•GeoDetector identified the magnitude of the driving factor effects on soil heavy metals.•MLR with decay function adequately quantified the contributions of pollution source landscapes.•Identified ...and quantified four main source landscapes of soil heavy metals pollution.
In this work, we propose a method that is not limited in the identification of the type of pollution source but it also suggests the land covers that emit heavy metals into the surrounding soils by introducing a three-stage procedure, as follows: (a) the Principal Component Analysis/Absolute Principal Component Scores technique is applied to the spatial distribution of soil heavy metal accumulations to identify the type of source that is responsible for soil heavy metal accumulation, (b) based on the spatial distribution of the principal component scores and on four selected driving factors (land cover, distance to mine or smelter, distance to road, and topographic elevation), the Geographical Detector model was used to identify the effect intensity of the driving factors on soil heavy metal accumulation and obtain the landscape type of pollutant sources, and (c) GIS analysis (buffer and overlap analysis) was performed on the principal component scores around the suspected land covers linked to the landscape type of pollutant sources to determine the land covers that, in fact, emit heavy metals into the surrounding soils. Based on the proposed approach, four mining and metallurgy land or land groups were determined to be the actual sources of soil heavy metal pollution in Daye city, Hubei Province, China. Lastly, a Multiple Linear Regression model with decay function was proposed to quantify the contributions of previously identified pollution sources to soil heavy metals accumulation. It was found that the HuangJin mountain quarry, the Tonglu mountain cooper mine (together with some related mineral processing and smelting enterprises), the Lion mountain mining and mineral processing base, and the large Oujia mountain mine are the four sources that contributed 3.2%, 34.3%, 8.3%, and 44% of the total soil heavy metal accumulations in the study area.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Coastal salt marsh, one of the blue carbon ecosystems that can adapt and mitigate climate change influence, is drawing global attention due to its high carbon sequestration capability. In China, ...however, coastal salt marsh has suffered great losses. Nation-wide analysis of salt marsh trends and management is critical to ecosystem protection and restoration. Thus, by analyzing previous coastal salt marsh studies, we found that the extent of coastal salt marsh varied greatly among the Liao River Delta, the Yellow River Delta, the middle coast of Jiangsu Province, Chongming Dongtan and Jiuduansha in Shanghai, with a 59% overall loss of salt marsh extent from the 1980s to the 2010s. The rate of salt marsh loss slowed down after the year 2000. Coastal land-claim (reclamation) is the most dominant driver of salt marsh loss. Climate change and coastal erosion, invasive species, and vegetation dynamics driven by competition and succession have also led to various effects on salt marsh extent and the ecological services they provide. Sea level rise, reclamation pressure and environmental pollution are the main factors, as negative drivers, together with conservation and restoration policies, as positive ones, affecting future trends in salt marshes. China has implemented several measures to protect and restore salt marshes, such as setting up protected areas, drawing marine ecological redline, and making strict regulations on reclamation. However, stronger legal protection for wetlands, more effective enforcement, and participation by local communities can further enhance salt marsh restoration, conservation and management.
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•Great losses (59%) of salt marshes occurred in China from the 1980s to the 2010s due to a few main drivers.•Salt marshes in China face natural and anthropogenic threats.•China has taken even tougher measures to conserve and restore salt marshes.•Legal basis for wetland protection, more effective enforcement and public participation are needed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Rainfallwas obtained fromTRMMdata for the period 1999–2008.•We model runoff and Water balance in the Tiaoxi catchment.•The gauge-adjusted TRMM 3B42 data can be a valuable tool for hydrologic ...modeling.
Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product’s role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This work presents recent methodological developments in geophysical assimilation research. We revisit the meaning of the term “solution” of a mathematical model representing a geophysical system, ...and we examine its operational formulations. We argue that an assimilation solution based on epistemic cognition (which assumes that the model describes incomplete knowledge about nature and focuses on conceptual mechanisms of scientific thinking) could lead to more realistic representations of the geophysical situation than a conventional ontologic assimilation solution (which assumes that the model describes nature as is and focuses on form manipulations). Conceptually, the two approaches are fundamentally different. Unlike the reasoning structure of conventional assimilation modeling that is based mainly on ad hoc technical schemes, the epistemic cognition approach is based on teleologic criteria and stochastic adaptation principles. In this way some key ideas are introduced that could open new areas of geophysical assimilation to detailed understanding in an integrated manner. A knowledge synthesis framework can provide the rational means for assimilating a variety of knowledge bases (general and site specific) that are relevant to the geophysical system of interest. Epistemic cognition‐based assimilation techniques can produce a realistic representation of the geophysical system, provide a rigorous assessment of the uncertainty sources, and generate informative predictions across space‐time. The mathematics of epistemic assimilation involves a powerful and versatile spatiotemporal random field theory that imposes no restriction on the shape of the probability distributions or the form of the predictors (non‐Gaussian distributions, multiple‐point statistics, and nonlinear models are automatically incorporated) and accounts rigorously for the uncertainty features of the geophysical system. In the epistemic cognition context the assimilation concept may be used to investigate critical issues related to knowledge reliability, such as uncertainty due to model structure error (conceptual uncertainty).
