企业文化与企业安全文化建设水平互动效应研究

为提高企业安全文化建设水平,基于人类协同国际环形和企业安全文化建设水平评价指标设计调查问卷,以赛轮金宇股份有限公司为研究对象,对其企业文化和企业安全文化建设水平进行相关性分析和多元逐步回归分析,探索二者的互动效应,从企业文化角度发掘安全文化建设重点。结果表明,自我实现文化、亲和文化、传统文化、成就文化与企业安全文化建设水平高度正相关,逃避文化则呈现高度负相关,其相关系数（绝对值）均在0.9以上;同时,多元逐步回归分析中,以上5个文化规则均进入回归方程,其标准化回归系数β分别为:0.395、0.182、0.169、0.129、-0.145。因此,这5种文化规则对企业安全文化建设水平具有很高的预测力,是企业安全文化建设的重点所在。     

有机过氧化物反应热的危险性预测研究

为了采用非实验的方法对安全物质学的研究内容及研究方法进行初探,基于定量结构-性质关系法,选择13种与有机过氧化物热危险性的影响因子密切相关的描述符,分别对起始分解温度T0和分解热△H的实验数据进行多元线性回归、偏最小二乘和支持向量机回归分析,从而获得3种相应的预测模型。对比T0与△H的实验值和预测值,结果发现:SVM预测模型的精度高于PLS预测模型,MLR预测模型的精度最低;同种预测模型对分解热的预测结果均优于起始分解温度。此外,分析各预测模型的稳定性数据发现:MLR模型的预测过程发生了过拟合现象,不具备预测能力;PLS模型的交互验证系数均大于0.5,具备较稳定的预测能力;SVM模型的交互验证系数均大于0.9,具备非常稳定的预测能力。     

基于优化参数的陕西省气温、降水栅格化方法分析

基于统计、地统计基本原理,运用传统插值法、地统计插值法、多元回归法和模拟气象站点法4 大类11 种方法,对陕西省2003-2012 年平均气温、降水量数据进行栅格化。结果表明:① 多元回归法和模拟气象站点法均能大幅提高气温数据的估测准确度,且多元回归法对气温的表示更加精细,其中以“回归+残差IDW(Inverse Distance Weighting)”法精度最高,MAE、RMSE、R²分别为0.498、0.775 和0.954 8;② OK(Ordinary Kriging)法对降水数据的估测准确度最高,MAE、RMSE、R²为46.934、69.251 和0.947 8;③ 气温、降水栅格化方法具有明显的区域性,不存在绝对普适的最优方法,应根据数据类型、地域特征对原始数据进行探索性分析,从而获得区域最适合的栅格化方法;④ 陕西2003-2012 年多年平均气温为10.925 ℃,标准差2.221 ℃,气温随纬度、海拔的增加而降低,具有鲜明的纬度和垂直地带性;平均降水量为664.446 mm,标准差213.226 mm,降水呈现由南向北逐渐递减的趋势,纬度地带性较强。     

松花江流域沉积物ATP微生物量的分布特征

分析松花江流域沉积物中三磷酸腺苷(ATP)微生物量的分布,结果表明,ATP分布具有显著的空间特征,在河流发源地及部分城市地区河流段沉积物中具有高的ATP含量,统计分析(主成分分析,多元回归树分析和响应模型分析)表明:ATP是沉积物的主要成分性质之一,在本研究所涉及11个因子中,影响ATP分布变异的第一因子是空间位置(纬度,39.79%),然后是环境因子(总氮,18.49%;硝氮,16.24%),其中ATP随纬度呈二次曲线变化(P<0.001),与总氮呈显著正相关(P=0.027),和硝氮呈显著负相关(P<0.01).同时ATP也随有机质、总磷和海拔的增加而呈增加趋势(P<0.05).沉积物ATP与多种营养元素及其不同形态具有显著相关性,表明通过ATP活性微生物量可以反映水体环境的营养水平.     

有机碳对土壤中有机氯污染物分布特征的影响

为了研究清洁地区土壤中w(OC)对有机氯污染物分布特征的影响,选择2006年春秋季采集于四川卧龙自然保护区高海拔地区的25个土壤样品,利用GC-HRMS方法测试有机氯污染物的残留量,利用回归分析方法研究有机氯污染物质量分数与w(OC)和海拔高度之间的关系. 结果表明:卧龙自然保护区有机氯污染物的质量分数均小于1 ng/g,与世界上其他边远地区的非农业土壤相似;土壤中w(OC)对有机氯污染物的分布特征有重要影响,分析的10种有机氯污染物质量分数〔以w(OC)校正〕与海拔高度均表现出较好的指数相关,且具有显著的统计学意义;有机氯污染物的质量分数〔以w(OC)校正〕随海拔高度上升而增加,表现出明显的“冷捕集”效应. 因此,在研究清洁地区土壤中有机氯污染物的分布特征时,以w(OC)进行校正是合理和重要的.      

