Boosting sensitivity of boron nitride nanotube (BNNT) to nitrogen dioxide by Fe encapsulation

The pristine boron nitride nanotube (BNNT) exhibits a poor chemical reactivity to some adsorbates, thus greatly limiting its application for the gas sensor. In the present work, using density functional theory (DFT) methods, we put forward a novel strategy to enhance the sensitivity of BNNT to nitrogen dioxide (NO₂) by the encapsulation of a single Fe atom inside its cavity. The results suggest that the NO₂ molecule can be only physically adsorbed on the pristine BNNT with a small adsorption energy (−0.10 eV). After the inclusion of the Fe atom inside BNNT (Fe@BNNT), the interaction of NO₂ molecules with this tube is significantly enhanced, leading to a transformation from the physisorption of on pristine BNNT to the current chemisorption. Interestingly, up to five NO₂ molecules can be adsorbed on this encapsulated BNNT along its circumference with the average adsorption energy of −0.52 eV, corresponding to a short recovery time (6 ms). Moreover, 0.38 electrons are transferred from the Fe@BNNT to the adsorbed NO₂ molecules, which is enough to induce the obvious change of its electrical conductance. Thus, we predict that the encapsulation of Fe atom inside BNNT would greatly boosts its sensitivity to NO₂ molecules, indicating its potential application as NO₂ sensors.     

Thermodynamic modeling of the Ca(NO3)2-MNO3 (M: alkali metal) systems

This work presents a thermodynamic evaluation of the Ca(NO₃)₂-MNO₃ (M: Li, Na, K, Rb, Cs) binary systems using the CALPHAD approach. The required Gibbs energy of liquid Ca(NO₃)₂ is missing in the literature and has been successfully evaluated in the present work with a fusion enthalpy of 23849 J mol⁻¹. The substitutional solution model can thus be employed to describe the Ca(NO₃)₂-base liquid phase. All the intermediate compounds are treated to be stoichiometric and their Gibbs energies comply with the Neumann-Kopp rule. Empirical functions relating mixing enthalpies to ionic parameters are employed to predict the corresponding values of binary melts which are used as input data to assist in parameters optimization for the liquid phases. The final calculated results show good agreement with most of the experimental and predicted data.     

Evaluation of a non-point source pollution model,AnnAGNPS, in a tropical watershed

Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km² watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R² = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R² = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R² = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0–1 t ha⁻¹ y⁻¹), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha⁻¹ y⁻¹. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify “hot spots” on the landscape.     

Gas sensing properties of ZnO thin films prepared by microcontact printing

In situ patterned zinc oxide (ZnO) thin films were prepared by precipitation of Zn(NO₃)₂/urea aqueous solution and by microcontact printing of self-assembled monolayers (SAMs) on Al/SiO₂/Si substrates. The visible precipitation of Zn(OH)₂ from the urea containing Zn(NO₃)₂ solution was enhanced by increasing the reaction temperature and the amount of urea. The optimized condition for the ZnO thin films was found to be the Zn(NO₃)₂/urea ratio of 1/8, the precipitation temperature of 80 °C, the precipitation time of 1 h and the annealing temperature of 600 °C, respectively. SAMs are formed by exposing Al/SiO₂/Si to solutions comprising of hydrophobic octadecylphosphonic acid (OPA) in tetrahydrofuran and hydrophilic 2-carboxylethylphosphonic acid (CPA) in ethanol. The ZnO thin film was then patterned with the heat treatment of Zn(OH)₂ precipitated on the surface of hydrophilic CPA. The ZnO gas sensor was exposed to different concentrations of C₃H₈ (5000 ppm), CO (250 ppm) and NO (1000 ppm) at elevated temperatures to evaluate the gas sensitivity of ZnO sensors. The optimum operating temperatures of C₃H₈, CO and NO gases showing the highest gas sensitivity were determined to be 350, 400 and 200 °C, respectively.     

