First module: It allows the automatic daily collection, storage and organization of the ... trajectories data are obtained by automatically running the (free-access) HYSPLIT model on a ... (ftp://toms.gsfc.nasa.gov/pub/omi/images/aerosol/). 5)-to-8) ...
Appendix to the paper by Barnaba et al., Atm. Env., 2017 http://dx.doi.org/10.1016/j.atmosenv.2017.04.038
APPENDIX A: A tool to operate the EC-Methodology and implementation over the Latium region To facilitate the operational implementation of the EC-Methodology over the DIAPASON Pilot Scale, a specific tool (MATLAB® code) was developed. This tool is composed of three modules. The first module allows to rapidly and automatically access all the web-based, desert dust daily information needed by that Methodology (point 1a in Section 2.2). The second module is dedicated to the selection of the desert dust-affected dates based on this collected information (point 1b in Section 2.2). This is driven by a Graphical User Interface (GUI, Figure A1) to help the operator in the evaluation of each one of the daily images collected. The third, automated module performs the computation of the daily PM10net_dust values in the Regional Background (RB) site on the dust-affected dates according to Eq. 1 (Section 2.2). For those networks having more than one RB site, the GUI allows to select the one to be considered in the analysis. First module: It allows the automatic daily collection, storage and organization of the following eight, web-based pieces of information required by the EC-Methodology: 1) Backward trajectories computed offline with the Lagrangian model HYSPLIT (Draxler and Hess, 1998; Stein et al., 2015) at three different altitudes (750 m, 1500 m, 2500 m) to track the possible desert origin of the air masses reaching the target area. In the software, backward trajectories data are obtained by automatically running the (free-access) HYSPLIT model on a local server; 2) Wind fields from the Regional Atmospheric Modeling System model (RAMS, Pielke et al., 1992). These are routinely produced by the Latium environmental agency (ARPA-Lazio), as part of the agency air quality forecasting system 3) True color (RGB) images from satellite observations to get information on the geographical extension of dust plumes. RGB images provided by the MODIS instrument on board the NASA Terra platform are used (http://lance-modis.eosdis.nasa.gov/imagery/subsets/?subset=Europe_3_02) 4) ‘Aerosol Index’ maps provided by the NASA-AURA OMI instrument to obtain information on the atmospheric load of absorbing aerosols (Torres et al., 1998); (ftp://toms.gsfc.nasa.gov/pub/omi/images/aerosol/) 5)-to-8) Desert-dust-PM10 at ground level provided by the following dust chemical transport models: - DREAM8b (http://www.bsc.es/projects/earthscience/visor/dust/med8/sfc/archive/; Basart et al., 2012b), - NAAPS (http://www.nrlmry.navy.mil/aerosol_web/globaer/ops_01/europe/; Hogan and Rosmond, 1991), - SKIRON (http://forecast.uoa.gr/dustindx.php, Spyrou et al., 2010) Besides this information (explicitly listed in the EC-Methodology), the vertically-resolved desert dust forecasts over Rome from the Tel Aviv University (TAU) model (http://wind.tau.ac.il/dust8/; Alpert et al., 2002) have been included in the application over the Latium region. The first module also performs automatic collection of the PM10 measurements recorded in the selected RB station within the monitoring network, necessary for the desert dust quantification (Eq. 1 in Section 2.2);
Appendix to the paper by Barnaba et al., Atm. Env., 2017 http://dx.doi.org/10.1016/j.atmosenv.2017.04.038
Figure A1: The Graphical User Interface (GUI) of the tool developed to easily implement the EC-Methodology
Second module: Through the GUI (Figure A1), the second module allows to load and display, dayby-day, the eight maps listed above and, by means of the 3-step procedure detailed below, translate/summarize this information into a ‘desert dust daily index’ (I), options being: ‘dust-day’, ‘no dust-day’ or ‘doubt event’.
1) For each day under investigation, and for each map, the user evaluates presence/absence of desert dust by means of pop-up menus within the GUI (Figure A1 inset). Possible options are reported in Table A1. Note that the option ‘heavy dust’ (and related code), is only activated for satellite and models maps as for back-trajectories and wind field is not possible to evaluate the ‘intensity’ of the desert dust event, but only whether or not the information is compatible with desert dust transport from North Africa. Image Classification Code (C) Not Available 0 no dust -1 doubt dust 1 dust 2 heavy dust* 3* Table A1: User options to assess presence of desert dust in the evaluation of the 8 images required by the ECMethodology tool (*only active for the satellite and model maps)
2) The index I is obtained as: 8
I Cn n 1
Kn 8
Eq. A1
Appendix to the paper by Barnaba et al., Atm. Env., 2017 http://dx.doi.org/10.1016/j.atmosenv.2017.04.038
In which, for the n = 8 maps/images, Cn are the codes in Table A1 and Kn are ‘weighting coefficients’, introduced to weight the relative importance of the nth map in the desert dust identification phase (Table A2). nth Image Coefficient (Kn) 1. Backtrajectories 1.2 2. Wind field 0.3 3. Modis-RGB 1.2 4. Nasa-Aura AI 0.5 5. Dream 1.2 6. Skiron 1.2 7. Naaps 1.2 8. Tau 1.2 Sum 8 Table A2: Weighting coefficients associated to the n = 8 maps used in the evaluation of the desert dust presence/absence within the EC-Methodology tool developed (see eq. A1).
