罐采样-加压进样-气相色谱法测定环境空气中非甲烷总烃研究

建立了罐采样-气相色谱法测定环境空气中非甲烷总烃的分析方法。用罐采集环境空气样品至微负压状态，经除烃空气加压后，用气相色谱仪进行分析。并对氮气空白、除烃空气、样品保存期限、不同压力进样、不同正压压力进样、不同湿度、不同种类物质对总烃的贡献等进行了研究，在最佳实验条件下，非甲烷总烃的方法检出限为0.04μmol/mol,标准曲线相关系数均大于0.999 5,低、中、高浓度水平测定结果相对标准偏差均小于2.0%,低、中、高浓度的质控样品相对误差均小于2.0%。利用该方法对环境空气样品进行了检测分析，该法完全满足国家标准质控的要求，适用于环境空气和污染源无组织废气中非甲烷总烃的准确测定。     

以江苏省河流湖泊为例探讨基于物种与面积关系确定大型底栖动物最适及高效采样量

以江苏省河流湖泊为研究对象,选取不同水体代表性点位,运用多生境采样法和形态学鉴定获取淡水大型底栖无脊椎动物的物种数及对应的采样面积,拟合实际结果的物种数与面积关系对数函数曲线。所研究采样量包括采样面积值、样方及样带数。研究发现,不同水体点位最适采样量面积为1.45~12.75 m²,样方及样带总数为6~16个,其要求十分耗时耗力。为解决这一问题,以物种数与面积关系对数函数的一阶导数曲线y≈1值为临界点,提出"高效采样量"概念,并提出3条采样原则供讨论。同时得到江苏地区高效采样量:涉水可过河流采样面积为1.36 m²,涉水不可过河流采样面积为1.5 m²,不可涉水河流和浅水湖泊采样面积为1.75 m²,均需采集4个样方,1个样带。     

长江口-杭州湾生态环境监测点位优化布设及水质分析

河口海湾位于陆海交错带,受径潮流作用的双重影响,具有物质输运格局复杂、生态环境变化梯度大等特点。生态环境监测点位布设与优化是科学、客观评估河口海湾生态环境状况的前提。采用空间平衡抽样法开展监测点位增设和优化方案研究,将2020年5月上海市海洋环境趋势性监测布设的142个监测点位,增设至200个,并进一步优化至170个,对优化前后的点位数据进行显著性检验、方差分析和空间自相关分析。结果显示:2组数据之间无显著性差异,并且优化后点位的方差和空间自相关值更小,说明空间平衡抽样法适用于河口海湾生态环境监测点位布设和优化,点位布设和优化结果可客观反映河口海湾的生态环境状况。随后,采用泰森多边形法确定了优化所得的170个离散点位的海域代表面积,克服现有空间插值法的缺陷,为河口海湾生态环境总体状况评价提供技术参考。     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.