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Neural tube defect (NTD) prevalence in northern China is among the highest worldwide. Dealing with the NTD situation is ranked as the number one task in China's scientific development plan in ...population and health field for the next decade. Physical and social environments account for much of the disease's occurrence. The environmental determinants and their effects on NTD vary across geographical regions, whereas factors that play a significant role in NTD occurrence may be buried by global statistics analysis to a pooled dataset over the entire study area. This study aims at identification of the local determinants of NTD across the study area and exploration of the epidemiological implications of the findings.
NTD prevalence rate is represented in terms of the random field theory, and Rushton's circle method is used to stabilize NTD rate estimation across the geographical area of interest; NTD determinants are represented by their measurable proxy variables and the geographical weighted regression (GWR) technique is used to represent the spatial heterogeneity of the NTD determinants.
Informative maps of the NTD rates and the statistically significant proxy variables are generated and rigorously assessed in quantitative terms.
The NTD determinants in the study area are investigated and interpreted on the basis of the maps of the proxy variables and the relationships between the proxy variables and the NTD determinants. No single determinant was found to dominate the NTD occurrence in the study area. Villages where NTD rates are significantly linked to environmental determinants are identified (some places are more closely linked to certain environmental factors than others). The results improve current understanding of NTD spread in China and provide valuable information for adequate disease intervention planning.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The blue carbon ecosystem, including the salt marsh ecosystem, possesses a significant carbon sequestration potential. Therefore, accurately quantifying the carbon storage within such ecosystems is ...crucial for the adequate accounting of carbon sequestration. The present work chose a Spartina alterniflora ecosystem in the Xiaogan Island (China) as the study area (approximately 11 ha), and employed the Bayesian maximum entropy (BME) approach to assimilate both hard organic carbon (OC) data and soft OC data measured from 2 cm and 10 cm stratified samples. A 3-dimensional model was developed for space-time OC estimation purposes based on the sediment chronology results. The 10-fold BME cross validation results demonstrated a high estimation accuracy, with the R2, RMSE and MAE values equal to 0.8564, 0.1026 % and 0.0748 %, respectively. A noteworthy outcome was the BME-generated carbon storage density maps with 1 m spatial resolution. These maps revealed that the carbon storage density at the top 30 cm sediment depth in the stable zone (with elder stand age of S. alterniflora) was higher than that in the rapid expansion zone, i.e., 71.79 t/ha vs. 69.82 t/ha, respectively. Additionally, the study found that the averaged carbon burial rate and the total carbon storage at the top 30 cm sediment depth across the study area were 266 g C/m2/yr and 781.50 t, respectively. Lastly, the proposed BME-based framework of carbon storage estimation was found to be versatile and applicable to other blue carbon ecosystems. This approach can foster the development of a standardized carbon sink metrological methodology for diverse blue carbon ecosystems.
•Carbon storage in salt marsh ecosystem was estimated at a spatial resolution of 1 m.•BME exhibited versatile ability for carbon storage estimation in a space-time domain.•Stand age of Spartina Alterniflora positively related to the carbon storage in sediment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Mapping the space–time distribution of heavy metals in soils plays a key role in contaminated site classification under conditions of in situ uncertainty, whereas uncertainty assessment is based on ...the quantification of the specific uncertainties in terms of exceedance probabilities. Geostatistical space-time kriging (STK) is increasingly used to estimate pollutant concentrations in soils. Sequential indicator simulation (SIS) technique is popular in uncertainty assessment of heavy metal contamination of soils. However, these techniques cannot handle multi-temporal data. In this work, spatiotemporal sequential indicator simulation (STSIS) based on an additive space–time semivariogram model (STSIS_A) and on a non-separable space–time semivariogram model (STSIS_NS) was used to assimilate multi-temporal data in the mapping and uncertainty assessment of heavy metal distributions in contaminated soils. Cu concentrations in soils sampled during the period 2010–2014 in the Qingshan district (Wuhan City, Hubei Province, China) were used as the experimental data set. Based on a number of STSIS realizations, we assessed different kinds of mapping uncertainty, including single-location uncertainty during 1 year and during multiple years, multi-location uncertainty during 1 year, and during multiple years. The comparison of the STSIS technique vs. SIS and STK techniques showed that STSIS performs better than both STK and SIS.
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•Heavy metals, and especially Cd, Pb and Hg, accumulate heavily in soils.•Industrial production is responsible for 72.85% and 51.16% of Hg and Pb pollution.•40.08% and 52.23% of Cr ...and As came from natural sources,•81.38% of Cd originated from agriculture activities.•Soils with different fertility grades exhibit different heavy metal contamination.
