Natural vegetation, such as trees and plants, absorbs pollutants from the air and releases oxygen, which helps to clean the air.

Air quality forecasting helps to identify areas with poor air quality and to develop strategies to reduce emissions and improve air quality.

Water vapor is a natural component of the atmosphere and is not considered an air pollutant.

Air pollution control refers to the methods and technologies used to reduce or eliminate air pollutants.

Sunlight is not a direct factor that affects air quality, although it can influence the formation of certain pollutants, such as ozone.

Air quality forecasting involves using mathematical models to predict future air quality conditions based on current and historical data.

Numerical weather prediction models are used to predict weather conditions, not air quality conditions.

Meteorological data and emissions data are the two primary input data used for air quality forecasting.

Meteorological forecasts are not a common output of air quality forecasting.

Air quality forecasting verification involves comparing forecasted air quality conditions with observed air quality conditions to assess the accuracy of the forecasts.

Numerical weather prediction is not a common method for air quality forecasting verification.

The primary goal of air quality forecasting verification is to identify and correct any errors in the forecasts so that they can be improved in the future.

Computational limitations are not a common challenge in air quality forecasting.

Air quality decision-making involves using air quality forecasts to make decisions about how to reduce emissions and improve air quality.

Predicting future weather conditions is not a common example of air quality decision-making.