Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data.

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Insight into environmental adaptability of antibiotic resistome from surface water to deep sediments in anthropogenic lakes by metagenomics.

The escalating antibiotic resistance threatens the long-term global health. Lake sediment is a vital hotpot in transmitting antibiotic resistance genes (ARGs); however, their vertical distribution pattern and driving mechanisms in sediment cores remain unclear. This study first utilized metagenomics to reveal how resistome is distributed from surface water to 45 cm sediments in four representative lakes, central China. Significant vertical variations in ARG profiles were observed (R2 = 0.421, p < 0.001), with significant reductions in numbers, abundance, and Shannon index from the surface water to deep sediment (all p-values < 0.05). ARGs also has interconnections within the vertical profile of the lakes: twelve ARGs persistently exist all sites and depths, and shared ARGs (e.g., vanS and mexF) were assembled by diverse hosts at varying depths. The 0-18 cm sediment had the highest mobility and health risk of ARGs, followed by the 18-45 cm sediment and water. The drivers of ARGs transformed along the profile of lakes: microbial communities and mobile genetic elements (MGEs) dominated in water, whereas environmental variables gradually become the primary through regulating microbial communities and MGEs with increasing sediment depth. Interestingly, the stochastic process governed ARG assembly, while the stochasticity diminished under the mediation of Chloroflexi, Candidatus Bathyarcaeota and oxidation-reduction potential with increasing depth. Overall, we formulated a conceptual framework to elucidate the vertical environmental adaptability of resistome in anthropogenic lakes. This study shed on the resistance risks and their environmental adaptability from sediment cores, which could reinforce the governance of public health issues.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region.

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

[Multi-scale Driving Mechanism of Urbanization on PM2.5 Concentration in Urban Agglomeration in the Middle Reaches of the Yangtze River].

Exploring the nonlinear effect of urbanization on PM2.5 concentration and its driving mechanism is crucial for controlling urban air pollution. Based on remote sensing data and statistical data from 2002 to 2020, spatial autocorrelation, systematic dynamic panel regression, and spatio-temporal geographical weighted regression models were used to analyze the spatio-temporal evolution of PM2.5 concentration in the urban agglomeration of the middle reaches of the Yangtze River and explore the driving mechanism of urbanization on PM2.5 concentration at different spatial scales. The results showed that:① PM2.5 concentration in the middle reaches of the Yangtze River showed an overall decreasing trend from 2002 to 2020, with a spatial distribution pattern of "high in the north and low in the south." ② Hot spot cities expanded towards the western part of the urban agglomeration, whereas cold spot cities showed enhanced spatial correlation. ③ The relationship between PM2.5 concentration and economic, land, and population urbanization followed N-shaped, U-shaped, and U-shaped curves, respectively. Secondary industry and energy consumption significantly promoted the change in PM2.5 concentration, and precipitation and vegetation helped mitigate PM2.5 pollution. ④ The overall driving effects of all urbanization factors in local areas were transformed, and the main areas of influence were concentrated in the southeast, northwest, and southwest of the study area. Considering the current urban development status and regional characteristics of the urban agglomeration in the middle reaches of the Yangtze River, promoting green industrial transformation, rational planning of urban spatial distribution and population distribution, and enhancing infrastructure construction will facilitate the coordinated development between urbanization and environmental protection.

Coupling coordination and driving mechanisms of water resources carrying capacity under the dynamic interaction of the water-social-economic-ecological environment system.

