吉林省城市大气降水pH值时空分异及成因

通过对吉林省８个代表城市１９９２－１９９７年大气降水观测数据的统计分析,对大气降水ＰＨ值的时空分异进行了探讨。结果表明：吉林省中西部城市大气降水基本呈中性;东部的图们,珲春两市大气降水酸化亚得,多年平均降水ＰＨ分别为５．１２和５．３８;全省碱性降水多集中在吉林市。     

江苏沿海地区化工事故时空变化分析与对策研究

统计汇总了2006—2009年江苏沿海地区发生的化工事故,分析事故发生的时间趋势、空间分布、事故原因、死亡人数以及类型等特点。结果表明:事故发生频次稳定,增长趋势不明显。71%的事故发生于园区内,29%的事故发生在分散企业。连云港事故主要发生于沿海化工区密集带,盐城事故主要分布于沿海化工区域,少数发生在内陆地区,南通事故发生地点较为分散。总体来看,江苏沿海地区化工事故具有数量多,影响广,造成的环境经济损失较大的特点。     

长江中游荆南三口断流时间演变特征及其影响机制

研究河道断流演变特征对认识区域水文过程及人类活动具有重要作用。论文依据1951—2014年水文气候实测数据,运用Mann-Kendall检验法,以及蒸散发-断流天数和降水-径流双累积曲线统计模型等方法分析了长江中游荆南三口断流时间演变特征及其影响机制,结果表明：1）对各时期荆南三口平均断流天数而言,1951—2014年呈逐期増加趋势,且趋势性变化显著,而在2003—2014年间,断流天数虽也增加,但趋势性变化不显著,即增加趋势逐渐减缓;2）影响荆南三口断流天数增减変化的是径流变化过程,而导致径流变化的又是以降水和蒸散发为代表的气候因素和以水利工程、各行业（农业）用水为标志的人类活动;3）影响荆南三口断流天数持续增加的主要驱动因素是人类活动,以各自时段前一时段为基准期,1959—1966、1967—1972、1973—1980、1981—2002、2003—2014年的5个变异期中,气候波动对荆南三口断流天数增加的贡献率依次为24.93%、19.05%、6.36%、10.38%、7.56%;人类活动影响的贡献率分别为75.07%、80.95%、93.64%、89.62%、92.44%。     

西宁市农牧源氨排放清单及其分布特征

以西宁市为研究区域,通过实地调研获得西宁市农牧源活动水平数据,利用排放因子法编制了西宁市2018年农牧源氨排放清单.分析了西宁市农牧源氨排放特征,利用ArcGIS进行3 km×3 km的空间网格化分配,利用蒙特卡罗模拟对畜禽养殖和氮肥施用氨排放清单进行不确定性分析.结果表明,西宁市2018年农牧源氨排放总量为4 644...     

长江源区各拉丹冬峰雪冰中微粒季节变化及其环境意义

分析了长江源区各拉丹冬峰冰川区不同海拔采集的3个雪坑SP1、SP2和SP3样品中δ18O、不溶微粒数量浓度及主要离子浓度,以探讨本研究区域雪冰微粒含量变化及其来源.结果显示,不同粒径微粒含量变化趋势一致,3个雪坑中细微粒含量分别占总微粒的88%、78%、86%.中微粒分别占10%、19%、11%;不同粒径微粒数量浓度之间的相关系数均达0.9以上(置信度可达99%),具有良好的相关性.雪冰中不溶微粒浓度同Ca2 、Mg2 、SO2-4一样,具有明显的季节波动.非季风期微粒浓度要高于季风期2～4倍.3个雪坑非季风期微粒通量分别占一个年层雪冰中微粒总通量的73.6%、92.3%、97%,即初春季节沙尘暴对于各拉丹冬雪冰中微粒沉降贡献远大于夏.秋季节.结合NCAR/NCEP再分析资料.应用HYSPLIT-4模式模拟的不同季节5 d后向气团轨迹图表明,各拉丹冬峰冰川I区雪冰中不溶性微粒可能来源于中亚、南亚和青藏高原本身,其中影响最大的可能是青藏高原自身的沙尘源区,同时冰川区裸露基岩对雪冰中不溶微粒含量亦有贡献.     

云南近百年来温度雨量的变化特征分析

用气候倾向率、Morlet小波方法,对云南10个站1901-2007年的年降水和平均温度资料分析表明:100年来云南平均增温速率为0.031℃/10a。近50年来全省平均增温速率为0.242℃/10a,略高于全国的增温,增温最大的是滇中的昆明、滇南的普洱,分别为0.442℃/10a和0.409℃/10a,并且西部明显高于东部。100年来云南年降水呈减少趋势,平均速率为7.288mm/10a,近50年来,年降水量减少平均速率为6.66mm/10a。但云南降水的区域分异非常明显,云南的东部、中部降水减少;而滇西北和滇东南地区降水增加。云南气温明显的年代际变化是32年,发生了3次突变。年降水呈明显的年代际及年际振荡,具有16～32年、4～8年周期。     

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.     

