Modeling seasonal variations of long-term soil CO<Subscript>2</Subscript> emissions in an orchard plantation in a semiarid area,SE Turkey

Emissions of soil CO₂ under different management systems have a significant effect on the carbon balance in the atmosphere. Soil CO₂ emissions were measured from an apricot orchard at two different locations: under the crown of trees (CO₂-UC) and between tree rows (CO₂-BR). For comparison, one other measurement was performed on bare soil (CO₂-BS) located next to the orchard field. Analytical data were obtained weekly during 8 years from April 2008 to December 2016. Various environmental parameters such as air temperature, soil temperature at different depths, soil moisture, rainfall, and relative humidity were used for modeling and estimating the long-term seasonal variations in soil CO₂ emissions using two different methods: generalized linear model (GLM) and artificial neural network (ANN). Before modeling, data were randomly split into two parts, one for calibration and the second for validation, with a varying number of samples in each part. Performances of the models were compared and evaluated using means absolute of estimations (MAE), square root of mean of prediction (RMSEP), and coefficient of determination (R²) values. CO₂-UC, CO₂-BR, and CO₂-BS values ranged from 11 to 3985, from 9 to 2365, and from 8 to 1722 kg ha^?1 week^?1, respectively. Soil CO₂ emissions were significantly correlated (p?<?0.05) with some environmental variables. The results showed that GLM and ANN models provided similar accuracies in modeling and estimating soil CO₂ emissions, as the number of samples in the validation data set increased. The ANN was more advantageous than GLM models by providing a better fit between actual observations and predictions and lower RMSEP and MAE values. The results suggested that the success of environmental variables for estimations of CO₂ emissions using the two methods was moderate.     

Soil Water Content Estimated by Support Vector Machine for the Assessment of Shallow Landslides Triggering: the Role of Antecedent Meteorological Conditions

Soil water content is a key parameter for representing water dynamics in soils. Its prediction is fundamental for different practical applications, such as identifying shallow landslides triggering. Support vector machine (SVM) is a machine learning technique, which can be used to predict the temporal trend of a quantity since training from past data. SVM was applied to a test slope of Oltrepò Pavese (northern Italy), where meteorological parameters coupled with soil water content at different depths (0.2, 0.4, 0.6, 1.0, 1.2, 1.4 m) were measured. Two SVM models were developed for water content assessment: (i) model 1, considering rainfall amount, air temperature, air humidity, net solar radiation, and wind speed; (ii) model 2, considering the same predictors of model 1 together with antecedent condition parameters (cumulated rainfall of 7, 30, and 60 days; mean air temperature of 7, 30, and 60 days). SVM model 2 showed significantly higher satisfactory results than model 1, for both training and test phases and for all the considered soil levels. SVM models trends were implemented in a methodology of slope safety factor assessment. For a real event occurred in the tested slope, the triggering time was correctly predicted using data estimated by SVM model based on antecedent meteorological conditions. This confirms the necessity of including these predictors for building a SVM technique able to estimate correctly soil moisture dynamics in time. The results of this paper show a promising potential application of the SVM methodologies for modeling soil moisture required in slope stability analysis.     

Modeling rhizofiltration: heavy-metal uptake by plant roots

The discovery of phytoaccumulation potential of plant species has led to its application for remediation of heavy-metal-contaminated soil and wastewater, which is termed as phytoextraction/rhizofiltration. For prediction, analysis, planning and cost-effective design of such systems, mathematical models not only are used as a screening tool but also provide optimal parameters like harvesting time, irrigation schedule, etc. Several laboratory and field scale studies have been carried out in the past, and mathematical expressions have been developed by various researchers for different phenomena like metal adsorption in soil, plant root growth with time, moisture and metal uptake by plant root, moisture movement in unsaturated zone, soil moisture relationship, etc. The complete design of any such phytoremediation program would require the knowledge of behavior of heavy-metal movement in soil, water and plant root system. In this paper, a model for simulating heavy-metal dynamics in soil, water and plant root system is developed and discussed. The governing non-linear partial differential equation is solved numerically by implicit finite difference method using Picard's iterative technique, and the formulation has been illustrated using a characteristic example. The source code is written in MATLAB.     

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the “Enter” method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685–3.65% and 0.9988–0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.     

Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.     