Fabrication and characterization of polyaniline-based gas sensor by ultra-thin film technology

Pure polyaniline (PAN) film, polyaniline and acetic acid (AA) mixed film, as well as PAN and polystyrenesulfonic acid (PSSA) composite film with various number of layers were prepared by Langmuir–Blodgett (LB) and self-assembly (SA) techniques. These ultra-thin films were characterized by ultraviolet–visible (UV–VIS) spectroscopy and ellipsometry. It is found that the thickness of PAN-based ultra-thin films increases linearly with the increase of the number of film layers. The gas-sensitivity of these ultra-thin films with various layers to NO₂ was studied. It is found that pure polyaniline films prepared by LB technique had good sensitivity to NO₂, while SA films exhibited faster recovery property. The response time to NO₂ and the relative change of resistance of ultra-thin films increased with the increase of the number of film layers. The response time of three-layer PAN film prepared by LB technique to 20 ppm NO₂ was about 10 s, two-layer SA film was about 8 s. The mechanism of sensitivity to NO₂ of PAN-based ultra-thin films was also discussed.     

Fluorescence detection of nitrogen dioxide with perylene/PMMA thin films

Thin films of polymethylmethacrylate (PMMA) doped with perylene provide selective, robust and easily prepared optical sensor films for NO₂ gas with suitable response times for materials aging applications. The materials are readily formed as 200 nm thin spin cast films on glass from chlorobenzene solution. The fluorescence emission of the films (λ_max=442 nm) is quenched upon exposure to NO₂ gas through an irreversible reaction forming non-fluorescent nitroperylene. Infrared, UV–VIS and fluorescence spectroscopies confirmed the presence of the nitro adduct in the films. In other atmospheres examined, such as air and 1000 ppm concentrations of SO₂, CO, Cl₂ and NH₃, the films exhibited no loss of fluorescence intensity over a period of days to weeks. Response curves were obtained for 1000, 100 and 10 ppm NO₂ at room temperature with equilibration times varying from hours to weeks. The response curves were fit using a numerical solution to the coupled diffusion and a nonlinear chemical reaction problem assuming that the situation is reaction limiting. The forward reaction constant fitted to experimental data was k_f∼0.06 (ppm min)⁻¹.     

Prediction of tropospheric ozone concentrations: Application of a methodology based on the Darwin’s Theory of Evolution

This study aims to predict the next day hourly average tropospheric ozone (O₃) concentrations using genetic programming (GP). Due to the complexity of this problem, GP is an adequate methodology as it can optimize, simultaneously, the structure of the model and its parameters. It is an artificial intelligence methodology that uses the same principles of the Darwinian Theory of Evolution. GP enables the automatic generation of mathematical expressions that are modified following an iterative process applying genetic operations.The inputs of the models were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO₂) and O₃, and some meteorological variables (temperature – T; solar radiation – SR; relative humidity – RH; and wind speed – WS) measured 24 h before. GP was also applied to the principal components (PC) obtained from these variables. The analysed period was from May to July 2004 divided in training and test periods.GP was able to select the most relevant variables for prediction of O₃ concentrations. The original variables, T, RH and O₃ measured 24 h before were considered significant inputs for prediction. The selected PC had also important contributions of the same variables and of NO₂. GP models using the original variables presented better performance in training period and worse performance in test period when compared with the models obtained using PC. The results achieved using the GP methodology demonstrated that it can be very useful to solve several environmental complex problems.     

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSE_ave) and average of mean absolute percentage error (MAPE_ave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSE_ave/RBF = 26.8 m³/s vs. RMSE_ave/LMFF = 40.2 m³/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF = 18.8 m³/s vs. RMSE/RBF = 19.2 m³/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test.     

