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

Mazzeo, Andrea; Huneeus, Nicolás; Ordóñez, César; Orfanoz-Cheuquelaf, Andrea; Menut, Laurent; Mailler, Sylvain; Valari, Myrto; Gon, Hugo Denier van der; Gallardo, Laura; Muñoz, Ricardo C.; Donoso, Rodrigo; Galleguillos, Mauricio; Osses, Mauricio; Tolvett, Sebastián

doi:10.1016/j.atmosenv.2018.06.043

Cited by 44 publications

(52 citation statements)

References 48 publications

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“…The boxplots together display a bimodal pattern with significant peaks during the morning rush hour, from 7 a.m. to 10 a.m. , and during the night, between 10 p.m. and 2 a.m. This can be associated with the topography and the persistence of subsidence conditions in the winter months (June–August) that induce thermal inversions (Gramsch et al, ) and weak winds in the morning and evening, preventing the dispersion of pollutant concentrations and leading to the accumulation of gases (CO, NO X , and VOCs) and aerosols (PM 10 and PM 2.5 ) in the air (see Ragsdale, Barrett, & Testino, ; Garcia‐Chevesich et al, ; Villalobos, Barraza, Jorquera, & Schauer, ; Perez & Gramsch, ; Yañez et al, ; Mazzeo et al, for more details). It is important to note that the high concentrations of PM 2.5 during the night and early morning hours may be also due to residential wood burning.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

“…Gramsch et al (), Yañez et al (), and Moisan et al () determined a negative correlation between temperature and wind speed with PM 2.5 and a positive correlation between RH and PM 2.5 ; however, a weak negative correlation was found for wind direction. Other authors have also considered weather variables to model PM 2.5 in Santiago (Ragsdale et al, ; Saide et al, ; Saide et al, ; Mazzeo et al, ).…”

Section: Exploratory Data Analysismentioning

confidence: 99%

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Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

et al. 2019

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Spatial prediction of exposure to air pollution in a large city such as Santiago de Chile is a challenging problem because of the lack of a dense air‐quality monitoring network. Statistical spatiotemporal models exploit the space–time correlation in the pollution data and other relevant meteorological and land‐use information to generate accurate predictions in both space and time. In this paper, we develop a Bayesian modeling method to accurately predict hourly PM2.5 concentrations in a 1‐km high‐resolution grid covering the city. The modeling method combines a spatiotemporal land‐use regression model for PM2.5 and a Bayesian calibration model for the input meteorological variables used in the land‐use regression model. Using a 3‐month winter‐time pollution data set, the output of sample validation results obtained in this paper shows a substantial increase in accuracy due to the incorporation of the linear calibration model. The proposed Bayesian modeling method is then used to provide short‐term spatiotemporal predictions of PM2.5 concentrations on a fine (1 km2) spatial grid covering the city. Along with the paper, we publish the R code used and the output of sample predictions for future scientific use.

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Section: Exploratory Data Analysismentioning

confidence: 99%

Section: Exploratory Data Analysismentioning

confidence: 99%

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

et al. 2019

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“…Figure 4) have been validated in terms of their consistency with concurrent estimates (e.g., regionalized greenhouse gas inventories), and compared with available estimates (DICTUC, 2007; DICTUC and UTFSM, 2017; USACH, 2014) but their accuracy requires further testing. Validation can be performed using in situ measurements and direct and inverse modeling techniques generally addressing CO and PM2.5 emissions (Gallardo et al, 2012b;Jorquera and Castro, 2010;Mazzeo et al, 2017;Mena-Carrasco et al, 2012;Saide et al, 2011;Saide et al, 2016;Schmitz, 2005). Extensive validation for other species has been hampered by the lack of speciated observations for particles and hydrocarbons, as well as by the lack of vertically resolved observations.…”

Section: Emission Changesmentioning

confidence: 99%

Evolution of air quality in Santiago: The role of mobility and lessons from the science-policy interface

