Search

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale. [ABSTRACT FROM AUTHOR]

Global warming has altered the uniformity of precipitation in Inner Mongolia, China, eventually leading to droughts. Further studies are necessary to determine the relationship between the concentration of precipitation and drought. Therefore, we assessed the spatial and temporal characteristics of the precipitation concentration degree (PCD), precipitation concentration period (PCP), and standardized precipitation evapotranspiration index (SPEI) in Inner Mongolia in the past (1961–2017) and predict changes in the three indices under different scenarios in the future (2018–2100) using measured model data and Sen's slope and Mann–Kendall trend analysis. The correlation between PCD/PCP and SPEI was explored using Pearson's correlation coefficient. The results showed that the spatial distribution of PCD and PCP in Inner Mongolia exhibited significant east–west differences. The PCD values were 0.42–0.76, with high‐value areas in the east. PCD showed a decreasing trend in both historical and future scenarios, indicating an even distribution of precipitation and an increased risk of drought. The PCP values were 190°–226°, with high‐value areas mainly in the western region. Except for in the Representative Concentration Pathways RCP4.5 and RCP8.5, PCP values in the historical and RCP2.6 scenarios showed a decreasing trend, indicating an earlier onset of maximum precipitation. SPEI values ranged between −1.23 and 1.17, with all future scenarios showing a decreasing trend and the historical scenario showing an increasing trend. The stations with positive correlation between SPEI and PCD accounted for 89.13, 67.39, 91.3, and 95.65% of Inner Mongolia, while those with positive correlation with PCP accounted for 43.47, 60.87, 56.52, and 4.35%, indicating that the correlation between drought variation and precipitation concentration is strong. These results can help reduce and prevent droughts and floods caused by changes in precipitation patterns and provide a basis for the rational use of water resources for preventing droughts and making relief decisions. [ABSTRACT FROM AUTHOR]

Hydrological connectivity affects the material cycling and energy transfer of ecosystems and is an important indicator for assessing the function of aquatic ecosystems. Therefore, clarification of hydrologic connectivity and its optimization methods is essential for basin water resources management and other problems; however, most of the current research is focused on intermittently flooded areas, especially in terms of optimization, and on hydrological regulation within mature water structures, while research on hydrological connectivity in dry, low rainfall plain areas remains scarce. Based on the graph and binary water cycle theories, this study assessed and hierarchically optimized the structural hydrological connectivity of the Dongliao River Basin (DRB), integrating artificial and natural connectivity, and explored the hydrological connectivity optimization method in the arid plain region at the basin scale to increase connectivity pathways. The spatial analysis and evaluation of hydrological connectivity was also carried out based on the results of the hierarchical optimization, and provided three scenarios for the construction of hydrological connectivity projects in the basin. The hierarchical optimization yielded a total of 230 new water connectivity paths, and the overall hydrological connectivity increased from 5.07 to 7.64. Our results suggest a large spatial correlation in hydrological flow obstruction in the DRB. The center of gravity of circulation obstruction shifted to the south after optimization for different levels of connectivity. With the increase in the optimization level of hydrological connectivity, the national Moran index rose and then fell. The magnitude of the increase in hydrological connectivity effects varied at different optimization levels, and there were sudden points' increase points. From an application point of view, Scenario 1 is necessary and the most cost effective is Scenario 2, which provides a scientific basis for guiding the construction of future ecological projects in the DRB. [ABSTRACT FROM AUTHOR]

Global warming has increased the prevalence and severity of natural disasters, such as the increased frequency and intensity of drought events. In this study, we employed the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) to analyze and predict the temporal and spatial variation of drought and its characteristics in the Songliao Plain under two global warming scenarios (1.5 and 2 °C). We used climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project. The results show that drought will become more frequent in the future; it is more serious at representative concentration pathway (RCP) 8.5 than RCP4.5 and under global warming of 1.5 °C than 2 °C. Geographically, the SPEI indicates that there are signs of drought in the northwest and northeast of Songliao Plain while the SPI indicates that drought decreases from north to south. In terms of drought characteristics, the drought barycenter expressed by the SPEI moves to the northeast, while the drought barycenter expressed by the SPI moves to the northwest. The SPI relies on a single meteorological factor, making the SPI mutation test more stable. Finally, compared to the historical period (1976–2005), the frequency and duration of drought have increased, and it is more reliable to use the SPEI to monitor drought in this area. [ABSTRACT FROM AUTHOR]

