Estimates of PM 2.5 distributions based on satellite data depend critically on an established relation between AOD and ground level PM 2.5 . In this study we performed an experiment at Cabauw to establish a relation between AOD and PM 2.5 for the Netherlands. A first inspection of the AERONET L1.5 AOD and PM 2.5 data showed a low correlation between the two properties. The AERONET L1.5 showed relatively many observations of high AOD values paired to low PM 2.5 values, which hinted cloud contamination. Various methods were used to detect cloud contamination in the AERONET data to substantiate this hypothesis. A cloud screening method based on backscatter LIDAR observations was chosen to detect cloud contaminated observations in the AERONET L1.5 AOD. A later evaluation of AERONET L2.0 showed that the most data that are excluded in the update from L1.5 to L2.0 were also excluded by our cloud screening, which provides confidence in both our cloud-screening method as well as the final screening in the AERONET procedure. The use of LIDAR measurements in conjunction with the CIMEL AOD data is regarded highly beneficial. Contra-intuitively, the AOD to PM 2.5 relationship was shown to be insensitive to inclusion of the mixed layer height. The robustness of the relation improves dependent on the time window during the day towards noon. The final relation found for Cabauw is PM 2.5 =124.5×AOD−0.34 and is valid for fair weather conditions. The relationship found between bias corrected MODIS AOD and PM 2.5 at Cabauw is very similar to the analysis based on the much larger dataset from ground based data only. We applied the relationship to a MODIS composite map to assess the PM 2.5 distribution over the Netherlands for the first time. The verification of the derived map is difficult because ground level artefact free PM 2.5 data are lacking. The validity and utility of our proposed mapping methodology should be further investigated.