361 DIFFERENTIAL PHOTOMETRIC STUDY OF THE EUROPEAN LIGHT EMISSION TO THE SPACE ALEJANDRO SÁNCHEZ DE MIGUEL ASAAF-UCM. Department of Astrophysics and Atmospheric Sciences, Universidad Complutense de Madrid. GPC. Spain. In the comparison of light pollution between two countries, there are some difficulties because of geographical, cultural and economical differences. The trouble is even worse considering all different systems of outdoor lighting. As first step, to contrast light pollution between countries, a group of close nations have been chosen with different population densities, size, built surface, etc. As a parameter to compare the emission of each country, the NOAA’s images (DMSP Satellite, OLS “VIS” band 0.40-1.10 um) flux were used and NASA’s software World Wind were employed to solve distortion problem of the Mercator projection. This software allow to draw frontiers over countries, so it is possible to make a parti- cular study of them. In these images all illuminated regions are saturated, so the number of counts don’t represent the flux emitted from that region, although that number is proportional to region’s area. As it is known the area of each country it is possible to calculate the density of illu- minated area per person and proportion of illuminated territory. These parameters allow to compare the aspect of the country from space. This parameter is very influenced by population’s density, so it is interesting to com- pare it with other parameters as urban surface, population density, street lamp density, etc. In this study we show some conclusions of the possible roots of the differences found between countries’ illumination. Data acquisition NASA´s software World Wind were used for the image visualization using the NOAA add-on of the Nightlights layer1. To discriminate the countries contribution the frontiers layer were employed. Then, countries night images were captured as closer as it was possible to minimise the per- spective effect. Then, using MaxIm DL, the tree components of the image were split and the red one were chosen because they are the most representative of the surface illuminated. PhotoShop were used to remove other countries from the image to make the measure of the counts from each country. Image analyses These images are not raw images. They have been removed from the lowest level of illumination and they have a bit less resolution. The intensity of bright points is almost always saturated level too. 362 As result, the flux measured is not proportional to the real flux, but it is proportional to the affected area. In sea area there are not significant signal. Geographic data To compare the light polluted area between countries some geographic data are needed to calculate the intensive values. For general data, the CIA The World FactBook, the EEA (European Environment Agency) and in some cases the government data were used. Data analyses There are some studies about the relationship between the population density and the light pollution2, although they are only a first approximation and if the deviations of this relationship are estimated, it is possible to extract political and quality effects of the illumination models. Twelve European countries have been chosen because they are the most uniform group of countries, as a consequence of the convergence European program. As it is possible to infer of the correlation between the density of population in built area, versus saturated area per built area, there is a significant correlation between these magnitudes. There are some points more difficult to be explained because of geographic effect. These countries are Luxemburg, Netherlands, and Spain. Luxemburg is a very small country and because of this it has a very high error. On the other cases more data are needed to get conclusions, but a group statistical analyses show that Spain is a outlier. To find the roots of this out rule value, a compilation of the Ministerio de Industria y Turismo de España3 data, some energetic waste values from different countries and Figure 1. Saturated Km2/Density 363 Final Report Lot 9: Public street lighting(RPSL)4 were made. These data let infer that pain has almost 50% more installed power peer light point. In this case CELMA7, data were used, because IDAE‘s data are more pessimistic.7 In other values, as power peer squared meter or light points per square kilometre (always using built area) Spain have a excess value. In Netherlands case, they have the lowest installed power of all studied countries, that could explain the deviation of the rule. This data show a relationship