Assessment of mean annual NO_2 concentration based on a partial dataset

摘要

NO2 is a pollutant harmful to both health and the environment. The European Union and the World Health Organization have developed guidelines in terms of pollutant. The value of 40 g/m(3) is set by both entities as the annual mean NO2 concentration not to be exceeded to prevent risks for human health. To assess this given value, yearlong in situ measurements are required. However, sometimes only partial data are available, such as having only NO. (NO + NO2) information, on the one hand, and, on the other hand, brief NO2 measurements performed over few months. To overcome the first hurdle, several methods exist in the literature to transform NOx data into NO2 data. The method of Derwent and Middleton is the most appropriate for France with less than 8% of deviation and even less deviation when considering rural and urban sites. For all values, NOx concentrations behave as expected with higher concentrations in autumn and winter than in spring and summer. However, for NO2 this trend changes around 80 g/m(3) for which the spring and summer values are higher. Therefore, to maximize measurements to assess an upper limit on annual NO2 concentration over a short period of time, those measurements should be done in winter if an annual concentration of less than 80 g/m(3) is expected, otherwise they should carry out in summer. To tackle the second issue, a second order polynomial approach is built on a Paris dataset covering years between 2013 and 2017 to determine annual mean concentrations with monthly mean concentrations and gives an overall error of 10%. The law built on Paris was then tested on several regions in France for the same period and resulted in predicted values with a mean error of about 15% compared to the measured ones. In the end, the presented methodology allows covering twelve times more ground with a single NO2 or NOx sensor with an acceptable error.