Comparison of alternative strategies for invasive species distribution modeling

Species distribution models (SDMs) can provide useful information for managing biological invasions, such as identification of priority areas for early detection or for determining containment boundaries. However, prediction of invasive species using SDMs can be challenging because they typically violate the core assumption of being at equilibrium with their environment, which may lead to poorly guided management resulting from high levels of omission. Our goal was to provide a suite of potential decision strategies (DSs) that were not reliant on the equilibrium assumption but rather could be chosen to better match the management application, which in this case was to ensure containment through adequate surveillance. We used presence-only data and expert knowledge for model calibration and presence/absence data to evaluate the potential distribution of an introduced mesquite (Leguminoseae: Prosopis) invasion located in the Pilbara Region of northwest Western Australia. Five different DSs with varying levels of conservatism/risk were derived from a multi-criteria evaluation model using ordered weighted averaging. The performance of DSs over all possible thresholds was examined using receiver operating characteristic (ROC) analysis. DSs not on the convex hull of the ROC curves were discarded. Two threshold determination methods (TDMs) were compared on the two remaining DSs, one that assumed equilibrium (by maximizing overall prediction success) and another that assumed the invasion was ongoing (using a 95% threshold for true positives). The most conservative DS fitted the validation data most closely but could only predict 75% of the presence data. A more risk-taking DS could predict 95% of the presence data, which identified 8.5 times more area for surveillance, and better highlighted known populations that are still rapidly invading. This DS and TDM coupling was considered to be the most appropriate for our management application. Our results show that predictive niche modeling was highly sensitive to risk levels, but that these can be tailored to match specified management objectives. The methods implemented can be readily adapted to other invasive species or for conservation purposes.     

Modeling soil and plant phosphorus within DSSAT

The crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) have served worldwide as a research tool for improving predictions of relationships between soil and plant nitrogen (N) and crop yield. However, without a phosphorus (P) simulation option, the applicability of the DSSAT crop models in P-deficient environments is limited. In this study, a soil-plant P model integrated to DSSAT was described, and results showing the ability of the model to mimic wide differences in maize responses to P in Ghana are presented as preliminary attempts to testing the model on highly weathered soils. The model simulates P transformations between soil inorganic labile, active and stable pools and soil organic microbial and stable pools. Plant growth is limited by P between two concentration thresholds that are species-specific optimum and minimum concentrations of P defined at different stages of plant growth. Phosphorus stress factors are computed to reduce photosynthesis, dry matter accumulation and dry matter partitioning. Testing on two highly weathered soils from Ghana over a wide range of N and P fertilizer application rates indicated that the P model achieved good predictability skill at one site (Kpeve) with a final grain yield root mean squared error (RMSE) of 535 kg ha⁻¹and a final biomass RMSE of 507 kg ha⁻¹. At the other site (Wa), the RMSE was 474 kg ha⁻¹ for final grain yield and 1675 kg ha⁻¹ for final biomass. A local sensitivity analysis indicated that under P-limiting conditions and no P fertilizer application, crop biomass, grain yield, and P uptake could be increased by over 0.10% due to organic P mineralization resulting from a 1% increase in organic carbon. It was also shown that the modeling philosophy that makes P in a root-free zone unavailable to plants resulted in a better agreement of simulated crop biomass and grain yield with field measurements. Because the complex soil P chemistry makes the availability of P to plants extremely variable, testing under a wider range of agro-ecological conditions is needed to complement the initial evaluation presented here, and extend the use of the DSSAT-P model to other P-deficient environments.     

Unified methodology for site-characterization and sampling of highway runoff

Hydrology, roadway traffic conditions, and atmospheric deposition are three essential data categories for the planning and implementation of highway-runoff monitoring and characterization programs. Causal variables pertaining to each data category could be site specific but have been shown to correlate with runoff pollutant loads. These data categories were combined to derive statistical relationships for characterization and prioritization of the respective pollutant loads at highway runoff sites. Storm runoff data of total suspended solids (TSS), total dissolved solid (TDS), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN) and total phosphorus (TP) collected from three highway sites in Charlotte, North Carolina, USA, were used to illustrate the development of site-specific highway-runoff pollutant loading models. This unified methodology provides a basis for initial assessment of the pollutant-constituent loads from highway runoff using hydrologic component variables. Improved reliability is achievable when additional traffic and/or atmospheric component variables are incorporated into the basic hydrologic regression model. In addition, operational guidance is suggested for implementing highway-runoff monitoring programs that are subject to sampling and resources constraints.     

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.     

江苏中部农业园小麦和土壤镉元素含量关系研究

为研究江苏中部农业园土壤和小麦镉元素含量［ω(Cd_soil)和ω（Cd_wheat）］关系,采集了土壤和小麦样品40组,采用多元线性回归分析方法建立ω(Cd_wheat)的预测模型。结果表明：（1）研究区表层土壤呈中性偏弱酸性,ω(Cd_soil)含量范围为0.083~0.239 mg/kg,平均值为0.152 mg/kg,均低于《土壤环境质量 农用地土壤污染风险管控标准（试行）》（GB 15618—2018）中农用地土壤污染风险筛选值,属于优先保护类土壤;（2）依据《食品安全国家标准 食品中污染物限量》（GB 2762—2017）中ω(Cd_wheat)限定值(0.1 mg/kg),小麦籽实Cd超标率为10%;（3）ω(Cd_wheat)主要受表层ω(Cd_soil)控制,同时受到土壤钼（Mo）、铅（Pb）、砷（As）、钙（Ca）和镉（Cd）等元素有效态影响,另外,还受土壤理化性质（pH值和有机质）的影响。