基于极大似然法的土壤重金属删失数据的相关性

基于极大似然法（MLE）,通过构建不同情形下的似然函数,提出了删失数据相关性估计的方法.分别研究了样本容量、删失比例、总体相关系数与干扰项等因素对估计值准确性的影响.同时,将MLE与替换法（通常将删失部分替换为LOD或LOD/2）和删除法（直接将删失部分删除）做对比,检测了MLE的精准度.以澳大利亚土壤普查数据作为实例对方法进行了实例应用.结果表明：样本容量越大,研究中的MLE的结果越准确,当样本容量达到2000时,估计值具有较好的稳定性,且受删失比例影响较小.MLE的相关系数估计值随删失比例（0%~90%）与总体相关系数变化程度较小,具有渐进无偏性和一致性.添加干扰项对MLE的准确性影响较小,表明其具有较强的鲁棒性.随着删失比例的提升,MLE精确性明显优于删除法和替换法.实际数据的应用结果表明澳大利亚土壤普查数据中,Ag与Hg有着较高的相关性,Hg与Hf之间相关系数几乎为0.     

基于最大谱的非平稳随机振动数据分析处理方法研究

目的解决非平稳随机振动环境实测数据无法采用功率谱密度分析方法的问题。方法基于最小均方误差准则,归纳总结FFT和数字滤波两种最大谱分析方法。通过对比两种方法在不同分辨率带宽时计算的优缺点,并分析其误差。对优选出来的最大谱分析方法进行举例验证。结果理论分析和仿真结果均表明基于比例分辨率带宽的FFT最大谱分析方法的误差最小。将FFT最大谱分析方法应用于某型炸弹9个架次自由飞振动数据的处理和环境条件确定中,结果合理有效。结论可用比例分辨率带宽的FFT法分析非平稳随机振动环境实测数据,为基于最大谱制定型号振动环境试验条件提供指导。     

贵州省闪电定位资料中小幅值地闪范围研究

采用2006—2015年贵州省ADTD闪电定位资料,应用Matlab软件中的曲线拟合工具箱对数据进行对数正态分布拟合,利用非参数正态分布检验中的Kolmogorov—Smirnov检验方法(简称K-S检验方法)分析各区间拟合效果,最终确定贵州省闪电定位系统资料的"小幅值地闪"干扰区间为-10~2k A,该结论为贵州省闪电资料修正提供理论依据,在使用ADTD闪电定位资料时可先将此区间误差数据剔除。该研究为利用闪电定位资料确定雷电灾害风险区划提供重要参考。     

基于信息熵的危险天气下进近管制风险评估

针对危险天气下进近管制系统运行风险性大、易发不安全事件的问题,提出根据一段时期内不安全事件的信息来评估该时期进近管制系统运行的风险,从而为以后进近管制系统的安全运行管理提供依据。首先,通过分析危险天气下管制系统运行过程的风险因素,建立风险评估指标体系。其次,给出评估指标灰色关联度属性的计算方法,利用其反映出的信息熵完成指标权重的确定和风险评估模型的建立。最后,应用该模型评估某进近管制中心一段时期内管制运行的风险状态水平,得到了风险值和风险级别,验证了该方法的适用性。     

基于源反演和气溶胶同化方法天津空气质量模式预报能力改进

为提升天津空气质量数值模式精细化预报能力,基于高分辨率排放源清单,技术应用源反演技术和气溶胶三维变分同化方法开展2020年天津空气质量数值预测分析,评估不同技术对空气质量模式预报能力改进,并结合气象因素评估模式系统性误差,以期提升天津空气质量精细化预报能力,服务分区精细化大气污染防治.结果表明,基于高分辨率排放源清单、源反演技术和气溶胶三维变分同化方法,可有效改进天津空气质量模式预报能力,调整后天津PM_2.5、 PM₁₀、 SO₂、 NO₂和O₃浓度预报平均偏差均在2μg·m^-3以内,其中高分辨率排放源清单应用后PM_2.5平均偏差为1.80μg·m^-3,源反演技术和气溶胶三维变分同化技术应用后平均偏差分别为-1.45μg·m^-3和-3.98μg·m^-3,均显著小于原模式的18.75μg·m^-3; PM_2.5浓度预报和实况的相关...     

