Paleoproductivity reconstruction for the eastern equatorial Pacific

Productivity at six core locations in the eastern equatorial Pacific (EEP) was reconstructed with a benthic foraminiferal transfer function. The core records show strong regionality, especially where affected by Peru margin upwelling of deeper Equatorial Undercurrent Water (EUC) (originally coming from the subantarctic). This "Peru margin" record differs from that seen along the equator where divergence leads to shallow upwelling, and it is generally inverse to that seen in cores outside the areas of equatorial upwelling. Principal components analysis shows that the main productivity pattern correlates well to the global oxygen isotope record and has lowest values during isotope stages 2 and 4. In addition to this, equatorial cores show a higher frequency pattern of variation which becomes much more pronounced during MIS 3 and 2. The reconstructions based on benthic foraminifera were tested against those from nonaccumulation rate based inorganic chemical proxies of export production. These were found to correlate well in the region influenced by Peru upwelling, and also to share common features for sites along the equator. All the nonaccumulation rate based paleotracers are consistent with one another and differ from accumulation rate derived proxies. The differences between the two classes of paleotracers may result from uncertainties in calculating actual biogenic fluxes since 230Th-normalized results conform more to those we obtained. Analysis of planktonic carbon isotope values for the EEP, and their comparison to the record of the Pacific subantarctic, indicates that the subantarctic contribution to the EUC was reduced during MIS 3 and 2.

Photosynthetic production in the Central Arctic during POLARSTERN cruise ARK-XXVII/3 (IceArc) in 2012

The ice-covered Central Arctic Ocean is characterized by low primary productivity due to light and nutrient limitations. It has been speculated that the recent reduction in ice cover could lead to a substantial increase in primary production, but still little is known as to the fate of the ice-associated primary production, and of nutrient supply with increasing warming. This study presents results from the Central Arctic Ocean collected during summer 2012, when sea-ice reached a minimum extent since the onset of satellite observations. Net primary productivity (NPP) was measured in water column, sea ice and melt ponds by 14CO2 uptake at different irradiances. Photosynthesis vs. irradiance (PI) curves were established in laboratory experiments and used to upscale measured NPP to the deep Eurasian Basin (north of 78°N) using the irradiance-based Central Arctic Ocean Primary Productivity model (CAOPP). In addition, new annual production was calculated from the seasonal nutrient drawdown in the mixed layer since last winter. Results show that ice algae can contribute up to 60% to primary production in the Central Arctic at the end of the season. The ice-covered water column had lower NPP rates than open water probably due to light limitation. According to the nutrient ratios in the euphotic zone, nitrate limitation was detected in the Siberian Seas (Laptev Sea area), while silicate was the main limiting nutrient at the ice margin influenced by Atlantic waters. Although sea-ice cover was substantially reduced in 2012, total annual new production in the Eurasian Basin was 17 ± 7 Tg C/yr, which is similar to previous estimates. However, when including the contribution by sub-ice algal filaments, the annual production for the deep Eurasian Basin (north of 78°N) is 16 Tg C/yr higher than estimated before. Our data suggest that sub-ice algae might be responsible for potential local increases in NPP due to higher light availability under the ice, and their ability to benefit from a wider area of nutrients as they drift with the ice.

Microalgal CO2 Sequestering - Modeling Microalgae Production Costs

Microalgae CO2 sequestering facilities might become an industrial reality if microalgae biomass could be produced at cost below $500.00 t-1. We develop a model for estimation of total production costs of microalgae as a function of known production-specific expenses, and incorporate into the model the effects of uncontrollable factors which affect known production-specific expenses. Random fluctuations were intentionally incorporated into the model, consequently into generated cost/technology scenarios, because each and every logically interconnected equipment/operation that is used in design/construction/operation/maintenance of a production process is inevitably subject to random cost/price fluctuations which can neither be eliminated nor a priori controlled. A total of 152 costs/technology scenarios were evaluated to find forty four scenarios in which Predicted Total Production Costs of Microalgae (PTPCM) was in the range $200 to $500 t-1 ha-1 y-1. An additional 24 scenarios were found with PTCPM in the range of $102 to $200 t-1 ha-1 y-1. These findings suggest that microalgae CO2 sequestering and the production of commercial compounds from microalgal biomass can be economically viable venture even today when microalgae production technology is still far from its optimum.

Bacterial abundance in the eastern Mediteranean Sea in August and September 2008 during SES_GR2

The SES_GR2_MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during August-September 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 ?m (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson).

Bacterial production in the eastern Mediteranean Sea in August and September 2008 during SES_GR2

The SES_GR2_MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during August-September 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Bacterial production was estimated by the 3H-leucine method (Kirchman et al. 1986, Kirchman 1993). At each depth, duplicate samples and a control were incubated with 20 nM L-[4,5 3H]-leucine. Samples were incubated in the dark, at in situ temperature.

Integrated net primary production during POLARSTERN cruise ARK-XXVII/3 (IceArc) in 2012

Depth-integrated in situ rates were calculated for each environment as a function of the available photosynthetically active radiation (PAR). Irradiance profiles were calculated for each environment (sea ice, melt pond, water under the ice and open water) from the daily average incoming solar shortwave irradiance measured by a pyranometer (Kipp & Zonen, Delft, Netherland) mounted on the ship. We used light attenuation coefficients of 10 m**-1 for snow, 1.5 m**-1 for sea ice (Perovich, 1996) and 0.1 m**-1 for Atlantic-influenced Arctic seawater, based on literature values and observations during the cruise. Planar irradiance was transformed to scalar irradiance according to Ehn and Mundy (2013) and Katlein et al., (2014). Water column production was integrated over the euphotic zone (1% of incoming irradiance) and sea ice production over the ice core thickness. Melt pond coverage and sea ice concentration were taken into account when calculating the total primary production per area.

