County Scale Research Articles

Spatially Explicit Life Cycle Global Warming and Eutrophication Potentials of Confined Dairy Production in the Contiguous US

Assessing the spatially explicit life cycle environmental impacts of livestock production systems is critical for understanding the spatial heterogeneity of environmental releases and devising spatially targeted remediation strategies. This study presents the first spatially explicit assessment on life cycle global warming and eutrophication potentials of confined dairy production at a county scale in the contiguous US. The Environmental Policy Integrated Climate model was used to estimate greenhouse gases (GHGs), NH3, and aqueous nutrient releases of feed production. The Greenhouse gases, Regulated Emissions, and Energy use in Transportation model and Commodity Flow Survey were used to assess GHGs and NH3 from feed transportation. Emission-factor-based approaches were primarily used to calculate GHGs from enteric fermentation, and GHGs, NH3, and aqueous nutrient releases from manure management. Characterization factors reported by the Intergovernmental Panel on Climate Change and Tool for Reduction and Assessment of Chemicals and other Environmental Impacts model were used to compute global warming and eutrophication potentials, respectively. The analyses revealed that life cycle global warming and eutrophication potentials of confined dairy production presented significant spatial heterogeneity among the US counties. For example, the life cycle global warming potential ranged from 462 kg CO2-eq/head to 14,189 kg CO2-eq/head. Surprisingly, sourcing feed locally cannot effectively reduce life cycle global warming and eutrophication potentials of confined dairy production. The feed supply scenarios with the lowest life cycle environmental impacts depend on the life cycle environmental impacts of feed production, geographic locations of confined dairy production, and specific impact categories. In addition, installing buffer strips in feed-producing hotspots can effectively reduce life cycle nutrient releases of confined dairy production. If 200 counties with the highest life cycle EP of corn adopt buffer strips, the reduction in life cycle EP of confined dairy production could reach 24.4%.

Characteristics of Spatiotemporal Changes in China's Carbon Budget at Different Administrative Scales

Currently, scientifically and reasonably specifying carbon emission reduction measures in the context of "double carbon" has become a common concern worldwide. China's administrative divisions have a notable impact on the formulation and implementation of relevant policies. Therefore the carbon emissions must be calculated accurately under China's administrative divisions at different scales. The spatiotemporal change characteristics of absorption and carbon emissions can provide scientific basis for the formulation of reasonable and differentiated carbon emission reduction policies in different administrative regions in China. To this end, this study used multi-source data such as remote sensing and statistics and integrated ecological models, statistics, and GIS space analysis and other methods to analyze the spatiotemporal dynamic change characteristics of carbon emissions and carbon absorption at different administrative scales （provinces, cities, and counties） in China. The results showed that： ① The total carbon absorption of vegetation in China continued to increase from 2000 to 2021 and the average value gradually increased. Differences were observed in spatiotemporal changes in carbon emissions at different administrative scales. The spatiotemporal changes at smaller scales were more evident. Carbon emissions showed obvious spatial differences of "high in the north and low in the south, high in the east and low in the west." ② The spatiotemporal distribution of CPI at the administrative scale was similar to that of carbon emissions and the overall trend was increasing annually. The pressure of carbon emissions on carbon absorption gradually weakened from the east to the central and western regions. ③ Spatiotemporal hotspot analysis showed that the overall spatial distribution of cold and hot spots in China's carbon absorption was as follows： In the spatial pattern of "hot in the east and cold in the west," the spatial distribution of cold and hot spots of carbon emissions showed agglomeration characteristics. The provincial scale was primarily oscillating hotspot whereas municipal and county scales were majorly continuous hot spots. Further results revealed that： ① Carbon absorption in different regions and periods in China showed significant variability, especially in the central and eastern regions. The possibility of offsetting carbon emissions by increasing carbon absorption remains. ② At the same scale, administrative regions （such as different provinces） and lower-level administrative regions at another scale （such as different cities in the same province） showed varying degrees of variability in carbon absorption and carbon emissions. Therefore, taking provincial administrative regions as an example for subsequent formulation considering carbon trading, emission reduction, and other policies, we should first consider the coordination of emissions between different cities in the province and then consider the coordination between provinces, which is expected to better promote the implementation of relevant policies.

Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Under global warming, surface urban heat islands (SUHI) threaten human health and urban ecosystems. However, scant research focused on exploring the complex associations between urban form factors and SUHI at the county scale, compared with rich studies at the city scale. Therefore, this study simultaneously examined the nonlinear and spatial non-stationary association between SUHI and urban form factors (e.g., landscape structure, built environment, and industrial pattern) across 2321 Chinese counties. An explainable spatial machine learning method, combining the Geographically Weighted Regression, Random Forest, and Shapley Additive Explanation model, was employed to deal with nonlinearity, spatial non-stationary, and interpretability of modeling. The results indicate the remarkable spatial disparities in the relationship between urban form factors and SUHI. Landscape structure contributes the most in southern counties, while the built environment is more important in northeastern counties. The impact of building density and building height increases with the county size and becomes the main driver of urban heat in mega counties. Most urban form factors exhibit nonlinear impacts on SUHI. For example, urban contiguity significantly affects SUHI beyond a threshold of 0.93, while building density does so at 0.17. By comparison, the influence of shape complexity remains stable above a value of 7. Factors such as industrial density and diversity have a varied influence on SUHI between daytime and nighttime. The results of local explanations and nonlinear effects provide targeted regional mitigation strategies for urban heat.

Applying the driver-pressure-state-impact-response model to ecological restoration: A case study of comprehensive zoning and benefit assessment in Zhejiang Province, China

Ecosystem degradation is a global problem that poses a significant threat to the sustainable development of human societies, particularly in developing countries, such as China. In response, China has implemented a series of ecological restoration (ER) policies over recent years. However, significant regional developmental disparities, pronounced spatial heterogeneity of ecological issues, and substantial historical debt for ER in China present considerable obstacles and financial burdens to the effective implementation of ER strategies. Delineating ER zones and assessing the ER benefits are essential for developing effective ER strategies and implementing ER projects. In this study, we constructed a comprehensive framework for ER utilizing the Driver-Pressure-State-Impact-Response (DPSIR) model, quantified the urbanization levels, ecological state, and restoration costs in Zhejiang Province to delineate ER zones, integrated the patch-generating land use simulation model with the ecosystem service value assessment method to quantify the benefits of ER, and ultimately developed tailored ER strategies. The results showed that: (1) The pattern of urbanization levels was characterized by high levels in the northeast and low levels in the southwest, which constrated with the ecological state. The areas of high restoration costs were located in the northeastern and southeastern regions, and the areas of low restoration costs were situated in the southwestern region. (2) The rate of construction land expansion is significantly curtailed under the ER scenario compared to the natural development scenario in 2035, while forest areas have seen effective protection and an increase from the levels of 2020. (3) The ER policy is projected to generate ecological benefits totaling CNY 8.23 billion by 2035, substantially reducing the rate of ecosystem degradation. (4) Zhejiang Province is divided into five zones at the county scale: ecological autonomous protection zone, ecological core protection zone, ecological priority restoration zone, ecological control zone, and moderate development zone. Strategies have been devised based on the forecasted benefits of ER, offering valuable insights into ecological management. These findings aim to enhance the understanding of ER and support the development and implementation of regional ecological policies.

Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China

Understanding the scale effect of relationships among ecosystem services (ESs) and the factors influencing these relationships, is vital for sustainable management of ESs. Previous research has focused less on multiple scale units, and the driving mechanisms behind ESs interactions remain unclear. Therefore, in this study, we assessed the spatial distribution of water yield (WY), water purification (WP), and soil conservation (SC) across four spatial scales (3 km × 3 km grid, 5 km × 5 km grid, sub-watershed, and county scales) in the Lishui River Basin of China during 2020. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, geographical weighted regression, and Pearson correlation analysis, we examined tradeoffs and synergies among these ESs at various scales. Additionally, we employed a geographic detector to analyze the reactions and scale effects among these tradeoffs/synergies and social-ecological factors. Our findings revealed the following: (1) Significant spatial heterogeneity in ESs was observed in the basin. The WY decreased from west (2511.65 mm) to east (890.06 mm), with high WP values concentrated in the eastern part, and high SC values (8.30 × 105 t/hm2) found in densely forested and grassland areas surrounding water bodies. Forested and cultivated lands were key contributors to ESs in the basin. (2) Across the four investigated scales, tradeoffs or synergies between the same ESs becoming more pronounced as the scale increased. Notably, most ES relationships displayed their strongest synergies at the county scale, with certain spatial areas demonstrating change in interaction directions. (3) The explanatory power of single- or double-factor interactions increased with scale. ESs interactions were found to be closely linked to climatic factors, as well as topographic features (including slope gradient and elevation), with precipitation exhibiting relatively high explanatory power. The interaction of natural and social factors significantly outweighed the combined interaction of dual social factors. This indicated that socio-ecological factors, especially climate and topography, jointly influenced the intensity and direction of ES tradeoffs/synergies. Scale effects arose from the “peak cutting and valley filling” phenomenon during scale transitions. These insights hold significant value for guiding hierarchical management strategies for regional ESs and informing decisions regarding resource allocation.

