Biomedical subjects

Peter B Sørensen

Publications and source records attributed to Peter B Sørensen.

12 recordsLinked to original sources

PubMed2005

Temporal development of brominated flame retardants in peregrine Falcon (Falco peregrinus) eggs from South Greenland (1986-2003).

A time trend between 1986 and 2003 was found for brominated flame retardants in peregrine falcon eggs from South Greenland, with significantly increasing concentrations of the polybrominated diphenyl ethers (PBDEs) 99, 100, 153, 154, and 209. For BDE-99 and -100, the concentration increased approximately 10% per year. The concentrations of PBDEs were among the highest detected in wildlife so far and ranged from 300 to 12,900 ng/g lipid weight (lw) for sigmaPBDE. While tetrabromobisphenol A (TBBPA) was below the limit of detection in all eggs, hexabromocyclododecane (HBCD), dimethyl-TBBPA, and brominated biphenyl BB-153 were detected in a majority of eggs, with median concentrations of 2.4, 230, and 550 ng/g lw, respectively. Analyses of eggs of the same bird showed no significant intra-clutch variation for PBDEs, BB-153, and HBCD but larger variations for dimethyl-TBBPA. Inter-clutch variations with increasing time trends exist for the BDEs 99, 100, 153, 154, and 209, while a decreasing contamination with the BDEs 183, 49, 47, 66 and 153 was indicated in a subset of eggs.

Bromine↗

PubMed2005

Risk of five polycyclic aromatic hydrocarbons in a terrestrial environment: influence of data variability.

The risk of five different pyrogenic polycyclic aromatic hydrocarbons (PAHs) toward two soil-dwelling organisms (i.e., springtail [Folsomia fimetaria] and earthworm [Eisenia veneta]) has been investigated with respect to lethality and reproduction at two soil depths in a typical Danish soil. Predicted environmental concentrations (PECs) are calculated with a model describing diffusion, bulk flow, and microbial degradation. Predicted no-effect concentrations (PNECs) are derived from laboratory experiments performed with nominal soil concentrations in the range from 0 to 300 microg PAH/g dry weight. Risk is estimated through a stochastic approach as well as with the conventional point estimate. The point estimate predicts a potential risk for pyrene, log PEC/PNEC = -0.01, with respect to springtail reproduction at 5 cm soil depth. In all other scenarios, the point-estimate log-ratios are significantly lower than 0. For the stochastic approach risk is defined when the probability for risk (i.e., the probability for log PEC/PNEC > 0), is larger than 5%. The results show that risk is present only for springtail and in the following five scenarios: For anthracene, the probability for risk with respect to lethality is 12% at 5 cm soil depth, and 17 and 5% with respect to reproduction at 5 and 50 cm soil depth, respectively; for pyrene the probability for risk with respect to reproduction is 49 and 14% at 5 and 50 cm, respectively. The results show that risk cannot be defined unambiguously with the two approaches. The probabilistic approach is less restrictive, and even small probabilities may be used as early-warning indications that risk may be posed under unfavorable circumstances.

Animals↗

PubMed2004

Ranking of chemical substances based on the Japanese Pollutant Release and Transfer Register using partial order theory and random linear extensions.