Toward improved calibration of watershed models: Multisite multiobjective measures of information

This paper presents a computational framework for incorporation of disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. The framework consists of three components: 1) an a-priori characterization of system behavior; 2) a formal and statistically valid formulation of objective function(s) of model errors; and 3) an optimization engine to determine the Pareto-optimal front for the selected objectives. The proposed framework was applied for calibration of the Soil and Water Assessment Tool (SWAT) in the Eagle Creek Watershed, Indiana, USA using three single objective optimization methods [Shuffled Complex Evolution (SCE), Dynamically Dimensioned Search (DDS), and DiffeRential Evolution Adaptive Metropolis (DREAM)], and one multiobjective optimization method. Solutions were classified into behavioral and non-behavioral using percent bias and Nash–Sutcliffe model efficiency coefficient. The results showed that aggregation of streamflow and NO_x (NO₃-N + NO₂-N) information measured at multiple locations within the watershed into a single measure of weighted errors resulted in faster convergence to a solution with a lower overall objective function value than using multiple measures of information. However, the DREAM method solution was the only one among the three single objective optimization methods considered in this study that satisfied the conditions defined for characterizing system behavior. In particular, aggregation of streamflow and NO_x responses undermined finding “very good” behavioral solutions for NO_x, primarily because of the significantly larger number of observations for streamflow. Aggregation of only NO_x responses into a single measure expedited finding better solutions although aggregation of data from nested sites appeared to be inappropriate because of correlated errors. This study demonstrates the importance of hydrologic and water quality data availability at multiple locations, and also highlights the use of multiobjective approaches for proper calibration of watershed models that are used for pollutant source identification and watershed management.     

Modeling small watersheds in Brazilian Amazonia with shuttle radar topographic mission-90 m data

This work presents a methodology for the refinement of shuttle radar topographic mission (SRTM-90 m) data available for South America to enable detailed watershed studies in Amazonia. The original data were pre-processed to properly map detailed low-order drainage features and allowed digital estimates of morphometric variables. Spatial-resolution refinement (3″ to 1″, or ∼90 to ∼30 m) through data kriging was found to be an interesting solution to construct digital elevation models (DEMs) with more adequate presentation of landforms than the original data. The refinement of spatial resolution by kriging interpolation overcame the main constraints for drainage modeling with original SRTM-90 m, such as spatial randomness, artifacts and unrealistic presentation due to pixel size. Kriging with a Gaussian semivariogram model caused a smoothing of the resulting DEM, but the main features for drainage modeling were preserved. Canopy effects on the modeled surface represented the main remaining limitation for terrain analysis after pre-processing. Data regarding a small watershed in Amazonas (∼38 km²), Brazil, were evaluated through visualization techniques, morphometric analyses and plot diagrams of the results. The data showed limitations for use in the original form, but could be applied for watershed modeling at relatively detailed scales after the described pre-processing.     

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al₂O₃ + TiN and Al₂O₃) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R², RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.     

Aviation emission inventory development and analysis

An up to date and accurate aviation emission inventory is a prerequisite for any detailed analysis of aviation emission impact on greenhouse gases and local air quality around airports. In this paper we present an aviation emission inventory using real time air traffic trajectory data. The reported inventory is in the form of a 4D database which provides resolution of 1° × 1° × 1000 ft for temporal and spatial emission analysis. The inventory is for an ongoing period of six months starting from October 2008 for Australian Airspace.In this study we show 6 months of data, with 492,936 flights (inbound, outbound and over flying). These flights used about 2515.83 kt of fuel and emitted 114.59 kt of HC, 200.95 kt of CO, 45.92 kt of NO_x, 7929.89 kt of CO₂, and 2.11 kt of SO_x. From the spatial analysis of emissions data, we found that the CO₂ concentration in some parts of Australia is much higher than other parts, especially in some major cities. The emission results also show that NO_x emission of aviation may have a significant impact on the ozone layer in the upper troposphere, but not in the stratosphere.It is expected that with the availability of this real time aviation emission database, environmental analysts and aviation experts will have an indispensable source of information for making timely decisions regarding expansion of runways, building new airports, applying route charges based on environmentally congested airways, and restructuring air traffic flow to achieve sustainable air traffic growth.     

