Insights to WRF-Chem sensitivity in a zone of complex terrain in the tropical Andes: Effect of boundary conditions, chemical mechanisms, nesting, and domain configuration

Cifuentes, Felipe; González, C.; Aristizábal, Beatriz Helena

doi:10.1016/j.apr.2021.101093

Cited by 7 publications

(8 citation statements)

References 46 publications

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“…Figure 2 shows the domain configuration of the model considering 3-nested domains (1:5 nesting ratio) with a downscaling process from 25-km resolution (parent domain) to 1-km resolution in the domain of interest, which covers the urban area of Manizales and surrounding areas. Details of model configuration are presented in Table S3, and major details about the reasons for the chosen configuration are described in the study by [18].…”

Section: Air Quality Modelingmentioning

confidence: 99%

“…The following statistical performance metrics were calculated using the software R and the Openair package [40]: mean bias (MB), mean gross error (MGE), root mean squared error (RMSE), normalized MB (NMB), normalized MGE (NMGE), normalized RMSE (NRMSE), and Pearson correlation coefficient (r). Different studies conducted around the world have used these statistics for model evaluation purposes [18,19,23,41,42]. Definitions of statistics used in this study are presented in Equations (S1)-(S7) of the Supplementary Materials.…”

Section: Field Observations and Air Quality Model Evaluationmentioning

confidence: 99%

“…Although EIs are essential tools in air quality management, they only provide information at a specific time and place without identifying the relationship with the observed data (ambient air measurements) [16,17]. In this sense, air quality modeling allows studying and forecasting the dynamics of ambient air, including the analysis of emission sources (EIs disaggregated spatially and temporally), meteorological processes, and physical and chemical processes [18][19][20]. Eulerian chemical transport models (CTM) are powerful tools for air quality studies at an urban or regional scale [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, air quality modeling allows studying and forecasting the dynamics of ambient air, including the analysis of emission sources (EIs disaggregated spatially and temporally), meteorological processes, and physical and chemical processes [18][19][20]. Eulerian chemical transport models (CTM) are powerful tools for air quality studies at an urban or regional scale [18,19]. Therefore, air quality modeling turns out to be useful as a validation tool for EIs and to corroborate the reliability of activity data and emission factors, by comparing the results with observations (ambient air measurements).…”

Section: Introductionmentioning

confidence: 99%

“…Modeling is also a fundamental tool in management plans. Some studies have integrated different tools for the assessment of air quality such as EI, transport models, and air quality models [5,[18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

et al. 2021

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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.

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Section: Air Quality Modelingmentioning

confidence: 99%