Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 1: Fine particle composition and organic source apportionment

Abstract

Project Description and Overview: Objectives: Determining the overall impact of atmospheric aerosols on radiative balance requires knowledge of the relative amounts of scattering and absorbing aerosols, their distributions, and their chemical and optical properties. This proposal was a continuation of measurements of aerosol scattering and absorption begun in Mexico City in 2003 in collaboration with MCMA 2003 and continuing in the Atmospheric Science Program field study, Megacity Aerosol Experiment-Mexico City, (MAX-Mex) during March of 2006 aimed at determining the variability of aerosol optical properties. A suite of instrumentation was deployed in MAX-Mex at site TO, located in the northern part of the Mexico City Metropolitan Area, (MCMA), for the characterization of the aerosol optical properties in the field. Measurements were made of the following aerosol properties: (1) aerosol absorption as a function of wavelength, measured at two minute intervals with a 7-wavelength Aethalometer (2) aerosol scattering as a function of wavelength, measured at one minute intervals with a 3-wavelength nephelometer; 3) aerosol scattering as a function of relative humidity (RH), measured at one minute intervals with 2 single-wavelength nephelometers operated under dry (10% RH) and wet (80% RH) conditions; and 4) collection of size-fractionated aerosol samples on quartz fiber filters at 12 hour intervals (day/night) for further laboratory characterization. Aerosol filter samples were also collected at site Tl (located north of MCMA) for comparison with those collected in the city center. Preliminary results from in situ measurements have indicated an enhanced UV absorption in the afternoon over that expected from black carbon (BC) aerosols alone. These results are directly applicable to both modeling of aerosol radiative forcing and satellite optical depth retrieval algorithms. Both of these applications assume that the aerosol absorption is due only to BC with a wavelength dependence of A, " whereas results obtained in MAX-Mex show that the aerosol wavelength exponent varies over Mexico City from-0.7 to-1.5. All of the data collected in the field from the measurement sets 1-3 have been made available to the ASP community via the MILAGRO data site housed at NCAR. The laboratory characterization of aerosol samples collected in the ASP MAX-Mex field study compared results from Mexico City to samples collected at other sites, including Chicago, Little Rock, and Mt. Bachelor, OR. The project focused on obtaining complete spectral characterization of aerosols-especially their absorption characteristics as they relate to basic chemical functional groups. Particular attention was given to organics and from biogenic derived organic compounds. This included determinations of the UV-Visible-NIR characteristics of the aerosol absorption as reported as Angstrom Absorption Exponents. Correlation of these results with IR band observations of carboxylic acid, and carboxylate groups were conducted, along with past correlations with carbon...

Three-dimensional factorization of size-resolved organic aerosol mass spectra from Mexico City

“…3), we therefore explore all solutions and use the criteria of finding physically meaningful factors to choose a good solution. Following the assumption that we will likely find the same factors from this dataset as Aiken et al (2009) reported for the HR-MS data in that 2-D factorization, we expect to need at least four factors in the solution. However, we also explore solutions with more factors in case new factors appear.…”

“…During the campaign, size-resolved submicron aerosol composition was measured near downtown Mexico City by highresolution time-of-flight aerosol mass spectrometry (HRToF-AMS). Previous factor analysis of the bulk submicron organic composition dataset, i.e., without making use of the size-resolved data, identified contributions to the organic aerosol from HOA, BBOA, OOA, and a locally occurring organic aerosol (LOA, Aiken et al, 2009). The 3-D factorization techniques used in this study allow us to obtain robust estimates of the size distributions of these aerosol components and perhaps identify additional components.…”

“…HOA and BBOA have similar unit-resolution mass spectra, making them hard to separate in some datasets (Lanz et al, 2008); this similarity likely contributes to the trading in these factors' time series. Aiken et al (2009) reported that factoring high-resolution spectra (i.e., the HR-MS dataset) substantially improved the separation of these two factors compared to factorization of the unit-mass-resolution spectral data. The improvement is the result of separating, before performing the factorization, the information from HR ions at the same nominal m/z that have different contributions to HOA and BBOA.…”

“…Then an estimation formula between the tracer and the component is applied. In this work, we use tracer-component relationships derived from MS-mode data for OOA from Ng et al (2011) and for HOA and BBOA from Aiken et al (2009). The tracer-component relationship for LOA is estimated as the size distribution of m/z 58/f 58 , where f 58 is the fraction of m/z 58 in the 3-D LOA factor mass spectrum.…”

Analysis of model-based PM2.5 emission factors for on-road mobile sources in Mexico

“…For example, recent field studies in Mexico City (Molina et al, 2007 have shown that mobile sources emit abundant amounts of primary PM and that their emissions estimates in current official emissions inventories are highly uncertain (Aiken et al, 2009;Zavala et al, 2009a). Uncertainties in emissions inventories may arise when model-based techniques are used for the estimation of PM emissions from mobile sources due to propagations of uncertainties in: (1) the databases for inventorying and classifying the vehicle's activity levels, number and fuel characteristics, as well as roads type, driving modes, and ambient data representative of the local conditions; (2) the use of non-locally measured and model-based emissions factors.…”