[Spatio-temporal Evolution and Prediction of Carbon Storage in Huaibei City Based on InVEST-PLUS Model].

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×105 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×105 t, with an average annual decrease of 1.22×104 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×105, 82.45×105, 82.82×105, and 82.51×105 t, respectively.

[Spatial Prediction Modeling for Soil pH Based on Multiscale Geographical Weighted Regression(MGWR) and Its Influencing Factors].

Anhui, Henan, Jiangsu, and Shandong provinces were selected as the study area. A total of 599 soil samples and nine environmental factors of soil pH were collected. The spatial distribution of soil pH was modeled based on multi-scale geographically weighted regression(MGWR), mixed geographically weighted regression(Mixed GWR), geographically weighted regression(GWR), and multiple linear regression(MLR) models. Then, the spatial difference in the effect of environmental factors on soil pH was revealed using MGWR and quantile regression models. The results showed that:① soil pH showed significant global and local spatial autocorrelation at different spatial distances, and the clustering characteristics were obvious. ② The MGWR model was the best among the four models, and the Radj2 of MGWR, Mixed GWR, GWR, and MLR were 0.64, 0.62, 0.59, and 0.48, respectively. The residual of MGWR had the strongest independent distribution and the weakest spatial autocorrelation with a global Moran's I of 0.07. ③ Three types of GWR predictions showed that the spatial distribution of soil pH decreased gradually from north to south in the study area, with the highest in northern Henan and the lowest in southern Anhui. ④ MGWR modeling results showed that there was strong spatial heterogeneity of mean annual precipitation(MAP), multi-resolution valley bottom flatness(MRVBF), and elevation affecting soil pH. MAP had a stronger effect on soil pH in northern Jiangsu and most parts of Shandong. The positive effect of MRVBF on soil pH was stronger in northern Jiangsu and western Shandong. The negative effect of elevation on soil pH was stronger in northern and central Jiangsu. ⑤ The quantile regression analysis showed that the mean annual precipitation had a significant negative effect on soil pH at different quantile levels of soil pH, and influence intensity decreased with the increase in pH quantile level. MRVBF had a significant negative effect on soil pH at a low quantile level(θ=0.1 to 0.4) but had no significant effect on soil pH at a high quantile level(θ=0.5 to 0.9). These results can provide an important reference for mapping soil properties and analyzing its influence factors based on the MGWR model in large regions.

Soil Microbial Fuel Cell Based Self-Powered Cathodic Biosensor for Sensitive Detection of Heavy Metals.

Soil microbial fuel cells (SMFCs) are an innovative device for soil-powered biosensors. However, the traditional SMFC sensors relied on anodic biosensing which might be unstable for long-term and continuous monitoring of toxic pollutants. Here, a carbon-felt-based cathodic SMFC biosensor was developed and applied for soil-powered long-term sensing of heavy metal ions. The SMFC-based biosensor generated output voltage about 400 mV with the external load of 1000 Ω. Upon the injection of metal ions, the voltage of the SMFC was increased sharply and quickly reached a stable output within 2~5 min. The metal ions of Cd2+, Zn2+, Pb2+, or Hg2+ ranging from 0.5 to 30 mg/L could be quantified by using this SMFC biosensor. As the anode was immersed in the deep soil, this SMFC-based biosensor was able to monitor efficiently for four months under repeated metal ions detection without significant decrease on the output voltage. This finding demonstrated the clear potential of the cathodic SMFC biosensor, which can be further implemented as a low-cost self-powered biosensor.

[Remote sensing monitoring and evaluation of spatial and temporal changes of ecological environment in Hangjin Banner, Inner Mongolia, China].

With the ecological environment problems being increasingly prominent and globalized, more and more attention is paid to environmental protection. Remote sensing technology is important in monitoring and evaluating ecological environment. In this study, based on the Landsat image data of 1992, 2000, 2008 and 2017, the remote sensing ecological index (RSEI) was constructed to monitor and evaluate the quality of ecological environment in Hangjin Banner, Inner Mongolia, aiming to provide a theoretical basis for local ecological environment protection. The results showed that from 1992 to 2017, the quality of ecological environment in Hangjin Banner was generally poor, with RESI grades of poor and inferior. The mean value of RESI increased from 0.31 (1992) to 0.37 (2008) and then decreased to 0.30 (2017). During the period, the change range was mainly from one grade to the next. In terms of spatial distribution, the regions with poor ecological environment quality were mainly in the desert plains of the central and western regions, that with good ecological quality mainly along the Yellow River and in the southeast, and that with large fluctuation of ecological quality grade mainly in the desert edge along the Yellow River and in the hilly and gully regions in the east. During the research period, the center of gravity of each ecological grade in Hangjin Banner substantially shifted, with spatiotemporal fluctuations. Our results suggest that ecological environment of Hangjin Banner was fragile and unstable. Ecological construction can promote the quality of ecological environment, but resources and land use should also be reasonably allocated.

[Assessing soil pH in Anhui Province based on different features mining methods combined with generalized boosted regression models]. / 基于不同特征挖掘方法结合广义提升回归模型估测安徽省土壤pH.

We explored the application of different feature mining methods combined with genera-lized boosted regression models in digital soil mapping. Environmental covariates were selected by two feature selection methods i.e., recursive feature elimination and selection by filtering. Using the original environmental covariates and the selected optimal variable combination as independent varia-bles, soil pH prediction model of Anhui Province was established and mapped based on the genera-lized boosted regression model and random forest model. The results showed that both kinds of feature mining methods could effectively improve the accuracy of soil pH prediction by generalized boosted regression models and random forest model, and could reduce dimensionality. Compared with the random forest model, the prediction accuracy of the validation set of the generalized boosted regression model was slightly lower. In the training set, the accuracy of the generalized boosted regression models was much higher than that of the random forest model, with higher interpretation and better overall effect. The main parameters of the random forest model, ntree and mtry, had limi-ted effect on the model. Different parameters and their combination could affect the prediction accuracy of the generalized boosted regression models, and thus should be tuned before modeling. The results of spatial mapping showed that soil pH in Anhui Province showed a pattern of "south acid and north alkali".

[Dynamics of recent cultivated land in Zhejiang Province and relevant driving factors].

Through the human-computer interactive interpretation of the 2000, 2005, and 2008 remote sensing images of Zhejiang Province with the help of RS and GIS techniques, the dynamic database of cultivated land change in the province in, 2000-2008 was established, and the driving factors of the cultivated land change were analyzed by ridge regression analysis. There was a notable cultivated land change in the province in 2000-2008. In 2000-2005 and 2005-2008, the annual cultivated land change in the province arrived -1.42% and -1.46%, respectively, and most of the cultivated land was changed into residential and industrial land. Non-agricultural population rate, real estate investment, urban green area, and orchard area were thought to be the main driving factors of the cultivated land change in Zhejiang Province, and even, in the developed areas of east China.