![]() ![]() This starts with green, which indicates very good quality air. To show the differences in air pollution, a colored 7-level scale (Airly CAQI) operates on our platform. In Airly, when providing the CAQI index, we consider PM10 and PM2.5 dust. It is a relative measure of the amount of air pollution. The air quality index used in Europe, CAQI, has five ranges, with the values presented on a scale from 0 (very low) to >100 (very high). ![]() The platform updates its data on average every 3 minutes, so the measurements shown are the rolling average of the last full hour. In our monitoring system, we use the hourly index, which describes the current air quality based on the average of all measurements from the last hour. Geo measure area calculator for land app has very simple user interface so everyone can use this awesome application. In European cities, a simpler presentation of air quality data is accomplished using various different indices, each converting their measurements into one easily understood number. One click to calculate area Different types of map views i.e normal, satellite view, terrain, hybrid etc Fields Area Measure App is a smart tool for measuring area and distance. Once you place your points on the map and then calculate area between all point. It is right next to the air quality forecast graph. Fields area Measure is a smart tool for measuring areas on the map. The percentage factor is visible in the side panel after selecting the measuring station on the map. The accuracy value is calculated for each Airly station, based on measurement data and forecasts collected from the last 14 days. We monitor the accuracy of our forecast on an ongoing basis and provide this information in our applications. In our opinion, this proves the high effectiveness of our predictive model. However, the difference between the expected and actual air quality is rarely big enough to make the forecast completely inaccurate. For example, we may predict very good air, but in reality, it will be "just" good. Similarly, when our forecast predicts a very high level of pollution, almost certainly smog awaits us - put on your masks and preferably do not leave the house! In the remaining 5% of cases, our model may be wrong, but usually, it is off by only 1 CAQI level. In other words, when our model predicts very good air, there is a 95% chance it will be so. ![]() This means that when forecasting air quality, in 95 cases out of 100, the absolute error of our prediction (relative to later-measured values) is not higher than 25 on the CAQI scale, i.e. According to our calculations, the accuracy of our air quality forecast is over 95%. ![]()
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