Areas Of Northeastern China Research Articles

Multiple approaches for heavy metal contamination characterization and source identification of farmland soils in a metal mine impacted area

Heavy metal and metalloid contamination of farmland soils are closely related to human health. Soil contamination caused by small-scale mining activities has been largely neglected, especially in developing countries. This research studies the characteristics and the source of heavy metals and metalloids in farmland soils in the Bailing Cu–Zn deposit area in Northeastern China through multiple approaches. Geochemical mapping conducted by portable X-ray fluorescence (pXRF) spectrometry and elemental fractionation analysis (sequential extraction) revealed the spatial variations in elemental concentration and fractionation of the farmland soils. The elemental variations in the sediment and water samples in a stream passing by the farmland were determined by inductively coupled plasma mass spectroscopy (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis. Results indicate elevated As, Zn, Fe, and Mn concentrations in the west bank of the farmland and the associated environmental risks caused by extremely high As concentrations (>500 mg kg−1). The upstream mining activities are identified as a dominated source of contamination in the studied farmland based on the variations of elemental concentrations and fractionations, as well as multivariate analysis results. This study revealed potential ecological risks caused by heavy metal and metalloid contamination in farmland soils in the metal mine impacted area and the stream as a primary pathway for the migration of pollutants associated with mining activities.

Long-term herbicide residues affect soil multifunctionality and the soil microbial community

Residues of herbicides with the extensive applications may impact the soil ecosystem and ultimately threaten agricultural sustainability. However, the effects of long-term herbicide residues on soil multifunctionality and the soil microbial community remain poorly understood. Here, we evaluated relationships between soil multifunctionality and soil microbial communities with residual herbicide concentrations by surveying and analyzing 62 black soil samples collected from an agricultural area in northeastern China. Total residual herbicide concentrations varied from 35 to 568 μg/kg in the soil samples. The response of soil multifunctionality to increasing residual herbicide concentrations exhibited an inverted U-shaped relationship with a peak at approximately 310 μg/kg, with net mineralized organic nitrogen (Nm) and total nitrogen (TN) exhibiting the same trend. Microbial community richness was significantly lower in soil samples with high residual herbicide concentrations (> 310 μg/kg, HG) compared to low residual herbicide concentrations (< 310 μg/kg, LG). In addition, the relative abundances of specific keystone microbial genera differed significantly between LG and HG: norank_f_Acetobacteraceae, norank_f_Caldilineaceae, Candidatus_Alysiosphaera, and Gonytrichum. The relative abundances of these genera were also significantly correlated with soil multifunctionality. Structural equation models (SEMs) further showed that herbicide residues influenced soil multifunctionality by affecting these specific keystone genera. Our study demonstrates that long-term herbicide residues significantly impact the multifunctionality of agricultural black soil, where low concentrations stimulate while high concentrations inhibit, underscoring the need for reasonable application of herbicides to maintain soil ecosystem health.

Tree stumps as passive samplers for trace metal dust deposition

Atmospheric dust is contaminated by trace metals in many parts of the world due to human industrial activities. This study evaluates tree stumps as passive samplers of dust deposition and bioindicators of trace metal dust contamination in a mine impacted environment. Tree stumps (n = 230) and adjacent soils from Cu–Zn mining areas in northeastern China were analysed in situ for their trace metal concentrations using portable X-ray fluorescence (pXRF). Arsenic and Zn concentrations in tree stumps and roadside soils of each location all decreased with distance from the road in a power-law relationship, indicating a common pollution source (resuspension and redeposition of road dust). Micro-XRF images of vertical tree stump slices indicate that As and Zn were concentrated on the cut (uppermost) surface of tree stumps. This indicates atmospheric deposition as a primary contributor to elevated As and Zn concentrations in tree stumps. Trace metals in soils may have other sources besides atmospheric deposition (e.g., migration from buried ore-bodies or rock weathering), which may confound distinguishing sources in polluted areas. Here, we show that the analysis of tree stumps are suitable indicators of atmospheric trace metal contamination. Compared to surface soils, tree stumps can better reflect road dust resuspension and redeposition because unlike soils, atmospheric deposition is the primary contributor to their trace metal concentrations.