Abundance and Biomass from heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus in the eastern Mediteranean Sea in October 2008 during SES_GR2

The HCMR_SES_LAGRANGIAN_GR2_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during October 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6?m and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus bacteria: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

Bacterial production in the Aegean Sea in April 2008 during SES_GR1

The HCMR_SES_LAGRANGIAN_GR1_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Bacterial production was estimated by the 3H-leucine method (Kirchman et al. 1986, Kirchman 1993). At each depth, duplicate samples and a control were incubated with 20 nM L-[4,5 3H]-leucine. Samples were incubated in the dark, at in situ temperature.

Abundance and Biomass from heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus in the Aegean Sea in April 2008 during SES_GR1

The HCMR_SES_LAGRANGIAN_GR1_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus biomass: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

Effects of RoHs and REACH regulations on firm-level production and export, and the role of global value chains : the cases of Malaysia and Vietnam

This paper uses firm-level data to examine the impact of foreign chemical safety regulations such as RoHS and REACH on the production costs and export performance of firms in Malaysia and Vietnam. This paper also investigates the role of global value chains in enhancing the likelihood that a firm complies with RoHS and REACH. We find that in addition to the initial setup costs for compliance, EU RoHS (REACH) implementation imposes on firms additional variable production costs by requiring additional labor and capital expenditures of around 57% (73%) of variable costs. We also find that compliance with RoHS and REACH significantly increases the probability of export and that compliance with EU RoHS and REACH helps firms enter a greater variety of countries. Furthermore, firms participating in global value chains have higher compliance with RoHS and REACH regulations, regardless of whether the firm is directly exporting, when the firm operates in upstream or downstream industries of the countries' supply chain.

Structure function and Multifractal spectrum applied to Digital Elevation Model

A Digital Elevation Model (DEM) provides the information basis used for many geographic applications such as topographic and geomorphologic studies, landscape through GIS (Geographic Information Systems) among others. The DEM capacity to represent Earth?s surface depends on the surface roughness and the resolution used. Each DEM pixel depends on the scale used characterized by two variables: resolution and extension of the area studied. DEMs can vary in resolution and accuracy by the production method, although there are statistical characteristics that keep constant or very similar in a wide range of scales. Based on this property, several techniques have been applied to characterize DEM through multiscale analysis directly related to fractal geometry: multifractal spectrum and the structure function. The comparison of the results by both methods is discussed. The study area is represented by a 1024 x 1024 data matrix obtained from a DEM with a resolution of 10 x 10 m each point, which correspond with a region known as ?Monte de El Pardo? a property of Spanish National Heritage (Patrimonio Nacional Español) of 15820 Ha located to a short distance from the center of Madrid. Manzanares River goes through this area from North to South. In the southern area a reservoir is found with a capacity of 43 hm3, with an altitude of 603.3 m till 632 m when it is at the highest capacity. In the middle of the reservoir the minimum altitude of this area is achieved.

On the complexity of radiation models for PV energy production calculation

Several authors have analysed the changes of the probability density function of the solar radiation with different time resolutions. Some others have approached to study the significance of these changes when produced energy calculations are attempted. We have undertaken different transformations to four Spanish databases in order to clarify the interrelationship between radiation models and produced energy estimations. Our contribution is straightforward: the complexity of a solar radiation model needed for yearly energy calculations, is very low. Twelve values of monthly mean of solar radiation are enough to estimate energy with errors below 3%. Time resolutions better than hourly samples do not improve significantly the result of energy estimations.

Explicit Expressions for Solar Panel Equivalent Circuit Parameters Based on Analytical Formulation and the Lambert W-Function

Due to the high dependence of photovoltaic energy efficiency on environmental conditions (temperature, irradiation...), it is quite important to perform some analysis focusing on the characteristics of photovoltaic devices in order to optimize energy production, even for small-scale users. The use of equivalent circuits is the preferred option to analyze solar cells/panels performance. However, the aforementioned small-scale users rarely have the equipment or expertise to perform large testing/calculation campaigns, the only information available for them being the manufacturer datasheet. The solution to this problem is the development of new and simple methods to define equivalent circuits able to reproduce the behavior of the panel for any working condition, from a very small amount of information. In the present work a direct and completely explicit method to extract solar cell parameters from the manufacturer datasheet is presented and tested. This method is based on analytical formulation which includes the use of the Lambert W-function to turn the series resistor equation explicit. The presented method is used to analyze commercial solar panel performance (i.e., the current-voltage–I-V–curve) at different levels of irradiation and temperature. The analysis performed is based only on the information included in the manufacturer’s datasheet.

Explicit expressions for solar panel equivalent circuit parameters based on analytical formulation and the Lambert W - Function

Due to the high dependence of photovoltaic energy efficiency on environmental conditions (temperature, irradiation...), it is quite important to perform some analysis focusing on the characteristics of photovoltaic devices in order to optimize energy production, even for small-scale users. The use of equivalent circuits is the preferred option to analyze solar cells/panels performance. However, the aforementioned small-scale users rarely have the equipment or expertise to perform large testing/calculation campaigns, the only information available for them being the manufacturer datasheet. The solution to this problem is the development of new and simple methods to define equivalent circuits able to reproduce the behavior of the panel for any working condition, from a very small amount of information. In the present work a direct and completely explicit method to extract solar cell parameters from the manufacturer datasheet is presented and tested. This method is based on analytical formulation which includes the use of the Lambert W-function to turn the series resistor equation explicit. The presented method is used to analyze the performance (i.e., the I - V curve) of a commercial solar panel at different levels of irradiation and temperature. The analysis performed is based only on the information included in the manufacturer's datasheet.