Toxic metals in soils pose hazards to food security and human health. Accurate source apportionment provides foundation for pollution prevention. In this study, a novel hybrid method that combines ...positive matrix factorization, Bayesian maximum entropy and integrative predictability criterion is proposed to provide a new perspective for exploring the heterogeneity of pollution sources in spatial random fields. The results suggest that Cd, As and Cu are the predominant pollutants, with exceedance rates of 27%, 12% and 11%, respectively. The new method demonstrates superiority in predicting toxic metals when combined major and all sources as auxiliary information., with the improvements of 44% and 46%, respectively, Although the major sources identified with the hybrid method are the primary contributors to the accumulation of toxic metals (e.g. coal combustion for Hg, traffic emission for Pb and Zn, industrial activities for As, agricultural activities for Cd and Cu and natural sources for Cr and Ni), the impact of nonmajor sources on toxic metal sin specific regions should not be ignored (e.g. industrial activities on Ni, Pb and Zn in the north and natural sources on Cd, Cu, As, Pb and Zn in the south). For better pollution control, specific local sources should be considered.
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•A novel hybrid method is proposed for source apportionment in spatial random fields.•Cd, As and Cu are the major pollutants, with exceedance rate of 27%, 12% and 11%.•The new method significantly improves the source analysis, with an RI-RMSE of 44%.•Cd, As and Cu are mainly from agricultural, industrial and composite sources.•The impact of nonmajor source on toxic metal in specific region should not be ignored.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract Coronaviruses belong to the family Coronaviridae , which primarily cause infection of the upper respiratory and gastrointestinal tract of hosts. Transmissible gastroenteritis virus (TGEV) is ...an economically significant coronavirus that can cause severe diarrhea in pigs. Silver nanomaterials (Ag NMs) have attracted great interests in recent years due to their excellent anti-microorganism properties. Herein, four representative Ag NMs including spherical Ag nanoparticles (Ag NPs, NM-300), two kinds of silver nanowires (XFJ011) and silver colloids (XFJ04) were selected to study their inhibitory effect on TGEV-induced host cell infection in vitro . Ag NPs were uniformly distributed, with particle sizes less than 20 nm by characterization of environmental scanning electron microscope and transmission electron microscope. Two types of silver nanowires were 60 nm and 400 nm in diameter, respectively. The average diameter of the silver colloids was approximately 10 nm. TGEV infection induced the occurring of apoptosis in swine testicle (ST) cells, down-regulated the expression of Bcl-2, up-regulated the expression of Bax, altered mitochondrial membrane potential, activated p38 MAPK signal pathway, and increased expression of p53 as evidenced by immunofluorescence assays, real-time PCR, flow cytometry and Western blot. Under non-toxic concentrations, Ag NPs and silver nanowires significantly diminished the infectivity of TGEV in ST cells. Moreover, further results showed that Ag NPs and silver nanowires decreased the number of apoptotic cells induced by TGEV through regulating p38/mitochondria-caspase-3 signaling pathway. Our data indicate that Ag NMs are effective in prevention of TGEV-mediated cell infection as a virucidal agent or as an inhibitor of viral entry and the present findings may provide new insights into antiviral therapy of coronaviruses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
To quantitatively assess heavy metal accumulation and potential ecological and human health risks as well as analyze the sources of metals in a typical soil-rice system located on the southeast coast ...of China, 120 topsoil samples and corresponding rice grain samples were collected across the study area. The concentrations of As, Cd, Pb, Cr, Hg, Zn, Cu, and Ni were analyzed. The results revealed that Hg, Cd, and Cu were the main pollutants in soils. Besides, according to geo-accumulation value of Hg, 18.3% of samples were at or above moderate contamination levels. Additionally, the soil was in moderate ecological risk from combined heavy metal pollution, and 49.7% and 27.0% of this risk could be attributed to Hg and Cd pollution, respectively, due to their high toxic-response factors. For the rice samples, Cd content showed the highest biological accumulation coefficient value (40.8%) in rice grains and was slightly greater than its maximum allowable value (MAV) (0.2 mg/kg) in 7.5% of samples, whereas the other metals were all lower than their corresponding MAVs. Heavy metal exposure (especially As exposure) via rice consumption causes significant carcinogenic and non-carcinogenic risks to adults, and non-carcinogenic risk to children, while the carcinogenic risk to children was at tolerable level. Greater rice consumption might be responsible for the greater health risk to adults than children. Natural sources (loaded heavily with Cr and Ni) such as lithogenic components and soil parent materials, agricultural activities (loaded heavily with Cd, Cu, and Zn), especially excessive use of pesticides and fertilizers, and industrial activities (loaded heavily with Hg, Pb, and As) including vehicle emissions, coal combustion, and those of the textile and chemical industries were identified as the main sources. Effective regulations should be enforced to guarantee the safety of farm produce and protect ecological and human health in the study area.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•BME combined with PMF are proposed for spatial quantitatively source apportionment.•1/5 of soils show different pollution degrees and Cd, Cu, As are the main pollutants.•Cd and As are the main ...elements causing ecological and health risks, respectively.•Industry like electroplating is the main source for As in northwest and northeast.•Agricultural activities are the main source for Cd in northwestern and north-central.
