Q&As

The CITEPA produces reference data on greenhouse gas (GHG) emissions and air quality at the national level. Our data is checked annually against the CITEPA’s data to ensure accuracy.

However, we differentiate ourselves by providing a spatialisation and temporalisation of emissions, providing monthly and per km² data.

In 2018, CITEPA produced a report on spatialised data, which allowed us to validate the consistency of our methodology. Overall, our data is consistent with CITEPA’s, but with a finer granularity.

The AASQAs (Associations Agréées de Surveillance de la Qualité de l’Air) publish their data in Open Data, which allows us to access it and use it as a basis for our analysis, as we can do with any inventory. We have therefore validated the consistency of our data.

However, our sensors collect different data. We use our own sensors to measure CO₂ concentrations in addition to the data provided by the AASQAs. We also do not rely on the air quality data measured by the AASQAs.

Yes, our methodology is certified by the World Meteorological Organisation‘s IG3IS label. In France, our inventory complies with the methodology set out in the PCIT guide issued by the French Ministry for Ecological Transition.

We compare the calculated emissions with the measured emissions in a given area. We therefore calculate the direct emissions that can be linked to Scope 1 of the Bilan Carbone®.

In addition, the quality of our data monitoring allows us to provide data on indirect energy-related emissions for the area. (Related to Scope 2 of the Bilan Carbone®)

However, Bilan Carbone® is a diagnostic tool developed by ADEME (French Environment and Energy Management Agency) using a defined technology.

Our sensors measure the global concentration of CO₂, but it is impossible to distinguish by measurement alone between the CO₂ emitted by industry, cars or buildings over the course of a year, and the CO₂ emitted by all living organisms since the beginning of the Earth, in particular through animal respiration and plant photosynthesis.

Therefore, the measurement needs to be coupled with a carbon inventory that tracks human activity in parallel. This method allows us to quantify CO₂ emissions from human activities and distinguish them from CO₂ naturally present in the atmosphere.

It is difficult to measure local anthropogenic emissions directly. However, we can measure the concentration of CO₂ resulting from current and past anthropogenic and biogenic fluxes.

Our sensors measure this total concentration to validate carbon inventory emissions.

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To assess emissions in the area, our sensors measure the concentration of CO₂ in the atmosphere before and after the wind.

The number of sensors needed to cover an urban area depends on its size and complexity. For the best possible coverage of the prevailing winds in an area, it is advisable to deploy 5 stations: one at each cardinal point and one in the centre of the area.

Under favourable conditions, a minimum of 3 sensors is required to provide basic coverage. Using a differential atmospheric model, we can determine the specific needs of each city. In some cases, up to 9 sensors may be required to ensure adequate coverage.

Air quality is measured on the basis of the pollutants present at ground level, close to the emission sources and at a level that makes it possible to assess the impact on human health.

We measure the total concentration of CO₂ in the atmosphere by placing our sensors above the canopy, usually on the roofs of buildings. As these measurements are logistically different, they are not compatible.

For the time being, therefore, we are not including air quality measurements in our analysis.

The atmospheric model simulates the transport of CO₂ in the atmosphere, taking into account the contributions of human activities, vegetation respiration and large-scale CO₂ transport.

The CO₂ emitted by anthropogenic sources (such as factories, vehicles, etc.) and natural sources (such as volcanoes, plant respiration, etc.) does not remain static. It is dispersed in the atmosphere under the influence of winds, atmospheric currents and other meteorological processes.

Atmospheric models simulate these processes to predict how CO₂ moves and is distributed over different areas and altitudes.

By comparing observations from our monitoring stations with model predictions, we can adjust the carbon inventory to improve its accuracy.

This allows us to correct for potential biases and errors in the source data.

This modelling is currently carried out using the WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry). This model is the result of collaborative development by the scientific community.

Under the leadership of the National Oceanic and Atmospheric administration, numerous international research centres and renowned universities work together to continuously improve this model. This collaboration makes WRF-Chem an essential reference for weather forecasting and scientific research.