between latitude-culture and the number of light points per square kilometre too. Italy, Portugal and Spain have the highest values. Figure 3. DendrogramFigure 2. CLUSPLOT (x) Figure 4. Km2 sat vs km2 built 364 Figure 5. Watts by Luminaries Figure 6. Light Points by km2 built Figure 7. Watts by square meter 365 Conclusions and future work • With the present data Spanish illumination can not be explained by demographic causes. • There is important evidence of excessive waste in Italy. • The population density in built land as intensive data is the best global parameter in order to represent the saturation of a country. • An appropriate illumination can be shown in satellite images. • It is needed further data of all the countries considered in the study. • Extend this study to all European countries, EEUU and Japan. • Search of an illumination model useful to countries in development. • Discussion of hypothesis about the type of light polluting sources. Acknowledgements I would like to thank to Jaime Zamorano Calvo from the Department of Astrofísica y CC. de la Atmós- fera UCM for his help and support, to Patricia García González for her job in the statistical analyses and finaly to Berenice Pila and Elena Manjavacas for they critical revision of the abstract and this document. Notes and References 1. Sensor DMSP/DMSP Visualization Date 2000-10-23 Credit Data courtesy Marc Imhoff of NASA GSFC and Christopher Elvidge of NOAA NGDC. Image by Craig Mayhew and Robert Simmon, NASA GSFC. 2. P. CINZANO , F. FALCHI , C.D. ELVIDGE, Monthly Notices of the Royal Astronomical Society, 328, 689-707 (2001) 3. SECRETARÍA GENERAL DE ENERGÍA, DIRECCIÓN GENERAL DE POLÍTICA ENERGÉTICA Y MINAS, MINISTERIO DE INDUSTRIA, TURISMO Y COMERCIO. La Energía en España - 2004. Pag.103 4. VAN TICHELEN,P., GEERKEN, T., JANSEN B., VANDEN BOSCH (LABORELEC),M., VAN HOOF, VANHOOYDONCK (KREIOS),L., VERCALSTEREN,A, January 2007, Final Report Lot 9: Public street lighting. 5. CIA World Factbook 6. G HAZEU, F PARAMO & J-L WEBER, June 2005. National statistics Form Land Cover Accounts (LEAC/CLC) (Source: EEA - Provisional results). 7. CELMA 4700000, IDAE(2005) 4200000 8. Wikipedia 9. PINDAR, A., PAPETTI, M., Public Procurement of Energy Saving Technologies in Europe (PROST) Report on the Country Study for Italy:Task 2a - Current Public Sector Purchasing, Building, and Replacement Practices Task 4b - PICO Feasibility Study. February 2002, Politecnico di Milano 10. Average power of luminaries at Vila do Gaia (EnLight). 11. Energy Efficiency Index(IEE)=(W/m2)*(100/10 lux). 366 Sp ai n P or tu ga l F ra nc e B el gi um Ir la nd U K G er m an y A us tr ia C ze ck R ep . It al y N et he rl an s L ux en bu rg To ta l A re a K m 2 (5 ) 49 95 42 91 95 1 54 56 30 30 27 8 68 89 0 24 15 90 34 92 23 82 44 4 77 27 6 29 40 20 33 88 3 25 86 B ui lt ar ea (% ) 2% 3% 5% 21 % 2% 11 % 8% 4% 6% 5% 13 % 8% Po pu la tio n(6 ) 40 39 78 42 10 60 58 70 60 87 61 36 10 37 90 67 40 62 23 5 60 60 91 53 82 42 22 99 81 92 88 0 10 23 54 55 58 13 35 09 16 49 14 61 47 44 13 D en si ty o f p op ul at io n 80 .8 7 11 5. 34 11 1. 57 34 2. 79 58 .9 7 25 0. 88 23 6. 02 99 .3 8 13 2. 45 19 7. 72 48 6. 72 18 3. 45 D en si ty o f p op ul at io n bu ilt a re a 49 86 45 11 22 56 16 59 30 26 22 10 28 14 23 25 21 37 40 60 36 44 22 89 L ig ht p oi nt s(4 ) 47 00 00 0 11 00 00 0 85 70 00 0 20 05 00 0 40 10 00 78 51 00 0 91 20 00 0 10 00 00 0 30 00 00 90 00 00 0 25 00 00 0 61 00 0 L ig ht p oi nt s pe r i nh ab ita nt 0. 12 0. 10 0. 14 0. 19 0. 10 0. 13 0. 11 0. 12 0. 03 0. 15 0. 15 0. 13 L P /k m 2 b ui lt 58 0 46 8 31 8 32 1 29 9 28 6 31 1 28 4 63 62 9 55 2 32 K m 2 / L P 0. 01 1 0. 00 8 0. 00 6 0. 00 3 0. 00 8 0. 00 5 0. 00 3 0. 00 4 0. 01 7 0. 00 5 0. 00 2 0. 01 2 K m 2 b ui lt 0. 00 13 0. 00 08 0. 00 08 0. 00 06 0. 00 08 0. 00 06 0. 00 03 0. 00 05 0. 00 05 0. 00 08 0. 00 03 0. 00 15 Sa tu ra te d A re a 53 76 6 84 56 48 69 3 58 35 33 12 37 10 7 28 83 2 44 83 50 21 45 21 2 55 36 70 8 Sa tu ra te d A re a % to ta l 11 % 9% 9% 19 % 5% 15 % 8% 5% 6% 15 % 16 % 27 % K m 2 s at / K m 2 b ui lt 6. 64 3. 60 1. 80 0. 93 2. 47 1. 35 0. 98 1. 27 1. 05 3. 16 1. 22 3. 42 C O 2(8 ) 6. 9 5. 8 6 9. 9 10 .9 9. 5 9. 7 7. 5 11 .6 7. 4 8. 8 18 .9 M W (4 ) 73 83 12 0 12 50 24 9 43 61 9 10 00 13 10 16 1 w at t p er L P 15 7 11 1 14 5 12 4 10 8 76 11 0 N o da ta N o da ta 14 5. 6 61 N o da ta E E I(1 1) 0. 91 1 0. 51 0 0. 46 3 0. 39 8 0. 32 0 0. 22 6 0. 34 1 0. 91 5 0. 35 6 W at /m 2 0. 09 1 0. 05 1 0. 04 6 0. 04 0 0. 03 2 0. 02 3 0. 03 4 0. 09 1 0. 03 6 W at t p er in ha bi ta nt 18 .2 7 11 .3 1 20 .5 3 23 .9 9 10 .5 9 10 .2 1 12 .1 3 22 .5 3 9. 76