The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature—dynamic simulations of autoregressive distributed lag (ARDL) models—to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change. [ABSTRACT FROM AUTHOR]

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a 'short-run' effect). The relationship between temperature and area burned exhibited a steady-state or 'long-run' relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe. We used an autoregressive distributed lag model, a common econometric analysis, to examine the climate correlates of area burned in the grasslands of Xilingol, China. Using mean monthly climate inputs from 2001 to 2014, the model identified temperature as an important influence on area burned. [ABSTRACT FROM AUTHOR]

Abstract To prove the theory that meteorological factors, especially winter drying events, usually affect trees by inducing water and energy imbalance, systematic studies of winter drying damage to Ligustrum lucidum trees in Shandong, China in 2018 were performed by analyzing digital and thermal images of the trees and integrating the results with meteorological and geostatistical analysis and the results of osmotic stress tests in saturated sucrose solution. The results indicate that sap water can not only be transported upward with assistance from cohesion-tension force, but also can be turned back from the terminals of leaves and branches under the inverse pulling force. Under extreme conditions during winter drying events, the breakage of sap water column and reduction of the terminal tissues or organs occur. Similar "∧"-shaped leaf symptoms can also be induced by freezing, summer drought and osmotic stress. The results support the theory of transpiration surface reduction characterized by unity of freezing and drying. Therefore, severe damage to juvenile plants of Ligustrum lucidum under these conditions may be caused by the combined effects of low temperature, drought, strong irradiation and saline soil as well as by the sensitive status of the plants. Highlights • Winter drying injury of Ligustrum lucidum associating with water and energy imbalance. • Digital and thermo images analysis, meteorological indices and geo-statistical analysis. • Sap water can turn back from the terminal of leaves and branches. • At the extreme situation transpiration reduction of the terminal tissue or organ occur. • Similar "∧"shaped symptoms can be induced by freezing, summer drought and osmotic stress. [ABSTRACT FROM AUTHOR]

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world. [ABSTRACT FROM AUTHOR]

The traditional studies on drought disaster risk were based on the ground point data, which were unable to realize the continuity of space and the timeliness. It is shown that the monitoring and evaluation precision on drought were reduced significantly. However, remote sensing data in adequate spatial and temporal resolution can overcome these limitations. It can better monitor the crop in large area dynamically. This study presents a methodology for dynamic risk analysis and assessment of drought disaster to maize production in the northwest of Liaoning Province based on remote sensing data and GIS from the viewpoints of climatology, geography and disaster science. The model of dynamic risk assessment of drought disaster was established based on risk formation theory of natural disaster, and the expression of risk by integrating data came from sky, ground and space. The risk indexes were divided into four classes by data mining method, and the grade maps of drought disaster risk were drawn by GIS. It is shown that the spatial and temporal risk distributions of maize at each growth stage changed over time. The model has been verified against reduction in maize yield caused by drought. It demonstrated the reasonability, feasibility and reliability of the model and the methodology. The dynamic risk assessment of regional drought disaster for maize can be used as a tool, which can timely monitor the status (the possibility and extent of drought) and trends of regional drought disaster. The results obtained in this study can provide the latest information of regional drought disaster and the decision-making basis of disaster prevention and mitigation for government management and farmers. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a Geographical Information Systems (GIS) and information diffusion-based methodology for spatio-temporal risk analysis of grassland fire disaster to livestock production in the grassland area of the northern China. We employed information matrix to analyze and to quantify fuzzy relationship between the number of annual severe grassland fire disasters and annual burned area. We also evaluated the consequences of grassland fire disaster between 1991 and 2006 based on historical data from 12 northern China provinces. The results show that the probabilities of annual grassland fire disasters and annual damage rates on different levels increase gradually from southwest to northeast across the northern China. The annual burned area can be predicted effectively using the number of annual severe grassland fire disasters. The result shows reliability as tested by two-tailed Pearson correlation coefficient. This study contributes a reference in decision making for prevention of grassland fire disaster and for stockbreeding sustainable development planning. The fuzzy relationship could provide information to make compensation plan for the disaster affected area. [Copyright &y& Elsevier]

