François De Vleeschouwer

Maud Ibanez

Nadine Mattielli

Claude Maerschalk

Nathalie Fagel

GEOCHEMICAL AND Pb ISOTOPIC SIGNATURE OF PEATY SEDIMENTS FROM CENTRAL-SOUTH CHILE: IDENTIFICATION OF PARTICLE SUPPLIES OVER THE HOLOCENE

Keywords: Peat soil, Trace elements, Pb isotopes, Ando soils, Lake District, Chile, Holocene

Two cores from tephra rich peat soils of the Chilean Lake District were investigated for mineral source and potential anthropogenic impact using elemental geochemistry, including rare earth elements, and lead isotopes. The dominant particle source to the area are the Trumaos which are the local ando soils derived from the weathering of volcanic deposits. In Galletué, although short term events of enhanced particle inputs occur, elemental and isotopic signatures show that the Trumaos are the only particle source to the area. In San Pablo de Tregua, punctual events of crustal particle inputs are recorded in Pb enrichment factors and isotopic profiles, reflecting a long range crustal involvement. This site also records the inception of the anthropogenic in the area in recent samples of which Pb enrichment factors and lead isotopic signatures shift drastically.

INTRODUCTION

During the last decade, many researches have been focused on peat cores as archives of atmospheric deposition, providing important data on palaeoclimatic and anthropogenic history [1][2][3][4][5] . Recent studies have for example demonstrated that ombrotrophic peat (i.e. exclusively fed by atmospheric deposition) is an excellent archive to record Pb atmospheric history throughout the Holocene 4,6 .

Studies of past atmospheric deposition are more rare in minerotrophic peat despite a common occurrence relative to ombrotrophic bogs. This is principally due to possible mobilization of elements linked to the fluctuation of watertable, or leaching by groundwater 7,8 . However other authors [9][10][11][12][13] have demonstrated the potential of mineralotrophic peat to record past climatic or anthropogenic changes.

In Southern Hemisphere, very few articles have investigated Holocene or anthropocene reconstruction derived from peat bog geochemistry [14][START_REF] Sapotka | Mineralogical, chemical, and isotopic (Sr, Pb) Composition of atmospheric mineral dusts in an ombrotrophic peat bog, Southern South America[END_REF][START_REF] Biester | [END_REF][17] . There are however numerous geochemical data from several sites of the Antarctic ice cap [18][19][20] are available and could be useful to decipher sources of particles and/or periods of pollution.

In this study, we focus on minerotrophic peat soils from the Chilean Lake District (Meridional Chile, 38-40°S). In an attempt to identify the main particle source (average Upper Continental Crust, mean Chilean Southern Volcanic Zone, and Trumaos, anthropogenic source), this study aims to decipher between local and global influence on peat records, using the depth evolution of geochemical and Pb isotopic compositions of peat soils.

MATERIAL AND METHODS

Site description

Two vertical cores (PB2 : 38°40'47''S -71°12'60''S -2.80 m; PB4 : 39°35'20''S -71°03'78''W -4.00 m) were drilled in peat soils from the Chilean Lake District (Fig. 1). The cores were retrieved using a Belorussiantype stainless steel D-corer 21,22 . Semi-cylindrical core are 50 cm long and 4.5 cm in diameter were sampled and bagged into PVC tubes and then wrapped in plastic. A pre-coring was performed to locate the thickest sediment accumulation.

The Chilean Lake District is located at the feet of the Andes, between 38° S and 42° S. The lakes are the results of the retreat of the Andean glaciers during the Late Glacial-Holocene transition [23][24][25][26] . This glacial landscape provides several peat infillings. This region, part of the Chilean Southern Volcanic Zone (SVZ), has been repeatedly under volcanic activity during the Holocene. Tephras were deposited from surrounding complex active volcanoes such as the Llaima, Lonquimay, Villarrica or Mocho-Choschuenco volcanoes [START_REF] González | Ferrán Volcanes de Chile[END_REF] . The entire area is covered by 4 to 6m thick andosoils formed by volcanic ash accumulation and weathering [START_REF] Besoaín | Suelos volcánicos de Chile[END_REF][START_REF] Bertrand | [END_REF] , locally called Trumaos (Fig. 1). These Trumaos provide a large amount of local mineral particles that could be transported by wind action and trapped by peat soils.

Figure 1. Locations of the two sites in the lake district map [START_REF] Bertrand | [END_REF] , and surface humidity zones. Grey shaded zone on the left map shows the extension of Andosoils, locally called Trumaos (reported from FAO-UNESCO).

In Galletué peat soil (PB2), vegetation is mainly composed of Juncus sp., Carex spp., a few Sphagnum spp., and several species of Gramineae [START_REF] Vargas-Ramírez | Proceedings the XIV Simposio de Palinología (A.P.L.E.)[END_REF] . In San Pablo de Tregua (PB4), Sphagnum spp. becomes abundant in the uppermost peat layers (20 cm). The actual vegetation of this mire presents an important cover of Carex spp. and Gramineae spp. Shrubs species like Escallonia alpina, Baccharis sp. [START_REF] Martínez-Miranda | [END_REF] and Chusquea spp. are also present, indicating a drying of the area. Both sites are fed by several little streams providing a non-atmospheric contribution to the peat soils.

C dating

Two samples from PB2 and four from PB4 have been dated either by AMS at Poznan Radiocarbon Institute (Poland), and by conventional (i.e. βcounting) technique at Centre for Isotope Research, University of Gröningen (The Netherlands). Results are reported in Table 1. Sediment accumulation covers more than 8500 years in PB2 and more than 11000 years in PB4.

Sediment characterization

High resolution magnetic susceptibility (HRSM), bulk density and total organic carbon (Fig. 2) were measured in both peat cores to detect specific lithologies that could induce geochemical fluctuations. HR-magnetic susceptibility was measured every 5 mm using an MS2 Magnetic Susceptibility System equipped with a MS2E spot-reading sensor (Bartington Instruments). Measurements were duplicated to assess reproducibility. HRSM exhibits sharp peaks when fine tephras are present. When tephras are coarser, their porosity prevents good contact between the sensor and the core. Recorded values represent in this case an underestimation of the real magnetic field. Additional HRSM fluctuations are rather due to other lithological variations. Bulk density is estimated by weighting a 1 cm thick semi-cylinder of sediment preliminary dried at 105°C for 12h. Total organic carbon was measured with a CS-200 Leco analyzer (CBR Harmignies, Belgium) on dried (105°C, 12h) and milled samples. Errors on C org measurements range from 5 to 10%.

Preparation and acid digestion of peat samples

Samples were taken out from the core using 5 ml acid cleaned plastic tools. Only the central part of the semi-cylinder sampled. Samples were dried (105° for 12h) and milled using an agate mortar. The sampling was constrained by the numerous tephra falls along the profiles and remained therefore irregular and quite large (sampling resolution up to 1 sample/40 cm).

All the samples were processed in clean air cabinets (ULB, Belgium). Digestion consisted in a two-step acid dissolution in closed Savilex  ® beakers: (1) organic matter is digested by repeated addition of H 2 O 2 30% p.a. and HNO 3 65% cc. sub. in a proportion of 3:1; (2) mineral matrix is then digested by addition of HF suprapur (Merck) + HNO 3 65% cc. sub. ± HClO 4 in a proportion of 1:6:0.5. The beakers were placed on a hot plate at 130°C for 4 days. After drying, 6N HCl was added and slowly evaporated.

Elemental geochemistry

Elemental geochemistry was measured using ICP-AES (IRIS Advantage) for major and transition elements and ICP-MS (VG PlasmaQuad PQ2) for trace elements at MRAC-Tervuren (Belgium). Si could not be analysed because of SiF 4 volatilisation during the HF-HNO 3 digestion procedure. Calibrations were made with In-Ru-Re-Bi internal standards (10 and 20 ppb), and two international reference materials: BCR CRM-100 beech leaves (EU certificated) and granite GA 33 , chosen to be representative of the current soil and volcanic samples. Accuracy varied between 1-11% for major elements, 1-2% for transition metals and 1-5% for other trace elements. Detection limits (i.e. mean + 3σ) were lower than 1 ppm for major elements excepted Na (2.7 ppm), K (1.1 ppm) and P (7.9 ppm). Detection limits for trace metals and other trace elements were lower than 1 and 10 ppb, respectively. To assess sample heterogeneity, replicates from both cores were analysed (PB2 -108 cm, PB4 -310 cm). Results for replicates are comparable within errors to the original analyses.

