Relation Between Individual Tree Mortality and Tree Characteristics in a Polluted and Non-Polluted Environmentx

Data on individual tree mortality in relatively healthy (Berezinskiy biosphere reserve) and damaged (surroundings of the mineral fertilizer plant ‘Achema’) even-aged Scots pine (Pinus sylvestris L.) stands are presented. Tree size and competition intensity were found to be the most significant predictors of individual tree mortality in all age groups of the relatively healthy Scots pine stands, however, an essential reduction in the closeness of relations between the tree mortality rate and these variables was determined with the aging of stands. An exponential decrease in tree mortality probability with an increase of tree size is characteristic for trees suffering different competition intensity, however, this decrease becomes much more pronounced as the competition pressure increases. The relations of different tree and stand variables with tree mortality probability have been found to become much weaker in the polluted environment. An exponential increase in tree mortality probability with an increase of crown defoliation was characteristic of damaged Scots pine stands, however, the rate of the increase was different in different age and diameter classes. The impact of crown defoliation on tree mortality rate increased with the aging of stands. At the same defoliation level, individual tree mortality probability was much higher for smaller suppressed trees, however, a relative increase in tree mortality probability along with an increase of crown defoliation was more pronounced for dominant trees. Conclusion: a higher mortality of damaged (defoliated) trees should be considered while assessing losses in forest productivity in a polluted environment.     

Ecohydrological impact–assessment modeling: an example for terrestrial ecosystems in Noord-Holland, the Netherlands

In the Netherlands the increasing demand for ecohydrological models on a regional (provincial) scale has resulted in the development of an empirical statistical impact–assessment model for semi-natural terrestrial herbaceous ecosystems. This model, called ITORS, describes the relationship between plant species and site factors such as soil, groundwater and management. The model, developed for the region of Noord-Holland, is applied to support provincial policy on soil and water management. The empirical statistical approach required the collection of a large set of field data, describing the biotic and environmental variation of a variety of semi-natural terrestrial ecosystems in Noord-Holland. These data were used to calculate species response models with help of logistic regression, resulting in significant models for 130 out of 144 species. Significant species response models were incorporated in the computer program ITORS. With help of ITORS the effect of various management scenarios can be easily evaluated, which is demonstrated by an example. This revised version was published online in July 2006 with corrections to the Cover Date.     

用“主成份子集合选择法”优选大气环境测点的缺陷

分析了主成份子集合选择法的应用过程,指出该方法只考虑了各测点的污染随机变化信息.而忽略了各测点的污染水平信息;用该方法所得到的优化点集不能正确地反映地区大气污染水平.因而主成份子集合选择法不宜应用于大气例行监测的优化布点研究中.     

Modeling of NO x Emissions from Oil Refinery Furnaces

The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO _x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO _x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO _x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO _x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO _x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper. This revised version was published online in July 2006 with corrections to the Cover Date.     

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO₄ phosphorus, NO₃ nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

Nitrate leaching in agriculture to upper groundwater in the sandy regions of the Netherlands during the 1992–1995 period

The Dutch National Monitoring Programme for Effectiveness of the Minerals Policy (LMM) was initiated to allow detection of a statutory reduction in nitrate leaching caused by a decreasing N load. The starting point, or baseline, was taken as the nitrate concentration of the upper metre of groundwater sampled on 99 farms in the 1992–1995 period in the sandy areas of the Netherlands, where predominantly grass and maize grow. We found here that a reduction in nitrate leaching of more than 20% in future would almost certainly be detected with the LMM. Detecting downward trends due to decreasing N load will require nitrate concentrations to also be related to soil drainage, precipitation excess leading to groundwater recharge and to location. Furthermore, we found that about 16% of the N load in the Dutch sandy regions was being leached to the upper metre of groundwater in the 1992–1995 period. The critical N load in approximately 1990 for exceeding the EC limit value for nitrate, NO₃, (50 mg L^–1) in the upper metre of groundwater for the mean situation for grassland, maize and arable land in the sandy area was found to be 210 kg ha^–1 a^–1. Because manure management has been altered, the critical load found will be lower than the current critical load .     

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.     

江西省“三线冲突”空间特征及其强度影响因素

科学划定“三线”（城镇开发边界、永久基本农田、生态保护红线）有利于合理安排国土空间资源,识别“三线冲突”强度影响因素,可加深对国土空间规划的认识。以省级空间规划试点区——江西省为例,通过“三线冲突强度指数”识别不同区域“三线冲突”差异,利用空间自相关分析探究空间特征,并应用地理加权回归模型分析不同冲突类型的影响因素。结果表明：研究区内,城镇—生态、城镇—农业、生态—农业、城镇—生态—农业冲突面积分别为4305.791 km²、2892.894 km²、1845.341 km²、159.444 km²,各类型冲突存在明显的空间聚集性;固定资产投资强度、建设用地适宜区面积占比和地形位指数分别为城镇—生态、城镇—农业和生态—农业冲突强度的主要影响因素,城镇化率和距河流距离对城镇—生态—农业冲突强度具有影响作用。研究结果可为今后合理布局“三线”、实现国土空间结构优化提供参考依据。     

皖西地区贫困空间分异的影响机制

区域性贫困是当前中国社会经济和区域发展不平衡的核心表现。以大别山连片特困区的皖西地区为例,以乡镇贫困发生率为贫困空间分异指标,以地理环境、经济环境、社会环境的20个因素为自变量,综合运用相关分析、空间回归、地理加权回归等方法,探究各因素对贫困空间分异的影响。结果表明：平均坡度、人均规模以上工业总产值、二三产业收入占纯收入比例、医疗卫生机构床位数是导致皖西地区贫困空间分异的核心因素,且其影响具有较强的空间异质性;地理机制、经济机制、社会机制是皖西地区贫困空间分异的主要机制,其中,地理环境是基础影响,经济环境的影响较强,社会环境影响广且大多与地理环境相关。研究结果对贫困干预的地理瞄准具有较强的借鉴意义。