未来气候变化对武夷山自然保护区毛竹异戊二烯排放速率的影响

采用全球气候模式Nor ESM1-M产生的RCP2.6、RCP4.5、RCP6.0和RCP8.5气候变化情景数据和植物异戊二烯排放计算模型,模拟分析了未来气候变化对武夷山自然保护区毛竹(Phyllostachys pubescens)异戊二烯排放速率的影响.结果显示,气候变化下武夷山自然保护区气温上升,年降水量和辐射强度波动较大,呈增加或下降趋势.毛竹异戊二烯平均日排放速率在未来气候变化情景下比基准情景下高约30μg·g~(-1)·d~(-1),在RCP8.5情景下比基准情景下高约48μg·g~(-1)·d~(-1);毛竹异戊二烯日排放速率在未来气候变化情景与基准情景下的差异在1~90 d和301~365 d较小,在91~300 d差异较大;相比基准情景,未来气候变化情景下毛竹异戊二烯日排放速率在1~190 d(平均增加15%以上)和271~365 d(平均增加20%)增幅较大,在191~270 d增幅较小,在RCP8.5情景下增幅最大(平均增加17%).另外,毛竹异戊二烯年排放速率在未来气候变化情景下比基准情景下约高10000μg·g~(-1)·a~(-1)以上,在RCP8.5情景下比基准情景下约高13%.研究表明,未来气候变化将使毛竹异戊二烯排放速率增加.     

气候变化和人类活动对武江流域年径流及最大日流量影响的定量分析

针对气候变化与人类活动对流域年径流及最大日流量变化影响的定量识别问题,以华南湿润区武江流域为例,分别采用HIMS（Hydro-Informatic Modeling System）模型和敏感性系数法,从日和年尺度定量模拟和评估气候变化与人类活动对流域年最大日流量和径流变化的影响过程及贡献率。结果表明：HIMS模型在武江流域适用性良好,日尺度模型率定期和验证期的纳西效率系数分别为0.85和0.77,水量平衡误差绝对值分别为3.1%和3.3%;两种方法均表明气候变化是引起流域年径流量增加的主要因素,人类活动导致了流域径流量的减少,但贡献率较小。气候变化与人类活动导致了流域年最大日流量的增加,气候变化对年最大日流量增加的贡献率为94%,而人类活动的贡献率则为6%。相较于年均径流量,气候变化对年最大日流量的影响更为显著。     

长江源区水文气象要素变化及其与大尺度环流因子关系研究

长江源区水文气象要素变化及其归因研究一直备受全球关注,现阶段研究多侧重于水文气象要素时空变化特征分析,针对长江源区水文气象要素与大尺度环流因子相互关系的研究不足。论文利用Mann-Kendall法、去趋势波动分析法和小波分析法,探究长江源区1957—2012年水文气象要素趋势性、波动性和周期性变化规律,分析水文气象要素与大尺度环流因子的相关关系,通过研究水汽通量揭示大尺度环流因子对水文气象要素变化的驱动机制。结果表明：20世纪90年代,长江源区气候暖干化,进入21世纪后,长江源区气候暖湿化趋势明显;长江源区水文气象要素序列具有正长程相关性,长江源区气候未来会继续呈现暖湿化变化趋势。长江源区水文气象要素都存在着1~5、10~24和25~45 a三种时间尺度周期变化规律。南亚季风是影响长江源区降水量和流量较为重要的大气环流因子,南亚季风驱动下的西南方向气流是长江源区主导气流和水汽来源。     

海洋环境低水位加速腐蚀研究进展

以海洋环境中一个非常重要的局部腐蚀形式——低水位加速腐蚀(ALWC)为对象,自其检测、发生原因、防护三个方面就国内外的文献报告进行综述分析。在检测方面,首先介绍了常用的宏观观察这一被动形式,并突出其特征,然后介绍了利用海水中可溶性无机氮含量作为ALWC发生概率预测这一主动形式,分析其优势与不足。在发生原因方面,在对将ALWC认定为一种典型的微生物腐蚀(MIC)形式的认知过程进行介绍之后,重点分析了微生物对ALWC作用机制不清晰的原因,并建议在后续研究中突出动态演变过程,结合高通量测序等分子生物学技术,确定在不同的阶段影响ALWC的关键微生物,且进一步在大气-海水体系下研究典型菌株及其协同作用的影响,提出微生物对ALWC的作用机制。在防护方面,根据新建和已建钢结构设施分别对传统和针对ALWC所具有的MIC与局部腐蚀特性的新型高效防护方法进行了介绍,并分析了防护方法的优缺点。     

经济手段防治白色污染的博弈分析

经济手段在环境管理中的作用及涉及到政府与各利益关系人之间的博弈过程。本文就政府与一次性餐具生产者，销售者之间的博弈关系，分析如何应用经济手段使其在防治“白色污染”中发挥更好的作用。     

阿图什市空气质量变化趋势分析

分析了阿图什市“八五”至“九五”期间空气质量监测数据不同年度、不同季节及不同污染因子的动态变化趋势．结合当地能源结构、气候特征、城市环境综合发展水平，指出影响阿图什市空气质量的主要因素，为防治和减轻阿图什市的空气污染提供了科学依据。     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Benchmarking for best practice environmental management

Benchmarking of environmental performance to demonstrate theachievement of best practice environmental management is acomponent of a new form of licensing of industrialdischarges in Western Australia. The paper describes theapproaches to benchmarking for the critical environmentalissues for an alumina refinery and wastewater treatmentplant. It also describes the lessons learnt from thebenchmarking process on appropriate methods, the benefitsand difficulties in the benchmarking process, and changesthat would assist benchmarking for best practiceenvironmental management.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.