Source Apportionment of Particulate Matter (PM10) and Indoor Dust in a University Building

A study on source apportionment of indoor dust and particulate matter (PM₁₀) composition was conducted in a university building by using chemometrics. The objective of this study was to investigate the potential sources of selected heavy metals and ionic species in PM₁₀ and indoor dust. PM₁₀ samples were collected using a low-volume sampler (LVS) and indoor dust was collected using a soft brush. Inductively coupled plasma spectrometry (ICP-MS) was used to determine the concentration of heavy metals, while the concentration of cations and anions was determined by atomic absorption spectrometer (AAS) and ion chromatography (IC), respectively. The concentration of PM₁₀ recorded in the building throughout the sampling period ranged from 20 ± 10 μgm^?3 to 80 ± 33 μgm^?3. The composition of heavy metals in PM₁₀ and indoor dust were dominated by zinc (Zn), followed by lead (Pb), copper (Cu), and cadmium (Cd). Principle component analysis (PCA) and multiple linear regression (MLR) showed that the main sources of pollutants in PM₁₀ came from indoor renovations (73.83%), vehicle emissions (16.38%), earth crust sources (9.68%), and other outdoor sources (0.11%). For indoor dust, the pollutant source was mainly earth crust. This study suggests that chemometrics can be used for forensic investigation to determine the possible sources of indoor contaminants within a public building.     

基于AHP&MLR的流程制造企业循环经济综合评价与实证分析——一项铝加工企业的案例研究

针对传统企业循环经济评价重物质、轻价值;重通用、轻特定;重数理、轻实践的不足,文章以资源流转平衡为核心,以铝加工企业资源的物质流转路线为基础,通过描绘并追踪其生产流程过程的资源投入、消耗、产出及废弃的价值信息,从投入、消耗与循环、输出3个环节构建循环经济评价指标,采用层次分析法和多元线性回归法综合评价方法对其循环经济发展进行了综合分析与评价,其评价的基本原理和方法可供冶金、建材、造纸及食品加工等高污染高能耗行业借鉴。     

Impacts of Meteorological Factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM₁₀) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.     

化学品厌氧生物降解QSBR预测模型研究

收集了155种有机化学品厌氧生物降解数据,以随机抽取的109种物质作为训练集,另外46种物质作为验证集,通过结构式拆分得到各基团,分别采用多元线性回归和BP人工神经网络2种算法对有机化合物结构与生物降解性定量关系(QSBR)进行研究。结果表明,多元线性回归模型验证集正确率为78.26%,总正确率为84.52%;BP人工神经网络模型验证集正确率为82.61%,总正确率为90.32%。可见,BP人工神经网络算法相对优于多元线性回归算法。     

基于QSPR方法的脂肪醇化合物闪点预测

为提高脂肪醇化合物闪点预测精度,提出基于定量结构-性质关系(QSPR)原理的脂肪醇化合物闪点预测方法。应用Dragon软件计算出91种脂肪醇的分子描述符,利用遗传函数算法(GFA)从1 481个描述符中筛选出3个与脂肪醇闪点关系最密切的分子描述符。分别用多元线性回归(MLR)方法和支持向量机(SVM)方法进行建模,并采用内部验证和外部检验的方式对模型的拟合度、预测性等性能进行验证。结果表明:预测集的MLR方法和SVM方法的平均绝对误差(AAE)分别为2.870 K和2.706 K;均方根误差(RMSE)为3.451 K和3.371 K。SVM模型在精度上略优于MLR模型,而MLR模型更为简单和方便。     

PMF和PCA/MLR法解析上海市高架道路地表径流中多环芳烃的来源

以上海市内环高架道路径流中的多环芳烃(PAHs)为研究对象,于2012年7~9月,实测了8场降雨的道路径流中的PAHs,了解了城市交通干道径流中PAHs的污染状况,并采用正定矩阵因子分解(PMF)和主成分分析/多元线性回归(PCA/MLR)两种模型对径流中的PAHs进行源解析.因子分析表明,径流中Σ16 PAHs的浓度范围为1.585~7.523μg·L-1,两种模型对于PAHs的来源有较为一致的判定.交通源为高架道路地表径流中PAHs的主要来源,PMF和PCA/MLR得到的源贡献率分别为37.7%和44.3%,其余3种来源石油源、燃气源和其他源,PMF得到的源贡献率依次为21.9%、26.4%、14.0%;与之对应,PCA/MLR得到的源贡献率依次为28.9%、18.3%、8.5%.PMF和PCA/MLR模型的计算值与实测值拟合较好,相关系数分别为0.961和0.997.     

基于受体模型与地统计的城市居民区土壤重金属污染源解析

选取西安城市居民区土壤为研究对象,通过绝对主成分分数/多元线性回归(APCS/MLR)与地统计相结合的方法解析其中As、Ba、Co、Cu、Mn、Ni、Pb、Sr、Zn、V的主要来源、各来源对各元素的贡献量、各来源贡献的空间分布特征.结果表明,相对于西安市褐土背景值西安城市居民区土壤中Pb、Cu、Sr、Co、Zn、Ba、Ni、As、Mn、V的累积程度依次减弱.西安城市居民区土壤中10种重金属的污染有自然源、交通源、化石燃料燃烧源这3种主要来源.As、Mn、Ni、V的来源以自然源为主,平均贡献量分别为69.63%、74.28%、68.65%、77.09%,其空间分布上表现为西北部低而东南部高的规律.Co、Pb、Sr、Zn的来源以化石燃料燃烧源为主,平均贡献量分别为59.84%、42.22%、44.50%、44.44%,空间分布上表现出自城市中心向外逐渐增加的特征.Ba以自然源和化石燃料燃烧源为主,平均贡献量分别为41.67%和48.27%;自然源、交通源、化石燃料源对Cu的平均贡献量分别为37.00%、20.47%、26.50%.