[Pollution Characteristics and Source Analysis of Soil Heavy Metal in Coal Mine Area near the Yellow River in Shandong].

To explore the pollution characteristics and source of soil heavy metal in a coal mine area near the Yellow River in Shandong, the geo-accumulation index method and improved Nemerow pollution index method were used to evaluate the pollution characteristics of soil heavy metal. The absolute principal component-multiple linear regression model (APCS-MLR) was used to quantitatively analyze the source of soil heavy metal, and the spatial distribution of Hg and Cd were analyzed using the Kriging spatial difference method in ArcGIS. The result accuracy of the APCS-MLR model was further verified. The results showed that:The measured contents of soil heavy metal Cu, Zn, Pb, Cr, Cd, Ni, As, and Hg all exceeded the normal site, among which, Hg and Cd exceeded the background values of soil elements in Shandong. The coefficient of variation (CV) of Hg was higher than 0.500, indicating significant spatial heterogeneity. Moreover, the correlation between Hg and other heavy metals was generally low, and the possibility of the same pollution source was small. The results of the geo-accumulation index and improved Nemerow pollution index showed that the overall soil heavy metal pollution was at a moderate level, among which the Hg pollution level was the highest, and its maximum value was at a slanted-heavy pollution level; Cu, Cd, and As in soil caused local pollution, which were at a slanted-light pollution level. Soil heavy metal pollution was closely related to mining activities, rehabilitation, and engineering construction in the coal mine area. The two major pollution sources of soil heavy metal in the research area were the compound source of the parent material and industrial and mining transportation sources (known source 1) and the compound source of atmospheric sedimentation and coal production (known source 2), the contribution rates of which were 76.705% and 16.171%, respectively. The results of the APCS-MLR model were shown to be reliable by analyzing the content distribution of Hg and Cd using the Kriging space difference mode. This research can provide scientific basis for the precise control and improvement of soil heavy metal pollution, ensuring the safety of food and agricultural products and improving the quality of the ecological environment in the coal mine area in the Shandong section of the Yellow River Basin.

A comparison of risk factor investigation and experts' opinion elicitation analysis for identifying foot-and-mouth disease (FMD) high-risk areas within the FMD protection zone of South Africa (2007-2016).

Foot-and-mouth disease is a controlled disease in accordance with the South African Animal Diseases Act (Act 35 of 1984). The country was classified by the World Organisation for Animal Health (WOAH) as having a FMD free zone without vaccination in 1996. However, this status was suspended in 2019 due to a FMD outbreak outside the controlled zones. FMD control in South Africa includes animal movement restrictions placed on cloven-hoofed species and products, prophylactic vaccination of cattle, clinical surveillance of susceptible species, and disease control fencing to separate livestock from wildlife reservoirs. The objectives of this study were to evaluate differences in identifying high-risk areas for FMD using risk factor and expert opinion elicitation analysis. Differences in risk between FMD introduction and FMD spread within the FMD protection zone with vaccination (PZV) of South Africa (2007-2016) were also investigated. The study was conducted in the communal farming area of the FMD PZV, which is adjacent to wildlife reserves and characterised by individual faming units. Eleven risk factors that were considered important for FMD occurrence and spread were used to build a weighted linear combination (WLC) score based on risk factor data and expert opinion elicitation. A multivariable conditional logistic regression model was also used to calculate predicted probabilities of a FMD outbreak for all dip-tanks within the study area. Smoothed Bayesian kriged maps were generated for 11 individual risk factors, overall WLC scores for FMD occurrence and spread and for predicted probabilities of a FMD outbreak based on the conditional logistic regression model. Descriptively, vaccine matching was believed to have a great influence on both FMD occurrence and spread. Expert opinion suggested that FMD occurrence was influenced predominantly by proximity to game reserves and cattle density. Cattle populations and vaccination practices were considered most important for FMD spread. Highly effective cattle inspections were observed within areas that previously reported FMD outbreaks, indicating the importance of cattle inspection (surveillance) as a necessary element of FMD outbreak detection. The multivariable conditional logistic regression analysis, which was consistent with expert opinion elicitation; identified three factors including cattle population density (OR 3.87, 95% CI 1.47-10.21) and proximities to game reserve fences (OR 0.82, 95% CI 0.73-0.92) and rivers (OR 1.04, 95% CI 1.01-1.07) as significant factors for reported FMD outbreaks. Regaining and maintaining an FMD-free status without vaccination requires frequent monitoring of high-risk areas and designing targeted surveillance.

Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation.

In order to determine the distribution area and amount of Artemisia annua Linn. (A. annua) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicated that the amount of A. annua in China was about 700 billion in 2019. A. annua was mainly distributed in the circular coastal belt of Shandong Peninsula, central Hebei, Tianjin, western Liaoning, and along the Yangtze River and in the middle and lower reaches of Jiangsu, Anhui, and the northern Chongqing provinces. The main factors affecting the amount are the precipitation in the wettest and the warmest seasons, the average annual precipitation, and the average temperature in the coldest and the driest seasons. The results show that the amount of A. annua is strongly influenced by precipitation and temperature.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China.

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

A water quality assessment model involving novel fluorescence technology.

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Isodose mapping of terrestrial gamma radiation in Sarawak-Borneo: Insights from surface-weathered soil analysis.

Presentation of baseline data on terrestrial gamma radiation (TGR) levels is crucial for assessing the annual effective dose received by the public due to natural radiation exposure. Cumulative doses from various sources can become significant, warranting a spatial understanding of TGR distribution. Few countries have comprehensively mapped TGR on a national scale, often facing challenges due to remote or inaccessible regions. This study investigated the influence of weathered soil groups on TGR dose rates in Sarawak-Borneo, Malaysia, to facilitate insights for TGR projection and isodose mapping. A total of 1044 TGR dose rate measurements were collected using NaI (Tl) scintillation detector survey meters, with a mean of 100 nGy h-1 and a range of 8-375 nGy h-1. Non-parametric statistical analyses of variance using Welch's ANOVA, Brown-Forsythe, and Kruskal-Wallis validated (P-sig.=.000) notable dissimilarities among six categories of superficial-weathered soil. Graphical analysis using Sinclair's cumulative plot revealed deviations at intervals of 50, 80, 100, 120, 175, and 205 nGy h-1. These deviations indicate distinct lithological influences. Skeletal soil (entisols) and podzolic soils had high mean dose rates (148 and 113 nGy h-1, respectively) due to limited development, thus preserving abundant uranium (U) and thorium (Th). Meanwhile, gleysols and thionic soils exhibited compatible means (90 and 82 nGy h-1, respectively), while alluvial (or transported soils) and organic soils displayed lower dose rate ranges (mean of 76 and 47 nGy h-1, respectively), reflecting rapid hydrolysis weathering processes. Simple linear regression analysis revealed a strong relationship between TGR dose rate and mean value of weathered soil groups (y = 0.851x + 0.141 nGy h-1), signifying the significance and magnitude of weathered soil groups' impact on TGR dose rates. The obtained R-value is 0.704, indicating a strong linear correlation among soil group variables, and a Durbin-Watson statistic of 1.41, suggesting positive autocorrelation among residuals, thus positive relationships. An isodose map was successfully developed using the Kriging technique, aligning with lithological features of the study area. Semivariogram analysis reveals spatial dependence within a range of 1.47°, supporting the Kriging technique's suitability for spatial inference. In conclusion, this study has successfully revealed the relationship between TGR dose rates and superficial-weathered soil in Sarawak-Borneo. While the linear relationship is applicable to the Sundaland-Borneo tectonic block, it has potential to be used as a valuable tool for spatial inference of TGR dose rates in isodose development with similar lithologial characteristics, aiding in radiation exposure assessment and environmental monitoring.

A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

Determining climate classifications and producing climate border maps with GIS of Safranbolu district, Karabük, Türkiye.

