Geostatistical analysis of Palmerton soil survey data

This paper presents a literature review focused on predictive technique audits, one of the types of audit considered to have the greatest potential role in improving environmental impact assessment practice. The literature review is limited to US literature with the exception of a few UK audits, one undertaken by Tomlinson at the University of Aberdeen. The authors are, however, aware that literature from other countries exists on this subject, for example from Canada and South Africa.In the review, predictive technique audits performed for or by the US Bureau of Land Management, the Electric Power Research Institute, the US Nuclear Regulatory Commission, the US Corps of Engineers, together with the Wisconsin Power Plant Impact Study are described. In addition, articles describing the auditing of models designed to predict environmental change are reviewed, before details of auditing activity in the UK are presented.     

Geostatistical strategy for soil sampling: the survey and the census

A soil sampling strategy for spatially correlated variables using the tools of geostatistical analysis is developed. With a minimum of equations, the logic of geostatistical analysis is traced from the modeling of a semi-variogram to the output isomaps of pollution estimates and their standard deviations. These algorithms provide a method to balance precision, accuracy, and costs. Their axiomatic assumptions dictate a two-stage sampling strategy. The first stage is a sampling survey, using a radial gird, to collect enough data to define, by a semi-variogram, the ranges of influence and the orientation of the correlation structure of the pollutant plume. The second stage is a census of the suspected area with grid shape, sizes and orientation dictated by the semi-variogram. The subsequent kriging analysis of this data gives isopleth maps of the pollution field and the standard error isomap of this contouring. These outputs make the monitoring data understandable for the decision maker.     

Magnetic susceptibility measurements as proxy method to monitor soil pollution: development of experimental protocols for field surveys

In the framework of the development of new methods for measuring and monitoring soil pollution, this paper deals with the use of magnetic methodologies to monitor the heavy metals presence in soils. In particular it shows a procedure for collecting magnetic susceptibility measurements in order to interpret them as proxy variable for monitoring heavy metals in soils. Magnetic measurements are carried out using a magnetic susceptibility meter with two different probes for in situ field surveys. The experimental procedure is divided in two parts. In the first part we carry out laboratory tests aimed to evaluate, for both the probes, the effective investigation depth for soil, the measurement reproducibility under different conditions, and the influence of water content. We complete this part comparing in situ measurements obtained by means of two probes with different characteristics. In the second part we carry out tests to evaluate the relationships between heavy metal levels and magnetic susceptibility values of soil samples. We investigate the variability of the magnetic susceptibility measurements contaminating different soil samples with well known concentration of heavy metals. Moreover we study the correlation between magnetic susceptibility values and metal concentrations, determined by means of AAS, in soil samples collected during a field survey. Results suggest that a careful check of the experimental procedure play a crucial role for using magnetic susceptibility measurements for heavy metals in situ monitoring. This is very helpful both for improving the quality of data and for making simpler data interpretation.     

Magnetic susceptibility measurements as proxy method to monitor soil pollution: the case study of S. Nicola di Melfi

The development of in situ, cheep, noninvasive, and fast strategies for soil monitoring is a crucial task for environmental research. In this paper, we present the results of three field surveys carried out in an industrial area of Southern Italy: S. Nicola di Melfi. The monitoring procedure is based on soil magnetic susceptibility measurements carried out by means of experimental protocols that our research group developed during the last years. This field surveys is supported by both geological characterization of the area and analytical determinations of metal concentrations in soils. Magnetic studies were carried out not only in situ but also in laboratory. Results show that, taking into account the influence due to the geomorphologic difference, soil magnetic susceptibility is an optimal indicator of the anthropogenic impact. So, our monitoring strategy discloses that the combined use of magnetic susceptibility measurements and soil geomorphology information may be used as a useful tool for the temporal monitoring of pollution evolution and for a fast screening of polluted zones.     

Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level

Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for sustainable use of groundwater. Regions in which were detected as critical areas need to be much more managed for using the current water resources efficiently. Conducting water harvesting systems especially in critical and hot areas in order to recharge the groundwater, and altering the current cropping pattern to another one that need less water requirement and applying modern irrigation techniques are highly recommended; otherwise, it is most likely that in a few years no more crop would be cultivated.     

Mapping Soil Gas Radon Concentration: A Comparative Study of Geostatistical Methods

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.     

Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions. Hence to review and devise strategies to reduce the adverse impacts of soil erosion is of utmost importance to the planners of watershed management programs in these regions. This paper demonstrates the use of satellite based remote sensing data coupled with the observational field data in a multi-criteria analytical (MCA) framework to estimate the soil erosion susceptibility of the sub-watersheds of the Rembiara basin falling in the western Himalaya, using geographical information system (GIS). In this paper, watershed morphometry and land cover are used as an inputs to the MCA framework to prioritize the sub-watersheds of this basin on the basis of their different susceptibilities to soil erosion. Methodology included the derivation of a set of drainage and land cover parameters that act as the indicators of erosion susceptibility. Further the output from the MCA resulted in the categorization of the sub-watersheds into low, medium, high and very high erosion susceptibility classes. A detailed prioritization map for the susceptible sub-watersheds based on the combined role of land cover and morphometry is finally presented. Besides, maps identifying the susceptible sub-watersheds based on morphometry and land cover only are also presented. The results of this study are part of the watershed management program in the study area and are directed to instigate appropriate measures to alleviate the soil erosion in the study area.     

Using of high-resolution topsoil magnetic screening for assessment of dust deposition: comparison of forest and arable soil datasets

Magnetic susceptibility (κ) is an easily detectable geophysical parameter that can be used as a proxy or semi-quantitative tracer of atmospheric industrial and urban dusts deposited in topsoil. An enhanced κ value of topsoil is in many cases also associated with high concentrations of soil pollutants (mostly heavy metals). High-resolution magnetic screening of topsoil in areas of high pollution influx is a useful tool for detection of pollution “hot spots”. General and regional screening maps with a grid density of 10 or 5 km have been performed on the basis of forest topsoil measurement only. The purpose of this study was to perform high-resolution magnetic screening with different grid densities in both forested and agricultural areas (arable land). Our large study area (ca. 200 km²) was located in a relatively more polluted region of the central part of Upper Silesia, and a second (small) one (ca. 100 m²) was located in the western part of Upper Silesia, with considerably lower influx of pollution. In the framework of this study, we applied a statistical comparison of data obtained in forested areas and on arable land. The arable soil showed statistically significantly lower κ values, the result of “physical dilution” of the arable layer caused by annual ploughing. Thus arable soils must be avoided during high-resolution field measurement. From semivariograms, it was clear that the spatial correlations in forest topsoil are much stronger than in arable soil, which suggests that a denser measurement grid is required in forested areas.     

Spatial 3D distribution of soil organic carbon under different land use types

Soil organic carbon (SOC) has been assessed in three dimension (3D) in several studies, but little is known about the combined effects of land use and soil depth on SOC stocks in semi-arid areas. This paper investigates the 3D distribution of SOC to a depth of 1 m in a 4600-ha area in southeastern Iran with different land uses under the irrigated farming (IF), dry farming (DF), orchards (Or), range plants on the Gachsaran formation (RaG), and range plants on a quaternary formation (RaQ). Predictions were made using the artificial neural networks (ANNs), regression trees (RTs), and spline functions with auxiliary covariates derived from a digital elevation model (DEM), the Landsat 8 imagery, and land use types. Correlation analysis showed that the main predictors for SOC in the topsoil were covariates derived from the imagery; however, for the lower depths, covariates derived from both the DEM and imagery were important. ANNs showed more efficiency than did RTs in predicting SOC. The results showed that 3D distribution of SOC was significantly affected by land use types. SOC stocks of soils under Or and IF were significantly higher than those under DF, RaG, and RaQ. The SOC below 30 cm accounted for about 59% of the total soil stock. Results showed that depth functions combined with digital soil mapping techniques provide a promising approach to evaluate 3D SOC distribution under different land uses in semi-arid regions and could be used to assess changes in time to determine appropriate management strategies.     

Physical characterization,magnetic measurements,REE geochemistry and biomonitoring of dust load accumulated during a protracted winter fog period and their implications

The winter fog in India is a recurrent phenomenon for more than a decade now affecting the entire Himalayan and sub-Himalayan regions covering an area of nearly 500,000 km². Every winter (December–January), the air and surface transports in cities of northern India (Amritsar, New Delhi, Agra, Gwalior, Kanpur, Lucknow, and Allahabad) are severely disrupted with visibility reduced to <50 m at times. Since dust particles are known to act as nuclei for the fog formation, this study is aimed to carry out physicochemical characterization of the dust particulates accumulated during a protracted fog period from one of the severely fog affected cities of north India (Allahabad; 25°27′33.40″N–81°52′45.47″E). The dust-loaded tree leaves belonging to Ficus bengalensis and Ficus religiosa from 50 different locations between January 24 and 31, 2010 are sampled and characterized. The mass of dust, color, grain shape, size, phase constituents, and mineral magnetic parameters, such as magnetic susceptibility, SIRM, χ _fd%, and S-ratio, show minor variation and the regional influence outweighs local anthropogenic contributions. The dust compositions show fractionated rare earth element pattern with a pronounced negative Eu anomaly similar to upper continental crust and further suggesting their derivation from sources located in parts of north and central India.     

Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer,Iran

Geostatistical methods are one of the advanced techniques to interpolate groundwater quality data. Geostatistical interpolation techniques employ both the mathematical and the statistical properties of the measured points. Compiling the data distribution on spatial and temporal domain is of crucial importance in order to evaluate its quality and safety. The main purpose of this paper is to assess groundwater quality of Arak plain, Iran, by an unbiased interpolated method so called Kriging. Therefore, seven quality variables of Arak plain aquifer including TDS, SAR, EC, Na⁺, TH, Cl^?, and SO₄ ^2? have been analyzed, studied, and interpreted statistically and geostatistically. Utilized data in this study were collected from 97 water well samples in Arak plain, in 2012. After normalizing data, variogram as a geostatistical tool for defining spatial regression was calculated and experimental variograms have been plotted by GS⁺ software, then the best theoretical model was fitted to each variogram based on minimum RSS error. Cross validation was used to determine the accuracy of the estimated data. The uncertainty of the method could be well assessed via this method since the method not only gave the average error (around 0 in this study) but also gave the standard deviation of the estimations. Therefore, more than 3800 points were estimated by ordinary Kriging algorithm in places which have not been sampled. Finally, estimation maps of groundwater quality were prepared and map of estimation variance, EV, has been presented to assess the quality of estimation in each estimated point. Results showed that the Kriging method is more accurate than the traditional interpolation algorithms not honoring the spatial properties of the database.     

Soil fauna and soil microflora as possible indicators of soil pollution

Research on biological indicators of soil pollution is hampered by soil variability and temporal and spatial fluctuations of numbers of soil animals. These characters on the other hand promote a high biological diversity in the soil. A high diversity combined with persistent soil pollutants increases the chance to select good indicators. However research on these topics is still limited. Examples of specific indicators are the changed arthropod species patterns due to pesticide influence and the changed soil enzyme activity under the influence of specific heavy metals. Another approach is to look for organisms that give a general indication of soil pollution. In this respect the earthworm species Allolobophora caliginosa proved to be sensitive for different types of manure especially pig manure with copper, for sewage sludge, for municipal waste compost and for fly ash. A third way of indication is by organisms accumulating pollutants. For some heavy metals (Cd, Zn), earthworms are very efficient accumulators. More research is needed especially on the specific relation between biological responses and abiotic soil characteristics.     

Rayleigh scattering measurements of several fluorocarbon gases

Integrating nephelometers are commonly used to monitor airborne particulate matter. However, they must be calibrated prior to use. The Rayleigh scattering coefficients (b(RS), Mm(-1)), scattering cross sections (σ(RS), cm(2)), and Rayleigh multipliers for tetrafluoromethane (R-14), sulfur hexafluoride, pentafluoroethane (HFC-125), hexafluoropropene (HFC-216), 1,1,1,2,3,3,3,-heptafluoropropane (HFC-227ea), and octafluorocyclobutane (C-318) are reported from measurements made using a Radiance Research M903 integrating nephelometer operating at λ = 530 nm and calibration with gases of known scattering constants. Rayleigh multipliers (±90% conf. int.) were found to be 2.6 ± 0.5, 6.60 ± 0.07, 7.5 ± 1, 14.8 ± 0.9, 15.6 ± 0.5, and 22.3 ± 0.8 times that of air, respectively. To the best of our knowledge, these are the first reported values for R-14, HFC-216, HFC-125, and C-318. Experimental accuracy is supported through measurements of values for SF(6) and HFC-227ea which agree to within 3% of previous literature reports. In addition to documenting fundamental Rayleigh scattering data for the first time, the information presented within will find use for calibration of optical scattering sensors such as integrating nephelometers.     

