Abstract
A method of retrieving PM10 particles concentrations at the ground level from AOT (Aerosol Optical Thickness) measurements is presented. It uses data obtained among five years during 2003 to 2007 summers in the Lille region (northern France). As PM10 concentration strongly depends on meteorological variables, we clustered the meteorologi- cal situations provided by the MM5 meteorological model forced at the lateral boundaries by the operational NCEP model in eight classes (local weather types) for which a robust statistical relationship between AOT and PM10 was found. The meteorological situations were defined by the hourly vertical profiles of temperature and (zonal and merid- ian) wind components. The clustering of the weather types were obtained by a self-organizing map (SOM) followed by a hierarchical ascending classification (HAC). We were then able to retrieve the PM10 at the surface from the AER- ONET AOT measurements for each weather type by doing non linear regressions with dedicated SOMs. The method is general and could be extended to other regions. We analyzed the strong pollution event that occurred during August 2003 heat wave. Comparison of the results from our method with the output of the CHIMERE chemical-transport mo- del showed the interest to tentatively combine these two pieces of information to improve particle pollution alert.
Highlights
Air pollution in cities has a major impact on human health and constitutes one of the major environmental and public-health issues human society has to address today
This paper presents a method for retrieving PM10 concentration from aerosol optical thickness (AOT) measurements done in the Lille region (France) with a sun photometer
As PM10 concentration strongly depends on meteorological variables, we first clustered the meteorological situations in a number of classes for which the AOT and PM10 relationship is expected to be simplified
Summary
PM10, especially, encompasses a wide range of particle types in terms of size (coarse, fine, and ultrafine) and can differ from chemical composition (dust, combustion particles, marine primary particles, secondary organic aerosol, secondary inorganic aerosol) and sources (natural or anthropogenic) as car traffic, industry, domestic household). This complex composition hampers the understanding of PM10 as a function of local sources, long-range transport and meteorology for a given site. Measuring stations are sparsely distributed and do not provide sufficient data for mapping particle concentration, since air quality can be highly variable both in space and in time. The key parameter for this purpose is the horizontal distribution of columnar aerosol optical thickness [1], but the relationship between the aerosol con-
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