Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.
Using statistical models, the average hourly ozone (O3) concentration was predicted from seven meteorological variables (Pearson correlation coefficient, R = 0.87 -0.90), with solar radiation and temperature being the most important predictors. This can serve to predict O3 for cities with real time meteorological data but no pollutant sensing capability. Incorporating other pollutants (PM2.5, SO2, and CO) into the models did not significantly improve O3 prediction (R = 0.91 -0.94). Predictions were also made for PM2.5, but results could not reflect its peaks and outliers resulting from local sources. Here we make a comparative analysis of three different statistical predictor models: (1) Multiple Linear Regression (MLR), (2) Support Vector Regression (SVR), and (3) Artificial Neuronal Networks (ANNs) to forecast hourly O3 and PM2.5 concentrations in a mid-sized Andean city (Manizales, Colombia). The study also analyzes the effect of using different sets of predictor variables: (1) Spearman coefficients higher than ± 0.3, (2) variables with loadings higher than ± 0.3 from a principal component analysis (PCA), (3) only meteorological variables, and (4) all available variables. In terms of the O3 forecast, the best model was obtained using ANNs with all the available variables as predictors. The methodology could serve other researchers for implementing statistical forecasting models in their regions with limited pollutant information.
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