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.

     

The use of ultrasound-assisted anaerobic compost tea washing to remove poly-chlorinated dibenzo-p-dioxins (PCDDs), dibenzo-furans (PCDFs) from highly contaminated field soils

The remediation of dioxin-contaminated soil of a specific coastal area previously employed for the manufacture of pentachlorophenol (PCP) in southern Taiwan’s Tainan City has attracted much attention of researchers there. This work addresses the possibility of providing an effective and environmentally friendly option for removing PCDD/Fs from soil in that field. Soil screening/sieving was first conducted to assess particle distribution. Fine sand was observed to be the major component of the soil, accounting for more than 60% of the total mass. A combination of ultrasonification and mechanical double-blade agitation was used to facilitate the washing of the soil using the biosurfactant anaerobic compost tea. More than 85 and 95% of total removal efficiencies were achieved for moderately and highly contaminated soils after 6 and 10 washing cycles, respectively, under ambient temperature, a soil/liquid ratio 1:2.5, 700 rpm, and over a relatively short duration. These results were achieved through the collision and penetration effects of this combined treatment as well as PCDD/F partitioning between the particles and anaerobic compost tea. This study represents the first to report the use of anaerobic compost tea solvent to wash soil highly contaminated by dioxin. It was concluded that anaerobic compost tea, rich in non-toxic bio-surfactants (e.g., alcohols, humic acids), can be used to improve bioavailability and bioactivity of the soil making bio-attenuation and full remediation more efficient.     

Cadmium (Cd) distribution and contamination in Chinese paddy soils on national scale

Rice is a staple food by an increasing number of people in China. As more issues have arisen in China due to rice contaminated by cadmium (Cd), Cd contamination in arable soils has become a severe problem. In China, many studies have examined Cd contamination in arable soils on a national scale, but little studies have focused on the distribution of Cd in paddy fields. This study explored the spatial pattern of Cd in paddy soils in China, made a preliminary evaluation of the potential risk, and identified the most critically contaminated regions based on the domestic rough rice trade flow. The results showed that Cd concentrations in paddy soils in China ranged from 0.01 to 5.50 mg/kg, with a median value of 0.23 mg/kg. On average, the highest Cd concentrations were in Hunan (0.73 mg/kg), Guangxi (0.70 mg/kg), and Sichuan (0.46 mg/kg) provinces. Cd concentrations in paddy soils in central and western regions were higher than those in eastern regions, especially the southeastern coastal regions. Of the administrative regions, Cd standard exceedance rate was 33.2 %, and the heavy pollution rate was 8.6 %. Regarding to Cd of paddy soil, soil environmental quality was better in Northeast China Plain than in Yangtze River Basin and southeastern coastal region. Mining activities were the main anthropogenic pollution source of Cd in Chinese paddy soil. Based on rice trade, more of the Chinese population would be exposed to Cd through intake of rice produced in Hunan province. Certain regions that output rice, especially Hunan province, should be given priority in the management and control of Cd contamination in paddy soil.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

活性碳纤维吸附废气中的丙烯酸和甲苯

采用动态吸附法探讨了在活性碳纤维对模拟废气中丙烯酸和甲苯的吸附过程中,各因素对活性碳纤维穿透吸附量和饱和吸附量的影响。实验结果表明,在模拟废气中丙烯酸质量分数为0.2%、双柱塞泵流量为6.1mL/h、载气流量为1.60L/min的条件下,穿透时间为10min,活性碳纤维的穿透吸附量为227.5mg/g,饱和吸附时间为40min,活性碳纤维的饱和吸附量为460.0m g/g。     

Identification of heavy metal pollutants using multivariate analysis and effects of land uses on their accumulation in urban soils in Beijing, China

In order to evaluate the current state of the environmental quality of soils in Beijing, we investigated contents of 14 metals in Beijing urban soils inside the 5th ring road by even grids sampling. Statistic analyses were conducted to identify possible heavy metal pollutants, as well as the effects of land uses on their accumulation. Our results revealed that the urban soils in Beijing were contaminated by Cd, Pb, Cu, and Zn. Land uses and urbanization ages affected the accumulation of the four heavy metals in soils significantly. Soils in industrial areas have the highest average Cu and Zn contents, while Pb contents in park areas and Cd in agricultural areas are the highest. The accumulations of Pb and Zn in urban soils increase significantly with sampling plots approaching the city center. And Pb, Cd, and Zn contents in soils in traffic areas also tend to increase in the city center. However, residential areas have the lowest contents of all the four heavy metals.     

