AERMOD模型在大气环境影响评价中的应用

通过实例,对AERMOD在大气环境影响评价中的应用进行讨论。根据2007年1月～12月达州市地面和高空气象数据、项目所在地的地形资料,应用AERMOD预测模型建立了空气扩散模型,对工程的大气环境影响进行了预测与评价。结果表明:AERMOD空气扩散模型实现了评价区域二维地形向三维地形的可视化转变,预测浓度分布能够反映出污染源分布和气象场变化以及地形等对大气污染物迁移和扩散的影响。     

中国CO《,2》排放的影响因素分解与预测

我国正处于2008年美国金融危机影响下,刺激经济一系列政策的落实观测期,要实现单位国内生产总值CO<,2>排放的目标,任务十分艰巨.本文采用投入产出结构分解技术,对1997-2004年我国应对亚洲金融危机时期的CO<,2>排放量变动进行了7因素的分解测算,结果表明经济增长是CO<,2>排放上升的主要驱动力,能源节约是驱动我国CO<,2>排放下降的主要因素,经济结构变动的影响相对较小.特别,国内最终需求、出口成长以及出口结构变动均推动CO<,2>排放上升,国内最终需求结构变动以及能源替代表现出了对CO<,2>排放的抑制影响等.同时,在我国经济"三步走"目标的前提下,结合能源节约设计了CO<,2>排放的3类情景,给出了未来CO<,2>排放的态势预测,提出了加强需求结构调整、能源效率提高以及可再生能源开发利用的保增长与CO<,2>减排并行不悖的建议.     

民航事故征候的灰色马尔可夫预测

民航事故征候的分析和预测是民航安全研究的重要内容.掌握民航事故征候的发展状况并据此提出相应的安全措施,可减少民航事故的发生.在民航事故征候灰色预测基础上,引入马尔可夫链(Markov Chains)预测理论,建立事故征候的灰色马尔可夫预测模型.该模型具有灰色预测和马尔可夫链预测的优点,不仅提高了对波动性较大的随机变量的预测精度,同时还拓宽了灰色预测的应用范围.对某一航空公司过载大事件的灰色马尔可夫预测和检验分析表明,事故征候的灰色马尔可夫预测模型精度高于GM(1,1)模型预测精度.研究表明,灰色马尔可夫预测模型可用于民航事故征候的预测.     

多元线性回归方法对北京地区PM2.5预报的改进应用

利用多元线性回归方法（REG）将多模式空气质量预报系统中3个模式（CMAQ、CAMx和NAQPMS）对北京市2016年PM_2.5的预报结果和观测数据进行集合,并对集合结果进行评估。结果表明：①不同模式的预报结果不尽相同,均能够反映2016年北京地区PM_2.5随时间的变化趋势,CMAQ、CAMx和NAQPMS相关系数为0.6~0.9,标准化平均偏差为-0.6~0.6。3个模式对重污染峰值预报都存在偏差,NAQPMS预报偏差低于其他模式;②基于多元线性回归集成预报模型能显著提高日均PM_2.5预报的准确率,能较好地改进不同季节模式整体高估或者低估的系统性偏差现象,春季国控平均偏差由-23 μg/m³改善至-2.3 μg/m³,冬季平均偏差降低近20 μg/m³;③利用多元线性回归方法对2016年红色预警期间小时PM_2.5订正结果显示,相关系数提高了0.13,均方根误差降低了20~30 μg/m³,并且对峰值浓度有较好的调整,预报峰值更为接近实况峰值,特别是对北部地区的改进效果较为明显,反映了实际观测数据对空气质量数值模式预报修正的研究意义和可行性。     

中国金属矿产的消费强度与回收潜力分析

从中国金属资源安全的角度来看,优化战略性金属矿产的供应结构,降低一次矿产的对外依存度,通过合理的政策导向有效地增加中国城市矿产开发效率,挖掘废旧金属的回收和利用潜力,是实现矿产资源安全保障的重要前提。本文以铁、铜、铝等三种金属矿产为例,采用美国、英国、法国、德国、日本等五个工业化国家及中国1949—2015年的面板数据,通过构建消费强度、回收密度和寿命分布函数分析了工业化五国金属消费和报废金属回收的历史规律,预测了2016—2030年中国三种金属消费和报废回收的变化趋势。结果显示:(1)五个工业化国家铁、铜、铝金属的消费强度经历了快速上升、平台缓降和较快下降的过程,而回收密度则经历了缓慢上升、较快增长和快速增长的三个阶段,在消费强度与回收密度的第三阶段呈现"脱钩"特征;(2)中国铁、铜、铝三种金属的消费强度大幅增加主要是集中在2000年以后,2015年我国铁、铜、铝的消费强度分别为540 kg/人、8 kg/人和23 kg/人,回收密度分别只有100kg/人、0.5 kg/人和3 kg/人,除铁、铝的消费强度进入平台下降期外,铜消费强度和三种金属回收密度仍处于增长的第一阶段;(3)预计2030年,中国铁、铜、铝的消费强度将分别为450 kg/人、9 kg/人和20 kg/人,仍处在平台缓慢下降阶段,回收密度将分别增加到220kg/人、3 kg/人和5 kg/人,回收密度与消费强度比例分别达到49%、33%和25%。通过对比可知,中国未来社会报废金属回收潜力巨大,如果能加以有效政策引导,加快回收利用,可大大缓解中国战略性金属的安全保障压力。     

Exploring Persistence in Streamflow Forecasting

In this study, the authors explore three persistence approaches in streamflow forecasting motivated by the need for forecasting model skill evaluation. The authors use streamflow observations with 15 min resolution from the year 2008 to 2017 at 140 United States Geological Survey streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 to 37,000 km². The study explores three approaches: simple persistence, gradient persistence, and anomaly persistence. The study shows that persistence forecasts skill has strong dependence on basin scales and weaker but non‐negligible dependence on geometric properties of the river network for a given basin. Among the three approaches explored, anomaly persistence shows highest skill especially for small basins, under about 500 km². The anomaly persistence can serve as a benchmark for model evaluations considering the effect of basin scales and geometric properties of river network of the basin. This study further reiterates that persistence forecasts are hard‐to‐beat methods for larger basin scales at short to medium forecast range.     

基于GM（1，N）灰模型预测企业主动性安全投入

本文基于GM（1,N）灰模型对企业主动性安全投入总量进行预测,研究把企业安全整体情况或整体表现作为一个“灰系统”对待时,依据该系统的特征量,来对企业主动性安全投入进行预测,为我国企业进行安全投入时提供决策依据。     

东北玉米热量指数预测方法研究(Ⅱ)——逐步回归预测方法

利用1960-2005年74类大气环流资料,从玉米播种期的5月开始到8月,逐月滚动建立了东北3省典型站玉米热量指数的逐步回归模型.各月模型都能较好地预测该区玉米生长季内的热量状况.检验结果表明,通过运用滚动预报的方法,可以有效地提高预测玉米生长季内热量指数的准确率.     

An integrated model for technology forecasting to reduce pollutant emission in China's pulp industry

The Chinese government has been implementing a strict pollutants gross control policy during the 11th Five Year Plan period. Primary pollutants reduction (COD & SO₂) is one of the major concerns of industrial environmental management. This paper constructs a technology selection model for industrial pollutants reduction by incorporating mass flow analysis of the production system, bottom-up modeling methodology, and linear programming for optimizing annualized discounted cost. A case study of COD emission control is carried out on the Chinese pulp industry, the nation's leading source of industrial COD discharge. The model is used to generate and analyze the technology prospects and COD emission situations under various scenarios for 2010, 2020 and 2030.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.