In 1997 a Pollutant Release and Transfer Register (PRTR) pilot project was initiated in Japan. In 1998 the project was expanded and in 1999 a law concerning the establishment of a national PRTR was adopted. Data on the emissions of chemical substances are therefore now being reported on a continuous base. In relation to the PRTR project data on toxicity have been collected. In order to make efficient use of the collected information on emission and toxicity it is useful to group or rank the chemical substances according to the impact on human health and the environment. It has recently been argued that partial order theory (POT) in combination with the use of linear extensions (LE) may be the most objective way to create a linear rank. The methodology has been further expanded to handle larger data sets by the use of random linear extensions (RLE). In this paper the Japanese PRTR data are ranked using the POT/RLE methodology. An average rank is established for chemical substances in the 1998 and 1999 PRTR in Japan. The top 10 chemical substances in the 1998 PRTR are: dichlorvos, inorganic arsenic compounds, cobalt compounds, beryllium compounds, fenitrothion, disulfoton, parathion, diazinon, 4,4'-diamino-3,3'-dichlorodiphenylmethane and antimony compounds. The top 10 chemical substances from the 1999 PRTR are PCBs, lead compounds, fenitrothion, dichlorvos, disulfoton, inorganic arsenic compounds, chlorothalonil, thiobencarb, chromium and HCFC-141b. The descriptor having the highest influence on the ranking of the 1998 PRTR data is the production volume, which, however, is not given in the 1999 PRTR. Further, the disagreement between the ranking with the lack of toxicity data substituted with mean and maximum values, respectively, strongly indicates a general need for further toxicological investigations.

Data Interpretation, Statistical↗

PubMed2003

Phthalates, nonylphenols and LAS in an alternately operated wastewater treatment plant--fate modelling based on measured concentrations in wastewater and sludge.

The performance of an alternately operated activated sludge wastewater treatment plant (WWTP) has been investigated with respect to six phthalates, nonylphenol (NP) and nonylphenol diethoxylate (NPDE) and linear alkylbenzene sulphonates (LAS). Samples of raw sewage, primary and secondary sludge and treated water were collected during an 8-day period in May 1999 and analysed for dissolved and sorbed substances. To evaluate the system performance with respect to substance removal through biodegradation and sorption to sludge the measured data were applied in a model describing the different bioreactors as one single reactor, corresponding to the concepts of, e.g. SimpleTreat. The most abundant of the investigated phthalates was di-(2-ethylhexyl)-phthalate (DEHP) with a measured mean inlet flow of 240g/day. Two percent of this amount was found in the treated water, 70% was biodegraded and 28% was found in the sludge. For LAS the mean inlet flow was 20,300g/day, of which less than 1% was found in the treated water, 84% was biodegraded and 15% was found in the sludge. The mean inlet flow of NP and NPDE was 44 and 590g/day, of which 4% and 2% was found in the treated water, 80% was biodegraded for both substances, and 16% and 18% was found in the sludge, respectively. The WWTP removal of the investigated substances was thus high compared to other studies of conventional activated sludge WWTPs. The simple model set-up presents a strong tool for predicting substance removal and system sensitivity related to changes in the inlet conditions, such as concentrations and flow. Furthermore, it allows the inclusion of complex alternately operated WWTPs in risk assessment tools such as e.g. SimpleTreat.

Alkanesulfonic Acids↗

PubMed2003

Model description of an alternately operated wastewater treatment plant--evaluation of the applicability of SimpleTreat.

Alternately operated wastewater treatment plants (WWTPs) are fundamentally different compared to conventional activated sludge WWTPs with respect to flow patterns and aeration in the biological reactors. Several model applications exist for conventional WWTPs, e.g. SimpleTreat, and in this study the effect of substituting a complex discontinuous operation, involving alternating degradation and flow conditions between two reactors, with one single bioreactor with continuos flow (SimpleTreat) has been investigated by setting up two models representing the respective operation schemes. The discontinuous operation induces fluctuations in the outlet concentrations that are not modelled with the single bioreactor model, however, the fluctuations and the associated uncertainties were found to be insignificant compared to the influence of the input parameter uncertainties on the model results. An empirical relationship between an aggregate pseudo-1st order degradation rate for the single bioreactor model and realistic aerobic and anoxic 1st order degradation rates, respectively, has been established. When using this aggregate degradation rate in the single bioreactor model an outlet concentration can be calculated that deviates no more than 2% from the mean outlet concentration from the alternating operation model. For substances with aerobic half-lives longer than approximately 2 h, which is valid for many chemical substances, the aggregate 1st order degradation rate can be set equal to the aerobic 1st order degradation rate.

