Association of exposure to multiple metals with papillary thyroid cancer risk in China

Papillary thyroid cancer (PTC) has inflicted huge threats to the health of mankind. Metal pollution could be a potential risk factor of PTC occurrence, but existing relevant epidemiological researches are limited. The current case-control study was designed to evaluate the relationships between exposure to multiple metals and the risk of PTC. A total of 262 histologically confirmed PTC cases were recruited. Age- and gender-matched controls were enrolled at the same time. Urine samples were used as biomarkers to reflect the levels of environmental exposure to 13 metals. Conditional logistic regression models were adopted to assess the potential association. Single-metal and multi-metal models were separately conducted to evaluate the impacts of single and co-exposure to 13 metals. The increased concentration of urinary Cd, Cu, Fe, and Pb quartiles was found significant correlated with PTC risk. We also found the decreased trends of urinary Se, Zn, and Mn quartiles with the ORs for PTC. These dose-response associations between Pb and PTC were observed in the single-metal model and remained significant in the multi-metal model (OR_25-50th=1.39, OR_50-75th=3.32, OR_>75th=7.62, p for trend <0.001). Our study suggested that PTC was positively associated with urinary levels of Cd, Cu, Fe, Pb, and inversely associated with Se, Zn, and Mn. Targeted public health policies should be made to improve the environment and the recognition of potential risk factors. These findings need additional studies to confirm in other population.

     

Distribution,source, and environmental risk assessment of phthalate esters (PAEs) in water,suspended particulate matter,and sediment of a typical Yangtze River Delta City,China

Phthalates (PAEs) in drinking water sources such as the Yangtze River in developing countries had aroused widespread concern. Here, the water, suspended particulate matter (SPM), and sediment samples were collected from 15 sites in wet and dry seasons in Zhenjiang, for the determination of six PAEs (DMP, DEP, DIBP, DBP, DEHP, and DOP) using the solid-phase extraction (SPE) or ultrasonic extraction coupled with gas chromatography-mass spectrometry (GC-MS). The total concentrations of six PAEs (Σ₆PAEs) spanned a range of 2.65–39.31 μg L^?1 in water, 1.97–34.10 μg g^?1 in SPM, and 0.93–34.70 μg g^?1 in sediment. The partition coefficients (K_d1) of PAEs in water and SPM phase ranged from 0.004 to 3.36 L g^?1 in the wet season and from 0.12 to 2.84 L g^?1 in the dry season. K_d2 of PAEs in water and sediment phase was 0.001–9.75 L g^?1 in the wet season and 0.006–8.05 L g^?1 in the dry season. The dominant PAEs were DIBP, DBP, and DEHP in water and SPM, DIBP, DEHP, and DOP in sediment. The concentration of DBP in water exceeded the China Surface Water Standard. The discharge of domestic sewage and industrial wastewater might be the main potential sources of PAEs. The risk quotient (RQ) method used for the risk assessment revealed that DBP (0.01 < RQ < 1) posed a medium risk, while DIBP and DEHP (RQ > 1) posed a high environmental risk in water, DIBP (RQ > 1) also showed a high risk in sediment.

     

反硝化条件下有机碳低丰度河床沉积层中的苯胺降解

采集渭河河床沉积物,研究了反硝化条件下有机碳低丰度的河床沉积层中苯胺降解.结果表明,反硝化条件下,苯胺在有机碳低丰度的河床沉积层中可生物降解.使该环境中苯胺(约50 mg/L)降解近95%,当硝酸盐为30.69、184.16、245.54 mg/L时,降解时间分别约为20、45、70 d.在上述环境中另加35.98 mg/L乙酸盐后,苯胺降解速度在硝酸盐为184.16 mg/L时最大,硝酸盐为30.69 mg/L时最小.当硝酸盐为30.69 mg/L,不加乙酸盐,27 d苯胺降解约95%;添加35.98 mg/L乙酸盐后,实验进行了47 d还仍有近13 mg/L苯胺残留,说明外加碳源(乙酸盐)对苯胺降解具有抑制作用.但当硝酸盐为184.16、245.54 mg/L时,外加碳源(乙酸盐)则强化苯胺降解.水合金属氧化物对苯胺降解具有促进作用.     