Knowing climate characteristics enables the detection of particular climate characteristics and their boundaries. This situation is essential in terms of providing sustainable use of areal resources and directing land use plans. For this reason, in this study, climate boundary maps of the Safranbolu district were created based on the need to form a basis for planning. For this purpose, measurement data of all meteorological stations in the district for the last 30 years were obtained; data were associated with the location, and the water balance of each station was calculated using the Thornthwaite climate classification method. In addition, the climate type was determined using different climate classification methods, and the results were compared. All applied methods have shown that Safranbolu has a humid climate; however, the humidity value in the north of Safranbolu is slightly higher than that in the central and southern parts. In addition, water shortage in the north of Safranbolu is observed in July-August, while water shortage in the central and southern parts is observed in July-August-September. Considering the long-term precipitation average of the Safranbolu district, the highest annual precipitation is observed in March and the lowest in August. Etp and Etr throughout the district are highest in July and lowest in January. Surplus water and surface flow occur in the months between December and May, with the highest amount of surface flow occurring in March. There is no month without rain in Safranbolu. Safranbolu, which is on the UNESCO World Heritage List, is a visiting area for local and foreign tourists because of its cultural, architectural, and historical features and geotourism potential. In addition to its current agricultural activities, the cultivation of the "Saffron" plant, which gives its name to the district, and its forest assets cause an increase in both the tourism capacity and population of the district. Considering all of these, studies on climate change risk management and water resources management in Safranbolu have been conducted.

Fine-tuning the use of moss transplants to map pollution by Potentially Toxic Elements (PTEs) in urban areas.

Mosspheres are a kind of moss transplants which offer a novel approach for detecting atmospheric pollution using devitalized mosses, as they reflect the atmospheric deposition of certain elements and polycyclic hydrocarbons. However, due to the unique features of the mosspheres such as the low elemental concentrations in the cultured material, the data treatment needs to be different from that of conventional biomonitoring studies. In this article, our objectives are to identify the best parameter for expressing the levels of chemical elements accumulated by mosspheres, and to apply a recently developed method to assess the probability of pollution of each sample and of the study area. To do this, we used data from a study in which 81 mosspheres were exposed in a medium-sized city in southwestern Europe. Comparing different pollution indices, we selected the enrichment rate (ER) as the most useful, as it is resilient to fluctuations in the initial concentrations and takes into account the time factor, allowing for greater comparability among studies. Then, we determined that the statistical distribution of the ERs of most elements fitted a normal distribution, showing that most samples did not differ significantly from the background concentrations for these elements. On the other hand, for Ni, Pb and Zn there was a subpopulation of samples above background values. In these cases, we determined the probability of pollution of each sample. Finally, we used indicator kriging to calculate the probability of pollution across the study area, identifying the polluted areas, which for some elements match the distribution of the main industries and highways, indicating that this is a suitable protocol to map elemental pollution in urban areas.

A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation.

During the process of seabed terrain exploration using a multi-beam echo system, it is inevitable to obtain a sounding set containing anomalous points. Conventional methods for eliminating outliers are unable to reduce the disruption caused by outliers over the whole dataset. Furthermore, incomplete consideration is given to the terrain complexity, error magnitude, and outlier distribution. In order to achieve both a high-precision terrain quality estimate and quick detection of depth anomalies, this study suggests a dual robust technique. Firstly, a robust polyhedral function is utilized to solve anomaly detection for large errors. Secondly, the robust kriging algorithm is used for refined outlier removal. Ultimately, the process of dual detection and anomaly removal is achieved. The experimental results demonstrate that DRS technology has the most favorable mean square error and error fluctuation range in the test set, with values of 0.8321 and [-2.0582, 1.9209], respectively, when compared to RPF, WT, GF, and WLS-SVM schemes. Furthermore, DRS is able to adjust to various terrain complexities, discrete distribution features, and cluster outlier detection, as shown by objective indicators and visual outcome maps, guaranteeing a high-quality seabed terrain estimate.

Spatial distribution of Pb and Zn in soils under native vegetation in Southeast Brazil.

