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A spatial approach to environmental risk assessment of PAH contamination.

https://arctichealth.org/en/permalink/ahliterature91935
Source
Risk Anal. 2009 Jan;29(1):48-61
Publication Type
Article
Date
Jan-2009
Author
Bengtsson Göran
Törneman Niklas
Author Affiliation
Department of Ecology, Lund University, Lund, Sweden. goran.bengtsson@ekol.lu.se
Source
Risk Anal. 2009 Jan;29(1):48-61
Date
Jan-2009
Language
English
Publication Type
Article
Keywords
Carbon - analysis
Creosote - chemistry
Ecology - methods
Electric Conductivity
Environment
Hydrogen-Ion Concentration
Models, Statistical
Normal Distribution
Polycyclic Hydrocarbons, Aromatic - analysis
Risk Assessment - methods
Soil
Soil Pollutants - analysis
Sweden
Temperature
Toxicology - methods
Abstract
The extent of remediation of contaminated industrial sites depends on spatial heterogeneity of contaminant concentration and spatially explicit risk characterization. We used sequential Gaussian simulation (SGS) and indicator kriging (IK) to describe the spatial distribution of polycyclic aromatic hydrocarbons (PAHs), pH, electric conductivity, particle aggregate distribution, water holding capacity, and total organic carbon, and quantitative relations among them, in a creosote polluted soil in southern Sweden. The geostatistical analyses were combined with risk analyses, in which the total toxic equivalent concentration of the PAH mixture was calculated from the soil concentrations of individual PAHs and compared with ecotoxicological effect concentrations and regulatory threshold values in block sizes of 1.8 x 1.8 m. Most PAHs were spatially autocorrelated and appeared in several hot spots. The risk calculated by SGS was more confined to specific hot spot areas than the risk calculated by IK, and 40-50% of the site had PAH concentrations exceeding the threshold values with a probability of 80% and higher. The toxic equivalent concentration of the PAH mixture was dependent on the spatial distribution of organic carbon, showing the importance of assessing risk by a combination of measurements of PAH and organic carbon concentrations. Essentially, the same risk distribution pattern was maintained when Monte Carlo simulations were used for implementation of risk in larger (5 x 5 m), economically more feasible remediation blocks, but a smaller area became of great concern for remediation when the simulations included PAH partitioning to two separate sources, creosote and natural, of organic matter, rather than one general.
PubMed ID
18808392 View in PubMed
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Species Distribution Modeling of Deep Pelagic Eels.

https://arctichealth.org/en/permalink/ahliterature290779
Source
Integr Comp Biol. 2016 10; 56(4):524-30
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Date
10-2016
Author
Shannon C DeVaney
Author Affiliation
Los Angeles Pierce College devanesc@piercecollege.edu.
Source
Integr Comp Biol. 2016 10; 56(4):524-30
Date
10-2016
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Keywords
Animal Distribution - physiology
Animals
Ecology - methods
Eels - physiology
Environment
Models, Biological
Software
Abstract
The ocean's midwaters (the mesopelagic and bathypelagic zones) make up the largest living space on the planet, but are undersampled and relatively poorly understood. The true distribution of many midwater species, let alone the abiotic factors most important in determining that distribution, is not well known. Because collecting specimens and data from the deep ocean is expensive and logistically difficult, it would be useful to be able to predict where species of interest are likely to occur so that sampling effort can be concentrated in appropriate areas. The distribution of two representative midwater fishes, the gulper eel Eurypharynx pelecanoides and the bobtail eel Cyema atrum (Teleostei: Saccopharyngiformes), were modeled with MaxEnt software to examine the viability of species distribution modeling (SDM) for globally distributed midwater fishes using currently available environmental data from the ocean surface and bottom. These species were chosen because they are relatively abundant, easily recognized, and unlikely to have been misidentified in database records, and are true midwater fishes, not known to undertake significant vertical diurnal migration. Models for both species show a generally worldwide distribution with some exceptions, including the Southern Ocean and Bering Sea. Variable contributions show that surface and bottom environmental variables correlate with species presence. Both species are more likely to be found in areas with low levels of silicate. SDM is a promising method for better understanding the ecology of midwater organisms.
PubMed ID
27252208 View in PubMed
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