Contrasting range changes and drivers of four forest foundation species under future climate change in China

Forest foundation species, vital for shaping community structure and dynamics through non-trophic level interactions, are key to forest succession and sustainability. Despite their ecological importance, the habitat ranges of these species in China and their responses to future climate change remain unclear. Our study employed the optimal MaxEnt model to assess the range shifts and their essential drivers of four typical forest foundation species from three climatic zones in China under climate scenarios, including Acer tegmentosum, Acer pseudo-sieboldianum (temperate zone), Quercus glandulifera (subtropical zone), and Ficus hispida (tropical zone). The optimal MaxEnt model exhibited high evaluation indices (AUC values > 0.90) for the four foundation species, indicating excellent predictive performance. Currently, we observed that A. tegmentosum and A. pseudo-sieboldianum are predominantly inhabited temperate forest areas in northeastern China, Q. glandulifera is primarily concentrated in subtropical forests in southeastern China, and F. hispida is mainly distributed across the tropical forests in southern China. Climate factors, particularly temperature, emerged as the primary environmental factors influencing the potential range of forest foundation species. Moreover, precipitation strongly influenced the potential range of A. tegmentosum and A. pseudo-sieboldianum, while elevation exhibited a greater impact on the range of Q. glandulifera and F. hispida. Under future climate scenarios, suitable areas for A. tegmentosum and A. pseudo-sieboldianum tend to expand southward, F. hispida tends to expand northward, while Q. glandulifera exhibited a tendency to contract towards the center. This study advances our understanding of the spatial and temporal dynamics of forest foundation species in China under climate change, providing critical insights for conservation efforts and sustainable forest management practices.

Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approximately 1 h by the PDE models. Such rapid and accurate prediction by the proposed model is important for evacuation planning, upstream flood control decisions and flood damage reduction.

Diversity of species and geographic distribution of tick-borne viruses in China.

Tick-borne pathogens especially viruses are continuously appearing worldwide, which have caused severe public health threats. Understanding the species, distribution and epidemiological trends of tick-borne viruses (TBVs) is essential for disease surveillance and control. In this study, the data on TBVs and the distribution of ticks in China were collected from databases and literature. The geographic distribution of TBVs in China was mapped based on geographic locations of viruses where they were prevalent or they were detected in vector ticks. TBVs sequences were collected from The National Center for Biotechnology Information and used to structure the phylogenetic tree. Eighteen TBVs from eight genera of five families were prevalent in China. Five genera of ticks played an important role in the transmission of TBVs in China. According to phylogenetic analysis, some new viral genotypes, such as the Dabieshan tick virus (DTV) strain detected in Liaoning Province and the JMTV strain detected in Heilongjiang Province existed in China. TBVs were widely distributed but the specific ranges of viruses from different families still varied in China. Seven TBVs belonging to the genus Orthonairovirus of the family Nairoviridae such as Nairobi sheep disease virus (NSDV) clustered in the Xinjiang Uygur Autonomous Region (XUAR) and northeastern areas of China. All viruses of the family Phenuiviridae except Severe fever with thrombocytopenia syndrome virus (SFTSV) were novel viruses that appeared in the last few years, such as Guertu virus (GTV) and Tacheng tick virus 2 (TcTV-2). They were mainly distributed in the central plains of China. Jingmen tick virus (JMTV) was distributed in at least fourteen provinces and had been detected in more than ten species of tick such as Rhipicephalus microplus and Haemaphysalis longicornis, which had the widest distribution and the largest number of vector ticks among all TBVs. Parainfluenza virus 5 (PIV5) and Lymphatic choriomeningitis virus (LCMV) were two potential TBVs in Northeast China that could cause serious diseases in humans or animals. Ixodes persulcatus carried the highest number of TBVs, followed by Dermacentor nuttalli and H. longicornis. They could carry as many as ten TBVs. Three strains of Tick-borne encephalitis (TBEV) from Inner Mongolia Province clustered with ones from Russia, Japan and Heilongjiang Province, respectively. Several SFTSV strains from Zhejiang Province clustered with strains from Korea and Japan. Specific surveillance of dominant TBVs should be established in different areas in China.