Heavy metals can play an important biological role as micronutrients but also as potentially toxic elements (PTEs). Understanding the natural concentrations of PTEs-Pb and Zn included-in soils allows for the identification and monitoring of contaminated areas and their role in environmental risk assessment. In this study, we aim to determine semi-total or natural and available concentrations of Pb and Zn in topsoils (0-20 cm depth) from 337 samples under native vegetation in the State of Minas Gerais, Brazil. Additionally, we sought to interpret the spatial geochemical variability using geostatistical techniques and quality reference values for these elements in soils were established. The semi-total concentrations were determined by flame and graphite furnace atomic absorption after microwave-assisted nitric acid digestion method. The available concentrations were extracted using the Mehlich-I extractor and determined by atomic absorption spectrometer. Spatial variability was modeled using semivariance estimators: Matheron's classic, Cressie and Hawkins' robust, and Cressie median estimators, the last two being less sensitive to extreme values. This allowed the construction of digital maps through kriging of semi-total Pb and Zn contents using the median estimator, as well as other soil properties by the robust estimator. The dominance of acidic pH and low CEC values reflects highly weathered low-fertility soils. Semi-total Pb contents ranged from 2.1 to 278 mg kg-1 (median: 9.35 mg kg-1) whereas semi-total Zn contents ranged from 2.7 to 495 mg kg-1 (median: 7.7 mg kg-1). The available Pb contents ranged from 0.1 to 6.92 mg kg-1 (median: 0.54 mg kg-1) whereas available Zn contents ranged from 0.1 to 78.2 mg kg-1 (median: 0.32 mg kg-1). The highest Pb and Zn concentrations were observed near Januária, in the northern part of the territory, probably on limestone rocks from the Bambuí group. Finally, the QRVs for Pb and Zn in natural soils were lower than their background values from other Brazilian region and below the prevention values suggested by Brazilian environmental regulations.

Long-term exposure to high perceived temperature and risk of mortality among patients with chronic kidney disease.

Health risks due to climate change are emerging, particularly from high-temperature exposure. The perceived temperature is an equivalent temperature based on the complete heat budget model of the human body. Therefore, we aimed to analyze the effect of perceived temperature on overall mortality among patients with chronic kidney disease. In total, 32,870 patients with chronic kidney disease in Seoul participated in this retrospective study (2001-2018) at three medical centers. The perceived temperature during the summer season was calculated using meteorological factors, including the air temperature near the automated weather station, dew point temperature, wind velocity, and total cloud amount. We assessed the association between perceived temperature using Kriging spatial interpolation and mortality in patients with CKD in the time-varying Cox proportional hazards model that was adjusted for sex, age, body mass index, hypertension, diabetes mellitus, estimated glomerular filtration rate, smoking, alcohol consumption, and educational level. During the 6.14 ± 3.96 years of follow-up, 3863 deaths were recorded. In multivariable analysis, the average level of perceived temperature and maximum level of perceived temperature demonstrated an increased risk of overall mortality among patients with chronic kidney disease. The concordance index for mortality of perceived temperature was higher than temperature, discomfort index, and heat index. When stratified by age, diabetes mellitus, and estimated glomerular filtration rate, patients with chronic kidney disease with young age (age <65 years) showed higher hazard ratio for mortality (interaction P = 0.049). Moreover, the risk of death in the winter and spring seasons was more significant compared to that of the summer and autumn seasons. Therefore, long-term exposure to high perceived temperature during summer increases the risk of mortality among patients with chronic kidney disease.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Upscaling ground-based backpack gamma-ray spectrometry to spatial resolution of UAV-based gamma-ray spectrometry for system validation.

Advances in the development of gamma-ray spectrometers have resulted in devices that are ideal for use in conjunction with the increasingly reliable systems of autonomously flying uncrewed aerial vehicles (UAVs) that have recently become available on the market. Airborne gamma-ray spectrometry (GRS) measurements have many different applications. Here, the technique is applied to a former uranium mining and processing site, which is characterized by relatively low specific activities and, hence, low count rates, requiring relatively large detectors and correspondingly big size UAVs. The future acceptance of the use of such UAV-based GRS systems for radionuclide mapping depends on their ability to measure absolute specific activities of natural radionuclides such as U-238 in near-surface soil that are consistent with the results of established and proven ground-based systems. To determine absolute specific activities on the ground, the gamma radiation data from airborne detectors must be corrected for attenuation caused by the flight altitude above ground. In recent years, mathematical procedures for altitude correction have been developed, that are specifically tailored to the working range of several tens of meters typical for UAVs. However, very limited experimental validation of these theoretical approaches is available. A very large dataset consisting of about 3000 UAV-based and 19,000 backpack-based measurements was collected at a low-grade uranium ore dump in Yangiabad, Uzbekistan. We applied different geostatistical interpolation methods to compare the data from both survey techniques by upscaling backpack data to airborne data. Compared to backpack systems, UAV-based systems have lower spatial resolution, so measurements average over larger areal units (or in geostatistical terminology: "spatial support"). Taking into account the change in spatial support, we illustrate that (1) the UAV-based measurements show good agreement with the upscaled backpack measurements and that (2) UAV surveys provide good delineation of contrasts of the relatively smooth U-238 specific activity distribution typical for former uranium mining and processing sites. We are able to show that the resolution of UAV-based systems is sufficient to map extended uranium waste facilities.

