The assessment of spatial distribution of soil salinity risk using neural network

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km²) results were used to upscale soil salinity to a district area (∼300 km²). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m⁻¹). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated.     

Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques

The Harran Plain is located in the southeastern part of Turkey and has recently been developed for irrigation agriculture. It already faces soil salinity problems causing major yield losses. Management of the problem is hindered by the lack of information on the extent and geography of the salinization problem. A survey was carried out to delineate the spatial distribution of salt-affected areas by randomly selecting 140 locations that were sampled at two depths (0 to 30 and 30 to 60 cm) and analyzed for soil salinity variables: soil electrical conductivity (EC), soluble cations (Ca^2+, Mg²⁺, Na⁺, and K⁺), soluble anions (SO ₄ ^2? , Cl^?), exchangeable Na⁺ (me 100 g^?1) and exchangeable sodium percentage. Terrain attributes (slope, topographical wetness index) were extracted from the digital elevation model of the study area. Variogram analyses after log transformation and ordinary kriging (OK) were applied to map spatial patterns of soil salinity variables. Multivariate geostatistical methods—regression kriging (RK) and kriging with external drift (KED)—were used using elevation and soil electrical conductivity data as covariates. Performances of the three estimation methods (OK, RK, and KED) were compared using independent validation samples randomly selected from the main dataset. Soils were categorized into salinity classes using disjunctive kriging (DK) and ArcGIS, and classification accuracy was tested using the kappa statistic. Results showed that soil salinity variables all have skewed distribution and are poorly correlated with terrain indices but have strong correlations among each other. Up to 65 % improvement was obtained in the estimations of soil salinity variables using hybrid methods over OK with the best estimations obtained with RK using EC_0–30 as covariate. DK–ArcGIS successfully classified soil samples into different salinity groups with overall accuracy of 75 % and kappa of 0.55 (p?<?0.001).     

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta,China

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0–15, 15–30, 30–45 and 45–60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.     

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.     

Examining the uncertainty of carbon emission changes: A systematic approach based on peak simulation and resilience assessment

Efforts to achieve carbon peak is one of the Chinese government's commitments, but the diversity of future development paths leads to the uncertainty of carbon emissions. Based on the carbon peak simulation, this study develops a framework to assess the carbon emission uncertainty, aiming to explore the potential low-carbon paths. The STIRPAT model is firstly introduced to explore the influence of population, economic and technology factors on carbon emissions, which is followed by emission peaks simulation. The resilience theory is then introduced to define the concept of low-carbon resilience (LCR), which refers to the ability to maintain a low level of carbon emissions. The uncertainty of carbon emission changes between different scenarios is identified by considering peaking time, cumulative increase and mitigation process. This study taking 10 Chinese coastal provinces as an example, and results show that all provinces can achieve the target of carbon emission peak in low-emissions scenario, the cumulative growth of carbon emissions is low and can be mitigated over a relatively short term, showing a strong LCR. In high-emissions scenario, Liaoning, Tianjin, Fujian and Guangxi may not have a peak before 2050, the uncertainty of carbon emission changes is relatively high, while Hebei, Jiangsu, Shanghai and Guangdong show relatively low uncertainty for the clear peaking time. The study also designs intermediate scenario to reduce the uncertainty of carbon emission changes to provide reference for each province's emission reduction path. These findings help to understand carbon uncertainty to reduce the risk of increasing cumulative emissions under the scenario of only focusing on peaking times, and provide a basis for future carbon resilience and sustainable emission reduction policies.     

Spatial variability of soil heavy metals in the three gorges area: multivariate and geostatistical analyses

There is a growing concern about environmental contamination in the three gorges area. The objectives of this study were to investigate the spatial variability and the possible influence factors of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in the center of this area based on multivariate and geostatistical approaches. All analyzed heavy metals were below their background levels, except Cd. The average concentrations of the analyzed elements in topsoil (0-20 cm) were 5.83 mg As kg(-1), 0.21 mg Cd kg(-1), 78.79 mg Cr kg(-1), 21.53 mg Cu kg (-1), 0.049 mg Hg kg(-1), 24.12 mg Pb kg(-1), and 68.5 mg Zn kg(-1). The concentration of As was mostly due to parent materials, whereas the source of Pb was mainly due to vehicle exhaust. The high concentration of Cd was resulted from agricultural practices and parent materials. The concentrations of Cr, Cu, Hg, and Zn were associated with parent materials and human activities.     