Alternative soil quality indices for evaluating the effect of intensive cropping, fertilisation and manuring for 31 years in the semi-arid soils of India

Soil quality assessment provides a tool for evaluating the sustainability of alternative soil management practices. Our objective was to develop the most sensitive soil quality index for evaluating fertilizer, farm yard manure (FYM), and crop management practices on a semiarid Inceptisol in India. Soil indicators and crop yield data from a long-term (31 years) fertilizer, manure, and crop rotation (maize, wheat, cowpea, pearl millet) study at the Indian Agricultural Research Institute (IARI) near New Delhi were used. Plots receiving optimum NPK, super optimum NPK and optimum NPK + FYM had better values for all the parameters analyzed. Biological, chemical, and physical soil quality indicator data were transformed into scores (0 to 1) using both linear and non-linear scoring functions, and combined into soil quality indices using unscreened transformations, regression equation, or principal component analysis (PCA). Long-term application of optimum inorganic fertilizers (NPK) resulted in higher soil quality ratings for all methods, although the highest values were obtained for treatment, which included FYM. Correlations between wheat (Triticum aestivum L.) yield and the various soil quality indices showed the best relationship (highest r) between yield and a PCA-derived SQI. Differences in SQI values suggest that the control (no NPK, no manure) and N only treatments were degrading, while soils receiving animal manure (FYM) or super optimum NPK fertilizer had the best soil quality, respectively. Lower ratings associated with the N only and NP treatments suggest that one of the most common soil management practices in India may not be sustainable. A framework for soil quality assessment is proposed.     

A methodology to identify representative configurations of sensors for monitoring soil moisture

Soil moisture is the key link among hydroecological compartments, responding dynamically to sequences of atmospheric processes and management conditions and modulating physical, chemical, and biological processes in the soil. Currently, there are a variety of monitoring techniques to measure, directly or indirectly, the soil moisture. However, some practical issues remain open like the definition a priori of the number, location and depth of the monitoring points, and the impact of failing or poor performance soil moisture sensors. Here, we present a set of techniques, namely Δθ time series, wavelet filtering, and time stability, to identify representative points and monitoring depths through an analysis of hourly soil moisture time series for different configuration of the monitoring network. We used real data from a monitoring network consisting of seven monitoring points, each one with four EC-5 probes (Decagon Devices Inc., Pullman, WA) at 20, 40, 60, and 100?cm. The use of simple time series of Δθ allowed us to assess the spatiotemporal influence of the monitoring points, while the wavelet periodograms allowed us to get insight about the response of the monitoring points at different time scales. Both methods are easy to implement or adapt to specific conditions, being coherent to the results derived from time stability analysis. For our case study, we concluded that we could reallocate 16 sensors (out of 28) without a significant loss of information. However, the final decision strongly relies on a deep knowledge of the site features and the objectives of the monitoring network.     

A Multivariate Analysis of Biophysical Parameters of Tallgrass Prairie Among Land Management Practices and Years

Six treatments of eastern Kansas tallgrass prairie – native prairie, hayed, mowed, grazed, burned and untreated – were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference VegetationIndex (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine whichvariables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was asignificant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects ofdifferent land management practices but not to yearlychange in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.     

Formulation and Validation of an Empirical Moisture Damage Index

An empirical moisture damage index was developed in order to reduce the subjectivity of the estimation of moisture damage in domestic residences, in relation to occupant health. The database was generated using information gathered from a sample of 164 houses that were examined by civil engineers, and questionnaire data collected from the occupants. The index was formulated to associate with the occupant reported respiratory symptoms by calculating weighted estimates of selected moisture damage attributes using 80% of the sample. The remaining 20% of the sample was used to verify the final index. The index associated strongly with the health symptoms of interest. This index is a tool that may be used as an indicator of moisture damage induced exposure in domestic residences.     

