Spatial variability of organochlorine pesticides （DDTs and HCHs） in surface soils from the alluvial region of Beijing, China

The spatial variability in the concentrations of 1,2,3,4,5,6-hexachlorocyclohexane (HCH) and 1,1,1-trichloro-2,2-bis-(p-chlorophenyl) ethane (DDT) in surface soils was studied on the basis of the analysis of 131 soil samples collected from the surface layer (0-20 cm depth) of the alluvial region of Beijing, China. The concentrations of total HCHs (including α-, β-, γ-, and δ-isomers) and total DDTs (i ncluding p,p'-DDT, p,p'-DDD, p,p'-DDE, and o,p'-DDT) in the surface soils tested were in the range from nondetectable to 31.72 μg/kg dry soil, with a mean value of 0.91, and from nondetectable to 5910.83 μg/kg dry soil, with a mean value of 32.13,respectively. It was observed that concentrations of HCHs in all soil samples and concentrations of DDTs in 112 soil samples were much lower than the first grade (50 μg/kg) permitted in "Environment quality standard for soils in China (GB15618-1995)". This suggests that the pollution due to organochlorine pesticides was generally not significant in the farmland soils in the Beijing alluvial region. In this study, the spatial distribution and trend of HCHs and DDTs were analyzed using Geostatistical Analyst and GS (513).Spatial distribution indicated how these pesticides had been applied in the past. Trend analysis showed that the concentrations of HCHs,DDTs, and their related metabolites followed an obvious distribution trend in the surface soils from the alluvial region of Beijing.     

喀斯特地区小尺度农业土壤砷的空间分布及污染评价

贵州岩溶地貌分布广泛,部分地区的土壤砷(As)污染问题比较突出.为了解高As背景地区农业土壤As含量分布特征,选取贵州省兴义市西南部具有代表性的As污染区(典型喀斯特区、亚喀斯特区)为研究对象,并以非喀斯特区作为对照组,采用地统计分析与GIS相结合的方法研究农业土壤As的空间变异和污染状况,并采用Moran's I系数分析在小尺度下土壤As的空间自相关性及其方向性特征.结果表明,不同地貌区土壤中As含量从高到低依次为:典型喀斯特区亚喀斯特区非喀斯特区,其中典型喀斯特区农业土壤As的算术均值与几何均值分别为47. 9 mg·kg~(-1)和43. 3 mg·kg~(-1),亚喀斯特区其值分别为36. 8 mg·kg~(-1)和30. 1 mg·kg~(-1),两个区域农业土壤As含量明显高于贵州土壤As背景值,As的超标率分别为98. 5%和96. 7%,表现出明显的As累积,而非喀斯特区As超标率仅为6. 7%;独立样本T检验结果表明,在3个不同地貌类型中,农业土壤与自然土壤As含量均无显著性差异(P 0. 05).农业土壤As的Moran's I系数为0. 45,Z值为11. 61,在本采样尺度下具有显著的正空间自相关(P 0. 05),存在空间聚集,尤其是东北-西南方向空间自相关性较好,以结构性变异为主.农业土壤样本As总体上处于轻微污染至轻度污染之间,分别占27. 10%、29. 02%,部分地区处于中度污染级别,而处于无污染状态的样本占41. 94%.     

天津市PM2.5碳组分空间差异性及来源解析

2021年在天津市不同功能区共设置4个点位同步采集细颗粒物（PM_2.5）样品,测定了其中8种碳质亚组分的含量.结果表明,采样期间各点位ρ[有机碳（OC）]为3.7~4.4 μg·m^-3,ρ[元素碳（EC）]为1.6~1.7 μg·m^-3,OC浓度在中心城区最高,EC浓度差别较小.采用最小比值法对二次有机碳（SOC）进行估算,结果表明环城区二次污染较为突出,SOC占OC的比例达48.8%.各功能区碳质亚组分间的相关性强弱呈现出外围区>中心城区>环城区的特征,均表现出EC₁与OC₂和EC₁与OC₄相关性最强.正定矩阵因子（PMF）来源解析结果显示,道路扬尘源（9.7%~23.5%）、燃煤源（10.2%~13.3%）、柴油车尾气（12.6%~20.2%）和汽油车尾气（18.9%~38.8%）是天津市PM_2.5中碳组分的主要来源.不同功能区碳组分污染源存在差异,中心城区和外围区主要受汽油车尾气影响;环城区受二次污染和柴油车尾气的影响更为突出.     