Soil pollution by toxic metals has become an important environmental problem over the last several decades. Because of environmental factor variation, specific spatial patterns of pollution and sources exist. However, commonly used methods rarely take natural spatial heterogeneity into account. Positive matrix factorization and Bayesian maximum entropy models combined with specific environmental factors were proposed for quantitative source apportionment to account for spatial heterogeneity. The proposed method was implemented in a region located in southeastern China using dense samples (3627 total samples containing Cd, Hg, As, Pb, Cr, Cu, Zn and Ni data). The results showed that more than one-fifth of soils in the northwest, north-central and southeast of the study region exhibited different degrees of integrated pollution. Cd, Cu and As were the main pollutants, with proportions that exceeded the national standards of 26%, 10% and 7%, respectively. In addition, Cd was the primary element responsible for ecological risk, and As was the greatest hazard to human health. Five main pollution sources were extracted: 72.11% of the toxic metal pollution could be ascribed to anthropogenic sources, and natural sources explained the remaining 27.89%. Traffic emissions (24.31%) consistent with the major road distribution were the main source of Pb and Zn, and atmospheric deposition during the coal combustion (18.04%) distributed across the study area, except for the southeastern mountain areas, was the main source of Hg. Agricultural activities (16.81%) distributed mainly in the north-central regions contributed the most to Cd and Cu, and industrial activities (12.95%) clustered in the northwestern areas contributed the most to As. In addition, natural sources were closely linked to Ni and Cr in the southeastern mountain areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Vertical water transport plays an important role in the development and maintenance of lakes in arid zones. However, previous study of this phenomenon has focused mainly on the effects of regional ...precipitation, rather than effects of groundwater recharge, on arid-zone environmental change. We selected a sedimentary profile from a seasonal lake basin located in the hinterland of the Badain Jaran Desert, northwestern China, to assess the Holocene environmental evolution of this region and its response to climate change. Our results show that peat deposition from 11 to 10 ka indicates humidification after Younger Dryas (YD) event; the lake sediment and lower EM2 and lower principal component analysis (PCA)-1 scores indicate relative more humid environment from 10 to 7.8 ka; the lake sediment and higher EM2 and higher PCA-1 scores indicate most humid environment from 7.8 to 5.8 ka; the transition from lacustrine facies to limnetic facies and increasing salinity indicate drying trend from 5.8 to 2.5 ka; the aeolian sand sediment and lowest EM2 and lowest PCA-1 scores indicate driest environment from 2.5 to 0.8 ka; and lacustrine facies and finest grain size indicate relative humid environment during the ‘Little Ice Age’ period. The environment was relative humid in early Holocene, most Humid in mid-Holocene, and dry in mid-late Holocene. The moisture pattern in the hinterland of the Badain Jaran Desert was synchronous with that in the northeastern of Tibetan Plateau, which implies that the groundwater recharge lake was dominated by the intensity of groundwater recharge from monsoon margin area, especially the northeastern of Tibetan Plateau. As a record from groundwater recharge lake without runoff, our study provides the groundwater output quantity record from northeastern of Tibetan Plateau and would benefit the environment change research and regional water resources allocation around the Tibetan Plateau.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Comparative evaluation of SOC baselines between global soil database (HWSD) and the recent soil survey for farmlands using a random forest model.
