北京市核心区冬春季大气离子沉降量特征及来源解析

近年来生态环境部和北京市不断加严降尘量控制指标,为了掌握降尘量中离子沉降量特征及其来源,采用过滤法和离子色谱法分别测定北京市核心区冬春季降尘量及离子沉降量,采用PMF模型开展离子沉降量来源解析.结果表明：(1)离子沉降量及其在降尘量中占比平均值分别为0.87 t·(km²·30 d)^-1和14.2%,工作日降尘量和离子沉降量分别是休息日的1.3倍和0.7倍.(2)离子沉降量与降水量、相对湿度、温度和平均风速的线性方程可决系数分别为0.54、 0.16、 0.15和0.02,离子沉降量与PM_2.5浓度和降尘量的线性方程可决系数分别为0.26和0.17,控制PM_2.5浓度对控制离子沉降量非常重要.(3)离子沉降量中阴、阳离子分别占61.6%和38.4%,SO^2-₄、 NO^-₃和NH⁺₄合计占60.6%;阴阳离子电荷沉降量比值为0.70,降尘呈碱性;离子沉降量中ρ(NO     

It is also in our nature: Genetic influences on work characteristics and in explaining their relationships with well‐being

Work design research typically views employee work characteristics as being primarily determined by the work environment and has thus paid less attention to the possibility that the person may also influence employee work characteristics and in turn accounts for the work characteristics–well‐being relationships through selection. Challenging this conventional view, we investigated the role of a fundamental individual difference variable—people's genetic makeup—in affecting work characteristics (i.e., job demands, job control, social support at work, and job complexity) and in explaining why work characteristics relate to subjective and physical well‐being. Our findings based on a national US twin sample show sizable genetic influences on job demands, job control, and job complexity, but not on social support at work. Such genetic influences were partly attributed to genetic factors associated with core self‐evaluations. Both genetic and environmental influences accounted for the relationships between work characteristics and well‐being, but to varying degrees. The results underscore the importance of the person, in addition to the work environment, in influencing employee work characteristics and explaining the underlying nature of the relationships between employee work characteristics and their well‐being. Copyright © 2016 John Wiley & Sons, Ltd.     

武威城乡融合发展核心区旅游与文化资源融合相关问题研究简

武威市被确定成为甘肃省统筹城乡发展的试验区,为当地经济、文化、旅游发展带来了新的战略机遇。虽然武威城乡融合发展核心区旅游业取得了长足发展,但是存在旅游文化产业要素分布不均衡,产业水平总体不高;文化资源与旅游产业的整合、融合度不足;旅游产品创新程度总体不高等问题。在对核心区文化旅游资源进行空间整合分析的基础上,深入挖掘了大靖、土门的历史文化资源特征,探讨了如何建立大靖历史文化名镇观光体验区、明代堡寨军事文化旅游体验区、土门故城体验旅游区、山陕会馆移民商贸文化体验旅游区等重点旅游项目。     

北运河浮游细菌集合群落空间变化的环境解释

环境选择在精细地理尺度河流浮游细菌多样性构建机制中的贡献已得到普遍认同.为回答浮游细菌集合群落中核心分类操作单元（Core Operational Taxonomic Units,Core OTUs）和特有分类操作单元（Unique Operational Taxonomic Units,Unique OTUs）是否具有相同的环境选择模式这一关键科学问题,本研究以高度人工化的北运河京津冀段为研究区域,尝试结合水质监测结果和高通量测序结果分别对核心OTUs局域群落和特有OTUs局域群落的空间变异进行环境解释.结果表明：北运河京津冀段水环境污染主要表现为再生水补给导致的无机氮和钙、镁盐分离子污染及再生水补给口物理扰动导致的有机污染两大类.浮游细菌集合群落中的核心OTUs局域群落结构的相似性明显高于特有OTUs局域群落结构,且二者空间变化规律及其环境驱动模式存在较大差异.核心OTUs局域群落空间分布规律与区域尺度地理空间分布模式具有较高一致性;与其空间变化相关程度最高的环境因子是TN、硝酸盐氮（NO₃^--N）、K⁺、Ca²⁺、Na⁺、Mg²⁺.氮和盐分离子随水流方向呈现显著下降趋势,与地理扩散距离表现出较强的协同变化规律.特有OTUs局域群落不具有显著的地理空间分布模式;与其空间分布有关的环境因子是温度（T）、氧化还原电位（ORP）和pH值;再生水补给口物理扰动对水文水质的间接影响.     

Temporal variations of C2 concentration near land surface and its response to meteorological variables in Heihe River Basin, northwest China

Atmospheric CO2 concentration （CC） near land surface and meteorological variables have been measured at four sites, named Yeniugou （alpine meadow and permafrost）, Xishui （mountainous forest）, Linze （oasis edge） and Ejina （lower desert）, respectively, in Heihe River Basin, northwest China. The results showed that, the half hourly CC at night was larger than in daytime, and the daily averaged CC was the largest in winter. The averaged CC of 932 d at the Linze was about 418 ppm, was about 366 ppm in the 762 d at the Ejina. In the same period from September 23 to November 9, 2004, the averaged CC was about 625,334, 436 and 353 ppm, at Yeniugou, Xishui, Linze and Ejina, respectively. The linear relationship between daily averaged CC and air temperature T was negative, between CC and relative humidity （RH） was positive. The linear CC-atmospheric pressure （A P） relationship was negative at the Linze and Yeniugou, was positive at the Ejina. The relationship between CC and global radiation R was exponent, and soil temperature Ts was negative linear, and soil water content was complex. The correlation between CC and wind speed was not existent. Using meteorological variables together to simulate CC, could give good results.