河流CO2与CH4排放研究进展

河流作为连接海-陆两大碳库的主要通道,其水-气界面二氧化碳(CO_2)与甲烷(CH_4)排放构成全球碳循环的重要环节,对全球气候变暖的贡献不容小觑.明确河流水体CO_2与CH_4产排过程、时空特征以及控制因素是认识河流生态学功能以及其对变化环境响应的重要内容.基于当前河流CO_2与CH_4排放研究进展,构建河流碳排放动力学概念框架(内源代谢、陆源输入),并从全球尺度、区域尺度、流域尺度综述了河流碳排放时空变异性特征以及存在的研究不足.在理解碳排放动力学概念框架和时空变异特征的基础上,构建了河流CO_2与CH_4动力学控制因子分层框架(内部因子:有机质、温度、营养盐;外部因子:水文、地貌、人类活动),深入探讨了河流碳排放的关键影响因素.最后,根据当前研究中存在的不足,提出河流碳排放应将纳入区域陆地碳平衡过程,今后研究重点应包括流域尺度上河流CO_2与CH_4内源产生与陆源输入相对贡献的量化研究、不同界面CO_2与CH_4产生与排放过程研究、高时空分辨率的监测数据的补充以及变化环境与人类活动干扰下河流碳排放的响应过程等,为理解河流生态学过程及生态系统功能提供基础,同时为我国进一步深入开展相关研究提供借鉴.     

川中丘陵县域土壤pH空间变异及影响因素分析——以四川仁寿县为例

利用2012年在仁寿县采集的555个表层土样(0~20cm),应用地统计学方法分析了该县域尺度表层土壤pH值的空间变异特征;并采用方差分析和回归分析量化主要影响因素对土壤pH值空间变异的影响程度。结果表明:研究区土壤pH值在4.02~8.14之间,平均为6.80,总体上以中性和碱性土壤为主;变异系数为14.48%,属中等程度的空间变异性。地统计分析表明,研究区土壤pH值变异函数的最佳理论模型为球状模型,具有中等程度的空间自相关性,且空间自相关范围较大。方差分析和回归分析表明,土壤类型、成土母质和土地利用方式是显著影响土壤pH值的主要因素。其中,成土母质和土地利用方式分别能独立解释76.2%和4.8%的土壤pH值空间变异。土壤类型的解释能力与分类级别有关,土类、亚类和土属可分别独立解释41.3%、57.3%和83.7%的土壤pH值空间变异,因而能反映成土过程和母质特性的土属是研究区土壤pH值空间变异的主控因素。研究结果可为川中丘陵县域尺度土壤pH空间变异分析及区域生态环境管理与建设提供有益参考。     

“城郊乡”梯度下土壤Cu、Zn、Pb含量的空间变异特征

为揭示城市化、工业化等人为活动对土壤环境质量的影响,选择能反映上海城郊乡梯度差异的中心城区、城郊结合部和远郊,采用地统计学方法对表层土壤样品Cu、Zn、Pb的空间变异结构和分布特征进行了对比分析。结果表明:城市土壤Pb、Cu、Zn的变异系数范围为0.24~0.62,均属中等变异强度。徐汇区土壤Cu、Pb、Zn符合正态分布,闵行区土壤Cu、Pb和Zn符合对数正态分布,奉贤区土壤Zn呈正态分布,土壤Cu、Pb符合对数正态分布。由中心城区到远郊,城市土壤Cu、Pb、Zn的各项统计特征值和变异系数均有较大差异,存在明显的空间分布差异。半方差函数分析结果表明,徐汇区作为中心城区,土壤Cu、Pb、Zn符合球状模型,土壤Cu、Zn具有强烈的空间相关性,土壤Pb具有明显的空间自相关。奉贤区以农业用地为主,土壤Pb符合线性模型,土壤Cu符合高斯模型,土壤Zn符合指数模型,具有强烈的空间相关性。闵行区地处城郊结合部,土壤Cu、Pb、Zn的半方差拟合模型均为线性模型,表现为纯块金形式,以随机变异为主,空间相关性弱。采用Kriging最优内插法进行无偏估值,绘制了表层土壤重金属含量的空间分布图,中心城区、城郊结合部、郊区土壤重金属的空间分布受城市化、工业化、城市交通等因素的影响,均表现出不同的空间分布规律。     