STUMOD—a Tool for Predicting Fate and Transport of Nitrogen in Soil Treatment Units

A typical onsite wastewater treatment system consists of a septic tank and a soil treatment unit to treat wastewater before it is discharged through the vadose zone to an aquifer. A tool was developed for the purpose of predicting the fate and transport of nitrogen in soil treatment units (STUMOD or Soil Treatment Unit Model). STUMOD calculates nitrogen species concentrations and the fraction of total nitrogen reaching the aquifer or a specified soil depth. Input data include parameters for hydraulics and nutrient transport and transformation. An analytical solution is used to calculate the profile of pressure based on Darcy’s equation and the relationships between suction head, unsaturated hydraulic conductivity, and soil moisture. Chemical transport is based on simplification of the advection–dispersion equation. STUMOD is relatively simple to use but accounts for important processes such as ammonium sorption, nitrification, and denitrification. STUMOD accounts for the effect of soil moisture content (a surrogate for redox conditions) on nitrification and denitrification reactions. The model has provisions to handle the influence of temperature and organic carbon content on nitrogen transformation. Model outputs, generated based on input parameters obtained from extensive literature review, were compared to a numerical model and data from laboratory tests and field sites. Both measured data and STUMOD outputs show a relatively higher removal in clayey soils compared to sandy soils. Consistent with literature data for most soils, STUMOD predicted ammonium conversion to nitrate within the first foot below the trench infiltrative surface.     

遥感监测土壤湿度综述

遥感技术具有大面积同步观测，时效性、经济性强等特点，为大面积动态监测土壤湿度提供了可能。本文对近年来国内外遥感监测土壤湿度的理论、方法的发展和应用进行了回顾，重点介绍了目前已经比较成熟和广泛应用的基于可见光与热红外波段的植被指数方法以及在干旱、半干旱地区的应用，通过对比分析了各种遥感监测方法的优缺点，指出了土壤湿度遥感监测方法存在的不足，展望了土壤湿度遥感监测方法的发展趋势。     

Development of a reference coastal wetland set in Southern New England (USA)

Various measures of plants, soils, and invertebrates were described for a reference set of tidal coastal wetlands in Southern New England in order to provide a framework for assessing the condition of other similar wetlands in the region. The condition of the ten coastal wetlands with similar hydrology and geomorphology were ranked from least altered to highly altered using a combination of statistical methods and best professional judgment. Variables of plants, soils, and invertebrates were examined separately using principal component analysis to reduce the multidimensional variables to principal component scores. The first principal component scores of each set of variables (i.e., plants, soil, invertebrates) significantly (p?<?0.05) correlated with both residential land use and watershed nitrogen (N) loads. Using cumulative frequency diagrams, the first principal component scores of each plant, soil, and invertebrate data set were plotted, and natural breaks and best professional judgment were used to rank the first principal component scores among the sites. We weighted all three ranked components equally and calculated an overall salt marsh condition index by summing the three ranks and then transforming the index to a 0–1 scale. The overall salt marsh condition index for the reference coastal wetland set significantly correlated with the residential land use (R?=???0.87, p?=?0.001) and watershed N loads (R?=???0.86, p?=?0.001). Overall, condition deteriorated in salt marshes and their associated discharge streams when subjected to increasing watershed residential land use and N loads.     

Soil water, salt, and groundwater characteristics in shelterbelts with no irrigation for several years in an extremely arid area

This paper is based on long-term monitoring data for soil water, salt content, and groundwater characteristics taken from shelterbelts where there has been no irrigation for at least 5 years. This study investigated the distribution characteristics of soil water and salt content in soils with different textures. The relationships between soil moisture, soil salinity, and groundwater level were analyzed using 3 years of monitoring data from a typical oasis located in an extremely arid area in northwest China. The results showed that (1) the variation trend in soil moisture with soil depth in the shelterbelts varied depending on soil texture. The soil moisture was lower in sandy and loamy shelterbelts and higher in clay shelterbelts. (2) Salinity was higher (about 3.0 mS cm^?1) in clay shelterbelts and lower (about 0.8 mS cm^?1) in sandy shelterbelts. (3) There was a negative correlation between soil moisture in the shelterbelts and groundwater level. Soil moisture decreased gradually as the depth of groundwater table declined. (4) There was a positive correlation between soil salinity in the shelterbelts and the depth of groundwater table. Salinity increased gradually as groundwater levels declined.     