Small watersheds are the best control and management unit for improving soil conservation services in karst areas

Climate factors and changes in landscape patterns are often recognized as the primary drivers of soil conservation services. The influence mechanism of climate factors and landscape patterns on soil conservation service is scale-dependent and spatial heterogeneous. However, it is not clear whether small watershed scale is more conducive to soil erosion control than large scale such as county scale and township scale. For the purpose of creating land use development plans that take local conditions into account, it is crucial to clarify the effects of climate and landscape pattern factors on soil conservation change. Wujiang River basin (WRB), a typical karst basin located in the catchment of the largest first-level tributary on the upper Yangtze River in China, was used as the study area in this research. Soil conservation services provided by water erosion control (SPC) in WRB from 2005 to 2020 were evaluated using the RUSLE model based on the modified rock exposure rate. By using stepwise regression model and multi-scale geographically weighted regression model (MGWR), the spatial heterogeneity of the influence of different driving factors on soil conservation service was comprehensively studied at the scale of district, township and small watershed. The results show that the SPC fluctuates obviously, but the trend is not significant. Climate factor is the dominant factor affecting SPC. With the change of scale from large to small, the adjusted R2 of the regression model gradually increases, especially the factors related to landscape pattern, and more driving factors can be revealed more comprehensively and effectively. Therefore, the small watershed scale is the best control unit to improve the SPC when formulating the regional management landscape plan. The findings of this research also have benchmark significance for other ecological fragile areas, and can provide more comprehensive suggestions for local ecosystem management and landscape planning.

Towards sustainable fruit production: Identifying challenges and optimization strategies

CONTEXTThe transition to sustainable fruit production is vital for global sustainability and agricultural development. However, implementing these insights into practice faces significant challenges. OBJECTIVESPomelo (Citrus maxima), a prominent citrus species in tropical and subtropical regions, has been cultivated to enhance farmer profitability but increasing yields often comes at the cost of environmental degradation. This study uses pomelo as a case study to assess key factors influencing pomelo production and establish optimal practices that meet consumption-based food while co-benefits for environmental and economic sustainability. METHODSA comprehensive study was conducted by combining 1155 farm surveys (from 2010, 2018, and 2022) with 31 field experiments. Life cycle assessment (LCA), classification and regression tree (CART) analysis, and scenario analysis were employed to develop pathways for sustainable pomelo production in Pinghe County, Fujian Province, China. RESULTS AND CONCLUSIONSResults show no significant differences in pomelo yield across the three years; however, pomelo production in 2022 demonstrated a 60 %–67 % lower in environmental footprints and a 64 %–76 % higher benefit-cost ratio (BCR) compared to 2010 and 2018. Tree age was identified as a critical factor influencing pomelo yield, with optimal yields observed in trees older than 12.5 years. The rate of phosphorus fertilizer application was a key determinant of the phosphorus footprint (PF), which was positively correlated with phosphorus fertilizer rates. Similarly, nitrogen fertilizer application significantly impacted both carbon and nitrogen footprints (CF and NF) as well as the BCR. CF and NF increased with higher nitrogen fertilizer rates, while BCR exhibited a negative correlation with nitrogen fertilizer application. Implementing Science and Technology Backyards (STB) followed by farmer training of slightly more than pomelo nutrient requirements practice (SNRP) or coordinate management by farmers and researchers of matching pomelo nutrients requirement practice (MNRP) had 50 %–91 % lower environmental footprints per ton of pomelo produced and 15 %–44 % higher BCR compared to current farming practices (FP) via reducing 46 %–77 % N, 49 %–92 % P2O5, and 42 %–73 % K2O per hectare. Moreover, adopting 100 % MNRP in pomelo production, along with sustainable fertilizer practices and the implementation of innovative fertilizers, could reduce greenhouse gas (GHG) emissions (0.76 Tg), reactive nitrogen (Nr) emissions (8.76 Gg), phosphorus (P) losses (0.84 Gg), and augment net ecosystem economic benefits (NEEB) (0.94 billion CNY) at the county scale. SIGNIFICANCEOur study provides evidence-based strategies for achieving sustainable pomelo production through multi-stakeholder collaboration of STB, government, enterprises, and smallholders.

