天津市环保产业信息管理系统研究

通过对环保产业信息获取,处理,应用等环节的研究,完成了对天津市自１９９３年来的首次环保产业调查,开发了配套的管理软件系统,同时对该系统的知识发现,网络化等问题进行了探讨。     

POTENTIAL EFFECTS OF CLIMATE CHANGE ON AGRICULTURAL WATER USE IN THE SOUTHEAST U.S.1

ABSTRACT: Climate change has the potential to have dramatic effects on the agricultural sector nationally and internationally as documented in many research papers. This paper reports on research that was focused on a specific crop growing area to demonstrate how farm managers might respond to climate-induced yield changes and the implications of these responses for agricultural water use. The Hadley model was used to generate climate scenarios for important agricultural areas of Georgia in 2030 and 2090. Linked crop response models indicated generally positive yield changes, as increased temperatures were associated with increased precipitation and CO2. Using a farm management model, differences in climate-induced yield impacts among crops led to changes in crop mix and associated water use; non-irrigated cropland received greater benefit since irrigated land was already receiving adequate moisture. Model results suggest that farm managers will increase cropping intensity by decreasing fallowing and increasing double cropping; corn acreage decreased dramatically, peanuts decreased moderately and cotton and winter wheat increased. Water use on currently irrigated cropland fell. The potential for increased water use through conversion of agriculturally important, but currently non-irrigated, growing areas is substantial.     

耐氨固氮菌对蔬菜作物生长的促进作用研究

研究并探讨了蔬菜作物在施用耐氨固氮菌后的生长、生理反应及增产的效果和原因。试验结果表明： 施用耐氨固氮菌显著增加了蔬菜苗期生物量、叶面积和叶片的叶绿素含量, 同时还增强了植株的根系活力, 并有明显的增产效果。耐氨固氮菌促进增产的作用不仅与其本身的固氮作用有关, 还可能与其分泌出某些生长刺激物 （ 或激素） 有关。     

扩展阴极型膜电解法处理含铜废水的数学模型

采用均匀设计法,研究离子膜扩展阴极电解法对低浓度含铜废水的处理效果,得到了反应条件与回收效果之间的线性模型,并比较了线性预测模型和BP网络的预测效果。结果表明,线性模型有较强的定性分析作用但预测能力不强,而BP网络具有很强的预测能力。两法结合,具有很强的理论和实践指导意义。模型预测结果和实验结果均表明,扩展阴极型膜电解法可带来很好的社会效益和经济效益。     

从委托代理理论谈中国的环境影响评价制度

根据委托代理理论,分析了中国环境影响评价制度中存在的问题,立足于绿色招商的理念,理顺建设项目环评委托代理关系,明确了建设单位、环评单位、招商部门、环保部门等相互之间的关系和职责,以使中国的环境影响评价制度更适应新型工业化道路发展的要求。     

突发性环境污染事故应急预警系统发展探讨

在介绍突发性环境污染事故的概念、类型与特性的基础上,综述了近年来国内外对这一领域的研究进展．并结合“5S”技术提出突发性环境污染事故应急预警系统的全程模拟。     

高架仓库内喷头热响应性能研究

基于喷头热响应性理论的研究成果,主要研究了在给定响应时间指数和喷头动作温度的务件下,高架仓库所用快速响应早期抑制喷头(ESFR)的热响应性能,并结合案例,对高架仓库内喷头热响应性能的主要影响因素进行了定量分析,得出喷头热响应性能与环境温度和火源功率之间的关系。研究结果可为高架仓库自动喷水灭火系统的设计和评估提供一定的参考。     

锡林郭勒盟植被物候枯黄期对干湿变化的时间多尺度响应

基于全球监测与建模组(GIMMS)1982～2015年第三代归一化植被指数(NDVI)GIMMS NDVI 3g数据集和气象观测数据,采用累计NDVI的Logistic曲线曲率极值法提取锡林郭勒草原植被枯黄期,并结合不同时间尺度标准化降水蒸散指数(SPEI)分析植被枯黄期对干湿变化的响应特征.结果表明:1982～201...     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.