Here are some common AI approaches we use to improve air pollution forecasting:

1. Regression Models: Linear regression, decision trees, and random forests can predict emission inventories based on historical data and relevant features.

2. Physics-Based Models with Machine Learning: Combining physics-based models (based on atmospheric science principles) with machine learning techniques to improve the representation of complex interactions in the atmosphere.

3. IoT and Sensor Networks: Integrating real-time data from IoT devices and sensor networks allowing for more accurate and timely air quality predictions.

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Managing people flow and decreasing air pollution concentrations involve implementing strategies that address transportation, urban planning, and lifestyle choices. Here are several approaches that can contribute to achieving these goals:

1. Encourage the use of public transportation, such as buses, trains, and subways, to reduce the number of individual vehicles on the road.

2. Promote walking, cycling, and other forms of active transportation to reduce reliance on motorized vehicles for short-distance trips.

3. Implement smart traffic light systems that optimize traffic flow, reduce congestion, and minimize unnecessary idling, which can contribute to air pollution.

4. Promote mixed-use development that integrates residential, commercial, and recreational spaces.

5. Increase the number of green spaces within urban areas to absorb pollutants and provide residents with alternative recreational spaces.

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