The purpose of this study is to investigate the current status of heavy metal soil pollution in one of the cradles of industry in China, the Tiexi Industrial District in the city of Shenyang, ...Ninety-three soil samples were collected from the top 15cm of the soil layer and were analyzed for heavy metal concentrations of Pb, Cu, Cr, Zn, Mn, Cd, As and Hg. The data reveal a remarkable variation in heavy metal concentration among the sampled soils; the mean values of all the heavy metal concentrations were higher than the background values, and the mean concentrations of Pb, Cu, Cd and As were as high as 5.75, 5.08, 12.12 and 13.02 times their background values, respectively. The results of principal component analysis (PCA) indicate that Pb, Cu, Zn, Cd, As and Hg are closely associated with the first principal component (PC1), which explained 46.7% of the total variance, while Cr and Mn are mainly distributed with the second component (PC2), which explained 22.5% of the total variance. Geostatistical analyses, including the calculation of semivariogram parameters and model fitting, further confirmed the results of the statistical analysis. In the estimated maps of heavy metals, several hotspots of high metal concentrations were identified; Pb and Cu showed a very similar spatial pattern, indicating that they were likely from the same source. There is a clear heavy polluted hotspot of Pb, Cu, Zn, Cd and As in the northeast part of the Tiexi Industrial District because of the Shenyang Smelting Plant, which was a backbone enterprise of China's metallurgical industry. There were also hotspots for other heavy metals in other areas. This is mainly the result of the industrial processing that occurred in the study area. All of these data confirm that Pb, Cu, Zn, Cd and As are a result of anthropogenic activities, especially from industrial processes. For Cr and Mn, the concentration patterns indicate low spatial heterogeneity, with low correlation to other metals, indicating that the concentration of Cr and Mn are mainly caused by natural factors such as soil parent materials. Although the city government of Shenyang has placed a high priority on improving the environment in recent years, it will require a long time to completely eliminate pollution in this area.
► We investigated the contamination pattern of heavy metal in an industrial city. ► The soil contamination was spatially coupled with land use by GIS. ► Obvious heavy polluted hotspots were indentified for Pb, Cu, Zn, Cd and As. ► The contamination of Pb, Cu, Zn, Cd and As were caused anthropogenic activities. ► The concentrations of Cr and Mn mainly come from natural factors.
The purpose of this study is to investigate the current status of metal pollution of the sediments from urban-stream, estuary and Jinzhou Bay of the coastal industrial city, NE China. Forty surface ...sediment samples from river, estuary and bay and one sediment core from Jinzhou bay were collected and analyzed for heavy metal concentrations of Cu, Zn, Pb, Cd, Ni and Mn. The data reveals that there was a remarkable change in the contents of heavy metals among the sampling sediments, and all the mean values of heavy metal concentration were higher than the national guideline values of marine sediment quality of China (GB 18668-2002). This is one of the most polluted of the world's impacted coastal systems. Both the correlation analyses and geostatistical analyses showed that Cu, Zn, Pb and Cd have a very similar spatial pattern and come from the industrial activities, and the concentration of Mn mainly caused by natural factors. The estuary is the most polluted area with extremely high potential ecological risk; however the contamination decreased with distance seaward of the river estuary. This study clearly highlights the urgent need to make great efforts to control the industrial emission and the exceptionally severe heavy metal pollution in the coastal area, and the immediate measures should be carried out to minimize the rate of contamination, and extent of future pollution problems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Central Asia has a land area of 5.6 × 10⁶ km²and contains 80–90% of the world's temperate deserts. Yet it is one of the least characterized areas in the estimation of the global carbon (C) ...stock/balance. This study assessed the sizes and spatiotemporal patterns of C pools in Central Asia using both inventory (based on 353 biomass and 284 soil samples) and process‐based modeling approaches. The results showed that the C stock in Central Asia was 31.34–34.16 Pg in the top 1‐m soil with another 10.42–11.43 Pg stored in deep soil (1–3 m) of the temperate deserts. They amounted to 18–24% of the global C stock in deserts and dry shrublands. The C stock was comparable to that of the neighboring regions in Eurasia or major drylands around the world (e.g. Australia). However, 90% of Central Asia C pool was stored in soil, and the fraction was much higher than in other regions. Compared to hot deserts of the world, the temperate deserts in Central Asia had relatively high soil organic carbon density. The C stock in Central Asia is under threat from dramatic climate change. During a decadal drought between 1998 and 2008, which was possibly related to protracted La Niña episodes, the dryland lost approximately 0.46 Pg C from 1979 to 2011. The largest C losses were found in northern Kazakhstan, where annual precipitation declined at a rate of 90 mm decade⁻¹. The regional C dynamics were mainly determined by changes in the vegetation C pool, and the SOC pool was stable due to the balance between reduced plant‐derived C influx and inhibited respiration.