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•Total of 29,927 farmland sites in ...Zhejiang, East China were surveyed for SOC stock.•Random forest model showed high predictive performance with R2 of 0.76.•Maps of fine-resolution SOC stock baseline and its uncertainty were estimated.•Considerable spatial discrepancies between this study and HWSD were revealed.•Carbon accounting based on SOC content of HWSD should be reinvestigated.
Soil organic carbon (SOC) is important to soil fertility and the global carbon cycle. Accurate estimates of SOC stock and its dynamics are critical for managing agricultural ecosystems and carbon accounting under climate change, especially for highly cultivated regions. We extensively surveyed the SOC levels in 29,927 sites in Zhejiang province, an intensively cultivated region of East China, from year 2007 to 2008. We then estimated the spatial distribution of topsoil (0–30cm) organic carbon stock using a random forest (RF) model, which is a powerful machine learning algorithm with superior predictive performance over parametric statistical models. The final RF model contained 23 predictor variables, covering soil properties, vegetation, climate, topography, land cover, farming practices, and locations. The RF model showed high performance in predicting the SOC stock, with a coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 10.63tCha−1. This performance was superior to the General Linear Model (GLM) (R2=0.35, RMSE=19.93tCha−1) and the ordinary kriging (OK) method (R2=0.57, RMSE=14.44tCha−1), and was equivalent to Boosted Regressing Trees (BRT) (R2=0.73, RMSE=11.26tCha−1). According to the variable importance evaluation, soil properties were the most important predictor variables, followed by climate and location, with relative importance values of 61%, 17%, and 14%, respectively. The predicted SOC stock ranged from 14.8 to 125.5tCha−1, with an average±standard deviation of 50.1±12.3tCha−1. The mean SOC level obtained from this survey was considerably lower than the value of 60.5tCha−1 reported for the same region in the Harmonized World Soil Database (HWSD), which is the most commonly used soil database worldwide. A large spatial discrepancy of SOC stock was observed between this survey and HWSD in regional and sub-regional levels. This study provided an updated regional baseline map of SOC levels for improving farmland management and refining carbon accounting under climate change.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Metals and metalloids accumulate in soil, which not only leads to soil degradation and crop yield reduction but also poses hazards to human health. Commonly, source apportionment methods generate an ...overall relationship between sources and elements and, thus, lack the ability to capture important geographical variations of pollution sources. The present work uses a dataset collected by intensive sampling (1848 topsoil samples containing the metals Cd, Hg, Cr, Pb, and a metalloid of As) in the Shanghai study area and proposes a synthetic approach to source apportionment in the condition of spatial heterogeneity (non-stationarity) through the integration of absolute principal component scores with geographically weighted regression (APCA-GWR). The results showed that three main sources were detected by the APCA, i.e., natural sources, such as alluvial soil materials; agricultural activities, especially the overuse of phosphate fertilizer; and atmospheric deposition pollution from industry coal combustion and transportation activities. APCA-GWR provided more accurate and site-specific pollution source information than the mainstream APCA-MLR, which was verified by higher R2, lower AIC values, and non-spatial autocorrelation of residuals. According to APCA-GWR, natural sources were responsible for As and Cr accumulation in the northern mainland and Pb accumulation in the southern and northern mainland. Atmospheric deposition was the main source of Hg in the entire study area and Pb in the eastern mainland and Chongming Island. Agricultural activities, especially the overuse of phosphate fertilizer, were the main source of Cd across the study area and of As and Cr in the southern regions of the mainland and the middle of Chongming Island. In summary, this study highlights the use of a synthetic APCA-GWR model to efficiently handle source apportionment issues with spatial heterogeneity, which can provide more accurate and specific pollution source information and better references for pollution prevention and human health protection.
Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a case study location, this research proposed the synthesis application of positive ...matrix factorization (PMF) and GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area. Although the mean concentrations of As, Cd, Cr, Hg, and Pb in the soils were lower than the National Environmental Quality Standards for Soils in China, the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively, indicating that heavy metals have been enriched in topsoil. Agricultural activities, industrial activities, and soil parent materials were the main sources of heavy metal pollution in the soils, accounting for 63.4%, 19.8%, and 16.8% of the total heavy metal accumulation, respectively. Cr was derived mainly from soil parent materials (80.72%). Cd was closely associated with agricultural activities (73.68%), such as sewage irrigation and application of fertilizer. Mercury was mainly attributed to industrial activities (92.38%), such as coal mining and smelting. As was related to agricultural (57.83%) and natural (35.56%) sources, and Pb was associated with industrial (42.42%) and natural (41.83%) sources. The new synthesis models are useful for estimating the source apportionment of heavy metals in soils.
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•A novel framework based on spatial analysis for source apportionment is proposed.•Combined with auxiliary data, the new model provides foundations for source analysis.•Cr (80.72%) was derived mainly from natural sources while As and Pb had mix sources.•Cd (73.68%) was closely associated with agricultural activities.•Hg (92.38%) was mainly attributed to industrial activities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Heavy metal pollution in soil has received much attention in recent decades. Many studies have analyzed the contamination status, spatial distribution, and pollution sources of heavy metals. Little ...information is available on the interaction between cultivated land quality and soil heavy metal pollution. Combining soil quality information and intensive heavy metal sampling surveys, this study analyzed heavy metal contamination and the ecological and health risks of various soils with different quality levels. Additionally, through the PMF model and risk assessment techniques, the ecological and health risks of specific pollution sources and their interaction with soil quality were investigated. The results showed that the mean content of the studied elements followed the increasing order of Hg (0.12 mg/kg) < Cd (0.19 mg/kg) < As (6.98 mg/kg) < Pb (25.57 mg/kg) < Cr (72.02 mg/kg). In addition, with increasing soil quality, the concentrations of Pb, Cr and Hg as well as the overall ecological risk increased significantly. Regarding health risks, heavy metal pollution posed a higher risk to children than adults, and ingestion was the main exposure pathway. The total hazard index and carcinogenic risk also increased with increasing soil quality. The PMF analysis showed that Pb and Cr mainly came from industrial activities, As could be attributed to natural sources, Cd was mainly derived from agricultural activities, and Hg pollution was determined by coal combustion. Considering the risks of specific pollution sources, agricultural activities and coal combustion were the major reasons for high ecological risks, whereas industrial activities and coal combustion posed significantly higher risks in suburban high-quality soil. Industrial activities mainly determined the health risk, which contributed more than 50% to the total risk. There was an upward health risk trend with increasing soil quality. Industrial activities in high-quality suburban soil posed the highest health risk to both adults and children. Reasonable and effective policies should be formulated to control industrial pollution and improve the ecological environment in this area.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The cyprinid Aral barbel L. brachycephalus is endemic to the Aral Sea of Uzbekistan and was introduced in China in 2003. With increased cultivation, there is a need for more effective breeding ...strategies. We sequenced 146 individuals from a full-sib family and obtained 66,771 single nucleotide polymorphisms (SNPs) using specific locus amplified fragment sequencing (SLAF-seq). Among these, 31,019 SNPs were assigned to 50 linkage groups. The linkage map contained 4304 unique loci and spanned 2419.2 cM in total, with an average length of 48.4 cM. The length for each linkage group ranged from 5.2 cM to 76.2 cM, with an average distance of 0.4 cM to 0.8 cM. Using the linkage map, 72 QTL regions were predicted to be significantly associated with three breeding-related traits: body height (BH), total length (TL), and head breadth (HB). This study provides useful tools for the molecular mark-assisted breeding of L. brachycephalus.
•The first high density genetic linkage map for Aral barbel was constructed.•The genetic linkage map consisted of 4304 loci allocated in 50 linkage groups (LGs) and spanned 2419.2 cM.•Seventy-two QTL regions were predicted to be significantly associated with three traits including BH, TL, and HB.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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