Abstract
Annual concentration is a key element to assess the air quality of an area for long-time exposure effects. Nonetheless, obtaining annual concentrations from sensors is costly since it needs to have a year of measurements for each required pollutant. To overcome this issue, several strategies are studied to assess annual particulate matter concentration from monthly data, with their pros and cons depending on the risk acceptance and measurement campaign costs. When applied on a French dataset, the error spans from 12–14% with one month of measurement to 4–6% for six months of measurement for PM10 and PM2.5, respectively. A relationship between the mean relative error and 95th percentile relative error is provided with an R2 of 0.99. The relationship between PM10 and PM2.5 was also investigated and improved compared to previous work by considering the seasonality and influence on emission reaching a mean relative error of 12%. Thus, this study provides tools for urban planners, engineers, researchers, and public authorities for improved monitoring of annual air pollution at a lower cost for particulate matter.
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