Determination of 15 phthalate esters in air by gas-phase and particle-phase simultaneous sampling

Based on previous research, the sampling and analysis methods for phthalate esters (PAEs) were improved by increasing the sampling flow of indoor air from 1 to 4 L/min, shortening the sampling duration from 8 to 2 hr. Meanwhile, through the optimization of chromatographic conditions, the concentrations of 9 additional PAE pollutants in indoor air were measured. The optimized chromatographic conditions required a similar amount of time for analysis as before, but gave high responsivity, the capability of simultaneously distinguishing 15 kinds of PAEs, and a high level of discrimination between individual sample peaks, as well as stable peak generation. The recovery rate of all gas-phase and particle-phase samples of the 15 kinds of PAEs ranged from 91.26% to 109.42%, meeting the quantitative analysis requirements for indoor and outdoor air sampling and analysis. For the first time, investigation of the concentration levels as well as characteristics of 15 kinds of PAEs in the indoor air from four different traffic micro-environments (private vehicles, busses, taxis and subways) was carried out, along with validation of the optimized sampling and analytical method. The results show that all the 9 additional PAEs could be detected at relatively high pollution levels in the indoor air from the four traffic micro-environments. As none of the pollution levels of the 15 kinds of PAEs in the indoor air from the 4 traffic micro-environments should be neglected, it is of great significance to increase the types of PAEs able to be detected in indoor air.     

金属尖化前缘模型主动式热疏导性能试验研究

目的为了更好地解决高超声速飞行器舵、翼前缘及头锥等气动热环境恶劣区域的热防护问题。方法采用主动式热疏导技术,以高温液态合金为工质,设计并制作具有主动式热疏导功能的尖化前缘金属试验模型(R=5 mm)。根据模型外尺寸设计加工一套石英灯仿形加热器和热流测试模型,开展地面热环境模拟试验。结果试件在前缘中心温度530℃左右时具有瞬态启动特性。前缘中心和大面积中心最大辐射热流密度分别为1000 kw/m^2和580 kw/m^2,试件在该环境中长时间受热状态下仍具有较好的热疏导能力。试验后试件无工质泄漏和结构破坏,具有一定的可重复使用性。结论可以此热疏导方式结合现有成熟热防护技术进一步开展工程设计与应用。     

绿地再生水灌溉土壤微生物量碳及酶活性效应研究

再生水灌溉引起了土壤理化性质的变化,其对土壤生物活性的影响备受关注.以再生水利用较典型的北京为研究区,分层采集了不同再生水灌溉历史的城区公园绿地与城郊农田表层土壤样品,测定并分析了一些常规理化指标、土壤微生物量碳及5种土壤酶（脲酶、碱性磷酸酶、蔗糖酶、脱氢酶和过氧化氢酶）活性,探讨长期再生水灌溉下土壤微生物量碳和酶活性的变化.结果表明,再生水灌溉下公园绿地土壤微生物量碳和酶活性均高于其自来水对照灌区,农田上升不明显.与对照相比,公园绿地与农田再生水灌区0～20 cm土层土壤微生物量碳平均含量分别上升了60.1%和14.2%,公园绿地再生水灌区0～20 cm土层中土壤酶活性平均增幅为36.7%,而农田为7.4%.调查区土壤微生物量碳及酶活性均随土壤深度的增加而降低,其中公园绿地0～10 cm与10～20 cm土层间差异极显著.因此,北京地区城市绿地使用再生水长期灌溉有助于提高土壤生物活性.