The water resources carrying capacity (WRCC) is a complex and comprehensive system that is jointly influenced by water resources, society, the economy and the ecological environment. Previous WRCC studies have primarily focused on estimating the overall level of regional WRCC. Few studies have explored the interactions among the various elements in the WRCC system and their influence on the WRCC evolution. Therefore, the purpose of this paper is, on the one hand, to explore the dynamic interactive relationships within the WRCC system from the perspectives of water resources, society, the economy and the ecological environment using a coupling coordination degree model and a panel vector autoregressive (PVAR) model, and on the other hand, to determine the evolutionary driving mechanism of the WRCC using the geographically and temporally weighted regression (GTWR) model to improve the regional WRCC. Taking 21 cities in Guangdong Province as an example, the results show that (1) the coupling coordination degree among the four WRCC subsystems in Guangdong Province shows an overall upward trend from 2009 to 2020, and the coordination between water resources utilization and other subsystems needs to be further strengthened. (2) The economic subsystem is the core of the WRCC system with reinforcing effects on both water resources and social subsystems but significant inhibitory effects on the ecological environment subsystem. Notably, the development of the ecological environment plays a crucial role in promoting social and economic development. (3) From 2009 to 2020, the development of the WRCC in Guangdong Province is initially driven by social and economic development, followed by economic development and ecological environmental protection, and then mainly by ecological environmental protection, which gradually becomes the primary driving force. This study provides a new entry point for studying the regional WRCC and formulating targeted measures for enhancing the regional WRCC.

Spatio-temporal characteristics and driving mechanism of land degradation sensitivity in Northwest China.

Northwest China has been experiencing severe land degradation for a long time due to various natural and social elements. Evaluating and analyzing the process of occurrence and driving mechanism of land degradation sensitivity in this area is crucial for enhancing the local ecological environment. In this study, 18 social and environmental elements were used to construct a land degradation sensitivity index (LDSI) evaluation system in the area from vegetation, climate, management, soil, and geomorphology five factors. The spatio-temporal characteristics of LDSI in Northwest China from 2000 to 2020 were evaluated on the basis of analyzing the developmental changes of each factor. Correlation analysis and multiscale geographical weighting regression (MGWR) were used to reveal the driving mechanism of land degradation sensitivity. The results indicated a high level of land degradation sensitivity in Northwest China, with >66 % of the area (190.96 × 104 km2) in the critical sensitive class from 2000 to 2020. But the land degradation sensitivity decreased in 18.52 % of the area (53.58 × 104 km2) from 2000 to 2020, the overall trend was weakening. The spatial distribution mainly showed stronger sensitivity in the northwest and weaker sensitivity in the southeast. By exploring the driving mechanism of land degradation sensitivity, it was found that vegetation and climate showed a strong correlation, with a correlation coefficient >0.8. Drought resistance played a strong role in the dynamic process of land degradation. The basic dynamic elements showed some spatial variability in land degradation in different regions. This study is of significance for land degradation prevention and sustainable development in Northwest China.

Impact of uranium on antibiotic resistance in activated sludge.

The emergence of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB) in the environment is well established as a human health crisis. The impact of radioactive heavy metals on ecosystems and ultimately on human health has become a global issue, especially for the regions suffering various nuclear activities or accidents. However, whether the radionuclides can affect the fate of antibiotic resistance in bacteria remains poorly understood. Here, the dynamics of ARB, three forms of ARGs-intracellular ARGs (iARGs), adsorbed extracellular ARGs (aeARGs), and free extracellular ARGs (feARGs)-and microbial communities were investigated following exposure to uranium (U), a representative radioactive heavy metal. The results showed that 90-d of U exposure at environmentally relevant concentrations of 0.05 mg/L or 5 mg/L significantly increased the ARB concentration in activated sludge (p < 0.05). Furthermore, 90-d of U exposure slightly elevated the absolute abundance of aeARGs (except tetO) and sulfonamide iARGs, but decreased tetracycline iARGs. Regarding feARGs, the abundance of tetC, tetO, and sul1 decreased after 90-d of U stress, whereas sul2 showed the opposite trend. Partial least-squares path model analysis revealed that the abundance of aeARGs and iARGs under U stress was predominantly driven by increased cell membrane permeability/intI1 abundance and cell membrane permeability/reactive oxygen species concentration, respectively. Conversely, the changes in feARGs abundance depended on the composition of the microbial community and the expression of efflux pumps. Our findings shed light on the variations of ARGs and ARB in activated sludge under U exposure, providing a more comprehensive understanding of antibiotic resistance risks aggravated by radioactive heavy metal-containing wastewater.

Understanding the driving mechanisms of site contamination in China through a data-driven approach.