Lead isotope signatures

Lead fraction was separated using a one step ion-exchange chromatography on AG1-X8 resin following previouly reported chemical procedures 34 . To monitor instrumental mass bias, Tl was added to each sample to achieve a Pb/Tl of = 5 and thus match the Pb/Tl ratio of the standard. During the analysis sessions, measurement of NBS 981 Pb standard was systematically performed after every two samples. It gave average individual error (2σ, 95% confidence) on Pb isotopic analyses for Galletué (PB2) and rock samples of 0.0200 for 208 Pb/ 204 Pb, 0.0150 for 207 Pb/ 204 Pb and 0.0180 for 206 Pb/ 204 Pb (for more details see Table 4). Acids were distilled (HNO 3 ) or sub-boiled (HF, HCl, HBr). Pb total blanks for the whole procedure range between 40pg and 200pg. This is negligible relative to Pb contents in the samples (150ng < PB2 content < 900ng, 25ng < PB4 content < 8.3µg).

Lead isotopic ratios were measured on Nu-Plasma MC-ICP-MS (ULB, Belgium) during three analysis sessions. All the standard values of this study fall in these ranges. Internal laboratory reproducibility are 0.027 for 206 Pb/ 204 Pb, 0.026 for 207 Pb/ 204 Pb, 0.003 for 208 Pb/ 204 Pb, 0.1 for 207 Pb/ 206 Pb and 0.15 for 208 Pb/ 206 Pb (n~600, 2σ level, 95% confidence) after several years of analyses. The total Pb beam intensities varied from 4 to 10V. Intensities during analyses of San Pablo de Tregua were sometimes lower (<1V), explaining the larger average individual errors for those samples. A duplicate of the whole procedure (i.e. including dissolution, column exchange and analysis) on a PB2 sample (108 cm depth) was also analysed. The sample and the replicate results show differences of 13x10 -3 for 206 Pb/ 204 Pb, 36x10 -5 for 207 Pb/ 206 Pb and 87x10 -7 for 208 Pb/ 206 Pb.

Selection of the reference element

Comparable profiles of major element concentrations variations as a function of depth suggest an important imprint of the soil lithology. To compensate for the influence of the lithology or matrix effect, trace and major element concentration values have been normalized. The peat soils of this study are located in a volcanic environment. Therefore the regional noncrustal geology of the Chilean Lake District has to be taken into account. Ti, Al, Zr, or Y are lithophile elements, well known for their resistance against weathering [START_REF] Wedepohl | L2[END_REF][START_REF] Nesbitt | [END_REF] . They are common in several minerals found in volcanic falls, such as, for example, glass shards (Al, Ti), plagioclase (Al), titanite (Ti), ilmenite (Ti), titano-magnetite (Ti), zircon (Zr). Zr and Y have been often used in literature 10 as reference elements, but they are in this study sometimes under detection limit. In contrast Al appears to be the most abundant element the samples. A slight fluctuation in [Al] will therefore strongly influence the fluctuation in the corresponding [trace element]/[Al] ratio. Consequently, Ti, a conservative element [START_REF] Wedepohl | L2[END_REF] moderately abundant in several resistant volcanic mineral phases, is chosen as the reference element. We assume in the studied area an almost entire Ti supply provided by weathering products of the SVZ volcanism (i.e. Trumaos). Because such a soft material is easily transported by wind, it is the most suitable source of particles in this area. Ti flux is obtained using the following formula 10 : Ti Flux = [Ti] sample x (density) x (accumulation rate), Total particle flux is calculated as follows : TPF = Ti Flux x 100/[Ti (%)] Trumaos Pb enrichment factors are calculated using the formula [START_REF] Shotyk | Peat bog archives of atmospheric Pb deposition[END_REF]

: E.F. (Pb) = ([Pb] sample /[Ti] sample )/([Pb] Trumaos /[Ti] Trumaos )

RESULTS

Lithology

The lithology of both cores is strongly influenced by weathering products from SVZ as the mineral residue of the samples is exclusively composed of volcanic minerals (e.g. plagioclase, pyroxene, olivine) and glass shards. Both peat soils were repeatedly blanketed by tephra falls up to 10 cm thick. These observations are consistent with the minerotrophic character of the two sites.

PB2 shows a low organic carbon content (mean C org = 5.4 %) and high density values (mean δ = 0.6). A peak in density (δ = 0.91) at 90 cm is correlated with a rapid decrease of the total organic carbon content (C org = 2.73%) and a slight positive shift in HRSM. This shift is not due to a tephra layer (as the nearest tephra is at 95 cm depth), but to a strong clayey and mineral-rich layer (Fig. 2). Below total organic carbon is very low and density is high, reflecting the strong minerogenic character of the sediment. This mineral character becomes more important under 250 cm depth as HRSM increases significantly. In contrast, PB4 displays an average higher total organic carbon content (mean C org = 31.7%) and lower density (mean δ = 0.2) values relative to PB2. HRSM is also very low (always under 50 x 10 -6 SI). This could reflect the lower contents of tephras and/or mineral particles in PB4. In this core, the Sphagnumdominated layers become increasingly mineralised from 0 to 70 cm depth. The density therefore increases while the total organic carbon content decreases. At 71 cm depth, the occurrence of a silty layer causes a strong positive shift of density (δ = 0.37 g/cm 3 ) and a negative shift of organic carbon content (C org = 10.1%). Below 71 cm, profiles are relatively linear with small fluctuations between pure peat values (i.e. value of the 1 cm depth sample) and silty values (i.e. value of the 71 cm depth sample), reflecting the clayey character of the samples. At the lowermost 20 cm, HRSM increases rapidly, indicating a strong lithological change that represents the base of the peat infilling.

Major elements

Major element analysis reveals that Al, Ca and Fe are the most abundant elements in PB2: average [Al] PB2 = 6.5 wt. %, average [Ca] PB2 = 3.5 wt. % and average [Fe] PB2 = 3.5 wt. % (Table 2). All the major elements display similar variation with depth: decreasing values from 198 cm depth, then increasing and peaking at 90 cm, finally decreasing again until the surface. Although concentrations are lower, PB4 displays the same trend in elemental abundance: average [Al] PB4 = 3.3 wt. %, average [Ca] PB4 = 0.97 wt. % and average [Fe] PB4 = 0.98 wt. %. P is often below detection limits. As in PB2, all the major elements show remarkably comparable profiles: concentrations peak slightly at 390, 310 and 160 cm depth, then decrease and peak strongly at 71 cm, to finally sharply decrease until the surface.

Trace elements

In PB2 (Table 3) Light Rare Earth elements (LREE) show a weak variation range relative to Heavy Rare Earth elements (HREE). Chondrite-normalized [START_REF] Anders | [END_REF] La/Lu ratio (La ch /Lu ch ) ranging between 2.78 and 2.84. The range of variation is more important in PB4 (La ch /Lu ch = 0.95-9.58). However, La ch /Lu ch averages suggest an enrichment in LREE relative to HREE in the two peat soils. Other trace elements show comparable trends as those of REE, reflecting a strong influence of a Trumaos-SVZ particle source. However, Pb values of PB2 and PB4 show a strong negative anomaly. Based on the behavior of Pb, several potential particle sources to the peat soils could therefore be involved : Trumaos, SVZ and/or Upper Continental Crust. These potential sources will be detailed later (see section 4).

Lead isotopes

Pb Isotopic compositions in PB2 and PB4 cores are shown in Table 4. The Pb isotopic ratios for Galletué (PB2) define relatively constant profiles and small variations: from 18.556 to 18.601 for 206

DISCUSSION

Natural vs. anthropogenic sources

Trace element concentrations normalized to chondrite values [START_REF] Anders | [END_REF] are reported as spider diagrams (Fig. 3). Profiles of average concentrations for the two peat soils are compared to average compositions of the Trumaos, the Chilean Southern Volcanic Zone (SVZ) soils and the average Upper Continental Crust (UCC). Trends suggest a dominant particle sources comparable to the SVZ products and Trumaos. Galletué area (PB2) is closer to the SVZ profile than PB4. It is consistent with the influence made by the numerous tephra layers found in the core. This is also consistent with the lithology: PB2 is rather a peaty sediment rich in volcanic particles, than a pure (i.e. organic) peat. San Pablo de Tregua (PB4) core as a whole has a comparable lithology to PB2 although it displays less abundant volcanic layers. [START_REF] Sun | Magmatism in the Ocean Basins[END_REF] normlized to chondrites [START_REF] Anders | [END_REF] for the two sites and Trumaos compared with SVZ [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] and Upper continental crust 58 .

