ALBERT

Description: A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability yet also be very biased. These results are likely dependent on characteristics of the data.

Description: A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data.

Description: During the MINOS campaign in August 2001 comprehensive two-dimensional gas chromatography (GC x GC) was applied to the in situ measurements of atmospheric volatile organic compounds (VOCs) at the Finokalia ground station, Crete. The measurement system employs a thermal desorption unit for on-line sampling and injection, and a GC x GC separation system equipped with a flame ionization detector (FID) for detection. The system was optimized to resolve C7-C14 organic components. Two-dimensional chromatograms from measurements of Finokalia air samples show several hundred well-separated peaks. To facilitate peak identification, cartridge samples collected at Finokalia were analyzed using the same GC x GC system coupled with a time-of-flight mass spectrometer (TOF-MS). The resulting mass spectra were deconvoluted and compared to spectra from a database for tentative peak identification. About 650 peaks have been identified in the two-dimensional plane, with significant signal/noise ratios (〉100) and high spectra similarities (〉800). By comparing observed retention indices with those found in the literature, 235 of the identifications have been confirmed. 150 of the confirmed compounds show up in the C7-C14 range of the chromatogram from the in situ measurement. However, at least as many peaks remain unidentified. For quantification of the GC x GC measurements, peak volumes of measured compounds have been integrated and externally calibrated using a standard gas mixture.

Description: Jakarta is the capital city of Indonesia located in the west-northern coast of Java island, within a deltaic plain and passes by 13 natural and artificial rivers. This megapolitan has a population of about 10.2 million people inhabiting an area of about 660 km2, with relatively rapid urban development. It has been reported for many years that several places in Jakarta are subsiding at different rates. The main causative factors of land subsidence in Jakarta are most probably excessive groundwater extraction, load of constructions (i.e., settlement of high compressibility soil), and natural consolidation of alluvial soil. Land subsidence in Jakarta has been studied using leveling surveys, GPS surveys, InSAR and Geometric-Historic techniques. The results obtained from leveling surveys, GPS surveys and InSAR technique over the period between 1974 and 2010 show that land subsidence in Jakarta has spatial and temporal variations with typical rates of about 3–10 cm year−1. Rapid urban development, relatively young alluvium soil, and relatively weak mitigation and adapatation initiatives, are risk increasing factors of land subsidence in Jakarta. The subsidence impacts can be seen already in the field in forms of cracking and damage of housing, buildings and infrastructure; wider expansion of (riverine and coastal) flooding areas, malfunction of drainage system, changes in river canal and drain flow systems and increased inland sea water intrusion. These impacts can be categorized into infrastructural, environmental, economic and social impacts. The risk and impacts of land subsidence in Jakarta and their related aspects are discussed in this paper.

Description: The technical steps involved in configuring a regional ocean model are analogous for all community models. All require the generation of a model grid, preparation and interpolation of topography, initial conditions, and forcing fields. Each task in configuring a regional ocean model is straight-forward – but the process of downloading and reformatting data can be time-consuming. For an experienced modeller, the configuration of a new model domain can take as little as a few hours – but for an inexperienced modeller, it can take much longer. In pursuit of technical efficiency, the Australian ocean modelling community has developed the Web-based MARine Virtual Laboratory (WebMARVL). WebMARVL allows a user to quickly and easily configure an ocean general circulation or wave model through a simple interface, reducing the time to configure a regional model to a few minutes. Through WebMARVL, a user is prompted to define the basic options needed for a model configuration, including the: model, run duration, spatial extent, and input data. Once all aspects of the configuration are selected, a series of data extraction, reprocessing, and repackaging services are run, and a "take-away bundle" is prepared for download. Building on the capabilities developed under Australia's Integrated Marine Observing System, WebMARVL also extracts all of the available observations for the chosen time-space domain. The user is able to download the take-away bundle, and use it to run the model of their choice. Models supported by WebMARVL include three community ocean general circulation models, and two community wave models. The model configuration from the take-away bundle is intended to be a starting point for scientific research. The user may subsequently refine the details of the model set-up to improve the model performance for the given application. In this study, WebMARVL is described along with a series of results from test cases comparing WebMARVL-configured models to observations and manually-configured models. It is shown that the automatically-configured model configurations produce a good starting point for scientific research.

Description: The biomisation method is used to reconstruct Latin American vegetation at 6000±500 and 18 000±1000 radiocarbon years before present (14C yr BP) from pollen data. Tests using modern pollen data from 381 samples derived from 287 locations broadly reproduce potential natural vegetation. The strong temperature gradient associated with the Andes is recorded by a transition from high altitude cool grass/shrubland and cool mixed forest to mid-altitude cool temperate rain forest, to tropical dry, seasonal and rain forest at low altitudes. Reconstructed biomes from a number of sites do not match the potential vegetation due to local factors such as human impact, methodological artefacts and mechanisms of pollen representivity of the parent vegetation. At 6000±500 14C yr BP 255 samples are analysed from 127 sites. Differences between the modern and the 6000±500 14C yr BP reconstruction are comparatively small. Patterns of change relative to the modern reconstruction are mainly to biomes characteristic of drier climate in the north of the region with a slight more mesic shift in the south. Cool temperate rain forest remains dominant in western South America. In northwestern South America a number of sites record transitions from tropical seasonal forest to tropical dry forest and tropical rain forest to tropical seasonal forest. Sites in Central America also show a change in biome assignment to more mesic vegetation, indicative of greater plant available moisture, e.g. on the Yucatán peninsula sites record warm evergreen forest, replacing tropical dry forest and warm mixed forest presently recorded. At 18 000±1000 14C yr BP 61 samples from 34 sites record vegetation that reflects a generally cool and dry environment. Cool grass/shrubland prevalent in southeast Brazil, Amazonian sites record tropical dry forest, warm temperate rain forest and tropical seasonal forest. Southernmost South America is dominated by cool grass/shrubland, a single site retains cool temperate rain forest indicating that forest was present at some locations at the LGM. Some sites in Central México and lowland Colombia remain unchanged in their biome assignments, although the affinities that these sites have to different biomes do change between 18 000±1000 14C yr BP and present. The "unresponsive" nature of these sites results from their location and the impact of local edaphic influence.

