201O年前郑州城市道路交通噪声污染预测和治理对策

根据郑州市１９９４～１９９８年城市道路交通噪声的监测数据以及影响城市道路交通噪声相关因素的数据，运用灰色系统理论，建立ＧＭ（１，Ｎ）预测模型对郑州市２０１０年前城市道路交通噪声进行预测；并利用灰色关联分析的方法进行分析，科学的得出产生城市道路交通噪声的主要因素，根据这一分析结果，提出了１９９９年到２０１０年期间治理对策。     

Enzymatic preparation of hydrophobic biomass with one-pot synthesis and the oil removal performance

Oil pollution is causing deleterious damage to aquatic ecosystems and human health. The utilization of agricultural waste such as corn stalk (CS) to produce biosorbents has been considered an ecofriendly and efficient approach for removing oil. However, most previous studies focused on the modification of the whole CS, which is inefficient due to the heterogeneity of CS. In this study, corn stalk pith (CP), which has excellent amphipathic characteristics, was selected to prepare a high-efficienc...     

磁湖水体BOD_5与COD_(cr)相关性分析

根据湖北省磁湖的实测水质数据资料,应用数理统计的方法分析了该水体BOD5和CODcr间的相关关系,同时也指出了一些BOD5和CODcr的相关性在实际应用中应该注意的问题。     

耦合遗传算法与RBF神经网络的PM2.5浓度预测模型

基于北京市空气质量监测点获取的空气污染物浓度数据,通过遗传算法搜索径向基人工神经网络的最优隐含层神经元数目和扩展常数,构建了耦合径向基人工神经网络算法与遗传算法的预测模型,预测北京市未来一天24h平均PM_2.5质量浓度.结果表明,预测精度与泛化性能良好.该模型不需要输入气象和地理位置信息等数据,具有依赖变量少、预测精度高（R²达0.75）和运算效率高等特征,并可以通过训练样本的驱动,使自身不断优化调整.该模型预测效果可以通过扩展输入特征、增加训练样本量等方法进一步提升,可对多种时空情境下的城市空气污染进行高效率且精确的预测.     

北京市餐饮业大气污染物排放特征

餐饮业是中国大型城市大气环境污染源之一.为了解餐饮业大气污染物的产生能力,本研究以北京为研究对象,选取41家不同菜系的餐饮企业,现场实地检测了净化设备前端的油烟、颗粒物和非甲烷总烃(NMHC)的产生浓度水平.结果表明,净化前油烟、颗粒物和NMHC的初始平均浓度约为1.93、 6.6和10.9 mg·m~(-3).提出了一种基于工作日与非工作日的估算污染物排放总量的计算方法.并基于北京市餐饮企业数量和本研究测得的排放因子,初步估算了2019年全市餐饮源主要污染物的初始产生总量,油烟、颗粒物和NMHC的年排放总量分别为5 512、 18 849和6 169 t.川湘菜、烧烤、烤鸭与家常菜产生的油烟与颗粒物浓度的Pearson系数均0.6,具有强相关性;其中川湘菜和烤鸭排放的Pearson系数均0.8,呈现极强相关性.     

Contributions of root cell wall polysaccharides to Cu sequestration in castor (Ricinus communis L.) exposed to different Cu stresses

Cell wall polysaccharides play a vital role in binding with toxic metals such as copper(Cu)ions.However, it is still unclear whether the major binding site of Cu in the cell wall varies with different degrees of Cu stresses.Moreover, the contribution of each cell wall polysaccharide fraction to Cu sequestration with different degrees of Cu stresses also remains to be verified.The distribution of Cu in cell wall polysaccharide fractions of castor(Ricinus communis L.) root was investigated with various Cu concentrations in the hydroponic experiment.The results showed that the hemicellulose1(HC1) fraction fixed 44.9%–67.8% of the total cell wall Cu under Cu stress.In addition, the pectin fraction and hemicelluloses2(HC2) fraction also contributed to the Cu binding in root cell wall,accounting for 11.0%–25.9% and 14.1%–26.6% of the total cell wall Cu under Cu treatments, respectively.When the Cu levels were ≤ 25 μmol/L, pectin and HC2 contributed equally to Cu storage in root cell wall.However, when the Cu level was higher than 25 μmol/L, the ability of the pectin to bind Cu was easy to reach saturation.Much more Cu ions were bound on HC1 and HC2 fractions, and the HC2 played a much more important role in Cu binding than pectin.Combining fourier transform infrared(FT-IR) and twodimensional correlation analysis(2 D-COS) techniques, the hemicellulose components were showed not only to accumulate most of Cu in cell wall, but also respond fastest to Cu stress.     