Figure 3. Spider diagrams

Pb isotopic data for Galletué (PB2) show a narrow range of variation and are included within the Trumaos-SVZ isotopic data field (Fig. 4.A.). A significant shift in 206 Pb/ 207 Pb is recorded between the two samples taken below (127.75 cm) and above (108.25 cm) the Alpehué Pumice, a well known rhyodacitic volcanic layer erupted from Caldera Sollipulli at 3000 yr cal BP 59 . The measured isotopic ratios are shifted from the Trumaos trend to the Alpehué Pumice isotopic composition (Fig. 4. B and C). This is due to contamination by grains from Alpehué Pumice dispersed in nearby upper layers, providing a pumiceous contribution to the isotopic signature of the peaty sediment. The shift shown by the sub-surface sample (3 cm depth) is too small (e.g. 206 Pb/ 204 Pb = 18.564 ± 0.0014) regarding to Pb isotopic ratios in actual Chilean cities aerosols 60 ( 206 Pb/ 204 Pb = 17.00 ± 0.02) to invoke any anthropogenic contribution to the Pb isotopic composition for the subsurface sample of Galletué core. The shallowest sample has probably been taken under the anthropogenically contaminated layers. Note that it was not possible to sample the subsurface layers due to contamination by sparse tephra grains. Moreover, in cores from Lake Galletué, no anthropogenic influence is recorded in terms of fossil fuel pollution 61 . Anthropogenic particles originating from a long distance source are negligible 61 . ) and PB4 (D.) SVZ: field of SVZ ratios (95% confidence) 41, 42, 46-48, 50-52, 55, 56, 62, 63 . SVZ*: possible extension to higher 207 Pb/ 206 Pb values 43,49,65 . Average Upper Continental Crust from litterature 58 . Actual Chilean industrial aerosols (younger than 1994) from the cities of Concepción, Villarrica and Punta Arenas 59 . B. and C. Influence of the pumice layer on the isotopic biplot and the 206 Pb/ 207 Pb vs. depth profile, respectively. E. Depth evolution of 206 Pb/ 207 Pb in San Pablo de Tregua (PB4).

Variation in Pb isotopic ratios ranges are ten times wider for San Pablo de Tregua (PB4) relative to PB2. Sediments sampled above 20 cm depth display isotopic ratios ( 206 Pb/ 207 Pb < 1.16, 208 Pb/ 206 Pb > 2.10) strongly shifted towards anthropogenic field (Fig. 4. D. and E) here reported by Actual Chilean industrial aerosols 59 . As it was already suggested by the high Pb E.F., Pb isotopic signatures of the three uppermost samples (1, 3, and 20 cm) reflect conjugated inputs of both SVZ volcanic particles and actual industrial aerosols. 206 Pb/ 207 Pb ratios were reported to vary between 1.12 and 1.14 associated Pb E.F. higher than 60 in Antarctic ice core samples 19,20 13), the third sample (16 cm depth) could be rather older. However, further sampling and dating of PB4 core would have been necessary to specify the exact age of this anthropogenic influence, but tephra grains contaminating surface samples peat make this sampling very tricky.

Changes in particle fluxes and Crustal involvment

PB2 samples show a Al/Ti profile with an opposite trend relative to Nd/Ti and Pb/Ti profiles (Fig. 5). All the normalized profiles are included within the range of Trumaos and SVZ values, except for Al/Ti. This latter profile ranges between (1) the Trumaos and/or the SVZ and (2) the average Upper Continental Crust Al/Ti values, suggesting a possible crustal contamination. Pb E.F. profile (calculated with Trumaos reference) suggests a unique mineral source for PB2. Nd/Ti and Pb/Ti profiles suggest a mineral particle source with a volcanic geochemical signature reflecting the Trumaos and/or SVZ compositions, as already shown by spider diagrams. Trumaos are easily eroded and carried by wind. It is therefore assumed that the SVZ signature is due to the Trumaos inputs rather than particles issued from solid volcanic rocks weathering or direct volcanic supply. 4). Upper continental crust: values from litterature 58 .

In PB4, Ti-normalized profiles of Al and Nd are roughly comparable. As a whole, the profiles are included within the composition ranges of SVZ and Trumaos. However, three peaks show significantly high ratios exceeding the SVZ and Trumaos composition ranges. They correspond to high Al/Ti values between at about 360 cm depth and high Al/Ti and Nd/Ti values between 210 and 246 cm depth (Fig. 5). These compositional shifts suggest a possible crustal particles input. Moreover, Pb/Ti and Pb E.F. profiles show also higher values trending towards crustal composition between 210 and 246 cm depth but not at 360 cm depth. Pb E.F. also shows higher values (approximately 3 and 5.5, respectively) between 210 cm and 246 cm but again not at 360 cm depth. These observations suggest involvement of a crustal particle source. The strong peak clearly observed at 71 cm depth in the Ti, Ti flux, and Pb E.F. profiles, is missing in Ti-normalized profiles. This contrast suggests that the 71 cm depth silty layer is mainly composed of volcanic derived material (Trumaos). Pb/Ti and Pb E.F. profiles display natural background (i.e. Trumaos) in the lower part of the core, while the uppermost 20 cm of sediments are strongly enriched in Pb. [Ti] flux and total particular flux average 0.18 g/m 2 /yr and 31 g/m 2 /yr, respectively. In the three profiles of PB4, a peak is located at 71 cm depth (Fig. 6). 147 Sm/ 144 Nd data mainly plot in the SVZ isotopic field (Fig. 6) except for five samples (130 cm, 210 cm, 246 cm, 270 cm and 310 cm). Samples 160 cm and 310 cm depth may reflect contamination by a crustal input as they are at the limit of the SVZ and Upper Continental Crust isotopic fields (Fig. 6). In addition they display opposite trends in Pb/Ti and Pb E.F. profiles. However, sample at 160 cm depth shows negative shifts (Pb/Ti = 2 x 10 -3 , Pb E.F. = 1.6) while sample at 310 cm shows positive ones (Pb/Ti = 7 x 10 -3 , Pb E.F.= 5.6). Therefore, it is doubtful to interpret them as contaminated by crustal particles. Samples from 210 cm, 246 cm and 270 cm depth are distinguishable from the others by significant lower 147 Sm/ 144 Nd values, reflecting potential contribution of a crustal source. This is especially consistent for the 210 and 246 cm samples because they present strong peaks in Al/Ti, Nd/Ti, Pb/Ti and Pb E.F. Those observations suggest a possible involvement of a long range crustal dust source during corresponding deposition period, strongly influencing 147 Sm/ 144 Nd signatures of these layers.

Summary and conclusions

We investigated peat cores from Chilean lake district. In this region, continental archives are frequently affected by volcanic falls. The availability of soft ash-derived Andosoils (i.e. Trumaos) material provides a continuous particle rain input in the Lake District. Short-term high fluxes of particles from various origins appeared to reflect punctual events. Cores from Galletué and San Pablo de Tregua peat soils (Chilean Lake District, South Chile) record ca 6000 and 10000 years of atmospheric particle deposition. Those peat soils are characterized by a minerotrophic matrix rich in volcanic dust with variable organic content. The use of major and trace elements data as well as Pb isotopes allow to decipher between three the particles categories supplied to the peat soils: a) SVZ weathering products (Trumaos) (0<Pb E.F.<2 and 1.185< 206 In Galletué (PB2) core, a clayey layer induces a peak in several elemental profiles at 90 cm depth. Particularly, the total particle flux reaches 158 g/m 2 /yr, which is extremely high. This particular clayey level could suggest a climatic change corresponding to a warmer period with a main volcanic atmospheric dust supply. Since this period, no crustal aerosol input is detected.

In San Pablo de Tregua (PB4) core, the particle source below 20 cm depth is the SVZ-Trumaos source. Above this depth, the signature is a mix between SVZ-Trumaos and the industrial sources, reflecting the inception of the anthropogenic influence. In PB4, two periods of Pb pollution could be distinguished by comparison with isotopic values from the Antarctic ice cap 19,20 . The sample at 16 cm could be related to the beginning of the 20 th century while the two uppermost ones reflect more likely the isotopic signature of the period between 1950 and 1987. Moreover the isotopic signatures between 210 cm and 246 cm suggest punctual crustal involvement, possibly linked to a long range particle transport.

Figure 2 .

 2 Figure 2. Lithological (left log) and tephrostratigraphical (right log) columns. In parallel are shown high resolution magnetic susceptibility dry bulk density and organic carbon content curves. Arrows indicate samples depth. 14 C calibrated 32 ages are also reported.

Figure 4 .