China currently faces significant environmental risks stemming from contaminated sites. The driving mechanism of site contamination, influenced by various drivers, remain obscured due to a dearth of quantitative methodologies and comprehensive data. Here, we used a data-driven causality inference approach to construct an interpretable random forest (RF) model. Results show that: (1) the trained RF model demonstrated remarkable predictive accuracy for identifying contaminated sites, with an accuracy rate of 0.89. In contrast to conventional correlation analysis, the RF model excels in discerning the key drivers through non-linear and genuine causal relationships between these drivers and site contamination. (2) Among the 25 potential drivers, we identified 18 key drivers of site contamination. These drivers encompass a broad spectrum of factors, including production and operational data, pollutant control level, site protection capability, pollutant characteristics, and physical-geographical conditions. (3) Each key driver exerts varying impacts on site pollution, with diverse directions, intensities, and underlying patterns. The partial dependence plots (PDPs) illuminate the role of each key driver, its critical value contributing to site pollution, and the interplay between these drivers. The key drivers facilitate the realization of three primary contamination processes: uncontrolled release, effective migration, and persistent accumulation. In light of our findings, environmental managers can proactively prevent site contamination by regulating single, dual, and multiple key drivers to disrupt critical pollution processes. This research offers valuable insights for devising targeted strategies and interventions aimed at mitigating environmental risks associated with contaminated sites in China.

Multiple isotopes reveal the driving mechanism of high NO3- level and key processes of nitrogen cycling in the lower reaches of Yellow River.

The continuous increase of nitrate (NO3-) level in rivers is a hot issue in the world. However, the driving mechanism of high NO3- level in large rivers is still lacking, which has limited the use of river water and increased the cost of water treatment. In this study, multiple isotopes and source resolution models are applied to identify the driving mechanism of high NO3- level and key processes of nitrogen cycling in the lower reaches of the Yellow River (LRYR). The major sources of NO3- were sewage and manure (SAM) in the low-flow season and soil nitrogen (SN) and chemical fertilizer (CF) in the high-flow season. Nitrification was the most key process of nitrogen cycling in the LRYR. However, in the biological removal processes, denitrification may not occur significantly. The temporal variation of contributions of NO3- sources were estimated by a source resolution model in the LRYR. The proportional contributions of SAM and CF to NO3- in the low-flow and high-flow season were 32.5%-52.3%, 44.2%-46.2% and 36.0%-40.8%, 54.9%-56.9%, respectively. The driving mechanisms of high NO3- level were unreasonable sewage discharge, intensity rainfall runoff, nitrification and lack of nitrate removal capacity. To control the NO3- concentration, targeted measures should be implemented to improve the capacity of sewage and wastewater treatment, increase the utilization efficiency of nitrogen fertilizer and construct ecological engineering. This study deepens the understanding of the driving mechanism of high nitrate level and provides a vital reference for nitrogen pollution control in rivers to other area of the world.

Research on the spatiotemporal evolution and driving mechanism of coupling coordinating between green transition of urban land use and urban land use efficiency: a case study of the Yangtze River Delta Region in China.

Analyzing the spatiotemporal characteristics and driving mechanisms of the coupling coordination between the Green Transition of Urban Land Use and urban land use efficiency can help explore the future development direction of sustainable land use in cities. This paper constructs a theoretical framework for the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency. We use several models, including the super-efficiency slack-based model, the coupling coordination degree model, the non-parametric kernel density estimation method, exploratory spatial data analysis, and the geographically and temporally weighted regression model to examine the real level of Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region from 2003 to 2020. Based on this, we investigate the spatiotemporal evolution characteristics and driving mechanisms of the two coupling coordination processes. The study found that (1) from 2003 to 2020, the overall trend of the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region tended to be coordinated and developed, but still at a primary coordination level, with sufficient room for improvement in the future. (2) The coupling coordination level of each city in the Yangtze River Delta region from 2003 to 2020 showed obvious spatial non-equilibrium and correlation characteristics, and overall dynamic polarization effects were exhibited during the study period; the spatial pattern of high-value areas showed a regularity of prioritizing Shanghai and Zhejiang Province, gradually penetrating into Jiangsu Province and Anhui Province. (3) Economic and social factors have a positive influence on the degree of coupling coordination; natural factors and policy factors have a predominantly negative influence on the degree of coupling coordination. Research conclusions include establishing a regional collaborative development mechanism, utilizing the spatial spillover effect of leading cities; emphasizing science, education, and culture, strengthening the introduction of scientific and technological talents, increasing fiscal inputs, raising the level of economic development, and further expanding the driving effect of economic and social factors; and optimizing the layout of urban and rural construction land, developing urban land in an orderly manner, appropriately strengthening environmental regulation, thereby suppressing the negative effects caused by natural and policy factors.

