Molecular modeling in environmental science∗

Quantum mechanic computer models are often used in chemistry to predict properties of molecules and to simulate reaction pathways. Such models calculate bond lengths, bond angles, dihedral angles, the energy of the molecular‐orbitals (MO's), the dipole moment, the ionisation potential, and the heat of formation. These results lead to detailed information on electronic structures like bond orders, electronic charges and levels of frontier orbitals (HOMO, LUMO).

It is possible to calculate properties of ground state molecules as well as ionic and radicalic structures, or reaction intermediates and other compounds with very short lifetimes.

From the exact knowledge of electronic structure, frontier orbitals, and reaction intermediates it is possible to predict stability of chemicals in the environment but also of fictive chemicals, which are not yet synthesized. Some authors have shown that there are correlations between electronic structure and toxicity.

The most interesting models for environmental chemistry are semiempirical models, such as MINDO, MNDO or MOPAC. Theses programs are able to handle molecules with 30 and more heavy atoms (all elements without hydrogen), and it is possible to install them on main frame computers (CPU‐time several minutes to hours) and on personal computers, with coprocessors (CPU‐time several hours to a few days).

Normally the molecule is read in from a Z‐matrix (a matrix of polar coordinates of atoms connected in the molecule) and with symmetry data. Furthermore, programs are available which create a Z‐matrix from the molecular structure plotted on the screen with a mouse.     

超声波/零价铁(US/Fe)协同降解氯酚类化合物的QSPR研究

在同一反应条件下测定US/Fe协同降解6种氯酚类化合物的降解速率常数(κ).采用软件Chemoffice2005内置的MOPAC2002计算氯酚类化合物的分子结构参数,运用偏最小二乘(PLS)回归分析方法,对氯酚类化合物进行定量结构-性质相关(QSPR)研究,得到预测性较好的模型.模型结果显示,影响US/Fe协同降解性能(logK)的主要因素是Vp、Ehomo、logKow、(Elumo-Ehomo)2和Elumo-Ehomo.推测在US/Fe协同体系中:(1)氯酚类化合物的降解反应不是以空化泡内热解反应为主;(2)Fe表面的吸附作用对降解反应起决定影响,且氯酚化合物与·OH的氧化反应占主导作用.     

天山北坡经济带城市PM2.5质量浓度时空分布及模拟分析

PM_2.5扩散/积聚有着复杂的物理、化学过程,其时空变化与前体污染物及气象条件密切相关.为探究区域城市ρ（PM_2.5）的变化规律,以天山北坡经济带城市为研究区,采用2015年1月-2017年10月大气污染物监测数据和气象数据,结合数理统计和GIS空间分析技术,分析研究区ρ（PM_2.5）的时空变化特征;借助普通OLS（最小二乘法）分析影响因子的多重相关性,通过PLS（偏最小二乘）法构建ρ（PM_2.5）估算模型.结果表明:①研究区各城市小时ρ（PM_2.5）呈"W"型双峰变化;各月份ρ（PM_2.5）呈"U"型特征,月均值为59.5 μg/m3,2月和9月分别为ρ（PM_2.5）最高月和最低月;各季节ρ（PM_2.5）排序为冬季（146.6 μg/m3）＞秋季（35.2 μg/m3）＞春季（34.1 μg/m3）＞夏季（26.8 μg/m3）;空间上ρ（PM_2.5）由西北部克拉玛依市向东南部乌鲁木齐市逐渐增大.②PLS法构建模型能有效克服自变量多重相关性的问题,模型可解释自变量95.7%和因变量80.1%的变异信息,年均模拟值与实测值偏差为9.82%.③研究区各城市ρ（PM_2.5）与ρ（CO）的相关性极显著,与气象因子中的风速和气温的相关性较显著,而与相对湿度的相关性不显著.研究显示,基于前体污染物和气象因子的PLS法构建模型是模拟ρ（PM_2.5）的有效方法.      

