Multiple imputation is a preferred technique for handling missing values in air quality data as it preserves the variability and relationships within the data.

Feature selection serves multiple purposes in air quality forecasting, including reducing data dimensionality, improving model interpretability, and reducing overfitting.

Support vector machines are often used in air quality forecasting due to their ability to capture non-linear relationships and handle high-dimensional data.

The primary goal of air quality forecasting is to predict future air pollutant concentrations, which can be used for various purposes such as public health alerts, air quality management, and policy development.

Mean absolute error, root mean square error, and coefficient of determination are commonly used statistical methods for evaluating the accuracy of air quality forecasts.

Ensemble methods, such as random forests, offer several advantages in air quality forecasting, including improved accuracy, reduced overfitting, and increased interpretability.

Air quality forecasting often utilizes a combination of data sources, including satellite remote sensing data, ground-based monitoring data, and meteorological data.

Meteorological data plays a crucial role in air quality forecasting as it helps predict wind patterns, estimate atmospheric stability, and account for temperature variations, all of which influence air pollutant dispersion and transport.

Time series analysis is a statistical technique specifically designed to analyze data collected over time, making it a suitable choice for identifying trends and patterns in air quality data.

Air quality forecasting serves multiple purposes, including informing public health decisions, supporting air quality management strategies, and assessing the effectiveness of air pollution control measures.

Geostatistics is a branch of statistics that deals with spatial data and is commonly used in air quality forecasting to handle the spatial variability of air pollutants.

The key difference between deterministic and probabilistic air quality forecasts is that deterministic forecasts provide a single predicted value, while probabilistic forecasts provide a range of possible values, representing the uncertainty in the prediction.

Multiple statistical techniques are commonly used to evaluate the performance of air quality models, including mean absolute error, root mean square error, and coefficient of determination.

Data assimilation plays a crucial role in air quality forecasting as it combines observations from different sources, estimates the initial conditions for the model, and improves the accuracy of the forecast.

Random forests are a machine learning algorithm that can handle high-dimensional data and are commonly used in air quality forecasting due to their ability to capture complex relationships between input features and air pollutant concentrations.