pudahuel, Cerrillos, Quilicura and Cerro Navia) with daily 39% decrease of NO . Finally, by combining both scenarios, HRT re- X mean concentrations between 100 and 120 μg/m3 while in the south (El duces NO and PM emissions in 42% and 33%, respectively. X 2.5 Bosque and Puente Alto) and in the city center (OHiggins) daily mean concentrations between 90 and 100 μg/m3 were observed. 2.5. Description of the simulated period At present, during autumn and winter a forecast for the me- tropolitan region is delivered every day at around 20 h (local time) on th th The selected period between the 15 and 28 of July is char- the air quality with respect to PM2.5 and PM10 of the following day. fi ff acterized by poor air quality with hourly concentrations of PM2.5 Following the national threshold limits, ve di erent episode levels can measured by the monitoring network ranging between 180 and 260 μg/ be declared by the authorities, namely Good, Regular, Alert, Pre- m3. Most affected areas were Cerro Navia (260 μg/m3) and Pudahuel in Emergency and Emergency (Table 3).

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th th Fig. 5. Observed hourly PM2.5 concentrations from the 15 to 28 of July 2015 at each station from the monitoring network from the MMA (Fig. 1).

Table 3 3. Results

Daily average concentration thresholds of PM2.5 defining Good, Regular, Alert, Pre-Emergency and 3.1. Validation of WRF simulations Emergency episodes according to Chilean environ- mental regulation. In order to analyze the variability of surface meteorological para- Episodes: [μg/m3 ] meters we focus on two DMC stations representative of urban and not- urban environments, San Pablo (SPB) and El Paico (EPC) respectively – Good 0 50 (Fig. 1). In general, the model reproduces the hourly variability of the Regular 50–80 Alert 80–110 RH, temperature and winds speed at both stations (Fig. 6). Moreover, Pre-Emergency 110–170 rapid changes of temperature, relative humidity and wind speeds as- Emergency > 170 sociated to coastal lows are also captured by the model. In terms of RH, observations outside of the urban limits at EPC are closer to the ob- servations than those from the urban station SPB, especially between Several restrictions to transport and residential wood combustion July 20th and 24th when RH is mainly underestimated (15–30%). Fur- are applied whenever one of the following episode levels is declared; thermore, the model mostly underestimates the temperature at the Alert, Pre-Emergency or Emergency. More restrictive prohibitions to beginning and end of the period of interest at both stations while from public and private transport circulation in Santiago are applied pro- the 20th to the 24th of July it underestimates the maximum tempera- gressively in the subsequent three episodes levels while residential tures and (only for SPB) overestimates the minimum temperature. The combustion of biomass is prohibited during pre-emergency and emer- general variability of wind speed is reproduced by WRF, mostly over- fi gency days and permitted only for certi cated emission sources during estimating the observations throughout the entire period. alert states (MMA, 2015). We clarify that this work explores the impact Saide et al. (2016) highlights the difficulties of WRF to resolve of the mitigation measures in the decontamination plan of Santiago general conditions of low wind speed typical during winter in central seeking to reduce concentration levels on the long-term and therefore Chile (average of 2.5 m/s) over the complex topography of Santiago in the emission baseline used does not include the above mentioned simulations with high resolution due to the lack of a parameterization emission reductions implemented in days of deteriorated air quality. for topographic effects. During the selected period, three days of Regular conditions, eight The models ability to capture vertical variations of the meteor- days of Alert and three Pre-Emergencies were declared based on the ological parameters are examined for the 25th and 26th of July (Fig. 7). forecast. Afterwards, a comparison can be made between observed le- We choose these two days because they coincide with the largest daily vels of PM2.5 and the corresponding forecast: two days of Good con- concentrations of PM2.5 observed. The model reproduces, in general, ditions, six days of Regular conditions, four Alerts episodes and two the vertical variations of temperature, RH and wind speed throughout Pre-Emergencies occurred (Table 4). the column. However, the model presents difficulties in reproducing the vertical structure of RH and wind speed above 2000 m, in particular for the 25th of July. The simulated planetary boundary layer height (PBLH) is compared

Table 4 Daily air quality episodes according to Chilean environmental regulations: G-Good, R-Regular, A-Alert, PE-Pre-Emergency and E-Emergency (Table 3); from the 15th to 28th of July according to observations, the operational forecast system in place in Santiago at that time, simulated by the model with the HTAP-USACH inventory and each one of the emission scenarios (i.e. HRES, HTRA and HRT).

