丰水期洪湖水质空间变异特征及驱动力分析

通过对丰水期不同程度富营养化的洪湖全湖139个监测点位的高密度采样分析,采用GIS空间插值技术得到洪湖水体中各污染因子、水生植物生物量和水质类别的空间分布特征图.结果表明,丰水期TN、TP、NH+4-N、高锰酸盐指数的质量浓度总体上呈现出自南向北逐步增高的变化趋势:北部河流入湖区围网养殖区开阔水体保护区南部长江入湖区;水质参数的贡献率依次为TNTP高锰酸盐指数NH+4-NDO.受工农业废水、生活污水、饵料投放、水生植物、水体交换等因素的影响,TN在丰水期超过目标水质59%,TP超标35.2%,高锰酸盐指数超标13.7%,NH+4-N超标4.3%,综合水质超标66.2%.在季风等环境因子的影响下水体中的DO达到水质目标.通过水质空间变异性分析,能直观反映人类活动、土地利用类型和环境因子之间相互作用对洪湖水环境产生的巨大影响.为了使洪湖水环境更有利于人们的生产和生活,应大力控制北部区域工农业废水和生活污水的排放,控制养殖面积,逐步恢复洪湖水体自净能力,实现洪湖水环境生态的可持续发展.     

Removal of heavy metals from aqueous solution by sawdust adsorption

The adsorption of lead, cadmium and nicel from aqueous solution by sawdust of walnut was investigated. The effect of contact time, initial metal ion concentration and temperature on metal ions removal has been studied. The equilibrium time was found to be of the order of 60 min. Kinetics fit pseudo first-order, second-order and intraparticle diffusion models, hence adsorption rate constants were calculated. The adsorption data of metal ions at temperatures of 25, 45 and 60~C have been described by the Freundlich and Langmuir isotherm models. The thermodynamic parameters such as energy, entropy and enthalpy changes for the adsorption of heavy metal ions have also been computed and discussed. Ion exchange is probably one of the major adsorption mechanisms for binding divalent metal ions to the walnut sawdust. The selectivity order of the adsorbent is Pb（I1）~Cd（II）〉Ni（I1）. From these results, it can be concluded that the sawdust of walnut could be a good adsorbent for the metal ions from aqueous solutions.     

准好氧矿化垃圾生物反应床的最佳工况研究

准好氧矿化垃圾生物反应床是一种新型、高效的渗滤液处理技术,为了确定准好氧矿化垃圾生物反应床处理渗滤液的最佳运行工况,设计了按L(934)运行的正交实验。研究表明,温度(A)、回灌频率(B)、水力负荷(C)、进水COD浓度(D)4个因素对COD、氨氮和总氮去除率3个指标项的影响程度各有不同,其中对COD的影响由大到小依次为C→D→A→B,氨氮为D→B→A→C,总氮为B→D→C→A。运用"综合平衡法"分析得其最佳运行工况为:温度30℃、回灌频率3 d/次、回灌量2 L/次、进水COD浓度50 000 mg/L,其水力负荷为30 L(/m3.次)、有机负荷约550 g(/m3.d)。结果表明,该技术不但操作运行简单,而且能有效去除渗滤液中的污染物,尤其是对TN的去除优势十分明显,是一种具有良好应用前景的渗滤液处理技术。     

北京市水生态监测评价方法构建及应用

根据北京市多年水生态监测的经验,确立了典型地表水体水生态监测的水文地貌、理化指标和生物要素。其中,生物要素评价因子主要包括浮游植物、浮游动物和底栖动物。并依据2014年监测结果进行了分析评价,将水生态状态等级分为5级。评价结果表明：北京市典型地表水体水生态状态综合评价结果中处于"中等"级别的监测断面数量为7个,占监测总数的41.2%,生物要素对最终评价结果影响略大。     

珠江口四大口门及近滩水域浮游藻类和关键水质因子的监测与分析

对珠江口四大口门虎门、蕉门、洪奇沥、横门及近滩为主的水域进行浮游藻类发生与关键水质因子 (溶解氧(DO)、活性磷 (PO3- 4-P)、硝酸盐 (NO- 3-N) )的监测与总结 ,得出二者间具有较高相关性。为珠江口水域水质、浮游藻类生态及赤潮研究做些基础工作与贡献。     

基于高分辨率MARGA分析成都市PM2.5中水溶性离子污染特征

利用高分辨率MARGA对2019年成都市PM_2.5中水溶性离子展开连续监测,结合气象参数分析水溶性离子污染特征.结果表明,MARGA监测的8种水溶性离子质量浓度与PM_2.5变化趋势一致,水溶性离子年均浓度为(23.1±13.6)μg·m^-3,在PM_2.5中占比为48.6%,表明水溶性离子为PM_2.5重要组分.各离子质量浓度大小顺序依次为:NO^-₃、 SO₄^2-、 NH⁺₄、 Cl^-、 Ca²⁺、 K⁺、 Mg²⁺和Na⁺,其中二次离子(NO^-₃、 SO₄^2-和NH⁺₄)年均质量浓度为(20.2±2.7)μg·m...     

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.