畜禽养殖中沙门氏菌污染的PCR快速检测

针对畜禽养殖是造成农村水环境生物性污染的主要原因,指出用常规生化检测技术耗时长、效率低,不能满足环境管理的要求：应用PCR技术检测沙门氏菌,特异性高、敏感性强、耗时短,与常规分离鉴定方法比较,完全符合实验证明,PCR技术可很好的应用于沙门氏菌污染的调查与监测。     

辽宁矿山固体废弃物对环境影响遥感调查分析

辽宁矿种繁多,矿山固体废弃物产出量巨大。为了加强生态环境建设,在矿山固体废弃物对生态环境影响遥感调查的基础上,论述了矿山固体废弃物调查对象与内容、技术路线与工作方法,并分析了辽宁矿山固体废弃物产出量及矿山固体废弃物对环境的影响。     

基于MODIS的西藏典型湖泊叶绿素a浓度反演

叶绿素a浓度是反映湖泊富营养化状态的一个重要参数。以MODIS L1B数据为基础,结合叶绿素a浓度实测数据,基于经验分析法实现了西藏典型湖泊叶绿素a浓度反演研究,并探索了西藏典型湖泊2019年春、夏、秋季叶绿素a浓度的时空变化特征。首先,利用叶绿素a浓度实测数据和MODIS L1B影像不同波段的反射率值进行组合试验,选择最佳波段组合建立模型;其次,分别选用2015年、2017年叶绿素a浓度实测值和反演值对模型进行对比验证;最后,利用叶绿素a浓度反演模型对西藏典型湖泊2019年春、夏、秋季叶绿素a浓度的时空变化特征进行分析。结果表明：在空间尺度上,西藏典型湖泊叶绿素a浓度整体上呈现出周围高、中部低的分布特征,且湖岸水体叶绿素a浓度变化较大;在季节尺度上,不同湖泊叶绿素a浓度的季节变化存在较大差异,格仁错和色林错的季节变化幅度较大,纳木错、塔若错和羊卓雍错的季节变化幅度较小。     

粒子群算法优化SVR的天德湖总氮遥感反演

以郑州天德湖为研究水域,利用实测的水质总氮数据和光谱反射率数据,获取反演总氮浓度的最佳波段组合,以此建立支持向量机回归（SVR）模型,并利用粒子群优化算法（PSO）优化SVR模型,建立PSO-SVR模型,再利用珠海一号高光谱（OHS）数据验证该模型的精度和适用性。结果表明：PSO-SVR模型的决定系数（R2）为0.923 6,均方根误差（RMSE）为0.103 3mg/L,平均相对误差（MRE）为3.656%;SVR模型的R2、RMSE和MRE分别为0.808 0、0.203 2mg/L和6.583%,PSO-SVR反演结果优于SVR模型。利用粒子群算法优化SVR模型,有助于提高天德湖总氮浓度反演精度,即利用实测光谱数据与卫星影像数据结合来反演总氮浓度可行。     

长-短NDVI时序检测植被局部突变特征方法研究

提出一种长-短NDVI时序检测植被局部变化特征方法,利用2000—2020年和田地区的MODIS数据,对研究区域同一格网内不同年度的NDVI时序作相似性检验以检测格网内植被是否发生突变,进而研究植被局部变化特征,检测植被局部发生生态突变的时间、位置及变化前后植被类型。结果表明：和田地区植被覆盖突变好转的年份为2006年、2013年、2016年和2018年,分布在平原绿洲、山区与平原交汇区域。除了开垦、耕作及其他逐利人为因素外,大部分植被覆盖突变发生在气候变化剧烈的南部山区,故和田地区局部植被退化是气候变化与人类活动共同作用的结果。     

基于表面增强拉曼光谱的微纳塑料肝脏内暴露检测方法研究

微纳塑料因其难以降解和小尺寸特性可能危害人类健康,目前已报道大量微纳塑料检测手段,但是主要集中于食物和环境样本中,尚缺乏评估微纳塑料生物体内暴露水平的研究。表面增强拉曼光谱（SERS）因其灵敏度高、检测速度快和数据处理简单等优点,具有良好的应用前景。本研究基于金核银壳为基底的SERS技术,旨在开发一种高灵敏准确的检测手段评估微纳塑料肝脏内的暴露水平。首先对尺寸为1 000 nm的聚苯乙烯微塑料进行物理化学性质表征。随后通过柠檬酸钠还原法成功制备形貌完整且粒径分布均一的30 nm金核银壳纳米粒子（Au@Ag NPs）。采用SERS对梯度稀释的微塑料标准品溶液进行检测,SERS成功检测浓度范围在10～0.05 mg·mL^-1的聚苯乙烯微塑料标准品,绘制标准曲线并计算最低检测限为0.0175 mg·mL^-1。构建小鼠微塑料尾静脉注射模型,使用SERS对小鼠肝脏内微塑料浓度进行检测,计算得到肝组织悬液中的微塑料浓度为0.493 mg·mL^-1。本研究建立的以Au@Ag为基底的SERS方法可实现肝脏内微塑料浓度的精确定量,弥补微塑料器官内暴露水平评价方法的空缺,为微塑料对人体危害的内暴露研究提供技术支持。     

Hazardous waste site identification using aerial photography: A pilot study in Burlington County,New Jersey,USA

The objective of the project was to identify all hazardous waste sites in Burlington County, New Jersey that could be detected on existing, medium-scale aerial photographs of the county. The complete set of over 1000 black- and-white stereopairs at a scale of 1:12,000 was carefully examined for initial identification of possible sites. All suspicious sites were examined again on color transparencies of the county at the same 1:12,000 scale. Out of the 1094 black- and-white photos, 250 required further checking on color transparencies using a zoom stereoscope. This examination resulted in a final identification of 67 sites, the locations of which were delineated on 1:24,000 USGS maps. The use of air photo interpretation techniques provided an effective procedure for identifying waste sites quickly as well as providing a useful demonstration program for county and state officials.     

Monitoring Forest Carbon Sequestration with Remote Sensing and Carbon Cycle Modeling

Sources and sinks of carbon associated with forests depend strongly on the management regime and spatial patterns in potential productivity. Satellite remote sensing can provide spatially explicit information on land cover, stand-age class, and harvesting. Carbon-cycle process models coupled to regional climate databases can provide information on potential rates of production and related rates of decomposition. The integration of remote sensing and modeling thus produces spatially explicit information on carbon storage and flux. This integrated approach was employed to compare carbon flux for the period 1992–1997 over two 165-km² areas in western Oregon. The Coast Range study area was predominately private land managed for timber production, whereas the West Cascades study area was predominantly public land that was less productive but experienced little harvesting in the 1990s. In the Coast Range area, 17% of the land base was harvested between 1991 and 2000. Much of the area was in relatively young, productive-age classes that simulations indicate are a carbon sink. Mean annual harvest removals from the Coast Range were greater than mean annual net ecosystem production. On the West Cascades study area, a relatively small proportion (< 1%) of the land was harvested and the area as a whole was accumulating carbon. The spatially and temporally explicit nature of this approach permits identification of mechanisms underlying land base carbon flux. Published online     

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630–690 nm), band 5 (1550–1750 nm) and the ratio between bands 1 (450–520 nm) and 4 (760–900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520–600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.