How much organic carbon could the soil store? The carbon sequestration potential of Australian soil.

Soil is a huge carbon (C) reservoir, but where and how much extra C can be stored is unknown. Current methods to estimate the maximum amount of mineral-associated organic carbon (MAOC) stabilized in the fine fraction (clay + silt, < 20 µm $$ <20\;\upmu \mathrm{m} $$ ) fit through the MAOC versus clay + silt relationship, not their maxima, making their estimates more uncertain and unreliable. We need a function that 'envelopes' that relationship. Here, using 5089 observations, we estimated that the uppermost 30 cm of Australian soil holds 13 Gt (10-18 Gt) of MAOC. We then fitted frontier lines, by soil type, to the relationship between MAOC and the percentage of clay + silt to estimate the maximum amounts of MAOC that Australian soils could store in their current environments, and calculated the MAOC deficit, or C sequestration potential. We propagated the uncertainties from the frontier line fitting and mapped the estimates of these values over Australia using machine learning and kriging with external drift. The maps show regions where the soil is more in MAOC deficit and has greater sequestration potential. The modelling shows that the variation over the whole continent is determined mainly by climate, linked to vegetation and soil mineralogy. We find that the MAOC deficit in Australian soil is 40 Gt (25-60 Gt). The deficit in the vast rangelands is 20.84 Gt (13.97-29.70 Gt) and the deficit in cropping soil is 1.63 Gt (1.12-2.32 Gt). Management could increase C sequestration in these regions if the climate allowed it. Our findings provide new information on the C sequestration potential of Australian soils and highlight priority regions for soil management. Australia could benefit environmentally, socially and economically by unlocking even a tiny portion of its soil's C sequestration potential.

Spatio-temporal trends of mercury levels in alluvial gold mining spoils areas monitored between rainy and dry seasons in the Peruvian Amazon.

Artisanal and small-scale gold mining (ASGM) in the Amazon has degraded tropical forests and escalated mercury (Hg) pollution, affecting biodiversity, ecological processes and rural livelihoods. In the Peruvian Amazon, ASGM annually releases some 181 tons of Hg into the environment. Despite some recent advances in understanding the spatial distribution of Hg within gold mine spoils and the surrounding landscape, temporal dynamics in Hg movement are not well understood. We aimed to reveal spatio-temporal trends of soil Hg in areas degraded by ASGM.,. We analyzed soil and sediment samples during the dry and rainy seasons across 14 ha of potentially contaminated sites and natural forests, in the vicinities of the Native community of San Jacinto in Madre de Dios, Peru. Soil Hg levels of areas impacted by ASGM (0.02 ± 0.02 mg kg-1) were generally below soil environmental quality standards (6.60 mg kg-1). However, they showed high variability, mainly explained by the type of natural cover vegetation, soil organic matter (SOM), clay and sand particles. Temporal trends in Hg levels in soils between seasons differed between landscape units distinguished in the mine spoils. During the rainy season, Hg levels decreased up to 45.5% in uncovered soils, while in artificial pond sediments Hg increased by up to 961%. During the dry season, uncovered degraded soils were more prone to lose Hg than sites covered by vegetation, mainly due to higher soil temperatures and concomitantly increasing volatilization. Soils from natural forests and degraded soil covered by regenerating vegetation showed a high capacity to retain Hg mainly due to the higher plant biomass, higher SOM, and increasing concentrations of clay particles. Disturbingly, our findings suggest high Hg mobility from gold mine spoil to close by sedimentary materials, mainly in artificial ponds through alluvial deposition and pluvial lixiviation. Thus, further research is needed on monitoring, and remediation of sediments in artificial to design sustainable land use strategies.

Fluvial flood adaptation using nature-based solutions: A comprehensive and effective assessment of hydro-meteorological risks.