Public responses to urban heat and payment for heat-resilient infrastructure: implications for heat action plan formulation.

Heat action plans should be urgently formulated to enable urban managers, planners, and designers to take appropriate actions for mitigation and adaptation. However, few studies have been conducted to investigate the societal needs and knowledge gaps regarding heat mitigation and adaptation. To address such research gaps, this paper presents an empirical study of 574 questionnaires in Chengdu, China, to explore heat-related impacts, public responses, and driving mechanisms. The results indicated that outdoor activities and work/study were more sensitive to extreme heat than transportation, sleep/rest, and diet. Heat-related impacts on physiological health were at the same level as those on psychological health, where digestive system illness and emotional irritability were the most prevalent physiological and psychological symptoms. Respondents' knowledge of heat-related threats, adaptation awareness, and adaptation knowledge were insufficient, compared with heat severity. The payment willingness among the respondents was not strong and payment amount was not high. Poorer, healthier, and the less affected in outdoor activities were positive groups in payment willingness, while the group which experienced heat-related impacts on outdoor activities could pay more compared with other groups. Overall, these results help to shape the main contents of heat action plans.

What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in the Northeast China (NEC) is essential for establishing suitable and sustainable cropping patterns that are adapted to local conditions, and for promoting the optimal use of black soil resources. Here, we classified the major grain crop cover types and investigated their spatiotemporal dynamic in the NEC by combining multi-source remote sensing imagery and phenological information based on the Google Earth Engine (GEE) platform. A number of typical cropping patterns from 2017 to 2021 were defined and extracted, and the characteristics of their spatial heterogeneity were analyzed. Driving mechanisms for the spatial heterogeneity of cropping patterns were revealed using Geodetector. The results concluded that over the past five years (2017-2021), there has been a shift from soybean to maize in the NEC, while rice has remained stable in terms of spatiotemporal dynamics. Seven dominant cropping patterns showed high spatial heterogeneity and positive spatial agglomeration. The center of gravity of the cropping pattern shifted southwards as the frequency of maize planting increased, while the center of gravity shifted northwards as the frequency of soybean planting increased, while the rice cropping pattern remained stable. The interaction between black-soil productivity index (BPI) and total grain income trend (TGIT) exhibits the most pronounced impact on the spatial heterogeneity of cropping patterns, with a q statistic of 0.523. Following closely are the interactions of soybean subsidies trend (SST), rice subsidies trend (RST), and maize subsidies trend (MST) with TGIT, with q statistics of 0.481, 0.472, and 0.452, respectively. Among the seven dominant cropping patterns, the soybean-based cropping pattern had the highest level of TGIT and BPI, followed by the maize-based cropping pattern, while the rice-based cropping pattern had the lowest level. All of the natural environmental, agri-economic and policy factors have a synergistic effect in contributing to the spatial heterogeneity of cropping patterns. Natural environmental factors determine the overall spatial distribution of cropping patterns in the NEC, while economic and policy factors combine to influence farmers' decisions, resulting in diverse regional cropping patterns. It is recommended that maize-soybean rotations such as Maize-Soybean Alternate Cropping (MSAC) and Maize-Soybean Rotational Cropping (MSRC) should be promoted, especially in the central and southern regions of the NEC, to meet agricultural market demand and stabilize soil productivity.

[Iron-based Bimetallic Catalysts for Persulfate Activation to Remove Antibiotics in Water: A Review].

