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The potential ecological impact of ongoing climate change has been much discussed. High mountain ecosystems were identified early on as potentially very sensitive areas. Scenarios of upward species movement and vegetation shift are commonly discussed in the literature. Mountains being characteristically conic in shape, impact scenarios usually assume that a smaller surface area will be available as species move up. However, as the frequency distribution of additional physiographic factors (e.g., slope angle) changes with increasing elevation (e.g., with few gentle slopes available at higher elevation), species migrating upslope may encounter increasingly unsuitable conditions. As a result, many species could suffer severe reduction of their habitat surface, which could in turn affect patterns of biodiversity. In this paper, results from static plant distribution modeling are used to derive climate change impact scenarios in a high mountain environment. Models are adjusted with presence/absence of species. Environmental predictors used are: annual mean air temperature, slope, indices of topographic position, geology, rock cover, modeled permafrost and several indices of solar radiation and snow cover duration. Potential Habitat Distribution maps were drawn for 62 higher plant species, from which three separate climate change impact scenarios were derived. These scenarios show a great range of response, depending on the species and the degree of warming. Alpine species would be at greatest risk of local extinction, whereas species with a large elevation range would run the lowest risk. Limitations of the models and scenarios are further discussed. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献
2.
基于OPAQ的城市空气质量预报系统研究   总被引:1,自引:1,他引:0  
空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。  相似文献
3.
In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.  相似文献
4.
Ulrich (1981) supposes in the hypothesis of humusdisintegrationthat the balance between polymerisation and breakdown of organicmaterial may be disturbed in chemically well buffered Europeanforest soils. This new aspect of aluminium toxicity may causenitrogen exceedance in forest ecosystems and subsequent seasonalnitrate output (Eichhorn and Hütterman, 1999).In a research program the substances in the seepage water aremonitored in a small woodland in central Germany. We explorethese multivariate data for examining possible influences on theprocess of humusdisintegration and its temporal evolution. As aresult, a regression model for carbon is developed, whichincludes covariables, i.e., other substances, as well as spatialand temporal terms describing systematic variability. Especiallyiron and aluminium turn out to be very influential in the model.So far our work is a basic step for monitoring the seepage waterdata by means of stochastic modelling.  相似文献
5.
In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.  相似文献
6.
The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.  相似文献
7.
This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.  相似文献
8.
The distributed lag effects of ambient particulate air pollution exposure on respiratory hospital admissions in Kathmandu Valley are modelled using daily time series data. The extended exposure to PM10 is accounted for by assigning weights to daily average PM10 which decline geometrically as the lag period increases in days. Results show that the percent increase in chronic obstructive pulmonary disease (COPD) hospital admissions and respiratory admissions including COPD, asthma, pneumonia, and bronchitis per 10 μg/m3 rise in PM10 are found to be 4.85% for 30 days lag effect, about 15.9% higher than that observed for same-day lag effect and 3.52% for 40 days lag effect, about 28.9% higher than the observed value for same-day lag effect, respectively.  相似文献
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