Electric vehicles do not produce tailpipe emissions, so they do not contribute to air pollution from transportation.

Carbon dioxide is a greenhouse gas, but it is not considered an air pollutant because it does not directly harm human health.

All of the above strategies are effective in reducing air pollution from transportation.

All of the above strategies are effective in reducing air pollution from power plants.

Land use is not a factor that directly influences air quality forecasting.

Gaussian dispersion models are the most common type of air quality model because they are relatively simple to use and can be applied to a wide range of scenarios.

Improving air quality forecasting is not a method for mitigating air pollution, but it can be used to help reduce air pollution by providing information that can be used to develop and implement effective air pollution control strategies.

The availability of data is the most important factor to consider when developing an air quality forecasting system because without data, it is not possible to develop an accurate model.

The cost of developing and running air quality models is not a challenge in air quality forecasting because there are a number of free and open-source air quality models available.

Air quality forecasting can help to save money by providing information that can be used to develop and implement effective air pollution control strategies, which can reduce the costs associated with air pollution.

Renewable energy sources are not a type of air pollution control technology because they do not remove pollutants from the air.

The National Oceanic and Atmospheric Administration (NOAA) is not responsible for air quality regulation in the United States.

State Implementation Plans (SIPs) are not a type of air quality standard.

Integrated monitoring stations are not a type of air quality monitoring station.

Long-term forecasts are not a type of air quality forecast.