In recent years, antibiotic residues are commonly detected in a variety of water bodies, causing serious threat to water ecosystems and human health. The removal of antibiotic contaminants from water based on the advanced oxidation process of activated persulfate has become a hot research topic due to its strong oxidative properties, high selectivity, and wide pH applicability range. Iron-based bimetallic materials with low cost, high stability, and excellent catalytic performance can effectively activate persulfate, which makes up for the defects of being a single iron activator, such as easy deactivation, low efficiency, and producing secondary pollution easily. Three typical Fe-based bimetallic catalysts, namely spinel ferrite, Fe-based layered double hydroxides, and Fe-based Prussian blue analogues, were investigated and analyzed for their activation of persulfate for antibiotic degradation. Several intrinsic mechanisms of persulfate activation by Fe-based bimetallic catalysts are systematically discussed, including the generation of free radicals, singlet oxygen, and high-valent metals; the process of electron transfer; and the direct oxidation process of persulfate. Finally, the general degradation pathways of four typical antibiotics, including fluoroquinolones, sulfonamides, tetracyclines, and ß-lactam antibiotics, are summarized to act as a reference for future studies on the application of Fe-based bimetallic catalysts and their modifications, derivatives, and complexes in the activating technology of persulfate.

Identifying the driving forces of cultivated land fragmentation in China.

Cultivated land fragmentation (CLF) has severely affected China's agricultural production efficiency, large-scale operations, agricultural modernization, and food security. Exploring the spatiotemporal evolution patterns and driving forces of CLF is crucial for agricultural modernization. However, the driving forces of CLF in different agricultural regions in China still need to be clarified. In this study, CLF was measured in 2000, 2010, and 2020 based on remote sensing data with landscape pattern metrics, and the driving forces of spatial differentiation were detected based on a geographical detector model. The overall cultivated land area has slightly declined during the study period while the CLF has intensified. CLF showed significant spatial autocorrelation, with CLF increased-cultivated land decreased (2000 to 2010) and CLF decreased-cultivated land decreased (2010 to 2020). The contribution rate of land use intensity on CLF was the highest among all agricultural regions, excluding the Qinghai Tibet Plateau. In contrast, the contribution rates of other factors significantly varied across agricultural regions. These findings provide scientific support in formulating policies to conserve cultivated land for sustainable use of agricultural resources and CLF management in China.

Assessing the influence of energy transition on economic-social growth: the case of China.

In recent years, because of the one-sided interpretation of the nexus between the green & low-carbon energy transition and the high-quality economic-social growth, some over-idealized due-oriented judgments have led to failures in theories or deviations from practices. It is necessary and typical to select the panel data of 30 provinces in China from 2010 to 2020 to demonstrate that nexus, since China plays a different role in both climate change and global energy security. This paper defines and measures both the high-quality economic-social growth and the green & low-carbon energy transition efficiency. After analyzing the structure characteristics of energy transition, it also discusses the impact of the transition efficiency on the high-quality growth. The results indicate that the inflection point from quantitative to qualitative change in the regional high-quality growth has not yet arrived, while the developed regions and the developing regions are still grappling with pronounced problems of imbalance and insufficiency. As far as now, the energy transition efficiency in the energy resource centers is much higher than that in the energy load centers. The provinces with a characteristic of energy supply-demand equilibrium have seamlessly shifted from the Introduction stage of green & low-carbon development to the Growth stage. Both the decrease in energy intensity and the increase in renewable share are able to promote high-quality economic-social development obviously; however, the reduction of per capita carbon emission cannot yield the expected positive externality in a short run.

Fluctuotaxis: Nanoscale directional motion away from regions of fluctuation.