Air pollution episodes for PM2.5 observed, forecasted and modelled during the simulated period 15–28 of July 2015

Date 15/07 16/07 17/07 18/07 19/07 20/07 21/07 22/07 23/07 24/07 25/07 26/07 27/07 28/07 Observations R A R R G R PE AAAPERGR Forecast R AAAARAPEAPEAPEAR Reference R A A R G R A A PE A R R G G HRES R A A G G R AAARRRGG HTRA R R R GGGRAARGGGG HRTGRRGGGGRRGGGGG

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Fig. 6. Observed (blue dots) and simulated (black lines) hourly averages of relative humidity (top) and temperature (centre) at 2 m.a.g.l and wind speed (bottom) at 10 m.a.g.l at El Paico (left) and San Pablo (right) stations (Fig. 1). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) against calculated PBLH based on observations from tethered balloon are within the urban area. With respect to its performance to reproduce and ceilometer backscatter data (Fig. 8). The PBLH based on ceilometer the vertical variations of meteorological parameters, the model shows observations is calculated following Muñoz and Undurraga (2010) and differences between its ability to simulate tethered balloon observations is only limited to daytime while PBLH based on tethered balloon are of the first 1000 m and radiosondes observations taken up to 5000 m. All calculated for both, day and nigth, based on potential temperature in all, the model presents better perfomance with parameters up to profiles considering a constant profile within the PBL and a positive 5000 m, except for RH, for which, smaller NRMSE and larger correlation gradient above it (Stull, 1988). are obtained for observations form the first 1000 m. In addition, MNB Both methods estimate the PBLH to reach heights between 400 and presents opposite signs between tethered balloon and radiosonde ob- 700 m during the day. During nighttime, shallow PBL occurs with servations. While the former are underestimated, RH and temperature heights below 100 m. The model mostly reproduces the observed from the latter are overestimated. We highlight that the model presents variability of the PBLH consistently underestimating it during nighttime much better correlation for wind speed observed from radiosonde than throughout most of the period, except for the 25th and 26th where the from tethered balloon. model overestimates it. The model, on average, underestimates the All in all, the configuration adopted for WRF is able to simulate the PBLH between 30 and 40% during day and night while an over- general circulation features of the metropolitan area. Surface statistics estimation around 40% during the day is observed from 24th to 26th. demonstrate WRF's ability to reproduce with good agreement the A quantitative analysis of the models ability to reproduce the ob- variability of meteorological parameters and in particular the strong servations of both networks shows that the model has a comparable and weak wind conditions. Rapid temperature and humidity changes performance in reproducing RH both from MMA and from DMC associated with coastal lows are also well reproduced. A similar (Table 5). While for temperature and wind speed the model has smaller agreement can also be observed in vertical statistics. The correct re- MNB and NRMSE (and comparable correlation) for observations from the production of these parameters is fundamental to reproduce the episode DMC stations than from the MMA. We recall the reader that while most of of bad air quality conditions during wintertime (Rutllant and Garreaud, the DMC stations are outside of the urban area, most of the MMA stations 2004).

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Fig. 7. Observed (blue dots) and simulated (black lines) radiosonde vertical profiles of relative humidity (left), temperature (centre) and wind speed (right) at 17:00 h (local time) on the 25th (top) and 26th (bottom) of July at the DMC site. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

3.2. Validation of CHIMERE simulations underestimated by the model throughout the entire period. The model

is able to reproduce the minimum levels of NOX but, in particular at the The validation of CHIMERE focuses on NOX and PM2.5 surface beginning and at the end of the simulation period, the model under- concentration in this analysis. estimates the concentrations between 10 and 15% at Pudahuel.

The model is able to capture large concentrations during weekdays Simulated vertical distribution of PM2.5 concentrations are com- as well as low concentrations during the week-end (18–19 and 25–26 of pared against backscatter data from the ceilometer at DGF (Fig. 10). July) for both pollutants. Larger differences in hourly concentrations Only a qualitative comparison is conducted considering that the ceil- with respect to NOX than for PM2.5 are observed (Fig. 9). Observed ometer provides backscatter attenuations and the model provides con- concentrations of PM2.5 are reproduced by the model throughout most centrations. The observations show the presence of particulate matter of the period with exception of the 25th of July when the model un- mostly in the first 300 m with maximum values in late afternoon. derestimates by more than a factor of two the observed concentrations. Cloudy conditions on the 19th generate the maximum backscattering

In addition, observed hourly NOX concentrations are mostly attenuations extending up to 600 m above ground. Consistent with

202 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

The statistical evaluation of CHIMERE considers six MMA stations, representative of different emission environments: industrial areas (Pudahuel and Quilicura), the city center (OHiggins), residential areas (Puente Alto and El Bosque) and semiurban areas outside of the city (Talagante). Statistical parameters for all six stations show that, in

general, the model performance to reproduce both NOX and PM2.5 are comparable in terms of mean bias and correlation. However, in terms of