Relationships between soil test phosphorus and county-level agricultural surplus phosphorus.

National nutrient inventories provide surplus phosphorus (P) estimates derived from county-scale mass balance calculations using P inputs from manure and fertilizer sales and P outputs from crop yield data. Although bioavailable P and surplus P are often correlated at the field scale, few studies have investigated the relationship between measured soil P concentrations of large-scale soil testing programs and inventory-based surplus P estimates. In this study, we assessed the relationship between national surplus P data from the NuGIS dataset and laboratory-measured soil test phosphorus (STP) at the county scale for Arkansas, North Carolina, and Oklahoma. For optimal periods of surplus P aggregation, surplus P was positively correlated with STP based on both Pearson (Arkansas: r=0.65, North Carolina: r=0.45, Oklahoma: r=0.52) and Spearman correlation coefficients (Arkansas: ρ=0.57, North Carolina: ρ=0.28, and Oklahoma: ρ=0.66). Based on Pearson correlations, the optimal surplus P aggregation periods were 10, 30, and 4 years for AR, NC, and OK, respectively. On average, STP was more strongly correlated with surplus P than with individual P inventory components (fertilizer, manure, and crop removal), except in North Carolina. In Arkansas and North Carolina, manure P was positively correlated with STP, and fertilizer P was negatively correlated with STP. Altogether, results suggest that surplus P moderately correlates with STP concentrations, but aggregation period and location-specific factors influence the strength of the relationship.

Multi-Scale Analysis of Ecosystem Service Trade-Offs/Synergies in the Yangtze River Delta

The transformation of ecosystem structure leads to changes in ecosystem services (ESs) and their relationship. However, most research in this area has focused on particular scales and timeframes, often overlooking the significance of spatial and temporal variations. Therefore, we used the equivalent value method to evaluate seven typical ESs in the Yangtze River Delta (YRD) between 2000 and 2020: food production (FP), water supply (WS), climate regulation (CR), environmental purification (EP), soil conservation (SC), biodiversity maintenance (BM), and aesthetic landscape (AL). We further employed the Spearman correlation coefficient and bivariate Moran’s I to evaluate the relationship of ESs and their spatial heterogeneity at grid, township, county and city scales. Our results show that (1) All ESs except AL exhibited a fluctuating upward trend from 2000 to 2020, resulting in a total increase in ecosystem service (ES) value of RMB 650.63 billion. (2) Approximately 70% of the ES pairs demonstrated a synergistic relationship, with the exception of FP and other ESs, which mainly showed a trade-off. (3) With the scale increased from grid to city level, the degree of trade-off between FP and other ESs strengthened at different levels, while the synergy degree of among other ESs gradually decreased. (4) The relationship between ESs demonstrated strong spatial heterogeneity, with FP and other ESs exhibiting trade-offs primarily in the northern and southern YRD, while other ES pairs exhibited mostly synergy in these regions. This study provides scientific information for governments to optimize land use distribution and improve ESs.

Characteristics of Spatial–Temporal Evolution of Carbon Emissions from Land Use and Analysis of Influencing Factors in Hubao-Eyu Urban Agglomerations, China

Exploring the dynamic relationship between land use change and carbon emissions is of great significance in promoting regional low-carbon sustainable development and “dual-carbon”. We reveal the characteristics of the evolution of spatial temporal patterns of land use carbon emissions at the county scale in resource-based urban agglomerations over the past 20 years and the analysis of influencing factors. The research results show that: (1) In terms of spatial and temporal characteristics, from 2000 to 2020, net carbon emissions from land use showed an overall upward trend, with construction land being the main source of increased carbon emissions; the spatial distribution pattern of carbon emissions shows a trend of further clustering of centers in the northeast-southwest direction, which mainly occurs in areas rich in coal resources; the economy-contributive coefficient is increasing, but ecological support coefficients are decreasing; (2) In the analysis of influencing factors, land use structure is the most significant factor contributing to the increase of carbon emissions, followed by economic level, while land use intensity per unit of GDP is the most significant factor inhibiting the increase of carbon emissions. The results of the study provide a useful reference for resource-based urban agglomerations to formulate regionally appropriate emission reduction strategies and realize low-carbon sustainable development.

