上海市臭氧污染时空分布及影响因素

分析2006—2016年上海市的监测数据发现,臭氧(O_3)浓度存在逐年上升趋势,污染持续时间有所增加,但除水平风速有下降趋势外,其他相关气象因素的年际变化趋势并不显著。空间分析结果表明,上海市O_3超标主要集中在西南部郊区,但市区O_3超标潜势不容忽视。O_3污染高发季节的污染玫瑰图分析发现,上海市南部地区是影响上海市O_3污染的关键区域;对于NO_2减排的影响分析发现,尽管上海市O_3平均浓度总体处于上升趋势,但在NO_2下降幅度最为明显的内环市区和北部郊区,O_3上升幅度低于NO_2下降幅度较小的内外环区域和西部郊区,表明上海市的O_3污染控制仍需持续推进NOx的减排,并同步推进VOCs的减排。     

近岸海域水质目标考核评价方法研究

为满足《水污染防治行动计划》近岸海域水质考核要求,根据目前近岸海域环境监测工作的实际情况,在考核范围、评价方法、目标分解、计分方法等方面进行了比较,在保证公平、公正的前提下,以一致性、可比性和适应性为原则,提出的考核评价方法以单因子评价法和点位评价法为基础,在未达标情况下,按照水质目标完成比例计分;达标情况下,在达标分值基础上按水质的保持和改善给予不同程度的加分,通过实例研究分析能够适应近岸海域水质状况变化的考核工作要求。     

公共场所火灾伤员转运护理应急资源评估

为保障伤员生命与健康,提升火灾伤员救治率,该文研究公共场所火灾伤员转运护理应急资源评估方法.以火灾伤员转运护理应急资源需求分析为基础,从应急人员、应急设备、应急环境信息与应急管理四个方面出发,共选取15个评估指标,构建公共场所火灾伤员转运护理应急资源评估指标体系,根据各指标采集相关数据并对数据实施量纲标准化处理.构建基...     

Diurnal concentrations variations,size distributions for ambient air particles and metallic pollutants (Cr,Mn, Ni,Cd, Pb) during summer season at a traffic area

This study characterized and discussed particulate ambient air particulate concentrations and seasonal variations for PM₁₈, PM₁₀, PM_2.5, and PM₁ during June 2013–July 2013 at this traffic sampling site. In addition, this study also characterized the ambient air particulates size distributions by using MOUDI-100S4 sampler to collect 1-day the ambient suspended particles (PM₁₈, PM₁₀, PM_2.5, and PM₁) at this sampling site. In addition, the study also showed that the main pollutants contributions were from traffic and residual areas. As for the pollutants seasonal concentrations variations, the results indicated that the average particle concentrations orders were all displayed as daytime?>?nighttime for PM₁₈, PM₁₀, PM_2.5 and PM₁ at this characteristic sampling site. The results further indicated that the mean highest of metal concentrations in this study indicated that the average metal concentration were all displayed as Mn?>?Cr?>?Ni?>?Pb?>?Cd for PM₁₈, PM₁₀, PM_2.5 and PM₁ on daytime and nighttime at this characteristic sampling site.     

Characteristics and assessment of phthalate esters in urban dusts in Guangzhou city, China

Phthalate esters (PAEs) were examined in indoor and outdoor dust samples from the subtropical city of Guangzhou, China. The ∑(16)PAEs concentrations ranged from 121 to 3,223 μg g(-1) dust, with the median concentration of 840 μg g(-1) dust. Significantly higher concentrations of PAEs in dust samples were found in offices where electrical and electronic devices, carpet pads, and office furniture were widely used. Of the 16 PAEs, diisobutyl phthalate (DiBP), di-n-butyl phthalate (DnBP), and di(2-ethylhexyl) phthalate (DEHP) dominated the PAEs in indoor and outdoor dust samples, and accounted for >96.8% and >93.1% of the ∑(16)PAEs concentrations, respectively. The median daily inhalation exposure of ∑(16)PAEs were 3.53 and 0.247 μg kg(-1) body weight day(-1), and at the 95(th) percentile were 7.62 and 0.530 μg kg(-1) body weight day(-1), up on the measured concentrations and estimated dust ingestion rates, respectively, for toddles and adults. The ubiquitous distribution of PAEs as noted in this study suggests the need for detailed assessment of PAEs concentrations using more sites and to further investigate the factors influencing PAEs exposure in China.     

近岸海域水质自动监测系统站位的布设

水质自动监测将是近岸海域环境监测的一个发展趋势。通过分析现有近岸海域自动监测站在站点布设过程中的经验,对近岸海域自动监测系统站位的设置原则、技术要求、基本条件及注意事项等进行阐述,为后续近岸海域自动监测系统的建设提供参考依据。     

流动注射分光光度法测定水中六价铬的研究

研究建立了测定水中六价铬的流动注射光度分析方法,探讨了利用抗坏血酸的还原性扣除水样中轻度色度或浊度干扰的可行性.本方法线性关系好,检出限低,精密度和准确度高,对地表水、地下水、生活污水、工业废水等实样的测定获得了令人满意的结果.与传统的二苯基碳酰二肼分光光度法相比,具有方法简单、灵敏度高、分析速度快等优点,适用于环境监测的分析工作.     

镇江地区土壤中重金属Cu和Pb污染状况与空间分布

为了锯镇江地区土壤中重金属Cu和Ph的污染状况与空间分布,对镇江地区表层土壤中的Cu和Pb进行了采样监测。结果表明,镇江地区表层土壤中cu的含量为19．2～273mg／kg,Pb的含量为20．6～3846mg／kg。与全省土壤背景值相比,均有一定程度的富集。对cu和Pb的相互关系进行分析可得出,镇江地区土壤在一定程度上受到农业面源的污染。     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.