RMSE, smaller error is observed for PM2.5 than for NOX (Table 6). Single station statistics reveal for PM2.5 lower bias inside the urban area than outside, with minimum values in the city center (OHiggins) and larger bias outside the urban area (Talagante). The RMSE shows a si- milar overestimation in almost all stations; lowest values are observed Fig. 8. Simulated (black line) and estimated boundary layer height (BLH) based in Puente Alto, while largest values are observed at OHiggins. Largest on tethered baloon (blue dots) and ceilometer (green dots) observations. We correlation coefficients are found in the northern area of the city, in highlight that while tethered baloon data are from the DMC site, the ceilometer particular for the stations Pudahuel and Quilicura while lower corre- data are from the DGF (Fig. 1). Simulated BLH corresponds to the DGF. Esti- lations are found in the city center (OHiggins), the south side (Puente mated boundary layer height (BLH) based on tethered baloon are calculated Alto) and in the semi urban areas outside of Santiago (Talagante). A following Stull (1988) and are for both day and night time whereas BLH esti- similar analysis for NOX reveals concentrations are underestimated in mated from ceilometer are estimated following Muñoz and Undurraga (2010) all stations except at El Bosque and OHiggins. The RMSE shows better and calculated only for daytime. (For interpretation of the references to colour agreement for the north side (Quilicura), in the south-east side (Puente in this figure legend, the reader is referred to the Web version of this article.) Alto) and outside the city (Talagante) while a lower agreement is ob- served in the city center (OHiggins). Finally, higher correlation coeffi- Table 5 cients are observed in the north-west (Pudahuel), and outside the urban Normalized mean bias (MNB), normalized root mean square error (NRMSE) and limits (Talagante) while lower values are observed in the south (Puente Pearson correlation coefficient (R) quantifying the performance of WRF to re- Alto) and north (Quilicura) side of the city. produce surface observations (top) of temperature (TEMP), relative humidity In terms of simulating the air quality conditions we highlight that (RH) and winds speed (WSPEED) considering stations from the air-quality monitoring network of Ministery of Environment (MMA, left) and the chilean the model reproduces observed AQ threshold limit classes in 9 out of 14 weather service (DMC, right). Same statistics are also computed considering considered days while the current forecast system in place only re- vertical profiles (bottom) from tethered ballon measurements up to 1000 m. produces it for 5 out of the 14 days (Table 4).The model in this study (left) and from radiosondes up to 5000 m. (right). was forced with observed winds and the performance is therefore not directly comparable with the forecast system used at the time. It Surface Statistics however illustrates an acceptable performance to reproduce AQ con- Networks: MMA DMC ditions in Santiago. However, exactly modelling concentrations just below or above a limit value is difficult and the model just over or MNB NRMSE R MNB NRMSE R underestimates the AQ episode classification for certain days. It un- st th TEMP −0.3 2.9 0.84 −0.2 1.1 0.85 derestimates the observed Pre-Emergency on the 21 and 25 as well as th RH −0.1 0.3 0.76 −0.1 0.4 0.73 the Regular conditions on the 28 by reproducing AQ conditions cor- WSPEED 0.9 7.2 0.35 0.3 4.8 0.40 responding to Alert, Regular and Good episodes respectively. Further-

more, the model overestimates daily levels of PM2.5 during a Regular Vertical Statistics th rd Observations: Tethered Balloon (1000 m) Radiosonde (5000 m) (17 ) and an Alert (23 ) episode reproduced as Alert and Pre-Emer- gency, respectively. TEMP −0.1 0.6 0.93 0.01 0.6 0.99 RH −0.3 0.5 0.87 0.2 8.5 0.70 − − WSPEED 0.1 4.8 0.12 0.01 0.9 0.87 3.3. Emission scenarios

Reduction of 50% of residential emissions (HRES) results in an PBLH shown in Fig. 8, the model underestimates the depth of the average decrease of 17% in PM concentrations, while reducing mixing layer extending it mostly up to 100 m. Furthermore, the model 2.5 transport activity by half (HTRA) results in a decrease of 23% in PM is not able to reproduce the observed aerosol layer on the 18th,19th, 2.5 concentrations (Fig. 11). As could be expected, the emissions reduction 26th and 27th. of both sectors by 50% has the largest impact, reducing the

th th Fig. 9. Simulated (black line) and observed hourly surface concentration of PM2.5 (left) and NOX (right) at Pudahuel station from the 15 to 28 of July 2015.

203 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

3 Fig. 10. Observed attenuated backscatter from ceilometer at DGF (left) and simulated vertical profiles of PM2.5 concentration in μg/m (right). Cloud base altitude (white dots) and boundary layer height (BLH, red dots) are also illustrated (left). BLH is estimated following Muñoz and Undurraga (2010). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Table 6

Bias, root mean square error (RMSE) and Pearson correlation coefficient (R) quantifying the performance of CHIMERE to reproduce surface concentrations of PM2.5 and NOX considering all stations of the air quality monitoring network (first row) and at six individual stations (center and bottom) from this network.

MMA Statistics for chemistry

NOX All Stations BIAS RMSE R PM2.5All Stations BIAS RMSE R −10.6 105.6 0.55 −9.7 33.0 0.52