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a "west−central−southwest" pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively. [ABSTRACT FROM AUTHOR]

Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake. [ABSTRACT FROM AUTHOR]

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data. [ABSTRACT FROM AUTHOR]

Ecological security early warning research is an effective tool for statistical unsustainable bottom line and watershed management. Recent research has shown that climatic elements influence the characteristics of ecological security and its variability and that landscape patterns constrain the basis of regional ecological security. Thus, there is a need to identify trends in ecological safety under the driving forces of climate and landscape pattern changes to enable early warning measures. In this study, a new ecological security early warning system (ESEW) was established, and a comprehensive index was constructed to assess the ESEW in the East Liaohe River Basin (ELRB), China during 2000–2020. The system used a geographic detector to reveal the internal influences, external drivers, and synergistic effects of ecological security warnings in the basin in different periods. A Bayesian network (BN) model was used to simulate the risk of different ecological security warnings occurring under different scenarios. The results showed that the ESEW level is higher in the northwestern part of the ELRB, and the area occupied by extreme warning decreased simultaneously as the area occupied by no warning decreased. The changes of ecological security warning situation in the ELRB are not only influenced by internal factors but also driven by external factors, and the synergy between the core factors and external factors can better explain the driving mechanism of ecological security alarm than a single factor. The BN model was more sensitive to the simulation of no warning and extreme warning. This study provided insights into the maintenance of the security and sustainable development of watershed ecosystems and new perspectives for watershed ESEW research. • A new ecological security early warning integrated system is constructe. • Identify internal influences and external drivers with Geographic detector. • Bayesian used by Multi-scenario simulation of ecological security risk warning. • High ecological security warning in the northwest of the basin. • High elevation-low urbanization rate reduces high ecological security warning risk. [ABSTRACT FROM AUTHOR]

In recent years, global warming has led to frequent waterlogging disaster (WLD), hindering the normal growth and development of crops, affecting the sustainable development of the peanut industry and food security in China. Henan Province, the main peanut-producing area with frequent WLD, was selected as the study area. Based on the soil-crop-atmosphere continuum system, this study constructed a comprehensive waterlogging index (CWI) to monitor waterlogging events. To determine the applicability of CWI, the standardized precipitation evapotranspiration index (SPEI) was calculated. A risk assessment model was constructed to evaluate the hazards of WLD during the different stages of development of spring peanut. The water stress at different growth stages of peanut on crucial physiological indices were studied to determine the natural vulnerability curve of WLD. As a consequence, based on the "Four-Factor" theory of disaster formation, this study established a dynamic WLD assessment model for peanut and evaluated the comprehensive risk of WLD in Henan from 1990 to 2020. The results indicated that the CWI is capable of accurately identifying waterlogging events and peanut responded significantly differently to water stress; the longer the duration of stress in the late growth period (LGP), the higher was the rate of yield reduction. The comprehensive risk of WLD to peanut rose from north to south during the three growth periods. During the LGP, high-risk regions constituted approximately 34.3% of the total population. This study provides a scientific basis and technical support for better exploration of peanut growth under waterlogging stress to manage and reduce the risk of WLD. • The comprehensive waterlogging index (CWI) can be used to identify waterlogging events. • Different water stress days in three periods had different effects on the growth of spring peanut. • Water stress in the late growth period had the greatest influence on the yield of spring peanut. • The risk was evaluated through "Four-Factor" theory, while the south of Henan had high-risk levels. [ABSTRACT FROM AUTHOR]

