How can you effectively calibrate data inputs for air quality modeling?

1 Define your objectives

Before you start calibrating your data inputs, you need to define your objectives and scope of your modeling project. What are the pollutants, sources, receptors, and time periods of interest? What are the regulatory standards or criteria that you need to meet or compare? What are the uncertainties and limitations of your data sources and model? These questions will help you determine the type, quality, and quantity of data inputs that you need for your model.

2 Collect and process your data

The next step is to collect and process your data inputs for your model. Depending on the model you use, you may need data on emissions, meteorology, terrain, land use, background concentrations, and other parameters. You can obtain these data from various sources, such as monitoring stations, inventories, satellites, or other models. However, you need to make sure that your data are consistent, complete, and representative of your modeling domain and period. You may need to perform quality assurance, gap filling, interpolation, or conversion of your data to match the format and resolution of your model.

3 Perform sensitivity analysis

Once you have your data inputs ready, you can perform sensitivity analysis to evaluate how your model responds to changes in your data inputs. Sensitivity analysis can help you identify the most influential data inputs, the optimal range of values, and the potential sources of error or uncertainty. You can use various methods for sensitivity analysis, such as perturbation, screening, variance-based, or gradient-based methods. You can also use software tools, such as Sensitivity Analysis for Everyone (SAFE) or Sensitivity Analysis Toolkit (SAT), to facilitate your sensitivity analysis.

4 Compare with observations

Another important step in calibrating your data inputs is to compare your model results with observations from independent sources, such as monitoring stations or satellites. Comparing with observations can help you validate your model performance, assess your model accuracy and bias, and identify the areas of improvement or refinement. You can use various statistical measures, such as mean error, root mean square error, correlation coefficient, or index of agreement, to quantify the agreement between your model and observations. You can also use graphical methods, such as scatter plots, time series plots, or spatial maps, to visualize the comparison.

5 Adjust and optimize your data inputs

Based on the results of your sensitivity analysis and comparison with observations, you can adjust and optimize your data inputs to improve your model performance and accuracy. You can use various methods for adjusting and optimizing your data inputs, such as manual tuning, inverse modeling, or optimization algorithms. You can also use software tools, such as Parameter Estimation and Uncertainty Analysis (PEST) or Model Optimization and Calibration Toolkit (MOZART), to assist you in adjusting and optimizing your data inputs.

6 Repeat and document your calibration

The final step in calibrating your data inputs is to repeat and document your calibration process until you achieve your desired model performance and accuracy. You may need to iterate several times to find the best combination of data inputs that fit your model and objectives. You also need to document your calibration process, methods, data sources, assumptions, results, and uncertainties. Documentation can help you communicate your modeling results, justify your modeling decisions, and support your modeling applications.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?