Comparison of the daily global solar radiation from different data sources in Northwest China climate

The solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.     

Effects of signals of disorder on fear of crime in real and virtual environments

Despite the fact that virtual environments are increasingly deployed to study the relation between urban planning, physical and social disorder, and fear of crime, their ecological validity for this type of research has not been established. This study compares the effects of similar signs of public disorder (litter, warning signs, cameras, signs of vandalism and car burglary) in an urban neighborhood and in its virtual counterpart on the subjective perception of safety and livability of the neighborhood. Participants made a walking tour through either the real or the virtual neighborhood, which was either in an orderly (baseline) state or adorned with numerous signs of public disorder. During their tour they reported the signs of disorder they noticed and the degree to which each of these affected their emotional state and feelings of personal safety. After finishing their tour they appraised the perceived safety and livability of the environment. Both in the real and in the simulated urban neighborhood, signs of disorder evoked associations with social disorder. In all conditions, neglected greenery was spontaneously reported as a sign of disorder. Disorder did not inspire concern for personal safety in reality and in the virtual environment with a realistic soundscape. However, in the absence of sound disorder compromised perceived personal safety in the virtual environment. Signs of disorder were associated with negative emotions more frequently in the virtual environment than in its real-world counterpart, particularly in the absence of sound. Also, signs of disorder degraded the perceived livability of the virtual, but not of the real neighborhood. Hence, it appears that people focus more on details in a virtual environment than in reality. We conclude that both a correction for this focusing effect and realistic soundscapes are required to make virtual environments an appropriate medium for both etiological (e.g. the effects of signs of disorder on fear of crime) and intervention (e.g. CPTED) research.     

秦皇岛市“创模”对策研究及效益分析

介绍了秦皇岛市创建国家环境保护模范城市的意义,重点阐述了完成创模任务的主要对策,包括:构建以旅游业为中心、特色产业互融互促的现代产业体系;加快城乡一体化建设;提升城镇发展质量和水平;加强重大基础设施建设;积极推广清洁能源,深入开展工程节能;积极推进循环经济发展等。秦皇岛市创建国家环境保护模范城市,将会在环境、经济和社会三方面产生明显的综合效益。     

喀斯特峡谷区工程性缺水原因及解决途径——以贵州省花江峡谷示范区为例

喀斯特峡谷区河深坡陡,地下水深埋,水资源利用难度大,加之经济和水利工程薄弱,是贵州省工程性缺水的典型区域。通过对花江峡谷示范区工程性缺水的原因剖析,提出一系列解决工程性缺水问题的措施,如雨水收集、表层喀斯特水和基岩裂隙水的开发利用、节水、加强对水利工程设施的管理和维护、加大水利投入以及提高植被覆盖率等,这对解决喀斯特峡谷及类似区域的工程性缺水问题有一定的指导和参考意义。     

Modelling the fate of hydrophobic organic contaminants in a boreal forest catchment: A cross disciplinary approach to assessing diffuse pollution to surface waters

The fate of hydrophobic organic compounds (HOCs) in soils and waters in a northern boreal catchment was explored through the development of a chemical fate model in a well-characterised catchment system dominated by two land types: forest and mire. Input was based solely on atmospheric deposition, dominated by accumulation in the winter snowpack. Release from soils was governed by the HOC concentration in soil, the soil organic carbon fraction and soil-water DOC content. The modelled export of selected HOCs in surface waters ranged between 11 and 250 ng day⁻¹ during the snow covered period, compared to 200 and 9600 ng/d during snow-melt; highlighting the importance of the snow pack as a source of these chemicals. The predicted levels of HOCs in surface water were in reasonable agreement to a limited set of measured values, although the model tended to over predict concentrations of HOCs for the forested sub-catchment, by over an order of magnitude in the case of hexachlorobenzene and PCB 180. This possibly reflects both the heterogeneity of the forest soils and the complicated and changing hydrology experienced between the different seasons.     

Herbicide Leaching on a Recharge Area of the Guarany Aquifer in Brazil

Abstract

The region of Ribeirão Preto City, located in Southeast of Brazil, São Paulo State, is an important sugarcane, soybean, and corn producing area with a high level of pesticides utilization. This region is also an important recharge area for groundwater supply of the Guarany aquifer. Since the past ten years atrazine, simazine, ametryn, tebuthiuron, diuron, 2,4-D, picloram, and hexazinone are the main herbicides used in this area. In order to study a possible leaching of some of these herbicides into the aquifer, surface, and groundwater samples were collected in a watershed during the years of 1996 to 2003, from different locations. To detect and quantify the herbicides a GC-MS (gas chromatograph/mass spectrometry) method was used. The response of the herbicides analyzed was linear over the concentration range of 0.02 to 2.0 μg/L. Analysis of groundwater revealed that the herbicides tebuthiuron, diuron, atrazine, simazine, and ametryn were not present in the samples. In the surface water collected in 1997, ametryn was present in two out of nine locations with concentrations ranging from 0.17 and 0.23 μg/L, which is above the allowable 0.1 μg/L according to the European safety level. The leaching potential of tebuthiuron, diuron, atrazine, simazine, 2,4-D, picloram, and hexazinone has been evaluated using CMLS-94, “Chemical Movement in Layered Soil,” as simulation model. No leaching into the depth of the water table at 40 m was found.     

Projecting Climate Change Effects on Forest Net Primary Productivity in Subtropical Louisiana,USA

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km × 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011–2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.     

Quantification of nitrate leaching from German forest ecosystems by use of a process oriented biogeochemical model

Simulations with the process oriented Forest-DNDC model showed reasonable to good agreement with observations of soil water contents of different soil layers, annual amounts of seepage water and approximated rates of nitrate leaching at 79 sites across Germany. Following site evaluation, Forest-DNDC was coupled to a GIS to assess nitrate leaching from German forest ecosystems for the year 2000. At national scale leaching rates varied in a range of 0–>80 kg NO₃–N ha⁻¹ yr⁻¹ (mean 5.5 kg NO₃–N ha⁻¹ yr⁻¹). A comparison of regional simulations with the results of a nitrate inventory study for Bavaria showed that measured and simulated percentages for different nitrate leaching classes (0–5 kg N ha⁻¹ yr⁻¹:66% vs. 74%, 5–15 kg N ha⁻¹ yr⁻¹:20% vs. 20%, >15 kg N ha⁻¹ yr⁻¹:14% vs. 6%) were in good agreement. Mean nitrate concentrations in seepage water ranged between 0 and 23 mg NO₃–N l⁻¹.     

Evaluating and reducing a model of radiocaesium soil-plant uptake

An existing model of radiocaesium transfer to grasses was extended to include wheat and barley and parameterised using data from a wide range of soils and contact times. The model structure was revised and evaluated using a subset of the available data which was not used for model parameterisation. The resulting model was then used as a basis for systematic model reduction to test the utility of the model components. This analysis suggested that the use of 4 model variables (relating to radiocaesium adsorption on organic matter and the pH sensitivity of soil solution potassium concentration) and 1 model input (pH) are not required. The results of this analysis were used to develop a reduced model which was further evaluated in terms of comparisons to observations. The reduced model had an improved empirical performance and fewer adjustable parameters and soil characteristic inputs.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.