Recent advances in the application of silica nanostructures for highly improved water treatment: a review

The demand for high-quality safe and clean water supply has revolutionized water treatment technologies and become a most focused subject of environmental science. Water contamination generally marks the presence of numerous toxic and harmful substances. These contaminants such as heavy metals, organic and inorganic pollutants, oil wastes, and chemical dyes are discharged from various industrial effluents and domestic wastes. Among several water treatment technologies, the utilization of silica nanostructures has received considerable attention due to their stability, sustainability, and cost-effective properties. As such, this review outlines the latest innovative approaches for synthesis and application of silica nanostructures in water treatment, apart from exploring the gaps that limit their large-scale industrial application. In addition, future challenges for improved water remediation and water quality technologies are keenly discussed.

     

Change in avian functional fingerprints of a Neotropical montane forest over 100 years as an indicator of ecosystem integrity

Ecologically relevant traits of organisms in an assemblage determine an ecosystem's functional fingerprint (i.e., the shape, size, and position of multidimensional trait space). Quantifying changes in functional fingerprints can therefore provide information about the effects of diversity loss or gain through time on ecosystem condition and is a promising approach to monitoring ecological integrity. This, however, is seldom possible owing to limitations in historical surveys and a lack of data on organismal traits, particularly in diverse tropical regions. Using data from detailed bird surveys from 4 periods across more than a century, and morphological and ecological traits of 233 species, we quantified changes in the avian functional fingerprint of a tropical montane forest in the Andes of Colombia. We found that 78% of the variation in functional space, regardless of period, was described by 3 major axes summarizing body size, dispersal ability (indexed by wing shape), and habitat breadth. Changes in species composition significantly altered the functional fingerprint of the assemblage and functional richness and dispersion decreased 35–60%. Owing to species extirpations and to novel additions to the assemblage, functional space decreased over time, but at least 11% of its volume in the 2010s extended to areas of functional space that were unoccupied in the 1910s. The assemblage now includes fewer large-sized species, more species with greater dispersal ability, and fewer habitat specialists. Extirpated species had high functional uniqueness and distinctiveness, resulting in large reductions in functional richness and dispersion after their loss, which implies important consequences for ecosystem integrity. Conservation efforts aimed at maintaining ecosystem function must move beyond seeking to sustain species numbers to designing complementary strategies for the maintenance of ecological function by identifying and conserving species with traits conferring high vulnerability such as large body size, poor dispersal ability, and greater habitat specialization. Article impact statement: Changes in functional fingerprints provide a means to quantify the integrity of ecological assemblages affected by diversity loss or gain.     

Sampling strategies in the assessment of long‐term exposures to toxic substances in air

The decision to mitigate exposures from vapor intrusion (VI) is typically based on limited data from 24‐hour air samples. It is well documented that these data do not accurately represent long‐term average exposures linked to adverse health effects. Limited decision guidance is currently available to determine the most appropriate sampling strategy, considering the cost of sampling alternatives along with the economic consequences of exposure‐related health effects. We present a decision model that introduces economic and statistical considerations in evaluating alternative VI sampling methods. The model characterizes the best sampling method by factoring economic and health consequences of exposure, the variability of exposure, the cost of sampling and mitigation, and the likelihood of false‐negatives and false‐positives. Decision‐makers can use results to select the sample size that maximizes net benefit. Conceptual and mathematical models are presented linking biological, statistical, and economic considerations to assess the cost and effectiveness of different sampling strategies. The model relates an average exposure concentration, determined statistically, to abatement costs and to the monetary value of health deterioration. The value of the information provided by different strategies is calculated and used to select the optimum sampling method. Simulations show that longer‐term sampling methods tend to be more accurate and cost‐effective than short‐term samples. The ideal sampling strategy shows significant seasonal variation (it is typically optimal to use longer samples in the winter) and also varies significantly with the stringency of regulatory standards. Longer‐term sample collection provides a more accurate representation of average VI exposure and reduces the likelihood of type I and type II errors. This reduces expected costs of mitigation and exposure (e.g., health consequences, legal and regulatory penalties), which in some cases can be quite significant. The model herein shows how these savings are balanced against the additional costs of longer‐term sampling.     

The effect of diazinon on blood glucose homeostasis: a systematic and meta-analysis study

Environmental Science and Pollution Research - Though evidence exists on the association between diazinon (DZN), an organophosphate pesticide, with hyperglycemia, contrasting reports also exist....     

Methyl methacrylate (MMA) and alumina recovery from waste artificial marble powder pyrolysis

Journal of Material Cycles and Waste Management - Owing to various advantages of artificial marble compared to natural marble, its application has been rising exponentially, which has resulted in...     

Special issue on contamination,remediation and health for pollutants in natural aquatic,soil, sediments and atmospheric environments

Environmental Geochemistry and Health -     

Development and evaluation of RAMP I – a practitioner’s tool for screening of musculoskeletal disorder risk factors in manual handling

RAMP I is a screening tool developed to support practitioners in screening for work-related musculoskeletal disorder risk factors related to manual handling. RAMP I, which is part of the RAMP tool, is based on research-based studies combined with expert group judgments. More than 80 practitioners participated in the development of RAMP I. The tool consists of dichotomous assessment items grouped into seven categories. Acceptable reliability was found for a majority of the assessment items for 15 practitioners who were given 1?h of training. The usability evaluation points to RAMP I being usable for screening for musculoskeletal disorder risk factors, i.e., usable for assessing risks, being usable as a decision base, having clear results and that the time needed for an assessment is acceptable. It is concluded that RAMP I is a usable tool for practitioners.     

Overestimation of self-reported driving exposure: Results from the SHRP2 Naturalistic Driving Study

Objectives: The accuracy of self-reported driving exposure has questioned the validity of using self-reported mileage to inform research questions. Studies examining the accuracy of self-reported driving exposure compared to objective measures find low validity, with drivers overestimating and underestimating driving distance. The aims of the current study were to (1) examine the discrepancy between self-reported annual mileage and driving exposure the following year and (2) investigate whether these differences depended on age and annual mileage.

Methods: Two estimates of drivers’ self-reported annual mileage collected during vehicle installation (obtained via prestudy questionnaires) and approximated annual mileage driven (based upon Global Positioning System data) were acquired from 3,323 participants who participated in the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study.

Results: A Wilcoxon signed rank test showed that there was a significant difference between self-reported and annual driving exposure during participation in SHRP 2, with the majority of self-reported responses overestimating annual mileage the following year, irrespective of whether an ordinal or ratio variable was examined. Over 15% of participants provided self-reported responses with over 100% deviation, which were exclusive to participants underestimating annual mileage. Further, deviations in reporting differed between participants who had low, medium, and high exposure, as well as between participants in different age groups.

Conclusions: These findings indicate that although self-reported annual mileage is heavily relied on for research, such estimates of driving distance may be an overestimate of current or future mileage and can influence the validity of prior research that has utilized estimates of driving exposure.     

洪涝灾害条件下疏散交通生成预测方法

为有助于有关部门更准确预测洪涝灾害受灾民众的疏散量,结合非集计数据和集计数据的优点,提出分区集计数据的概念,设计了受灾区域分区方法,并通过意向偏好(SP)调查法对我国居民在洪涝条件下疏散交通需求数据进行调查。在此基础上,引入BP神经网络建立基于分区集计数据的疏散交通生成预测模型。利用调查数据进行实证分析发现,所设计方法取得了较好的预测效果,鲁棒性较好,平均相对预测误差仅为1.8%,其预测效果明显优于现有的非集计和整集计模型。