•We modeled carbon dynamics in Central Asia using Biome-BGC grazing model.•An inventory approach was also employed to estimate CO2-eq emission.•Grazing resulted in 0.25–1.39Pg Carbon release in the ...last 33 years.•Proper grazing intensities promote grassland productivity.
Dryland grasslands in Central Asia were prone to concurrent high levels of grazing intervention and climatic variability in the past decades. However, the influences of grazing on carbon cycling under climate change are still uncertain in this region. We modeled the carbon dynamics in Central Asia for different grassland types (i.e., Temperate Grassland, TG; Desert Grassland, DG; Forest Meadow, FM) that varied in grazing intensity from 1979 to 2011 by using the modified Biome-BGC grazing model. In addition, an inventory approach was also employed to estimate the CO2-eq emission from meat and milk production. The regional simulation estimated that the grassland ecosystems in Central Asia acted as a net carbon source with a value of 0.83PgC for the last 33 years (1Pg=1015g). However, Central Asian grasslands had a weak carbon sink of 0.10Pg when the grazing effect was eliminated. Grazing resulted in the release of 0.93PgC in Central Asia according to the modeling approach and 0.25–1.39PgC to inventory approach. Nevertheless, proper grazing intensities for TG, DG, and FM at approximately 0.23, 0.35, and 0.35headha−1, respectively, can result in overcompensation, which means that plants have higher productivity after herbivory compared with ungrazed condition under proper grazing intensity. These results can be attributed to the decreasing evapotranspiration (ET) in grazed grasslands, which can effectively promote grass growth. Therefore, restricting the grazing intensity to less than 0.23, 0.35, and 0.35headha−1 for TG, DG, and FM, respectively, to mitigate the degradation and maintain its carrying capacity for livestock is important. Our research explored the possible implications for grazing management of grasslands in Central Asia and concluded that grazing can eventually be assembled into a set of biophysical tools for climate adaptation and mitigation.
Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the ...environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO
fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.
Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this ...study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.
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•Disaggregated different human activities related to LULCC over a complex region.•Quantified the contribution of each driving factor to biophysical parameters using RF.•Strong spatial ...heterogeneity of biophysical parameters in the NTM.•Amplified signals of biophysical parameters over climate change.•Human-induced trends in biophysical parameters produced significant signatures.
Quantifying the variation of biophysical parameters and their driving mechanisms is essential for monitoring land surface environmental changes and for understanding the land–atmosphere interaction in the arid region. Due to the complexity of human activities, most researches are limited to climate change, whereas the response analysis of human activities to changes in biophysical parameters are still lacking or not comprehensively considered. Therefore, large biases and uncertainties still exist in the estimates of regional responses to global change. Firstly, we specifically quantified the main human activities related to land use/land cover change (LULCC) in the northern Tianshan Mountains (NTM), and identified the spatiotemporal changes of primary biophysical parameters, including Albedo, leaf area index (LAI), land surface temperature (LST), and Normalized Difference Vegetation Index (NDVI). Then, we tested the performance of the five models used, including multiple linear regression (MLR), random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), and K-nearest neighbor (KNN). RF outperformed others and was used to quantify and disaggregate the contribution of climate change and human activities to land surface parameters in the NTM. We found a strong spatial heterogeneity in the spatial variation of all biophysical parameters. Except for LST, the annual maximum Albedo, LAI, and NDVI showed a significant increasing trend in the NTM from 2000 to 2019 (p < 0.05). Generally, climate change contributed more to the biophysical parameters than human activities. However, the contribution of human activities to NDVI was 0.51, which was greater than that of climate change during 2000–2015. This study provides new insight on the impact of climate change and human activities on biophysical parameters and a scientific basis for model parameterization in the arid region.