For the purpose of pollution assessment and source apportionment of heavy metals in agricultural soils with different fertility levels, an intensive sampling (1848 samples) was conducted in Shanghai city. Various indices, including the pollution index (PI), Nemerow integrated pollution index (NIPI), geoaccumulation index (GI) and potential ecological risk index (RI), were employed to assess the pollution status caused by heavy metals. In view of the spatial heterogeneity of heavy metal concentrations, in this work a synthesis of principal component analysis (PCA) with spatial lag modeling (SLM) is proposed to explore quantitatively the sources of heavy metals and the contributions of each component. The results showed that the mean heavy metal concentrations followed a decreasing order: Cr (72.02 ± 8.90 mg/kg) > Pb (25.57 ± 7.38 mg/kg) > As (6.98 ± 1.97 mg/kg) > Cd (0.19 ± 0.10 mg/kg) > Hg (0.12 ± 0.08 mg/kg). Although the mean heavy metal concentrations did not exceed the corresponding national standards, the percentages of sampling locations exhibiting higher concentrations relative to the background values were found to be equal to 43.29% for Pb, 32.63% for Cd, 27.54% for Hg, 11.26% for Cr, and 10.93% for As, which indicate significant local accumulation of heavy metal pollutants within the study area. Industrial activities, natural sources and agricultural activities are the main pollution sources that account for 27.92%, 27.48% and 20.64% of the total pollution, respectively. Industrial activities with high Pb and Hg loadings have a large contribution to good fertility soils (39.38%). It was found that agricultural activity is the main contributor of Cd pollution, having a large contribution (33.46%) to low fertility soils. Cr and As pollution comes mainly from natural sources, with relatively equal contribution to soils with various fertility levels. The present study improves understanding of the pollution status of heavy metals in Shanghai agricultural soils, and also serves as reference for pollution source apportionment in other regions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Sampling and estimation of geographical attributes that vary across space (e.g., area temperature, urban pollution level, provincial cultivated land, regional population mortality and state ...agricultural production) are common yet important constituents of many real-world applications. Spatial attribute estimation and the associated accuracy depend on the available sampling design and statistical inference modelling. In the present work, our concern is areal attribute estimation, in which the spatial sampling and Kriging means are compared in terms of mean values, variances of mean values, comparative efficiencies and underlying conditions. Both the theoretical analysis and the empirical study show that the mean Kriging technique outperforms other commonly-used techniques. Estimation techniques that account for spatial correlation (dependence) are more efficient than those that do not, whereas the comparative efficiencies of the various methods change with surface features. The mean Kriging technique can be applied to other spatially distributed attributes, as well.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This work is the first systematic quantitative analysis of the heavy metal situation along the Zhejiang coastal region focusing on the integrative assessment of the concentrations of seven heavy ...metals (Cu, Cd, Hg, Zn, Pb, Cr, and As) in surface sediments during the 2012–2015 period. Different heavy metal contamination indices were used for surface sediment quality assessment purposes. The numerical results revealed a noticeable spatial fluctuation of the degree of contamination throughout the region during the four years considered. Higher contamination levels and ecological risks were detected in the southern part of the Zhejiang coastal region. It was found that the Cu, Cd and Hg were the predominant contaminants along the Zhejiang coast with mean regional concentrations varying between 29.1 and 34.2, 0.12–0.17, and 0.044–0.052 mg/kg, respectively. The Cr and Pb exhibited lower contamination levels than the other metals during each one of the years 2012–2015. Stochastic site indicators of heavy metal contamination were used to assess regional uncertainties and obtain useful physical interpretations of the state of contamination of the Zhejiang coast. These indicators can be expressed explicitly in terms of probabilities of heavy metal contamination (either at a global scale or spatially distributed over the coastal region), and therefore they can be considered as risk indicators. It was found that the fraction of the coastal region where excess contamination occurred could never exceed the ratio of the mean heavy metal contamination over the selected threshold. In half of the coast study region, the degree of heavy metal contamination was higher than the median spatial contamination values during the month of August of the years 2012–2015. The spatial means of excess contamination and excess differential contamination increased as the relative area of over-contamination increased. Within the substantially contaminated sub-region of the Zhejiang coast, stronger contamination correlations were observed between locations separated by shorter distances. These correlations were higher when smaller thresholds were considered. As regards the spatial connectivity of the corresponding contamination risks, it was found that 44%, 31%, 39% and 63% of the location pairs in the Zhejiang coast simultaneously experienced moderate risks during the years 2012, 2013, 2014 and 2015, respectively. The ratio of the probability of excess contamination at both locations separated by distances < 20 km over the probability of excess contamination at either one of these two locations was high even for large thresholds, indicating that locations with high contamination are concentrated rather than being dispersed along the Zhejiang coast. Lastly, another interesting finding is that the characterization of the Zhejiang coastal region as over-contaminated is very sensitive to the DC threshold considered, that is, a small increase in the threshold selected can reduce significantly the probability that region is characterized as over-contaminated.
•Higher contamination levels and ecological risks were detected in the southern part of the Zhejiang coastal region.•Different indices show different spatial fluctuation of the degree of contamination.•Cu, Cd and Hg were the predominant contaminants, Cr and Pb exhibited lower contamination levels.•Stochastic site indicators are useful for uncertainty assessment and physical interpretations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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