The growing prominence of Nature-based Solutions (NbS) for disaster risk reduction (DRR) has sparked increased interest. This study is motivated by the need to establish a quantifiable and standardized method for assessing the risks mitigated by NbS in engineering applications. The goal is to establish a comprehensive and effective system framework for assessing hydro-meteorological risks related to NbS in engineering applications. The proposed framework considers flood disaster mechanisms, uncertain factors, and ecosystem services, integrating them to comprehensively assess the benefits of NbS. Specifically, 2-D hydraulic analysis and an in-house adaptive Kriging-based reliability analysis are developed and applied to establish flood prevention standards for NbS. Additionally, the InVEST toolkit is utilized to evaluate ecosystem services. To demonstrate the applicability of the framework, the Baoli River Watershed located in Pingtung County of Taiwan is selected as a case study. It is found that NbS can effectively withstand a 25-year return period flood and reduce flooding on agricultural land by 46.03 %. Furthermore, the probability of flooding decreased from 100 % to 27 % for a 20-year return period flood. NbS was found to provide approximately NT$1.20-4.65 million more in total benefit value compared to the engineering governance strategy. The supporting source codes are available at https://github.com/johnthedy/Adaptive-Kriging-Using-PSO-HHs-in-HECRAS3D.git.

A spatiotemporal XGBoost model for PM2.5 concentration prediction and its application in Shanghai.

This paper innovatively constructed an analytical and forecasting framework to predict PM2.5 concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, improvements are made on XGBoost prediction accuracy and stability so that prediction deviation at extreme points can be avoided. The main findings of this paper can be summarized as follows: 1) Compared with the original model, the goodness of fit of the modified XGBoost model on the test set increased by 17 %, and the root mean square error decreased by 28 %; 2) The variation of PM2.5 concentration in Shanghai has a significant seasonal (cyclical) effect, and its fluctuation period is 3 months (a quarter). In winter, the frequency of extreme value points is significantly higher than that in other seasons; 3) In terms of spatial distribution, the PM2.5 concentration in the central city of Shanghai is higher than that in the rural areas, and the PM2.5 concentration gradually decreases from center city to the surrounding areas. The innovation and contribution of this paper can be summarized as follows: 1) EEMD algorithm verified by SSA was used to decompose the original model without reconstructing all subsequences and get the best weighing among each part of the hybrid model by using variable weight assignment; 2) The city was cut into pieces according to administrative districts in avoid of the duplicate analysis when utilizing advised Kriging interpolation; 3) IDW method was applied to verified Kriging interpolation to increase the accuracy; 4) The latitude and longitude were innovatively converted into the arc length of the corresponding spherical surface; 5) Hierarchical analysis method was used to obtain the order of importance among the PM2.5 monitoring stations, which could improve the accuracy and achieve dimension reduction.

Soil organic carbon stock retrieval from Sentinel-2A using a hybrid approach.

Digital soil maps find application in numerous fields, making their accuracy a crucial factor. Mapping soil properties in homogeneous landscapes where the soil surface is concealed, as in forests, presents a complex challenge. In this study, we evaluated the spatial distribution of soil organic carbon stocks (SOCstock) under forest vegetation using three methods: regression kriging (RK), random forest (RF), and RF combined with ordinary kriging of residuals (RFOK) in combination with Sentinel-2A satellite data. We also compared their accuracies and identified key influencing factors. We determined that SOCstock ranged from 0.6 to 10.9 kg/m2 with an average value of 4.9 kg/m2. Among the modelling approaches, we found that the RFOK exhibited the highest accuracy (RMSE = 1.58 kg/m2, NSE = 0.33), while the RK demonstrated a lack of spatial correlation of residuals, rendering this method inapplicable. An analysis of variable importance revealed that the SWIR B12 band of the Sentinel-2A satellite contributed the most to RFOK predictions. We concluded that the RFOK hybrid approach outperformed the others, potentially serving as a foundation for digital soil mapping under similar environmental conditions. Therefore, it is essential to consider spatial correlations when mapping soil properties in ecosystems that are inaccessible for capturing the spectral response of the soil surface.

A Bayesian maximum entropy model for predicting tsetse ecological distributions.

BACKGROUND: African trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information. METHODS: This paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible. RESULTS: Under the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts. CONCLUSIONS: This work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.