秸秆炭化还田对滴灌棉田土壤微生物代谢功能及细菌群落组成的影响

通过5 a田间定位试验,研究持续秸秆直接还田和炭化还田对滴灌棉田土壤微生物功能多样性以及细菌群落组成的影响.试验设置3个处理:单施化肥(对照,CK)、秸秆直接还田+化肥(ST)和秸秆炭化还田+化肥(BC).秸秆直接还田和炭化还田均显著提高土壤有机质、全氮及速效养分含量,其中秸秆炭化还田的作用更显著.秸秆直接还田处理土壤微生物碳源代谢活性最高,其次是秸秆炭化还田,均显著高于对照.秸秆直接还田主要促进了碳水化合物类和胺类碳源的代谢;秸秆炭化还田显著提高多聚物类碳源的代谢.秸秆直接还田和炭化还田显著提高土壤细菌群落多样性.与对照相比,秸秆直接还田显著增加变形菌门、放线菌门、拟杆菌门以及黄单胞菌科、酸微菌科、微杆菌科和噬纤维菌科的相对丰度;秸秆炭化还田处理酸杆菌门、芽单胞菌门、硝化螺旋菌门以及Blastocatellaceae (Subgroup₄)菌科、芽单胞菌科、亚硝化单胞菌科的相对丰度显著增加.相关性分析表明黄单胞菌科、酸微菌科与碳水化合物类、氨基酸类、羧酸类、胺类碳源代谢显著正相关,噬纤维菌科、微杆菌科与碳水化合物类、胺类碳源代谢显著正相关,Blastocatel...     

新型环境监测仪器户外小型水质自动监测站性能研究

随着中国地表水水质自动监测站建设工作的大力推进,近几年一种体积小、集成度高、功能完备并且直接应用于户外的小型水质自动监测站设备成为研发和应用热点。为了探究目前市场上户外小型水质自动监测站产品的仪器性能和技术特征,笔者选取6种型号的户外小型水质自动监测站进行功能检查和性能测试。结果表明:户外小型水质自动监测站具备采配水、预处理、水质多参数分析、数据传输等功能;不同参数标准溶液的测定结果具有较高的准确度和精密度,不进行任何维护的情况下整个系统能够在户外连续稳定运行一个星期以上。     

汉江流域金水河与淇河潜在硝化速率及硝化功能基因的影响因子

河流是地表连接陆地和海洋生态系统生源要素的关键通道,河流生态系统中包括硝化作用在内的氮转化过程对全球氮循环具有重要影响.本研究选择位于秦岭南坡流域内人为干扰强度低的金水河及人为干扰强度高的淇河进行比较研究,探讨人为干扰强度对河流潜在硝化速率及硝化功能基因丰度的影响.结果表明,同时期的淇河潜在硝化速率显著高于金水河(P<0.05).金水河和淇河的AOA-amo A基因丰度都显著高于AOB-amo A(P <0.05),但枯水期淇河的AOB-amo A基因丰度显著高于金水河,金水河与淇河AOA-amo A基因丰度无显著差异.潜在硝化速率与水温、p H、水体NO₃^-和沉积物NO₃^-浓度呈显著正相关(P <0.05).硝化功能基因丰度与水温、p H、水体及沉积物NO₃^-、NH₄⁺及有机碳浓度相关性显著(P <0.05).潜在硝化速率与AOA-amo A基因丰度无显著相关性,但与AOB-amo A基...     

基于GWR的陕西省生态系统服务价值时空演变特征及影响因素分析

基于生态系统服务价值(ESV)评估体系、地理探测器与GWR模型,分析了2000—2015年的陕西省107个县级行政单位ESV时空演变特征、影响因素及主导因子作用强度的空间分异特征。结论如下:(1)15年间陕西省ESV先升后降,其中供给服务与调节服务是陕西ESV的核心功能。(2)历年ESV高低值分布都表现出中心低,边缘高,北低南高的空间特征,ESV差值变化特征较为明显,全境基本被负值覆盖,高值区域集中于陕北。(3)经济因素对ESV的解释作用最强,社会因素与自然因素次之。(4)各主导因子对ESV的作用的空间强度不同,其中土地垦殖率呈现出由南向北逐渐递减的特征,人均GDP从南到北逐渐增强,人口密度由东北向西南逐渐增强,城乡居民人均收入对ESV则表现出由北向南从负值向正值过度且作用逐渐递增的特征。     

路堑节理边坡开挖稳定性及影响因素分析

以某公路节理岩质边坡为工程背景,首先利用FLAC3D建立该边坡的数值分析模型,计算得到5台阶开挖的安全系数分别为2.62、2.39、1.61、1.06、0.67,并确定其破坏模式为双滑块平面剪切破坏;然后针对边坡的破坏模式建立理论计算模型,分别改变节理的倾角和力学参数(c,),分析它们对稳定性的影响。结果表明:变化下滑块节理面的倾角时,安全系数先变小,而后逐渐增大;变化上滑块节理面的倾角时,安全系数逐渐增大;当c1、c2取值较小时,两者的变化对于安全系数的影响基本相同,但随着取值的增大,c2的影响效果逐渐大于c1。     

城市群碳排放空间关联网络结构及其影响因素

基于社会网络分析(SNA)及二次分配(QAP)方法,利用珠江三角洲城市群2001~2019年地级市数据,探究珠江三角洲城市群碳排放空间关联性及其影响因素.结果表明,珠江三角洲城市群碳排放空间相关性呈现出复杂的网络结构形态,空间关联的紧密程度呈现周期性变化,表现出“依政策波动”特征.碳排放空间关联网络呈现显著的“核心-边缘”分布模式,广州和深圳等经济发达城市处于网络核心,发挥“中介”和“桥梁”作用,惠州,江门等发展较为落后的城市处于网络边缘,对网络的控制和影响能力较为微弱.碳排放空间关联网络划分为“净受益”,“净溢出”,“双向溢出”和“经纪人”4个板块,各板块之间的联动效应显著.经济发展水平,能源利用效率,技术水平和环保力度差异的扩大促进了碳排放空间关联关系的形成.研究结果将有助于决策者为珠江三角洲城市群各城市界定减排责任和减排目标,制定更公平,更有针对性的城市群协同减排方案提供借鉴.     

Analysis on the economic effect of Sino-US trade friction from the perspective of added value

Environment, Development and Sustainability - Based on the theory of trade added value, this paper discusses the potential actual trade scale and benefit damage degree of the two countries under...     

Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities

Environment, Development and Sustainability - According to the World Urbanization Prospects of United Nations, the global urban population has increased rapidly over past few decades, reaching...     

Research progress of the POP fugacity model: a bibliometrics-based analysis

Environmental Science and Pollution Research - With the emergence of environmental issues regarding persistent organic pollutants (POPs), fugacity models have been widely used in the concentration...     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.