Characteristics and Influencing Factors of the Granite Weathering Profile: A Case Study of a High Latitude Area in Northeastern China

Rock weathering, an important geological process on the earth’s surface, plays a key role in shaping surface morphology, providing nutrients needed by the ecosystem, and regulating the global climate. However, the regimes for controlling rock weathering in different regions are still controversial. In this respect, the heavy minerals, elements, Sr-Nd isotope, magnetic susceptibility, and chromaticity of the granite weathering profile in Harbin, a high latitude area of China, were analyzed for understanding the weathering characteristics and mechanisms of the granite weathering profile in the cold area. The results indicate that the profile underwent strengthened physical weathering (large volumes of cracks in the granite parent rock) and low–moderate chemical weathering (CIA = 56~68). However, the chemical weathering does not clearly affect the composition of the weathering products, making the weathering products a good inheritance from the parent rocks, as evidenced by the binary diagram (e.g., TiO2-Zr, and La/Sc-Co/Th) and geochemical genes (LG01 and LG03). The development process of the weathering profile has been affected by the input of external materials and biological activities, as evidenced by the Sr-Nd isotopic composition, Th/Zr values, chemical depletion fractions, ΔAl/Ti ratios, χfd%, and geochemical genes (LG03). Notably, a progressively decreasing weathering degree with a reduced depth is observed in the profile, which is likely to be related to an addition of external materials and/or effects of biological activities. In addition, the weathering regime of the profile does not vary in geology time, characterized by a kinetic-limited weathering regime with a limited supply.

Biomass carbon sink stability of conifer and broadleaf boreal forests: differently associated with plant diversity and mycorrhizal symbionts?

Forest biomass carbon stability is crucial in achieving carbon neutrality in the high-latitude northern hemisphere, and identifying the differences among forest types and decoupling their associations with plant traits and geoclimatic conditions is the basis for precise forest management. We conducted a large-scale field survey in state-owned forest areas in northeastern China, covering a total of 280,000 km2 forest area, 1275 arbor plots (30m × 30m), 5285 shrub plots (5m × 5m), and 7076 herb plots (1m × 1m). We hypothesized that the conifer and broadleaf forest differences in biomass carbon (C) storage and stability (environmental stability to climatic changes-ES and recalcitrant stability to be decomposed-RS) are associated with mycorrhizal abundance (EcM: ectomycorrhizal, AM: arbuscular mycorrhizal, NM-AM: non-mycorrhizal or arbuscular mycorrhizal), taxon diversity traits (richness, Simpson, Shannon-Wiener, and evenness), and structural differences (diameter, height, and density) in the arbor, shrub, and herb layers. Our results showed that (1) conifer forests had 13.1Mg/ha higher C stocks and 30.9% higher RS, but 8.6% lower ES than broadleaf forests (p < 0.05). Trees in conifer forests had 1.5m taller and 2.4cm thicker trees, but 15% less tree density than those in broadleaf forests. Herbs in conifer forests were 14% shorter and 57% denser than in broadleaf forests. (2) The abundance of EcM-symbiont trees in conifer forests was 15% higher than in broadleaf forests, while their EcM-symbiont shrubs and AM-symbiont herbs were 5-6% lower (p < 0.05). Broadleaf forests had 7% higher tree richness and 19% higher herb richness but 9% lower shrub richness than conifer forests (p < 0.05). Tree and herb evenness was 5-6% higher in conifer forests (p < 0.05). (3) Variations of biomass C sink traits could be explained more by plant diversity in conifer forests (7%) than in broadleaf forests (3.4%). Mycorrhizal symbionts could explain more in broadleaf forests (9.7%) than conifer forests (6.7%). In conifer forests, fewer EcM trees (higher AM trees) and AM herbs, higher tree richness were accompanied by higher biomass C storage and ES. Broadleaf forests underwent similar changes, characterized by an elevation in both RS and ES. (4) Our research emphasized that variations in carbon sequestration between conifer and broadleaf forests could be attributed to mycorrhizal symbionts and species diversity besides tree size-related structural differences. Our findings support the precise management of boreal forests to achieve carbon neutrality based on leaf blade types, plant diversity, and mycorrhizal symbionts.

