Abstract. Eddy covariance (EC) flux measurements of the atmosphere/surface exchange of gases over an urban area are a direct way to improve and evaluate emissions inventories, and, in turn, to better understand urban atmospheric chemistry and the role that cities play in regional and global chemical cycles. As part of the MCMA-2003 study, we demonstrated the feasibility of using eddy covariance techniques to measure fluxes of selected volatile organic compounds (VOCs) and CO 2 from a residential district of Mexico City (Velasco et al., 2005a, b). During the MILAGRO/MCMA-2006 field campaign, a second flux measurement study was conducted in a different district of Mexico City to corroborate the 2003 flux measurements, to expand the number of species measured, and to obtain additional data for evaluation of the local emissions inventory. Fluxes of CO 2 and olefins were measured by the conventional EC technique using an open path CO 2 sensor and a Fast Isoprene Sensor calibrated with a propylene standard. In addition, fluxes of toluene, benzene, methanol and C 2 -benzenes were measured using a virtual disjunct EC method with a Proton Transfer Reaction Mass Spectrometer. The flux measurements were analyzed in terms of diurnal patterns and vehicular activity and were compared with the most recent gridded local emissions inventory. In both studies, the results showed that the urban surface of Mexico City is a net source of CO 2 and VOCs with significant contributions from vehicular traffic. EvapCorrespondence to: E. Velasco (evelasco@mce2.org) orative emissions from commercial and other anthropogenic activities were significant sources of toluene and methanol. The results show that the emissions inventory is in reasonable agreement with measured olefin and CO 2 fluxes, while C 2 -benzenes and toluene emissions from evaporative sources are overestimated in the inventory. It appears that methanol emissions from mobile sources occur, but are not reported in the mobile emissions inventory.

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Abstract. Eddy covariance (EC) flux measurements of the atmosphere/surface exchange of gases over an urban area are a direct way to improve and evaluate emissions inventories, and, in turn, to better understand urban atmospheric chemistry and the role that cities play in regional and global chemical cycles. As part of the MCMA-2003 study, we demonstrated the feasibility of using eddy covariance techniques to measure fluxes of selected volatile organic compounds (VOCs) and CO2 from a residential district of Mexico City (Velasco et al., 2005a, b). During the MILAGRO/MCMA-2006 field campaign, a second flux measurement study was conducted in a different district of Mexico City to corroborate the 2003 flux measurements, to expand the number of species measured, and to obtain additional data for evaluation of the local emissions inventory. Fluxes of CO2 and olefins were measured by the conventional EC technique using an open path CO2 sensor and a Fast Isoprene Sensor calibrated with a propylene standard. In addition, fluxes of toluene, benzene, methanol and C2-benzenes were measured using a virtual disjunct EC method with a Proton Transfer Reaction Mass Spectrometer. The flux measurements were analyzed in terms of diurnal patterns and vehicular activity and were compared with the most recent gridded emissions inventory. In both studies, the results showed that the urban surface of Mexico City is a net source of CO2 and VOCs with significant contributions from vehicular traffic. Evaporative emissions from commercial and other anthropogenic activities were significant sources of toluene and methanol. The data show that the emissions inventory is in reasonable agreement with measured olefin and CO2 fluxes, while C2-benzenes and toluene emissions from evaporative sources are overestimated in the inventory. It appears that methanol emissions from mobile sources occur, but are not present in the mobile emissions inventory.

To identify the sources of PM2.5 – bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th – 95th percentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r = 0.74 – 0.88) and lowest CODs (0.32 – 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r = 0.47 – 0.55) and higher CODs (0.41 – 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th – 95th percentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r = 0.77 – 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.

The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003–2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio–ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.