Bioreactors↗

PubMed2003

Evaluation of the ranking probabilities for partial orders based on random linear extensions.

Partial order theory and Hasse diagrams appears to be a promising tool for decision-making in environmental issues. Alternatives or objects are said to be partial ordered when it is impossible to find a mutual relationship (< or >) for all criteria. This is often the case in complicated real life situations. However, sometimes it is attractive to apply a total order, i.e. linear rank, and not just the partial order. Based on ranking probabilities and linear extensions it is possible to derive a total order. A linear extension is a projection of the partial order into a total order that comply with all the relations in the partial order. When all linear extensions are known the ranking probabilities can be found as the probability for an object to occupy a specific rank. However, the total number of linear extensions is proportional with the faculty of the number of objects in the partial order. Therefore it is practically impossible to identify all possible linear extensions for partial orders with more than around 20 objects. This study reviews and evaluates a method which estimates the ranking probability based on sampling of a minor random fraction of the linear extensions. Using standard statistics the necessary number of random linear extensions is described as a function of the ranking probability estimate and the restrictions on the confidence interval around the ranking probability. The analysis reveals a smaller systematic uncertainty, which occurs due to the random selection of ranking between two incomparable objects. The discrepancy appears to be dependent on the structure of the partial order. The method using random linear extensions thus appears as a valuable tool for analysing larger partially ordered sets, which are practically impossible to handle using the total set of linear extensions.

Decision Making↗

PubMed2003

Analysis of monitoring data of pesticide residues in surface waters using partial order ranking theory.

In this investigation, a new and simple way to analyze, interpret, and generalize monitoring data of occurrence of pesticide active ingredients in surface waters was developed. The occurrence is quantified using the variables frequency of detection and the concentration level. These two parameters are associated with basically different ecotoxicological effects; for example, a high frequency of detection may be related to bioaccumulation problems, while the level of concentration also controls the acute toxicological effects. The active ingredients were ranked on the basis of the monitoring data in relation to both the frequency of finding and concentration level using the concept of partial ordered sets. The resulting rankings was correlated with other rankings based on descriptors such as sprayed area, applied dose, adsorption to soil organic carbon, vapor pressure, and soil dissipation half-life. A similarity index was applied in order to compare the ranking of the monitoring data with the ranking of the descriptors. It is shown how partial order theory can be used to evaluate the relevance of every single descriptor. The dosage is found to be the most important descriptor, followed by the sprayed area and the adsorption to organic carbon ending up a very close similarity between, respectively, the rankings using monitoring data and rankings using these three descriptors.

Data Interpretation, Statistical↗

PubMed2003

Ranking contaminated sites using a partial ordering method.

In this project, we apply the method of partial ordering on the ranking of 74 contaminated sites located in the county of West Zealand (Denmark). The method is based on the concept that the parameters are kept separated through the ranking analysis, and thus no weighing of the different parameter values is necessary. The ranking is displayed in a graphical form by the Hasse diagram technique to ease the interpretation. A critical comparison is made of the ranking of contaminated sites by the partial ordering method and an index function used by the county of West Zealand. Comparing the ranking by the partial ordering method to the index function shows that the choice of score points and index function highly influences the ranking result, as only four sites are equally ranked. The importance of the parameters used to identify the environmental hazard of the contaminated sites is analyzed in order to evaluate the influence of each parameter on the ranking. From among a total of six different parameters, two have high influence, two medium, and two low because of both the construction of the scoring system and the characteristics of the data.

Denmark↗

PubMed2002

Comparison of the combined monitoring-based and modelling-based priority setting scheme with partial order theory and random linear extensions for ranking of chemical substances.