Gallardo

Barraza

Ceballos

et al. 2018

Elementa: Science of the Anthropocene

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Worldwide, urbanization constitutes a major and growing driver of global change and a distinctive feature of the Anthropocene. Thus, urban development paths present opportunities for technological and societal transformations towards energy efficiency and decarbonization, with benefits for both greenhouse gas (GHG) and air pollution mitigation. This requires a better understanding of the intertwined dynamics of urban energy and land use, emissions, demographics, governance, and societal and biophysical processes. In this study, we address several characteristics of urbanization in Santiago (33.5°S, 70.5°W, 500 m a.s.l.), the capital city of Chile. Specifically, we focus on the multiple links between mobility and air quality, describe the evolution of these two aspects over the past 30 years, and review the role scientific knowledge has played in policy-making. We show evidence of how technological measures (e.g., fuel quality, three-way catalytic converters, diesel particle filters) have been successful in decreasing coarse mode aerosol (PM10) concentrations in Santiago despite increasing urbanization (e.g., population, motorization, urban sprawl). However, we also show that such measures will likely be insufficient if behavioral changes do not achieve an increase in the use of public transportation. Our investigation seeks to inform urban development in the Anthropocene, and our results may be useful for other developing countries, particularly in Latin America and the Caribbean where more than 80% of the population is urban.

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“…In this context, most of the aforementioned conclusions for CHIMERE (the model applied here) have been drawn for the Île de France region, which is densely populated, relatively flat and not directly influenced by sea-salt emissions. The model has been applied for a number of other regions, but the map is still incomplete and sensitivity testing is not the main focus of the corresponding studies (Mazzeo et al, 2018;Menut et al, 2018;Monteiro et al, 2018;Brasseur et al, 2019;Deroubaix et al, 2019). This is where the present study comes into play: A series of sensitivity test has been run with CHIMERE over the northwestern Iberian Peninsula, a region characterized by a complex coastline, forested mountain terrain and the advection of sea-salt from the surrounding Atlantic Ocean.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of CHIMERE to changes in model resolution and chemistry over the northwestern Iberian Peninsula

Brands

Fernández-García

Montecelo

et al. 2019

Preprint

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Abstract. Here, the capability of the chemical weather forecasting model CHIMERE (version 2017r4) to reproduce summertime surface ozone, particulate matter and nitrogen dioxide concentrations in complex terrain is investigated. The study area is the northwestern Iberian Peninsula, where both coastal and mountain climates can be found in direct vicinity and a large fraction of the land area is covered by forests. Fed by lateral boundary conditions from the ECMWF Composition Integrated Forecast System, meteorological data from the Weather Research and Forecasting Model (WRF) and the HTAP v2.2 emission inventory, CHIMERE's performance compared to observations is tested with a range of sensitivity experiments, exploring the role of horizontal and vertical resolution and the effects of applying distinct chemistry mechanisms. Using a high horizontal and vertical resolution yields the most balanced verification results. If both the daily maximum and minimum values are important for the given application, then the full Melchior mechanism should be used. If, however, the daily maxima are considered more important than the minima, SAPRC should be used instead. In any case, model performance for nitrogen dioxide is clearly not satisfactory for our study region, probably indicating deficiencies in the emission inventory.

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

Cited by 44 publications

References 48 publications

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

Evolution of air quality in Santiago: The role of mobility and lessons from the science-policy interface

Sensitivity of <i>CHIMERE</i> to changes in model resolution and chemistry over the northwestern Iberian Peninsula

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

Cited by 44 publications

References 48 publications

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

Evolution of air quality in Santiago: The role of mobility and lessons from the science-policy interface

Sensitivity of &lt;i&gt;CHIMERE&lt;/i&gt; to changes in model resolution and chemistry over the northwestern Iberian Peninsula

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Sensitivity of <i>CHIMERE</i> to changes in model resolution and chemistry over the northwestern Iberian Peninsula