湘江子流域重点污染企业影响区土壤重金属镉污染源识别

土壤重金属Cd（镉）是威胁人类健康最严重的重金属污染元素之一.土壤Cd污染来源复杂,明确土壤重金属分布特征,探索重点污染企业影响区土壤重金属源解析方法,对土壤Cd污染防治具有重要意义.2018年5-9月,以湘江流域头山、大河滩两个子流域为研究对象,采用系统布点法,借助2 km×2 km的网格在研究区内均匀布点共计2 638个,分析重点污染企业对土壤Cd污染的影响及w（Cd）的空间分布特征,并借助双变量局部莫兰指数方法（bivariate local Moran's I）、广义加性模型（GAM）量化土壤中w（Cd）与企业分布的空间相关性及不同行业源的贡献率.结果表明:①研究区土壤中w（Cd）为0.032~14.01 mg/kg,平均值（0.63 mg/kg）较高,高值区主要分布在研究区西部,低值区位于研究区东部.②研究区重点污染企业总体聚集程度不高（Moran's I=0.45）,而分行业企业呈显著的高聚集分布（0.88 ≤ Moran's I ≤ 0.97）特征,尤其是冶炼与压延加工业的集聚性最为明显.③土壤中w（Cd）与不同行业的空间相关聚类分析发现,存在较大区域的H-H[土壤w（Cd）高值与企业高密度]聚类分布,说明土壤中w（Cd）受企业分布的影响较大,在企业集聚区存在较多的土壤w（Cd）高值区.④在企业影响区内,土壤w（Cd）分布受行业影响排序依次为采选业>化学品制造业>冶炼和压延加工业>其他行业,其方差解释率分别为51.2%、48.0%、46.2%、19.3%.研究显示,研究区土壤中w（Cd）分布受企业的影响明显,在污染源识别中采选业对土壤Cd污染影响最突出,研究结果可为湘江流域土壤重金属污染防治和企业污染源的管理提供依据.      

基于空间插值的西安市重空气污染期间主要污染物时空变化特征及相关性分析

西安市是我国承东启西、连接南北的战略性枢纽城市,但其长期受到重空气污染的影响.基于2018年11月24日-12月3日西安市及其周边7个地级市共38个环境质量监测站点的逐时数据,利用空间插值、趋势分析和相关性分析方法,研究了西安市一次重空气污染期间六大污染物（PM_2.5、PM₁₀、CO、NO₂、SO₂和O₃）的质量浓度时空变化及彼此间的相关关系.结果表明:①IDW（inverse distance weighting,反距加权插值法）和OKri（ordinary Kriging,普通克里格插值法）均能较好地获得西安市空气污染物的时空变化情况,但IDW的插值精度优于OKri,距离指数为7的IDW可以满足西安市空气污染物时空变化模拟的要求.②研究期间,西安市首要污染物为PM_2.5和PM₁₀,二者分别是中度-重度污染及严重-"爆表"污染天气的首要贡献因子.③ρ（PM_2.5）、ρ（PM₁₀）、ρ（CO）、ρ（NO₂）和ρ（SO₂）均呈中部高、两边低,北部高、南部低的空间分布特点,而ρ（O₃）则相反;PM_2.5、PM₁₀、O₃污染程度日趋严重,NO₂污染程度逐渐缓解.④ρ（PM_2.5）、ρ（NO₂）、ρ（CO）之间呈中等正相关,三者在时空变化上具有较高的一致性;ρ（SO₂）与ρ（PM_2.5）、ρ（NO₂）、ρ（CO）均呈弱正相关;ρ（O₃）与ρ（NO₂）、ρ（CO）均呈弱负相关.受扬尘天气和特殊风向及地形共同影响,西安市PM₁₀出现"爆表"现象,导致ρ（PM₁₀）与其他污染物质量浓度之间的相关性不明显.研究显示,距离指数为7的IDW适合西安市空气污染情况时空变化的模拟,重污染天气条件下,西安市ρ（PM_2.5）、ρ（NO₂）、ρ（CO）之间具有较高的同源性,但各污染物间时空变化和相关性关系较复杂.      

室内微生物污染水平预测关键技术研究综述

室内微生物气溶胶来源多样,并受很多环境因素影响,可以利用统计学方法对室内微生物污染水平进行实时预测.本文首先介绍了预测模型的建立方法,并通过大量的文献调研,按照室内微生物污染水平预测模型建立的思路,介绍了现有研究得出的室内微生物气溶胶浓度与环境参数（换气次数、温湿度、人数、颗粒物、气流组织、CO₂）的关系,总结了室内微生物污染水平预测模型,并给出了未来发展方向.     

Two-dimensional correlation spectroscopic analysis on the interaction between humic acids and aluminum coagulant

In this study, two-dimensional correlation spectroscopy integrated with synchronous fluorescence and infrared absorption spectroscopy was employed to investigate the interaction between humic acids and aluminum coagulant at slightly acidic and neutral p H. Higher fluorescence quenching was produced for fulvic-like and humic-like fractions at p H 5. At p H 5, the humic-like fractions originating from the carboxylic acid, carboxyl and polysaccharide compounds were bound to aluminum first, followed by the fulvic-like fractions originating from the carboxyl and polysaccharide compounds. This finding also demonstrated that the activated functional groups of HA were involved in forming the Al-HA complex, which was accompanied by the removal of other groups by co-precipitation.Meanwhile, at p H 7, almost no fluorescence quenching occurred, and surface complexation was observed to occur, in which the activated functional groups were absorbed on the amorphous Al(OH)3. Two-dimensional FT-IR correlation spectroscopy indicated the sequence of HA structural change during coagulation with aluminum, with IR bands affected in the order of COOH COO-NH deformation of amide Ⅱ aliphatic hydroxyl C\OH at p H 5, and COO-aliphatic hydroxyl C\OH at p H 7. This study provides a promising pathway for analysis and insight into the priority of functional groups in the interaction between organic matters and metal coagulants.