二氧化硫排放总量控制若干问题的思考

根据国家《“十一五”规划纲要》对削减二氧化硫排放总量的要求，在对电力行业脱硫现状及前景的具体阐述中指出实现这个目标的可能性，并从多方面对二氧化硫总量控制的关键问题进行了详尽的论述与分析。     

尿中氚水和总氚的分析测定

用蒸馏-液闪法和氧化蒸馏-液闪法分别测量了氚污染人员尿中的氚水浓度和总氚(氚水和有机氚)浓度。根据72个高于本底水平的尿中氚水和总氚浓度分析比较，认为在氚内污染工作人员的尿中，有机氚与氚水的浓度比值为(5.4 3.7)％。     

除湿机的冷凝水化学成分与空气环境质量的关系

本文采用家用除湿机作为空气水溶性物质采样器方法，并验证了该方法作为空气水溶性物质采样嚣的可行性。本文通过空气冷凝水化学成分与空气总悬浮微粒(TSP)可溶性化学成分的对比，得到水汽中大部分阴阳离子的含量只比TSP低1个数量级，而水汽中的微量元素含量与各种碳的浓度不都比TSP低，TSP中一般检测不出亚硝酸根，而水汽中则检出大量亚硝酸根。     

ENHANCING FECAL COLIFORM TOTAL MAXIMUM DAILY LOAD MODELS THROUGH BACTERIAL SOURCE TRACKING1

ABSTRACT: Surface water impairment by fecal coliform bacteria is a water quality issue of national scope and importance. In Virginia, more than 400 stream and river segments are on the Commonwealth's 2002 303(d) list because of fecal coliform impairment. Total maximum daily loads (TMDLs) will be developed for most of these listed streams and rivers. Information regarding the major fecal coliform sources that impair surface water quality would enhance the development of effective watershed models and improve TMDLs. Bacterial source tracking (BST) is a recently developed technology for identifying the sources of fecal coliform bacteria and it may be helpful in generating improved TMDLs. Bacterial source tracking was performed, watershed models were developed, and TMDLs were prepared for three streams (Accotink Creek, Christians Creek, and Blacks Run) on Virginia's 303(d) list of impaired waters. Quality assurance of the BST work suggests that these data adequately describe the bacteria sources that are impairing these streams. Initial comparison of simulated bacterial sources with the observed BST data indicated that the fecal coliform sources were represented inaccurately in the initial model simulation. Revised model simulations (based on BST data) appeared to provide a better representation of the sources of fecal coliform bacteria in these three streams. The coupled approach of incorporating BST data into the fecal coliform transport model appears to reduce model uncertainty and should result in an improved TMDL.     

香港写字楼消防安全需求的调查研究

本文对非工业场所-写字楼的消防安全需求进行了总结,引入了整体消防安全理念,概述了现有写字楼提升消防安全的要求,指出了采用消防工程手段的必要性.     

FOREST HARVESTING AND STREAMWATER INORGANIC CHEMISTRY IN WESTERN NORTH AMERICA: A REVIEW1

The solution chemistry of forested streams primarily in western North America is explained by considering the major factors that influence this chemistry — geological weathering; atmospheric precipitation and climate; precipitation acidity; terrestrial biological processes; physical/chemical reactions in the soil; and physical, chemical, and biological processes within streams. Due to the complexity of all these processes and their varying importance for different chemicals, stream water chemistry has exhibited considerable geographic and temporal variation and is difficult to model accurately. The impacts of forest harvesting on stream water chemistry were reviewed by considering the effects of harvesting on each of the important factors controlling this chemistry, as well as other factors influencing these impacts ‐ extent of the watershed harvested, presence of buffer strips between streams and harvested areas, nature of post‐harvesting site preparation, revegetation rate following harvesting, pre‐harvesting soil fertility, and soil buffering capacity. These effects have sometimes reinforced one another but have sometimes been counterbalancing or slight so that harvesting impacts on stream water chemistry have been highly variable. Eight major knowledge gaps were identified, two of which — a scarcity of detailed stream chemical budgets and knowledge of longitudinal variation in stream chemistry — relate to undisturbed streams, while the remainder relate to forest harvesting effects.     

Influence of social capital on farmer household income gap: total effect and structural effect

Based on the survey data of typical villages in Shaanxi Province, China, the effect of social capital on the income gap of farmers’ households was analyzed using the Shapley value of the total amount of social capital and the social capital structure. The results show the following: first, social capital can expand the household income gap, and the effect of this index on the household income gap is 7.54%. Second, the indexes of the social capital dimension can expand the household income gap, and the structural effects of the household income gap on social networks, social trust, and social participation are 3.17%, 3.64%, and 0.65%, respectively. Third, no dimension of the path is the same as the effect on the household income gap.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Emerging Tools for Continuous Nutrient Monitoring Networks: Sensors Advancing Science and Water Resources Protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well‐known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as the primary example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real‐time nutrient data. The concurrent emergence of new tools to integrate, manage, and share large datasets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous monitoring to rapidly move forward. We highlight several near‐term opportunities for federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large‐scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation's water resources.