Cumulative effects of developed road network on woodland—a landscape approach

Population growth, during the twentieth century, has increased demand for new farmlands. Accordingly, road networks have rapidly been developed to facilitate and accelerate human access to the essential resources resulted in extensive land use changes. The present study aims at assessing cumulative effects of developed road network on tree cover of Golestan Province in northern Iran. In order to detect changes over the study period of 1987–2002, the LULC map of the study area was initially prepared from the satellite images of Landsat TM (1987) and ETM+ (2002) using maximum likelihood supervised classification method. Afterwards, a total number of seven landscape matrices were selected to detect cumulative effects of the developed road network on woodland cover. The obtained results indicated that the fragile patches are mainly located at a distance of 171–342 m from the roadside. Furthermore, the majority of the patches affected by cumulative effects of development activities are situated at a distance of 342–684 m from the roadside, over an approximate area of 55 ha. The analysis of landscape metrics revealed that the developed road network has increased the landscape metrics of “the number of patches” and “patches perimeter-area ratio”. It has also followed by a decrease in metrics such as “patches area”, “Euclidean nearest neighbor distance”, “patches proximity”, “shape index”, “contiguity”, and “mean patches fractal dimension”. The road network has also increased the “number of patches” and decreased the “mean patches area” representing further fragmentation of the landscape. With identification of highly affected wooldland cover patches, it would be possible to apply adaptive environmental management strategies to preserve and rehabilitate high-priority patches.     

Systematic monitoring of heathy woodlands in a Mediterranean climate—a practical assessment of methods

Practical and useful vegetation monitoring methods are needed, and data compatibility and validation of remotely sensed data are desirable. Methods have not been adequately tested for heathy woodlands. We tested the feasibility of detecting species composition shifts in remnant woodland in South Australia, comparing historical (1986) plot data with temporal replicates (2010). We compared the uniformity of species composition among spatially scattered versus spatially clustered plots. At two sites, we compared visual and point-intercept estimation of cover and species diversity. Species composition (presence/absence) shifted between 1986 and 2010. Species that significantly shifted in frequency had low cover. Observations of decreasing species were consistent with predictions from temperature response curves (generalised additive models) for climate change over the period. However, long-term trends could not be distinguished from medium-term dynamics or short-term changes in visibility from this dataset. Difficulties were highlighted in assessing compositional change using historical baselines established for a different purpose in terms of spatial sampling and accuracy of replicate plots, differences in standard plot methods and verification of species identifications. Spatially clustered replicate plots were more similar in species composition than spatially scattered plots, improving change detection potential but decreasing area of inference. Visual surveys detected more species than point-intercepts. Visual cover estimates differed little from point-intercepts although underestimating cover in some instances relative to intercepts. Point-intercepts provide more precise cover estimates of dominant species but took longer and were difficult in steep, heathy terrain. A decision tree based on costs and benefits is presented assessing monitoring options based on data presented. The appropriate method is a function of available resources, the need for precise cover estimates versus adequate species detection, replication and practical considerations such as access and terrain.     

Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—a potential tool for assessing the hazard degree of dust and salt storms

With the recession of the Aral Sea in Central Asia, once the world’s fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000–2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4–8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.     

Analysis of heavy metal concentrations in sediments of selected estuaries of Malaysia—a statistical assessment

Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making     

Assessment of dietary intakes of nineteen pesticide residues among five socioeconomic sections of Hyderabad—a total diet study approach

Total diet study approach was used to assess the dietary intakes of pesticide residues among the select population in Hyderabad. When assessed by a food frequency questionnaire, it was found that the food intakes varied among five socioeconomic sections (SES). Therefore, we intended to compare the intakes of pesticide residues through these foods among the five SES. A total of 195 foods from different markets were collected and analyzed for 19 pesticides. The residues were analyzed with a gas chromatograph and were confirmed with mass spectrometry. About 51 % of the samples were detected with one or more residues. Thirteen out of the 19 residues were present in levels above detection limits in various concentrations. The median concentrations of the residues in all the samples tested, ranged from 0.00010 to 0.33 mg/kg. Highest median concentration was for β-HCH in water samples. Exposures to all the residues were below the respective ADIs at both mean and 95th percentile levels of food intakes with highest estimated dietary intakes (EDIs) of β-HCH in both the cases. The EDIs of β-HCH were the highest among all the residues at both the intake levels among all the SES. The EDIs of β-HCH were significantly higher in lower SES than higher SES possibly due to the consumption of rice cooked in water contaminated with β-HCH. EDIs for other residues did not differ significantly among the five SES.     