灰霾天气的形成与演化

由于经济规模迅速扩大和城市化进程加快,大气气溶胶污染日趋严重,由气溶胶造成的能见度恶化事件越来越多,这些人类活动排放的污染物,包括直接排放的气溶胶和气态污染物通过化学转化与光化学转化形成的细粒子二次气溶胶,可形成灰霾(特指人类活动源排放的大气污染物诱发的低能见度事件),致使能见度下降。我国东部地区灰霾天气迅速增加,灰霾天气的本质是细粒子气溶胶污染,与光化学烟雾相关联,形成灰霾天气的气溶胶组成非常复杂。近年来由于灰霾天气日趋严重引发的环境效应问题和气溶胶辐射强迫引发的气候效应问题,广泛地引起科学界、政府部门和社会公众的关注,而成为热门话题。文章讨论了目前对灰霾天气的认识,灰霾的定义与判别标准,灰霾天气与光化学烟雾、气象条件的关系,也涉及灰霾天气对人体健康的影响。展望了灰霾研究的前景与主要研究方向和内容。     

Adapting to new policy environment – past pattern and future trend in us-sino waste plastic trade flow

Plastics are one of the most used materials in human activities, where consumer consumption and industrial production together has imposed vast rise in demand for this material in last century. While plastic is ideally derived from crude oil as a primary source from manufacturers’ perspective, varying crude oil prices are driving manufacturers economically to seek for alternative sources for plastics production. Waste plastic recovered from obsolete consumer products thus becomes an economic substitution for virgin plastics, which is further intensified with the possibility of international waste plastic trading. This study focuses on waste plastic trade between the US and mainland China by performing a correlation analysis of trade data. It is suggested in this study that although waste plastics are traded from the US to mainland China in general, as many of us believes, the route is gradually shifting in the past years. With tightening Chinese customs regulations, waste plastic from the US now tends to take a transit in a third destination (Hong Kong SAR for instance) for preliminary treatment to bypass Chinese customs inspection. Such phenomenon is worth noting, as a complication in waste plastic trading route hinders waste plastic transboundary movement monitoring. Furthermore, it will have adverse consequent consumer, industrial, and environmental impacts. It is thus necessary for national competent authorities to strengthen cooperative study and communication capacity in the future as a response to the changing waste plastic trade pattern.     

生态毒理数据筛查与评价准则研究

化学品生态风险评价和水质基准研究需要大量生态毒理数据,由于目前关于化学品毒性效应的研究较多,不同文献对同一测试终点的报道常常存在一定的差异,数据选择不当会直接影响最终评价结果。为了降低专业人员在数据筛选过程中的主观影响,有必要制定一套科学合理、操作性强的数据筛查与评价准则。本文整理比较了美国、荷兰、德国和澳大利亚等4个国家的5个毒理数据筛查与评价方法,并以荷兰的CRED方法为主,结合另外4个筛选方法以及现有的水质基准推导指南和生态风险评价技术导则,从可靠性、相关性、精确性三个方面详细阐述了数据质量评价标准与使用规范。其中数据质量评价包括五个方面:(1)实验设计,包括测试标准、操作规程、数据有效性、对照组设置;(2)实验试剂的纯度及其杂质的物理化学性质;(3)受试生物的基本信息和来源;(4)暴露条件,包括试验系统、暴露浓度设置及变化、暴露时间、生物负荷;(5)数据分析,包括平行样、统计分析方法、浓度-效应关系、原始数据;数据使用规范主要考虑受试生物、测试终点和暴露场景与评价目标的相关性,以及生态风险评价和水质基准推导对数据精确性的要求。这些均可为我国从事生态风险评价和水质基准研究的工作人员提供有益借鉴,使数据筛选过程更加客观、统一,同时还可以作为毒理实验论文撰写依据,提高数据报道质量。     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.