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Ecological connectivity in landscapes is crucial for plant diversity conservation. The barrier risk to ecological connectivity represents the risk to ecological connectivity loss or weakening, resulting from the barrier to biological information exchange among habitats. Therefore, clarifying the barrier risk to the ecological connectivity of plant diversity in space can reveal the spatial impacts of reduced ecological connectivity on plant diversity. This study analyzed effects of karst peak, river network, arable land, and impervious surface on plant diversity in karst natural, countryside, urban, and island landscapes in Guizhou Province with fragile environment. Then, we calculated the barrier distance of ecological connectivity to reveal the barrier risk to the ecological connectivity of plant diversity in space. The results showed that karst peak was the source of high plant diversity, and plant diversity could diffuse about 400m around karst peaks. River network and arable land enhanced the connectivity among karst peaks to maintain plant diversity, and the effect on enhancing the connectivity was about 300m and 450 m, respectively, while the weakening effect of impervious surface on connectivity was about 350 m. Based on the distance for plant diversity diffusing around karst peaks, the barrier distance of ecological connectivity was determined by the combination type of river network, arable land and impervious surface in landscapes. From low to high, the barrier risk to the ecological connectivity of plant diversity was about 1,110 m in the combination of river network and arable land, about 790m in the combination of river network, arable land and impervious surface, about 520 min the combination of arable land and impervious surface, about 400m in the combination of river network and impervious surface. Our findings clarify the barrier risk to the ecological connectivity of plant diversity in space, and provide a scientific basis for plant diversity conservation from the perspective of ecological connectivity. [ABSTRACT FROM AUTHOR]

The transport characteristics of phosphorus in soil and the assessment of its environmental risk have become hot topics in the environmental and agricultural fields. The Sanjiang Plain is an important grain production base in China, and it is characterised by serious land use change caused by large-scale agricultural exploitation. Agricultural inputs and tillage management have destroyed the soil nutrient balance formed over long-term conditions. There are few studies on non-point source phosphorus pollution in the Sanjiang Plain, which is the largest swampy low plain in a mid-high-latitude region in China. Most studies have focused on the water quality of rivers in marsh areas, or the export mechanism of phosphorus from specific land uses. They were conducted using experimental methods or empirical models, and need further development towards mechanism models and the macro-scale. The question is how to find a way to couple processes in phosphorus cycling and a distributed hydrological model considering local hydrological features. In this study, we report an attempt to use a distributed phosphorus transport model to analyse non-point source total phosphorus pollution from different land uses and soil types on the Sanjiang Plain. The total phosphorus concentration generally shows an annually increasing trend in the study area. The total phosphorus load intensity is heterogeneous in different land use types and different soil types. The average total phosphorus load intensity of different land use types can be ranked in descending order from paddy field, dry land, wetlands, grassland, and forestland. The average total phosphorus load intensity of different soil types can be ranked in descending order: paddy soil, bog soil, planosol, meadow soil, black soil, and dark brown earth. The dry land and paddy fields account for the majority of total phosphorus load in the study area. This is mainly caused by extensive use of phosphate fertilizer on the cultivated land. This has important implications for future agricultural management and non-point source control in this agricultural area of the mid-high latitude region. [ABSTRACT FROM AUTHOR]

Taking advantage of the opportunity provided by the nationally funded Water Resources Integrated Planning and Investigation of China program (WRIPI), a model was established to estimate non-point source pollutant loads in a large-scale basin (ENPS-LSB) on the basis of hydrological processes and pollutant transport. The model made use of Environment for Visualizing Images software (ENVI) and Interactive Data Language (IDL) and took the characteristics of present-day China into account: a huge rural population scattered widely, marginal rural infrastructure and livestock cultivation based on scatter-feeding. The model was divided into two sections: one on dissolved and one on adsorbed pollutant loads. The dissolved loads were divided into four different types: those originating from agricultural fields, urban areas, rural residential areas, and livestock. The study was undertaken in the Yangtze River Basin. The results showed the chemical oxygen demand (COD), the total nitrogen (TN), the total phosphorous (TP), and the ammonia nitrogen (NH-N) loads to be 2.6 × 10, 1.6 × 10, 9.3 × 10 and 3.1 × 10 tons, respectively, in the year 2000. The dissolved COD resulted mainly from rural residential areas and livestock; 76.8% of the dissolved TN and 86.4% of the dissolved TP produced were from agricultural fields. The Yangtze River Delta, the lower reaches of the Han River, and part of the Dongting Lake, Taihu Lake and Poyang Lake basins all had relatively high dissolved pollutant loads; adsorbed nitrogen and phosphorus loads were mainly observed in the middle and upper reaches of the Yangtze River Basin. Dissolved loads were most affected by human activity, whereas adsorbed loads were most affected by natural factors. The results showed that the model performed well on large scale, describing pollutant loads effectively. This makes it possible to properly consider not only point source pollutant discharge but also non-point source pollution in the Yangtze River Basin. Combining point source discharge investigation with the ENPS-LSB model could assist environmental management with controlling water pollution. [ABSTRACT FROM AUTHOR]

