Monitoring of Cr, Cu, Pb, V and Zn in polluted soils by laser induced breakdown spectroscopy (LIBS)

Laser Induced Breakdown Spectroscopy (LIBS) is a fast and multi-elemental analytical technique particularly suitable for the qualitative and quantitative analysis of heavy metals in solid samples, including environmental ones. Although LIBS is often recognised in the literature as a well-established analytical technique, results about quantitative analysis of elements in chemically complex matrices such as soils are quite contrasting. In this work, soil samples of various origins have been analyzed by LIBS and data compared to those obtained by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). The emission intensities of one selected line for each of the five analytes (i.e., Cr, Cu, Pb, V, and Zn) were normalized to the background signal, and plotted as a function of the concentration values previously determined by ICP-OES. Data showed a good linearity for all calibration lines drawn, and the correlation between ICP-OES and LIBS was confirmed by the satisfactory agreement obtained between the corresponding values. Consequently, LIBS method can be used at least for metal monitoring in soils. In this respect, a simple method for the estimation of the soil pollution degree by heavy metals, based on the determination of an anthropogenic index, was proposed and determined for Cr and Zn.     

Designing Environmental Databases for Statistical Analyses

The Environmental Monitoring and Assessment Program (EMAP) collects data that are used to statistically assess the environmental condition of large geographic regions. These data are then posted on the EMAP web site so that anyone can use them. Databases used for the statistical analyses, "analytical" databases, differ in design from the "general-use" databases used by a secondary audience. Their scope is usually restricted in time, in geographic extent, and in type and content of data, often being limited to a single scientific discipline. Their structure may be more horizontal than vertical, so that statistical programs can import the data easily. Their design is strongly influenced by the nature of the scientific analysis because the goal is to create a good computing environment for that analysis. We illustrate these aspects of design with an analytical database for estuaries in the U.S. mid-Atlantic region.     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

Environmental impact of Information Communication Technology: A review of econometric assessment methods,influential mechanism,and influential direction

In recent decades, the rapid development of the global society and economy has brought serious environmental problems, such as air pollution, greenhouse effect, and ecosystem degradation, all of which have attracted wide attention. Simultaneously, the use of Information Communication Technology (ICT) has been on the rise since the 1980s, which stimulates the growth of the economy and has a certain degree of impact on the ecological environment. Accordingly, the scientific community's interest in assessing this impact has grown considerably, and econometric methods are widely used as evaluation tools. However, the research results are often different in different regions, periods, environmental proxies, and ICT. Some studies indicate that the development of ICT is beneficial to environmental improvement, while others state that it is not. Therefore, a comprehensive analysis of the methods, mechanisms, and relative policy suggestions of this topic is desiderated. To address this gap, this paper carries out a systematic literature review of the usage of econometric methods in the assessment of the environmental impact of ICT. Among the 472 articles retrieved from scientific literature databases, 46 met the inclusion criteria and were analyzed in detail. This study aims to provide scholars with a mental picture of the current state of scientific research, identify the factors that influence the direction of ICT's impact on the environment, and enable future researchers to use more reliable and accurate estimation methods. The methods used in selected articles were divided into two main groups (i.e., time series models and panel data models), and panel data models were further subdivided into five categories. The detailed flow of these methods is summarized, and new advanced pre-test methods and estimators are highlighted. Furthermore, the analysis also indicates that among all the countries and regions involved in the selected articles, there is a non-strict inverted U-shaped curve between the level of socio-economic development and the direction of the impact of ICT on the environment. Finally, current research challenges are discussed, and potential directions for future research on this topic are also projected.     

Comparison of Point Estimation Techniques in the Spatial Analysis of Radium-226, Radium-228 and Potassium-40 in Soil

Many environmental surveys require the implementation of estimation techniques to determine the spatial distribution of the variable being investigated. Traditional methods of interpolation and estimation, for example, inverse distance squared and triangulation often ignore features of the data set such as anisotropy which may have a significant impact on the quality of the estimates produced. Geostatistical techniques may offer an improved method of estimation by modelling the spatial continuity of the variable using semi-variogram analysis. The theoretical model fitted to the semi-variogram is then used in the assignation of weighting factors to the samples surrounding the location to be estimated. This paper outlines the results of a comparison between three common estimation methods, polygonal, triangulation and inverse distance squared and a geostatistical method, in the estimation of soil radionuclide activities. The geostatistical estimation method known as kriging performed best over a range of parameters used to test the performance of the methods. Kriging exhibited the best correlation between actual and estimated values, the narrowest error distribution and the lowest overall estimation error. Polygonal estimation was best at reproducing the data set distribution. Conditional bias was evident in all the methods, low values being over-estimated and high values being under-estimated.     

