大气红外辐射超高光谱探测仪临边探测—污染气体的反演精度和光谱通道评估

利用RFM模式,模拟计算了在研仪器—大气红外辐射超高光谱探测仪的临边探测模式下大气污染气体体积混合比的权重函数.结合仪器的可探测亮温阈值（0.3 K）,利用权重函数线性化方法,计算并分析了6种大气状态下,气体混合比廓线在不同反演精度条件下可获得的光谱通道数随切点高度的变化,并给出了可用光谱通道在不同切点高度的位置.结果表明,随着相对反演精度的降低,可用光谱通道数量增加,除热带大气外,其它5种大气在10%臭氧反演精度和5%甲烷反演精度条件下有足够的通道,可用于反演4.6 km以上的混合比廓线.CO在15%反演精度条件下,6种大气均能获得反演6.9 km以上廓线的光谱通道.近地面切点高度反演比较困难,很难获得较高的反演精度.可用光谱通道位置与气体吸收线位置特别是峰值区域一致,也与气体模拟亮温高值区一致.     

基于GA-BP的渗透系数多目标反演分析模型研究

针对渗透系数反演结果不唯一问题,以扬压力和渗流量2类渗流监测效应量为反演目标,建立了基于正交设计、有限元正分析和GA-BP相结合的渗透系数多目标反演分析模型。结合水口大坝2009—2018年渗流监测时间序列数据,开展了大坝坝体、坝基岩体渗透系数反演研究,并对反演结果进行了验证。研究结果表明:建立的渗透系数反演模型可较好地解决渗流单目标反演的缺陷,反演结果为绝对渗透系数,且反演结果真实可靠。     

用实测资料对山区逆温的分析

利用云南早期温度层结探空实测资料对山区逆温的特征进行了分析,结果表明:干季逆温出现频率高,山区逆温维持时间长,逆温主要发生在低层。并对同一区域内代表不同下垫面的山区与河谷同期探空资料作了逆温层结的对比分析。     

乌鲁木齐-奎屯高等级公路沿线污染气象分析

就乌鲁木齐－奎屯高等级公路沿线的污染气象条件进行分析，为研究道路二侧汽车尾气污染提供必要的参数。     

基于无人机多光谱影像的小微水域水质要素反演

总磷(TP)、悬浮物浓度(SS)、浊度(TUB)3种水质参数可以直接通过遥感反演得到,常用于评价区域水环境的污染状况.以浙江农林大学东湖为研究对像,使用无人机携带多光谱传感器(Mica Sense Red Edge)获取多光谱影像,进而提取16个光谱参数,分别构建东湖水域TP、SS、TUB的反演模型.结果表明:光谱参数V5(NIR 0.770～0.890μm)与TP、SS相关性显著(r分别为0.470、-0.537,p0.05),V4(0.670～0.760μm)与TUB相关性显著(r=0.486,p0.05).在建立的TP反演模型中,指数函数模型精度最高,决定系数R~2为0.7829;在建立的SS、TUB反演模型中,多项式函数模型精度最高,决定系数R~2分别为0.7503、0.7334.经检验,TP、SS、TUB模型估测值与实测值线性拟合曲线的决定系数R~2分别为0.7374、0.8978、0.6726,满足水质要素反演的精度要求.最后利用建立的模型,结合多光谱影像数据,建立了东湖水域各参数的空间分布图,实现了水质参数的可视化,可为小微水域的污染防治提供技术支撑.     

水源区河流非点源污染物入河量计算的水质方程反演方法

以水质一维流方程为基础,建立了水源地区非点源污染物入河量计算的反演模型;计算了浙江省湖州市老虎潭水库水源地区各条支流的非点源污染物入河量;并通过对实测水文水质参数的统计,采用Monte Carlo模拟方法,分析了模型各输入参数的敏感性.由于该模型所有敏感参数都可以实测获得,避免了关键参数取值的人为主观因素的影响,保证了非点源污染过程定量分析的客观性;该模型在计算非点源污染物入河量时不受时间的限制,可以根据研究区域的水文水质监测频率计算任意时段内的非点源污染物入河量.结果表明,该水源区所有溪流中的氮、磷污染物负荷量都与溪流流量呈极显著正相关(r0.90,p0.01),因此,丰水期是非点源污染物入河的集中期;对输入参数的敏感性分析显示,对模型输出结果影响最大的是溪流的流量,其次是段末浓度和背景浓度,综合降解系数和流速的影响较小.     

