地动噪声RMS值计算方法的适用性及误差概率分析

对比两种计算RMS值的方法,结合误差概率统计给出结果的误差概率分布。在使用有限脉冲响应（FIR）带通滤波器进行滤波并计算RMS值的过程中,滤波器窗口函数和阶数是影响计算结果误差的主要因素,其中阶数的作用更大。通过计算功率谱密度（PSD）反算RMS值的误差主要受pwelch函数的窗口函数类型、窗口长度及重叠率等参数影响,其中窗口长度作用更大。从同等误差水平的概率分布看,在合理设置参数的前提下,使用PSD反算RMS值的方法更优。     

Foliar Particulate Matter Distribution in Urban Road System of Beijing,China

Particulate matter(PM), one of the most important pollutants of traffic emission, threatens the health of urban ecosystems and citizens. Roadside trees play an important role in trapping PM, and the foliar PM load is a useful indicator for air PM pollution in road systems. To detect the relationships of foliar PM load with road structures, urbanization, and meteorology in road systems, we studied a widely-planted tree Sophora japonica L. in 100 roads and 10 yards of Beijing, China, and found that the foliar PM loads increased with the distances from the urban centre(DUC) linearly, while decreased with the road density. All-subsets regression analysis indicated that DUC, average monthly relative humidity, average monthly wind speed, and mean annual wind speed were the most important factors in predicting foliar PM load, rather than general situation of buildings and road cleanliness. The monthly relative humidity and monthly wind speed had a negative correlation to foliar PM, while the annual relative humidity and annual wind speed had a positive correlation to foliar PM. Suburban areas and these effective factors should be highlighted in PM control in Beijing.     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.     

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

Journal of Geographical Sciences - In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial...     

Self-thinning Rules at Chinese Fir (Cunninghamia lanceolata) Plantations—Based on a Permanent Density Trial in Southern China

Data selection and methods for fitting coefficients were considered to test the self-thinning law. The Chinese fir (Cunninghamia lanceolata) in even-aged pure stands with 26 years of observation data were applied to fit Reineke’s (1933) empirically derived stand density rule (N $propto$ d¯ ^-1.605, N = numbers of stems, d¯ = mean diameter), Yoda’s (1963) self-thinning law based on Euclidian geometry (v¯ $propto$ N ^-3/2, v¯ = tree volume), and West, Brown and Enquist’s (1997, 1999) (WBE) fractal geometry (w¯ $propto$ d¯ ^-8/3). OLS, RMA and SFF algorithms provided observed self-thinning exponents with the seven mortality rate intervals (2%-80%, 5%-80%, 10%-80%, 15%-80%, 20%-80%, 25%-80% and 30%-80%), which were tested against the exponents, and expected by the rules considered. Hope for a consistent allometry law that ignores species-specific morphologic allometric and scale differences faded. Exponents α of N $propto$ d¯^α, were significantly different from -1.605 and -2, not expected by Euclidian fractal geometry; exponents β of w¯ $propto$ N^β varied around Yoda’s self-thinning slope -3/2, but was significantly different from -4/3; exponent γ of w¯ $propto$ d¯^γ tended to neither 8/3 nor 3.     

基于长期密度试验的杉木人工林自然稀疏线研究（英文）

采用杉木人工同龄纯林26年连续观测数据,拟合Reineke(1933)经典的林分密度效应法则、Yoda(1963)基于欧几里德几何的自疏法则以及West, Brown和Enquist的分形几何(1997, 1999)。通过OLS、RMA和SFF算法得到7种枯损率区间(2%-80%, 5%-80%, 10%-80%, 15%-80%, 20%-80%, 25%-80%and 30%-80%)的自稀疏指数,对这些指数与考虑的几种法则相应指数进行验证。发现Reineke密度效应法则的指数显著区别于-1.605和-2,而不是预想的欧几里德分形几何结果。Yoda自疏法则指数在-3/2左右变动,但显著区别于-4/3。West、Brown和Enquist的分形几何式中指数参数既不趋向于8/3,亦不趋向于3。     

温度、盐度和光照对球形棕囊藻生长和卤代烃释放的影响

氯甲烷(CH_3Cl)、溴甲烷(CH_3Br)和碘甲烷(CH_3I)是挥发性卤代烃的3种主要成分,对温室效应和大气臭氧破坏有重要影响。由于海藻可以在一定条件下产生卤代烃,本文选择了球形棕囊藻进行室内培养,研究了不同温度、盐度和光照对其释放CH_3Cl、CH_3Br和CH_3I含量的影响。在不同温度(15、20、25℃),盐度(19、23、29)和光照(14、25、38μmol·m^-2·s^-1)条件下,藻细胞密度和卤甲烷的释放受温度的影响较明显。温度偏低(15～20℃)更适宜藻的生长和CH_3I的存在(CH_3I最大平均浓度为5.90～6.29pmol·L^-1),但25℃释放的CH_3Cl和CH_3Br最多(最大平均浓度分别为452.48和9.49pmol·L^-1),表明CH_3Cl的最大平均浓度比CH_3Br和CH_3I高约10倍。总体上CH_3Cl和CH_3Br的释放随着温度、盐度和光照强度的增加而增大,但盐度和光照的变化对藻的生长和卤甲烷释放的影响不明显。     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。     

一种基于蓝牙室内指纹定位的贝叶斯改进算法

贝叶斯估计是重要的位置指纹定位算法,但传统的等值贝叶斯先验概率在动态定位中不适用。针对该问题,本文提出了一种基于贝叶斯指纹定位的改进算法。首先,借助陀螺仪获取的航向信息和高斯核函数模型建立概率投票算法,计算先验概率;然后,结合先验概率和信号强度计算待测点位于参考点上的后验概率;最后,选取概率最高的参考点,以概率为权重计算待测点的最或然值。以智能手机为试验对象,在规则路径试验中,改进算法的平均定位误差为1.15 m,定位误差小于2 m的概率为96.1%,不规则路径试验中,平均定位误差为0.50 m,定位误差在1 m的可信度为94.8%;并且改进算法对定位中位置跳变的现象有明显改善,具有较好的稳健性。     

确定似大地水准面的Hotine-Helmert边值解算模型

空间大地测量技术的发展使大地高的观测成为可能,从而为第二大地边值问题的研究带来了新的机遇,本文对基于Helmert第二压缩法的第二边值问题(简称为Hotine-Helmert边值问题)展开研究。首先介绍了地形直接、间接影响的定义与算法,然后推导了Hotine-Helmert边值问题的解算模型。Hotine-Helmert边值理论无须计算地形压缩对重力的次要间接影响,因而较Stokes-Helmert边值理论更简单。此外,文中引入了一种低阶修正的Hotine截断核函数,该核函数较传统的截断核函数能有效地改善似大地水准面的解算精度。为了验证本文构建的Hotine-Helmert边值解算模型的有效性和实用性,本文将EIGEN-6C4模型的前360阶作为参考模型,利用Hotine-Helmert边值解算模型构建了我国中部地区6°×4°范围、1.5′×1.5′分辨率的重力似大地水准面,其精度达到±4.8 cm。