Pudahuel Puente Alto Quilicura

BIAS RMSE R BIAS RMSE R BIAS RMSE R

NOX −59.1 108.8 0.69 −8.6 70.1 0.47 −44.4 92.9 0.41

PM2.5 −15.8 32.2 0.70 −14.6 31.8 0.32 −15.8 32.2 0.70

O´Higgins Talagante El Bosque

BIAS RMSE R BIAS RMSE R BIAS RMSE R

NOX 90.9 167.8 0.50 −56.4 77.1 0.61 20.6 121.7 0.54

PM2.5 7.8 38.5 0.43 −21.5 35.0 0.44 −13.2 37.8 0.49

The reduction on PM2.5 concentration from HRES is mostly in the outskirts of the city whereas most of the decrease in PM2.5 concentra- tions from HTRA and HRT is within the city (Fig. 12). However, and

contrary to PM2.5, concentrations of NOX are reduced more homo- genously both within and outside the city for scenarios reducing transport emissions (HTRA and HRT). The impact of the emission reduction scenarios on the number of Alerts and Pre-Emergencies produced by the model is now further ex- amined. According to the observations a total of 2 Pre-Emergencies, 4 Alerts, 6 Regular and 2 Good conditions occurred during the study period while the model (Reference) simulates 1 Pre-Emergency, 5 Alerts, 5 Regular and 3 Good conditions (Table 4). The HTRA scenario is more effective in improving the AQ conditions in Santiago compared to HRES. Although both are effective in reducing Pre-Emergency con- ditions, HTRA is also successful in reducing the number of days with Fig. 11. Decrease in NOX (left) and PM2.5 (right) surface concentrations (in %) Alert conditions to two and a corresponding increase of days with Good for the city domain (red square in Fig. 2) for HRES (blue), HTRA (red) and HRT conditions to seven. The HRES scenario however maintains the number (yellow) emission scenarios. Reduction in surface concentrations are computed of days with Alert and Regular AQ conditions. The reduction of both, with respect to the original HTAP-USACH emission inventory (section 2.4). (For residential combustion and transport emissions (HRT) notably im- interpretation of the references to colour in this figure legend, the reader is proves AQ conditions in Santiago; no Alert conditions days occur, days referred to the Web version of this article.) with Regular AQ conditions are decreased by one and the number of Good episodes increases from 3 to 10. concentrations of PM2.5 by 41%. Contrary to PM2.5, for NOX little im- The daily concentrations observed at Pudahuel and Cerro Navia pact is seen with HRES while large, and comparable, reductions are stations (Fig. 13) are usually responsible for triggering episodes of bad seen in both scenarios reducing transport activity (HTRA and HRT), AQ during winter. They have the highest concentrations throughout consistent with the fact that NOX emissions in the urban area are mainly most of the analyzed period (Fig. 5). At Pudahuel, the model re- due to transport activity. produces, in general, magnitude and evolution of the daily

204 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

Fig. 12. Decrease of PM2.5 (top) and NOX (bottom) surface concentration (in %) for scenarios HRES (left), HTRA (centre) and HRT (right). Location of stations from the air quality monitoring network are included: Cerrillos (1), Cerro Navia (2), El Bosque (3), Independencia (4), La Florida (5), Las Condes (6), O'Higgins Park (7), Pudahuel (8), Puente Alto (9), Quilicura (10) and Talagante (11). concentrations but underestimates the observations on the 21st and trends between the 21st and 24th of July. Furthermore, observed con- from the 24th to 26th of July (Fig. 13). At Cerro Navia, a similar centrations at Cerro Navia on these two days are close to the threshold variability as at Pudahuel is observed but with higher concentrations. limits for Alert episodes and thus the difficulties of the model to re- These two stations present the most deteriorated AQ conditions from produce these conditions can be explained by the negative bias of the the 21st to the 25th of July. For these days the model is successful in model (Table 6). reproducing the AQ level for the 22nd and the 24th of July. Furthermore, Our scenarios consisted of 50% reduction of emissions. To test the model overestimates the concentrations on the 23rd reproducing whether the impact of measures would be linear between the 0 and conditions of Pre-Emergency (Table 4) and is not able to reproduce the 50% reduction we also performed a simulation with 10% transport Pre-Emergency levels observed on the 21st and 25th. We highlight that emissions reduction (10PCTRA). For policy makers it would be im- Cerro Navia and Pudahuel are two nearby stations only 1.8 km apart portant to know if the implementation of a measure already is pro- from each other and therefore the model has difficulties in reproducing portionally effective or only achieves results when it is fully im- the concentration gradient between these stations and their opposing plemented. Especially for PM this is an important question because part

3 Fig. 13. Observed (blue line) and simulated PM2.5 daily surface concentration (in μg/m ) at Cerro Navia (left) and Pudahuel (right) for REFERENCE (red), HRES (black), HTRA (yellow) and HRT (green) emission scenarios. Additionaly, threshold values for Alert (80–110 μg/m3) and Pre-Emergency (110–170 μg/m3) episodes are reported (dashed red lines). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

205 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

3 Fig. 14. Observed (left) and simulated (right) PM2.5 to NOX hourly concentrations (in μg/m ) at O'Higgins Park (top) and Pudahuel (bottom) stations. Linear regression equations are also shown for each plot.

of the PM2.5 concentration consists of secondary PM, particles formed day, with the consequent change in ratio of observed NOX to PM2.5. from precursor gases in the atmosphere. However, also for NOX a Ideally the model-to-model comparison should also reflect this. The nonlinear reaction over the 0–50% reduction could be possible because model captures to some extent this fluctuation in Pudahuel but not in

