PubMed Health⌕ Search

Biomedical subjects

Ole John Nielsen

Publications and source records attributed to Ole John Nielsen.

5 recordsLinked to original sources

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↗

Knudsen cell construction, validation and studies of the uptake of oxygenated fuel additives on soot.

INTENTION, GOAL, SCOPE, BACKGROUND: The properties of atmospheric particles are important to public health, radiative forcing of the atmosphere and to elucidating the chemical reactivity of atmospheric particles. We have constructed a Knudsen cell to study the uptake of organic compounds on soot. This article describes the construction and validation of the instrument, and our results on commercial soot concerning the uptake coefficient of ethanol, acetone, 1-butanol and diethoxymethane. OBJECTIVES: First, a technical description of the instrument is presented. Next, its performance is validated by measuring the uptake of NO2 on hexane soot. Finally, the uptake coefficients of four oxygenated hydrocarbons on commercial soot are presented. The objective is to contribute to the understanding of the formation of particles in motor vehicle exhaust. METHODS: A Knudsen cell is used to measure the uptake of specific gas-surface systems. A quadrupole mass spectrometer is used to determine the decay rate of a pulse of reagent gas in the reaction chamber. RESULTS AND DISCUSSION: The BET surface area of the commercial soot was 12.6 m2/g. The uptake coefficient (gamma) has been determined for ethanol (gamma0,BET = 7.7 +/- 4.8 x 10(-8)), 1-butanol 1.4 +/- 0.54 x 10(-7)), acetone (gamma0BET = 1.5 +/- 0.15 x 10(-7)) and diethoxymethane (gamma0,BET = 2.6 +/- 0.61 x 10(-7). These results are characteristic of the specific soot sample used. The ordering of the uptake coefficients, ethanol < 1-butanol approximately acetone < diethoxymethane, can be ascribed to a combination of physical (size and mass) and chemical effects. In addition, the initial uptake coefficient for NO2 on fresh hexane soot was determined to be gamma0,BET = 1.7 +/- 1.1 x 10(-4). CONCLUSIONS: In conclusion, we demonstrate that this instrument is able to measure uptake coefficients that are in agreement with accepted literature values. New data is presented concerning four light oxygenated hydrocarbons. RECOMMENDATIONS AND OUTLOOK: A large amount of detailed information concerning individual heterogeneous reactions is necessary in order to model the composition of motor vehicle emissions. We look forward to increasing the size of this database. Results for a series of alcohols and alkanes will be presented in a forthcoming publication.

Carbon↗

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↗

A comparison of partial order technique with three methods of multi-criteria analysis for ranking of chemical substances.

An alternative to the often cumbersome and time-consuming risk assessments of chemical substances could be more reliable and advanced priority setting methods. An elaboration of the simple scoring methods is provided by Hasse Diagram Technique (HDT) and/or Multi-Criteria Analysis (MCA). The present study provides an in depth evaluation of HDT relative to three MCA techniques. The new and main methodological step in the comparison is the use of probability concepts based on mathematical tools such as linear extensions of partially ordered sets and Monte Carlo simulations. A data set consisting of 12 High Production Volume Chemicals (HPVCs) is used for illustration. It is a paradigm in this investigation to claim that the need of external input (often subjective weightings of criteria) should be minimized and that the transparency should be maximized in any multicriteria prioritisation. The study illustrates that the Hasse diagram technique (HDT) needs least external input, is most transparent and is least subjective. However, HDT has some weaknesses if there are criteria which exclude each other. Then weighting is needed. Multi-Criteria Analysis (i.e. Utility Function approach, PROMETHEE and concordance analysis) can deal with such mutual exclusions because their formalisms to quantify preferences allow participation e.g. weighting of criteria. Consequently MCA include more subjectivity and loose transparency. The recommendation which arises from this study is that the first step in decision making is to run HDT and as the second step possibly is to run one of the MCA algorithms.

Chemical Industry↗