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1、英文参考文献Fuzzy approaches to environmental decisions: applicationto air qualityBernard E.A. Fishera b s t r a c tThis paper considers flexible approaches to decisions designed to improve environmental quality having regard to uncertainty. The performance of simple and complex models, for forecasting ai
2、r quality are reviewed, and both types are shown to involve considerable uncertainty regarded as typical of environmental systems. This means that decisions usually depend on combining two or more quite uncertain environmental criteria, and it is shown that this can be approached systematically if a
3、 fuzzy logic framework is adopted. Fuzzy set aggregation includes, as special cases, other decision-making frameworks, such as multi-criteria analysis and conventional probability based methods. Examples are presented of how it can be applied to situations involving models and used to incorporate br
4、oader factors involving risk, and socio-economic considerations.1. The problemIdeally environmental models should contain the bestknown science, be tested against measurements, and then used for prediction and decision-making if they are supported by the necessary input data and perform well against
5、 measurements. This has led in recent years to the development of more complex, fundamental models, in which every part of the environmental system is described in as much detail as possible, the so-called reductionist approach. If the development in modelling produced better predictions this would
6、be the way to proceed. From examples in the field of air pollution, it is argued in this paper that limitations in process description, and the lack of detailed data on concentrations, deposition or emissions etc., mean that this approach has not produced usefully better predictions. Even the most a
7、dvanced models are still associated with large uncertainties (Hunt, 2000; Funtowicz and Ravetz, 2005; Saloranta, 2001).The alternative approach to forecasting is to consider the decision that is likely to bring environmental improvements and to consider models which allow decisions to be made, so-ca
8、lled fit for purpose models. These models may involve greater uncertainty than more complex models, but they facilitate more readily the treatment of uncertainty, through sensitivity analysis. Moreover, the data requirements to run them are less stringent. They may also allow broader factors to be i
9、ncluded into the decision-making process, incorporating risk, and optimising social and economic factors, etc.An environmental decision ought to depend on a criterion meeting a numerical objective with uncertainty treated explicitly (Royal Commission on Environmental Pollution, 1998). One way of inc
10、luding the uncertainty is to assume that the criterion involves the membership of a fuzzy set. This is an obvious application of fuzzy logic to environmental decision-making. In some cases, the environmental criterion is vague or imprecise, such as when dealing with the quality of life (Mendes and M
11、otizuki, 2001), the ranking of ecosystemsin terms of environmental conditions and impacts (Tran et al., 2002), or environmental impact assessment (Enea and Salemi, 2001).A fuzzy set is a generalisation of a normal set for which there is not a sharp boundary between belonging and not belonging to the
12、 set. A fuzzy set is defined by its membership function and two examples are shown in Fig. 1, in which two hypothetical environmental quality criteria (relating to health and noise) along a cross-section though a city are shown, where x is the distance from the city centre. In both cases near the ci
13、ty centre (at small values of x), the environmental quality is poorer, and therefore there is a greater possibility that environmental quality objectives are exceeded. This is shown by the higher membership functions at short distances from the city centre for the fuzzy sets describing unsatisfactor
14、y health and noise. If the environmental quality were described by a normal set, the membership function would equal 1 out to the distance at which the environmental quality was judged satisfactory and the membership function would be set equal to 0 at greater distances.Qualitative factors, involvin
15、g expert judgement of the pedigree of a model, should be part of an assessment ofmodel performance, going beyond traditional scientific approaches, such as sensitivity analysis and model validation (Van der Sluijs et al., 2003). Wider judgements are required when reviewing the formulation of a model
16、 or decidingwhether it is appropriate to use an established model in a novel application. Because of its history of testingmodel performance, and the use of objectives in air quality management, air quality provides a good test case of fuzzy decision-making. However, fuzzy decision-making is closely
17、 related to other environmental decision-making frameworks, such as multi-criteria decision analysis for flood defence (Tung, 2002), or the weighted utility approach for abrupt climate change (Perrings, 2003). It is not proposed that fuzzy decision-making is superior to these other methods, but it d