NOX is involved in ozone chemistry and depending if the regime is O'Higgins where concentrations and emissions seem to be more domi- VOCs or NOX limited the impact of NOX emission reduction on NO2 nated by a single source or a fixed combination of sources in the model. concentrations may differ. If the model predicts proportional impacts A potential source explaining the difference could be the resuspension over the 0–50% reduction range, the impact of a 10% reduction would processes by transportation or urban dust, none of them included in the be 20% of the impact of a HTRA scenario. When we look at the impact inventory used in this study. This is obviously strongly associated with on NOX and PM2.5 concentrations of the 10PCTRA scenario we find that transport activity (and therefore with exhaust emissions from transport) in fact the impact of the 10PCTRA is 20.0% for NOX and thus linear but may also fluctuate independently from transport activities because whereas for PM2.5 it is slightly higher, namely 21.4%. For PM2.5 it resuspension is strongly influenced by climatic conditions (lower during suggests that the first steps in the reduction might be slightly more rain events or over wet surface areas, (Amato et al., 2012)). An addi- efficient than the later steps e.g. going from 40 to 50% emission re- tional source potentially explaining the difference in bandwith between duction but the difference is not dramatic. observations and model is the formation of secondary organic aerosols

To further understand the relation between emissions and con- (SOA) which is not included in the model but has an impact on PM2.5 centrations we plot for Pudahuel and OHiggins the observed and concentrations and could even exceed the contribution of primary or- modelled PM2.5 as a function of observed and modelled NOX, respec- ganic aerosols (Robinson et al., 2007). tively (Fig. 14). The results show that in both cases (observations and model) PM2.5 and NOX concentrations are strongly related. Given that 4. Conclusions the dominant source of NOX emissions in an urban environment is transport these results illustrate the strong relation between PM2.5 and Although air quality has improved over the years (Barraza et al., transport activity. However, differences exist in the bandwidth between 2017; Gallardo et al., 2018), poor air quality remains a worrying issue observed to-observed and model-to-model results with the former for Santiago in the winter, particularly in terms of particulate matter. having a larger bandwidth than the latter, especially for OHiggins. The occurrence of days with elevated concentrations of pollutants is Different sources like traffic or residential combustions have different thought to be due to a combination of high emissions from residential ratios on NOX to PM2.5 emissions. A larger bandwidth then suggests that heating and road transport in combination with limited ventilation and the relative importance of the sectors fluctuates over time, or within the mixing. The government has implemented various decontamination

