高原机场飞机噪声监测与计算结果比较分析

高原机场飞机架次较少,机型单一,飞行周期稳定,以及高原特殊的环境特征,有时会导致现场实际噪声监测数据无效。应用综合噪声模型(INM)软件对青海玉树机场飞机噪声进行计算,其结果与监测结果比较显示,计算值和监测值误差在±6 d B的范围以内。探讨提出采用飞机噪声计算模式,可以为高原机场飞机噪声管理提供可靠技术支撑。     

江苏省空气质量预报准确度及改进方法分析

介绍了江苏省重污染天气监测预报预警系统以及大气重污染预警会商流程,将2015年13个地级市的模式预报、人工预报结果分别与实际观测值进行比较。结果表明:人工预报更准确,PM_(2.5)日均值、臭氧日最大8 h平均值、AQI 3个指标人工预报和实况的相关性分别比模式预报高出12.8%、0.3%、11.4%,平均标准误差(MNE)分别低20.7%、3.1%、23.1%。依据国家空气质量预报技术指南评分办法,对各市2015年全年空气质量级别为"良"时进行评分。通过开展07∶00预报更新,使2015年上半年空气质量预报级别得分平均提高了0.9分,全年级别得分平均提高了2.6分;通过改进模式预报参数,使PM_(2.5)日均预报值、臭氧日最大8 h平均预报值、AQI预报值和实际观测值的相关性比上年同期分别提高26.0%、5.0%、33.9%,MNE分别降低3.6%、31.3%、7.6%。     

Coupling upland watershed and downstream waterbody hydrodynamic and water quality models (SWAT and CE-QUAL-W2) for better water resources management in complex river basins

Effective water resources management programs have always incorporated detailed analyses of hydrological and water quality processes in the upland watershed and downstream waterbody. We have integrated two powerful hydrological and water quality models (SWAT and CE-QUAL-W2) to simulate the combined processes of water quantity and quality both in the upland watershed and downstream waterbody. Whereas the SWAT model outputs water quality variables in its entirety, the CE-QUAL-W2 model requires inputs in various pools of organic matter contents. An intermediate program was developed to extract outputs from SWAT at required subbasin and reach outlets and converts them into acceptable CE-QUAL-W2 inputs. The CE-QUAL-W2 model was later calibrated for various hydrodynamic and water quality simulations in the Cedar Creek Reservoir, TX, USA. The results indicate that the two models are compatible and can be used to assess and manage water resources in complex watersheds comprised of upland watershed and downstream waterbodies.     

Hydrologic–Economic Model for Managing Irrigation Intensity in Irrigation Areas under Watertable and Soil Salinity Targets

This paper gives mathematical details and sample applications of SWAGMAN Farm (SWAGMAN, Salt Water and Groundwater Management), a farm-scale hydrologic economic model that integrates agronomic, climatic, irrigation, hydrogeological and economic aspects of irrigated agriculture. The model is capable of determining optimum mix of land use to keep watertable and soil salinity within acceptable limits while maximising the economic returns. Alternatively, the model can simulate water and salt balance and economics of a given cropping preference. Web-based and Geographic Information Systems versions of the model are available for integration with the environmental reporting systems of the irrigation areas.     

The Role of Abatement Costs in GHG Permit Allocations: A Global Stabilization Scenario Analysis

Our objective is to propose permit allocation schemes that lead to a fair distribution of the net abatement cost among regions in a global greenhouse gas (GHG) stabilization scenario. We use a detailed technology-based energy model, World-MARKAL, to determine efficient abatement decisions, and to calculate the regional gross abatement costs (before permit allocation and trading). The net abatement costs are then calculated and used for different permit allocation schemes.     

天津城市污水泵站恶臭气体组分浓度及污染现状

为了解天津城市污水泵站恶臭的污染程度,选取天津市一个典型的城市污水泵站进行为期3天的采样,采集该污水泵站距离污染源强不同距离处的恶臭气体样品,用气相色谱-质谱联用仪（GS-MS）和分光光度法对采集样品的组分含量进行检测,比较了恶臭排放源点和不同距离处的恶臭组分的浓度特征和污染现状。结果表明,该泵站恶臭污染物质以硫化氢、二氯甲烷和苯系物为主,还有一些烯烃和芳香烃衍生物;恶臭物质的浓度变化范围是n.d.~0.073 7 mg/m³,大部分恶臭物质浓度一般由高至低表现为源点>下风向5 m,源点>背景值,环境空气采样点与恶臭排放源相比,其降幅明显;虽然检测到的恶臭组分较多,但是其浓度与《恶臭污染物排放标准》（GB 14554—1993）等标准中规定的排放限值相比,均未超标。     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

街道峡谷内车辆流动对反应性污染物传播特性影响的模拟研究

采用CFD（计算流体力学）数值模拟的研究方法,使用动网格技术,分析简单光化学反应下车辆流动及不同来流风速对双车道三维街道峡谷内污染物传播特性的影响.结果表明,车辆移动改变了峡谷内气流结构,以及背风侧与迎风侧活性污染物浓度分布的相对大小,有利于污染物在峡谷中的传播扩散;来流风使机动车尾气向建筑背风侧汇聚,并随着风速增加而加强,对近迎风侧车道车辆尾气淹没射流的影响比近背风侧车道大.在车辆移动与来流风的综合作用下,污染物的扩散能力得到显著增强.     

基于3种时间序列模型的九龙江河流库区藻华预测

湖库富营养化和有害藻华是全球性生态环境问题,藻华预测与早期预警是保障湖库水源地供水安全的关键技术.如何基于高频水生态在线监测数据进行藻华的实时动态预测成为水生态管理领域的重大需求.本研究以福建省九龙江江东库区(水源地)为例,利用3年连续观测的逐时平均总叶绿素a浓度数据,对比研究了SARIMA、Prophet和LSTM(长短期记忆神经网络)3种时间序列模型在藻华(日平均叶绿素a大于15μg·L^-1)预测方面的效果.结果表明：(1)时间序列模型要求参数少,灵活性强,能清晰反映水质特征和未来变化趋势,可弥补传统藻类监测预警方法的局限性;(2)基于深度学习框架的LSTM模型,具有独特的迭代优化算法,对藻类非线性变化特征的识别和预测能力较强,其总叶绿素a逐日预测和7日预测效果均显著优于SARIMA模型和Prophet模型;(3)输入数据长度会在一定程度上影响模型预测效果,最优的输入数据时间长度为7 d;输入数据频率对预测效果也有影响,在预测非藻华日时,小时数据的预测效果优于日频率数据;在预测藻华日时,两种频率数据无显著差异,但日频率数据能更准确识别藻华日特征.总结起来,基于...