18、oes provide a structured framework within which other methodsmaybeincorporated. In this paper, we consider how conventional approaches to air quality models can be extended within a fuzzy logic framework to include wider considerations, not just those concerned with the accuracy of the model. These
19、wider, generally policy related, considerations are relevant to how the modelling results will be used, and hence should also be an integral part of the decision.2. Performance of air quality models: simple and complex2.1. Short-range modelsA number of assessments of dispersion models performance to
20、 predict concentrations over short distances out to 30 km have been made. Some of these have been summarised by Ireland (2003), and for urban areas by Fisher (2005a). They show that various summary statistics can be used to judge model performance. Model performance also varies according to the way
21、results are interpreted. For example, for point sources much of the error can arise from errors in the prediction of the direction of the plume centreline. The error is much smaller if predicted and measured maximum groundlevel concentrations are compared without regard to the location of the maxima
22、. Generally, studies suggest that most predictions are likely to be within a factor of two of the predictions, a simple, useful measure of performance, but performance is not consistently better than a factor of two. The uncertainty in model predictions could be based on other estimators, such as th
23、e residual sum of squares. The factor of two is easy to explain to decision makers and would be in agreement with the uncertainty associated with setting the boundary of air quality management areas, recommended in informal guidance produced by National Society for Clean Air (2003).The uncertainty a
24、ssociated with errors in model formulation, or whether the model is appropriate to an application are more difficult to deal with. In part, this may be addressed by using more than one air quality dispersion model in predictions (Fisher, 2003; Fisher et al., 2002). The overall uncertainty can only b
25、e addressed by recourse to comparisons with measurements of suitable quality and quantity. The pedigree of the model can help (Van der Sluijs et al., 2003) and would bring in considerations as to whether the model had been used for similar applications and if the formulation of the model is readily
26、available for review by potential users. When the structure of the environmental model is very complex, which is often the case in regional air quality models, sensitivity analysis to choose the best values for parameters, is not usually undertaken, because either the run times are too long, or the
27、data sets of observations are too limited to evaluate each component in detail.2.2. Regional modelsThe prediction of concentrations and deposition over long and short distances using air quality models depends in part on the uncertainty in the input values for the parameters in the model. This can b
28、e assessed in a relatively straightforwardway, if possible errors in model formulation are ignored. Two recent studies have determined the uncertainty in model predictions for acid deposition (Abbott et al., 2003) and dispersion calculations (Hall et al., 2000a,b) based on sensitivity studies, Monte
29、-Carlo simulation and scena rio analysis. The degree of uncertainty is again broadly within a factor of two i.e. changing input values within their bounds of uncertainty generally leads to predictions which lie within a range of two of a central value, but predictions better than a factor of two can
30、not be obtained. Abbott et al. (2003) have also shown that the commonly applied methods of deriving critical loads have uncertainties of comparable magnitude.An example of a complex integrated atmospheric model to calculate long-term annual average atmospheric concentrations and depositions over a r
31、egion such as Europe, is the use of Models-3. Recent research (Cocks et al., 2004) has demonstrated that meso-scale pollution transport models, such as Models-3, can be run to produce hourly time-series of pollution concentration and deposition fields over long periods, such as a year, providing dir
32、ect comparisons with percentile exceedences of short-term air quality objectives. Long-term averages derived from time-series also provide a benchmark for comparison with older established statistical acid deposition models, which may only be used to calculate long-term averages. Issues regarding th
33、e practical use of Models-3 for regulatory assessment purposes remain, but the demonstration supports the viability of the concept of a modular assessment framework incorporating complex interactions. As an integrated, reductionist model, the results provide extra information on quantities, such as
34、secondary particulate matter.The running of a meso-scale model is a very large computing exercise, and a large amount of preparatory work is involved in the production of the data sets used. Unlike the simpler models, the attribution, or contribution, of a single source or a set of sources to the to
35、tal concentration or deposition, is not a simple output of such models. The calculations provide the concentration, as time series, at chosen receptor points. However, as the model includes nonlinear processes, the simple addition or subtraction of individual source contributions is not appropriate.