• An integrated susceptibility index was constructed based on the "Soil-Plant-Atmosphere continuum". • The central and southeastern regions of Shandong province were more susceptible to pine caterpillar outbreaks than the rest. • Susceptibility of pine caterpillar outbreaks increased with time. • Synergism between environmental factors induced greater susceptibility of pine caterpillar outbreaks. Outbreaks of the pine caterpillar (Dendrolimus spp.) have substantial impacts on coniferous forest ecosystem structure, distribution, and productivity, as well as the economy. Furthermore, there is emerging evidence that increasing frequencies of fires and windstorms driven by climate change are contributing to more severe pine caterpillar outbreaks. Therefore, there is a need to identify areas susceptible to pine caterpillar outbreaks to apply prevention measures efficiently and facilitate sustainable forest management. This study constructed an integrated index for assessing the susceptibility of forest ecosystems in Shandong Province, China to pine caterpillar outbreaks based on the indicators of mainly forest structure, climate, topography, landform, and soil conditions. This index suggests the risk and susceptibility of a forest area to a pine caterpillar outbreak. In addition, a geodetector model was used to identify the synergistic or antagonistic effects of environmental factors on risk of pine caterpillar outbreak. The results showed that: (1) mountain and hilly areas are more susceptible to pine caterpillar outbreaks, whereas surrounding areas show a medium susceptibility in central and southeastern part of Shandong Province; (2) there was an increasing susceptibility of the study area to pine caterpillar outbreak in the 1990s, 2000s and 2010s; (3) synergism between certain topography and climate variables enhanced the susceptibility of the study area to pine caterpillar outbreaks, including the interaction between elevation and sunshine hours and between slope and humidity. The integrated susceptibility index was shown to perform well in indicating the risk and susceptibility of forest ecosystems to pine caterpillar outbreaks. The present study can act as a reference for forest management within the identification of areas with high susceptibility to pine caterpillar outbreak. [ABSTRACT FROM AUTHOR]

[Display omitted] • New evaluation index system and perspectives for ECC and its influencing factors in river basin. • ECC showed different geospatial distribution and variation patterns. • The variations of ECC in the DRB are closely associated with the nature and topography factors. Ecological carrying capacity (ECC) is a key indicator for assessing the sustainable development capacity of a region. A comprehensive assessment of regional ECC and the influencing factors has scientific and practical significance for promoting functional and sustainable regional development. The Driver-Pressure-State-Impact-Response-Management (DPSIRM) framwork and subjective-objective combination method were used to develop a framework to establish an ECC evaluation index system. In this study, six internal and six external factors from the perspectives of nature, topography, society, and landscape patterns were selected from the Dongliao River Basin (DRB) in the Jilin province, China. Geographically weighted regression, redundancy analysis, and multiple linear regression models were used to reveal the coupling relationships among the internal and external factors. The results showed that the ECC in the study area was spatially heterogeneous with higher ECC in the southeast and northwest areas of the DRB and lower ECC in the central of DRB had a low carrying capacity. ECC showed a gradual upward trend from 2005 to 2018, which was attributed to the implementation of the ecological environment protection policy in DRB. Primary internal factors that influenced the ECC were precipitation, temperature, altitude, and slope. Precipitation had a positive effect on ECC by influencing Shannon's diversity and landscape shape, while temperature, altitude, and slope had a negative effect on ECC by affecting aggregation and patch density. The methods used herein and the presented results are instructive to sustainable development and ecological restoration in the DRB and other similar areas. [ABSTRACT FROM AUTHOR]

Changes in land use and human interference influence the structures of space use and ecological processes. Since 1990, the ecosystem service value (ESV) of the Dongliao River Basin (DRB) has undergone remarkable temporal and spatial changes. However, the response of ESV to production-living-ecological space (PLES) changes in the DRB remains controversial. In the present study, multi-source data and cluster analysis methods were employed to assess and analyze the patterns and spatial differences of the ESV of the DRB from 1990 to 2018 at a 30-m scale. An increase in the ESV of 149.61 million yuan was found for the DRB; this increase was mainly caused by the reduction of production space and the expansion of ecological space. The ecological space increased from 19.25% in 1990 to 21.59% in 2018, while production space decreased from 75.26% to 71.77%. The high-high clustering area impacting the ESV was mainly distributed in the middle and southeast of the DRB. Furthermore, the causes of the spatial and temporal changes in the ESV were used to provide a scientific basis for decision makers to scientifically manage spatial resource allocation in river basins. • Multi-source data and cluster analysis methods were used to evaluate and analyze the pattern and spatial difference of regional ESV. • Revealing the mechanism by which the value of ecosystem services responds to changes in PLES. • Exploring how changes in land use and human disturbance affect the structure of space use and ecological processes. • Policy recommendations for scientific management of watershed space were put forward. [ABSTRACT FROM AUTHOR]