 4 Figure 4. 206 Pb/ 207 Pb vs.208 Pb/ 206 Pb isotopic biplot of PB2 (A.) and PB4 (D.) SVZ: field of SVZ ratios (95% confidence)41, 42, 46-48, 50-52, 55, 56, 62, 63 . SVZ*: possible extension to higher 207 Pb/ 206 Pb values43,49,65 . Average Upper Continental Crust from litterature58 . Actual Chilean industrial aerosols (younger than 1994) from the cities of Concepción, Villarrica and Punta Arenas59 . B. and C. Influence of the pumice layer on the isotopic biplot and the 206 Pb/ 207 Pb vs. depth profile, respectively. E. Depth evolution of 206 Pb/ 207 Pb in San Pablo de Tregua (PB4).

  aging from 1950 A.D. to 1987 A.D. Moreover, ice samples dating from the early 1900's A.D. to about 1940 A.D. show preferentially 206 Pb/ 207 Pb varying between 1.15 and 1.18, and lower Pb E.F. Therefore, despite a lack of dating for our samples, we could assume a young age (1940 A.D.-2000 A.D.) for the two uppermost samples of PB4 on the basis of their low 206 Pb/ 207 Pb ratios (1.12-1.14), strong Pb enrichment and extremely high Pb E.F. With a 206 Pb/ 207 Pb ratio of 1.15 and lower Pb E.F. (

Figure 5 .

 5 Figure 5. Ratio profiles (Al/Ti, Nd/Ti, Pb/Ti) and Pb enrichment factor (calculated using Trumaos values) of the two profiles. SVZ: mean value (for references, see figure 4). Upper continental crust: values from litterature 58 .

Figure 6 presents

 6 Figure6presents Ti concentrations, Ti fluxes and particulate fluxes for the two peat soils. Galletué (PB2) samples show extremely high fluxes in Ti and particles, averaging 0.93 g/m 2 /yr and 158 g/m 2 /yr, respectively. For comparison, in Swiss peat bogs10 , the Younger Dryas is recorded by a strong dust peak averaging 11.5 g/m 2 /yr while the natural background dust flux is less than 0.5 g/m 2 /yr. At 90 cm depth, a shift in [Ti] is correlated with a strong peak in Ti flux and particulate flux. Between 130 and 180 cm, a slight increase in [Ti] is also recorded in the three profiles. However, 147 Sm/ 144 Nd signatures calculated from Sm and Nd concentrations (for more details, see figure7 caption) of the Galletúe (PB2) samples confirm that the dominant source of atmospheric particles to the peat soils is related to the SVZ-Trumaos component (Fig.6). The possible contribution of a crustal sources for the 90 cm depth sample, previously suggested by the Al/Ti ratio, is inconsistent with the 147 Sm/ 144 Nd results. The high values recorded by the PB2 sample reflect abundant inputs of Trumaos and other volcanic weathering particles inputs.

Figure 6 .

 6 Figure 6. Ti concentration, Ti flux, total particle flux and 147 Sm/ 144 Nd profiles. Arrows indicate samples depth. SVZ: mean value from litterature 40-57 , so as Upper continental crust values 58 . 147 Sm/ 144 Nd is calculated using [Sm]/[Nd] in sample multiplied by the ratio between natural abundance of 147 Sm in Sm and natural abundance of 144 Nd in Nd (i.e. 0.60847). 147 Sm/ 144 Nd results for 1 cm and 3 cm depth samples in PB4 are not represented, because Sm and Nd concentration measurements are under detection limits.

  Pb/ 207 Pb<1.195) providing a great amount of erodible material easily transported by wind, and constituting the local and main particles source; b) a mix of SVZ weathering products and long range crustal particles (2<Pb E.F.<6 and 147 Sm/ 144 Nd < 0.105 to 0.115), only recorded in San Pablo de Tregua; c) an anthropogenic undifferentiated source (Pb E.F.>6 and 207 Pb/ 206 Pb>0.865) only recorded in the uppermost 20 cm of San Pablo de Tregua (PB4).

Table 1 .

 1 AMS (Poz samples) and conventional (GR samples) dating and associated Oxcal 3.832 calibrated ages from samples from PB2 and PB4. * depth dated by tephra correlation of a dated sampwle in a neighboring outcrop.

	Site	Sample	Depth	Age yr B.P.	Error	Age ranges
		number	(cm)			yr cal B.P.
	PB2
		Poz-252	*120.5	2900	± 45	2920 -3170
		GrN-27083 282.5	7350	± 200	7750 -8550
	PB4
		Poz-6863	45	645	± 30	550 -670
		Poz-1320	90	3970	± 50	4250 -4540
		Poz-1319	190	7490	± 40	8180 -8380
		GrN-27085	340	9480	± 160	10250 -11200

Table 2 .

 2 Weight percentage of major elements in samples from Galletué (PB2) and San Pablo de Tregua (PB4). <DL: under detection limits (i.e. < 3σ); values in italic: quantitative measurements (i.e. > 9σ); underlined values: semiquantitative measurements (i.e. 3σ -6σ).

	Depth
	(cm)	Ti Al Fe Mn Mg Ca Na	K	P
	Galltetué (PB2)
	3.5 0.35 4.8 4.4 0.06 1.5 3.6 1.2 0.27 0.07
	29 0.20 2.6 1.6 0.02 0.44 1.7 0.64 0.15 0.05
	70 0.35 4.25 2.3 0.04 0.60 2.3 1.0 0.23 <DL
	90 0.63 9.9 4.8 0.09 1.4 6.4 2.4 0.43 0.07
	108 0.55 6.6 4.0 0.07 1.1 3.8 1.9 0.44 0.10
	Replicate 108 0.55 6.7 3.9 0.07 1.2 4.1 2.0 0.48 <DL
	128 0.58 6.3 3.2 0.06 0.80 3.3 1.8 0.41 0.12
	152 0.62 6.6 3.6 0.08 0.84 3.4 2.3 0.47 0.09
	177 0.61 7.7 3.3 0.06 0.94 3.5 2.1 0.42 <DL
	198 0.66 8.4 4.2 0.09 1.1 4.1 2.7 0.62 0.10
	227 0.61 7.5 3.5 0.07 0.84 3.5 2.3 0.48 0.12
	San Pablo de Tregua
	(PB4)
	1 0.01 0.17 0.09 0.06 0.30 1.2 0.17 0.24 <DL
	3 0.01 0.15 0.08 0.05 0.31 1.2 0.15 0.26 <DL
	16 0.12 1.7 1.6 0.03 0.26 1.3 0.48 0.19 <DL
	47 0.48 5.5 2.1 0.04 0.51 1.5 1.7 0.76 0.15
	71 0.89 7.4 2.4 0.05 0.70 2.1 1.8 0.73 <DL
	105 0.32 4.2 0.71 0.01 0.16 0.68 0.35 0.09 <DL
	160 0.39 5.0 0.92 0.02 0.27 0.98 1.0 0.32 0.19
	210 0.09 2.6 0.51 0.01 0.07 0.28 0.13 0.05 <DL
	246 0.09 2.5 0.38 0.01 0.07 0.25 0.12 <DL <DL
	270 0.13 2.0 0.41 0.01 0.06 0.25 0.08 <DL <DL
	310 0.11 1.9 0.57 0.01 0.12 0.41 0.18 0.09 <DL
	Replicate 310 0.12 1.9 0.57 0.01 0.11 0.43 0.16 <DL <DL
	340 0.39 5.6 1.9 0.02 0.63 1.3 0.59 0.12 <DL
	360 0.19 4.8 1.1 0.02 0.34 1.1 0.89 0.22 <DL
	Alpehué Pumice	0.28 7.9 2.5 0.06 0.47 1.7 2.9 1.6 0.05
	Trumaos	0.79 6.0 4.2 0.16 1.8 3.7 0.89 0.20 0.07
	Replicate PB2 %-deviation	1 2.3 0.3 0.4 6.2 6.6 5.3 9.5	-
	Replicate PB4 %-deviation	1 0.0 0.0 0.0 8.3 4.8 11.1 -	-

Table 3 .

 3 Pb/ 204 Pb, from 15.602 to 15.607 for 207 Pb/ 204 Pb and from 38.462 to 38.500 for 208 Pb/ 204 Pb. Except for the three uppermost samples, the Pb isotopic compositions of San Pablo de Tregua (PB4) also vary in narrow ranges: between 18.371 and 18.655 for 206 Pb/ 204 Pb, between 15.579 and 15.669 for 207 Pb/ 204 Pb, and between 38.238 and 38.617 for 208 Pb/ 204 Pb. Isotopic ratios of the three surface samples shift strongly towards lower values (0.868 < 207 Pb/ 206 Pb < 0.875 and 2.108 < 208 Pb/ 206 Pb < 2.118). Elemental content (ppm) of trace elements in samples from Galletué (PB2). <DL: under detection limits; values in italic: quantitative measurements; underlined values: semi-quantitative measurements.