Upper Amu Darya (UAD).
The UAD has abundant hydropower potential (HP). This study focuses on the potential sites of small hydropower stations (sHPs) and explores the impact of future climate change ...on discharge and HP.
The conflict between water resources and hydropower in Central Asia is a key issue affecting regional development. Developing sHPs provides a new direction for alleviating energy shortages in Central Asia. Combining the hydrological model with the geographic information decision-making method, we have determined the potential sites for ten sHPs in the UAD and calculated the HP for each station. Based on future climate data from two shared socioeconomic pathways and four global climate models, we found that future precipitation and temperature are projected to increase, with precipitation increasing mainly in spring and winter. The average discharge will increase by 19.1∼36.6% in the near-term (2031–2050) and by 29.7∼106.8% in the long-term (2071–2090). In addition, climate change has significantly increased HP in winter, and the increase of HP in relatively high altitudes is higher than that in low altitudes. This implies that the HP development prospect of UAD is broad in future, but for sHPs construction schemes, it is necessary to further consider the impact on both environment and society.
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•Ten potential sites for small hydropower stations have been identified.•The discharge will increase by 19.1–36.6% in 2031–2050 and 29.7–106.8% in 2071–2090.•Future climate change will significantly increase hydropower potential in winter.•The peak flow will increase in relatively low altitudes, and the base flow will increase in relatively high altitudes.
•Quantified the climatic and oasification effect on NPP variation.•Using a machine learning method to separate climate-induced NPP and oasification-induced NPP.•Vegetation NPP in the mid-Tianshan ...Mountain is more sensitive to water-related factors.•The impact of oasification on NPP is gradually increasing over the last 20 years.
Net primary productivity (NPP) has been substantially changed under the intense oasification in the urban agglomerations on the northern slopes of mid-Tianshan Mountain (UANSTM) and climate change. However, the temporal variations of NPP under the oasification remain unclear, and the relative contribution of oasification and climate change on annual NPP variation is still under debate. By using remote sensing data, reanalysis data, modified Carnegie–Ames-Stanford Approach (CASA) model, and a machine learning method, we explored the spatial–temporal variation of NPP in the UANSTM region and quantified the contribution of oasification and climate change to NPP variation from 2001 to 2020. Our study indicated that: (1) the NPP presents an overall increasing trend in the most of region and the region presented decreasing trend mainly due to the cropland conversion to the urban area; (2) the oasification-dominated NPP area concentrated in the built-up land and cropland; (3) during 2001–2020, the NPP increased by about 5.4 Tg·C, and the contribution of climatic and oasification to NPP increase were quantified (73.1% and 26.9%, respectively); (4) water-related factors was the main driver of NPP variation in the UANSTM region.
Ili River, the main river in the Lake Balkhash basin.
This study introduces a novel hybrid model by coupling the Soil and Water Assessment Tool (SWAT) hydrological model with machine learning ...algorithms. It aims to simulate and forecast the streamflow of the Ili River, clearly delineating the roles of meteorological factors and anthropogenic factors in the process of streamflow change.
During the period 1960–2020, the contribution of climate change to streamflow variation was 104.59%-113.07%, the contribution of land use ranged from −10.75% to −4.59%, and the impact of reservoir construction was −2.27%. The predictive outcomes of the hybrid model indicate that, under the SSP2–4.5 and SSP5–8.5 scenarios, the streamflow of the Ili River is projected to increase by 12.8% and 14.3% respectively in the future period (2021–2100), in comparison to the historical period (1960–2020). The warming and humidification from 2021 to 2100 will lead to changes in the streamflow components of the Ili River, with a decrease in the proportion of groundwater flow and an increase in the proportion of surface flow. The results of this study provide a reference for the rational utilization and scientific management of water resources in the Ili River Basin.
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•Hydrological model coupled with machine learning constructs a novel hybrid model.•Meteorological factors are the major drivers for variation of Ili River streamflow.•The basin's warming and humidification drive increased streamflow in the future.
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