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.     

Assessment and monitoring of nutrient loading in the sediments of tidal creeks receiving shrimp farm effluent in Quang Ninh, Vietnam

Coastal shrimp farming may lead to the contamination of sediments of surrounding estuarine and marine ecosystems as shrimp farm effluent often contains high levels of pollutants including a range of organic compounds (from uneaten feed, shrimp feces, and living and dead organisms) which can accumulate in the sediments of receiving waterways. The assessment and monitoring of sediment quality in tidal creeks receiving shrimp farm effluent can support environmental protection and decision making for sustainable development in coastal areas since sediment quality often shows essential information on long-term aquatic ecosystem health. Within this context, this paper investigates nutrient loadings in the sediments of tidal creeks receiving shrimp farm effluent in Quang Ninh, Vietnam, which now have a high concentration of intensive and semi-intensive shrimp farms. Sediment samples taken from inside creek sections directly receiving effluent from concentrated shrimp farms (IEC), from main creeks adjacent to points of effluent discharge outside concentrated shrimp farms (OEC), and few kilometers away from shrimp farms (ASF) as reference sites were collected and analyzed before and after shrimp crops to investigate spatial and temporal variation. The results showed that there were statistically significant differences in the concentrations of total nitrogen, total phosphorus, and total organic carbon among IEC, OEC, and ASF sites while the seasonal variation being limited over study times. A sediment nutrient index (SNI) computed from coefficient scores of the factor analysis efficiently summarizes sediment nutrient loads, which are high, albeit quite variable, in canals directly receiving effluents from farms but then decline sharply with distance from shrimp farms. The visualization and monitoring of sediment quality data including SNI on maps can strongly support managers to manage eutrophication at concentrated shrimp farming areas, contributing to sustainable development and management at coastal zones.     

三明市酸、碱降水成因研究

三明市属福建省内陆山区,在东、西相差仅四公里的两个测点,常常一边下硫酸盐型酸雨,一边下钙盐型碱雨这一特殊降水过程。从离子成分、离子浓度比进行分析,并从局地污染源、气象等因素就其成因作了探索性研究,将影响降水ｐＨ值的污染因子进行灰色关联优势分析。结果表明,三明市降水污染相当严重,在重视酸性降水污染的同时,也不应忽视污染更为严重的碱性降水     

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity’s influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed’s influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.     

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.     

Some comments on analysis techniques for censored water quality data

When investigating trace substances in ambient water, a proportion of water sample concentrations is usually below limits of detection. In medical and industrial reliability studies, comparisons are often made of time to event data which includes right censored observations indicating only that an observation is greater than a specified value. In this paper consideration is given to the application of non-parametric procedures, widely used in the analysis of time to event data, to water quality data which is left censored.A non-parametric estimate of the cumulative distribution function for left censored water quality data can be generated quite easily. For the comparison of levels of trace substances it is necessary to combine an unconditional likelihood for the proportion of observations below a detection limit with a partial likelihood for the portion of the distribution above the detection limit in order to make use of regression methodology. The details of this are outlined and an example is given which compares levels of toxic substances at the head and mouth of the Niagara river.When comparisons are based on matched pair data, further modifications are necessary. A development paralleling that for time to event data is given. Consideration is also given to model extensions which allow for a dependence between observations at the same location over a period of time.The presentation is introductory and designed to illustrate the potential of some available methodology for use in the analysis of water quality data.     

Modified Grey Model and its application to groundwater flow analysis with limited hydrogeological data: a case study of the Nubian Sandstone, Kharga Oasis, Egypt

Groundwater flow at Kharga Oasis, located in the western desert of Egypt, was previously analyzed using numerical models; however, the lack of basic data often limits the implementation of these models, as well as introducing a problem for model calibration and validation. The Grey Model (GM) was used to overcome these difficulties of data limitation and uncertainty of hydrogeological conditions. However, no clear theories exist for selecting the number of input model trends and the most suitable values of input parameters. Therefore, in the current study, a modification of the GM is newly proposed and called the Modified Grey Model (MGM) in an attempt to determine a process for selecting the best input models' trends with the appropriate values of input parameters to achieve acceptable fitting to observations. The sensitivity analysis results showed that the MGM produced more stable results than the GM using a wide range of values for input parameters. Moreover, the MGM reduced the calculation time required for fitting the measured piezometric level trends by 99.8 %. Three development scenarios of groundwater withdrawal were proposed that involved either expanding the present extraction rate or redistributing the groundwater withdrawal over the recent working production wells (RWPWs). The results concluded that the groundwater table in the northern part of the oasis could be temporally recovered to an economical piezometric level; however, the table in the southern part is severely decreased. Therefore, new production wells are recommended to be constructed in the southern part far enough from the RWPWs.     