36、 In order to estimate the contribution of a set of sources, the calculation would need to be repeated again, so that computer results with, and without, the specified sources can be compared. The run times can be reduced by running parts of themodel in parallel, but run times and costs are still a s
37、ignificant disincentive to the routine application of such models. Source-receptor relationships are available when supercomputing facilities are used, such as for the EMEP model (Tarrason et al., 2003) used in international negotiations to set limits on transboundary pollution. However, estimates o
38、f the uncertainty cannot be easily derived from this model, or Models-3, because of the computing requirements.2.3. Examples of regional model performanceEstimates of the likely uncertainties in predicted concentrations and deposition using the simpler regional transport models are possible, because
39、 they have short running times. Abbott et al. (2003) found that 300 runs of the model TRACK using a plausible range of input parameter values, describing emissions, processes and meteorology, encompassed almost all the annual wet deposition measurements made at UK sites in 1997.Comparisons of model
40、performance against measurements were possible for both simple and complex models. In the above study (Abbott et al., 2003), the baseline model TRACK for which normal parameter input values are used, had correlation coefficients1 of 0.6, 0.6 and 0.4, when measured and modelled annual wet deposition
41、of sulphur, nitrate and reduced nitrogen were compared at 31 UK acid deposition sites. The annual wet deposition of sulphur, nitrate and reduced nitrogen for 1997 from the statistical national assessment model HARM, which has in recent years been used to make assessments of possible critical load ex
42、ceedences from a variety of sources, had slightly better correlation coefficients of 0.7, 0.7 and 0.6. The national assessment model FRAME, though not the latest version, produced a correlation coefficient of 0.3 for reduced nitrogen comparisons for 1997. A comparison of the annual wet deposition at
43、 48 European sites from the EMEP model yielded correlation coefficients of 0.6, 0.7 and 0.7 over the period 19851996. The correlation coefficients from the limited spatial analysis of the Models-3 annual deposition over the UK (Cocks et al., 2004) gave values of 0.75 and 0.5 for comparisons of the c
44、alculated and measured annual wet deposition of sulphur and nitrate at 13 UK acid deposition sites for 1999. No correlation was found for reduced nitrogen, though predictions were in the right range. One should be cautious about drawing firm conclusions from these numbers, which apply to annual depo
45、sition over different years, periods and spatial domains, but it is apparent that none of the models was markedly better at predicting annual spatial patterns than any of the others.However, complex models (Models-3, or EMEP) do have the advantage that they can be used to predict time series of conc
46、entrations or deposition, which is not possible with the simpler models. Models-3 (Cocks et al., 2004) produced correlation coefficients for weekly time-series of observations in the range 0.60.8 for sulphur, 0.40.8 for nitrate and 0.30.8 for reduced nitrogen at UK acid deposition sites. A similar c
47、omparison of the results of the EMEP model, at monthly averaged, European acid deposition sites, appeared to suggest slightly lower correlation coefficients. Without dismissing the progress that reductionist models may achieve in advancing the science, one may consider more direct approaches to deci
48、sion-making. The studies cited above suggest that models of concentration or deposition over long, or short-ranges, produce results, which lie within, but are not much better than, a factor of two of observations. To the authors knowledge this degree of uncertainty is the best achievable for forecas
49、ts in all environmental media i.e. land, air and water. Given this degree of uncertainty in the environmental quantities predicted by models, decisions based on models should not be regarded as certain. Instead a fuzzy logic framework should be applied, which additionally has the advantage that other, broader aspects of the decision-making process might be included within the same framework. 3. Uncertain environmental objectives3.1. Critical load exampleAlthough national air quality objectives, in terms of concentrations an