Aims: Pulsed field ablation (PFA) is an emerging non-thermal ablative modality demonstrating considerable promise for catheter ablation of atrial fibrillation (AF). However, these PFA trials have almost universally included only Caucasian populations, with little data on its effect on other races/ethnicities. The PLEASE-AF trial sought to study the 12-month efficacy and the safety of a multi-electrode hexaspline PFA catheter in treating a predominantly Asian/Chinese population of patients with drug-refractory paroxysmal AF., Methods and Results: Patients underwent pulmonary vein (PV) isolation (PVI) by delivering different pulse intensities at the PV ostium (1800 V) and atrium (2000 V). Acute success was defined as no PV potentials and entrance/exit conduction block of all PVs after a 20-min waiting period. Follow-up at 3, 6, and 12 months included 12-lead electrocardiogram and 24-h Holter examinations. The primary efficacy endpoint was 12-month freedom from any atrial arrhythmias lasting at least 30 s. The cohort included 143 patients from 12 hospitals treated by 28 operators: age 60.2 ± 10.0 years, 65.7% male, Asian/Chinese 100%, and left atrial diameter 36.6 ± 4.9 mm. All PVs (565/565, 100%) were successfully isolated. The total procedure, catheter dwell, total PFA application, and total fluoroscopy times were 123.5 ± 38.8 min, 63.0 ± 30.7 min, 169.7 ± 34.6 s, and 27.3 ± 10.1 min, respectively. The primary endpoint was observed in 124 of 143 patients (86.7%). One patient (0.7%) developed a small pericardial effusion 1-month post-procedure, not requiring intervention., Conclusion: The novel hexaspline PFA catheter demonstrated universal acute PVI with an excellent safety profile and promising 12-month freedom from recurrent atrial arrhythmias in an Asian/Chinese population with paroxysmal AF., Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT05114954., Competing Interests: Conflict of interest: V.Y.R. has served as consultant to and has received equity compensation from Hangzhou Dinova EP Technology Co., Ltd; and unrelated to this manuscript, he serves as a consultant for and has equity in Ablacon, Acutus Medical, Affera-Medtronic, Apama Medical-Boston Scientific, Anumana, APN Health, Aquaheart, Atacor, Autonomix, Axon Therapies, Backbeat, BioSig, CardiaCare, CardioNXT/AFTx, Circa Scientific, CoRISMA, Corvia Medical, East End Medical, EPD-Philips, EP Frontiers, Epix Therapeutics-Medtronic, EpiEP, Eximo, Farapulse-Boston Scientific, Field Medical, Focused Therapeutics, HRT, Intershunt, Javelin, Kardium, Keystone Heart, LuxMed, Medlumics, Middlepeak, Neutrace, Nuvera-Biosense Webster, Oracle Health, Restore Medical, Sirona Medical, SoundCath, Valcare; unrelated to this work, has served as a consultant for Abbott, AtriAN, Biosense-Webster, BioTel Heart, Biotronik, Boston Scientific, Cairdac, Cardiofocus, Cardionomic, CoreMap, Fire1, Gore & Associates, Impulse Dynamics, Medtronic, Novartis, Philips, and Pulse Biosciences; and has equity in DRS Vascular, Manual Surgical Sciences, Newpace, Nyra Medical, Surecor, and Vizaramed. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001-2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011-2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The environmental risks posed by polycyclic aromatic hydrocarbons (PAHs) and the diversity of their anthropogenic origins make them a global issue. Therefore, it is of utmost significance for protecting the aquatic environment and the growth of neighboring populations to identify their possible origins and ecological risk. Here, we detail the contamination profiles of 15 PAHs found in the East Liao River's surface waters in Jilin Province and use the receptor model Absolute Principal Component Analysis - Multiple Linear Regression (APCS-MLR) and diagnostic ratios method to identify the primary potential sources of pollution. Based on the natural hazard risk formation theory (NHRFT), an ecological risk assessment (ERA) model for PAHs in the East Liao River was developed. The method assesses the ecological risk status of PAHs by integrating the risk quotient (RQ) approach and the DPSIRM (driving force, pressure, state, impact, response, management) conceptual framework. Total concentrations in the surface water body were between 396.42 and 624.06 ng/L, with an average of 436.99 ng/L. The source research revealed that coal, biomass, and traffic emission sources are the most likely PAH contributors to the East Liao River. The ERA found that the majority of the sites' locations of the study were at low risk for PAHs in surface water bodies (30.7 % and 32.2 %, respectively), while only a tiny percentage of sites were at high or very high risk (1.8 % and 13.6 %). The study results provide theoretical support for the East Liao River's ecological, environmental protection, and policy formulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In recent years, the main kiwifruit producing region, central-south Shaanxi Province, has often suffered from the threat of extreme high temperatures. Assessing the risk of high-temperature disasters in the region is essential for the rational planning of agricultural production and the development of resilience measures. In this study, a database was established to assess the risk of a high-temperature disaster to kiwifruit. Then, four aspects, hazard, vulnerability, exposure and disaster prevention and mitigation capacity, were taken into account and 19 indexes were selected to make an assessment of the risk of a high-temperature disaster. At the same time, 16 indexes were selected for the assessment of the climatic suitability of kiwifruit in terms of light, heat, water, soil and topography, and were used as one of the indexes for exposure assessment. The analytic hierarchy process and the entropy weighting method were combined to solve the weights for each index. The results reveal that: (1) The Guanzhong Plain has a high climatic suitability for kiwifruit, accounting for 15.14% of the study area. (2) The central part of the study area and southern Shaanxi are at high risk, accounting for 22.7% of the study area. The major kiwifruit producing areas in Shaanxi Province (e.g., Baoji) are at a low risk level, which is conducive to the development of the kiwifruit industry. Our study is the first to provide a comprehensive assessment of the risk of a high-temperature disaster to the economic fruit kiwifruit, providing a reference for disaster resilience and mitigation.

