Assessment of environmental impacts part two: Data collection

Intervention analysis is a relatively new branch of time series analysis. The power of this technique, which gives the probability that changes in mean level can be distinguished from natural data variability, is quite sensitive to the way the data are collected. The principal independent variables influenced by the data collection design are overall sample size, sampling frequency, and the relative length of record before the occurrence of the event (intervention) that is postulated to have caused a change in mean process level.For three of the four models investigated, data should be collected so that the post-intervention record is substantially longer than the pre-intervention record. This is in conflict with the intuitive approach, which would be to collect equal amounts of data before and after the intervention. The threshold (minimum) level of change that can be detected is quite high unless sample sizes of at least 50 and preferably 100 are available; this minimum level is dependent on the complexity of the model required to describe the response of the process mean to the intervention. More complex models tend to require larger sample sizes for the same threshold detectable change level.Uniformity of sampling frequency is a key consideration. Environmental data collection programs have not historically been oriented toward data analysis using time series techniques, thus eliminating a potentially powerful tool from use in many environmental assessment applications.     

INTERVENTION ANALYSIS OF WATER QUALITY RECORDS1

The impoundment of the Kootenai River by Libby Dam caused changes in discharge and water quality in the river downstream from Lake Koocanusa. The changes observed downsteam were largely attributable to the depth of withdrawal from the reservoir and the reservoir's ability to store and mix various influent water masses. The preimpoundment and postimpoundment time series of discharge and six water quality variables were autocorrelated and exhibited strong seasonality. Intervention analysis, a technique employing Box-Jenkins time series models, was used to quantify the nature and magnitude of the changes in water quality after the construction of Libby Dam. The models were developed with data from June 1967 through February 1981 and were able to satisfactorily forecast riverine conditions from March 1981 through January 1982.     

土壤微塑料污染及其分析方法

微塑料作为环境介质中的新型污染物,其对生态系统的影响越来越受到关注.土壤是环境中微塑料的重要蓄积库,结合近些年来国内外关于土壤中微塑料的研究,针对土壤中微塑料的来源、生态效应、分析方法及污染防治进行综述,以便更全面的了解土壤中微塑料污染的现状及其潜在的生态环境风险.塑料包装废弃物、农用地膜破碎、大气沉降是土壤中微塑料的...     

基于GIS油田环境监测信息管理系统的研究和应用

以大庆油田为例,通过在区域环境安全信息管理与建筑物环境安全信息管理系统中采用环境地理信息系统(EGIS),构建大庆油田环境地理信息系统(DOFESIMS),并阐述了其技术结构、数学模型、功能及功能扩展,为区域环境安全信息管理的规划与实现提供科学依据。在实现对油田环境有效管理的基础上,对大庆油田污染物排放进行了预测与分析,并将结果进行空间可视化展示,以此证明环境地理信息系统可有效解决区域环境安全的信息管理。     

MODELING HYDROLOGIC TIME SERIES FROM THE ARCTIC1

The general intervention model is applied to hydrologic and meteorologjc time series from the Canadian Arctic. The authors show how the model is able to account for environmental interventions, missing observations in the data, changes in data collection procedures, the effects of external inputs, as well as seasonality and autocorrelation. Methods for identifying transfer functions by making use of a physical understanding of the processes involved are demonstrated and sample applications of the general intervention model to Arctic data are shown.     

An approach to optimise nutrient management in environmental sanitation systems despite limited data

The material flow analysis method can be used to assess the impact of environmental sanitation systems on resource consumption and environmental pollution. However, given the limited access to reliable data, application of this data-intensive method in developing countries may be difficult. This paper presents an approach allowing to develop material flow models despite limited data availability. Application of an iterative procedure is of key importance: model parameter values should first be assessed on the basis of a literature review and by eliciting expert judgement. If model outputs are not plausible, sensitive input parameters should be reassessed more accurately. Moreover, model parameters can be expressed as probability distributions and variable uncertainty estimated by using Monte Carlo simulation. The impact of environmental sanitation systems on the phosphorus load discharged into surface water in Hanoi, Vietnam, is simulated by applying the proposed approach.     

POLLUTANT LOADS IN URBAN STORMWATER: REVIEW OF METHODS FOR PLANNING-LEVEL ESTIMATES1

ABSTRACT: A review of methods for planning-level estimates of pollutant loads in urban stormwater focuses on transfer of charac. teristic runoff quality data to unmonitored sites, runoff monitoring, and simulation models. Load estimation by transfer of runoff quality data is the least expensive, but the accuracy of estimates is unknown. Runoff monitoring methods provide best estimates of existing loads, but cannot be used to predict load changes resulting from runoff controls, or other changes of the urban system. Simulation models require extensive calibration for reliable application. Models with optional formulations of pollutant build up, washoff, and transport can be better calibrated and the selection of options should be based on a statistical analysis of calibration data. Calibrated simulation models can be used for evaluation of control alternatives.     

