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Air Quality Forecasting: Combining Traditional and AI Methods

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Learn about the two main methods used to predict air quality and how combining them can provide more accurate predictions.

As the summer of 2023 brought wildfire smoke from Canada, people became increasingly concerned about air quality and its impact on health. Poor air quality can trigger asthma and exacerbate heart and respiratory problems, leading to emergency room visits and even death. While it’s easy to find daily air quality index scores for most cities, predicting air quality in the days ahead is more challenging.

Chemical Transport Models vs Machine-learning Models

As a professor of civil and environmental engineering, I work on air quality forecasting. Two main methods are used to predict air quality a few days ahead or longer: chemical transport models and machine-learning models. Chemical transport models use known chemical and physical formulas to calculate the presence and production of air pollutants. They use data from emissions inventories reported by local agencies and meteorology data to simulate the flow and chemical reactions of air pollutants. However, their simulations involve multiple variables with huge uncertainties, which can make the results less accurate.

Machine-learning models, on the other hand, learn patterns over time from historical data to predict future air quality for any given region and then apply that knowledge to current conditions to predict the future. However, the downside of machine-learning models is that they do not consider any chemical and physical mechanisms, as chemical transport models do. Also, the accuracy of machine-learning projections under extreme conditions, such as heat waves or wildfire events, can be off if the models weren’t trained on such data.

Machine Learning Measurement Model Fusion

Scientists have determined that neither model is accurate enough on its own, but using the best attributes of both models together can help better predict the quality of the air we breathe. This combined method, known as the machine–learning–measurement model fusion (ML-MMF), has the ability to provide science-based predictions with more than 90% accuracy. It is based on known physical and chemical mechanisms and can simulate the whole process, from the air pollution source to your nose. Adding satellite data can help inform the public on both air quality safety levels and the direction pollutants are traveling with greater accuracy.

Recent research has shown that the combined model is 66% more accurate than the chemical transport model and 12% more accurate than the machine-learning model alone. While the chemical transport model is still the most common method used today to predict air quality, applications with machine-learning models are becoming more popular. For example, the U.S. Environmental Protection Agency’s AirNow.gov relies on machine learning for its regular forecasting method. The site also compiles air quality forecast results from state and local agencies, most of which use chemical transport models.

Conclusion

In conclusion, predicting air quality is a complex task that requires the use of multiple methods. While chemical transport models and machine-learning models have their own strengths and weaknesses, combining them can provide more accurate predictions. As information sources become more reliable, the combined models will become more accurate ways to forecast hazardous air quality, particularly during unpredictable events like wildfire smoke.

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