National assessment of sea level rise using topographic and census data for Turkish coastal zone

Turkish coastal zone elevation to sea level rise was illustrated by using digital elevation model and Geographical information systems methods. It was intended to determine several parameters such as population, settlements, land use, wetlands, contribution to national agricultural production and taxes at risk by using high resolution SRTM topographic, orthorectified Landsat Thematic Mapper Mosaics and census data with GIS methods within 0-10 m elevation of national level. All parameters were examined for coastal cities, coastal districts, settlements and villages' status. As a result of the analysis of data set, it was found that approximately 7,319 km(2) of land area lies below 10 m contour line in Turkey, and is hence highly vulnerable to sea-level rise. 28 coastal cities, 191 districts and 181 villages or towns are located below 10 m contour line in study area. In the short term, for the struggle of negative impact of sea level rise, the findings suggest that the Ministry of Environment should declare new areas as protection areas and develop special environmental programs for national level.     

Towards a framework for the assessment of saltwater intrusion in coastal aquifers

Saltwater intrusion (SWI) represents a threat to coastal aquifers worldwide by rendering groundwater quality not viable for its intended purposes. Therefore, understanding SWI impacts is indispensable for informed decision-making on aquifer management. Despite advances in methods to assess the impact of SWI, it remains challenging to select appropriate methods that are effective, timely, and affordable under the influence of a range of factors including aquifer characteristics, hydro-geochemical dynamics, shoreline geomorphology, biochemical reactions, and data availability among others. This study examines commonly used methods that assess the impact of SWI towards the development of an assessment framework in coastal aquifers underlying densely populated urban areas. The methods were selected using complexity-functionality criteria and then tested at a pilot aquifer by coupling Strengths, Weaknesses, Opportunities and Threats (SWOT) and Multi-Attribute Decision Making (MADM) analyses to evaluate the effectiveness of the methods and identify elements of the framework. The framework proved functional in synthesizing parametric results, assessing the dynamics of SWI and quantifying its potential impact, as well as providing an effective platform for informed impact assessment and planning for sustainable exploitation of coastal aquifers.     

Impacts of seawater intrusion on soil salinity and alkalinity in Bafra Plain, Turkey

This study was carried out to investigate possible seawater intrusion into groundwater along the coastal lines of the Bafra Plain and salinity–alkalinity problems over land areas irrigated with water exposed to seawater intrusion were evaluated. For this purpose, 32 groundwater wells were selected over the plain, water samples were taken from these wells between October 2007 and September 2008, and chemical analyses were performed over these samples. Soil samples were taken from the fields irrigated with this water at 32 different locations at the end of the irrigation season in September 2008 from 0–30, 30–60, 60–90, and 90–120 cm soil depths and textures. EC, pH, Na, Ca, Mg, and K analyses were performed over these samples. Excessive seawater intrusion was observed in some parts of the plain and impacts of seawater intrusion decreased with the distance from the coastal line. It was determined that groundwater quality was significantly affected from seawater intrusion. Salinity and especially alkalinity problems were observed in land areas irrigated with this water and alkalinity increased with the rate of intrusion.     

Monitoring and assessment of toxic metals in Gulf War oil spill contaminated soil using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was applied for the detection of toxic metals in oil spill contaminated soil (OSCS). The OSCS samples were collected from Khursania Saudi Arabia along the coast of Persian Gulf exposed to oil spills in 1991 Gulf war. Environmentally important elements like Aluminum Magnesium, Calcium, Chromium, Titanium, Strontium, Iron, Barium, Sodium, potassium, Zirconium and Vanadium from the contaminated soil have been detected. Optimal experimental conditions for analysis were investigated. The LIBS system was calibrated using standard samples containing these trace elements. The results obtained using Laser-Induced Breakdown Spectroscopy (LIBS) were compared with the results obtained using Inductively Coupled Plasma Emission Spectroscopy (ICP). The concentrations of some elements (Ba and Cr) were found higher than permissible safe limits. Health risks associated with exposure to such toxic elements are also discussed.     