A值法研究大气总量控制的环境质量达标保证率

在总量控制区用A值法实行总量控制后，总量控制区在符合污染物年允许排放总量限值时，污染物的日平均浓度和小时平均浓度不一定能达到国家或地区大气环境质量标准。为了反映总量控制区环境质量达标情况，本文提出了A值法研究大气总量控制的环境质量达标保证率的概念，通过总量控制A值法的基本原理，给出小时／日环境质量达标保征率的量化公式，经过分析得到；环境质量达标保证率与污染物种类有关，同一污染物的保证率与A值的年分布情况密切相关。同时利用长江下游某地的2000年气象资料分析该地区A值的变化趋势，求得该地区SO2和N02的小时保征率分别为97．4％、90.2％，日保征率分别为90.2％、79．5％。     

基于深度学习的大数据空气污染预报

为了更好地反映环境污染变化趋势,为环境管理决策提供及时、全面的环境质量信息,预防严重污染事件发生,开展城市空气质量预报研究是十分必要的.本文针对环境大数据时代下的城市空气质量预报,提出了一种基于深度学习的新方法.该方法通过模拟人类大脑的神经连接结构,将数据在原空间的特征表示转换到具有语义特征的新特征空间,自动地学习得到层次化的特征表示,从而提高预报性能.得益于这种方式,新方法与传统方法相比,不仅可以利用空气质量监测、气象监测及预报等环境大数据,充分考虑污染物的时空变化、空间分布,得到语义性的污染物变化规律,还可以基于其他空气污染预测方法的结果(如数值预报模式),自动分析其适用范围、优势劣势.因此,新方法通过模拟人脑思考过程实现更充分的大数据集成,一定程度上克服了现有方法的缺陷,应用上更加具有灵活性和可操作性.最后,通过实验证明新方法可以提高空气污染预报性能.     

干旱生态敏感区水电站环境影响后评价指标体系研究

针对干旱生态敏感区水电站环境影响后评价的要求,设计备选指标集,采用频次分析法进行指标初选,采用专家咨询法进行指标精选,建立由4项二级指标、14项三级指标构成的干旱生态敏感区水电站环境影响后评价指标体系,采用层次分析法给每个指标赋予权重.将该评价指标体系用于某水电站建设的实例分析,结果表明,水电站运行以来总体上有利影响大...     

川西古镇大气环境容量计算模型

通过对川西古镇大气环境污染特点的分析。根据箱体内污染物“物质守恒”原理,以箱式模型为基础,推导出箱体内污染物之浓度随时问和空间的变化规律。建立了适合于川西古镇大气环境容量的数值计算模型。在此基础上,根据污染物自身特点,引入k1、k2两个浓度修正系数,分别建立了适合于该区域的SO2和TSP环境容量计算的修正模型。当研究区域具有下垫面变化显著或大气污染源源强变化显著等特点时,应使用改进后的单箱模式。     

从水污染防治角度出发的全国分省域水环境安全研究

针对我国日益严峻的水环境安全问题,本文从水污染防治角度出发,依据影响我国水环境安全的关键污染问题,即水环境质量、水污染物排放、水环境风险,构建了水环境安全指标体系。利用相关分析法、主成分分析法,简化、优化评价指标体系。利用层次分析法与专家打分法,估算各指标权重,采集各指标数据对全国31个省(自治区、直辖市)2011—2013年的水环境安全状况进行评估。结果表明:该指标体系在一定条件和范围内是合理的,我国水环境安全大体呈西高中东低的空间分布,京津冀区域水环境安全评价值较低。"十三五"时期,京津冀地区应着重于降低主要污染物浓度,削减劣V类断面比例,提高水环境质量,海南、福建、广东等地区着力于减少农业等水污染物排放强度,湖南等地区则需要重点关注涉重企业的污染物排放。     

CURVE NUMBER HYDROLOGY IN WATER QUALITY MODELING: USES,ABUSES, AND FUTURE DIRECTIONS1

ABSTRACT: Although the curve number method of the Natural Resources Conservation Service has been used as the foundation of the hydrology algorithms in many nonpoint source water quality models, there are significant problematic issues with the way it has been implemented and interpreted that are not generally recognized. This usage is based on misconceptions about the meaning of the runoff value that the method computes, which is a likely fundamental cause of uncertainty in subsequent erosion and pollutant loading predictions dependent on this value. As a result, there are some major limitations on the conclusions and decisions about the effects of management practices on water quality that can be supported with current nonpoint source water quality models. They also cannot supply the detailed quantitative and spatial information needed to address emerging issues. A key prerequisite for improving model predictions is to improve the hydrologic algorithms contained within them. The use of the curve number method is still appropriate for flood hydrograph engineering applications, but more physically based algorithms that simulate all streamflow generating processes are needed for nonpoint source water quality modeling. Spatially distributed hydrologic modeling has tremendous potential in achieving this goal.     

