基于快速聚类方法分析常州市区PM2.5的统计特性

运用统计方法研究常州市区2013~2014年6个国控点六项基本污染物(SO_2、NO_2、CO、O_3、PM_(2.5)和PM_(10))月平均浓度变化,结果表明,除O_3外,其它五项污染物月平均浓度夏季较低冬季较高.颗粒物与风速之间的关系为PM_(2.5)浓度随风速的升高一直降低,PM_(10)随风速的升高浓度先降低后升高.采用快速聚类分析(k-means)并运用SWV和DIV指数对六项基本污染物进行分类,得到4个样本分类.与依据颗粒物化学成分或粒径谱对PM进行源解析方法不同,本研究更多是从PM_(2.5)与其它污染物相关关系以及污染程度等角度按照欧式距离进行分类.不同类中PM_(2.5)来源明显不同,类1中PM_(2.5)与化石燃料燃烧排放密切相关,类2与O_3密切相关,类3与城市不完全燃烧排放、区域灰霾污染密切相关,类4可以归类于城市"背景"类.快速聚类分析结果也表明常州市区PM_(2.5)有着复杂的来源.     

基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案

针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考.     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失,提高滑坡预测的准确性,通过搭建灾害模拟平台获得滑坡的实验数据,在获得多组模拟实验数据后,分析各变量的特性。首先,通过层次分析(Analytic Hierarchy Process,AHP)算法,对滑坡进行危险度划分;然后,通过支持向量机(Support Vector Machine,SVM)建立模型,遗传算法(Genetic Algorithm,GA)再优化SVM参数,提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据,通过GA与SVM结合建立的模型精度较好,实验预测结果与实际结果较为吻合,与单一SVM相比,精度更高,结果更好,更加适用于多变量的复杂非线性滑坡预警。     

我国安全生产行政执法统计指标体系研究与应用

我国安全生产行政执法统计指标体系自施行以来,一直存在统计指标体系过于繁杂、部分统计指标较笼统,未充分体现安全生产重点工作及其成效等问题,创新改革安全生产行政执法统计制度具有重要的理论意义和实用价值。通过梳理安全生产行政执法统计指标体系演变脉络,在实地调研和现场访谈的基础上,基于PDCA理论提出以执法人员-执法对象-执法行为“三位一体”的安全生产行政执法统计指标体系,明确包含执法力量、执法对象、执法检查和事故查处等方面的综合评价指标,并在实践中得以应用和检验,为全面分析安全生产执法效能拓展空间,统计制度的后续修订和安全生产综合分析工作奠定基础。     

Combining Logistic Models with Multivariate Methods for the Rapid Biological Assessment of Rivers Using Macroinvertebrates

This work represents an attempt to define a simple method to classify the relative degree of disturbance of sites in lotic systems on the basis of comparison of their faunistic composition with reference sites. Two ecotypes were selected in northern Portugal where benthic invertebrates were sampled in reaches with different levels of contamination. As a first stage, previous Geographic Information System information was used to define reference sites in each ecotype. Afterwards, multivariate techniques and non linear estimation models were combined to assess biological quality. This method allowed us to quantify sites according to increasing levels of contamination, after the probabilities of occurrence of taxa along a gradient of contamination taking into account the reference condition. The results suggest that this method is sensitive to organic pollution, easy to interpret, namely the species tolerance, and could be a good framework to establish regional rankings depending on the ecological impact of river sites.     

Modelling Phosphorus Retention in Lakes and Reservoirs

Steady-state models for the prediction of P retention coefficient (R) in lakes were evaluated using data from 93 natural lakes and 119 reservoirs situated in the temperate zone. Most of the already existing models predicted R relatively successfully in lakes while it was seriously under-estimated in reservoirs. A statistical analysis indicated the main causes of differences in R between lakes and reservoirs: (a) distinct relationships between P sedimentation coefficient, depth, and water residence time; (b) existence of significant inflow–outflow P concentration gradients in reservoirs. Two new models of different complexity were developed for estimating R in reservoirs: , where τ is water residence time (year), was derived from the Vollenweider/Larsen and Mercier model by adding a calibrated parameter accounting for spatial P non-homogeneity in the water body, and is applicable for reservoirs but not lakes, and , where [P_in] is volume-weighted P concentration in all inputs to the water body (μg l⁻¹), was obtained by re-calibrating the OECD general equation, and is generally applicable for both lakes and reservoirs. These optimised models yield unbiased estimates over a large range of reservoir types.     

基于FHWA的兰州市道路交通噪声预测模型的建立

结合美国道路交通噪声污染预测模型(FHWA)和国内学者在该方面的大量研究成果,选择兰州市主、次干道共计52条、142个监测点的建模采样数据,并应用统计学原理分析了影响道路交通噪声的各个因子与道路交通噪声的相关性,最终得出了符合兰州市道路交通特征的噪声污染统计预测模型.随后通过兰州市15个监测点的预测与实测对比验证后发现二者具有较高的一致性,此模型可应用在兰州市道路交通噪声污染的预测评价中.     

运用QSPR预测有机磷农药的色谱保留值

通过计算43种有机磷农药的各种结构参数,运用多元线性回归分析方法比较了适用于有机磷农药色谱保留值的定量关系表达式,建立了有机磷农药结构参数对色谱保留值的QSPR模型.模型分析表明:磷酸酯与硫逐磷酸酯两类有机磷农药的模型非交叉验证相关系数R2分别为0.991和0.998,标准误差SE分别为0.0539和0.2874,交叉验证相关系数Q2分别为0.976和0.990,标准偏差Scv分别为0.086和0.610.在已知磷酸酯与硫逐磷酸酯两类有机磷农药结构参数的情况下,此模型可有助于有机磷农药的色谱分析.     

一种新的汛期降水集中期划分方法

汛期降水集中期是近期气象学者提出的表征汛期气候的一种新的特征量,它在气候研究中体现了较好的灵活性、客观性,通过对其分析,可为汛期气候的诊断和预测提供依据。但现在普遍使用的降水集中期在计算方法和时间长度上存在缺陷,特别是运用到时间跨度较长时段的气候分析时,特征量表征作用就有所缺失,而且计算方法较为复杂。为更好地使用降水集中期这一特征量,提出了以15天作为时长,用滑动统计来划定汛期降水集中期的新方法,并运用统计方法、天气气候学方法进行了论证,同时在长江下游主雨季降水集中期分析和金华地区汛期分析两个实例中进行了应用检验。结果表明,汛期降水集中期新方法划定的特征量与汛期降水总量存在时间上的相对独立性和总趋势上的显著相关性,且在汛期气候极端灾害事件上有较强的描述能力。因此认为,15天滑动统计新方法划定的汛期降水集中期使用便捷,天气气候意义明确,在实际应用中更为客观有效。     

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

Abstract: Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient‐reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight‐digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.