Estimates of soil erosion using cesium-137 tracer models

The soil erosion was studied by ¹³⁷Cs technique in Yatagan basin in Western Turkey, where there exist intensive agricultural activities. This region is subject to serious soil loss problems and yet there is not any erosion data towards soil management and control guidelines. During the soil survey studies, the soil profiles were examined carefully to select the reference points. The soil samples were collected from the slope facets in three different study areas (Kırtas, Peynirli and Kayısalan Hills). Three different models were applied for erosion rate calculations in undisturbed and cultivated sites. The profile distribution model (PDM) was used for undisturbed soils, while proportional model (PM) and simplified mass balance model (SMBM) were used for cultivated soils. The mean annual erosion rates found using PDM in undisturbed soils were 15 t ha⁻¹ year⁻¹ at the Peynirli Hill and 27 t ha⁻¹ year⁻¹ at the Kırtas Hill. With the PM and SMBM in cultivated soils at Kayışalan, the mean annual erosion rates were obtained to be 65 and 116 t ha⁻¹ year⁻¹, respectively. The results of ¹³⁷Cs technique were compared with the results of the Universal Soil Loss Equation (USLE).     

Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.     

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.     

The use of spatial empirical models to estimate soil erosion in arid ecosystems

The central objective of this project was to utilize geographical information systems and remote sensing to compare soil erosion models, including Modified Pacific South-west Inter Agency Committee (MPSIAC), Erosion Potential Method (EPM), and Revised Universal Soil Loss Equation (RUSLE), and to determine their applicability for arid regions such as Kuwait. The northern portion of Umm Nigga, containing both coastal and desert ecosystems, falls within the boundaries of the de-militarized zone (DMZ) adjacent to Iraq and has been fenced off to restrict public access since 1994. Results showed that the MPSIAC and EPM models were similar in spatial distribution of erosion, though the MPSIAC had a more realistic spatial distribution of erosion and presented finer level details. The RUSLE presented unrealistic results. We then predicted the amount of soil loss between coastal and desert areas and fenced and unfenced sites for each model. In the MPSIAC and EPM models, soil loss was different between fenced and unfenced sites at the desert areas, which was higher at the unfenced due to the low vegetation cover. The overall results implied that vegetation cover played an important role in reducing soil erosion and that fencing is much more important in the desert ecosystems to protect against human activities such as overgrazing. We conclude that the MPSIAC model is best for predicting soil erosion for arid regions such as Kuwait. We also recommend the integration of field-based experiments with lab-based spatial analysis and modeling in future research.     

Sediments deposition due to soil erosion in the watershed region of Mangla Dam

Soil erosion is the most important reason of sedimentation load of water reservoirs in the world. In Pakistan, Mangla dam is one of the most important water reservoirs used for the production of electricity and for the supply of water for irrigation purposes. However, the capacity of Mangla dam reservoir has reduced by more than 20% since its construction. This study highlights the impact of rainfall on soil erosion and consequently on sedimentation deposition in Mangla dam reservoir. Sedimentation, annual rainfall, and normal rainfall data of 39 years were used in this study. Shuttle Radar Topographic Mission data were used to calculate the total drainage area of the Mangla watershed region. The sedimentation data of Mangla reservoir from 1967 to 2005 were retrieved from Water and Power Development Authority in Pakistan. The meteorological observatories in the Mangla watershed region are identified. Annual rainfall data from 1967 to 2005 for the meteorological observatories in the Mangla watershed regions were retrieved from Pakistan Meteorological Department (PMD). In addition, normal rainfall data for the years 1949 to 1978 and for the years 1979 to 2008 were also retrieved from PMD. The impact of annual rainfall is observed on sedimentation load in Mangla dam. The correlation coefficient between annual rainfall and sedimentation load is 0.94. This study shows that with an increase in rainfall, the soil erosion of the area increases which subsequently is responsible for the increase in the rate of sedimentation load in Mangla dam. This study further demonstrates that better soil management can reduce the sedimentation load in the Mangla reservoir.     

Wind tunnel experimental study on the effect of PAM on soil wind erosion control

In recent years, high-molecular-weight anionic polyacrylamide (PAM) have been widely tested on a variety of soils, primarily in water erosion control. However, little information is available regarding the effectiveness of PAM on preventing soil loss from wind erosion. The research adopted room wind tunnel experiment, two kinds of soils were used which were from the agro-pastoral area of Inner Mongolia, the northwest of China, the clay content of soils were 22.0 and 13.7%, respectively. For these tests, all the treatments were performed under the condition of wind velocity of 14 m s(-1) and a blown angle of 8.75%, according to the actual situation of experimented area. The study results indicated that using PAM on the soil surface could enhance the capability of avoiding the wind erosion, at the same time, the effect of controlling wind soil erosion with 4 g m(-2) PAM was better than 2 g m(-2) PAM's. Economically, the 2 g m(-2) PAM used in soil surface can control wind erosion effectively in this region. The prophase PAM accumulated in soil could not improve the capability of avoiding the wind erosion, owing to the degradation of PAM in the soil and the continual tillage year after year. The texture of soil is a main factor influencing the capability of soil avoiding wind erosion. Soil with higher clay content has the higher capability of preventing soil from wind erosion than one with the opposite one under the together action of PAM and water.     