A comprehensive assessment approach for multiscale regional economic development: Fusion modeling of nighttime lights and OpenStreetMap data

Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress. Traditional evaluation methods focus on basic economic metrics like population and capital, which may not fully reflect the complexities of economic activities. Nighttime light (NTL) has been validated as an alternative indicator for regional economic development, yet limitations persist in its evaluation. This study integrates OpenStreetMap (OSM) data and NTL data, providing a novel data integration approach for evaluating economic development. The study uses mainland China as a case, applying Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to evaluate OSM and NTL data across provincial, municipal, and county levels. It compares OSM, NTL, and their combined use, providing key empirical insights for enhancing data fusion models. The study results reveal: (1) NTL data is more accurate for provincial-level economic activity, while OSM data excels at the county level. (2) GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales. (3) Through the integration of both datasets, it is observed that, compared to single-data modeling, the performance is enhanced at the city scale and county scale. The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial, municipal, and county levels. The study offers a straightforward and efficient approach to data integration. The findings offer new research perspectives and scientific support for sustainable regional economic growth, particularly valuable in data-scarce, underdeveloped areas.

The Relationship between Extreme Precipitation and Damaging Floods in the Northeastern United States

Abstract The northeastern United States has experienced an increase in extreme precipitation over the last three decades, which has substantial implications for flooding. However, the relationship between extreme precipitation and societally relevant flooding in the Northeast is not well understood. Here, we investigate the association between precipitation, antecedent wetness, and damage-producing warm-season (June–November) flood reports in the Northeast, 1996–2020. We employ 4-km gridded daily precipitation data aggregated to the county scale to calculate 1- and 3-day precipitation and a standardized precipitation index at multiple time scales. From these, we determine extreme precipitation and extreme antecedent wetness thresholds to identify hydroclimatic extreme events and relate them to damaging flood occurrences from the NOAA Storm Events Database. We find that damaging floods frequently occur with relatively high precipitation and antecedent wetness, although there are exceptions in several counties. However, the majority (54%) of damaging floods are not associated with extreme precipitation or extreme antecedent wetness, and more than 90% of hydroclimatic extreme events are not associated with floods. We explore limitations to the definitions of extreme precipitation and extreme antecedent wetness by varying the threshold. Lower hydroclimatic extreme event thresholds not only have a greater likelihood of being associated with damaging floods but also have a greater number of hydroclimatic extreme events not associated with a damaging flood; the opposite holds for higher thresholds. We address these challenges by combining precipitation and antecedent wetness to identify coordinated thresholds that best capture damaging flooding in each county.

Effects of Forest Conversion on the Stocks and Stoichiometry of Soil Carbon, Nitrogen, and Phosphorus at a County Scale in Subtropical China

The decline in primary natural forests worldwide has intensified research on the effects of forest transformation on soil carbon (C), nitrogen (N), and phosphorus (P) cycles and stocks. However, the extent to which soil C, N, and P stocks and stoichiometry are affected by forest conversion remains unclear. Here, we examined the effects of forest transformation on soil nutrient storage capacity and stoichiometric characteristics in native broadleaf forests (BFs), plantation forests (PFs), tea gardens (TGs), cultivated lands (CLs), and urban artificial green spaces (GSs) at a county scale in subtropical China. The results showed that the other forest types exhibited significantly reduced soil C and N contents and stocks but increased soil P content and stock compared to BFs. The soil C:N:P stoichiometric ratios for BFs and the converted PFs, TGs, GSs, and CLs were sequentially decreased as follows: 444.8:24.2:1, 95.0:10.0:1, 30.2:3.9:1, 23.1:3.7:1, and 19.4:1.9:1, respectively. Within the altitude (AL) span of 180 to 1200 m surveyed, the AL decided the type of forest conversion and significantly influenced the stock levels and stoichiometric ratios of soil C, N, and P. The results of this study highlight the importance of the ecological management of TGs and the optimization of soil P production in CLs, TGs, and GSs.