新型树脂对氯酚类物质的吸附研究

合成了乙酰氯修饰树脂NDA-O,与Purolite公司的MN-200相比,合成的树脂比表面积较小,但酸性官能团较多.用两种树脂吸附2-氯酚、4-氯酚和2,4-二氯酚发现,吸附结果可以用Langmuir和Freundlich等温吸附方程很好地模拟,合成的NDA-O对3种氯酚类物质的吸附能力优于MN-200.两种树脂对氯酚类物质的吸附量随着温度的升高而减少,表明吸附是放热过程,同时对氯酚类物质的吸附焓变随着吸附量的增大而减小.对同一种树脂来说,吸附量随着氯代程度的增加而增加,取代氯数相同的时候,吸附量与吸附质的K..成正相关.进一步研究了pH对吸附的影响,在pH大于9时,3种氯酚类物质的吸附量均急剧减少.     

综合资源立法是资源可持续利用的法律保证

资源的有效保护和永续利用在我国可持续发展战略中占有十分重要的地位。本文分析了我国资源法制建设中存在的问题，阐述了综合资源立法在资源法制建设体系中的意义。     

Comparative study on performances of a heat-pipe PV/T system and a heat-pipe solar water heating system

The heat-pipe solar water heating (HP-SWH) system and the heat-pipe photovoltaic/thermal (HP-PV/T) system are two practical solar systems, both of which use heat pipes to transfer heat. By selecting appropriate working fluid of the heat-pipes, these systems can be used in the cold region without being frozen. However, performances of these two solar systems are different because the HP-PV/T system can simultaneously provide electricity and heat, whereas the HP-SWH system provides heat only. In order to understand these two systems, this work presents a mathematical model for each system to study their one-day and annual performances. One-day simulation results showed that the HP-SWH system obtained more thermal energy and total energy than the HP-PV/T system while the HP-PV/T system achieved higher exergy efficiency than the HP-SWH system. Annual simulation results indicated that the HP-SWH system can heat the water to the available temperature (45°C) solely by solar energy for more than 121 days per year in typical climate regions of China, Hong Kong, Lhasa, and Beijing, while the HP-PV/T system can only work for not more than 102 days. The HP-PV/T system, however, can provide an additional electricity output of 73.019 kWh/m², 129.472 kWh/m², and 90.309 kWh/m² per unit collector area in the three regions, respectively.     

The determination and matching analysis of pinch point temperature difference in evaporator and condenser of organic rankine cycle for mixed working fluid

Exergo-economic analysis of the pinch point temperature difference (PPTD) in both evaporator and condenser of sub-critical organic Rankine cycle system (ORCs) are performed based on the first and second laws of thermodynamics. Taking mixture R13I1/R601a as a working fluid and the annual total cost per net output power Z as exergo-economic performance evaluation criterion, the effects of PPTD in evaporator ΔT_e, and the PPTD ratio of condenser to evaporator y, on the exergo-economic performance of ORCs are analyzed. Moreover, how some other parameters influence the optimal PPTD in evaporator ΔT_e,opt and the optimal PPTD ratio of condenser to evaporator y_opt are also discussed. It has been found that the exergo-economic performance of ORCs is remarkably influenced by ΔT_e and y, and there exists ΔT_e,opt and y_opt. In addition, ΔT_e,opt and y_opt are affected by heat transfer coefficient ratio of condenser to evaporator ß, the temperature of working fluid at dew point in condenser T_1a, and composition of R13I1/R601a: larger ß and T_1a lead to lower ΔT_e,opt and y_opt; by contraries, larger mass fraction of R13I1 makes ΔT_e,opt and y_opt increase, and y_opt increases linearly. The effects of the temperature of working fluid at bubble point in evaporator T_3a, mass flow rate of exhaust flue gas m_g, and inlet temperature of exhaust flue gas T_gi on ΔT_e,opt and y_opt are very slight. For comparison, three additional working fluids, namely R601a, R245fa, and 0.32R245fa/0.68R601a, are also taken into account.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.