206 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208 plans with mitigation strategies aimed at reducing emissions and thus Acknowledgements improving AQ in Santiago. To examine the impact of residential combustion and transport This work was conducted within the framework of the FONDECYT emission on AQ in Santiago a modelling system composed by the WRF project 1150873 and the first author of this work was funded by the meteorological model and CHIMERE chemical and transport model is Center for Climate Research and Resilience (CR2) (FONDAP implemented for Central Chile and applied to simulate simultaneously 15110009). N.Huneeus was partially funded by the Anillo Project the dispersion of gaseous pollutants and particulate matter. The system ACT1419. Powered@NLHPC: This research was partially supported by was used to investigate the impact of a set of emissions scenarios in the supercomputing infrastructure of the NLHPC (ECM-02). reducing the number of days with bad AQ conditions during two winter weeks in July 2015. References The performance of the modeling system is assessed against avail- able surface meteorological measurements, observed surface con- Amato, F., Schaap, M., van der Gon, H.A.D., Pandolfi, M., Alastuey, A., Keuken, M., centrations, vertical profiles of temperature, relative humidity and Querol, X., 2012. Effect of rain events on the mobility of road dust load in two Dutch – fi and Spanish roads. Atmos. Environ. 62, 352 358. wind speed from eld measurements and ceilometer backscatter pro- Anav, A., Menut, L., Khvorostiyanov, D., Viovy, N., 2011. Impact of tropospheric ozone on files at downtown Santiago. The model presents better performance in the Euro-Mediterranean vegetation. Global Change Biol. 17. reproducing surface meteorological observations than vertical ones. Barraza, F., Lambert, F., Jorquera, H., Villalobos, A.M., Gallardo, L., 2017. Temporal fi evolution of main ambient pm2.5 sources in santiago, Chile, from 1998 to 2012. Furthermore, pro les from radiosondes up to 5000 m are better re- Atmos. Chem. Phys. Discuss. 2017, 1–21. produced than observations from tethered balloon over the first Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., 1000 m. In addition, the model has difficulties in reproducing the ob- Manders, A., Stern, R., Tsyro, S., Garcıá Vivanco, M., Thunis, P., Pay, M.-T., Colette, ı served planetary boundary layer height. The model, in general re- A., Couvidat, F., Meleux, F., Rou ¨l, L., Ung, A., Aksoyoglu, S., Baldasano, J.M., Bieser, J., Briganti, G., Cappelletti, A., D'Isidoro, M., Finardi, S., Kranenburg, R., produces the variability of daily concentrations from the air quality Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., monitoring network of Santiago. However, the model has difficulties in Prévôt, A.S.H., Roberts, P., White, L., 2016. Presentation of the eurodelta iii inter- – simulating small scale concentration gradients observed in the city; for comparison exercise evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with me teorology. Atmos. Chem. Phys. 16 (19), Cerro Navia and Pudahuel for instance, two stations responsible for 12667–12701. triggering most of the pollution events in Santiago and with occasional Borrego, C., Monteiro, A., Ferreira, J., Miranda, A., Costa, A., Carvalho, A., Lopes, M., opposing trends during the analyzed period, are only 1.8 km apart, a 2008. Procedures for esti mation of modelling uncertainty in air quality assessment. Environ. Int. 34 (5), 613–620. distance comparable to the spatial resolution used in CHIMERE. Carbone, S., Saarikoski, S., Frey, A., Reyes, F., Reyes, P., Castillo, M., Gramsch, E., Oyola, The analysis of the relationship between PM2.5 and NOX reveals that P., Jayne, J., Worsnop, D.R., Hillamo, R., 2013. Chemical characterization of sub- the dynamics of the model does not fully capture all the real-world micron aerosol particles in Santiago de Chile. Aerosol and Air Quality Research 13 (2), 462–473. emission variations, however the strong correlation between these Carter, W.P., 2010. Development of the saprc-07 chemical mechanism. Atmos. Environ. pollutants seen both in the observations and the model results is en- 44 (40), 5324–5335 atmospheric Chemical Mechanisms: Selected Papers from the couraging. 2008 Conference. ff Collins, W., Rasch, P., Boville, B., Hack, J., McCaa, J., Williamson, D., Kiehl, J., Briegleb, Di erent scenarios within the range of the emissions reduction B., 2004. Description of the Ncar Community Atmosphere Model (Cam 3.0). Tech. targeted in the decontamination plan for Santiago from the Ministry of rep., NCAR -National Center For Atmospheric Re search. the Environment (MMAplan, 2014) were tested: 50% decrease of re- FNL, N., 2000. Ncep Fnl Operational Model Global Tropospheric Analyses, Continuing sidential emissions (HRES), 50% decrease of transport emissions from July 1999. https://doi.org/10.5065/D6M043C6. Franck, U., Leitte, A.M., Suppan, P., 2014. Multiple exposures to airborne pollutants and (HTRA) and 50% decrease of residential combustion and transport hospital admissions due to diseases of the circulatory system in Santiago de Chile. Sci. emissions (HRT). Consistent with the main sectors contributing to Total Environ. 468–469, 746–756. emissions of PM and NO , the largest reduction in total emissions, for Gallardo, L., Olivares, G., Langner, J., Aarhus, B., 2002. Coastal lows and sulfur air pol- 2.5 X lution in central Chile. At mospheric Environment 36 (23), 3829–3841. PM2.5, was seen for HRES whereas for NOX it is obtained with HTRA. In Gallardo, L., Barraza, F., Ceballos, A., Galleguillos, M., Huneeus, N., Lambert, F., Ibarra, coherence with the spatial distribution of residential combustion and C., Munizaga, M., O'Ryan, R., Osses, M., et al., 2018. Evolution of air quality in transport emissions, the impact of HRES is located mostly in the out- santiago: the role of mobility and lessons from the science policy interface. Elem Sci Anth 6 (1). skirts of Santiago while the impact of HTRA is mostly found within the Garreaud, R.D., Rutllant, J., 2003. Coastal lows along the subtropical west coast of south city. Furthermore, HTRA is more efficient than HRES in improving AQ America: numerical simulation of a typical case. Mon. Weather Rev. 131 (5), in Santiago expressed by fewer days with Alert conditions and more 891–908. Garreaud, R.D., Rutllant, J.A., Fuenzalida, H., 2002. Coastal lows along the subtropical with Good conditions. The largest improvement was obtained by west coast of south ameri ca: mean structure and evolution. Mon. Weather Rev. 130 combining emission reductions in both sectors (HRT). Overall, our re- (1), 75–88. sults suggest that measures aiming to reduce transport emissions are Gramsch, E., Cereceda-Balic, F., Oyola, P., von Baer, D., 2006. Examination of pollution ff trends in Santiago de Chile with cluster analysis of pm10 and ozone data. Atmos. more e ective in reducing PM2.5 and NOX concentrations in Santiago Environ. 40 (28), 5464–5475. than policies reducing residential emissions. Furthermore, a sensitivity Gramsch, E., Caceres, D., Oyola, P., Reyes, F., Vasquez, Y., Rubio, M., Sanchez, G., 2014. fl test revealed over the range of emission reduction from 0 to 50% a In uence of surface and subsidence thermal inversion on PM2.5 and black carbon – linear relation for NO and almost linear for PM . This implies that concentration. Atmos. Environ. 98, 290 298. X 2.5 Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P., Geron, C., 2006. Estimates reductions smaller than 50%, will still have a proportional benefitin of global terrestrial iso prene emissions using megan (model of emissions of gases and terms of improvements in AQ. aerosols from nature). Atmos. Chem. Phys. 6, 3181–3210. We highlight that this study would need to be complemented with a Hauglustaine, D.A., Balkanski, Y., Schulz, M., 2014. A global model simulation of present and future nitrate aero sols and their direct radiative forcing of climate. Atmos. cost analysis associated to each emission scenario considered in this Chem. Phys. 14, 6863–6949. study in order to determine the cost effectiveness of each measure. Our Huneeus, 2017. Transport of Particle Pollution into the Maipo valley: winter 2015 analysis also reveals that while the strong relation observed between Campaign Results. In Preparation. Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., PM2.5 and NOX is captured by the model, it does not fully capture all the Keating, T., Zhang, Q., Kurokawa, J., Wankmuller, R., Denier van der Gon, H., real-world emission variations, especially for PM2.5, pointing to pos- Kuenen, J.J.P., Klimont, Z., Frost, G., Darras, S., Koffi, B., Li, M., 2015. Htap v2.2: a sible missing emissions in the inventory and/or missing aerosol for- mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15 (19), 11411–11432. mation processes such as the formation of SOA.