A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling

Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA

To evaluate the significant sources contributing to water quality parameters, we used principal component analysis (PCA) for the interpretation of a large complex data matrix obtained from the Kandla creek environmental monitoring program. The data set consists of analytical results of a seasonal sampling survey conducted over 2 years at four stations. PCA indicates five principal components to be responsible for the data structure and explains 76% of the total variance of the data set. The study stresses the need to include new parameters in the analysis in order to make the interpretation of principal components more meaningful. The PCA could be applied as a useful tool to eliminate multi-collinearity problems and to remove the indirect effect of parameters.     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

Factors influencing the sinuosity of Pannagon River, Kottayam, Kerala, India: An assessment using remote sensing and GIS

The present study was carried out to identify the factors influencing the sinuosity of the Pannagon river, using the IRS P6 LISS III data and Geographical Information System (GIS) on 1:50,000 scale. The river follows meandering course and exhibits a narrow, highly sinuous and incised channel. Several lines of evidence including satellite and topographic data, geological maps and field investigations and the generated themes on lithology, structure, geomorphology, slope, riparian vegetation and hydrology have analyzed to understand the controls on the channel morphology of the Pannagon river. The average of sinuosity index of the selected reaches in the river on 1967 was 1.6 and of 2004 was 1.8. The sinuous patches are more in the lower reaches of the river and most of the area comes under floodplain with thick column of alluvial deposits. The analysis shown that the style and degree of sinuosity of the Pannagon river depends on a number of geological factors, including tectonics and the riparian vegetation also plays a major role.     

Practitioners,professional cultures,and perceptions of impact assessment

The very nature of impact assessment (IA) means that it often involves practitioners from a very wide range of disciplinary and professional backgrounds, which open the possibility that how IA is perceived and practised may vary according to the professional background of the practitioner. The purpose of this study is to investigate the extent to which a practitioner's professional background influences their perceptions of the adequacy of impact assessment in New Zealand under the Resource Management Act (RMA). Information gathered concerned professional affiliations, training, understanding of impact assessment practise, and perceptions of adequacy in relation to impact assessment.The results showed a dominance of a legalistic, operational perspective of impact assessment under the Resource Management Act, across all the main professions represented in the study. However, among preparers of impact assessments there was clear evidence of differences between the four main professional groups – surveyors, planners, engineers and natural scientists – in the way they see the nature and purpose of impact assessment, the practical steps involved, and what constitutes adequacy. Similarly, impact assessment reviewers – predominantly planners and lawyers – showed variations in their expectations of impact assessment depending on their respective professional affiliation.Although in many cases the differences seem to be more of a matter of emphasis, rather than major disputes on what constitutes a good process, even those differences can add up to rather distinct professional cultures of impact assessment. The following factors are seen as leading to the emergence of such professional cultures: different professions often contribute in different ways to an impact assessment, affecting their perception of the nature and purpose of the process; impact assessment training will usually be a secondary concern, compared with the core professional training, which will be reflected in the depth and length of such training; and any impact assessment training provided within a profession will often have the “cultural” imprint of that profession.     

Source Apportionment Of Groundwater Pollution Around Landfill Site In Nagpur, India

The present work attempts statistical analysis of groundwater quality near a Landfill site in Nagpur, India. The objective of the present work is to figure out the impact of different factors on the quality of groundwater in the study area. Statistical analysis of the data has been attempted by applying Factor Analysis concept. The analysis brings out the effect of five different factors governing the groundwater quality in the study area. Based on the contribution of the different parameters present in the extracted factors, the latter are linked to the geological setting, the leaching from the host rock, leachate of heavy metals from the landfill as well as the bacterial contamination from landfill site and other anthropogenic activities. The analysis brings out the vulnerability of the unconfined aquifer to contamination.