Using hydrochemical signatures to characterize the long-period evolution of groundwater information in the Dagu River Basin,China

• The long-period groundwater evolution was identified by hydrochemical signatures. • The dominant processes in the groundwater evolution were verified. • Groundwater quality in the coastal areas was susceptible to deterioration due to SI. • Groundwater contamination arose from fertilizer, livestock manure & domestic sewage. The evolution of hydrochemical compositions influenced by long-period interactions between groundwater and the geo-environment is a fundamental issue for exploring groundwater quality and vulnerability. This study systematically investigated the hydrochemical processes and anthropogenic interference occurring in the river basin by bivariate plots, Gibbs diagrams, saturation index, and the major ions ratios. Apparent changes in groundwater hydrochemistry have been observed in the study area, illustrating the origins of major ions are affected by various internal and external factors. Results highlighted that TDS varied from freshwater to brackish water, ranging between 187.90 and 2294.81 mg/L. Ca²⁺ and HCO₃⁻ are the dominant ions in the studied samples. The results gained by Gibbs diagrams, bivariate plots, saturation index, and the major ions ratios demonstrated that minerals dissolution/precipitation, cation exchange, and human inputs play crucial roles in the unconfined aquifers. Moreover, the overuse of nitrogen fertilizer, livestock manure, and industrial/domestic sewage led to nitrate and nitrite contamination and brought significant challenges to the surrounding hydrogeo-environment. The present study could make an unambiguous identification of natural processes and anthropogenic interventions influencing groundwater hydrochemistry’s long-period evolution and create a preliminary strategy for groundwater resources management.     

基于Landsat数据的关中盆地腹地AOD时空格局及城市化对其影响

气溶胶光学厚度（aerosol optical depth,AOD）是气溶胶最基本的光学特性之一,表征气溶胶对光的消减作用,对区域大气污染有一定的指示作用.首先,以2000~2019年长时间序列MOD09A1产品数据为基础,利用ASTER波谱数据库,构建了Landsat TM/OLI蓝光波段地表反射率数据集,利用深蓝算法（deep blue algorithm,DB）对关中盆地腹地AOD进行了反演估算.其次,通过测算2000~2019多年平均/各年平均AOD和季节AOD值,研究分析了AOD空间分布特征和时间变化趋势;利用经验正交函数分析方法（empirical orthogonal function,EOF）剖析了AOD主要的空间分布模式.最后,探索分析了城市化对AOD时空分布特征的影响.结果表明：①研究区多年平均AOD空间分布受地形和人类活动影响显著,低海拔且人类活动强度大的盆地内部AOD值和变化幅度均高于周围山系.AOD呈现出明显的季节差异性：春季（0.34） > 夏季（0.33） > 秋季（0.23） > 冬季（0.12）,夏季AOD区域差异性最大.②年均AOD呈先增大后减小再增大的变化趋势,2005年达到最大值;春夏两季AOD数值分布离散,而冬季则表现为低值集聚的状态.③EOF分析结果表明,研究区AOD存在3种主要的空间分布模态：第一,AOD空间分布模式表现为区域一致型,特征向量空间分布与海拔梯度一致,反映了不同海拔下AOD变化程度的差异;第二,以秦岭山脉为界,大致呈现南北反相的分布特征,反映出关中盆地地域发展的独特性和与南部秦岭山区的差异性;第三,"东南-西北"的分布格局,表明了AOD在"城镇-乡村"呈反相变化趋势;④研究区AOD值与常住人口密度（R²=0.707,P<0.05）、不透水面密度（R²=0.377,P<0.05）以及工业POI密度（R²=0.727,P<0.5）呈正相关关系,说明城市化对AOD时空分布有一定影响.本研究对加强关中盆地空气质量监测和城市生态环境建设具有重要的意义.     

城市纳污河流有色溶解有机物时空演变特征

利用平行因子分析和主成分分析方法,结合紫外吸收光谱与三维荧光光谱技术,研究了典型城市纳污河流-河北洨河水体有色溶解有机物(CDOM)的来源及随季节和空间变化特征,探讨了水体CDOM与水质指标间关系.结果表明:洨河水体CDOM主要为新近微生物源产生,受人类活动影响较大.CDOM浓度在时间变化上表现为春夏(2、5月)低,而秋冬(8、11月)高,从上游至下游呈现先升高再降低变化特征.荧光鉴别出的4种组分:组分1(类富里酸),组分2(类腐殖酸)及组分3(类胡敏酸)为类腐殖质,组分4为类蛋白物质.不同季节各组分来源及分布存在差异,除夏季外其他季节水体类蛋白与类腐殖质来源相似,尤其与类胡敏酸组分;类蛋白组分在各季节分布变化显著,冬春含量相对较高.洨河水体荧光物质与氨氮、亚硝氮具有共同来源,其中类腐殖质对COD贡献较为明显.采用光谱分析法并结合平行因子、主成分分析及聚类分析方法可识别污染源空间分布,揭示河体CDOM随季节变化规律.