洋河水库蓝藻水华爆发预测影响因子研究

为了预测洋河水库富营养化变化趋势和蓝藻水华爆发的风险,对洋河水库水体进行采样,分析其环境质量.研究结果表明,近年来洋河水库叶绿素a含量处于高位,氮、磷含量不断上升,特别是总氮质量浓度在5 mg/L左右,主要以硝态氮的形式存在,富营养化程度不断加剧,具有大规模爆发蓝藻水华的风险.对洋河水库藻华爆发的环境条件进行分析,提出...     

我国未来能源消费及其对环境的影响分析

随着我国经济的发展和城市化水平的提高,未来的能源消费状况及由此带来的环境污染问题日益受到人们的关注.本文简要回顾了1 980年以来我国能源消费状况并分析了相应的环境问题,对我国未来能源消费状况及可能带来的环境影响设置了3种情景进行预测和分析,得出我国今后因能源消费的SO₂、NO_x、CO₂和烟尘等排放量依然很高,但不同情景的预测结果有较大区别,并提出通过提高能源效率以及加强清洁、可再生能源的开发利用来解决能源消费带来的环境污染问题.     

基于ELCOM-CAEDYM模型的太湖蓝藻水华早期预测探讨

结合太湖蓝藻水华形成的"四阶段理论", 基于澳大利亚西澳大学水研究中心开发的ELCOM-CAEDYM耦合模型框架构建了太湖水华蓝藻生态动力学模型,对蓝藻水华的形成进行模拟,分析了太湖蓝藻水华早期预测的可行性.结果表明:该模型在较长时间尺度上对春季复苏阶段及生长上浮阶段蓝藻水华的形成模拟效果较好,蓝藻生物量模拟值与站点调查值的误差变化范围在1.0%~70.4%,平均误差为28.0%,与MODIS卫星反演值的误差变化范围在3.8%~83.9%,平均误差40.5%;但越冬阶段蓝藻生物量模拟输出值与站点调查值的误差变化范围在3.0%~143.6%,平均误差为40.1%,与MODIS卫星反演值的误差变化范围在9.7%~118.4%,平均误差为48.8%,表明模型对蓝藻越冬过程模拟能力还不强,应在蓝藻越冬机制模拟计算方面进一步改进,以满足蓝藻水华早期预测的需要.     

公路路网建设项目声环境影响评价研究

以甘肃省某市新区基础设施建设中的路网建设为例,根据公路路网的交通参数、道路地形地貌条件、路面设施等,利用导则推荐模式预测路网噪声贡献值。该新区路网中道路距离多在500m以上,不论道路平行还是相交,噪声影响以主干道为主,以次干道为辅,相互影响;根据路网噪声环境影响的特点,提出切实可行的声环境影响减缓措施。     

安徽省近10年能源足迹测度及驱动因子分析

安徽省为生态建设省,随着中部崛起战略与皖江城市带承接产业转移示范区建设的实施,能源消费给当地生态环境形成了较大压力. 依据生态足迹模型对安徽省2000—2009年能源足迹进行了时间序列测度,基于STIRPAT模型揭示了能源足迹驱动因子的边际贡献,运用灰色GM(1,1)模型预测了2015年与2020年能源足迹. 结果表明:安徽省人均能源足迹由2000年的0500 8 hm2增至2009年的1043 5 hm2,人均能源足迹赤字由2000年的0432 9 hm2扩至2009年的0975 5 hm2;煤炭足迹对能源足迹平均贡献率为7629%,占主导地位;能源足迹强度由2000年的1083 3 hm2104元降至2009年的0704 7 hm2104元;研究时段内,能源足迹生态压力指数均大于5 人均GDP和第二产业在经济中所比例均与能源足迹呈正相关,二者边际弹性系数分别为0000 073 57和0006;而单位工业增加值能耗与能源足迹呈负相关,其边际弹性系数为-0186.      

Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.     

回归分析方法在环境领域中的应用评述

回归分析是一种处理变量间相关关系的有效数理统计方法,回归分析模型目前已应用于环境领域的多个方面,并在实际应用中证实了其准确性和可行性。基本回归分析方法有一元线性回归、多元线性回归和逐步回归等,通过概述了这几种基本回归分析方法的原理及其在环境领域中的应用现状,评述了其应用效果,并预测了回归分析技术在环境领域中应用的发展方向;对在众多环境科学与工程研究领域中,更好地发挥回归分析的作用具有很好的参考价值。     

京津冀臭氧污染特征、气象影响及基于神经网络的预报效果评估

基于生态环境监测和气象观测数据,分析了2016~2020年京津冀13个城市臭氧（O₃）浓度特征,讨论了O₃污染高发月份日最高温度（T_max）、日均地面气压（p）、日均地面相对湿度（RH）和日均地面风速（v）等气象要素对O₃-8h浓度和O₃-8h超标情况的影响规律,并采用AQI级别预报准确率、O₃浓度范围预报准确率和O₃级别预报准确率等方法,评估了基于神经网络的O₃统计预报效果.结果表明,2016~2020年期间京津冀13城市ρ（O₃-8h-90per）分别为157.4、177.2、177.3、190.6和175.6μg·m^-3,区域臭氧浓度5a上升了11.6%,2016~2019年期间总体呈波动上升趋势,2020年环比下降;2020年与2016年相比,除北京、张家口和承德略有下降外,其他10个城市ρ（O₃-8h-90per）上升了6~45.5μg·m^-3.O₃-8h月均值呈现"两头低,中间高"现象,ρ（O₃-8h）在4~9月的月均值超过了100 μg·m^-3,在6月最高,为158.10 μg·m^-3.城市O₃-8h超标率范围为8.6%~19.2%,97.8%的O₃-8h超标情况发生在4~9月.区域尺度上O₃-8h浓度与日最高温度相关性最强,当T_max在25~28℃区间时,所有城市开始出现O₃-8h超标.O₃-8h浓度与日均地面气压呈负相关关系;当RH在60%以下时,大部分城市O₃-8h浓度随相对湿度上升缓慢增长;当RH在61%~70%以上时,大部分城市O₃-8h浓度随日均相对湿度上升而下降.O₃-8h超标时的地面主导风向主要为偏南风,大部城市O₃-8h浓度高值易集中出现在2~3m·s^-1及以下低风速区间.OPAQ统计模式提前1~9 d预报相关系数范围为0.72~0.86,AQI级别预报平均准确率为67%~86%,O₃-8h浓度范围预报平均准确率为63%~84%.在O₃-8h超标情况多发的4~9月,模式对O₃轻度污染和O₃-8h超标情况提前3 d预报准确率分别为69%和66%,可为O₃-8h超标管控提供参考依据.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.     

石油石化企业环境预警系统研究

通过对石油石化企业环境突发事件风险分析,提出建设环境预警系统的必要性,再对国内外常见的地表水环境预警系统构成、环境质量预测模拟软件以及环境预警决策支持系统进行总结,提出符合石油石化行业污染特征的环境预警系统的基本框架。