Good knowledge of the technological language and science of air quality measurement helps to choose the best air quality station on the market
We are currently facing many environmental crises: global warming, hazardous waste, resource depletion, and air pollution, among others. Millions of people die each year from diseases caused by exposure to outdoor air pollution. The Air Quality Index (AQI) is an important indicator for reflecting on and evaluating this air quality.
According to the United States Environmental Protection Agency (EPA), AQI is calculated using six main pollutants: fine particulate matter (PM2.5), particulate matter (PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2 ), and carbon monoxide (CO). Furthermore, the AQI measures general air quality on a scale from 0 to 300 divided into six levels (Good, Moderate, Harmful for sensitive people, Harmful, Very Harmful, and Dangerous). These levels show the impact of air quality on human health and provide a good reference for policy decision-making on health, economy, or mobility.
Statistical and scientific concepts
To measure these pollutants precisely, Libelium Air Quality Station uses Machine Learning. Libelium engineers have used statistical and Artificial Intelligence techniques. Machine Learning algorithms were trained with loads of data to obtain advanced models that accurately enhance sensor quality. ML models ‘learn’ as a result of the observation of tons of data and spontaneously find a solution. Then, the model makes predictions, finds patterns, and draws conclusions without human interaction.
Libelium engineers have used statistical and Artificial Intelligence techniques. Machine Learning algorithms were trained with loads of data to obtain advanced models that accurately enhance sensor quality
The goal is to get the best R² in the market. The term R² is a very common and vital concept, but it can be complex and misleading. The coefficient of determination (R²) measures a comparison between an air quality device and another taken as a reference. The closer to 1, the better the device imitates the performance of the reference device.
R² is always an important indicator of the quality of the sensor. Remember that, depending on the equation applied, R² does not include the error, and it must be read with other indicators, for example with error parameters like the MAE (Mean Absolute Error), RMSE (Root-Mean-Square Error) or the statistical distribution of the error. The RMSE allows us to evaluate the dispersion of the errors too because it penalizes especially the longest distances. This metric is always highly valuable since it describes well the accuracy of a sensor.
Unlike other data providers, the R² parameter calculated by Libelium is directly related to the RMSE.
How to get the best R²
But it is easy to get a great R² when the air quality station is located in low contaminated areas or with always the same temperature, for example. The algorithm needs to learn from the more various air conditions.
Beta testing call
Hostile air conditions are wanted
to push the Libelium Air Quality Station algorithm to the limit
Libelium has launched a beta testing call so that our Air Quality Station can learn in the environments with the most varied air quality and achieve the best R² on the market.
I want to participate
As you can see, talking properly about air quality can get quite technical, and not everyone has a background and knowledge of science and technology.
Therefore, to make it easier to reach people who are not so fluent in the matter, Libelium has developed a glossary of the most important terms in the measurement of air quality. In this manner, you will know what to look for when exploring the best solution on the market for measuring pollutants in the air.
In conclusion, his glossary provides definitions of common terms used in the context of air pollution. Everything you need to know about air quality in a unique document that you can download and share.
Index of the Air Quality Glossary
- Air pollution
- AQI (Air Quality Index)
- ppb (parts per billion)
- ppm (parts per million)
- µg/m³ (micrograms per cubic meter)
- Chemical parameters
- Particulate matter (PM)
- Nitrogen dioxide (NO2)
- Ozone (O3)
- Carbon monoxide (CO)
- Sulfur dioxide (SO2)
- Carbon dioxide (CO2)
- Statistics / mathematics / computer science
- Artificial Intelligence (AI)
- Machine Learning (ML)
- R² (coefficient of determination)
- MAE (Mean Absolute Error) and RMSE (Root-Mean-Square Error)
If choosing the best air quality station is a struggle for you, grab this glossary to get started with this exciting and evolving industry that can make the world more livable.
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