AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Preconcentration of cadmium and copper ions on magnetic core–shell nanoparticles for determination by flame atomic absorption

Magnetic core–shell nanoparticles modified by (3-aminopropyl) trimethoxy silane were prepared and used as adsorbent for the extraction and preconcentration of cadmium and copper ions. The ions were desorbed with nitric acid followed by determination with flame atomic absorption spectrometry. The extraction conditions were investigated systematically. The method was applied for the determination of Cd(II) and Cu(II) ions in different water samples. The accuracy was also evaluated through analysis of certified reference material.     

Estimating abundance: a non parametric mark recapture approach for open and closed systems

We present a novel, non-parametric, frequentist approach for capture-recapture data based on a ratio estimator, which offers several advantages. First, as a non-parametric model, it does not require a known underlying distribution for parameters nor the associated assumptions, eliminating the need for post-hoc corrections or additional modeling to account for heterogeneity and other violated assumptions. Second, the model explicitly deals with dependence of trials by considering trials to be dependent; therefore, cluster sampling is handled naturally and additional adjustments are not necessary. Third, it accounts for ordering, utilizing the fact that a system with a small population will have a greater frequency of recaptures “early” in the survey work compared to an identical system with a larger population. We provide mathematical proof that our estimator attains asymptotic minimum variance under open systems. We apply the model to a data set of bottlenose dolphins (Tursiops truncatus) and compare results to those from classic closed models. We show that the model has an impressive rate of convergence and demonstrate that there’s an inverse relationship between population size and the proportion of the population that need to be sampled, while achieving the same degree of accuracy for abundance estimates. The model is flexible and can apply to ecological situations as well as other situations that lend themselves to capture recapture sampling.     

Bootstrap confidence intervals for adaptive cluster sampling design based on Horvitz–Thompson type estimators

Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) introduced several bootstrap methods under adaptive cluster sampling using a Horvitz–Thompson type estimator. Using a simulation study, they showed that their proposed methods provide confidence intervals with highly understated coverage rates. In this article, we first show that their bootstrap methods provide biased bootstrap estimates. We then define two bootstrap methods, based on the method of Gross (Proceeding of the survey research methods section. American Statistical Association, Alexandria, VA, pp 181–184, 1980) and Bootstrap With Replacement, that provide unbiased bootstrap estimates of the population mean with bootstrap variances matching the corresponding unbiased variance estimator. Using a simulation study, we show that the bootstrap confidence intervals based on our proposed methods have better performance than those based on available bootstrap methods, in the sense of having coverage proportion closer to the nominal coverage level. We also compare the proposed intervals to empirical likelihood based intervals in small samples.     

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling (ACS) is an efficient sampling design for estimating parameters of rare and clustered populations. It is widely used in ecological research. The modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators based on small samples under ACS have often highly skewed distributions. In such situations, confidence intervals based on traditional normal approximation can lead to unsatisfactory results, with poor coverage properties. Christman and Pontius (Biometrics 56:503–510, 2000) showed that bootstrap percentile methods are appropriate for constructing confidence intervals from the HH estimator. But Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) showed that bootstrap confidence intervals from the HT estimator are even worse than the normal approximation confidence intervals. In this article, we consider two pseudo empirical likelihood functions under the ACS design. One leads to the HH estimator and the other leads to a HT type estimator known as the Hájek estimator. Based on these two empirical likelihood functions, we derive confidence intervals for the population mean. Using a simulation study, we show that the confidence intervals obtained from the first EL function perform as good as the bootstrap confidence intervals from the HH estimator but the confidence intervals obtained from the second EL function perform much better than the bootstrap confidence intervals from the HT estimator, in terms of coverage rate.     

Impact of Silver Nanoparticles Synthesized by Irradiated Polyvinylpyrrolidone on Spodoptera littoralis Nucleopolyhedrosis Virus Activity

Journal of Polymers and the Environment - A Green nanomaterial synthesis could be considered as an alternative bio-controller source of insect pest management as well as saving the environment....     

Mechanochemical activation for lead extraction from spent cathode ray tube

Journal of Material Cycles and Waste Management - In this study, a mechanochemical activation technique to effectually extract lead from the funnel glass of cathode ray tube (CRT) is proposed. A...     

