Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model

Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.

Evaluation and Prediction of Ecological Sustainability in the Upper Reaches of the Yellow River Based on Improved Three-Dimensional Ecological Footprint Model

Ecological footprint is an important method for regional sustainable assessment. Scientific assessment of the ecological sustainability of the upper reaches of the Yellow River is of great significance to the realization of a win-win situation for the ecological environment protection and economic development of the entire Yellow River basin. Based on the improved three-dimensional ecological footprint model, this paper measures and spatially portrays the ecological footprint per capita depth (EF_depth), ecological footprint per capita size (EF_size), and ecological footprint per capita 3D (per capita EF_3D) of the upper Yellow River region from 2011 to 2020. Then, the ecological footprint diversity index (EFDI), integrated land stress index (I_{comprehensive}), ecological stress index (ETI), and ecological coordination coefficient (ECC) are used to evaluate its ecological safety and sustainability. The results of the study indicate that: (1) From 2011 to 2020, the three-dimensional ecological footprint of all provinces and regions in the upper reaches of the Yellow River was in a fluctuating upward trend as a whole, and NMG had the highest growth, from 2.6256 hm²/person to 3.3163 hm²/person, with an average annual growth rate of 2.36%. (2) In the past 10 years, the ETI index of the upper reaches of the Yellow River increased from 2.13 in 2011 to 3.28 in 2020, which is a serious insecurity. The EFDI index fluctuates slightly, but increases year by year. (3) The capital flow occupancy rate of the upper reaches of the Yellow River has been above 86.67%, and fluctuated during the study period, reaching a peak of 88.61% in 2020. (4) In the four periods, the number of land comprehensive pressure states and ecological security pressure states of the provinces and regions in the upper reaches of the Yellow River show a distribution pattern that the northeast region is better than the southwest region. This study is expected to provide scientific reference for land use in the upper reaches of the Yellow River, building the ecological security barrier of the Qinghai Tibet Plateau, and promoting sustainable socio-economic development.

Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella

Objective

This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend.

Methods

For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM (1,1), made GM (1,1)-Markov prediction.

Results

This study collected 37,223 cases from China Information System for Disease Control and Prevention's “Disease Prevention and Control Information System” and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.17:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. The relative error would be 5.06%.

Conclusions

Varicella data had its unique development characteristics. The accuracy of GM (1,1)—Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12898-3.

Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment

The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter “Reservoir Area”). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area.

Atmospheric emissions of Cu and Zn from coal combustion in China: Spatio-temporal distribution, human health effects, and short-term prediction

China has become the largest coal consumer and important emitter of trace metals in the world. A multiple-year inventory of atmospheric copper (Cu) and zinc (Zn) emissions from coal combustion in 30 provinces of China and 4 economic sectors (power plant, industry sector, residential sector, and others) for the period of 1995-2014 has been calculated. The results indicated that the total emissions of Cu and Zn increased from 5137.70 t and 11484.16 t in 1995-7099.24 t and 14536.61 t in 2014, at an annual average growth rate of 1.90% and 1.33%, respectively. The industrial sector ranked as the leading source, followed by power plants, the residential use, and other sectors. The emissions of Cu and Zn were predominantly concentrated in the northern and eastern regions of China due to the enormous consumption of coal by the industrial and the power sectors. The emissions of Cu and Zn were closely associated with mortality and life expectancy (LE) on the basis of multiple regression analysis. Spatial econometric models suggested that Cu and Zn emissions displayed significantly positive relevance with mortality, while they exhibited negative correlation with LE. The influence of the Cu emission peaked in the north of China for both mortality and LE, while the impacts of the Zn emission on mortality and LE reached a maximum value in Xinjiang Province. The results of the grey prediction model suggested that the Cu emission would decrease to 5424.73 t, whereas the Zn emissions could reach 17402.13 t in 2020. Analysis of more specific data are imperative in order to estimate the emissions of both metals, to assess their human health effects, and then to adopt effective measures to prevent environmental pollution.