Particle release transport in Danshuei River estuarine system and adjacent coastal ocean: a modeling assessment

A three-dimensional hydrodynamic model was created to study the Danshuei River estuarine system and adjacent coastal ocean in Taiwan. The model was verified using measurements of the time-series water surface elevation, tidal current, and salinity from 1999. We conclude that our model is consistent with these observations. Our particle-tracking model was also used to explore the transport of particles released from the Hsin-Hai Bridge, an area that is heavily polluted. The results suggest that it takes a much longer time for the estuary to be flushed out under low freshwater discharge conditions than with high freshwater discharge. We conclude that the northeast and southwest winds minimally impact particle dispersion in the estuary. The particles fail to settle to the bottom in the absence of density-induced circulation. Our model was also used to simulate the ocean outfall at the Bali. Our experimental results suggest that the tidal current dominates the particle trajectories and influences the transport properties in the absence of a wind stress condition. The particles tend to move northeast or southwest along the coast when northeast or southwest winds prevail. Our data suggest that wind-driven currents and tidal currents play important roles in water movement as linked with ocean outfall in the context of the Danshuei River.     

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0–30cm, 30–60cm, 60–90cm and 90–120cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.     

Insecticide residues in soil and water in coastal areas of vegetable production in Togo

Some common organochlorine, organophosphorus and pyrethroid insecticides were analysed in agricultural soil samples (n?=?35) and surface water and groundwater samples (n?=?25) collected from coastal areas of vegetable production in Togo. Analytical methods included solvent extraction of the insecticide residues and their subsequent quantification using GC-ECD. δ-HCH, heptachlor epoxide, 4,4-DDE, endosulphan (α, β and sulphate), lambda-cyalothrin and chlorpyrifos were found in the soil samples with concentrations that varied from non-detectable (ND) to 26.93 μg kg^?1 dry weight. For water samples, heptachlor epoxide, 2,4-DDD, 4,4-DDD, 4,4-DDE and endosulphan (α, β, and sulphate) were found at contamination levels that varied from ND to 0.116 μg L^?1. The concentration of insecticide residues detected in the water samples was below the limits set by the World Health Organization (WHO) and also by the European Union (EU), with the exception of the concentration of endosulphan sulphate at the Aného site, which was 0.116 μg L^?1.     

An assessment of persistent organic pollutants in Scottish coastal and offshore marine environments

Concentrations of persistent organic pollutants (POPs), including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were determined in sediment and biota (fish liver) from around Scotland. The concentrations were investigated using assessment criteria developed by OSPAR and ICES. Organic contaminant concentrations, PAHs, PCBs and PBDEs in sediment, and PCBs and PBDEs in fish liver, were significantly higher in the Clyde compared to all other sea areas. This is mainly due to historic industrial inputs. Highest PCB and PAH concentrations were found in the strata furthest up the Clyde estuary, with concentrations of POPs in these strata being at levels such that there is an unacceptable risk of chronic effects occurring in marine species. Furthermore, for PAHs in Clyde sediment there was a significant negative gradient going from north to south towards the open sea. PAH and PCB concentrations in sediment and biota in all other Scottish sea areas (except for PCBs in sediment from East Scotland) were unlikely to give rise to pollution effects, being below relevant assessment criteria. Although no assessment criteria are available for PBDEs, the concentrations observed in Scottish sediments were low with all congeners below the limit of detection (LoD; 0.03 μg kg(-1) dry weight) in 140 out of a total of 307 samples analysed. Where PBDEs were detected, the dominant congeners were BDE47 and BDE99. PBDEs were detected in fish livers, although concentrations were less than 150 μg kg(-1) lipid weight in all sea areas except the Clyde where concentrations ranged between 8.9 and 2202 μg kg(-1) lipid weight. Few trends were detected in contaminant concentrations in biota or sediment at any Scottish site with more than five years data. Downward trends were detected in PAHs in sediment from the Clyde, Irish Sea and Minches and Malin Sea and PCBs in fish liver from the Moray Firth. Rules were developed for the aggregation of the contaminant data across a sea area. An overall assessment for each sea area was then assigned, looking at the frequency of sites or strata within each sea area that were above or below the relevant assessment criteria. Overall the status of the various sea areas, with respect to the assessed POPs, can be considered to be acceptable in that they were below concentrations likely to result in chronic effects for all sea areas except the Clyde.     

