Elemental potassium has been extensively used as an indicator of biomass burning in the source apportionment of PM2.5. We present a method to estimate the fraction of potassium associated with biomass burning (Kb) based on a linear regression with iron that can be applied at any site where PM2.5 chemical speciation is available. The estimated fraction has a significantly greater correlation with levoglucosan (R2=0.63), an organic tracer of biomass burning, than total potassium (R2=0.39). We explore temporal and spatial variability of Kb over a period of six years in the Atlanta area. Kb is larger in spring when biomass burning activity is more prevalent and during weekends due to the use of fireplaces in winter and outdoor charcoal cooking in summer. Kb is the predominate form of potassium for the rural site in this study. The use of Kb in a receptor model results in a lower fraction of PM2.5 apportioned to biomass burning and a greater fraction to mobile sources when compared to the use of total K. Results suggest that Kb is a good indicator of biomass burning

We built an emission inventory (EI) for the megacity of Bogotá, Colombia for 2012, which for the first time augments traditional industrial and mobile sources by including commercial sources, biogenic sources, and resuspended dust. We characterized the methodologies for estimating each source annually, and allocated the sources to hourly and 1 km² spatial resolution for use as inputs for air quality modeling purposes. A resuspended particulate matter (RPM) emission estimate was developed using the first measurements of road dust loadings and silt content for the city. Results show that mobile sources dominate emissions of CO₂ (80%), CO (99%), VOC (68%), NO_x (95%), and SO₂ (85%). However, the newly estimated RPM comprises 90% of total PM₁₀ emissions, which are at least onefold larger than the PM₁₀ emissions from combustion processes. The 2012 EI was implemented in a chemical transport model (CTM) in order to understand the pollutants’ fate and transport. Model evaluation was conducted against observations from the city’s air quality monitoring network in two different periods. Modeling results for O₃ concentrations showed a good agreement, with mean fractional bias (MFB) of +11%, and a mean fractional error (MFE) of +35% with observations, but simulated PM₁₀ concentrations were strongly biased high (MFB +57%, MFE +68%), which was likely due to RPM emissions being overestimated. NO_x, CO, and SO₂ were also biased high by the model, which was probably due to emissions not reflecting current fleet conditions. Future work aims to revise emission factors for mobile sources, which are the main sources of pollutants to the atmosphere.

Bogota is the largest city in Colombia and is considered one of the most ones polluted in Latin America. The annual average PM10 concentration in the city is 55μg/m3, being as high as 90μg/m3 in the western region of the city. In this study, two sites in the western region were selected to assess the PM10 contribution from different sources. Two sets of fifty five 24–hour PM10 samples were taken at each site on quartz and Teflon filters. Chemical analysis of these samples was conducted to determine the ion, metal, and organic and elemental carbon concentrations. Ionic balance and mass closure were performed to check the consistency of chemical analysis. Positive Matrix Factorization (PMF) was then applied to determine the source contributions. Mobile sources and fugitive windblown dust were found to be the most significant sources of PM10 at both sites. An ion factor and a secondary aerosol source factor were identified at one site, whereas industry–related factors were identified at the other site, as expected in an area with a high density of small and medium industrial facilities. While it is true that source apportionment studies have been conducted worldwide, this is the first time that the Positive Matrix Factorization (PMF) model is applied in Bogota using full PM10 chemical speciation data, including carbonaceous materials, metals and ions. It is also the first time that a receptor model is applied simultaneously in two sites of the city. We aim that the results from this study will support environmental authorities in designing effective air pollution abatement measures in the city