基于深度学习的华东地区PM2.5浓度遥感反演

PM_2.5作为大气污染的主要来源,对人类身体健康有着极大的影响.本文提出基于深度学习模型的多要素联合PM_2.5反演方法,以PM_2.5浓度作为真值数据,引入Himawari气溶胶光学厚度(AOD)日数据产品与温度、相对湿度和气压等10个要素作为反演要素.为验证方法的有效性,采用华东地区2016～2018年的数据分季节开展实验,并与传统反演方法进行对比.结果表明,PM_2.5浓度与AOD、降水、风速、高植被覆盖指数呈正相关关系,与矮植被覆盖指数呈负相关关系,与温度、湿度、气压以及DEM的相关性随季节的变化而改变;基于深层神经网络(DNN)反演的PM_2.5精度高于传统的线性和非线性模型,各个季节R²均在0.5以上并且误差较小,其中秋季的反演效果最好R²为0.86,夏季为0.75,冬季为0.613,春季为0.566;模型的可视化结果显示,DNN模型的反演结果更接近地面监测站点插值的PM_2.5浓度分布,分辨率更高且更精确.     

北京地区采暖期SO2熏烟型污染及评估

随着北京城市建设的发展，为了保护环境，北京市的新建生活区均采用集中供热系统。燃烧后的有害气体通过烟排向大气，经过高空稀释后的烟气可以降低污染负荷，但是在某此气象条件下仍然造成了地面局部污染，本文在北京市丰台区一个生活小区的供热系统污染源环境影响评价的基础上，讨论了熏烟污染的发生及危害。     

CRITICAL ELEMENTS IN DESCRIBING AND UNDERSTANDING OUR NATION'S AQUATIC RESOURCES1

ABSTRACT: Despite spending $115 billion per year on environmental actions in the United States, we have only a limited ability to describe the effectiveness of these expenditures. Moreover, after decades of such investments, we cannot accurately describe status and trends in the nation's aquatic ecosystems or even those in specific regions. Why? This situation has arisen in part because we have excluded the fundamental principles of probability designs that are widely used in other fields and we have often ignored direct measures of biota, the subjects of greatest concern. To demonstrate the results of ignoring these powerful statistical and biological tools, we present four case studies. These studies compare estimates of aquatic resource status derived from using (1) a probability-based study design, often with biological measures of condition; and (2) a nonstatistical study design, often using chemical surrogates. In three of the four cases, the results derived from the nonstatistical perspective underestimate the degree of biological degradation.     

Direct calculation of likelihood-based benchmark dose levels for quantitative responses

A benchmark dose (BMD) for quantitative responses is a lower confidence limit (LCL) on the effective dose corresponding to a specified risk level r. A commonly adopted method for calculating the BMD is to obtain a pointwise upper confidence curve U(d) on the risk function and then invert this relationship by solving the equation U(d)=r. The solution d is taken to be the BMD. Sciullo et al. (2000) have shown that the coverage achieved by this inversion method is at least as great as the coverage achieved by U (·) but that there is otherwise no general relationship between the two coverage probabilities. The present paper develops a method for direct calculation of the BMD based on the asymptotic distribution of the likelihood ratio statistic. It is further shown that the direct method and the inversion method are equivalent when U (·) is also based on the likelihood ratio. Since the direct method is known to be asymptotically correct, it follows that the LR-based inversion method is also asymptotically correct. However, the direct method is computationally faster and easier to program. Finally, some simulation studies are conducted to assess the small sample coverage probabilities of the direct method when responses follow either a normal or a gamma distribution.