Performance of AERMOD at different time scales

As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be verified. This paper evaluates the performance of AERMOD in estimating sulfur dioxide (SO₂) concentrations in Dallas and Ellis counties in Texas. Results suggest that SO₂ concentrations simulated by AERMOD at the 8 h, daily, monthly, and annual intervals match their respective observed concentrations much better compared with the simulated 1 and 3 h SO₂ concentrations. In addition, AERMOD performs better in simulating SO₂ concentrations when combined point and mobile emission sources are used as model inputs rather than using point or mobile emission sources alone. Results also suggest that, at the monthly scale, AERMOD performs much better in simulating the high end of the spectrum of SO₂ concentrations in the study area compared to results at the 1, 3, 8 h, and daily scales. These results not only help us better understand the performance of AERMOD but also provide useful information to researchers who are interested in applying AERMOD in various applications, such as the utilization of AERMOD in chronic exposure assessment in epidemiological studies where long-term (i.e., monthly and/or annual) air pollution concentration estimations are often used.     

RBF neural network inferential sensor for process emission monitoring

Inferential sensing, or soft sensing, gained popularity in recent years as an alternative to continuous emission monitoring systems because of its simplicity, reliability, and cost effectiveness as compared to analogous hardware sensors. In this paper we address the problem of NO_x emission using a model of furnace of an industrial boiler, and propose a neural network structure for high performance prediction of NO_x as well as O₂. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NO_x modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NO_x pollutant, enabling a better understanding on how and where NO_x are produced. The boiler was simulated under various operating conditions. The generated data is then used for initial development and assessment of neural network soft sensors for emission prediction based on the conventional process variable measurements. The performance of the proposed soft sensor is then evaluated using actual data from an industrial boiler. The developed soft sensor achieves comparable accuracy to the continuous emission monitor analyzer, however, with substantial reduction in the cost of equipment and maintenance.     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Artificial neural network-based model for predicting VO2max from a submaximal exercise test

The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict maximal oxygen uptake (VO₂max) of fit adults from a single stage submaximal treadmill jogging test. Participants (81 males and 45 females), aged from 17 to 40 years, successfully completed a maximal graded exercise test (GXT) to determine VO₂max. The variables; gender, age, body mass, steady-state heart rate and jogging speed are used to build the ANN prediction model. Using 10-fold cross validation on the dataset, the average values of standard error of estimate (SEE), Pearson’s correlation coefficient (r) and multiple correlation coefficient (R) of the model are calculated as 1.80 ml kg^?1 min^?1, 0.95 and 0.93, respectively. Compared with the results of the other prediction models in literature that were developed using Multiple Linear Regression Analysis, the reported values of SEE, r and R in this study are considerably more accurate.     

Algorithmic complexity bounds on future prediction errors

We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution μ by the algorithmic complexity of μ. Here we assume that we are at a time t > 1 and have already observed x = x₁ ⋯ x_t. We bound the future prediction performance on x_t+1x_t+2 ⋯ by a new variant of algorithmic complexity of μ given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.     

Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation

The spatial uncertainty of a topography based rainfall runoff model (TOPMODEL) is addressed in this study to assess its variability in simulating watershed hydrologic response with regards to the change of digital elevation model (DEM) resolution. Twelve DEM realizations of different grid sizes ranging from 30 m to 3000 m for each of two case watersheds are used for comparative examinations. The study shows that DEM grid size has significant influence on the topographic index distribution which represents the effect of topography on watershed hydrology in TOPMODEL. The smoothing effect of grid size increase may result in deteriorated topographic index distributions at coarse resolutions as the ratio of grid cell area to watershed area gets larger. The simulated discharges and model efficiencies using a same set of TOPMODEL parameters are sensitive to DEM grid size especially at coarse resolutions. This sensitivity, however, can be moderated by parameter calibrations as the optimization runs show that fairly equal efficiencies can be preserved by the compensation effect of transmissivity parameter T₀ within a large extent of DEM resolution for each watershed. The interaction between T₀ and the topographic index distribution with respect to TOPMDOEL model performance is also examined. It is found that both study watersheds demonstrate a similar pattern of change in model performance along with the increase of the grid-to-watershed ratio. The analysis reveals that the ratio poses an important factor in controlling the effect of DEM grid size on TOPMODEL performance. A ratio of less than 5% is suggested in DEM resolution selection for TOPMODEL applications based on the results of this study.