Analyzing multiscale associations and couplings between integrated development and eco-environmental systems: A case study of the central plains urban agglomeration, China

In the Anthropocene, urban agglomeration is increasingly recognized as a crucial facilitator in advancing urbanization to more developed stages, requiring comprehension of the multiscale associations and couplings supporting urban agglomeration integrated development and eco-environmental systems. This study introduces a novelty research framework for analyzing the intricate associations and couplings between the degree of integrated development and eco-environmental quality in the Central Plains Urban Agglomeration (CPUA), which operates simultaneously at the city, county, and landscape scales. The results suggest that the CPUA degree of integration is moderately low, with areas exhibiting a high degree of integration comprising <1% and predominantly located within the sphere of influence of core cities, such as Zhengzhou. Regions with high eco-environmental quality are located in regions with natural advantages in the northwestern, southwestern, and southeastern regions. It is worth noting that a generally inverse coupling relationship was observed between the urban agglomeration degree of integration and eco-environmental quality, exhibiting varied characteristics across different spatial scales and regional contexts. Stakeholders should focus more on environmental management and policy-making at the county administrative level. This study proposes a multiscale approach for revealing the complex associations and couplings between integrated development and eco-environmental systems, offering valuable insights into the sustainable development of urban agglomerations that encompass natural, social, and economic dimensions.

Identification of priority conservation areas in Beijing-Tianjin-Hebei using multi-scenario trade-offs based on different spatial scales and their drivers

Identifying priority conservation areas (PCAs) on the basis of ecosystem services (ESs) trade-offs is a crucial step in achieving optimal ecosystem management in the BTH, noting the complex spatio-temporal and scaling effects inherent in these ecosystems. A cross-scale quantification of spatial and temporal changes in ecosystem services and their correlations was conducted in the BTH. Furthermore, the Ordered Weighting Algorithm (OWA) was introduced to simulate a variety of scenarios in order to determine the PCAs and to explore the drivers of ES in the PCAs. The results indicated that Food supply (FS), Water yield (WY), and Soil retention (SR) in the BTH increased by 16.4 × 104t/hm2, 81.81 mm, and 37081.29 t/hm2, respectively. In contrast, Carbon storage (CS) and Habit quality (HQ) exhibited a decrease of 0.01 t/hm2 and 0.03. Spatially, the high-value areas of ESs at the county scale exhibited greater clustering. At the county scale, there were two trade-offs and eight synergies in 2000, and four trade-offs and six synergies by 2020. In contrast, at the raster scale, there were two trade-offs and eight synergies from 2000 to 2020. The mechanisms of change were similar and exhibited spatial heterogeneity. Scenario 6 represents the optimal priority conservation area, exhibiting the most balanced conservation efficiency of each ecosystem service. This scenario enables the simultaneous conservation of multiple ESs. The natural factors within the PCAs exert a stronger influence on ESs than socio-economic factors. The inclusion of interacting factor effects will enhance the explanatory power of the drivers. The findings of this study can serve as a theoretical foundation for cross-scale ecosystem service management decisions.

Impact of landscape pattern on habitat quality in the Yangtze River Economic Belt from 2000 to 2030

Recently, rapid economic development of the Yangtze River Economic Belt region in China and the growing problem of regional habitat quality have seriously threatened the process of biodiversity conservation in the Yangtze River Basin. Studying the impact of changes in landscape pattern indexes on habitat quality in the Yangtze River Economic Belt can provide policy insights for biodiversity conservation in the Yangtze River Basin. However, existing studies are still lacking in exploring the effects of changing landscape pattern indices on habitat quality under future scenarios. Based on the land use data of the Yangtze River Economic Belt from 2000 to 2020, this study simulates the land use situation in 2030, and analyzes the landscape pattern indexes and habitat quality during the 30 years, to investigate the impact of landscape pattern indexes on habitat quality at global and local scales through various methods. The results show that landscape patterns were significantly affected by human activities and land use types, with the Yangtze River Economic Belt experiencing increased levels of land use and structural stabilization during the three decades (Shannon Diversity Index increased from 0.90 in 2000 to 0.94 in 2030), fragmentation of the landscape (Landscape fragmentation index increase from 0.094 in 2000 to 0.102 in 2030), and weakened connectivity (contagion index decrease from 48.213 in 2000 to 45.437 in 2030). The Habitat Quality has a large variation in spatial distribution at the county scale, with an average change in habitat quality of 1.04 % from 2000 to 2030, and an overall downward trend. There is a strong localized spatial autocorrelation between landscape pattern indices and habitat quality, with significant spatial heterogeneity and some form of variability at time scales. The results of this study help to further understand the eco-environmental problems of the Yangtze River Economic Belt, and provide theoretical references for the formulation of ecological environmental protection policies and ecological functional area planning.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Ecological security assessment of Yunnan Province, China in the context of Production-Living-Ecological space division.