First report of Rice panicle blight caused by Fusarium sporotrichioides in China.

Rice (Oryza sativa L.) panicle blight, caused by various fungal and bacterial pathogens, is an emerging threat to rice production, due to the impact on rice yield and quality. In the autumn of 2020, a survey was conducted to understand the etiology of the disease in Liaoning province, an important rice growing area in northeastern China. Rice seeds with typical reddish or brown spots on the glumes were collected from various rice fields. Symptomatic seeds were sterilized with 5% sodium hypochlorite and 75% ethanol, rinsed in sterile distilled water, and placed on potato dextrose agar (PDA) plates. After 3 to 5 days of incubation at 25°C, suspected Fusarium strains showing cream to salmon colonies on PDA were purified by the single-spore isolation method. The identification of these strains were performed based on morphological and molecular characteristics. Fusarium incarnatum-equiseti species complex was the most frequently, followed by the members of Fusarium graminearum species complex and Fusarium fujikuroi species complex. However, one strain was identified as F. sporotrichioides Sherb. based on the following results: (I) Colonies on PDA produced dense mycelia and typical red pigment on the undersurface. Macroconidia were usually 3- to 5-septate, moderately curved to straight, and 27.46 ± 7.16 × 3.78 ± 0.8 μm (n = 50). Microconidia were ellipsoid to fusoid and 9.77 ± 2.29 × 2.99 ± 0.65 μm (n = 50). (II) Genomic DNA was extracted by AxyPrep Multisource Genomic DNA Miniprep Kit according to the manufacturer's instructions; the sequence analysis of partial translation elongation factor-1α (TEF-1α) and RNA polymerase II second largest subunit (RPB2) were accomplished with the primers EF1/2 and RPB5F/7CR, respectively. BLAST searches of the obtained sequences had 99-100% homology with several F. sporotrichioides strains from GenBank. DNA sequences of partial TEF-1α and RPB2 were deposited in GenBank as OQ068267 and OQ068269, respectively. (III) This strain can synthesis T-2, HT-2, diacetoxyscirpenol (DAS), and Neosolaniol (NEO) toxin at the concentration of about 5000, 600, 700 and 8000 μg/kg in rice culture, respectively, according to the previous culture and analysis methods (1,2). (IV) Pathogenicity tests were conducted with the rice variety Nanjing 9108 by spraying spore suspension (106 conidia/ml) on rice spikes (5 mL per spike) at the flowering stage. Control spikes were inoculated with sterile distilled water. Three weeks after inoculation, the inoculated rice glumes showed similar symptoms with the original samples in the field. No symptoms were observed on controls. Besides, F. sporotrichioides was successfully recovered from the inoculated rice spikes but not from controls. To our knowledge, this is the first report of F. sporotrichioides causing rice panicle blight in China and this disease appears to be a serious risk to food safety and human health. Funding: This work was supported by Jiangsu Agriculture Science and Technology (CX(21)1005).