As a major agricultural province in China, it is necessary to study the content of heavy metals in farmland soil and crop in Jilin Province and to evaluate the risks to ecology and human health. This study presented the work completed on 79 soil samples, 10 rice samples, 66 maize samples and 15 soybean samples collected from Jilin Province farmland and evaluated six heavy metals (Zn, Cu, Pb, Cd, Hg and As) concentrations. The results showed that the concentrations of the six heavy metals in farmland soil and crop samples from Jilin Province basically met the soil standards and food health standards of China. The agricultural soil pollution spatial distribution was the most serious in the south of Jilin Province and the lightest in the west. The non-carcinogenic risks faced by children eating crops were higher than those of adults, but the carcinogenic risks were lower than those of adults. Both of the two health risks to adults and children from eating crops were very limited. The results would help determine the heavy metals pollution in farmland soil in Jilin Province efficiently and accurately and helped decision makers to achieve a balance between production and environmental regulation.

Floods are a devastating kind of natural disaster. About half of the population in China lives in rural areas. Therefore, it is necessary to assess the flood disaster risk of rural housings. The results are valuable for guiding the rescue and relief goods layout. In this study, we take the severe flood disaster that happened at Kouqian Town in Jilin, China in 2010 as an example to build an risk assessment system for flood disaster on rural housings. Based on the theory of natural disaster risk formation and "3S" technology (remote sensing, geography information systems and global positioning systems), taking the rural housing as the bearing body, we assess the flood disaster risk from three aspects: hazard, exposure and vulnerability. The hazard presented as the flood submerging range and depth. The exposure presented as the values of the housing and the property in it. The vulnerability presented as the relationship between the losses caused by flood and flood depth. We validate the model by the field survey after the flood disaster. The risk assessment results highly coincide with the field survey losses. This model can be used to assess the risk of other flood events in this area.

