Prevalence and distribution of fecal indicator organisms in South Florida beach sand and preliminary assessment of health effects associated with beach sand exposure

Fecal indicator levels in nearshore waters of South Florida are routinely monitored to assess microbial contamination at recreational beaches. However, samples of sand from the surf zone and upper beach are not monitored which is surprising since sand may accumulate and harbor fecal-derived organisms. This study examined the prevalence of fecal indicator organisms in tidally-affected beach sand and in upper beach sand and compared these counts to levels in the water. Since indicator organisms were statistically elevated in sand relative to water, the study also considered the potential health risks associated with beach use and exposure to sand. Fecal coliforms, Escherichia coli, enterococci, somatic coliphages, and F(+)-specific coliphages were enumerated from sand and water at three South Florida beaches (Ft. Lauderdale Beach, Hollywood Beach, and Hobie Beach) over a 2-year period. Bacteria were consistently more concentrated in 100g samples of beach sand (2-23 fold in wet sand and 30-460 fold in dry sand) compared to 100ml samples of water. Somatic coliphages were commonly recovered from both sand and water while F(+)-specific coliphages were less commonly detected. Seeding experiments revealed that a single specimen of gull feces significantly influenced enterococci levels in some 3.1m(2) of beach sand. Examination of beach sand on a micro-spatial scale demonstrated that the variation in enterococci density over short distances was considerable. Results of multiple linear regression analysis showed that the physical and chemical parameters monitored in this study could only minimally account for the variation observed in indicator densities. A pilot epidemiological study was conducted to examine whether the length of exposure to beach water and sand could be correlated with health risk. Logistic regression analysis results provided preliminary evidence that time spent in the wet sand and time spent in the water were associated with a dose-dependent increase in gastrointestinal illness.     

某储油库地下水有机污染健康风险评价

本文以USEPA推荐使用的污染场地健康风险评价方法为基础, 结合污染场地实际情况, 分析、评价了某储油库地下水有机污染对场址内暴露人群造成的健康风险。评价结果表明: 该储油库地下水有机污染物为1,2-二氯乙烷、苯、三氯甲烷和甲苯。污染场址内的工人和居民受到的非致癌风险均小于1, 在可接受范围; 而污染对场址内的工人和居民产生的致癌风险较大, 分别为1.7×10?4、9.0×10?3, 是不可接受的。产生致癌风险的主要污染物为1, 2-二氯乙烷, 占总致癌风险的99.80%, 可致人产生多种形态的肿瘤, 并具有潜在的遗传毒性。主要暴露途径是吸入吸收, 占总致癌风险比例大于70%, 其次为口入吸收。皮肤接触暴露途径产生的致癌风险较小, 占总致癌风险比例小于1%。     

基于DPSIR模型的河西走廊农田防护林健康评价

农田防护林健康对维护绿洲生态安全、减少风沙灾害、改善局地小气候、提高农作物产量等具有重要的意义。综合运用熵值法和层次分析法确定指标权重,基于驱动力-压力-状态-影响-响应（DPSIR）模型对河西走廊农田防护林健康状况进行了评价。结果表明：（1）河西走廊农田防护林整体上处于亚健康水平,瓜州县、酒泉市肃州区、嘉峪关市和临泽县的农田防护林处于健康水平,民勤、民乐、高台和阿克塞的农田防护林处于不健康水平,其余区县农田防护林处于亚健康状态。（2）林带连续性、疏透度、修枝情况和地区经济收入是驱动区域农田防护林健康状况的关键因素。     

一种基于改进SEIR模型的突发公共卫生事件风险动态评估与预测方法——以欧洲十国COVID-19为例

突发公共卫生事件会严重影响社会公众生命健康,风险评估和预测可为突发公共卫生事件有效防控提供科学依据。本文提出了一种基于SEIR模型的突发公共卫生事件风险动态评估与预测方法,将突发公共卫生事件传播与人口、医疗、经济情况相结合,耦合危险性与脆弱性,建立合理的风险评估综合指标体系,利用熵值—层次分析组合模型实现突发公共卫生事件风险动态评估。此外,本文建立了传染病传播动力学修正SEIR模型,将传染病传播动力学模拟预测与风险评估相结合,实现突发公共卫生事件演变趋势的预测和风险的动态预测。2019年12月底的COVID-19疫情是一次传播速度快、感染范围广、防控难度大的重大突发公共卫生事件。本文以欧洲10国COVID-19疫情为例,开展风险评估与风险动态预测研究,依据欧洲10国自疫情开始至2020年4月16日的疫情数据,预测了2020年4月17日—2020年5月10日疫情演变的趋势,进而实现了10国的疫情风险动态预测。本文模型预测结果表明至2020年5月10日欧洲10国疫情形势仍然严峻,预测数据与真实数据的拟合优度R ²大于0.92,预测结果与疫情真实情况基本一致,在此情况下,复工复产对于疫情防控仍然是不利的。本文提出的基于SEIR模型的公共卫生事件风险动态评估与预测方法为疫情已然传播开的国家和地区提供了风险持续评估和预测的可能,为后期疫情防控决策提供了支持,同时也可用于今后新的疫情发生时期或其他突发性公共卫生事件下风险的应急评估和预测。     

Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests

This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R² value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths.     

