The process of air pollution has been going on not the first millennium, but never before had it been so intense like in recent decades.
The results did not keep us waiting; urban environmental issues (in Beijing for the second time in December a "red" level of anxiety is declared because of the smog), depletion of the ozone layer, which protects Earth's surface from the harmful for all living ultraviolet radiation, global warming, entailing an increase in global sea level, which can have catastrophic consequences for all mankind.
Activities of heavy industry, in particular the burning of fossil fuels, has led to the fact that global temperatures have risen by one degree (in average), causing sea level rise of more than 20 cm. That caused concern of the heads of governments of leading countries and led to a series of global solutions at the recent climate summit in Paris.In order to deal with some phenomenon issue, it is important to know what it is about at the level of specific data and to be able to evaluate it.
Important indicators of air pollution are: carbon CO2, ozone O3, nitrogen dioxide NO2 and sulfur dioxide S02.The values of these indicators are very different not only from region to region, but even within few kilometers, depending on many factors. The presence and the proximity of the source of fuel combustion, such as thermal power plants - carbon dioxide (CO2) and sulfur dioxide (S02). Direction of the wind. Traffic on the streets - the brown dioxide (NO2) can be visually observed in the air of some cities, hence the name. The other possible factors - the presence of chemical plants, cement plants, actively ongoing agriculture etc.
Therefore, to determine the measures required to reduce air pollution, data accuracy is very important.By the end of this year OpenWeatherMap released a new API data on air pollution, including the main indicators: CO2, O3, NO2 and S02.
In OpenWeatherMap there are three components of the data model - the global data with a large coverage, more detailed regional data, and local data from the stations. This model allows you to produce results for a specific location with high accuracy.
All the data is processed, validated and, if necessary, aggregated with other data sources.