Green plants play an important role in energy flows and material cycles. The net primary productivity (NPP) reflects the capability of vegetation to convert solar energy into photosynthate (fixed carbon). Understanding the factors that contribute to variations in NPP is of key importance for improving the rock-desertification environment in karst areas. In this paper, the NPP model (Light Use Efficiency model) is modified on the basis of remote sensing data [moderate resolution imaging spectroradiometer (MODIS)], climate data and observed information. Then the model is employed to estimate the spatial–temporal variations of NPP in the Guizhou Province, China. Finally, the NPP differences between karst area and non-karst area, and the relationships between NPP and climate factors are analyzed. The results show that the NPP estimated using MODIS data are reasonable. The mean NPP of territorial vegetation is 421.46 gC m−2 year−1; the NPP in the non-karst area is 13.3% higher than that in the karst area; the correlation degree between NPP and precipitation is better in southeastern and western districts. [ABSTRACT FROM AUTHOR]

This study sought to analyze heavy metal (Pb, Zn, Cu, Ni, Mn, and Fe) pollution status in the waters of Aibi Lake in northwest China through the use of an applied comprehensive pollution index, health risk model, and multivariate statistical analyses in combination with the lake's land use types. Results showed that (1) the maximum (average) values of the heavy metals Pb, Zn, Cu, Ni, Mn, and Fe were 0.0644 (0.0123), 0.0006 (0.0002), 0.0009 (0.0032), 0.1235 (0.0242), 0.0061 (0.0025), and 0.0222 (0.0080) μg/L, respectively. Among these, in all the samples, Pb and Ni exceeded the standard and acceptable values put forth by the World Health Organization by 21.13 and 25.67%, respectively. Ni also exceeded (30.16%) the third grade of the Environmental Quality Standards for Surface Water of China. The levels of the six heavy metals were all within the fishery and irrigation water quality standard ranges in China. (2) The average values for single pollution index of heavy metals Pb, Zn, Cu, Ni, Mn, and Fe were 1.000, 0.0006, 0.0009, 3.000, 0.060, and 0.070, respectively, among which Ni levels indicated moderate to significant pollution, while others indicated healthy levels. (3) Health risk evaluation showed that the Rn values for Pb, Zn, Cu, Mn, and Fe were 1.8 × 10−4, 5.33 × 10−9, 4.80 × 10−7, 1.08 × 10−6, and 2.51 × 10−7 a−1, respectively, of which, in all samples, Pb and Ni contents all exceeded the maximum acceptable risk levels according to the International Commission on Radiological Protection (ICRP) as well as the U.S. Environment Protection Agency. (4) Combining with multivariate statistical analyses along with the land use distribution within the lake basin, Pb, Zn, Cu, Ni, and Mn were mainly influenced by the agriculture production and emission from urban lives and traffics, and Fe mainly originated from the natural environment. The results of this research can provide reference values for heavy metal pollution prevention in Aibi Lake as well as for environmental protection of rump lakes in the arid regions of northwest China and Central Asia. [ABSTRACT FROM AUTHOR]

In order to timely determine the dynamic changes of the ecological environment quality and future development laws of the urban agglomeration on the northern slope of the Tianshan Mountains, combined with the actual situation of the urban agglomeration, 11 indicators were selected from the three aspects of natural ecology, social ecology, and economic ecology. To reduce the dimensions of the indicators, principal component analysis, coefficient of variation, and analytic hierarchy process were used based on RS and GIS technology methods, and the ecological environmental quality (EQI) from 2000 to 2018 was dynamically evaluated. Further, the CA-Markov model was introduced to simulate the development status in 2026 for predictive purposes. The main results are as follows: the overall ecological environment of the area exhibited a gradually improving distribution change from southwest to northeast; the proportion of ecological environment classification exhibited a gradually decreasing change pattern; the spatial differentiation of ecological environment quality exhibited a significant spatial positive correlation; from the influencing factors, an observation can be made that natural ecological factors were highly significant; the prediction accuracy verification revealed that the CA-Markov model was suitable for the prediction of the ecological environment quality in the region and had high accuracy; and the comprehensive regional ecological environment quality indexes were 5.7392, 6.1856, and 6.4366, respectively, while the forecasted value for 2026 was predicted to be 6.6285, indicating that the overall ecological environment quality of the region will improve and develop well. The present research results reveal the law of dynamic changes and future development of the ecological environment quality in the region, which can be used as a theoretical reference for the formulation of ecological environmental protection measures., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020-2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)