Estimating the Soil Erosion Response to Land-Use Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Heilongjiang Province, China

Understanding soil erosion in the northeastern area of China with black soil is vital for protecting the natural environment and preserving food security. Although spatial and temporal studies of soil erosion have been conducted, further research is needed on the correlation between soil erosion and land use type changes. In this study, the soil erosion modulus is computed using RUSLE. The model that is most suitable to the research area was produced by contrasting three different approaches to estimating the rainfall erosion factor. The RUSLE based on the multi-year continuous high-density hourly average precipitation had the best performance of the bunch, with a MAPE of 15.49%, RMSPE of 7.99%, and R2 of 0.99. Based on this model, simulated soil erosion trends in the study region from 1980 to 2020 were examined, along with the link between soil erosion and land use change. The results showed that 40.47% of the overall erosion area is made up of cultivated land, and 97.83% of it is low erosion. The most severe soil erosion occurred on unused land, with moderate and above soil erosion occupying 48.93%. Since 2000, there has been an increase in the erosion of soil in the study region, which is primarily spatially represented in the rise in the soil erosion of forests in the central and northern mountainous areas. The study’s findings serve as a guide for land planning and the development of sustainable agriculture.

Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model’s learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics.

The Effect of Uncertainty of Risks on Farmers’ Contractual Choice Behavior for Agricultural Productive Services: An Empirical Analysis from the Black Soil in Northeast China

Regulating farmers’ choice of agricultural production service (APS) contracts can help maintain the stability of transactions, enhance agricultural production efficiency, protect farmers’ rights and welfare, stimulate the healthy and sustainable development of service organizations, and promote the economic benefits of agriculture. Under the uncertainty of risk (RU), farmers’ willingness to sign a regulated service contract after purchasing APS is a key factor in reaching an efficient and high-quality partnership. Based on the survey data of the black soil area in northeastern China, this study uses the Heckman two-stage model and analyzes it under the logical framework of APS purchases and contractual choice behavior (CCB) under the role of RU. The main findings are as follows. First, the RU has a significant “inducing” effect on farmers’ CCB, and the higher the RU, the more farmers tend to make formal written contracts; among them, each unit increase in business risk increases farmers’ choice of written contracts by 0.797 units, which is an important factor affecting CCB. Second, farmers’ personal trust level played a positive moderating role in influencing CCB. Compared to the low trust level group, farmers in the high trust level group had a diminished degree of influence on CCB, but the intensity was relatively weak. Third, farmers’ personal, family, and social characteristics all influence their behavioral decision-making processes. In order to reduce the risk level of cooperation between farmers and service providers, which can promote the standardization of cooperation contracts, this study suggests that the government should use the publication of manuals and other forms to carry out legal literacy, promote labor market information transparency, reduce service risks, and enhance the effectiveness of the linkage between farmers and service providers.

Toxoplasma gondii Antibodies in Raw Milk and Sera of Cows in China.

Toxoplasma gondii is a worldwide food-borne protozoa that has harmful influences on animal and human health. Raw milk containing T. gondii has been considered as one of the possible infectious sources for humans. Although China is one of the world’s leading milk consumers, there is still no study to investigate the seroprevalence of T. gondii in raw cow milk in China; especially for cows in rural areas. Thus, we conducted this study to examine the specific anti-T. gondii IgG-antibody in the raw milk and sera of domestic cows in China. In total, 894 cows were randomly selected from rural areas in northeastern China. The positive rate of T. gondii in the milk and serum samples were 6.38% (57/894) and 7.16% (64/894), respectively. Moreover, a history of abortion (OR = 2.03, 95% CI: 1.11–3.72, p = 0.022) was identified as the only risk factor for T. gondii infection in the studied cows. This study investigated the seroprevalence of T. gondii in the raw milk and sera of cows in China; it provided timely and useful data for public health and food safety, especially in rural areas.

Effects of Winter Heating on Urban Black Carbon: Characteristics, Sources and Its Correlation with Meteorological Factors

Coal combustion for winter heating is a major source of heavy atmospheric pollution in China, while its impacts on black carbon (BC) are not yet clear. A dual-spot Aethalometer was selected to monitor the atmospheric BC concentration in Zhengzhou, China, during the heating season, which is from 15 November through 15 March of the following year, and the non-heating season (days other than heating season). The characteristics and sources of BC were analyzed, and a concentration weight trajectory (CWT) analysis was conducted. The results showed that the BC concentrations in the heating season were generally higher than those in the non-heating season. The diurnal variation in BC concentrations during heating season was bimodal, and that during the non-heating season was unimodal. The α-values in the heating and non-heating seasons indicated that combustion of coal and biomass and vehicle emissions were the major BC sources for the heating season and non-heating season, respectively. BC concentrations were positively correlated with PM2.5, PM10, CO, and NOX. There was a strong negative correlation between wind speed and BC concentrations, and that for relative humidity was the opposite. BC concentration during heating season was mainly influenced by the northwestern areas of China and the eastern part of Henan, and that in the non-heating season was mainly from the northeastern areas of China and southern Henan.

Characteristics of Hydro-Geochemistry and Groundwater Pollution in Songnen Plain in Northeastern China

Agricultural production may cause groundwater pollution. This study investigated the characteristics of shallow groundwater pollution in a typical black land agricultural production area in Northeastern China and the geochemical behavior of major pollutants. A total of 27 and 23 shallow groundwater samples were collected for measuring on-site parameters and major components in 2000 and 2014, respectively. The improved integrated approach was used to assess groundwater contamination. The results showed that the groundwater was slightly polluted by agricultural activities. The average concentrations of major ions of shallow groundwater were found to be in the following order: Ca2+ &gt; Na+ &gt; Mg2+ &gt; K+ for cations and HCO3− &gt; SO42− &gt; Cl− &gt; NO3− for anions. Percentages of 7.4% and 34.8% of the total groundwater sample in 2000 and 2014, respectively, indicated that the shallow groundwater quality has gradually worsened in the past few decades. The concentration of NO3− was a major factor that influenced the observed groundwater quality changes. Scientific and effective fertilization of rice cultivation is an effective way to avoid groundwater pollution, and the improved groundwater quality evaluation methods can further improve the standard of groundwater resource management effectively.

A novel fire danger rating model based on time fading precipitation model — A case study of Northeast China

With the increase of extreme climate and global warming, forest fires have become more frequent. Therefore, it is important to accurately predict whether fires will occur in forest in the future. Precipitation is an important factor that affects the probability of the occurrence of forest fires in the future. Previous models selected annual average precipitation, monthly average precipitation or drought days as the precipitation value, which the attenuation of precipitation is not considered. In this study, a time fading model is used to calculate the comprehensive precipitation index, which is an exponential weight decay model. The earlier the precipitation time, the smaller the weight. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, in this study, discrete fire points are converted into a continuous fire-point density. The structure of the prediction model is more reasonable, which is conducive to obtaining higher-precision prediction results. Besides, the SVM regression model was used to construct a forest fire danger rating model. In the same area, considering the comprehensive precipitation index compared with the average precipitation value, the accuracy of the three forest areas in northeastern China in the test set has been improved by about 5%. The accuracy rates of 90.13%, 93.04% and 87.5% can be achieved respectively.

The Early Effect of Black Land Protection Plan in Northeast China on Industrial Pollution Using Synthetic Control Method

This study used relevant industrial pollution emission data collected between 2004 and 2020 in northeastern China. It utilized the synthetic control method (SCM) and used entire county-level areas within the scope of the typical black land protection as the experimental group. Thirty regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan), including the three non-black land areas of Liaoning, Jilin, Heilongjiang, and Inner Mongolia, and the other 26 entire provinces were taken as the control group. We studied whether the Outline of Black Land Protection Plan in Northeast China (2017–2030) (BLPP) has reduced local industrial pollution emissions since it was issued in 2017. The study found that implementation of the BLPP reduced industrial wastewater and sulfur dioxide emissions in black land areas significantly. Between 2017 and 2020, local industrial wastewater production decreased by 29.3% compared to the period without implementation of the outline, while industrial sulfur dioxide decreased by 12%. These results remained robust under the placebo and difference-in-difference (DID) tests. This once again showed that implementation of the outline had a significant effect on reducing industrial pollutant emissions in the black land area of northeastern China.

The danger and indeterminacy of forfeiting perching space of bryophytes from climate shift: a case study for 115 species in China.

Identifying the danger and expressing the indeterminacy of forfeiting perching space of species induced by rapid climate warming is crucial for biodiversity risk management under future changes in climate conditions. The scenarios of climate shift named the representative concentration pathways, the categorizing technique with regard to fuzzy-set, and Monte Carlo scheme was employed to survey the indeterminacy and the danger of forfeiting perching space caused by climate warming for 115 bryophytes in China. For the deterministic scenarios of climate shift, the richness of 115 bryophytes improved in several areas in north-eastern China, while it dropped in some areas in southern, eastern, south-eastern, and central China. In addition, for the deterministic scheme of altering climatic state, the count for bryophytes with the proportion of contracting the present areal range as less than 20%, 20-40%, 40-60%, 60-80%, and over 80% was belike 34-38, 19-38, 24-35, 9-19, and 4-9, separately; the count of bryophytes with the ratio of the occupying entire areal range as over 80%, 60-80%, and less than 20% was roughly 97-109, 4-14, and 2-8, separately. For the scenarios of randomly change in climate state, the number of bryophytes with a various proportion of forfeiting the present perching space dropped with enhancing the possibility; with the likelihood beyond 0.6, the count of bryophytes with forfeiting present perching space as less than 20%, 20-40%, 40-60%, 60-80% and high than 80% of the present areal range was approximately 7-14, 2-10, 0-7, 2-9, and 13-20, separately; the number of bryophytes with the ratio of occupying the whole areal range as less than 20%, 20-40%, 40-60%, 60-80%, and over 80% was more or less 1-3, 0-3, 1-5, 1-3, and 38-44, separately. Roughly 48 bryophytes would face the risk of extinction from climate warming, including endemic and non-endemic species. Forfeiting perching space induced by climate warming would cause variations in species composition and the disappearance of some ecological functions associated with these bryophytes. The inconstancy of forfeiting areal range caused by climate warming should be incorporated into the policy-making of conservation bryophytes for adaptation of climate warming.

Hyperlipidemia and hypertension have synergistic interaction on ischemic stroke: insights from a general population survey in China

BackgroundHyperlipidemia (HLP) and hypertension (HTN) are both independent risk factors for ischemic stroke. This study aimed to assess whether HTN and HLP have a synergistic effect on the risk of ischemic stroke.MethodsBetween January and August 2013, 11,695 subjects in rural areas of northeastern China were enrolled. The additive and multiplicative scales were used to evaluate the interaction.ResultsThe prevalence of ischemic stroke was 5.7%. Using the healthy group (without HTN or HLP) as the reference group, subjects with both HTN and HLP had a higher risk of ischemic stroke (odds ratio [OR]: 3.369, 95% confidence interval [CI]: 2.579–4.402), and this OR was greater than that of subjects with only HTN (OR: 1.995, 95% CI 1.526–2.610) or HLP (OR: 1.321, 95% CI 0.937–1.862) (adjusting for age, sex, race, education level, family income, current smoking and drinking status, physical activity, body mass index, diabetes, family history of stroke, and atrial fibrillation). Regarding the additive scale, the relative excess risk due to interaction (OR: 1.053, 95% CI 0.458–1.648) was positive after adjusting for confounders. Moreover, the attributable proportion was 31.3%, which means that 31.3% of the total risk of ischemic stroke was due to the synergistic interaction between HTN and HLP. Furthermore, the synergistic index (S) of ischemic stroke was 1.8 (95% CI 1.157–2.801), which also indicates a synergistic interaction between HTN and HLP. Regarding the multiplicative scale, the interaction effect was also significant after adjusting for confounders (OR: 2.163, 95% CI 1.817–2.575).ConclusionThe results suggest that the synergistic effect of HTN and HLP on ischemic stroke is significantly higher than the sum of their independent effects. The quantification of the combined effect should help to promote healthy blood pressure and blood lipid levels among the general population.

Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.