Air quality assessment of Estarreja, an urban industrialized area, in a coastal region of Portugal

Despite the increasing concern given to air quality in urban and industrial areas in recent years, particular emphasis on regulation, control, and reduction of air pollutant emissions is still necessary to fully characterize the chain emissions–air quality–exposure–dose–health effects, for specific sources. The Estarreja region was selected as a case study because it has one of the largest chemical industrial complexes in Portugal that has been recently expanded, together with a growing urban area with an interesting location in the Portuguese coastland and crossed by important road traffic and rail national networks. This work presents the first air quality assessment for the region concerning pollutant emissions and meteorological and air quality monitoring data analysis, over the period 2000–2009. This assessment also includes a detailed investigation and characterization of past air pollution episodes for the most problematic pollutants: ozone and PM10. The contribution of different emission sources and meteorological conditions to these episodes is investigated. The stagnant meteorological conditions associated with local emissions, namely industrial activity and road traffic, are the major contributors to the air quality degradation over the study region. A set of measures to improve air quality—regarding ozone and PM10 levels—is proposed as an air quality management strategy for the study region.     

Risk assessment and distribution of soil Pb in Guangdong, China

In this study, the spatial distributions of soil lead (Pb) concentration in three horizontal soils in Guangdong, China, were surveyed and analyzed using geostatistics and geography information systems (GIS). Findings indicated that the Pb geometric mean concentration of 23.3 mg/kg in surface soils was found to be higher than those in global soils, which ranged from 2.3–235 mg/kg. In addition, the Pb geometric mean concentrations from A- to C-horizon were found to be 23.3, 27.2, and 28.6 mg/kg, respectively. The classification of a soil Pb environmental risk in an area was likewise presented based on the different levels of environmental quality of Pb and was done by GIS technology. Accordingly, there is a higher local concentration of Pb in the surrounding areas of Guangzhou where there is higher population density and in the north of Guangdong, which is a historic mining area. The results obtained by the environmental risk assessment reveal that about 46% of total surveyed area was above the background value, that is, 2.7% of the total area was at risk of pollution.     

Assessing the soil quality of alpine grasslands in the Qinghai-Tibetan Plateau using a modified soil quality index

Soil degradation has caused various problems on the planet. Human disturbance and land use changes always negatively affect soil quality. In this study, we used a modified soil quality index (SQI) to assess soil quality under differing degrees of human disturbance and land use. The alpine grasslands were studied at different levels of degradation [i.e., severely degraded grassland, heavily degraded grassland, moderately degraded grassland, and non-degraded grassland (NDG)] in a case study conducted in Qinghai-Tibetan Plateau (QTP) to test the feasibility of using the SQI. Fifteen chemical, physical, and biological soil parameters were measured in each type of grassland. Significant variations in SQI were found across the different types of grasslands according to severity of human disturbance and changes in land use. Urease, the ratio of microbial biomass nitrogen to total nitrogen, proteinase, and soil organic carbon were found to be the most important indicators for assessing soil quality. NDG had a higher SQI than the other three types of grasslands. It was concluded that SQI is effective for assessing the soil quality of alpine grasslands in the QTP. The intensity of human disturbance had a negative effect on soil quality in the QTP.     

Impact of model parameterization on predictive uncertainty of regional groundwater models in the context of environmental impact assessment

Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.