基于PLS1的哈尔滨市PM2.5与空气污染物相关性分析

以往对PM2.5的研究多集中在气象因子或单一空气污染物对PM2.5质量浓度变化的影响,未考虑多种空气污染物对PM2.5质量浓度的协同作用。通过哈尔滨市环保局发布的2014年1月份（共31 d）市区内主要空气污染物SO2、NO2、PM10、CO、O3、PM2.5的质量浓度数据,运用相关性分析、PLS1和通径分析方法,研究哈尔滨市区内主要空气污染物对PM2.5质量浓度变化的直接影响、通过其他空气污染物的间接影响及污染物之间的协同作用。结果表明,SO2、NO2、PM10、CO 与 PM2.5质量浓度显著性相关,O3与 PM2.5质量浓度相关性不显著,SO2、NO2、PM10、CO 之间存在严重的复相关性。依据相关性分析结果,建立了SO2、NO2、PM10、CO对PM2.5质量浓度的PSL1模型,模型的拟合优度r2为0.852,模型拟合良好。对所建立的模型进行通径分析,结果显示,SO2、NO2、PM10、CO对PM2.5质量浓度变化的直接作用分别为0.005、-0.142、-0.140、1.191,CO对PM2.5质量浓度变化的直接影响作用最大。SO2、NO2、PM10通过CO对PM2.5质量浓度变化的间接作用分别为0.706、1.011、1.118均大于它们对PM2.5质量浓度变化的直接作用。SO2、NO2、PM10、CO对PM2.5质量浓度变化的总决定系数为85.9%。CO是主要空气污染物中影响PM2.5质量浓度变化的主要因素,降低冬季煤炭供暖期CO的排放量,有利于提高空气环境质量,降低对人体的健康危害。     

北京市能源消费碳足迹影响因素分析

为了量化人口、城市化和技术等因素对城市碳排放的影响,以北京市为研究对象,引入修正的碳足迹方法计算1990~2011年城市能源消费碳足迹,采用STIRPAT模型和偏小二乘模型对城市能源消费碳足迹的影响因素进行评估.结果显示,城市化、人均收入、人口是碳排放最主要的正向驱动因素,而能源消费强度、产业结构和研发投入比重等因素导致碳排放降低.北京市二氧化碳排放不存在环境库兹涅茨曲线,城市碳排放总量虽然整体状况还在增加,但增长速度在逐步放缓,经济发展与环境保护尚未实现协同.根据模拟结果,降低碳排放和建设低碳城市的建议是发展集中型和紧凑型的城镇功能组团,控制人口过快增长,合理引导居民绿色消费,依靠科技创新和技术进步.     

基于偏最小二乘的絮体图像信息与水质的相关性

针对污水处理运行过程COD、BOD5、TN和TP实时测量的问题,提出一种基于絮体形态参数的偏最小二乘法模型.采用图像分析技术对污泥絮体形态特征进行提取,基于相关性从提取的形态参数和试验期间的运行参数中选择模型的输入变量,通过偏最小二乘法建立输入变量与4个水质指标的预测关系模型.结果表明,COD、BOD5、TN、TP的交叉有效性系数分别为0.736、0.682、0.839、0.618,絮体形态参数与水质指标有明显的相关性,可用于预测出水水质.     

环境行业标准方法测定COD的PLS改进

文章采用偏最小二乘法改进校正模型,在测定COD值的高量程和低量程范围内分别建立PLS模型,预测结果与GB法测定COD值的相对误差为4.47%和4.09%,具有较好的一致性。研究解决了环境行业标准快速消解分光光度法测定水质化学需氧量中直线拟合回归不确定性的问题。     

Long-Term Monitoring of Scots Pine Litter Decomposition Rates Throughout Sweden Indicates Formation of a More Recalcitrant Litter in the South

Decomposition studies were carried out at sites throughout Sweden, including the four Integrated Monitoring sites. Scots pine needle litterbag weight loss measurements over 3 or 5 years were determined at 26 sites and repeated up to 27 times, depending on the site. Humus layer respiration rates were determined for 20 sites in 1987–1989 and repeated in 2007–2008. Partial Least Squares (PLS) regression was used to elucidate the relative importance of climatic and soil factors. Annual needle weight losses decreased only slowly (20–10%) over 3–5 years for all northern (>60°N) sites but decreased sharply from 30 to 10% in the third year in southern (<60°N) sites. Respiration rates of southern sites were less (40% on average) than those of northern sites. Humus layer N was positively correlated to needle weight loss during the first and the second years, but negatively correlated in the third year and to respiration rates. The results indicated that litter formed in southern Sweden became more recalcitrant in later stages of decomposition compared to litter produced in northern Sweden.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.