The factors that have most influenced climate change in recent years are related to human activities. The burning of fossil fuels such as coal and oil increased the concentration of carbon dioxide (CO2) in the atmosphere as well as industrial activities and deforestation for agriculture (NASA, [20--]).

 The main greenhouse gases emitted by human activities in 2018 in the United States were 81% carbon dioxide (CO2), 10% Methane (CH4), 7% Nitrogen Oxide (N2O) and 3% fluorinated gases, totalizing 6,677 million metric tons of carbon dioxide equivalent. In the same year 28% of gases emitted came from transport, 27% from electricity, 22% from industry, 12% from commerce and homes and 10% from agriculture (EPA, 2018). The emission of carbon dioxide from fossil fuel (ffco2) was 10,008 in 2018. In Brazil, with a population of 209,469,323, in the same year, the flaring of gas was 3.2 MtCO2 (CENTER FOR GLOBAL ENVIRONMENTAL RESEARCH, 2019; GLOBAL CARBON ATLAS, 2018).

Another difficulty is the frequency in which the data are made available, usually in the range of months.

A solution to collect high density and accurate data of atmospheric composition, in real time, to process data and make a clustering mapping accessible by public. The next picture shows a spectrum in UV-VIS and NIR regions made by a simple camera.

Gases absorbs radiations in certain paterns like show in the next picture:

In this way, the application can collect atmospheric composition data wherever there is a user, send it for processing on a server and with the processed data present a detailed map of the atmospheric composition that even allows the crossing of data in the form of clustering for the identification of major sources of gaseous pollutants.

This technology is important because it allows for real-time monitoring, warning of unhealthy gas concentration conditions, identification of punctual gas sources and planning of public policies on gas emissions. As the data can have high density, it will be possible to identify local differences caused by small activities, as well as the emission of gases in unlicensed activities.

It is expected to achieve a refinement of atmospheric composition data at a global level, which will help to monitor in the short, medium and long term the impacts of anthropogenic and natural activities.

A gamification method linked to physical exercise APPs will be used to show air quality during activity monitoring. Filters for Instagram showing the concentration of gases where people take selfies can be made available to encourage the use of the resource and increase its user base. All this can be made by a API availability.