Literature DB >> 22878102

Source apportionment of ambient PM2.5 in Santiago, Chile: 1999 and 2004 results.

Héctor Jorquera1, Francisco Barraza.   

Abstract

A receptor model analysis has been applied to ambient PM(2.5) measurements taken at Santiago, Chile (33.5°S, 70.7°W) in 2004 (117 samples) and in 1999 (95 samples) on a receptor site on the eastern side of the city. For both campaigns, six sources have been identified at Santiago and their contributions in 1999/2004 are: motor vehicles: 28 ± 2.5/31.2 ± 3.4%, wood burning: 24.8 ± 2.3/28.9 ± 3.3%, sulfates: 18.8 ± 1.7/16.2 ± 2.5%, marine aerosol: 13 ± 2.1/9.9 ± 1.5%, copper smelters: 11.5 ± 1.4/9.7 ± 3.3% and soil dust: 3.9 ± 1.5/4.0 ± 2.4%. Hence relative contributions are statistically the same but the absolute contributions have been reduced because ambient PM(2.5) has decreased from 34.2 to 25.1 μg/m(3) between 1999 and 2004 at Santiago. Similarity of results for both data sets - analyzed with different techniques at different laboratory facilities - shows that the analysis performed here is robust. Source identification was carried out by inspection of key species in source profiles, seasonality of source contributions, comparison with published source profiles and by looking at wind trajectories computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) from USA's National Oceanic and Atmospheric Administration (NOAA); for the wood burning sources the MODIS burned area daily product was used to confirm wildfire events along the year. Using this combined methodology we have shown conclusively that: a) marine air masses do reach Santiago's basin in significant amounts but combined with anthropogenic sources; b) all copper smelters surrounding Santiago - and perhaps coal-fired power plants as well - contribute to ambient PM(2.5); c) wood burning is the second largest source, coming from residential wood burning in fall and winter and from regional wildfires in spring and summer. The results of the present analysis can be used to improve emission inventories, air quality forecasting systems and cost-benefit analyses at local and regional scales.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22878102     DOI: 10.1016/j.scitotenv.2012.07.049

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

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Authors:  Gea Oliveri Conti; Behzad Heibati; Itai Kloog; Maria Fiore; Margherita Ferrante
Journal:  Environ Sci Pollut Res Int       Date:  2017-01-04       Impact factor: 4.223

2.  Trace element contents in fine particulate matter (PM2.5) in urban school microenvironments near a contaminated beach with mine tailings, Chañaral, Chile.

Authors:  Stephanie Mesías Monsalve; Leonardo Martínez; Karla Yohannessen Vásquez; Sergio Alvarado Orellana; José Klarián Vergara; Miguel Martín Mateo; Rogelio Costilla Salazar; Mauricio Fuentes Alburquenque; Dante D Cáceres Lillo
Journal:  Environ Geochem Health       Date:  2017-05-23       Impact factor: 4.609

3.  PM2.5 monitoring during a 10-year period: relation between elemental concentration and meteorological conditions.

Authors:  Pamela B Sanguineti; Bethania L Lanzaco; María Laura López; Mariana Achad; Gustavo G Palancar; Luis E Olcese; Beatriz M Toselli
Journal:  Environ Monit Assess       Date:  2020-04-25       Impact factor: 2.513

Review 4.  Chile Confronts its Environmental Health Future After 25 Years of Accelerated Growth.

Authors:  Paulina Pino; Verónica Iglesias; René Garreaud; Sandra Cortés; Mauricio Canals; Walter Folch; Soledad Burgos; Karen Levy; Luke P Naeher; Kyle Steenland
Journal:  Ann Glob Health       Date:  2015 May-Jun       Impact factor: 2.462

5.  Combining Cluster Analysis of Air Pollution and Meteorological Data with Receptor Model Results for Ambient PM2.5 and PM10.

Authors:  Héctor Jorquera; Ana María Villalobos
Journal:  Int J Environ Res Public Health       Date:  2020-11-15       Impact factor: 3.390

  5 in total

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