环评中锅炉房大气污染源强的确定

在环境评价中,污染源强的确定对环境影响因素评价的分析结果有重要作用。对锅炉房污染物排放的分析表明,影响锅炉房大气污染物的主要因素有燃料的构成、发热量和燃烧方式等。确定锅炉房大气污染物的方法主要有物料衡算法、实测法和经验系数法。在这三种方法中,物料衡算法被普遍采用。在确定锅炉房大气污染物的排放量时,也可以采用物料衡算法和实测法相结合的方法。     

不完全竞争行业中的环境工具有效性研究:一个理论框架

各级政府贯彻落实绿色发展的理念,实现经济增长与环境保护的共赢,才能真正实现"绿水青山就是金山银山"。在经济"新常态"背景下,防治环境污染是个复杂而系统性的工程,需要进行经济追因与综合治理,其中,制定实施有效的环境政策工具尤为关键。本文把微观经济学中的双寡头产量竞争模型作为基准模型拓展用于环境经济分析,并把环境税、环境规制、排污权交易这三类主要环境工具对企业生产决策的影响纳入基准模型,旨在对比研究不完全竞争行业中环境工具的有效性问题。结果表明:主要基于市场机制的环境经济政策比行政色彩浓厚的环境规制更有效率。提高环境税率或排污权价格均能显著削减行业污染物排放量,如果环境税率恰好等于排污权价格,则环境税与排污权交易这两类环境工具的效力相等。这对进一步建立和完善我国尚处于探索或初始实施阶段的环境工具,促进石化、电力、钢铁等不完全竞争行业的主要污染物减排,具有一定参考价值和借鉴意义。     

试论环保诚信机制的构建

在经济利益的驱动下,一些排污单位隐瞒实情、偷排暗排、推卸责任、逃避监管等不诚实不守信的行为时有发生,不仅严重污染了环境,对其自身的生存发展也极为不利,建立排污单位环保诚信机制实为当务之急。排污单位环保诚信内容包括诚信排污、诚信自律、诚信整改和诚信担当等多个方面,主要通过经济、技术、舆论、公权、公众支持等综合措施合力促进和保障排污单位环保诚信机制的建立,以提高排污单位环境保护意识,保护生态环境,促进企业健康可持续发展。     

TREND ASSESSMENT OF WATER QUALITY TIME SERIES1

ABSTRACT A general methodology is described for identifying and statistically modeling trends which may be contained in a water quality time series. A range of useful exploratory data analysis tools are suggested for discovering important patterns and statistical characteristics of the data such as trends caused by external interventions. To estimate the entries in an evenly spaced time series when data are available at irregular time intervals, a new procedure based upon seasonal adjustment is described. Intervention analysis is employed at the confirmatory data analysis stage to rigorously model changes in the mean levels of a series which are identified using exploratory data analysis techniques. Furthermore, intervention analysis can be utilized for estimating missing observations when they are not too numerous. The effects of cutting down a forest upon various water quality variables and also the consequences of acid rain upon the alkalinity in a stream provide illustrative applications which demonstrate the effectiveness of the methodology.     

FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY1

ABSTRACT: Fractional differencing is a tool for modeling time series which have long-term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box-Jenkins ARIMA(p, d, q) models to take nonintegral values; they permit the simultaneous modeling of the long-term and short-term behavior of an observed time series. This paper discusses the usefulness of fractional differencing to time-series modeling, with emphasis on hydrologic applications. A methodology for fitting fractionally differenced ARIMA models is described, and examples are presented.     

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.     

An Exploration of Dynamical Systems Modelling as a Decision Tool for Environmental Policy

A weakness often found in conventional environmental management tools is that of treating the dynamics of environmental effects and the underlying dynamics of structural change as external factors, precluding their ability to capture the evolutionary behaviour which characterizes economic systems. This article suggests a spatial dynamic modelling framework which addresses explicitly complex sectorial linkages and their environmental effects. The economic dynamics of the model are given spatial reference by means of a set of sector-based suitabilities, which allows us to explore both local environmental degradation effects as they unfold, and how these impacts then affect the economic productivity of the system as a whole. The approach is illustrated by calibrating the model with 1971-1981 data for the island of Crete, and then running it to explore how the economic dynamics of the island might evolve to the year 2001. This article presents a means through which to endogenize the environment and its explicit linkages with sectorial economic dynamics which can be of considerable use to environmental management in all its dimensions.