COVID-19 and beliefs about tobacco use: an online cross-sectional study in Iran

Environmental Science and Pollution Research - There is mixed evidence surrounding the relationship between tobacco use and COVID-19 infection/progression. The current study investigates beliefs...     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

Markers of oxidative stress and toxicant exposure among young waterpipe smokers in the USA

Waterpipe (aka hookah) tobacco smokers are exposed to toxicants that can lead to oxidative DNA and RNA damage, a precursor to chronic disease formation. This study assessed toxicant exposure and biomarkers of DNA [8-oxo-7, 8-dihydro-2′-deoxyguanosine (8-oxodG)] and RNA [8-oxo-7,8-dihydroguanosine (8-oxoGuo)] oxidative damage during smoking of flavored and non-flavored waterpipe tobacco. Thirty waterpipe smokers completed two counterbalanced 2-h lab waterpipe smoking sessions (flavored vs. non-flavored waterpipe tobacco). Urinary concentrations of 8-oxodG and 8-oxoGuo and expired carbon monoxide (eCO) were measured before and after the smoking sessions. A significant increase in the urinary concentrations of 8-oxodG (from 2.12 ± 0.83 to 2.35 ± 0.91 ng/mg creatinine, p = 0.024) and 8-oxoGuo (from 2.96 ± 0.84 to 3.45 ± 0.76 ng/mg creatinine, p = 0.003) were observed after smoking the non-flavored and flavored waterpipe tobacco, respectively. Our results also showed that the mean ± SD of eCO increased significantly after smoking the flavored (from 1.3 ± 1.1 to 20.3 ± 23.6 ppm, p < 0.001) and non-flavored waterpipe tobacco (from 1.8 ± 1.2 to 24.5 ± 26.1 ppm, p < 0.001). There were no significant differences in the means of 8-oxodG (p = 0.576), 8-oxoGuo (p = 0.108), and eCO (p = 0.170) between the flavored and non-flavored tobacco sessions. Smoking non-flavored and flavored waterpipe tobacco leads to oxidative stress and toxicant exposure. Our findings add to the existing evidence about the adverse effects of waterpipe tobacco smoking (WTS) and the need for strong policies to inform and protect young people from the risks of WTS.

     

非潮汐淹水对白骨壤膜脂过氧化系统的影响

通过室内模拟非潮汐淹水,探索不同淹水水位条件下,红树植物白骨壤(A.marina(Forsk.)Vierh).超氧化物歧化酶(SOD)、过氧化氢酶(CAT)、丙二醛(MDA)随时间的变化情况。结果表明,经历第二个淹水周期后,较第一个周期各梯度,白骨壤超氧化物歧化酶活性(SOD)从高到低排序不变,过氧化氢酶活性(CAT)、丙二醛的量(MDA)也表现出相同的规律。本研究结论为红树植物在人工非潮汐生境下,红树林造林的关键技术之一——淹水水位的控制提供了数据参考。     

Comparison of a two-stage sampling design and its composite sample alternative: An application to soil studies

The objective of a long-term soil survey is to determine the mean concentrations of several chemical parameters for the pre-defined soil layers and to compare them with the corresponding values in the past. A two-stage random sampling procedure is used to achieve this goal. In the first step, n subplots are selected from N subplots by simple random sampling without replacement; in the second step, m sampling sites are chosen within each of the n selected subplots. Thus n · m soil samples are collected for each soil layer. The idea of the composite sample design comes from the challenge of reducing very expensive laboratory analyses: m laboratory samples from one subplot and one soil layer are physically mixed to form a composite sample. From each of the n selected subplots, one composite sample per soil layer is analyzed in the laboratory, thus n per soil layer in total. In this paper we show that the cost is reduced by the factor m — 1 when instead of the two-stage sampling its composite sample alternative is used; however, the variance of the composite sample mean is increased. In the case of positive intraclass correlation the increase is less than 12.5%; in the case of negative intraclass correlation the increase depends on the properties of the variable as well. For the univariate case we derive the optimal number of subplots and sampling sites. A case study is discussed at the end.     

Minimizing the cost of translocation failure with decision‐tree models that predict species’ behavioral response in translocation sites

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision‐tree models for species’ translocation, we used data on the short‐term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision‐tree algorithms (decision tree, decision‐tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. Minimizar el Costo del Fracaso de la Reubicación con Modelos de Árboles de Decisión que Predigan la Respuesta Conductual de la Especie en los Sitios de Reubicación     

New analytical formulations for calculation of dispersion parameters of Gaussian model using parallel CFD

New analytical formulations are presented for calculation of most effective parameters in the Gaussian plume dispersion model; the standard deviations of concentration for horizontal and vertical dispersion in neutral atmosphere conditions. Employing parallel Computational Fluid Dynamics (CFD) as a powerful tool, some well-known analytical generations of Pasquill–Gifford–Turner experimental data are modified. To achieve this aim, CFD simulations are carried out for single stack dispersion on flat terrain surface and ground level concentrations are determined in different distances. An inverse procedure in Gaussian plume dispersion model is then applied and standard deviations of horizontal and vertical dispersions are obtained. The values are compared with those of the well-known methods of Doury, Briggs and Hanna in two cases: the experimental data for release of krypton-85 from 100 m high and pollution dispersion from three 28 m high stacks of Besat power plant near Tehran. The comparison indicates that new formulations for plume dispersion are more accurate than other well-known formulations.     

Improved power density and Cr/Pb removal using ozone in a microbial desalination cell

Environmental Chemistry Letters - Microbial desalination cells are promising bio-electrochemical technologies for water desalination, treating wastewater and bioelectricity production. In this...     

Seasonal trend and source identification of polycyclic aromatic hydrocarbons associated with fine particulate matters (PM2.5) in Isfahan City,Iran, using diagnostic ratio and PMF model

Environmental Science and Pollution Research - Particulate matters (PMs) and their associated chemical compounds such as polycyclic aromatic hydrocarbons (PAHs) are important factors to evaluate...     

Quantifying the efficiency of soil sampling designs: A multivariate approach

The objective of this paper is to quantify and compare the loss functions of the standard two-stage design and its composite sample alternative in the context of multivariate soil sampling. The loss function is defined (conceptually) as the ratio of cost over information and measures design inefficiency. The efficiency of the design is the reciprocal of the loss function. The focus of this paper is twofold: (a) we define a measure of multivariate information using the Kullback–Leibler distance, and (b) we derive the variance-covariance structure for two soil sampling designs: a standard two-stage design and its composite sample counterpart. Randomness in the mass of soil samples is taken into account in both designs. A pilot study in Slovenia is used to demonstrate the calculations of the loss function and to compare the efficiency of the two designs. The results show that the composite sample design is more efficient than the two-stage design. The efficiency ratio is 1.3 for pH, 2.0 for C, 2.1 for N, and 2.5 for CEC. The multivariate efficiency ratio is 2.3. These ratios primarily reflect cost ratios; influence of the information is small.