With the rapid development of population, society and economy, human activities have caused serious adverse impacts on the environment, ecosystems and landscape patterns over the long term. In order to address the series of impacts of human activities on the environment, territorial space and resource use, the study of Production-Living-Ecological Space (PLES) and ecological security have all become academic frontiers in the field of sustainable development. In this study, we applied multi-source data and GIS technology to construct an ecological security evaluation model based on the results of PLES delineation and the Pressure-State-Response (PSR)framework, and carried out the three-period PLES ecological security evaluation for 2000, 2010 and 2020 at the county and grid scales in Yunnan Province. The PLES pattern in Yunnan Province is dominated by ecological space, which accounts for 75%, followed by 23% of production space, with ecological space shrinking from 2000 to 2020. Ecological security in ecological space and living space shows an improving trend from 2000 to 2020. The ecological security of production space improved in 2010 compared to 2000 but then showed a decreasing trend in 2020. Ecological security in ecological space shows that north-western and southern Yunnan is safer than central Yunnan, while ecological security in living space is safer in central Yunnan, and ecological security in production space is better in southern Yunnan than in northern Yunnan. Comparison with related research results shows that the ecological security evaluation results of PLES in Yunnan Province in this study are scientific and reasonable. The ecological security evaluation model of PLES constructed in this study solves the problem of complex and incomplete ecological security evaluation indexes in the past, and the results of the study are more refined and precise, which provides new ideas for the study of regional ecological security.

Spatial and temporal distribution characteristics research of esophageal cancer in China

Objectives: To explore the spatial distribution characteristics, trend changes, and spatial clustering of esophageal cancer among residents in China at the county (city, district) scale, a spatial epidemiological approach was used, with the aim of providing localized evidence for the prevention and treatment of esophageal cancer in China. Methods: The data source was the incidence (crude rate) and mortality (crude rate) of esophageal cancer from 2005 to 2016 in the 2008-2019 edition of China Cancer Registration Annual Report published by the National Cancer Center. The Joinpoint model was used for time trend analysis. The tumor registration area in 2016 was selected as the study area for spatial feature analysis, with a total of 487 counties (cities and districts), covering 27.6% of the national population. Spatial autocorrelation analysis was performed to reveal spatial distribution characteristics by using Arcgis 10.6 software, and spatial scanning statistics was used to analyze spatial clustering characteristics by using SaTScan 9.5 software. The log-likelihood ratio (LLR) and relative risk (RR) were calculated in different windows, and the region with the largest LLR value represented the most likely cluster. Results: From 2005 to 2016, the incidence and mortality rate of esophageal cancer in China showed a trend of increasing at first and then decreasing. The incidence and mortality rate of esophageal cancer in 2016 were characterized by spatial positive correlation. High incidence and high mortality were mainly concentrated in the areas through which the Huaihe River flowed. The primary clusters (taking high incidence rate as an example LLR=6 374.41, RR=2.37, P＜0.001) were mainly distributed in Jiangsu, Anhui and Shandong in eastern China and eastern Henan and southern Hebei in central China, and secondary clusters (taking high incidence rate as an example LLR=1 971.19, RR=1.91, P＜0.001) in Gansu, Ningxia Hui Autonomous Region, Shaanxi, Sichuan and other central and western regions. Conclusions: The incidence and mortality of esophageal cancer in China have decreased since 2010. The disease burden of esophageal cancer has obvious spatial differences, and measures should be taken according to local conditions in high-risk cluster areas such as the Huaihe River basin.

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu–Chongqing Economic Zone, China, from 2000 to 2020

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.