利用地面三维激光扫描进行桥梁挠度变形分析

针对常规桥梁挠度变形分析方法仅能获取有限数量离散监测点的几何变形,且存在自动化程度低、效率低、局限性大的问题,本文提出了一种基于点云数据的桥梁挠度变形分析方法。首先通过地面三维激光扫描仪获取桥梁变形体上的点云数据,经过精确配准,获取可靠点云数据。然后利用滑动窗口法得到桥梁线形,并通过两期线形叠差分析得到桥梁挠度变化。最后以跨度40 m+65 m+40 m的连续钢构桥实体工程为试验对象,利用本文方法发现该桥梁存在最小值为8 mm、跨中最大值为17.5 mm、平均值为14.9 mm的挠度变形。结果表明,该方法理论严密、计算效率高、简单实用。研究结果对三维激光扫描技术在变形监测领域的深度应用有重要借鉴意义。     

典型矿区深层地下水重金属含量特征及健康风险评价——以皖北矿区为例

以皖北矿区为例,分析测试了不同含水层(松散、煤系、太灰、奥灰)中的6种重金属元素(Cd、Cr、Cu、Zn、Pb、Ni),对其含量特征及健康风险评价进行了研究。结果表明,6种重金属在不同含水层含量大小次序各不一样,从整体来看,研究区地下水中所测重金属含量依次为NiZnPbCuCdCr,与太灰水一致;整体地下水中Cr、Cu和Zn均未超过(GB/T14848—93)中Ⅲ类水质标准,Cd、Pb和Ni有部分水样超过标准限值。化学致癌物Cd和Cr在各含水层所致健康危害风险值数量级在10-6~10-4 a-1,Cr健康风险值在各含水层中均大于Cd,Cr在煤系含水层危害风险值(1.29×10-4 a-1)已超过美国环境保护局(USEPA)最大可接受风险(1×10-4 a-1),为研究区首要的环境健康风险管理控制指标。化学非致癌物Cu、Zn、Pb、Ni四种重金属健康危害风险值较小,数量级在10–11~10-8 a-1,Pb和Ni健康危害风险值相对较高,也应引起重视。各含水层总的健康风险值大小次序为:煤系太灰奥灰松散,前三者已超过国际辐射防护委员会(ICRP)推荐的(5×10-5 a-1)最大可接受风险,其中煤系含水层总的健康风险值为1.46×10-4 a-1,已超过USEPA(1×10-4 a-1)推荐的最大可接受风险。对矿区深层地下水开展重金属含量分析和健康风险评价,可为地下水水资源的开采利用和保护提供参考。     

Health risk assessment for typical and abandoned chromium-contaminated sites

Chromium pollution were investigated by sampling the abandoned chromium contaminated sites in Chongqing. Its health risk was assessed following the procedures made by USEPA. The results showed that the most seriously polluted land was found at the former production area （G4） with average Cr（VI） concentration of 3369.2 mg kg-1 on the surface soil. The assessment indicated that there was high risk of non-carcinogens for children. The assessed risk of the first layer of office area （G1） for children, chromium slag transition （G2）, drainage pipeline chromium slag transition （G3） and production area （G4） for both children and adults were not acceptable （〉1.00× 106）. It was strongly suggested that the accessible measures of remediation should be taken for a portion of contaminated sites before the reuse of abandoned lands.     

A review of the climatological characteristics of landfalling Gulf hurricanes for wind, wave, and surge hazard estimation

The climatological characteristics of landfalling Gulf of Mexico hurricanes are presented, focusing on the basic parameters needed for accurately determining the structure and intensity of hurricanes for ocean response models. These include the maximum sustained wind, radius of maximum winds, the Holland-B parameter, the peripheral or far-field pressure, the surface roughness and coefficient of drag, and the central pressure for historical hurricanes in the Gulf.Despite evidence of a slight increase in the annual number of named storms over the past 50 years, presently there is no statistically significant trend in tropical storms, hurricanes, or major hurricanes in the Gulf of Mexico. In addition, the long-term variability of tropical cyclones in the Gulf reflects the observed variability in the Atlantic basin as a whole. Analyses of hurricane winds from multiple sources suggest the presence of a bias toward overestimating the strength of winds in the HURDAT dataset from 7% to 15%. Results presented comparing HURDAT with other sources also show an overestimation of intensity at landfall, with an estimated bias of ~10%.Finally, a review of recent studies has shown that hurricane frequencies and intensities appear to vary on a much more localized scale than previously believed. This exacerbates the sampling problem for accurate characterization of hurricane parameters for design and operational applications.     

Ecosystem health assessment of Honghu Lake Wetland of China using artificial neural network approach

Honghu Lake, located in the southeast of Hubei Province, China, has suffered a severe disturbance during the past few decades. To restore the ecosystem, the Honghu Lake Wetland Protection and Restoration Demonstration Project (HLWPRDP) has been implemented since 2004. A back propagation (BP) artificial neural network (ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland. And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project. Particularly, 12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs. The output is one layer of ecosystem health index. After training and testing the BP ANNs, an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland. The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP (in 2002) to middle health after the implementation of the HLWPRDP (in 2005). It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.