207 A. Mazzeo et al. Atmospheric Environment 190 (2018) 195–208

Jorquera, H., 2002a. Air quality at santiago, Chile: a box modeling approach i. carbon G.S., Liu, Z., Snyder, C., Chen, F., Barlage, M.J., Yu, W., Duda, M.G., 2017. The monoxide, nitrogen oxides and sulfur dioxide. Atmos. Environ. 36 (2), 315–330. weather research and forecasting model: overview, system efforts, and future direc-

Jorquera, H., 2002b. Air quality at santiago, Chile: a box modeling approach ii. PM2.5, tions. Bull. Am. Meteorol. Soc. 98 (8), 1717–1737. coarse and PM10 particulate matter fractions. Atmos. Environ. 36 (2), 331–344. Robinson, A.L., Donahue, N.M., Shrivastava, M.K., Weitkamp, E.A., Sage, A.M., Grieshop, Madronich, S., McKenzie, R.L., Björn, L.O., Caldwell, M.M., 1998. Changes in biologically A.P., Lane, T.E., Pierce, J.R., Pandis, S.N., 2007. Rethinking organic aerosols: semi- active ultraviolet radiation reaching the earth's surface. J. Photochem. Photobiol. B volatile emissions and photochemical aging. Science 315 (5816), 1259–1262. Biol. 46 (1), 5–19. Rutllant, J., Garreaud, R., 1995. Meteorological air pollution potential for santiago, Chile: Markakis, K., Valari, M., Perrussel, O., Sanchez, O., Honore, C., 2015. Climate-forced air- towards an objective episode forecasting. Environ. Monit. Assess. 34 (3), 223–244. quality modeling at the urban scale: sensitivity to model resolution, emissions and Rutllant, J., Garreaud, R., 2004. Episodes of strong flow down the western slope of the meteorology. Atmos. Chem. Phys. 15 (13), 7703–7723. subtropical andes. Mon. Weather Rev. 132 (2), 611–622. Mena-Carrasco, M., Oliva, E., Saide, P., Spak, S.N., de la Maza, C., 2012. Estimating the Saide, P.E., Carmichael, G.R., Spak, S.N., Gallardo, L., Osses, A.E., Mena-Carrasco, M.A.,

health benefits from nat ural gas use in transport and heating in santiago, Chile. Sci. Pagowski, M., 2011. Forecasting urban PM10 and PM2.5 pollution episodes in very Total Environ. 429, 257–265. stable nocturnal conditions and complex ter rain using wrf–chem co tracer model. Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Atmos. Environ. 45 (16), 2769–2780. Curci, G., Foret, F., Hodzic, A., Mailler, S., Meleux, F., Monge, J., Pison, I., Siour, G., Saide, P.E., Mena-Carrasco, M., Tolvett, S., Hernandez, P., Carmichael, G.R., 2016. Air

Turquety, S., Valari, M., Vautard, R., Vi vanco, M., 2013. CHIMERE 2013: a model for quality forecasting for win ter-time PM2.5 episodes occurring in multiple cities in regional atmospheric composition modelling. Geosci. Model Dev. (GMD) 6, central and southern Chile. J. Geophys. Res.: Atmosphere 121 (1), 558–575. 981–1028. Schmitz, R., 2005. Modelling of air pollution dispersion in Santiago de Chile. Atmos. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A., 1997. Radiative Environ. 39 (11), 2035–2047. transfer for inhomoge neous atmospheres: Rrtm, a validated correlated-k model for Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, M., Huang, X., Wang, W., the longwave. J. Geophys. Res.: Atmosphere 102 (D14), 16663–16682. Powers, J., 2008. A description of the advanced research WRF version 3. NCAR MMA, 2015. Final Report to Manage Critics Pollution Episodes of Respirable Particulate Technical Note 123.