Sampling for landscape elements—a case study from Lower Saxony,Germany

The estimation of coverage, i.e., the proportion of the total area in a study region covered by a given target class, is essential to many aspects of environmental monitoring. We analyze and compare the efficiency of different sample-based approaches for the estimation of coverage of different land cover classes from aerial imagery in a case study in Lower Saxony, Germany on the basis of the estimated standard errors. A complete delineation of vegetation classes in n?=?279 aerial photo plots of 400 × 400 m thrown onto the study region of 1,117.7 km² in accordance with a systematic grid is compared to different configurations of line intercept sampling and clusters of points. The observation designs under study are characterized by different complexity and total size of the observation units and therefore also to the efforts related to yield a single observation. Especially for those classes that cover a relatively large proportion of the sampling frame, our results show that difference in performance between the different designs are negligible. A cluster of four transects of 200 m each allows estimating the area of land cover classes with high coverage with nearly similar precision as a complete mapping of fixed area plots of 16 ha each. Clusters of points show unexpected high precision for the estimated coverage of land cover classes with relatively high coverage.     

Development of tiger habitat suitability model using geospatial tools—a case study in Achankmar Wildlife Sanctuary (AMWLS), Chhattisgarh India

Geospatial tools supported by ancillary geo-database and extensive fieldwork regarding the distribution of tiger and its prey in Anchankmar Wildlife Sanctuary (AMWLS) were used to build a tiger habitat suitability model. This consists of a quantitative geographical information system (GIS) based approach using field parameters and spatial thematic information. The estimates of tiger sightings, its prey sighting and predicted distribution with the assistance of contextual environmental data including terrain, road network, settlement and drainage surfaces were used to develop the model. Eight variables in the dataset viz., forest cover type, forest cover density, slope, aspect, altitude, and distance from road, settlement and drainage were seen as suitable proxies and were used as independent variables in the analysis. Principal component analysis and binomial multiple logistic regression were used for statistical treatments of collected habitat parameters from field and independent variables respectively. The assessment showed a strong expert agreement between the predicted and observed suitable areas. A combination of the generated information and published literature was also used while building a habitat suitability map for the tiger. The modeling approach has taken the habitat preference parameters of the tiger and potential distribution of prey species into account. For assessing the potential distribution of prey species, independent suitability models were developed and validated with the ground truth. It is envisaged that inclusion of the prey distribution probability strengthens the model when a key species is under question. The results of the analysis indicate that tiger occur throughout the sanctuary. The results have been found to be an important input as baseline information for population modeling and natural resource management in the wildlife sanctuary. The development and application of similar models can help in better management of the protected areas of national interest.     

Contamination assessment of arsenic and heavy metals in a typical abandoned estuary wetland—a case study of the Yellow River Delta Natural Reserve

Coastal and estuarine areas are often polluted by heavy metals that result from industrial production and agricultural activities. In this study, we investigated the concentration trait and vertical pattern of trace elements, such as As, Cd, Ni, Zn, Pb, Cu, and Cr, and the relationship between those trace elements and the soil properties in coastal wetlands using 28 profiles that were surveyed across the Diaokouhe Nature Reserve (DKHNR). The goal of this study is to investigate profile distribution characteristics of heavy metals in different wetland types and their variations with the soil depth to assess heavy metal pollution using pollution indices and to identify the pollution sources using multivariate analysis and sediment quality guidelines. Principal component analysis, cluster analysis, and pollution level indices were applied to evaluate the contamination conditions due to wetland degradation. The findings indicated that the concentration of trace elements decreased with the soil depth, while Cd increases with soil depth. The As concentrations in reed swamps and Suaeda heteroptera surface layers were slightly higher than those in other land use types. All six heavy metals, i.e., Ni, Cu, As, Zn, Cr, and Pb, were strongly associated with PC1 (positive loading) and could reflect the contribution of natural geological sources of metals into the coastal sediments. PC2 is highly associated with Cd and could represent anthropogenic sources of metal pollution. Most of the heavy metals exhibited significant positive correlations with total concentrations; however, no significant correlations were observed between them and the soil salt and soil organic carbon. Soil organic carbon exhibited a positive linear relationship with Cu, Pb, and Zn in the first soil layer (0–20 cm); As, Cr, Cu, Ni, Pb, and Zn in the second layer (20–40 cm); and As, Cr, Cu, Ni, Pb, and Zn in the third layer (40–60 cm). Soil organic carbon exhibited only a negative correlation with Cd (P?I _geo values), which averaged less than 0 in the three soil layers, this finding indicates that the soils have remained unpolluted by these heavy metals. The mean concentrations of these trace elements were lower than Class I criteria. The degradation wetland restoration suggestions have also been provided in such a way as to restore the reserved flow path of the Yellow River. The results that are associated with trace element contamination would be helpful in providing scientific directions to restore wetlands across the world.     

The value of the environment – Is it a matter of approach?

This paper discusses why estimates of the benefits of reduced air pollution differ in accordance with the approach used. Estimates based on bottom-up studies of the damage costs related to air pollution usually turn out much lower than estimates based on assessments of the utility of reduced air pollution, obtained for instance by willingness to pay assessments. This is usually explained by the fact that the willingness to pay approach includes the utility aspect of non-market values, and for this reason, it is often preferred to the damage cost approach. This is, however, not the whole story. The paper shows why alternative approaches should not be considered as being in conflict, but rather as means to get supplementary information necessary to put a value on environmental quality. Information from bottom-up assessments of damage costs and from studies of the willingness to pay is used in a macroeconomic model to carry out an evaluation of the social costs of energy saving measures in Hungary.     

Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual interpretation for the complete archipelago. In the second step, we identified two island groups from the Andaman to investigate and compare the forest strata density. The third and final step involved more of a localised phytosociological module that focused on the North Andaman Islands. The results based on the analysis of the high-resolution satellite data show that more than 75% of the mangroves are under high- to very high-density canopy class. The framework developed would serve as a significant measure to forest health and evaluate management concerns whilst addressing issues such as gap identification, conservation prioritisation and disaster management—principally to the post-tsunami assessment and analysis.     

Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA. This revised version was published online in July 2006 with corrections to the Cover Date.     

Spatial and seasonal distribution of nitrate-N in groundwater beneath the rice�Cwheat cropping system of India: a geospatial analysis

Increased use of nitrogenous fertilizers in the intensively cultivated rice (Oryza sativa)?Cwheat (Triticum aestivum) cropping system (covers a 13.5-ha m area in South Asia) has led to the concentration of nitrates (NO₃-N) in the groundwater (GW) in Haryana State of India. Six districts from the freshwater zone were selected to identify factors affecting NO₃-N enrichment in GW. Water and soil samples were collected from 1,580 locations and analyzed for their chemical properties. About 3% (26,796, and 10,588 ha) of the area was estimated to be under moderately high (7.5?C10 mg l^???1) and high (>10 mg l^???1) risk categories, respectively. The results revealed that NO₃-N was 10?C50% higher during the pre-monsoon season than in the monsoon season. Nitrate-N decreased with the increase in aquifer depth (r ²?=?0.99). Spatial and proximity analyses using ArcGIS (9.2) revealed that (1) clay material in surface and sub-surface texture restricts N leaching, (2) piedmont and rolling plains act as an N sink, and (3) perennial rivers bring a dilution effect whereas seasonal rivers provide favorable conditions for NO₃ ^? enrichment. The study concludes that chemical N fertilizers applied in agro-ecosystems are not the sole factor determining the NO₃ in groundwater; rather, it is an integrated process governed by several other factors including physical and chemical properties of soils, proximity and type of river, and geomorphologic and geographical aspects. Therefore, future studies should adopt larger area (at least watershed scale) to understand the mechanistic pathways of NO₃ enrichment in groundwater and interactive role of the natural drainage system and surrounding physical features. In addition, the study also presents a conceptual framework to describe the process of nitrate formation and leaching in piedmont plains and its transportation to the mid-plain zone.     

Elemental contamination in urban parks of Rawalpindi/Islamabad—a source identification and pollution level assessment study

The soil quality of urban parks is of vital importance as the children playing in the parks get themselves easily contaminated. A study was conducted to assess the level of elemental pollution caused by constant urbanization and industrialization, in various parks of Islamabad and Rawalpindi. The soil samples, collected from 14 urban parks of Islamabad and Rawalpindi areas, were analysed for their elemental concentrations. In each sample, 32 elements were quantified using semi-absolute k (0)-instrumental neutron activation analysis and flame atomic absorption spectrophotometry. The quality of analysis was assured by analysing the International Atomic Energy Agency (IAEA) reference material IAEA-S7 (soil). The pollution level was assessed by enrichment factor, pollution load index and our suggested indicator called "average toxic element concentration". The elemental concentrations measured in the parks of two cities were compared by t test. Four sources of different elements in the soils were identified by employing principal component analysis and cluster analysis. The results of multivariate techniques grouped all parks into four classes. The use of enrichment factor indicated the presence of Ni at slightly higher level in all parks while the pollution load index revealed that the parks of Rawalpindi were relatively more polluted as compared to that of Islamabad. The hot spot areas of elemental concentration were closely related to high traffic conditions.     

Towards a more holistic sustainability assessment framework for agro-bioenergy systems — A review

The use of life cycle assessment (LCA) as a sustainability assessment tool for agro-bioenergy system usually has an industrial agriculture bias. Furthermore, LCA generally has often been criticized for being a decision maker tool which may not consider decision takers perceptions. They are lacking in spatial and temporal depth, and unable to assess sufficiently some environmental impact categories such as biodiversity, land use etc. and most economic and social impact categories, e.g. food security, water security, energy security. This study explored tools, methodologies and frameworks that can be deployed individually, as well as in combination with each other for bridging these methodological gaps in application to agro-bioenergy systems. Integrating agronomic options, e.g. alternative farm power, tillage, seed sowing options, fertilizer, pesticide, irrigation into the boundaries of LCAs for agro-bioenergy systems will not only provide an alternative agro-ecological perspective to previous LCAs, but will also lead to the derivation of indicators for assessment of some social and economic impact categories. Deploying life cycle thinking approaches such as energy return on energy invested-EROEI, human appropriation of net primary production-HANPP, net greenhouse gas or carbon balance-NCB, water footprint individually and in combination with each other will also lead to further derivation of indicators suitable for assessing relevant environmental, social and economic impact categories. Also, applying spatio-temporal simulation models has a potential for improving the spatial and temporal depths of LCA analysis.