Are Air Quality Forecasts Actually Accurate?
Clean air is vital for our personal health and well-being, but that’s not all. It’s also crucial we monitor air quality, so we can combat everyday pollution and wildfires.
Today, the need for accurate air quality forecasting has become more crucial than ever. Air quality forecasts help us understand risks associated with air quality and make decisions to protect ourselves.
But how reliable are these air quality forecasts?
Here, we’ll look deeper into how air quality is forecasted as well as the factors that can influence the forecast and its accuracy.
How Accurate Are Air Quality Forecasts?
Air quality forecasts can vary in accuracy depending on several factors, like weather and emissions. However, air quality forecasts are generally improving. Thanks to technological advancements and a better understanding of air quality causes, forecasts are more accurate than they were historically.
The accuracy of air quality forecasts also varies depending on the time scale. Short-term forecasts of a few hours are generally better than longer-term forecasts over several days.
How Air Quality Forecasting Works
Air quality forecasting involves using mathematical models and data analysis techniques to predict air quality in a specific area over a particular period. The process typically involves the following steps:
- Data Collection: Air quality data is collected from air quality monitoring stations, satellites, and other sources. The data collected includes; Levels of various air pollutants, such as ozone, particulate matter, and nitrogen dioxide. Meteorological data, such as temperature, wind speed, and humidity.
- Data Analysis: The collected data is analyzed to identify patterns and trends in air quality. This analysis can help identify air pollution sources, such as traffic, industry, or natural events like wildfires.
- Model Development: Mathematical models are developed based on data analysis to predict air quality levels over time.
- Forecasting: Based on the models, air quality forecasts provide information about expected air quality levels over the next few hours or days.
Machine learning (ML) is now more commonly used to make air quality predictions based on the data collected from air quality monitoring networks. ML algorithms can learn patterns and relationships in large and complex datasets, allowing them to accurately predict air quality based on meteorological conditions, pollutant emissions, and historical air quality data.
The advantages of using ML for air quality predictions are that they can:
- Handle large and complex datasets. This allows them to capture the nonlinear relationships between factors affecting air quality.
- Adapt to changing data patterns and improve the accuracy of their predictions over time.
However, ML algorithms can be computationally intensive and require large amounts of data to train. And this can be challenging for some applications.
4 Factors That Affect Air Quality Forecasting
Several factors can affect the accuracy of air quality forecasting, including:
#1 - Meteorological Conditions
Meteorological conditions play a significant role in determining air quality. This can include temperature, wind speed, and humidity.
For example, changes in weather patterns, like an increase in rainfall or wind, can affect the movement of air pollutants. This can, in turn, affect air quality forecasting. Naturally, this is why shorter-term forecasting tends to be more accurate than long-term forecasts.
#2 - Emissions
Another factor is the emissions data which can come from various sources, including industrial, transportation, and natural sources such as wildfires or dust storms. The accuracy of emissions data used can impact the air quality forecast.
#3 - Chemical Reactions
Next, the chemical reactions in the atmosphere can also affect air quality forecasting. Pollutants can undergo complex reactions with other chemicals in the atmosphere, making it difficult to predict how air quality will change over time.
#4 - Data Quality
Finally, the quality and availability of data to drive air quality forecasting models can also affect their accuracy. Poor quality data can lead to inaccurate forecasts. Missing data can also make it difficult to predict how air quality will change over time. That is why it is so important cities build their own air quality network to fill in the data gaps.
Air quality forecasts are an important tool for public health and policy decision-making. They provide valuable information to individuals, government agencies, and industries that can help them make informed decisions to reduce exposure to air pollution and protect public health.
Worried About Your Air Quality?
Monitor the Particulate Matter levels around the world with our free, real-time PurpleAir Map or join PurpleAir's mission to make air quality data accessible to everyone by investing in an air quality monitor for your home.
Together, we can be informed and make changes in our daily habits and the community to improve air quality.