| 12 Jun 2025
Origin, transport and processing of organic aerosols at different altitudes in coastal Mediterranean urban areas
Abstract. Organic molecular markers in atmospheric PM10 were analysed by off-line GC-MS techniques in an urban background site (81 m above sea level (asl)) and in a nearby elevated sub-urban background site (415 m asl), in cold and warm periods in Barcelona; situated in the Western Mediterranean Basin. Previous studies reported similar PM concentrations and substantial organic matter contributions in both sites but did not analyze the organic molecular composition, which is expected to vary within the city's vertical airshed due to a weakening influence of local emission sources and enhanced influence of regional air masses. Multi-variant analysis of organic molecular marker concentrations, together with major air quality parameters (NO, NO2, O3, PM10), resolved six components that represented primary emissions sources and secondary organic aerosol formation processes: 1) diurnal traffic 2) nocturnal traffic, 3) biomass burning, 4) biogenic with primary and secondary organic markers, 5) fresh secondary, and 6) regional secondary. Urban traffic emissions reached the elevated site during daytime through the sea-mountain breeze, while nocturnal traffic emissions accumulated in the nighttime urban atmosphere, when the two sites were often disconnected by temperature inversions. Biomass burning, dominant in the cold period, was the main contributor to toxic PAHs in these two background sites. Regional secondary organic aerosol contribution was more abundant in the elevated background site. Several SOA formation mechanisms were identified such as the oxidation of traffic emissions by NOx, the aqueous-phase oxidation under high relative humidity, and formation of fresh SOA under conditions of low relative humidity.
Comments to the manuscript titled “Origin, transport and processing of organic aerosols at different altitudes in coastal Mediterranean urban areas” by Clara Jaén et al.
The authors measured 68 organic molecular markers in diurnal and nocturnal PM10 in an urban background site and in a nearby elevated sub-urban background site, in cold and warm periods in Barcelona. By applying MCR-ALS method, six different sources were resolved based on the profile of organic molecular markers. This manuscript has a good discussion of the influence of sources, altitude, seasons and diurnal differences. However, there are some concerns about the source apportionment methods and stability of the results. Firstly, the manuscript lacks the basic information of the MCR-ALS method and the rational of choice among the other source apportionment methods, e.g., Positive Matrix Factorization (PMF). Secondly, two weeks of diurnal and nocturnal sampling in cold and warm periods will have 28 samples, which is far behind the substance number (i.e., 67) input in the model. Therefore, the stability of results and the choice among different runs will be a big concern for the solidity of results of discussion. For example, for receptor models, depending on the number of degrees of freedom per variable, the suggested minimum number of samples (N) needs is N>30+(V+3)/2, where V represents the number of species and should be 65 here in the study (Henry et al., 1984). PMF has several sensitivity analysis, i.e., factor choosing, bootstrapping (BS) mapping and displacement (DISP) diagnostics. Please indicate the similar analysis for MCR-ALS to make sure the source apportionment result is solid and strong enough to support the subsequent discussions. Thirdly, given the limited samples number involved in the analysis, some interpreted source is not trusty, e.g., diurnal and nocturnal traffic and fresh secondary. Specially, for fresh secondary, the sampling matric, i.e., PM10 is far from the new particle generation (e.g., PM1 or smaller). Fourthly, the interpretation is based on source indicators like PAHs, hopanes, retene and others but in the real environment most of them are not single-source specific, fossil fuel combustion, vehicle emission and biomass burning will be integrated (Bi et al., 2008; Shen et al., 2012). Therefore I suggest the authors to reduce the number of sources and make the results more solid. Other multivariate analysis that don’t have strict limit on degrees of freedom, i.e., unsupervised hierarchical clustering analysis, could be supplemented to support the source apportionment results. Also, the source apportionment of other studies conducted in Barcelona should be compared with and discussed in the manuscript in detail. Given the above main concern, for now the manuscript is not qualified for ACP but it could be considered after major revision.
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References:
Bi, X., Simoneit, B.R.T., Sheng, G., Fu, J., 2008. Characterization of molecular markers in smoke from residential coal combustion in China. Fuel 87, 112–119. https://doi.org/10.1016/j.fuel.2007.03.047
Henry, R. C., Lewis, C. W., Hopke, P. K., & Williamson, H. J., 1984. Review of receptor model fundamentals. Atmospheric Environment (1967), 18(8), 1507-1515. https://doi.org/10.1016/0004-6981(84)90375-5
Shen, G., Tao, S., Wei, S., Zhang, Y., Wang, R., Wang, B., Li, W., Shen, H., Huang, Y., Yang, Y., Wang, W., Wang, X., Simonich, S.L.M., 2012. Retene emission from residential solid fuels in China and evaluation of retene as a unique marker for soft wood combustion. Environ. Sci. Technol. 46, 4666–4672. https://doi.org/10.1021/es300144m