Scale,context, and decision making in agricultural adaptation to climate variability and change

This work presents a framework for viewing agricultural adaptation, emphasizing the multiple spatial and temporal scales on which individuals and institutions process information on changes in their environment. The framework is offered as a means to gain perspective on the role of climate variability and change in agricultural adaptation, and developed for a case study of Australian agriculture. To study adaptation issues at the scale of individual farms we developed a simple modelling framework. The model highlights the decision making element of adaptation in light of uncertainty, and underscores the importance of decision information related to climate variability. Model results show that the assumption of perfect information for farmers systematically overpredicts adaptive performance. The results also suggest that farmers who make tactical planting decisions on the basis of historical climate information are outperformed by those who use even moderately successful seasonal forecast information. Analysis at continental scales highlights the prominent role of the decline in economic operating conditions on Australian agriculture. Examples from segments of the agricultural industry in Australia are given to illustrate the importance of appropriate scale attribution in adapting to environmental changes. In particular, adaptations oriented toward short time scale changes in the farming environment (droughts, market fluctuations) can be limited in their efficacy by constraints imposed by broad changes in the soil/water base and economic environment occuring over longer time scales. The case study also makes the point that adaptation must be defined in reference to some goal, which is ultimately a social and political exercise. Overall, this study highlights the importance of allowing more complexity (limited information, risk aversion, cross-scale interactions, mis-attribution of cause and effect, background context, identification of goals) in representing adaptation processes in climate change studies.     

基于观测数据空间化的北京市PM_(2.5)时空分异

PM2.5在地球表面上的变化是连续的,但由于地面海拔起伏和站点分布的相对稀疏,直接内插法很难顾及这种情况。文章运用反距离权重(IDW)、Kriging、经验贝叶斯克里金法(EBK)、多元回归+残差内插的综合空间化手段,对北京市2015年1月的PM2.5数据进行分析。结果表明:利用多元回归+残差内插方法空间化更优,且PM2.5存在显著的正局部空间自相关,中度以上污染主要分布在北京市东南部和房山区。时间上,PM2.5质量浓度逐时均值呈"W"型。3 h尺度上,PM2.5质量浓度与相对湿度和露点温度正相关,与能见度、大气压和风速负相关,与温度不相关。     

Climatic and genetic controls of yields of switchgrass, a model bioenergy species

The U.S. Renewable Fuel Standard calls for 136 billion liters of renewable fuels production by 2022. Switchgrass (Panicum virgatum L.) has emerged as a leading candidate to be developed as a bioenergy feedstock. To reach biofuel production goals in a sustainable manner, more information is needed to characterize potential production rates of switchgrass. We used switchgrass yield data and general additive models (GAMs) to model lowland and upland switchgrass yield as nonlinear functions of climate and environmental variables. We used the GAMs and a 39-year climate dataset to assess the spatio-temporal variability in switchgrass yield due to climate variables alone. Variables associated with fertilizer application, genetics, precipitation, and management practices were the most important for explaining variability in switchgrass yield. The relationship of switchgrass yield with climate variables was different for upland than lowland cultivars. The spatio-temporal analysis showed that considerable variability in switchgrass yields can occur due to climate variables alone. The highest switchgrass yields with the lowest variability occurred primarily in the Corn Belt region, suggesting that prime cropland regions are the best suited for a constant and high switchgrass biomass yield. Given that much lignocellulosic feedstock production will likely occur in regions with less suitable climates for agriculture, interannual variability in yields should be expected and incorporated into operational planning.