Relationship between musculoskeletal disorders and anthropometric indices among bus drivers in Zahedan city

Introduction. Most work-related musculoskeletal disorders (MSDs) are associated with improper postures and poorly designed workstations. This study is an attempt to examine the prevalence and severity of MSDs as well as anthropometric dimensions among city bus drivers. Methodology. This cross-sectional study was carried out on 60 male bus drivers. A body discomfort chart was used to evaluate MSDs. Spearman correlation was employed to examine the relationship between body size and the severity of discomfort. Results. Data analysis showed that discomfort reported in the lower back (33.3%), upper back (18.3%) and knee (15%) was severe and there was a direct and significant correlation between the body mass index and the severity of discomfort in the shoulder, arm and hip (p?p?Conclusion. The results of the study can be used to address ergonomic risk factors and reduce their associated disorders. The findings of the study can be used in training programs to educate drivers how to decrease the risk associated with work-related MSDs by adopting appropriate behavior strategies.     

Loofa egyptiaca as a novel adsorbent for removal of direct blue dye from aqueous solution

In this paper, Loofa egyptiaca (LE), an agricultural plant cultivated in Egypt, was used to prepare low-cost activated carbon (LE_C1 and LE_C2) adsorbents. The adsorbents (LE, LE_C1 and LE_C2) were evaluated for their ability to remove direct blue 106 dye from aqueous solutions. Batch mode experiments were conducted using various parameters such as pH, contact time, dye concentration and adsorbent concentration. The surface chemistry of LE, LE_C1 and LE_C2 was analyzed by scanning electron microscopy (SEM). The experimental data were examined using Langmuir, Freundlich, Temkin and Harkins–Jura isotherms. The results showed that the adsorption of direct blue 106 was maximal at the lowest value of pH (pH = 2). Removal efficiency was increased with an increase in dye concentration and a decrease in amount of adsorbent. Maximum adsorption capacity was found to be 57.14, 63.3 and 73.53 mg/g for LE, LE_C1 and LE_C2 respectively. Kinetics were also investigated using pseudo-first-order, pseudo-second-order and intra-particle diffusion models. The experimental data fitted very well with the pseudo-first-order and pseudo-second-order kinetic models. The results indicate that LE, LE_C1 and LE_C2 could be employed as adsorbents for the removal of direct blue dye from aqueous solutions.     

Health risk assessment of potentially harmful elements and dietary minerals from vegetables irrigated with untreated wastewater,Pakistan

In the developing world, vegetables are commonly grown in suburban areas irrigated with untreated wastewater containing potentially harmful elements (PHEs). In Pakistan, there is no published work on the bioaccessibility aspect of PHEs and dietary minerals (DMs) in sewage-irrigated soil or the vegetables grown on such soils in Pakistan. Several industrial districts of Pakistan were selected for assessment of the risk associated with the ingestion of vegetables grown over sewage-irrigated soils. Both the total and bioaccessible fraction of PHEs (Cd, Co, Cr, Ni, and Pb) and DMs (Fe, Cu, Mn, Zn, Ca, Mg, and I) in soils and vegetable samples were measured. The concentrations of these PHEs and DMs in sewage-irrigated and control soils were below published upper threshold limits. However, compared to control soils, sewage irrigation over the years decreased soil pH (7.7 vs 8.1) and enhanced dissolved organic carbon (1.8 vs 0.8 %), which could enhance the phyto-availability of PHEs and DMs to crops. Of the PHEs and DMs, the highest transfer factor (soil to plant) was noted for Cd and Ca, respectively. Concentrations of PHEs in most of the sewage-irrigated vegetables were below the published upper threshold limits, except for Cd in the fruiting portion of eggplant and bell pepper (0.06–0.08 mg/kg Cd, dry weight) at three locations in Gujarat and Kasur districts. The bioaccessible fraction of PHEs can reduce the context of dietary intake measurements compared to total concentrations, but differences between both measurements were not significant for Cd. Since the soils of the sampled districts are not overly contaminated compared to control sites, vegetables grown over sewage-irrigated soils would provide an opportunity to harvest mineral-rich vegetables potentially providing consumers 62, 60, 12, 104, and 63 % higher dietary intake of Cu, Mn, Zn, Ca, and Mg, respectively. Based on Fe and vanadium correlations in vegetables, it is inferred that a significant proportion of total dietary Fe intake could be contributed by soil particles adhered to the consumable portion of vegetables. Faecal sterol ratios were used to identify and distinguish the source of faecal contamination in soils from Gujranwala, Gujarat, and Lahore districts, confirming the presence of human-derived sewage biomarkers at different stages of environmental alteration. A strong correlation of some metals with soil organic matter concentration was observed, but none with sewage biomarkers.