External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

In the mid nineteen eighties the Dutch NO_x air quality monitoring network was reduced from 73 to 32 rural and city background stations, leading to higher spatial uncertainties. In this study, several other sources of information are being used to help reduce uncertainties in parameter estimation and spatial mapping. For parameter estimation, we used Bayesian inference. For mapping, we used kriging with external drift (KED) including secondary information from a dispersion model. The methods were applied to atmospheric NO_x concentrations on rural and urban scales. We compared Bayesian estimation with restricted maximum likelihood estimation and KED with universal kriging. As a reference we also included ordinary least squares (OLS). Comparison of several parameter estimation and spatial interpolation methods was done by cross-validation. Bayesian analysis resulted in an error reduction of 10 to 20% as compared to restricted maximum likelihood, whereas KED resulted in an error reduction of 50% as compared to universal kriging. Where observations were sparse, the predictions were substantially improved by inclusion of the dispersion model output and by using available prior information. No major improvement was observed as compared to OLS, the cause presumably being that much good information is contained in the dispersion model output, so that no additional spatial residual random field is required to explain the data. In all, we conclude that reduction in the monitoring network could be compensated by modern geostatistical methods, and that a traditional simple statistical model is of an almost equal quality.

Structural changes in lake functioning induced from nutrient loading and climate variability

Climate variability is increasingly recognized as an important regulatory factor, capable of influencing the structural properties of aquatic ecosystems. Lakes appear to be particularly sensitive to the ecological impacts of climate variability, and several long time series have shown a close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food web dynamics. Thus, understanding the complex interplay among meteorological forcing, hydrological variability, and ecosystem functioning is essential for improving the credibility of model-based water resources/fisheries management. Our objective herein is to examine the relative importance of the ecological mechanisms underlying plankton seasonal variability in Lake Washington, Washington State (USA), over a 35-year period (1964–1998). Our analysis is founded upon an intermediate complexity plankton model that is used to reproduce the limiting nutrient (phosphate)–phytoplankton–zooplankton–detritus (particulate phosphorus) dynamics in the lake. Model parameterization is based on a Bayesian calibration scheme that offers insights into the degree of information the data contain about model inputs and allows obtaining predictions along with uncertainty bounds for modeled output variables. The model accurately reproduces the key seasonal planktonic patterns in Lake Washington and provides realistic estimates of predictive uncertainty for water quality variables of environmental management interest. A principal component analysis of the annual estimates of the underlying ecological processes highlighted the significant role of the phosphorus recycling stemming from the zooplankton excretion on the planktonic food web variability. We also identified a moderately significant signature of the local climatic conditions (air temperature) on phytoplankton growth (r = 0.41), herbivorous grazing (r = 0.38), and detritus mineralization (r = 0.39). Our study seeks linkages with the conceptual food web model proposed by Hampton et al. [Hampton, S.E., Scheuerell, M.D., Schindler, D.E., 2006b. Coalescence in the Lake Washington story: interaction strengths in a planktonic food web. Limnol. Oceanogr. 51, 2042–2051.] to emphasize the “bottom-up” control of the Lake Washington plankton phenology. The posterior predictive distributions of the plankton model are also used to assess the exceedance frequency and confidence of compliance with total phosphorus (15 μg L⁻¹) and chlorophyll a (4 μg L⁻¹) threshold levels during the summer-stratified period in Lake Washington. Finally, we conclude by underscoring the importance of explicitly acknowledging the uncertainty in ecological forecasts to the management of freshwater ecosystems under a changing global environment.     

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.     

某炼钢厂净、浊循环系统水质稳定配方相互影响的研究

通过对照实验,研究了净循环系统中A系列药剂缓蚀、阻垢的效果,以及循环系统中加入A、B两系列药剂对缓蚀、阻垢的影响,说明了在B系列药剂存在的循环系统中加入A系列药剂,其缓蚀、阻垢效果基本上不产生相互影响.     

公共租赁住房项目建设生态环境影响评价——以济南市西蒋峪片区为例

在分析济南市西蒋峪片区公共租赁住房评价区生态环境现状的基础上,从土地利用、生物、水土流失和景观类型等方面分析了施工期和运营期项目对生态环境的影响,在此基础上提出了公租房建设中的景观生态建设和植被保护与恢复等生态保护和恢复的对策。     

山东省孔孟文化遗产地建设生态环境影响评价

本文在分析山东省孔孟文化遗产地建设项目区生态环境现状的基础上,从土地利用、生物量、水土流失和景观等方面分析了施工期和运营期项目对生态环境的影响,在此基础上提出了相应的生态保护和恢复的对策。     

重庆市劳动力流动的影响因素及对策建议

改革开放以来,劳动力流动已经成为一种十分普遍和非常重要的社会经济现象。运用经济计量模型,实证考察了流动距离、预期收益、人力资本比率对省际劳动力流动的影响效应,并由此得出劳动力流动的政策建议。     

中国城镇碳排放的区域差异和影响因素

修正了IPCC关于电力碳排放系数的计算方法,根据中国(省市)能源平衡表估算了1995-2008年中国30个省市的城镇碳排放,划分了高、中、低三个不同排放区域,分析城镇碳排放的区域差异,采用STIRPAT模型分析城镇碳排放及区域差异的影响因素.结果表明,城镇碳排放是中国碳排放的主体;城镇碳排放总体快速增长趋势,2001年后表现尤为明显;中国城镇碳排放存在很强的区域差异,2008年高排放区域的八个省市城镇碳排放总量占全国城镇碳排放总量的50％以上;城镇居民人均收入对城镇碳排放影响最大,然后是城镇化率和能源强度,人口总量对碳排放影响较小;城镇居民人均收入增加、城市化进程不断推进、能源强度降低对城镇碳排放的影响存在区域差异,这种影响强度的差异是导致城镇碳排放存在区域差异的主要原因.一方面,我国城镇居民收入的不断提高和城市化进程的推进决定了城镇发展需要一定的碳排放空间,另一方面,城镇需要走可持续的低碳发展道路.     

关于测定水中总硬度的分析

在测定水中总硬度时,影响分析结果的因素很多,如水样pH值、溶液的温度、滴定的时间等以及缓冲溶液的配制,操作不当均会给分析结果带来影响。因此文中针对上述问题提出了解决的办法。     

基于Divisia分解法的江苏沿海地区碳排放影响因素研究

能源消费是碳排放的主要来源。随着江苏沿海经济的快速发展,能源消费的增长以及以煤为主的能源结构在短期内难以改变,因此碳排放呈增长趋势。为了研究影响江苏沿海地区碳排放的因素,采用对数平均权重Divisia分解法,建立江苏省沿海地区人均碳排放量的影响因素分解模型,分析了2000～2008年经济发展、能源结构和能源效率对江苏省沿海地区人均碳排放的影响。研究结果表明：经济发展对江苏省沿海地区人均碳排放的贡献值和贡献率均呈指数增长趋势,能源效率的提高和能源结构的改善对人均碳排放的抑制作用比较有限,经济发展是造成江苏省沿海地区人均碳排放量快速增长的主要因素。通过研究,为江苏沿海地区控制和减少碳排放提供一些针对性的措施