Background.China has undergone a rapid industrial revolution and urbanization during the past three decades. This expansion is largely responsible for the release of a large amount of heavy metals into soils and is increasingly raising concerns over the potential effects on human health and the environment. The problem is drawing increasing attention, especially after an extensive nationwide soil survey report in 2014. A number of studies have examined soil contamination by heavy metals in China. However, most of these studies have been small in scale and it is therefore challenging to get a general overview of the level of contamination across the entire country.Objectives.The present study is aimed at presenting a synthesized overview of the extent, pattern, and impact of heavy metal contamination of soils in China, including mitigation approaches.Methods.Eighty-six journal articles and other literature such as reports, internet sources, and statistical yearbooks were narratively and critically synthesized to compile a holistic summary of sources of heavy metals, the extent of pollution, spatial distribution and impact of heavy metal contamination in China. The major findings from these studies are presented, along with mitigation approaches applicable to China.Discussion.A synthesis of major findings from recent scientific journals shows that about 10.18% of farmland soils which supports 13.86% of grain production in China is affected by heavy metals. The main sources of pollution are anthropogenic activities. Even though the spatial distribution of pollution is highly variable owing to natural and human factors, provinces with intensive industrial activities such as Henan, Shandong, and Sichuan are more highly polluted than others. These regions are top grain producing areas and hence require close follow-up for development of feasible approaches to mitigating crop contamination and associated health risks emerging in parts of China. The government recently launched a program aimed at determining sound reclamation strategies.Conclusion.Mitigation of heavy metal contamination in China requires coordination of different actors and integration of all feasible reclamation approaches.Competing Interests.The authors declare no competing financial interests.
Wolkite is a town like many developing countries, faces problems associated with poor solid waste management. The town has only one major landfill site, which is found at Gasore kebele, near to the town. However, the waste dumping in this site has been affecting the surrounding community. The objectives of this study were to evaluate the current solid waste deposal site and socioeconomic impact of the current solid waste disposal site in the study area. The data were collected through field observation, key informant interview, focus group discussion, and household survey. Geospatial data were also used to evaluate the current solid disposal site. In this study, fifty-two household and two focus group discussion were participated. The quantitative data coded and analyzed using SPSS software. The data described using descriptive statistics and qualitative data were also expressed using narrative description whereas the geospatial data were analyzed by ArcGIS. The study result showed that the landfill site is proximate to stream and river, church, mosque, rural settlement, main road, and vegetation. The disposal site has affecting negatively to the local community; besides, the municipal waste is disposed arbitrarily on open field, roadside, dumped everywhere and solid waste disposed jointly with liquid at the landfill site, therefore, this study recommends to select suitable landfill site in the of the town.
Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.
Background: Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since, variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit for optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely-sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the RF and Classification and Regression Tree (CART).Results: The results indicated that the RF model has good prediction performance with corresponding R2 and RMSE values of 0.96, and 0.91 mg/g, respectively. The distribution of SOC content showed variability across landforms (CV=78.67%), land-use (CV=93%), and lithology (CV=64.67%). Forestland had the highest SOC (13.60 mg/g) followed by agriculture (10.43 mg/g), urban (9.74 mg/g), and water body (4.55 mg/g) land-uses. Furthermore, bauxite and laterite lithology had the highest SOC content (14.69 mg/g) followed by fluvial (14.52 mg/g) and shale (13.57 mg/g), whereas the lowest was predicted in sandstone (5.53mg/g). The mean SOC concentration was 11.70 mg/g, where the majority of area was classified as humous and organo-humus, distributing in the mountainous regions. The biophysical land surface indices, brightness removed vegetation indices, topographic indices (, and soil spectral bands, respectively were the most influential predictors of SOC. Conclusion: The spatial variability of SOC may be influenced by landform, land-use, and lithology of the study area. Remotely-sensed predictors including land moisture, land surface temperature and built-up indices added valuable information for prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm sub-tropical urban regions.
Soil salinity is a severe soil degradation problem mainly faced in arid and semi-arid regions. About 11 million ha of land in the arid, semi-arid, and desert parts of Ethiopia is salt-affected, especially in the Awash River basin, including Afambo irrigated area. Remote sensing approaches are significant tools for accurately predicting and modeling accurately predicting and modeling soil salinity in various world regions. This study aims to analyze and model soil salinity status in the case of Afambo irrigated areas using Landsat-8 and sentinel-2A, Afar region, Ethiopia, by applying remote sensing with field measurements. Thirty-two soil samples were collected from the topsoil (0-30 cm); out of these, 25 soil samples with various EC ranges were selected for modeling, and the remaining 7 samples were utilized to validate the model. Landsat-8 and Sentinel-2A images acquired in the same month were used to extract soil salinity indices. Linear regression analyses correlated the EC data with corresponding soil salinity spectral index values derived from satellite images. The best-performing model was selected for salinity mapping. The soil salinity indices extracted from both Landsat-8 and Sentinel-2A bands estimated soil salinity with high acceptable accuracy of R2 values of SI, 0.78 and 0.81, respectively. The model results in three salinity classes with varying degree of salinity, namely, highly saline, moderately saline, and slightly saline, which covers 15.1%, 39.8% and 45.1% of the total area for Landsat-8, respectively and 26.1%, 32%, and 41.9% for sentinel 2A, respectively. Generally, the results revealed that the expansion rate of salt-affected soils has been increasing. From this study, it is possible to infer that if the present irrigation practice continues, it is expected that total the cultivated lands will become sterile within a short period. Thus, it needs to be monitored regularly to secure up-to-date knowledge of their extent to improve management practices and take appropriate actions.
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