	Depth (cm)	Sr	Y	Zr	Nb Ba La Ce Pr Nd Eu Sm Gd Dy Ho	Er	Yb Lu Hf Pb Th U
	Galletué (PB2)
	3.5 232 12	44	1.35 115 4.8 11 1.6 7.3 0.62 1.9 2.1 2.1 0.43 1.3 1.2 0.18 1.3 3.7 0.80 0.25
	29 109 7.9 31	0.87 71 3.5 7.8 1.1 5.1 0.4 1.3 1.4 1.4 0.29 0.86 0.81 0.13 0.85 2.7 0.62 0.20
	70 154 11	40	1.22 95 4.4 10 1.4 6.5 0.55 1.7 2.0 1.9 0.38 1.1 1.1 0.17 1.1 3.7 0.62 0.26
	90 425 18	69	1.84 166 6.5 15 2.3 10 1.0 2.8 3.2 3.2 0.66 1.9 1.8 0.27 1.8 5.8 0.90 0.33
	108 285 20	81	2.14 172 8.2 19 2.8 13 1.1 3.3 3.8 3.6 0.74 2.2 2.1 0.32 2.2 6.5 1.4 0.46
	Replicate 108 305 22	89	2.36 189 9.1 21 3.1 14 1.2 3.7 4.1 4.0 0.84 2.4 2.3 0.35 2.4 7.2 1.5 0.51
	128 282 22	92	2.44 186 9.2 22 3.1 14 1.2 3.6 4.1 3.9 0.78 2.3 2.2 0.33 2.4 7.3 1.5 0.50
	152 318 21	85	2.17 214 8.9 21 3.0 14 1.3 3.6 4.1 3.8 0.76 2.2 2.1 0.32 2.3 7.8 1.5 0.51
	177 316 21	82	2.31 187 8.7 20 2.9 13 1.1 3.3 3.7 3.6 0.74 2.1 2.1 0.33 2.2 6.8 1.3 0.42
	198 385 26 106 2.93 253 11 26 3.7 17 1.4 4.2 4.5 4.5 0.89 2.7 2.6 0.40 2.7 8.6 2.1 0.67
	227 337 24	95	2.78 221 11 25 3.6 16 1.3 3.9 4.3 4.0 0.80 2.4 2.3 0.35 2.4 8.2 1.8 0.59
	San Pablo de Tregua (PB4)
												w
	1 70 0.85 <DL <DL 29 0.46 0.65 0.10 0.44 <DL <DL <DL 0.1 <DL 0.07 <DL <DL <DL 83 <DL 0.03
	3 80 0.66 <DL <DL 48 0.37 0.45 0.08 0.35 <DL <DL <DL <DL <DL <DL <DL <DL <DL 70 <DL
	16 113 4.3 <DL 0.51 83 1,6 2.8 0.42 2.0 0.18 0.53 0.60 0.62 0.13 0.39 0.38 0.06 <DL 16 <DL 0.10
	47 152 34 132	4.9 121 16 24 4.2 18 1.1 4.0 4.54 4.5 0.92 2.7 2.5 0.39 3.0 13 3.5 1.1
	71 204 33 155	6.5 124 15 25 4.2 18 1.5 4.2 4.73 4.6 0.91 2.7 2.5 0.37 3.4 19 3.9 1.3
	105 67 41 106	2.9 114 25 27 5.0 21 1.3 4.5 5.46 5.6 1.1 3.2 2.8 0.40 2.4 12 <DL 0.88
	160 129 4.2 <DL 0.34 16 3.4 3,1 0.64 2.5 0.13 0.47 0.57 0.54 0.12 0.33 0.26 0.04 <DL 0.78 <DL 0.08
	210 24 88 <DL 0.66 37 48 34 10 44 1.6 7.2 10.11 8.4 1.8 4.8 3.5 0.52 <DL 2.3 <DL 0.38
	246 20 86 <DL 0.67 34 44 25 7.9 33 1.2 5.3 7.45 6.9 1.5 4.4 3.3 0.50 <DL 2.3 <DL 0.46
	270 21 40	22	0.91 31 19 11 2.8 11 0.4 1.7 2.34 2.4 0.6 1.9 1.6 0.25 0.45 2.3 <DL 0.22
	310 37 57	48	1.9	90 23 20 4.1 17 0.85 3.2 4.08 4.1 0.97 3.1 2.8 0.45 <DL 4.8 <DL 0.52
	Replicate 310 41 66	56	2.2 107 27 24 4.9 20 1.0 3.9 4.76 4.8 1.1 3.6 3.2 0.52 <DL 5.6 <DL 0.59
	360 146 5.0 <DL <DL 12 2.4 3.6 0.59 2.6 0.16 0.55 0.61 0.61 0.13 0.41 0.39 0.06 <DL 0.50 <DL 0.03
	390 99 46	96	2.2	61 15 17 4.1 19 1.7 4.2 4.78 4.8 1.0 3.2 3.2 0.52 2.0 5.4 1.8 0.58
	Alpehué Pumice	229 26.2 238 7.71 569 25.7 51.5 6.36 23.4 1.04 4.6 4.37 3.85 0.9 2.55 2.81 0.44 6.1 21.3 9 2.55
	Trumaos	166 26.1 127 4.11 143 14.2 31.3 4.28 19.1 1.44 4.59 5.29 4.87 0.96 2.88 2.81 0.41 3.22 10 3.74 0.91
	Replicate PB2 %-deviation 6.9 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2
	Replicate PB4 %-deviation 11 15.4 15.3 15.8 18.1 18.0 18.7 18.2 16.7 17.9 18.2 16.5 16.8 15.9 17.4 15.5 14.9 -16.4 -13.3
	Depth		206 Pb/ 204 Pb	2σ	207 Pb/ 204 Pb	2σ	208 Pb/ 204 Pb	2σ	208 Pb/ 206 Pb	2σ	207 Pb/ 206 Pb	2σ
	(cm)				x 10 -4		x 10 -4		x 10 -4		x 10 -5	x 10 -5
	Galletué (PB2)
	3.5		18.5564	14	15.6053	12	38.4622	31	2.0727	5	0.8409	2
	29		18.5820	17	15.6072	16	38.4916	40	2.0714	3	0.8399	1
	70		18.5727	13	15.6020	11	38.4761	30	2.0716	4	0.8400	1
	90		18.5722	16	15.6037	13	38.4771	32	2.0718	5	0.8402	2
	108		18.5710	9	15.6039	7	38.4737	17	2.0717	3	0.8402	1
	128		18.5956	10	15.6064	12	38.4974	30	2.0703	4	0.8393	1
	152		18.5927	10	15.6045	9	38.4950	25	2.0705	4	0.8393	1
	177		18.6014	14	15.6070	11	38.5002	28	2.0699	4	0.8391	1
	Replicate 177		18.8800	9	15.6026	9	38.4756	27	2.0699	5	0.8394	1
	198		18.5855	10	15.6057	9	38.4881	26	2.0709	5	0.8397	1
	227		18.5820	12	15.6032	13	38.4850	32	2.0711	4	0.8397	1
	San Pablo de Tregua (PB4)

Table 4 .

 4 Analyses of lead isotopes in samples from Galletué (PB2) and San Pablo de Tregua (PB4). Italic value (when 2σ < 100x10 -4 ) have to be considered as qualitative data.

ACKNOWLEDGEMENTS

We would like to thank O.S.T.C. project EV/12/10B (Coord. M. De Batist, RCMG Gent, Belgium), INQUA and Lefranc Foundation (ULg) for field financial support. Analyses were partially financed by the Belgian National Found for Scientific Research (F.N.R.S.). We thank especially J. De Jong (ULB), J. Navez and L. Monnin (MRAC, Tervuren) for helpful laboratory and analytical assistance, and Sébastien Bertrand and Lourdes Vargas for field assistance. We are also grateful to M. Stürm, E. Chapron (ETH-Zurich) and F. Charlet (RCMG Gent, Belgium) for giving us the possibility to use HR-magnetic susceptibility system. This manuscript has received the kind improvement of D. Weis, M. Streel, Maria Gehrels and two anonymous reviewers. During this investigation, François De Vleeschouwer was a F.R.I.A. (F.N.R.S.) fellow.