Objective: The present study aimed to evaluate a new method for validation of complete linear block at the left atrial (LA) roof., Background: Linear lesions at the LA roof have been reported to improve the success rate of catheter ablation of atrial fibrillation (AF)., Methods: Complete linear block at the LA roof was evaluated in 31 patients after complete isolation of pulmonary vein antrum (PVA) using a simple method. We hypothesized that complete linear block of the LA roof could be confirmed during sinus rhythm if (1) a continuous line of double potentials could be recorded at the LA roof, and (2) the activation sequence along the posterior wall changed to caudocranial. The results of this method were compared with standard pacing techniques., Results: Thirty-nine lines were assessed using this method: 8 before and 31 after achievement of complete conduction block. After validation of complete linear block at the LA roof, double potentials with a mean interpotential interval of 60 +/- 13 ms were recorded in 25 patients and electroanatomical mapping of the left atrium showed the activation sequence of posterior wall changed to caudocranial in all 31 patients during sinus rhythm. The sensitivity, specificity, positive and negative predictive values of this method for validating complete linear block were 100%, 88.9%, 96.8%, and 100%, respectively., Conclusion: A corridor of double potentials along the roof line and a caudocranial activation sequence along the posterior wall during sinus rhythm can indicate conduction block at the LA roof after PVA isolation.

Background: Catheter ablation can restore sinus rhythm in selected patients with atrial fibrillation (AF), but its preventive function for thromboembolic events (TE) is still underdetermined., Methods: We retrospectively studied 520 consecutive patients with AF who were referred for circumferential pulmonary vein ablation. The incidence of late TE was investigated, and the predictors of late TE were identified from analysis of the basic clinical variables of patients with late TE., Results: Eight (1.5%) patients experienced late TE during 28+/-8 months follow-up. When TE occurred, most (6/8, 75%) patients were documented with recurrence episode. The recurrence of AF was more frequent in patients with late TE than patients without late TE (75.0% vs 30.5%, P=0.007). Multivariate logistic regression analysis identified that all risk factors, including all clinical variants prior ablation and the CHADS2 score, were not related to late TE but the presence of recurrence was the only predictor of late TE (odds ratio, 5.542; 95% confidence interval [1.416-45.013], P=0.019)., Conclusions: Our study demonstrates a strong relationship between the recurrence after ablation and TE post-operation and supports the proposal that free of recurrence is an indication for discontinuation of anticoagulation after ablation.

Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is an inherited heart muscle disease associated with increased risks of sudden death, particularly in young, otherwise healthy, patients. The pathologic features are progressive myocardial atrophy and fibrofatty replacement. Plakophilin-2 (PKP2) is reported as the most common ARVD/C-causing gene in Western countries. In this study we aimed to determine the prevalence of PKP2 mutations in Chinese patients with ARVD/C and their phenotype characteristics. Genotype and phenotype were investigated in a cohort of 18 unrelated Chinese patients with a clinical diagnosis of ARVD/C. Direct sequencing of PKP2 led to the identification of 5 novel heterozygous mutations (R158K, Q211X, L419S, A793D, and N852fsX930) in 39% of patients (7 of 18) with ARVD/C. Among them, N852fsX930 was found in 3 unrelated young patients who presented with symptomatic ventricular tachyarrhythmia. Nevertheless, no significant difference could be detected between patients with ARVD/C with (n = 7) and without (n = 11) PKP2 mutations with regard to the phenotype characteristics and clinical outcomes. Decreased penetrance was prominent in family members. In conclusion, 5 novel PKP2 mutations were identified in a cohort of symptomatic Chinese patients with ARVD/C. N852fsX930 appeared to be a hot-spot mutation in which patients presented with a severe ARVD/C phenotype, and 2/3 had early onset of arrhythmic events. No significant difference was found in phenotype characteristics between patients with ARVD/C with and without PKP2 mutations. The decreased penetrance indicated that an ARVD/C diagnosis cannot solely rely on genotyping results.