Description: During the MINOS campaign in August 2001 comprehensive two-dimensional gas chromatography (GC×GC) was applied to the in situ measurements of atmospheric volatile organic compounds (VOCs) at the Finokalia ground station, Crete. The measurement system employs a thermal desorption unit for on-line sampling and injection, and a GCxGC separation system equipped with a flame ionization detector (FID) for detection. The system was optimized to resolve C7−C14 organic components. Two-dimensional chromatograms from measurements of Finokalia air samples show several hundred well-separated peaks. To facilitate peak identification, cartridge samples collected at Finokalia were analyzed using the same GC×GC system coupled with a time-of-flight mass spectrometer (TOF-MS). The resulting mass spectra were deconvoluted and compared to spectra from a database for tentative peak identification. About 650 peaks have been identified in the two-dimensional plane, with significant signal/noise ratios (〉100) and high spectra similarities (〉800). By comparing observed retention indices with those found in the literature, 235 of the identifications have been confirmed. 150 of the confirmed compounds show up in the C7−C14 range of the chromatogram from the in situ measurement. However, at least as many peaks remain unidentified. For quantification of the GCxGC measurements, peak volumes of measured compounds have been integrated and externally calibrated using a standard gas mixture.

Description: The technical steps involved in configuring a regional ocean model are analogous for all community models. All require the generation of a model grid, preparation and interpolation of topography, initial conditions, and forcing fields. Each task in configuring a regional ocean model is straightforward – but the process of downloading and reformatting data can be time-consuming. For an experienced modeller, the configuration of a new model domain can take as little as a few hours – but for an inexperienced modeller, it can take much longer. In pursuit of technical efficiency, the Australian ocean modelling community has developed the Web-based MARine Virtual Laboratory (WebMARVL). WebMARVL allows a user to quickly and easily configure an ocean general circulation or wave model through a simple interface, reducing the time to configure a regional model to a few minutes. Through WebMARVL, a user is prompted to define the basic options needed for a model configuration, including the model, run duration, spatial extent, and input data. Once all aspects of the configuration are selected, a series of data extraction, reprocessing, and repackaging services are run, and a “take-away bundle” is prepared for download. Building on the capabilities developed under Australia's Integrated Marine Observing System, WebMARVL also extracts all of the available observations for the chosen time–space domain. The user is able to download the take-away bundle and use it to run the model of his or her choice. Models supported by WebMARVL include three community ocean general circulation models and two community wave models. The model configuration from the take-away bundle is intended to be a starting point for scientific research. The user may subsequently refine the details of the model set-up to improve the model performance for the given application. In this study, WebMARVL is described along with a series of results from test cases comparing WebMARVL-configured models to observations and manually configured models. It is shown that the automatically configured model configurations produce a good starting point for scientific research.

Description: The biomisation method is used to reconstruct Latin American vegetation at 6000±500 and 18 000±1000 radiocarbon years before present (14C yr BP) from pollen data. Tests using modern pollen data from 381 samples derived from 287 locations broadly reproduce potential natural vegetation. The strong temperature gradient associated with the Andes is recorded by a transition from high altitude cool grass/shrubland and cool mixed forest to mid-altitude cool temperate rain forest, to tropical dry, seasonal and rain forest at low altitudes. Reconstructed biomes from a number of sites do not match the potential vegetation due to local factors such as human impact, methodological artefacts and mechanisms of pollen representivity of the parent vegetation. At 6000±500 14C yr BP 255 samples are analysed from 127 sites. Differences between the modern and the 6000±500 14C yr BP reconstruction are comparatively small; change relative to the modern reconstruction are mainly to biomes characteristic of drier climate in the north of the region with a slight more mesic shift in the south. Cool temperate rain forest remains dominant in western South America. In northwestern South America a number of sites record transitions from tropical seasonal forest to tropical dry forest and tropical rain forest to tropical seasonal forest. Sites in Central America show a change in biome assignment, but to more mesic vegetation, indicative of greater plant available moisture, e.g. on the Yucatán peninsula sites record warm evergreen forest, replacing tropical dry forest and warm mixed forest presently recorded. At 18 000±1000 14C yr BP 61 samples from 34 sites record vegetation reflecting a generally cool and dry environment. Cool grass/shrubland is prevalent in southeast Brazil whereas Amazonian sites record tropical dry forest, warm temperate rain forest and tropical seasonal forest. Southernmost South America is dominated by cool grass/shrubland, a single site retains cool temperate rain forest indicating that forest was present at some locations at the LGM. Some sites in Central Mexico and lowland Colombia remain unchanged in the biome assignments of warm mixed forest and tropical dry forest respectively, although the affinities that these sites have to different biomes do change between 18 000±1000 14C yr BP and present. The "unresponsive" nature of these sites results from their location and the impact of local edaphic influence.