Chiara Aghemo

Joseph Virgone

Vincenzo Giovanni

Anna Fracastoro

Laura Pellegrino

Jérôme Blaso

Kévyn Savoyat

Johannes

G V Fracastoro

Anna Pellegrino
email: anna.pellegrino@polito.it

L Blaso

J Savoyat

Kevyn Johannes

Management and monitoring of public buildings through ICT based systems: control rules for energy saving of lighting and HVAC

Keywords: energy efficiency, existing public buildings, control strategies, dynamic simulations

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

One of the major challenges in today's society concerns the reduction in energy use and CO 2 footprint in existing public buildings without significant construction works. In this context, key challenges are concerned with the design and the development of a monitoring and control infrastructure to manage appliances so as to effortlessly optimize energy efficiency usage (without compromising comfort for occupants) and to offer to decision makers dedicated tools to plan and manage energy saving strategies. Within this frame, at the end of 2010 a project entitled "Smart Energy Efficient Middleware for Public Spaces" (SEEMPubS) was funded by the European Commission. The project specifically addresses reduction in energy usage and CO 2 footprint in existing public buildings and spaces by implementing an intelligent ICT-based building monitoring and managing system. Actions of retrofitting on building envelopes or services to reduce energy consumptions are not always possible or economically convenient in existing buildings and in particular in historical buildings where conservation is a matter of priority. In SEEMPubS a system to monitor and control buildings services and appliances will be designed and implemented with the aim of optimizing energy usage without compromising occupants comfort and offering decision makers strategies and tools needed to plan energy saving measures.

The proposed system will make use of "LinkSmart", which is a service-oriented middleware developed in a previous European project [START_REF] Jahn | Hydra Middleware for Developing Pervasive Systems: a Case Study in the eHealth Domain[END_REF][START_REF] Lardies | Deploying Pervasive Web Services over a P2P Overlay[END_REF] and designed to support the development of networks of embedded systems, simplifying the interconnection of heterogeneous devices that communicate using different protocols. The project will use the LinkSmart middleware potential to create services and applications across heterogeneous devices to develop an energy-aware platform.

The SEEMPubS platform will provide functions and tools to add energy efficiency features to the Building Management System (BMS), by monitoring environmental and energy data in real time, and by controlling the operation of active systems like lighting and HVAC services, taking advantage of natural resources (daylight and solar energy) to ensure the best possible comfort conditions with the most efficient use of energy.

A real-time energy-awareness service for all users of the Public Spaces will be also created and it will combine awareness services with a community portal to actively involve people in energy saving issues.

The functionality of the designed platform will be demonstrated in existing buildings at the Politecnico di Torino Campuses (Italy). Buildings built in different periods, from historical to contemporary constructions, were chosen as pilot study, to take into account potentials and drawbacks resulting from the use of smart ICT based systems in buildings with different features and constraints.

In the paper the activity carried out during the first year of the project to define the new BMS architecture and in particular the control strategies proposed for implementation in the existing buildings is presented. Furthermore a special focus is set on the procedure adopted to evaluate the effectiveness of the proposed control strategies: dynamic simulations have been carried out to assess the saving potentials with respect to the energy performance goals.

Control strategies and control logics

The activity carried out during the first year of the project was mainly focused at preparing the groundwork for the development, implementation and validation of the BMS in the Politecnico buildings. To demonstrate the effectiveness of the new system in reducing energy consumption, increasing occupants comfort and simplifying facility managers activities, some rooms, from each Campus site, were selected as case studies [START_REF] Virgone | Building definition: criteria, methodologies and description of the selected buildings, D1.1, WP1[END_REF]. Rooms were chosen on the basis of the following criteria:

representativeness with respect to Politecnico Campus and other Public Buildings energy saving potentials characteristics of the existing services.

Both private and public spaces in historical and modern buildings were selected, in particular classrooms, student offices, single offices and open plan offices were chosen as demonstrator. Energy saving potentials related to the control of HVAC services, lighting plants and other electrical appliances, were supposed according to the rooms architectural and plants features (volume, orientation, type of envelope, type of heating and lighting plant, shading system, etc.), the type of activity carried out, the occupancy patterns and the solar/daylight availability.

In figure 1 some images of the rooms that will be used for the study are presented.

Insert Fig. 1

Six pairs of rooms were selected for the study. Each test room has a reference room -a room with similar architectural characteristics, building services, type of activity etc. The six reference rooms will run with the already present systems and with manual controls, whereas the six similar test rooms, will be equipped with the new BMS for lighting, heating/cooling and electrical appliances control and monitoring. The monitoring of the energy consumptions in each couple of room will provide information on the savings achievable with the designed control system.

Further analyses on the selected rooms were carried on to determine the future control strategies: indoor and outdoor microclimatic and lighting conditions were monitored during different periods of the year and an overall energy auditing was carried out to determine the present energy performance of the Politecnico buildings [START_REF] Virgone | Energy audit certification, D1.2, WP1, Smart Energy Efficient Middleware for Public Spaces SEEMPubS project[END_REF].

Lighting control strategies

To define the most appropriate lighting control strategies, a general overview of the state of the art on lighting control was first carried out (Destefanis et al.2011[START_REF] Dubois | Energy saving potential and strategies for electric lighting in future North European, low energy office buildings: a literature review[END_REF][START_REF] Doulos | Quantifying energy savings in daylight responsive systems: the role of dimming electronic ballasts[END_REF], Guo et al. 2010[START_REF] Ryckaert | Criteria for energy efficient lighting in buildings[END_REF], Tiller et al. 2010). The most recurrent solutions for energy savings are:

time switching daylight harvesting occupancy control a combination of the previous.

Time switching is the strategy that entails to turn automatically on and off luminaires at scheduled times to avoid useless lighting out of working hours. This is the simplest and more spread solution in public and commercial buildings.

Daylight harvesting is the control strategy that entails to automatically adjust luminaires light flux (dimming) in order to maintain a predetermined illuminance in the room, taking into account the contribution of daylight. The aim of this lighting control strategy is to ensure adequate lighting condition throughout the working hours and to save energy by means of reducing the lighting power as a function of daylighting supply. This strategy is especially effective in those rooms or buildings that are characterised by high daylight availability and all-day working hours.

Occupancy control will turn luminaires on and off when the presence or absence of people is detected in a space. Also in this case the automatic control avoids energy waste produced by the possibility that users leave the spaces forgetting the lights on. The control logic could provide for either switching on and off or for single off, leaving the user, in this second case, the possibility to turn on the light when desired. A lighting control based only on presence detection will be effective in those spaces where user absence is highly probable, and in particular in public or commercial buildings, where users are little motivated at caring about the use of light.

Proposed control logics

According to the information on the rooms features achieved during the first phase of the project, some lighting control logics were initially proposed [START_REF] Pellegrino | Initial energy control and conservation strategies, D2.3.1, WP2, Smart Energy Efficient Middleware for Public Spaces SEEMPubS project[END_REF]. In particular, for spaces with high daylight availability and medium/high users absence probability, a combination of daylight harvesting and occupancy control was proposed. For spaces with low daylight availability, a lighting control based on occupancy sensing was proposed, while a particular solution was planned for large classrooms with no daylight: students absence probability during the day is very low, but the classroom area could be only partially occupied when a small number of students attend the lectures. In this case it was supposed to use several occupancy sensors to dim to a minimum the light corresponding to the unoccupied parts of the classroom. In all cases the possibility to override the automatic control via manual command is considered.

Based on these assumptions, some recurrent combinations of lighting control strategies were initially proposed for the selected rooms:

time switching and manual control: this mix of control strategies was proposed for the Student Offices where daylight availability is very low and the general lighting is provided by means of High Pressure Metal Halide lamps, which can not be switched on and off instantaneously and can not be dimmed daylight harvesting, occupancy detection and manual control: this combination of control strategies was proposed for the private offices (DIST offices, Administrative offices and DAUIN offices). In these cases the automatic dimming of lights based on a daylight harvesting control strategy could be applied successfully thanks to the high daylight availability and it could be useful to control lights on the basis of room's occupancy too, as a medium absence probability could be truthfully assumed for these offices occupancy detection and manual control: these control strategies were proposed for the classrooms and for the open-plan offices, as daylight is not allowed to enter the rooms (in classrooms) or is quite low due to the glazing characteristics and the external obstructions produced by the structure of the double façade in the open-plan offices shading device control based on outdoor illuminance or solar radiation: this control strategy was proposed for the offices with motorized blinds on the roof lights to control overheating and glare from solar radiation.

In the following diagrams the lighting control logics proposed for the selected case studies are presented. Figure 2 describes the control logics corresponding to a strategy of both daylight harvesting and occupancy control. The lights will be switched on and off when user presence or absence is detected by the occupancy sensor and will be dimmed up or down to integrate daylighting in order to achieve the required lighting conditions for visual comfort.

Insert Fig. 2 Likewise, figure 3 shows the control logics for controlling lighting as a consequence of users presence. In these cases the lights will be switched on and off when user presence or absence is detected by the occupancy sensors.

Insert Fig. 3 Figure 4 describes the lighting control logics designed for big classrooms with no daylight, and figure 5 shows the logics for those rooms where, together with the electric lighting, shading devices can be automatically controlled on the bases of the sun position and the solar radiation measured on the building roof.