The combined monitoring-based and modelling-based priority setting scheme (COMMPS) used to establish a priority setting list within the EU Water Framework Directive plays a major role in the European environmental policy on chemical substances. The COMMPS procedure can be classified as a so-called scoring method. The applied functional relationship and weight factors are established based on expert judgement, which unfortunately appears to be vulnerable to subjective inputs. In this study an alternative priority setting methods based on partial order theory (POT) and random linear extensions (RLE) is suggested and compared to the COMMPS procedure. The POT/RLE is characterised as being based on fewer assumptions concerning functional relationships and does not apply weighting factors. Using the POT/RLE methodology a different ranking result occur than when using the COMMPS procedure. Eight of the top 20 substances from the COMMPS procedure are not ranked within the top 20 when using POT/RLE. From the viewpoint of environmental protection, especially the substances that have been given low priority in the COMMPS procedure, but a high rank in POT/RLE, are of interest in a regulatory context. These substances are naphthalene, trichloromethane, isoproturon, metolachlor, endosulfan, acenaphthene, alachlor and dichloromethane. An analysis of the ability of the descriptors to separate the single substance discloses that the most significant descriptor is the concentrations detected in the environment. Further, the frequency of detection is not applied as a descriptor in the COMMPS procedure. However, if this descriptor was to be applied the analysis revealed that it would have been the third most significant descriptor.

Algorithms↗

PubMedDate not supplied

Improving the predicting power of partial order based QSARs through linear extensions.

Partial order theory (POT) is an attractive and operationally simple method that allows ordering of compounds, based on selected structural and/or electronic descriptors (modeled order), or based on their end points, e.g., solubility (experimental order). If the modeled order resembles the experimental order, compounds that are not experimentally investigated can be assigned a position in the model that eventually might lead to a prediction of an end-point value. However, in the application of POT in quantitative structure-activity relationship modeling, only the compounds directly comparable to the noninvestigated compounds are applied. To explore the possibilities of improving the methodology, the theory is extended by application of the so-called linear extensions of the model order. The study show that partial ordering combined with linear extensions appears as a promising tool providing probability distribution curves in the range of possible end-point values for compounds not being experimentally investigated.

Computer Simulation↗

PubMedDate not supplied

Improved estimation of the ranking probabilities in partial orders using random linear extensions by approximation of the mutual ranking probability.

The application of partial order theory and Hasse diagram technique in environmental science is getting increasing attention. One of the latest developments in the field of Hasse diagram technique is the use of random linear extensions to estimate ranking probabilities. In the original algorithm for estimating the ranking probability it is assumed that the order between two incomparable pair of objects can be chosen randomly. However, if the total set of linear extensions is considered there is a specific probability that one object will be larger than another, which can be far from 50%. In this study it is investigated if an approximation of the mutual ranking probability can improve the algorithm. Applying an approximation of the mutual ranking probability the estimation of the ranking probabilities are significantly improved. Using a test set of 39 partial orders with randomly chosen values the relative mean root square difference (MRSD) decrease in average from 7.9% to 2.2% and a maximum relative improvement of 90% can be found. In the most successful case the relative MRSD goes as low as 0.77%.

Journal Article↗

PubMedDate not supplied

Estimation of averaged ranks by a local partial order model.

This paper continues the series of publications about applications of partial ordering. The focus of this publication is the derivation of approximate analytical expressions for the averaged rank and the ranking probabilities. To derive such combinatorial formulas a local partial order is suggested as an approximation. The performance of the approximation is rather high; we therefore conclude that three very simple descriptors of the local partial order seem to be sufficient to get a rough impression of the linear order, induced by the averaged ranks and the ranking probabilities of empirical partially ordered sets. Linear order derived from the partial order, ranking probabilities, and other characteristics are considered as parts of a so-called "General Ranking Model" (GRM). Following the local partial order, the averaged rank of an object x can be estimated applying the following simple formula: Rk(av) = (S+1)*(N+1)/(N+1-U). S is the number of successors of the object x, N is the total number of objects (of the quotient set), and U is the number of objects incomparable with x. More complex formulas for the ranking probabilities are given in the text. A list of abbreviations and symbols can be found in Tables 3 and 4.

Journal Article↗