Effect of land use land cover change on soil erosion potential in an agricultural watershed

Universal soil loss equation (USLE) was used in conjunction with a geographic information system to determine the influence of land use and land cover change (LUCC) on soil erosion potential of a reservoir catchment during the period 1989 to 2004. Results showed that the mean soil erosion potential of the watershed was increased slightly from 12.11 t ha^???1 year^???1 in the year 1989 to 13.21 t ha^???1 year^???1 in the year 2004. Spatial analysis revealed that the disappearance of forest patches from relatively flat areas, increased in wasteland in steep slope, and intensification of cultivation practice in relatively more erosion-prone soil were the main factors contributing toward the increased soil erosion potential of the watershed during the study period. Results indicated that transition of other land use land cover (LUC) categories to cropland was the most detrimental to watershed in terms of soil loss while forest acted as the most effective barrier to soil loss. A p value of 0.5503 obtained for two-tailed paired t test between the mean erosion potential of microwatersheds in 1989 and 2004 also indicated towards a moderate change in soil erosion potential of the watershed over the studied period. This study revealed that the spatial location of LUC parcels with respect to terrain and associated soil properties should be an important consideration in soil erosion assessment process.     

The role of forest stand density in controlling soil erosion: implications to sediment-related disasters in Japan

The role of forest stand density in controlling soil erosion was investigated in Ehime Prefecture, Japan. The main objective was to compare soil erosion under different forest conditions including forest type, species composition, and stand density as influenced by thinning operations. Relative yield index (Ry) was used as an indicator of stand density to reflect the degree of management operations in the watershed. Eleven treatments were established based on the above forest conditions. Soil loss was collected in each of the 11 treatments after each rainfall event for a period of 1 year. The paper presents summary data on soil loss as affected by forest conditions and rainfall patterns. Findings showed that an appropriate forest management operation, which can be insured by stand density control, is needed to reduce soil loss. The present study plays an important role in clarifying technical processes related to soil erosion, while it helps linking these elements to current Japanese forestry issues and bringing new inputs to reducing sediment-related disasters in Japan.     

Spatiotemporal analysis of the PM10 concentration over the Taranto area

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area.     

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.     

Demonstrating PM<Subscript>2.5</Subscript> and road-side dust pollution by heavy metals along Thika superhighway in Kenya,sub-Saharan Africa

This study assessed the level of heavy metal in roadside dust and PM_2.5 mass concentrations along Thika superhighway in Kenya. Thika superhighway is one of the busiest roads in Kenya, linking Thika town with Nairobi. Triplicate road dust samples collected from 12 locations were analysed for lead (Pb), chromium (Cr), cadmium (Cd), nickel (Ni), zinc (Zn), and copper (Cu) using atomic absorption spectrophotometry (AAS). PM_2.5 samples were collected on pre-weighed Teflon filters using a BGI personal sampler and the filters were then reweighed. The ranges of metal concentrations were 39–101 μg/g for Cu, 95–262 μg/g for Zn, 9–28 μg/g for Cd, 14–24 μg/g for Ni, 13–30 μg/g for Cr, and 20–80 μg/g for Pb. The concentrations of heavy metals were generally highly correlated, indicating a common anthropogenic source of the pollutants. The results showed that the majority of the measured heavy metals were above the background concentration, and in particular, Cd, Pb, and Zn levels indicated moderate to high contamination. Though not directly comparable due to different sampling timeframes (8 h in this study and 24 h for guideline values), PM_2.5 for all sites exceeds the daily WHO PM_2.5 guidelines of 25 μg/m³. This poses a health risk to people using and working close to Thika superhighway, for example, local residents, traffic police, street vendors, and people operating small businesses. PM_2.5 levels were higher for sites closer to Nairobi which could be attributed to increased vehicular traffic towards Nairobi from Thika. This study provides some evidence of the air pollution problem arising from vehicular traffic in developing parts of the world and gives an indication of the potential health impacts. It also highlights the need for source apportionment studies to determine contributions of anthropogenic emissions to air pollution, as well as long-term sampling studies that can be used to fully understand spatiotemporal patterns in air pollution within developing regions.     