Evaluation of geochemical associations as a screening tool for identifying anthropogenic trace metal contamination

Geochemical association plots are used as a screening tool for environmental site assessments and use empirical log–log relationships between total trace metal concentrations and concentrations of a major (i.e., reference) soil metal constituent, such as iron (Fe), to discern sites with naturally elevated trace metal levels from sites with anthropogenic contamination. Log–log relationships have been consistently observed between trace metal and reference metal concentrations and are often considered constant. Consequently, we used a regional geochemistry data set to evaluate background trace metal/Fe log–log associations across soils with highly diverse composition. Our results indicate that, although geochemical associations may be proportional, they significantly differ across predominant United States Department of Agriculture (USDA) soil orders. This suggests that highly complex interactions between soil-forming factors and variable secondary clay mineral composition affect the ratio of trace metals to Fe concentrations in soils. Also, intra-order variability in trace metal/Fe ratios generally ranged multiple orders of magnitude which suggest that the order level of the USDA soil taxonomic system is insufficient to reasonably classify background trace metal concentrations. Consequently, geochemical association plots are a useful screening tool for environmental site assessments, but ubiquitous application of generic background metal data sets could result in erroneous conclusions. Because significantly different ratios were observed across predominant USDA soil orders, an agglomerative clustering technique was used to elucidate hierarchical patterns of association. We present these results as a mechanism to aid environmental assessors in screening candidate background metal data sets for their applicability to site-specific soil composition; although site-specific background metal data should be utilized if ample pristine reference sites with similar (i.e., sub-order) soil composition can be identified and sampled.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Fluroxypyr biodegradation in soils by multiple factors

Fluroxypyr (4-amino-3,5-dichloro-6-fluoro-2-pyridinyl-1-methylheptyl ester) is a widely used herbicide for controlling weeds, fungi, and insects. However, extensive use of the herbicide has led to its high accumulation in ecosystems and contamination to soils and crops. Environmental behaviors and fate of herbicides are dependent on many physiochemical and biological factors. Whether fluroxypyr is significantly affected and how it is degraded under the environmental conditions is largely unknown. The present study investigated the effects of soil microbe, soil type, dissolved organic matter (DOM), temperature, soil moisture, and surfactant on fluroxypyr degradation in soils. Application of DOM derived from sludge and straw to fluroxypyr-contaminated soils increased degradation of fluroxypyr. Environmental factors such as temperature, moisture, soil microbe and soil type could affect the rate of fluroxypyr dissipation. Also, the microorganism affected the degradation of fluroxypyr. Analysis by gas chromatography-mass revealed that the reaction in soils might include the removal of 1-methylheptyl ester to generate fluroxypyr acid (4-amino-3,5-dichloro-6-fluoro-2-pyridiny). Our results provided initial data that a set of biological and physiochemical factors coordinately regulates the decay of fluroxypyr in soils.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Mixed Integer Linear Programming Models for Selecting Ground-Level Ozone Control Strategies

The traditional strategy for ground-level ozone control is to apply emission reductions across the board throughout certain time periods and locations. In this paper, we study various mixed integer linear programming (MILP) models that seek to select targeted control strategies for the Dallas Fort-Worth (DFW) region to reduce emissions, in order to achieve the State Implementation Plan (SIP) requirements with minimum cost. Statistics and optimization methods are used to determine a potential set of cost-effective control strategies for reducing ozone. These targeted control strategies are specified for different types of emission sources in various time periods and locations. Three MILP models, a static model, a sequential model, and a dynamic model, are studied in this research. These different MILP models allow decision makers to study how the targeted control strategies change under different circumstances. Meanwhile, two types of auxiliary variables are considered as supplemental control strategies in the optimization if the current set of control strategies is unable to reduce ozone to comply with the 8-h ozone standard. Results from the different models can provide decision makers with information concerning how the effectiveness of the control strategies varies with daily emission patterns and meteorology.     

Estimation of Biogas Production Rate in a Thermophilic UASB Reactor Using Artificial Neural Networks

Biogas production rate was modeled and estimated in a thermophilic upflow anaerobic sludge blanket digester. Data set covers a time period of both steady-state conditions and an abnormal operation condition, i.e., organic loading shocks. Multilayer neural networks topology was used as the modeling tool. Half of the experimental data were used for the training of the model and the remaining half were used for the testing stage. Model results were evaluated from the point of view of both steady conditions and abnormal conditions. It was seen from the time series trends of the estimated data that biogas production rates at steady state operation conditions were closely estimated by the model while the results for organic loading shocks were sufficiently followed. Artificial neural network models gave encouraging estimation results for the online control of thermophilic reactors.