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Abstract: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 km 2 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 CO 2 (80%), CO (99%), VOC (68%), NO x (95%), and SO 2 (85%). However, the newly estimated RPM comprises 90% of total PM 10 emissions, which are at least onefold larger than the PM 10 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 O 3 concentrations showed a good agreement, with mean fractional bias (MFB) of +11%, and a mean fractional error (MFE) of +35% with observations, but simulated PM 10 concentrations were strongly biased high (MFB +57%, MFE +68%), which was likely due to RPM emissions being overestimated. NO x , CO, and SO 2 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.

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A hypothesis by Maul (1977), stating the rate of change of loop current (LC) volume is related to deep Yucatan Channel (YC) transport, is tested with a continuous 54-year simulation of the Gulf of Mexico (GoM) using a regional 1/25° resolution Hybrid Coordinate Ocean Model (HYCOM) configuration. The hypothesis states that the imbalance of transport between the upper YC and the Florida Straits controls the rate of change of the LC volume and that the imbalance is compensated by transport through the deep YC. Bunge et al. (2002) found a strong relationship between the deep YC transport and the LC area using 7.5 months of data from a mooring array in the YC, but the observational record length was relatively short compared to the time scale of LC variability. The 54-year HYCOM simulation provides a much longer and spatially complete data set to study the LC variability. Results show that the time evolution of the LC between two shedding events can be viewed as a combination of relatively high-frequency fluctuations superimposed on a low-frequency trend. The high-frequency variability of the LC area time derivative and the deep YC transport are related. The low-frequency variability is examined by comparing the LC area time series with time-integrated transport in the deep YC, and statistically similar trends are identified, supporting the Maul (1977) theory.

Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.