Insert Fig. 4 and5 2.2 HVAC control strategy

Methodology of work

The goal of the SEEMPubS project is to decrease the energy consumption of buildings while ensuring the comfort of the users. We have to take into account two different factors: 1-the type of heating system used in the selected rooms;

2-the intermittent use of the building.

The economic advantage of intermittent use of heating systems in intermittently-occupied buildings (e.g. schools, office buildings) is no longer in doubt. Nevertheless, optimal energy consumption requires that the heating restart time be defined with great precision. In many cases, to avoid risks, the lowering of the temperature during non-occupancy period is limited, which leads to reductions in energy savings.

An intermittent heating controller allows the internal temperature to be lowered during non-occupancy periods, while maintaining the desired temperature during occupancy periods, when these periods are clearly defined (Fraisse et al, 1997a(Fraisse et al, , 1997b[START_REF] Fraisse | A numerical comparison of different methods for optimizing heating restart time in intermittently occupied buildings[END_REF]. The different phases of heating are as follows and are illustrated in figure 5:

-Upper control during occupancy period. The internal air temperature Ta must be maintained at the high set-point temperature T h by means of the heating law. -Minimum power: switching-off of heating at the end of the occupancy period.

-Lower control during the non-occupancy period if the internal temperature reaches the low set-point temperature T l . The low set-point temperature avoids the risk of condensation or frost.

-Recovery at maximum power, so as to reach the high set-point temperature T h from the beginning of the following occupancy period scheduled. Restart time is optimized by the controller.

-Further possibility to integrate in the process: anticipated stopping of heating with an optimized time delay before the end of occupation period scheduled.

Insert Fig. 6

In our case, we want to go further by having a detection of the occupancy. A schedule is always defined allowing turning off the heating when the room is unoccupied, unless a presence is detected. Nevertheless, the user can always act on the system by changing the set point temperature of the fan coils with individual switcher. In fact, lot of classrooms or offices are not used during all the time of the scheduled period. If no presence is detected, the heating is stopped after a little delay. Furthermore, since access to all the information is simultaneously available, strategies of interoperability may be found allowing for example the anticipation of the heating stop if the other loads (solar or internal) are enough.

Heating control strategy

The control strategy proposed is based on a communication between all sub-systems (heating/cooling, lighting, other electricity used). The algorithm logic for heating is presented in figure 7:

-The strategy supposes that every room possesses its own panel of circuit breaker: a circuit breaker for heating (in the case of fan coils it is on the ventilator that we act), one for lighting and one for electricity plugs.

-A minimum indoor temperature should be defined which the heating has to restart.

Insert Fig. 7 and 8

Two practical examples of communication between sub-systems are described hereafter:

-The heating schedule is on "unoccupied", but someone enters the office for work. If necessary, lights will be switched on thanks to the presence detector, which will also switch on the heating system for the comfort of the person using the office.

-Another example is to anticipate the heating system switch off when heat gains from lighting and electrical devices (computers, …) exceed the heating power needs. A normal feedback control would let the indoor temperature increase above the set-point before stopping the heating systems, with an unnecessary waste of energy. The graphic in figure 8 explains this point.

Theoretical application

The following schema explains the adoption of this strategy on a room (figure 9).

Insert Fig. 9 3. Numerical simulations

The potential in saving energy of the proposed control solutions has been assessed by means of building performance simulations. The procedure adopted was as follow:

analysis of available software and selection of the most appropriate with respect to the simulation goal;

monitoring of environmental condition in the selected rooms (illuminance, air temperature, relative humidity);

models construction and simulation of environmental conditions;

comparison between monitored data and simulation results;

further set up of rooms model;

simulation of rooms performance in terms of energy consumption and environmental conditions when applying the new control strategies.

Lighting simulations

Methodology

To assess the effectiveness of the proposed lighting control solutions, for each analysed room, the savings from future control strategy respect to the present one has been calculated from total annual electric lighting energy consumptions.

In general, the total annual energy used for lighting depends on different aspects, mainly related to: building usage in terms of target illuminance and occupancy profile, user behaviour, lighting plant characteristics, lighting control system and indoor daylight availability. Therefore, to calculate the energy consumption for those rooms where daylight is allowed to enter a dynamic Climate-Based Daylight Modelling (CBDM) approach [START_REF] Reinhart | Dynamic daylight performance metrics for sustainable building design[END_REF]) [START_REF] Mardaljevic | Simulation of annual daylighting profiles for internal illuminance[END_REF]) [START_REF] Mardaljevic | Daylight metrics and energy savings[END_REF]) was used to estimate annual daylight availability and, as a consequence, to calculate the electric energy consumption as a function of the type of lighting control system adopted.

Climate-based daylight modelling allows the prediction of various lighting quantities using sun and sky conditions that are derived from standard meteorological datasets. At present there is quite few software that allows a climate-based daylight simulation. Among them Radiance and Daysim are the most well-known and used in the lighting field.

Daysim calculates the annual daylight availability in arbitrary buildings based on the Radiance backward ray tracer, using external daylighting conditions derived from standard meteorological local datasets. It includes specific occupant behaviour model algorithms to mimic occupant use of personal controls such as light switches and venetian blinds and to predict the electric lighting use due to automated lighting controls such as occupancy sensors and photocell controlled dimming systems. In addition to daylighting metrics, among its outputs, the total annual energy used for lighting [kWh] and the LENI value [kWh/m 2 year] are included (EN 15193, 2007).

For the SEEMPUBS project both Radiance [START_REF] Larson | Rendering with Radiance[END_REF] and Daysim [START_REF] Reinhart | Daylight Availability and Manual Lighting Control in Office Buildings[END_REF] were used. The first one to validate the models, the second one to estimate the lighting energy use and the savings obtained with the proposed control solutions.

Model validation

To start the lighting simulation with Radiance or Daysim a 3D model of the selected rooms and their external environment were prepared (Figure 10). The rooms' models were prepared using the parametric software Revit and then imported in Radiance and Daysim through the software Ecotect.

To correctly characterise the model's opaque and transparent surfaces, in field measurements of luminous reflectances and transmittances were carried out. The models' validation was done comparing the output of the Radiance simulations with the quantities measured inside the rooms, with same sun position and sky condition [START_REF] Mardaljevic | Validation of a lighting simulation program under real sky conditions[END_REF][START_REF] Reinhart | The simulation of annual daylight illuminance distributions -a state-of-the-art comparison of six RADIANCE-based methods[END_REF][START_REF] Reinhart | Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds[END_REF][START_REF] Reinhart | Experimental validation of 3ds Max® design 2009 and Daysim 3.0[END_REF].

As an example, table 1 presents some first results obtained for a clear and an overcast sky condition. The measurements were done in correspondence of two points on a horizontal plane: one near the window (point 1), one far from the window (point 2). The relative differences between calculated and measured values ranges from -12% to 29.4%, therefore confirm an acceptable accuracy of the model used for the lighting simulation.

Insert Fig. 10

Insert Table 1 3

.1.3 Simulation results for lighting energy demand

Annual energy demand for lighting, in a building or room, depends on several factors:

indoor daylight availability;

building usage, in terms of occupancy profile, required illuminance and user behaviour with respect to both electric and daylighting systems; lighting plant characteristics, mainly concerned with the installed electric power density and the adopted lighting control system.

To calculate the total annual energy used for lighting Daysim requires to define the arrival and departure time, the presence of lunch or intermediate breaks during working hours and the target illuminance, while, for the user's behaviour with respect to lighting and blinds, some different algorithms can be used. Different stochastic user behaviour models, based on field study data, are in fact implemented in Daysim to mimic how building occupants interact with manual controls of lighting plants or shading devices: active users who operates the electric lighting in relation to ambient daylight condition, open blinds in the morning, and partly close them to avoid visual discomfort; passive users who keep the electric lighting switched on throughout the working day and keep the blinds lowered throughout the year.

When running a simulation two different options for user behaviour can be selected:

-Active (equals design intention); passive (equals design risk): according to this option the energy demand for lighting is calculated considering an active user but, since there is the possibility that a user is passive, the passive simulation is carried out to see, through the daylighting metrics, how robust a building design is against users working in opposition to it.

-Mixed use: both type of users are considered, the simulation is run twice, once for each user type, and the resulting mean energy use is reported.

The options to simulate lighting and shading control system are:

shading control: manual, automatic or no movable shading; lighting control: six different control strategies are considered: 1) manual on/off switch near the door, 2) switch off occupancy sensor, 3) switch on/off occupancy sensor, 4) photosensor controlled dimming system 5) combination switch off occupancy and dimming system 6) combination on/off occupancy and dimming system. Furthermore, as for the characteristics of the lighting plant, the following data are required:

installed lighting power density (W/m 2 ); standby power (W/m 2 ) for occupancy sensor, photosensor, etc; ballast loss factor (%); delay time of the occupancy sensor (minutes).