Matter (PM10). Final report. Ministerial Secretariat of the Environment Minister for Stull, R.B., 1988. Measurement and Simulation Techniques. Springer, pp. 405–440. the Metropolitan region, Santiago. USACH, U., 2014. Informe final: Actualizacion y sistematizacion del inventario de emi- MMAplan, 2014. Prevention and Decontamination Plan for the Metropolitan Region of siones de contaminantes at mosfericos en la region metropolitana. Informe final. Santiago - Decontamination Plan Strategy 2014-2018. Informe final. Ministerial Universidad de Santiago de Chile. Secretariat of the Environment Minister for the Metro politan region, Santiago. Valari, M., Menut, L., 2010. Transferring the heterogeneity of surface emissions to http://portal.mma.gob.cl/planes-de-descontaminacion-atmosferica-estrategia-2014- variability in pollutant concentrations over urban areas through a chemistry trans- 2018/. port model. Atmos. Environ. 44, 3229–3238. Monteiro, A., Miranda, A., Borrego, C., Vautard, R., Ferreira, J., Perez, A., 2007. Long- van Leer, B., 1979. Towards the ultimate conservative difference scheme. v. a second- term assessment of particu late matter using chimere model. Atmos. Environ. 41 (36), order sequel to godunov's method. J. Comput. Phys. 32 (1), 101–136. 7726–7738. Van Loon, M., Vautard, R., Schaap, M., Bergström, R., Bessagnet, B., Brandt, J., Builtjes, Mullins, J., Bharadwaj, P., 2015. Effects of short-term measures to curb air pollution: P., Christensen, J., Cuve lier, C., Graff, A., et al., 2007. Evaluation of long-term ozone evidence from santiago Chile. Am. J. Agric. Econ. 97 (4), 1107–1134. simulations from seven regional air quality models and their ensemble. Atmos. Muñoz, R., Alcafuz, R.I., 2012. Variability of urban aerosols over santiago, Chile: com- Environ. 41 (10), 2083–2097. parison of surface pm10 concentrations and remote sensing with ceilometer and Vautard, R., Builtjes, P., Thunis, P., Cuvelier, C., Bedogni, M., Bessagnet, B., Honore, C., lidar. Aerosol and air quality research 12, 8–19. Moussiopoulos, N., Pirovano, G., Schaap, M., et al., 2007. Evaluation and inter-

Muñoz, R.C., Undurraga, A.A., 2010. Daytime mixed layer over the santiago basin: de- comparison of ozone and PM10 simulations by sev eral chemistry transport models scription of two years of observations with a lidar ceilometer. Arch Meteorol. over four european cities within the citydelta project. Atmospheric Envir onment 41 Climatol. 49 (8), 1728–1741. (1), 173–188. Nakanishi, M., Niino, H., 2004. An improved mellor–yamada level-3 model with con- Villalobos, A., Barraza, F., Jorquera, H., Schauer, J., 2015. Chemical speciation and densation physics: its design and verification. Boundary-Layer Meteorol. 112 (1), source apportionment of fine particulate matter in santiago, Chile, 2013. Sci. Total 1–31. Environ. 512–513, 133–142. Nenes, A., Pilinis, C., Pandis, S., 1998. Isorropia: a new thermodynamic model for in- Wu, W.-S., Purser, R.J., Parrish, D.F., 2002. Three-dimensional variational analysis with organic multicomponent atmospheric aerosols. Aquat. Geochem. 4, 123–152. spatially inhomogeneous covariances. Mon. Weather Rev. 130 (12), 2905–2916. Olivares, G., Gallardo, L., Langner, J., Aarhus, B., 2002. Regional dispersion of oxidized Zhao, Y., Feng, D., Yu, L., Wang, X., Chen, Y., Bai, Y., Hernandez, H.J., Galleguillos, M., sulfur in central Chile. Atmos. Environ. 36 (23), 3819–3828. Estades, C., Biging, G.S., Radke, J.D., Gong, P., 2016. Detailed dynamic land cover

Pérez, P., Gramsch, E., 2016. Forecasting hourly PM2.5 in Santiago de Chile with emphasis mapping of Chile: accuracy improvement by integrating multi-temporal data. Rem. on night episodes. Atmos. Environ. 124, 22–27. Sens. Environ. 183, 170–185.

Pérez, P., Trier, A., Reyes, J., 2000. Prediction of PM2.5 concentrations several hours in Zyryanov, D., Foret, G., Eremenko, M., Beekmann, M., Cammas, J., D'Isidoro, M., Elbern, advance using neural net works in santiago, Chile. Atmos. Environ. 34 (8), H., Flemming, J., Friese, E., Kioutsioutkis, I., Maurizi, A., Melas, D., Meleux, F., 1189–1196. Menut, L., Moinat, P., Peuch, V., Poupkou, A., Ra zinger, M., Schultz, M., Stein, O., Powers, J.G., Klemp, J.B., Skamarock, W.C., Davis, C.A., Dudhia, J., Gill, D.O., Coen, J.L., Suttie, e. a., 2012. 3-d evaluation of tropospheric ozone simulations by an ensemble Gochis, D.J., Ah madov, R., Peckham, S.E., Grell, G.A., Michalakes, J., Trahan, S., of regional chemistry transport model. Atmos. Chem. Phys. 12 (7), 3219–3240. Benjamin, S.G., Alexander, C.R., Di mego, G.J., Wang, W., Schwartz, C.S., Romine,

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