Assessing the effects of land use changes on soil sensitivity to erosion in a highland ecosystem of semi-arid Turkey

There has been increasing concern in highlands of semiarid Turkey that conversion of these systems results in excessive soil erosion, ecosystem degradation, and loss of sustainable resources. An increasing rate of land use/cover changes especially in semiarid mountainous areas has resulted in important effects on physical and ecological processes, causing many regions to undergo accelerated environmental degradation in terms of soil erosion, mass movement and reservoir sedimentation. This paper, therefore, explores the impact of land use changes on land degradation in a linkage to the soil erodibility, RUSLE-K, in Cankiri–Indagi Mountain Pass, Turkey. The characterization of soil erodibility in this ecosystem is important from the standpoint of conserving fragile ecosystems and planning management practices. Five adjacent land uses (cropland, grassland, woodland, plantation, and recreational land) were selected for this research. Analysis of variance showed that soil properties and RUSLE-K statistically changed with land use changes and soils of the recreational land and cropland were more sensitive to water erosion than those of the woodland, grassland, and plantation. This was mainly due to the significant decreases in soil organic matter (SOM) and hydraulic conductivity (HC) in those lands. Additionally, soil samples randomly collected from the depths of 0–10 cm (D ₁) and 10–20 cm (D ₂) with irregular intervals in an area of 1,200 by 4,200 m sufficiently characterized not only the spatial distribution of soil organic matter (SOM), hydraulic conductivity (HC), clay (C), silt (Si), sand (S) and silt plus very fine sand (Si + VFS) but also the spatial distribution of RUSLE-K as an algebraically estimate of these parameters together with field assessment of soil structure to assess the dynamic relationships between soil properties and land use types. In this study, in order to perform the spatial analyses, the mean sampling intervals were 43, 50, 64, 78, 85 m for woodland, plantation, grassland, recreation, and cropland with the sample numbers of 56, 79, 72, 13, and 69, respectively, resulting in an average interval of 64 m for whole study area. Although nugget effect and nugget effect–sill ratio gave an idea about the sampling design adequacy, the better results are undoubtedly likely by both equi-probable spatial sampling and random sampling representative of all land uses.     

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 μg/m³, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 μg/m³) in comparison with 2013 (23.3 μg/m³). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m³, 2014 26.9 μg/m³) and urban (2013 27.7 μg/m³, 2014 31.3 μg/m³) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m³, 2014 24.5 μg/m³) and urban-background (2013 23.0 μg/m³, 2014 26.1 μg/m³) areas, respectively.     

Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China

Soil erosion is a serious environmental problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China. Unfortunately, Guizhou Province suffers from a lack of financial resources to research, monitor and model soil erosion at large watershed. In order to assess the soil erosion risk, soil erosion modeling at the watershed scale are urgently needed to be undertaken. This study integrated the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) to estimate soil loss and identify the risk erosion areas in the Maotiao River watershed, which is a typical rural watershed in Guizhou Province. All factors used in the RUSLE were calculated for the watershed using local data. It was classified into five categories ranging from minimal risk to extreme erosion risk depending on the calculated soil erosion amount. The soil erosion map was linked to land use, elevation and slope maps to explore the relationship between soil erosion and environmental factors and identify the areas of soil erosion risk. The results can be used to advice the local government in prioritizing the areas of immediate erosion mitigation. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil erosion. It thus indicates that RUSLE-GIS model is a useful and efficient tool for evaluating and mapping soil erosion risk at a large watershed scale in Guizhou Province.     

PM2.5 and PM10 Concentrations from the Qalabotjha Low-Smoke Fuels Macro-Scale Experiment in South Africa

This article presents results from the particulate monitoringcampaign conducted at Qalabotjha in South Africa during the winter of 1997. Combustion of D-grade domestic coal and low-smoke fuels were compared in a residential neighborhood to evaluate the extent of air quality improvement by switchinghousehold cooking and heating fuels.Comparisons are drawn between the gravimetric results from the two types of filter substrates (Teflon-membrane and quartz-fiber) as well as between the integrated and continuous samplers. It is demonstrated that the quartz-fiber filters reported 5 to 10% greater particulate mass than the Teflon-membrane filters, mainly due to the adsorption of organic gases onto the quartz-fiber filters. Due to heating of sampling stream to 50 °C in the TEOM continuous sampler and the high volatile content of the samples, approximately 15% of the particulate mass was lost during sampling.The USEPA 24-hr PM_2.5 and PM₁₀ National Ambient Air Quality Standards (NAAQS) of 65 g m^-3 and 150 g m^-3, respectively, were exceeded on several occasions during the 30-day field campaign. Average PMconcentrations are highest when D-grade domestic coal was used, and lowest between day 11 and day 20 of the experiment when a majority of the low-smoke fuels were phased in. Source impacts from residential coal combustion are also found to be influenced by changes in meteorology, especially wind velocity.PM_2.5 and PM₁₀ mass, elements, water-soluble cations (sodium, potassium, and ammonium), anions (chloride, nitrate, and sulfate), as well as organic and elemental carbonwere measured on 15 selected days during the field campaign. PM_2.5 constituted more than 85% of PM₁₀ at three Qalabotjha residential sites, and more than 70% of PM₁₀ at the gradient site in the adjacent community of Villiers. Carbonaceous aerosol is by far the most abundant component, accounting for more than half of PM mass at the three Qalabotjha sites, and for more than a third of PM mass at the gradient site. Secondary aerosols such as sulfate, nitrate,and ammonium are also significant, constituting 8 to 12% of PM mass at the three Qalabotjha sites and 15 to 20% at the Villiers gradient site.     

