The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils

This paper proposes a multistep approach for creating a 3D stochastic model of total petroleum hydrocarbon (TPH) grade in potentially polluted soils of a deactivated oil storage site by using chemical analysis results as primary or hard data and classes of sensory perception variables as secondary or soft data. First, the statistical relationship between the sensory perception variables (e.g. colour, odour and oil–water reaction) and TPH grade is analysed, after which the sensory perception variable exhibiting the highest correlation is selected (oil–water reaction in this case study). The probabilities of cells belonging to classes of oil–water reaction are then estimated for the entire soil volume using indicator kriging. Next, local histograms of TPH grade for each grid cell are computed, combining the probabilities of belonging to a specific sensory perception indicator class and conditional to the simulated values of TPH grade. Finally, simulated images of TPH grade are generated by using the P-field simulation algorithm, utilising the local histograms of TPH grade for each grid cell. The set of simulated TPH values allows several calculations to be performed, such as average values, local uncertainties and the probability of the TPH grade of the soil exceeding a specific threshold value.     

Climate change effect on outdoor ambiences in Iranian cities

In present research work the effect of climate change over the humidex in Iran is analyzed. From this research, we can conclude that the PD_IAQ index showed a special sensibility to changes in temperature and relative humidity, hence, it can be an interesting tool, may be even better than the humidex to show the expected effect of climate change in perception of air quality. Thus, it was found that the humidex expected in the future will be more elevated than that in the preceding period and, on the other hand, the PD_WRC has clearly reached a lower value than in the preceding period. The same effects, other than those using the earlier indexes in a more detailed manner, were observed with the PD_IAQ. This index showed a higher sensitivity to variations in temperature and relative humidity, than the humidex. Finally, the main results obtained must be considered at the time of design and during construction of future buildings, since buildings that are being constructed today will be occupied in the future years of the 2100s.     

Seasonal baseline of nutrients and stable isotopes in a saline lake of Argentina: biogeochemical processes and river runoff effects

The seasonal variability of inorganic and organic nutrients and stable isotopes and their relations with plankton and environmental conditions were monitored in Lake Chasicó. Principal component analysis evidenced the strong influence of the river runoff on several biogeochemical variables. Silicate concentrations were controlled by diatom biomass and river discharge. Higher values of nitrate and soluble reactive phosphorus (SRP) indicated agricultural uses in the river basin. Elevated pH values (～9) inhibiting nitrification in the lake explained partially the dominance of ammonium: ～83 % of dissolved inorganic nitrogen (DIN). The low DIN/SRP ratio inferred nitrogen limitation, although the hypotheses of iron and CO₂ limitation are relevant in alkaline lakes. Particulate organic matter (POM) and dissolved organic matter (DOM) were mainly of autochthonous origin. The main allochthonous input was imported by the river as POM owning to the arid conditions. Dissolved organic carbon was likely top-down regulated by the bacterioplankton grazer Brachionus plicatilis. The δ¹³C signature was a good indicator of primary production and its values were influenced probably by CO₂ limitation. The δ¹⁵N did not evidence nitrogen fixation and suggested the effects of anthropogenic activities. The preservation of a good water quality in the lake is crucial for resource management.     

Baseline element composition of foliose and fruticose lichens along the steep climatic gradient of SW Patagonia (Aisén Region, Chile)

Samples of foliose (Nephroma antarcticum) and fruticose (Usnea sp.) lichens were collected across a steep climatic and vegetation gradient in a remote, almost pristine region of SW Chilean Patagonia. Concentrations of major and trace elements in lichens from the rainforest were among the lowest ever reported worldwide for foliose and fruticose lichens and can be considered background levels for the region. The two lichen growth forms showed different elemental compositions mainly due to the greater capacity of foliose thalli to intercept elements from windborne and canopy-leached particles. The patterns of spatial variation in the chemical composition of lichens were effectively explained by statistical methods and reflected the different availability of wet and dry deposition along the steep climatic gradient. Baseline values established for N. antarcticum samples growing in temperate Nothofagus forests were therefore distinct from those of samples growing in more open, drier habitats. The fruticose Usnea sp. showed a higher affinity for atmophile Hg, low concentrations of lithophilic elements, and the same baseline composition whether from temperate forests or from dry, barren environments. The provided background and baseline values against which variations can be measured will be useful in the early detection of local or regional climatic and environmental change, especially in view of the planned construction of hydropower dams under the recently approved HidroAysén Project.     

A methodology to identify representative configurations of sensors for monitoring soil moisture

Soil moisture is the key link among hydroecological compartments, responding dynamically to sequences of atmospheric processes and management conditions and modulating physical, chemical, and biological processes in the soil. Currently, there are a variety of monitoring techniques to measure, directly or indirectly, the soil moisture. However, some practical issues remain open like the definition a priori of the number, location and depth of the monitoring points, and the impact of failing or poor performance soil moisture sensors. Here, we present a set of techniques, namely Δθ time series, wavelet filtering, and time stability, to identify representative points and monitoring depths through an analysis of hourly soil moisture time series for different configuration of the monitoring network. We used real data from a monitoring network consisting of seven monitoring points, each one with four EC-5 probes (Decagon Devices Inc., Pullman, WA) at 20, 40, 60, and 100?cm. The use of simple time series of Δθ allowed us to assess the spatiotemporal influence of the monitoring points, while the wavelet periodograms allowed us to get insight about the response of the monitoring points at different time scales. Both methods are easy to implement or adapt to specific conditions, being coherent to the results derived from time stability analysis. For our case study, we concluded that we could reallocate 16 sensors (out of 28) without a significant loss of information. However, the final decision strongly relies on a deep knowledge of the site features and the objectives of the monitoring network.     

Use of cellular automata in the study of variables involved in land use changes

The study of changes in land use has been included lately in territorial processes in order to optimize future management decisions. The different functions that the territory plays (production, aesthetic, and natural functions, etc.) make planning choices more difficult. This work focuses on the selection and combination of a set of indicators to analyze the variables through which the changes occur in the land used in viticulture for wine production. The proposed approach makes use of the Geographic Information System (GIS) in the development of a map of land use scenario. It is applied to a case study through a model involving cellular automata (CA) implemented with maps of suitability for viticulture and Markov chains. The use in this case of the CA is aimed at validating the scenario map in order to deduce the variables and the orientations of the farmers in the field of wine production.