用灰色系统理论GM(1,1)建立大气降水中酸雨pH值的预测模型

本文用灰色系统理论GM(1,1)建立了大气降水中pH值的预测模型。经三种不同方法检验。对我市1990—1993年大气降水中酸雨pH值进行了预测,结果令人满意。     

Release Characteristics of Nutrients from Polymer-coated Compound Controlled Release Fertilizers

Reliable information regarding release characteristics of nutrients from a polymer-coated controlled release fertilizer (CRF) is essential for beneficial agronomic and environmental results. Significant knowledge regarding nitrogen release from polymer-coated urea was gained while the information regarding the release of the different nutrients contained in polymer-coated compound N–P–K CRF remains limited. An experiment in which major factors affecting the differential release of nutrients from two coated compound CRF was performed in free water, water saturated sand and sand at field capacity. In general, nitrate release was the fastest, followed by ammonium and potassium whereas phosphate was significantly slower, with a rate of linear release in free water 45–70% slower than that of nitrate. Little differences were obtained for the lag periods of nitrate, ammonium and potassium release (2–10 days) under the experimental conditions, whereas for P they were one order of magnitude larger. The main factor slowing the release was assumed to be the lower solubility of ions with P being the least soluble. Release into free water was, expectedly, somewhat faster than that into saturated sand and significantly faster as compared to sand at field capacity and particularly so for P. Raising the temperature from 20 °C to 40 °C increased the rate of linear release of the different nutrients. The energy of activation, EA_rel, estimated for the linear release, of the different nutrients, was narrow ranging between 37 to 46 (KJ mol⁻¹) whereas the mean values obtained for the two CRF, differing by 50% in coating thickness, was non-significant. However, EA_rel was significantly different in different media. The complex effect of temperature on the lag period and nutrient interactions during release deserve further investigation.     

面向城市水环境治理的智慧水管家模式

近年来我国城市水环境治理取得了显著成效,已迈入水资源、水环境、水生态统筹治理的新阶段。传统水环境治理模式难以适应新阶段的问题和挑战,亟需探索一条可持续、系统性、高质量的水环境治理模式。智慧水管家模式是三峡集团在推进长江大保护过程中,系统构建形成的一套可复制推广的机制、理念、方法与技术支撑体系。从治理机制和理念、核心业务着力点以及科技支撑三个层面系统阐述了智慧水管家模式的核心内容,以期为我国城市水环境治理提供新思路。     

火灾规律双重性模型及其对室内漏油火灾的分析

火灾的规律具有双重性:确定性和随机性。只有既研究其确定性又研究其随机性,并进而研究二者的综合才能完整地认识火灾的规律。本文提出火灾双重性规律的学术思想,通过构造室内漏油火灾的确定性和随机性模型,阐明运用火灾双重性规律表述火灾过程的具体方法。文中用微机实施的大量计算表明,该模型能方便地预言:发生室内漏油火灾时,室内设备是否会损坏以及可能被损坏的概率.     

Prevalence-adjusted optimisation of fuzzy models for species distribution

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.     

Comparing environmental influences on coral bleaching across and within species using clustered binomial regression

Differential susceptibility among reef-building coral species can lead to community shifts and loss of diversity as a result of temperature-induced mass bleaching events. We evaluate environmental influences on coral colony bleaching over an 8-year period in the Florida Keys, USA. Clustered binomial regression is used to develop models incorporating taxon-specific responses to the environment in order to identify conditions and species for which bleaching is likely to be severe. By building three separate models incorporating environment, community composition, and taxon-specific responses to environment, we show observed prevalence of bleaching reflects an interaction between community composition and local environmental conditions. Environmental variables, including elevated sea temperature, solar radiation, and reef depth, explained 90% and 78% of variability in colony bleaching across space and time, respectively. The effects of environmental variables were only partially explained (33% of variability) by corresponding differences in community composition. Taxon-specific models indicated individual coral species responded differently to local environmental conditions and had different sensitivities to temperature-induced bleaching. For many coral species, but not all, bleaching was exacerbated by high solar radiation. A 25% reduction in the probability of bleaching in shallow locations for one species may reflect an ability to acclimatize to local conditions. Overall, model results indicate predictions of coral bleaching require knowledge of not just the environmental conditions or community composition, but the responses of individual species to the environment. Model development provides a useful tool for coral reef management by quantifying the influence of the local environment on individual species bleaching sensitivities, identifying susceptible species, and predicting the likelihood of mass bleaching events with changing environmental conditions.     

京津冀PM_(2.5)的主要影响因素及内在关系研究

大气污染物的源排放是形成灰霾天气的内因,气象条件是形成灰霾天气的外因。本研究通过构建PM_(2.5)浓度的两段式分布滞后模型,结合自然环境因素及经济因素对PM_(2.5)的影响因素进行了综合分析。在第一段模型中构建了PM_(2.5)和大气污染物排放量的分布滞后模型,第二段模型中构建了不同的大气污染源对大气污染物排放量的影响因素模型。大气污染物排放源主要包括工业源、生活源、机动车源、集中式污染治理设施源。在工业源中,工业废气重度污染行业是大气污染物排放主要的贡献者;在生活源中,燃煤消费量对大气污染物排放影响很大,这也是冬季供暖期间PM_(2.5)剧增的原因;在机动车源中,尽管黄标车的保有量仅占汽车保有量的10%左右,但却占据了颗粒物排放量的绝大部分。利用京津冀代表性城市PM_(2.5)日度数据研究得出平均气温、平均风速、日照时数、平均气压、降雨量、平均相对湿度、沙尘暴等因素对PM_(2.5)浓度的负向与正向作用。研究发现,大气污染物排放量对PM_(2.5)浓度具有聚集的滞后效应,当期大气污染物排放量、滞后一期、滞后两期、滞后三期大气污染物对PM_(2.5)浓度具有显著的正向作用,且影响依次递减。构建的大气污染物排放量的污染源影响因素模型揭示一个地区煤炭消费量、工业废气重度污染行业工业增加值、黄标车保有量对该地区大气污染物排放量具有显著影响。本研究对优化能源消费结构和产业结构,减少空气污染物排放提出了对策建议。     

泥石流危险范围的模型实验预测法

泥石流危险范围的模型实验预测旨在确定一次泥石流可能堆积泛滥的最大范围.通过泥石流不同冲出量、堆积区坡度和容重的31次模型实验,获取了泥石流堆积面积.最大堆积长度、宽度和厚度的实验数据.采用量纲分析方法,建立了一次泥石流危险范围的预测模型,并由实验资料确定了模型的修正系数.在云南和甘肃的应用实践表明,预测模型精度高,适用范围广,可在我国泥石流危险范围预测预报中使用.     

城市综合减灾规划模式研究

根据城市规划的基本原理和城市灾害的特点，提出了城市综合减灾规划模式。