In the following some first results of lighting energy demand for one of the rooms selected as demonstrator in the SEEMPubS project are presented. The selected case study for this first analysis is a single office, with internal movable shading device, southwest oriented. Inputs for simulation are:

occupancy profile: from 9,00 a.m. to 6,00 p.m., with lunch and intermediate breaks;

user requirements and behaviour: target illuminance of 500 lx, mix of active and passive user's behaviour for lighting and active behaviour depending on the user's need to avoid direct sunlight on work plan for blinds; lighting control systems: the initially proposed control solution for the considered office was a combination of switch on/off based on occupancy and dimming based on photosensor. In this phase of the study all control options available in Daysim were actually simulated to compare their effectiveness with respect to the manual control, which is the solution used at present in the Politecnico buildings.

Results are presented in figure 11 andtable 2. The control solution proposed at first (combination on/off based on occupancy plus dimming) seems not to provide relevant savings with respect to manual control (16%). This result is consistent if we consider that a user with mixed active and passive behaviour was selected for manual control and that the presence of the shading device in a southwest oriented room may reduce greatly the annual daylight availability.

The results obtained with Daysim demonstrate that, for this room, the most effective control strategy would be a combination of dimming based on light availability and switch off based on occupancy. In this case energy savings with respect to manual control rise up to 39%.

Insert Fig. 11

Insert Table 2 3.2 HVAC simulations

3.2.1

Software presentation

TRNSys is a internationally recognized software dedicated to the simulation of dynamic systems, developed by the Solar Energy Laboratory (SEL), University of Madison in the United States. It allows performing dynamic simulations of the thermal behaviour of a mono or multi-zone building. It can also follow hourly consumption (heating, air conditioning) or production (solar panels) annual energy facilities of a building.

TRNSys is in fact a software which compiles different modules (called "types") and allows to link them one to the other to create a realistic model of the building (or the room) tested. Each type has it own part in the simulation. For example, three types are used to create meteorological data of the simulated place, and a special type to model the building (walls and their compositions, windows...).

a conference day: Thursday.

-Weekend day: Saturday and Sunday.

In the next table (table 3) the results on the heating needs of the two offices during this cold week are presented:

Insert Table 3 The next figure (figure 15) presents both internal temperatures of the rooms during the week with the external temperature of Torino used to perform the simulation.

Insert Fig. 15

As we can see on table 3, the control strategy used in the test office allows to perform an important energy saving (-66% compare to the reference room). This result is a consequence of the management of periods without occupants. In a classic office like the reference room, the occupancy is managed by a general schedule for the entire building which cut off the heating from 6pm to 8am and during weekends but the heating is on every day, no matter if the occupant is out for the day. In the test office, a presence detector is used which allows to turn off the heating if no presence is detected for more than 30min.

Furthermore, in the classical office, the fan speed is controlled by the occupant who usually put it to the maximal value, which is 3, so the heating will always be to its maximal power to reach the set point temperature in the room (no matter which the real occupation is). On the other hand, in the test office, a regulator is used to choose the fan speed; for example if the set point temperature is 21°C and the indoor temperature of the room is 20°C, the regulator will select the minimal speed to reach 21°C, but if the temperature is 16°C in the office, it is the maximal speed which will be chosen.

Conclusions

Actions of retrofitting on building envelopes or services to reduce energy consumptions are not always possible or economically convenient in existing buildings and in particular in historical buildings where conservation is a matter of priority. Nevertheless savings can be achieved by designing intelligent ICT-based service to monitor and control environmental conditions, energy loads and plants operation.

The paper has presented some activities carried on in the first phase of the SEEMPubS project and in particular it describes the methodology adopted to define and evaluate the control strategies to be applied in the demonstrator buildings of the Politecnico di Torino to prove the efficacy of the ICT-based BMS that will be designed and implemented in the following phases of the project.

Based on the analysis of buildings and rooms' characteristics, new control strategies have been proposed, both for lighting and for heating, in order to provide energy savings in buildings where heavy retrofitting works are not possible, like for historical buildings. The effectiveness of the proposed control strategies is being evaluated through building performance dynamic simulations.

As far as lighting is concerned, the first energy saving results, obtained for a single office southwest oriented, demonstrated that the initially proposed control solution wouldn't provide the expected savings, while better results could be achieved with a different combination of occupancy based and daylight harvesting control. Higher savings are expected for rooms with greater annual daylight availability (for instance rooms with a minor use of shadings).

A further consideration regards the impact of occupancy profile and users' behaviour: at present the algorithms implemented in Daysim have been used to calculate the energy demand, but different results could be obtained by implementing an "ad hoc" occupancy profile or user behaviour algorithm, as was done for thermal simulations.

To conclude with the thermal simulation part, a first answer about energy saving potential of the control strategy imagined for the SEEMPubS project has been achieved in design conditions. This turned out to be about 60% on this test simulation, but it was based on a reference case with a scarce control. This result has to be completed by an analysis of thermal comfort discrepancies induced by this control and by a yearly study, which also includes warmer days. Combination switch off occupancy and dimming system -39%

Combination on/off occupancy and dimming system -16%

Fig. 1 .

 1 Fig.1. Internal view of some rooms that were selected for the study in the historical Politecnico di Torino Campus (Valentino Castle)

Fig. 3 .

 3 Fig. 3. Control logic in case of occupancy control in open plan offices

Fig. 11 .

 11 Fig. 11. Energy demand for different control strategies

Table 1 . Comparison between measured and calculated illuminances

 1

	Sky	Point	Measured	Calculated	Relative
	condition		Illuminance (lx)	Illuminance (lx)	difference (%)
	Clear Sky	1	26618	31840	19.6
		2	1088	958	-12.0
	Overcast	1	246	276	11.9
	sky
		2	74	96	29.4

Table 2 . Calculated percentage of energy saving with different control strategies, respect to manual control

 2

	Control strategies	Energy savings with respect to manual control (%)
	Switch off occupancy sensor	-26%
	Switch on/off occupancy sensor	+14%
	Photosensor controlled dimming system	-18%

Table 3 . Maximal power P and heating needs E resulting from the simulation

 3

		Reference office	Test office	Savings (%)
	P (W)	8794	6755	-23
	E (kWh)	169	58	-66
	P/A (W/m²)	148.4	114	-23
	E/A kWh/m²)	2.86	.97	-66

The new version of TRNSys (v17) was used to perform those simulations. The new 3D plug-in of this version was used on several cases of the project to go further in the interoperability between parametric architectural software (Revit) and energy simulation software (TRNSys; Daysim, etc.)

Acknowledgements

We acknowledge the contribution of whole consortium of the SEEMPubs project which collaborated at the achievement of the presented results.

Modelling of two offices in historical building

The figure below (figure 12) presents the TRNSys simulation diagram used to simulate the buildings with the different types to model all the specifications of the Politecnico campus (fan coils or AHU...).

Insert Fig. 12

To approach more precisely the reality of the Politecnico campus, two special types were used to simulate fan coils (present in nearly all the rooms of the SEEMPubS project). Those types use the hot water temperature and the flow rate of the fan to calculate the exact energy consumption of the system. A schedule has also been adopted to recreate the occupancy and no occupancy periods of the general heating system of Politecnico (figure 13).

Insert Fig. 13

The hot water temperature of the general heating system was supposed to vary as a function of the outdoor temperature in Torino according to a classical heating law (figure 14).

Insert Fig. 14

Simulation results

In this section we present the first results of the simulation concerning two offices in the Valentino Castle (Politecnico di Torino). One of the offices, the reference room, represents the present situation of the office (occupancy, fan speed selected manually by the occupant, heating based on a automatic schedule which doesn't take into account the occupancy ...). The second office is used to test the control strategy described in the precedent paragraph.

The aim of this study is to measure the energy saving potential of the control strategy proposed in the SEEMPubS project. To perform this goal, a simulation has been made of one cold week (winter condition) and the heating needs and the internal temperature of the two offices were measured.

An important part of our strategy is based on the management of the heating (or cooling) during periods without occupancy. To see the influence of our strategy an occupancy schedule was created, which simulates four types of day:

an office day: occupation of the office from 8am to 6pm, a teaching day: occupancy from 8am to 10am, then after lunch from 1pm to 2pm, and finally after the afternoon class from 4pm to 6pm, a conference day: no occupancy during the day, -Weekend: no occupancy.

In the simulation performed here we have in the two offices this repartition: