Search

Featured Application: Thus far, limited research has been carried out on a large scale that considers landscape characteristics, in-situ soil condition, or land cover. We aim to verify hitherto lacking evidence on the ability of vegetation characteristics and topographical factors to lessen pollutants in tropical regions in China. Soil heavy metals along roadsides pose a great threat to ecosystems while their spatial variations and influencing factors still remain unclear in some regions, especially in tropical areas with complex landscape characteristics. Our study was carried out to determine how the land use, vegetation characteristics, topographical factors and distance to the road affect the soil heavy metal distribution. Taking Jinghong county in Yunnan Province, Southwest China as a case, soil samples were collected at different distances off roads and canonical correspondence analysis (CCA) methods were used to determine the relative importance of different factors. Our results showed that heavy metal sources were obtained mainly from the road, based on the principle component analysis (PCA) identification. There were no obvious trends of soil quality index (SQI) with distance to the road in natural soils, while SQI nutrients and SQI metals in farmlands had a decreasing and increasing trend, respectively, which could both be expressed by logarithm models. However, soil properties showed little differences for road levels while they showed significant differences under land use types. The CCA further showed that heavy metal variations in natural soils were jointly affected by distance, plant coverage, relative elevation and soil properties in decreasing order. [ABSTRACT FROM AUTHOR]

• We established a partitioning indicator system of China considering 19 indicators and then constructed the ecological value and development value layers. • China is classified into four types of functional areas, which are defined as key protected area, development area, low-tension area and conflict area. • Key protected areas coincide with ecological security barrier and mainly distributed in the Qinghai-Tibet Plateau, and the existing nature reserves can protect most of the high-value regions of protected areas. • Most of the land in southeast China are zoned as conflict areas. With the acceleration of China's urbanization and economic development, the ecological conditions of some areas have deteriorated, which has seriously affected the balance of ecological sustainability and socio-economic development. Therefore, how to formulate scientific partitioning strategies for both regional socio-economic development and ecological protection has become a key issue. Aimed at maintaining and improving regional sustainability, we established a partitioning indicator system of China considering 19 value indicators. We constructed the separate sets of the ecological values and economic-development values layers. Based on these two categories, China is classified into four types of functional areas, which are defined as key protected area, development area, low-tension area and conflict area. Finally, the Zonation model was used to identify the important regions of ecological sublayers, then these regions were overlapped with the conflict area to identify the key regions in conflict status which need more attention for ecological sustainability. The main research results are as follows: The ecological situation is generally better in southern China, while the development degree is higher in eastern China. The partitioning map shows that key protected areas mainly distributed in the Qinghai-Tibet Plateau and parts of northeast China, and the existing nature reserves can protect most of the high-value regions of protected areas. In addition, most of the land in southeast China is identified as conflict areas. Though only 12% of high-quality habitats identified by Zonation are located in conflict areas, but nearly one-third of these habitats are in extreme-conflict status. The research results can provide a scientific basis for decision-making agencies to optimize regional ecological value and socio-economic benefits for regional sustainability. [ABSTRACT FROM AUTHOR]

• Tree cover showed significantly linear relationship with the species richness. • The explanatory power between tree cover and species richness showed spatial variability. • GWR was effectively in exploring the spatial correlation between tree cover and richness. Explicit biodiversity level at large spatial scale is often indicated by species richness. The spatial variability of species richness has been postulated to be related with influencing factors such as climate, habitat and human activities using many geostastical methods. Understanding the relationship between species richness and its influencing factors is of critical importance for its conservation. As tree cover data with high resolution can reflect the terrestrial habitat with environment, its efficiency for modelling spatial pattern of biodiversity was investigated in this study. The spatial distribution of tree cover and its influencing factors in Yunnan province, and the relationship between tree cover and species richness of mammals and birds were analyzed by linear regression method and Geographically Weighted Regression (GWR) model. The results showed that tree cover in this study region exhibited a substantially spatial heterogeneity, which was affected by human activities, topography and climate. Linear regression analysis showed that tree cover was significantly correlated with the species richness of mammals and birds. The explanatory power (local R2) of GWR model between tree cover and species richness of mammals and birds showed great spatial variability. The predicted spatial distribution of species richness of mammals and birds was consistent with the actual observed values. Our study revealed that the GWR model provides local details of spatial variation in species richness of mammals and birds. These results will be helpful to understand the impact of tree cover on variation of species richness of mammals and birds under global change and will be of theoretical significance for better conservation and management of biodiversity. [ABSTRACT FROM AUTHOR]

Highlights • An evaluation system considering pressure and pressure-bearing was established. • Grid scale ecological security was evaluated using this system and entropy method. • County scale security level was evaluated based on entropy matter-element model. • Changes of ecological security situation between 2000 and 2015 were explored. Abstract Ecological security is important for social and economic sustainable development which needs stable resources and eco-environment support. In this study, we established an evaluation indicator system considering pressure and pressure-bearing to quantify the ecological security for Kunming city as it is a socioeconomic center in Southwest China with important ecological niche and rapid urbanization. According to the weights assigned by entropy method, we overlaid 13 indicators which were divided into 2 units, pressure indicators and pressure-bearing indicators, to evaluate the ecological security on grid scale. And then, ecological security level on district-county scale was evaluated based on the entropy matter-element model. On this basis, we explored their spatial distribution characteristics and the change trend from 2000 to 2015. Results showed that spatial distribution characteristic of ecological security evaluation was similar with the results of pressure evaluation, showing the scores and levels gradually increased from the urban area to the surrounding areas. Compared the ecological security levels in 2000 and in 2015, both of the highest value and the lowest value increased slightly. In general, the area with improved ecological security situation accounted for 89.10% of the total area. In terms of the change trend of ecological security levels, eight counties deteriorated while five counties remained unchanged which were driven by different deterioration reasons. The evaluation results of this study can provide comprehensive and targeted support for regional sustainable development. [ABSTRACT FROM AUTHOR]

• The spatial and temporal variations in wind erosion on the Qinghai-Tibet Plateau from 1980 to 2015 were evaluated. • Soil wind erosion modulus of the Qinghai-Tibet Plateau had an obvious decline during 1980–2015. • The relative contribution of climate change and human activities to wind erosion changes was identified spatially. • Climate change was the predominant factor that affected wind erosion across the Qinghai-Tibet Plateau. Land degradation caused by wind erosion seriously threatens the supply of ecosystem services on the Qinghai-Tibet Plateau (QTP). Assessing wind erosion variations is critical to identify key change areas and formulate desertification control strategies. In this study, we evaluated the spatiotemporal dynamics in wind erosion on the QTP from 1980 to 2015 using the Revised Wind Erosion Equation (RWEQ). Furthermore, we analysed the spatial correlation between wind erosion dynamics, climate change and human activities and identified the main driving factors. The results showed that wind erosion intensity on the QTP increased continuously from southeast to northwest. Areas with moderate or high soil erosion intensity (>25 t·hm−2·a−1), accounting for 53.48% of the region, were mostly distributed on the western and northern areas of the plateau. The mean annual soil wind erosion modulus (SWEM) of the entire region during 1980–2015 was 32.08 t·hm−2·a−1, with an evident decline at a rate of approximately 0.14 t·hm−2·a−1 in this period. However, wind erosion increased significantly in 9.99% of the QTP, mainly in the northwestern part of the region. Areas with significant changes in wind erosion driven by climate change and human activities accounted for 54.74% and 18.89% of the plateau, respectively. The influence of climate change on wind erosion is characterized by large scale and spatial continuity, while the impact of human activities on wind erosion occurs in local areas and distributes in patches. This study has implications for spatially revealing the climate and human drivers of wind erosion changes and identifying pivotal zones for desertification prevention on the QTP. [ABSTRACT FROM AUTHOR]

Air pollution increases the risks of all-cause mortality, cardiovascular mortality and respiratory mortality across China. However, the urban-rural differences in the associations between air pollution and mortality have not been clearly identified. In this study, a distributed lag nonlinear model was used to examine whether the air pollutants-mortality associations vary between urban and rural areas. Then, we used logistic regression analyses to evaluate the air pollutants-mortality relations. Also, generalized additive models were simulated to evaluate the nonlinear curves. Our results showed that the relative risks of air pollution-related mortality were generally higher in rural areas, where PM 2.5 pollution was the dominant factor (p -value < 0.05). Mortality risks for all-cause, cardiovascular and respiratory will increase when average annual PM 2.5 concentrations exceed approximately 38 μg/m3, 41 μg/m3 and 41 μg/m3, respectively, all of which exceed the annual Grade II standards. In urban areas, PM 10-2.5 and NO 2 were associated with mortality (p -value < 0.05). We proposed some area-specific strategies for controlling the NO 2 pollution and PM 10-2.5 pollution in urban areas and the PM 2.5 pollution in rural areas to eliminate the gaps. Our findings identify that rural residents are more sensitive to air pollution than urban residents in China, and this result challenges previous assumptions about the more adverse effects of urbanization on residents' health in developing countries. • Risks of air pollution-related mortality were higher for rural residents in China. • PM 10-2.5 and NO 2 pollution were associated with mortality in urban areas. • PM 2.5 pollution was the dominant factor for mortality in rural areas. • Different area-specific strategies should be developed in China. Our researches contribute to the limited evidence on air pollution's effects on rural residents' mortality in China. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants is a significant trigger of asthma in China. However, there is little evidence of which pollutants are associated most with asthma at the national level, and little research on pollutants' critical values based on concentration-response (C-R) curves. Further, the potential influential variables of air pollutants have not been identified clearly. In this study, logistic regression analyses were used to evaluate the associations between air pollutants and asthma based on 14176 samples across 12 provinces in China in 2015. We also fit a generalized additive model to evaluate the nonlinear C-R curves to identify the critical values. Our results showed that PM 10-2.5 (odds ratios, 95% confidence intervals: 0.95, 0.90–1.00) and NO X (odds ratios, 95% confidence intervals: 0.98, 0.96–1.00) were associated significantly with asthma's prevalence (p < 0.05) in China, in which the correlation between NO X pollution and asthma's prevalence was relatively more significant. The risk of asthma increased significantly when the average annual PM 10-2.5 and NO X concentrations exceeded approximately 40 μg/m3 and 67 μg/m3, respectively. The results of structural equation modelling confirmed that economic scale was the most important driving force for PM 10-2.5 and NO X pollution. Economic scale primarily had a direct influence on increased PM 10-2.5 concentrations, while it primarily had an indirect influence on increased NO X concentrations. Our findings contribute to the limited evidence on PM 10-2.5 and NO X pollution's effects on asthma's prevalence in China. • PM 10-2.5 and NO X were associated significantly with prevalence of asthma in China. • The critical value of PM 10-2.5 concentrations for asthma was 40 μg/m3 in China. • The critical value of NO X concentrations for asthma was 67 μg/m3 in China. • Economic scale had the strongest impact on PM 10-2.5 and NO X pollution. [ABSTRACT FROM AUTHOR]

With the intensification of climate warming and human activities, the ecosystems on the Qinghai-Tibet Plateau (QTP) are facing increasing threats which leads to extensive ecological degradation. Ecological restoration measures need to be implemented to improve biodiversity and ecosystem services to mitigate the impact of climate change and human disturbances. However, the key priority areas (KPAs) for ecological restoration are not clear on the QTP, and the benefits of ecosystem services for ecological restoration are often ignored. In this study, we are the first to identify the KPAs based on the quantitative evaluation method and multicriteria optimization algorithm under five restoration scenarios aiming at ecosystem service improvement on the QTP. Results showed that: (1) The benefits of ecological restoration for climate change mitigation and associated costs under different scenarios showed generally similar spatial variability, exhibiting higher in the south and lower in the west, which were different from those for biodiversity. (2) The restoration priorities in Sichuan and Yunnan were generally higher under scenarios Ⅱ and Ⅴ, while in Xinjiang, Sichuan, Yunnan, and western and southern Tibet were higher under scenarios Ⅰ, Ⅲ and Ⅳ. (3) For different ecosystems, the similarities lied in that the restoration priorities of wetland ecosystem were the highest, while those of desert ecosystem were the lowest under five restoration scenarios. (4) When the restoration area requirement was 25% of the total degraded area, the highest restoration priority levels under scenarios Ⅰ, Ⅲ and Ⅳ were mainly distributed in Guinan, Renbu, Nierong and Chayu counties, and under scenarios Ⅱ and Ⅴ were mainly distributed in Renbu, Lang and Guinan counties. When the restoration area requirements were 50% and 75% of the total degraded area, the counties with higher restoration priority levels under scenario Ⅱ were different from those under other four scenarios. This study identified the KPAs under different restoration scenarios, which provided references for the restoration measures implementation on the QTP., 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 © 2022 Elsevier Ltd. All rights reserved.)

Nitrogen (N) plays a central role in livestock development and food production in agricultural and pastoral regions, while its flow and loss can affect environmental quality, biodiversity and human health. A comprehensive understanding of the sources, patterns and drivers of N flow helps to alleviate its negative effects and promote sustainable management. We developed a county-scale N flow model to quantitatively analyze the N use efficiency (NUE), N losses and their driving forces in the food production and consumption system (FCPS) on the Qinghai-Tibet Plateau (QTP). More sustainable N utilization was further investigated through scenario analyses. Our results revealed that N fluxes doubled from 1998 to 2018 to maintain the growing demands for human food production and consumption in Ledu County, which was related to the increasing N losses to the atmosphere and water environment. The surging N fluxes greatly changed the N distribution pattern, resulting in a relatively low NUE (mean value: 29.41%) in the crop-production subsystem (CPS) and a relatively high NUE (mean value: 23.50%) in the livestock-breeding subsystem (LBS). The CPS contributed the most to the N losses. The urban population, animal-derived consumption, crop planting structure, imported fodder and N fertilizer application level were closely associated with N losses. The scenario analysis indicated that combined reasonable changes in planting structure, precision animal feeding, fertilizer management, diets and conversion of cropland into pasture could reduce N losses in 2030 to 5%-61% of Business as usual level. Our results highlighted the strong anthropogenic impact on the N flow of food production and consumption and suggested a sustainable N flow management strategy to harmonize the relationship between N flow and anthropogenically driven factors on the QTP., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The effects of ecological restoration based on ecosystem services (ES) have attracted more and more attention, while the simulation and cost-benefit analysis of ecological restoration scenarios are not well investigated. In this study, four ecological restoration scenarios were simulated at a typical watershed on the Qinghai-Tibetan Plateau (QTP) based on the land use conversion. Scenario 1 was only grassland restoration, Scenario 2 and 3 were mainly farmland to shrub, and Scenario 4 was mainly grassland restoration with bare land converting to forest and shrub. The ecosystem services value (ESV) and the cost-benefits of these scenarios were quantified and compared in 25 years after the restoration investment. The results showed there were significant differences in the ESV under four scenarios, among which Scenario 4 had the largest ESV and Scenario 1 had the smallest ESV. The spatial distribution of ESV was uneven, and high-value regions were concentrated in the southwest and central regions. There were great variations between original scenario and simulated scenarios, but a little difference between Scenarios 2, 3, and 4. The largest loss of farmland abandonment was regulating service, followed by supporting service, provisioning service, and cultural service. From the perspective of payback period, Scenario 1 was the fastest, and it could obtain net benefits first. From the short- and long-term (6 and 25 years after investment) benefits, Scenarios 1 and 4 had the largest cumulative ESV increase, respectively. The results of this study can provide a basis for the formulation and implementation of ecological policies.

Grasslands are the dominant ecosystem of the Qinghai-Tibet Plateau (QTP), and they play an important role in climate regulation and represent an important ecological barrier in China. However, the spatial differentiation characteristics of net primary productivity (NPP) and normalized differential vegetation index (NDVI) and the main influencing factors that vary with grassland type on the QTP are not clear. In this study, standardized precipitation evapotranspiration index (SPEI), digital elevation model (DEM), precipitation, temperature, slope, photosynthetically active radiation (PAR) and grazing intensity were considered the driving factors. First, a grey relational degree analysis was performed to test for the quantitative relationships between NPP, NDVI and factors. Then, the geographical detector method was applied to analyze the interaction relationships of the factors. Finally, based on the geographically weighted regression (GWR) model, the influence of factors varied with grassland type on the NPP and NDVI was revealed from the perspective of spatial differentiation. The results were as follows: (1) The NPP and NDVI had roughly the same degrees of correlation with each impact factor by the grey relational degree analysis, each factor was closely related to the NPP and NDVI, and the relational degree between grazing intensity and NPP was greater than that between grazing intensity and NDVI. (2) The interaction relationships between influencing factors and NPP and NDVI varied with the grassland type and presented bivariate enhancement and nonlinear enhancement, and the interaction effects between grazing intensity and any factor on each grassland type had a greater impact on NPP. (3) The main influencing factors of the spatial heterogeneity of NPP were grazing intensity and PAR, which were "high from northeast to southwest, low from northwest to southeast" and "low in the middle and high around". The main influencing factors on the NDVI were precipitation and PAR, which were "low in the middle and high around" and "high in the north, low in the south".

Exposure to NO 2 pollution has a significant adverse effect on residents' health. However, few studies have assessed the health effects associated with NO 2 pollution. Compared with PM 2.5 pollution, the harmfulness of NO 2 pollution has not been quantitatively studied or clearly identified. In this study, we assessed the NO 2 exposure-related health effects by non-linear and linear methods, taking advantage of online monitoring and survey data. We also assessed the economic cost of NO 2 pollution in 338 cities in China. Our results showed that the average annual concentration of NO 2 in the top fifteen cities with more than ten million permanent residents (except for Shenzhen, in the Guangdong province) exceeded the annual Grade II standards (40 μg/m 3 ). The estimated national NO 2 -related all-cause mortality for non-linear and linear methods were 388.5 × 10 3 (95% CI: 198.1 × 10 3 -748.2 × 10 3 ) and 374.1 × 10 3 (95% CI: 194.3 × 10 3 -695.9 × 10 3 ), respectively. The total calculated national economic cost was about 28.8 billion US$ (95% CI: 14.7-55.4) in 2016. In addition, the comparison results showed that the harm caused by PM 2.5 pollution was about four times that of NO 2 pollution. Our statistics contribute to the limited research on NO 2 pollution's effects on health and the economy in China., 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 © 2020 Elsevier B.V. All rights reserved.)

The grassland soils of the Qinghai-Tibet Plateau (QTP) store a large amount of organic carbon because of the cold, humid climate, and topsoil organic carbon is quite sensitive to global climate changes. However, the spatio-temporal dynamics and factors that influence the soil organic carbon (SOC) on the QTP's grassland are not understood well. Moreover, there are few comparative analyses of different approaches to estimate the QTP' SOC. In this study, we estimated the storage and patterns of SOC density (SOCD) using several methods, including MODIS (moderate-resolution imaging spectroradiometer) retrieval, field data and previous empirical models (Models1-4, and soil organic matter (SOM)). And their relations with aboveground biomass, soil moisture, temperature, elevation, and soil conductivity were further explored. The results showed that SOC showed a similar variation trend in the different models, in which it decreased with increasing bulk density (BD) in the topsoil at 30 cm. For meadow and steppe grasslands, Models 1, 2, and 4 showed similar estimated values of SOCD, while Model3 had a lower value than them. SOC storage in the BD 3 and SOM methods had abnormal values, while the MODIS-NDVI, BD 1, 2, and 4 methods had similar SOC stock values for meadow and steppe grassland. Moreover, meadow grassland had a higher SOC storage than did steppe grassland, with means values of 397.9×1010 kg and 242.2×1010 kg, respectively. SOCD's spatial distribution using MODIS-NDVI method differed clearly from the empirical models, with a significant tendency for spatial variation that increased from the northwestern to southeastern regions on the QTP. Therefore, based on the values estimated and spatial variation features, the MODIS-NDVI method may be a more feasible and valid model to estimate SOC. Moreover, the mean annual SOCD values during 2000-2015 showed an increasing trend, with a higher mean value in meadow and a lower mean value in steppe. Further, SOCD was correlated significantly and positively with aboveground biomass and soil moisture, and negatively correlated with elevation and soil conductivity. Increasing temperature had negative effects on SOCD, which was consistent with the global trend. These results indicated that topsoil moisture plays a key role in SOCD spatial patterns. Our results provide valuable support for the long-term estimation of SOCD in future research on the QTP., Competing Interests: The authors have declared that no competing interests exist.