SAOZ measurements of NO2 at Aberystwyth

We present in this paper fifteen years' measurements, from March 1991 to September 2005, of stratospheric NO2 vertical columns measured by a SAOZ zenith-sky visible spectrometer. The instrument spent most of its time at Aberystwyth, Wales, with occasional excursions to other locations. The data have been analysed with the WinDOAS analysis program with low-temperature high-resolution NO2 cross-sections and fitting a slit function to each spectrum. Because of a change in detector in May 1998 there is some uncertainty about the relative changes before and after this date, which are partially constrained by the results of an intercomparison exercise. However, the effect of the Mt Pinatubo aerosol cloud is very evident in the data from 1991-94, with a decrease of 10% in NO2 in the summer of 1992 (the SAOZ was located in Lerwick, Scotland during the winter of 1991-92 and observed very low NO2 values but these cannot be directly compared to the Aberystwyth data). To focus more on interannual and long-term variations in NO2, a seasonal variation comprising an annual and semi-annual component was fitted to the morning and evening twilight separately from 1995 to the present. This fit yielded average NO2 columns of 4.08 x 10(15) cm(-2) and 2.68 x 10(15) cm(-2) for the evening and morning twilight, respectively, with a corresponding annual amplitude of +/-2.08 x 10(15) cm(-2) and +/-1.50 x 10(15) cm(-2). Departures from the fitted curve show a trend of 6% per decade, consistent with that reported elsewhere, for the period 1998-2003, but in the past two years a distinct interannual variation of amplitude of approximately 8% has emerged.     

日本土壤环境质量标准与污染现状

介绍了日本土壤污染的环境质量标准以及日本市区、农用地土壤和有毒有害化学物质的污染现状     

Spatial modeling of forest stand susceptibility to logging operations

The susceptibility of residual, non-harvested, live trees to damage caused by the harvesting of other nearby trees has received moderate attention over the last four decades through observational studies prompted by concerns over ecological and economic consequences of logging operations. We developed models to predict the potential level of damage to residual trees that could be caused by selective timber harvesting. Three machine-learning methods, i.e., classification and regression tree (CART), random forest (RF), and boosted regression tree (BRT), were assessed for this purpose. Through an observational study of a harvested area in the Hyrcanian forests of Iran, we recorded damage to trees >7.5 cm diameter at breast height along transects and grouped them into three types: (1) scars >100 cm², (2) >50% crown removal, and (3) trees leaning >10°. These field observations were associated with the spatially explicit characteristics of the forest stand, i.e., slope angle, slope aspect, altitude, slope length, topographic position index, stand type, stand density, and distance from the nearest roads and skid trails, that were considered as the explanatory variables to the modeling processes. To determine whether the CART, RF, and BRT models performed well in estimating the probability of damage occurrence, they were validated using the Akaike information criterion (AIC) and area under the receiver operating characteristics (AUC) curve. The results revealed that the BRT model with AIC = −276 and AUC = 0.89 generated the most accurate spatially explicit distribution map of stand susceptibility to damage from logging operations, followed by RF (AIC = −263 and AUC = 0.87) and CART (AIC = −23 and AUC = 0.62). We found that the spatial extent of residual stand damage was highly influenced by slope terrain and stand density. Our study has practical implications for reorganizing and planning reduced-impact logging operations and provides forest engineers with insights into the utility of machine learning methods in domains of forestry and forest engineering.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.     

downscaling procedures as a tool for integration of multiple air issues

In assessing the risks associated with climate change,downscaling has proven useful in linking surfacechanges, at scales relevant to decision making, tolarge-scale atmospheric circulation derived from GCMoutput. Stochastic downscaling is related to synopticclimatology, weather-typing approaches (classifyingcirculation patterns) such as the Lamb Weather Typesdeveloped for the United Kingdom (UK), the EuropeanGrosswetterlagen (Bardossy and Plate, 1992) and thePerfect Prognosis (Perfect Prog) method from numericalweather prediction. The large-scale atmosphericcirculation is linked with site-specific observationsof atmospheric variables, such as precipitation, windspeed or temperature, within a specified region. Classifying each day by circulation patterns isachieved by clustering algorithms, fuzzy rule bases,neural nets or decision trees. The linkages areextended to GCM output to account for climate change. Stochastic models are developed from the probabilitydistributions for extreme events. Objective analysiscan be used to interpolate values of these models toother locations. The concepts and some applicationsare reviewed to provide a basis for extending thedownscaling approach to assessing the integrated riskof the six air issues: climate change, UV-B radiation,acid rain, transport of hazardous air pollutants, smogand suspended particulates.