3) To assign one of the three options (‘dust-day’, ‘no dust-day’ or ‘doubt event’) to I, it is compared with the minimum (Imin) and maximum (Imax) values it can reach, i.e., Imin is obtained considering each code Cn as “no dust” (Cn = -1, Table A1), and Imax is obtained considering each Cn as either “heavy dust” (n = 3 to 8) or “dust” (n = 1, 2): 8 Kn m in I n 1 8 2 8 I m ax 2 K n 3 K n 8 8 n 3 n 1
Eq. A2
The assignment is as follows: I I m in 0.5 " dust event" m in m in I 0.5 I I 0.3 " doubt event" m in I I 0.3 " no dust event"
Eq. A3
where I max I min Third module: Once all dates within the investigated period have been classified, the third module allows to select the Regional Background site to be used as reference (see Section 2.2), and automatically evaluates the desert dust contribution to the PM10 values (PM10net_dust, following Eq. 1, Section 2.2). As indicated in the main text (Section 2.2), the out-of-dust PM10 background (PM10outofdust_RB) is computed as the 30-day-moving 50th-percentile PM10 value. We performed specific tests on the sensitivity of the EC-Methodology outcome over the Latium region to the choice of this percentile. In particular, for a whole ‘test’ year (2009, chosen from an ex-ante analysis as particularly rich in Saharan events), we computed the desert dust impact on the PM10 metrics using the 30th, 40th, and 50th percentiles. We found no difference in the desert-dustscreened PM10 yearly average at all sites, and at most one single case of difference in the yearly number of exceedances due to desert dust. Therefore, the more conservative approach has been preferred. Furthermore, the EC Guidelines document itself (EC, 2011) specifies that: ‘the monthly moving 40th percentile is a site specific indicator which reproduces the background concentration existing in the Iberian Peninsula during days with prevailing atmospheric advective conditions. The
Appendix to the paper by Barnaba et al., Atm. Env., 2017 http://dx.doi.org/10.1016/j.atmosenv.2017.04.038
use of this indicator in other countries has not been validated and no certainty exists on its accuracy. In absence of specific studies that identify the statistical indicator that better reproduce PM10 background concentration the use of a more conservative indicator, like the average of the PM10 concentrations registered during 15 days before and 15 days after the analyzed dust outbreak episode excluding the days with the identified episode, or the moving 50th percentile of 30 days, should be preferred’. Overall, the final output of the procedure is an ascii file containing the daily net concentrations PM10net_dust and the ‘desert dust daily index’. Being user-driven, the outcome of the dust identification phase is highly user-dependent, i.e., subjective to some extent.
References (additional to those included in the main text): Alpert, P., S.O. Krichak, M. Tsidulko, H. Shafir and J.H. Joseph, A dust prediction system with TOMS initialization, Mon. Wea. Rev., 130, 9, 2335-2345, 2002. Draxler, R.R. and G.D. Hess, An overview of the HYSPLIT_4 modelling system for trajectories, dispersion, and deposition, Aust. Meteor. Mag. 47, 295-308, 1998. Hogan, T. F. and Rosmond, T. E., The description of the Navy Operational Global Atmospheric Prediction Systems spectral forecast model, Mon. Weather Rev., 119, 1786–1815, 1991. Pielke, R. A., W. R. Cotton, R. L. Walko, C. J. Tremback, W. A. Lyons, L. D. Grasso, M. E. Nicholls, M. D. Moran, D. A. Wesley, T. J. Lee, J. H. Copeland, A comprehensive meteorological modeling system—RAMS, Meteorol. Atmos. Phys., 49 (1-4), 69–91, doi:10.1007/BF01025401, 1992. Spyrou, C., C. Mitsakou, G. Kallos, P. Louka, and G. Vlastou, An improved limited area model for describing the dust cycle in the atmosphere, J. Geophys. Res., 115, D17211, doi:10.1029/2009JD013682, 2010. Stein, A.F., Draxler, R.R, Rolph, G.D., Stunder, B.J.B., Cohen, M.D., and Ngan, F., NOAA's HYSPLIT atmospheric transport and dispersion modeling system, Bull. Amer. Meteor. Soc., 96, 2059-2077, doi: 10.1175/BAMS-D-14-00110.1, 2015. Torres, O., P.K. Bhartia, J.R. Herman, Z. Ahmad, J. Gleason, Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: theoretical basis, J. Geophys. Res., 103, 17099–17110, 1998.