Check dam sediments: an important indicator of the effects of environmental changes on soil erosion in the Loess Plateau in China

Check dam sediments document the process of soil erosion for a watershed. The main objectives of this research are as follows: first, to determine whether the sediments trapped in check dams can provide useful information about local erosion and the environment, and second, to obtain the extent to which they can be stratigraphically interpreted and correlated to the land use history of an area controlled by check dams. Particle size and the concentration of ¹³⁷Cs in sediments are the indicators used to study the effects of environmental changes on soil erosion in the Loess Plateau, China. A total of 216 soil samples were collected from four sediment profile cores at the Yangjuangou watershed check dam constructed in 1955 and fully silted with sediments by 1965. The results indicated that ¹³⁷Cs dating and sediment particle size can characterize the sediment deposition process. Silt makes up more than 50 % of the sediment; both the clay and silt sediment fractions decrease gradually in the upstream direction. The sediment profiles are characterized by three depositional layers. These layers suggest changes in the land use. The top layer showed tillage disturbance, with moderate sediments and new soil mixed from 0 to 20 cm. A transition stage from wetlands (characterized by vegetation such as bulrush) to cropland is inferred from sediments at depths of 20–85 cm. Below 85 cm, sedimentary layering is obvious and there is no tillage disturbance. At the downstream site, A0, the average rate of sediment deposition from 1958 to 1963 was approximately 6,125.4 t year^?1 km^?2. Because of their high time resolution, check dam sediments indicate the effects of environmental changes on soil erosion, and they can provide a multiyear record of the soil erosion evolution at the local scale in the middle reaches of the Yellow River.     

Desertification of the eastern Karoo,South Africa: Conflicting paleoecological,historical, and soil isotopic evidence

The desertification debate in South Africa has benefitted greatly in recent years from the contributions of a wide range of disciplines. In this paper we review the conflicting and supporting evidence for degradation in the eastern Karoo as reported in recent archaeological, historical, and stable carbon isotope studies as it relates to three key aspects of the debate: the precolonial environment, the rate and nature of change, and the relative contributions of humans and climate to the process. First, all studies suggest a greater grassiness at some time in the past, but researchers disagree on the timing of the switch to more shrubby conditions in the eastern Karoo. Second, regional rainfall records for the past 2 decades reveal an above-average rainfall period, and numerous long-term surveys show an increase in grass cover over the same period. These findings question the expanding Karoo hypothesis as well as the argument that the Karoo's carrying capacity has decreased in recent years. Finally, the relative responsibilities of humans and climate in the degradation process remain poorly understood and generally have not formed the focus of investigation.     

Heavy metals in urban ambient PM10 and soil background in eight cities around China

The ambient PM₁₀ and background soil samples were collected and analyzed with ICP-AES in eight cities around China to investigate the levels of ten heavy metals (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The mean concentrations of ten heavy metals in PM₁₀ of the eight cities of China followed the order of Zn?>?Pb?>?Mn?>?Cu?>?Ni?>?Cr?>?Co?>?V. The metals in the ambient PM₁₀ and soil were compared in each city to evaluate the heavy metal mass fraction from anthropogenic sources in ambient air. The CD values in these cities were all above 0.2, indicating that the ingredients spectrums of PM₁₀ and soil vary markedly. Most heavy metals were enriched in PM₁₀, except Fe and Ti. The results showed that almost all the cities suffer important heavy metal pollution from anthropogenic sources. The eight cities were also grouped according to their similarity in heavy metals of ambient PM₁₀ by cluster analysis to investigate the relationship between the heavy metals and the pollution sources of each city. The conclusion was that the eight cities were divided into three clusters which had similar industrial type and economy scale: the first cluster consisted of Shenzhen, Wuxi, and Guiyang; followed by Jinan and Zhengzhou as the second grouping; and the third group had Taiyuan, Urumqi, and Luoyang.     

Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete

Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.