Regulating the motion of nanoscale objects on a solid surface is vital for a broad range of technologies such as nanotechnology, biotechnology, and mechanotechnology. In spite of impressive advances achieved in the field, there is still a lack of a robust mechanism which can operate under a wide range of situations and in a controllable manner. Here, we report a mechanism capable of controllably driving directed motion of any nanoobjects (e.g., nanoparticles, biomolecules, etc.) in both solid and liquid forms. We show via molecular dynamics simulations that a nanoobject would move preferentially away from the fluctuating region of an underlying substrate, a phenomenon termed fluctuotaxis-for which the driving force originates from the difference in atomic fluctuations of the substrate behind and ahead of the object. In particular, we find that the driving force can depend quadratically on both the amplitude and frequency of the substrate and can thus be tuned flexibly. The proposed driving mechanism provides a robust and controllable way for nanoscale mass delivery and has potential in various applications including nanomotors, molecular machines, etc.

[Spatio-temporal Variation and Multi-dimensional Detection of Driving Mechanism of PM2.5 Concentration in the Chengdu-Chongqing Urban Agglomeration from 2000 to 2021].

Studies on the spatio-temporal variation and driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration are of great significance for regional atmospheric environment protection and national economic sustainable development. Based on PM2.5 remote sensing data, DEM data, in situ meteorological data, MODIS NDVI data, population density data, nighttime lighting data, road network data, and land use type data, a series of mathematical methods such as Theil-Sen Medium analysis and Mann-Kendall significance test, combined with the Geo-detector model were used to analyze the spatio-temporal variation and multi-dimensional detection of the driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration. The results showed that the overall PM2.5 concentration showed a fluctuating downward trend in the Chengdu-Chongqing urban agglomeration from 2000 to 2021, and the PM2.5 pollution was the most prominent in winter. PM2.5 concentration exhibited obvious spatial heterogeneity with "high in the middle and low in the surrounding areas." The high-PM2.5 concentration areas were mainly concentrated in Zigong, Neijiang, Ziyang, and Guang'an, and the areas with a PM2.5 concentration decrease were mainly concentrated in the west of Chongqing. Influencing detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was influenced by the combined effects of climate factors, topographic factors, vegetation cover, and anthropogenic factors. Furthermore, elevation, slope, and road network density were regarded as the dominant factors influencing the spatial heterogeneity of PM2.5 concentration in the study area. Topographic factors and climate factors showed the highest and lowest contribution rate to the spatial heterogeneity of PM2.5 concentration, respectively. The contribution rate of topographic factors and anthropogenic factors had gradually increased, and the contribution rate of climate factors and vegetation cover had gradually decreased in the study area from 2000 to 2021. Interaction detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was mostly affected by the interaction effects of elevation and road network density, slope, precipitation, sunshine duration, and land use type. The interaction detection results exhibited obvious regional differences on the city level. For instance, the spatial heterogeneity of PM2.5 concentration in Chengdu, Deyang, and Leshan was mostly affected by the interaction between different influencing types, and the spatial heterogeneity of PM2.5 concentration in Dazhou, Meishan, Ya'an, Ziyang, Neijiang, and Zigong was mostly affected by the interaction within a single influencing type.

Spatial relationship between land urbanization and ecosystem health in the Yangtze River Basin, China.

Globally, land-based urbanization had far-reaching impacts on ecosystem health. Determining the spatial relationship between land urbanization and ecosystem health is important for sustainable socioeconomic development and ecological protection. However, existing studies lack research on these relationships in basin regions, which may limit the implementation of effective basin ecological management measures. Based on multi-source data, this study analyzed the spatiotemporal patterns and spatial correlations of land urbanization rate (LUR) and ecosystem health index (EHI) in the Yangtze River basin (YRB) with a series of spatial analysis methods. The results showed that EHI in the YRB decreased by 0.024 during 2000-2020, with a decreasing range of 3.133 %, while LUR increased by 0.216, with an increasing range of 54.135 %. LUR has a significant negative spatial correlation with EHI, with high EHI and high LUR (9.814% in 2020) and high EHI and low LUR (12.397% in 2020) being the main types of agglomeration. The global regression results showed that LUR significantly negatively affected EHI. At the local scale, the LUR positively affected the EHI in the mountainous region, while the opposite was confirmed in the plain region. This study can provide scientific reference for the development of sustainable urban land control measures and basin ecological management measures.

Urban park system on public health: underlying driving mechanism and planning thinking.

The driving mechanism and planning thinking of the impact of urban park system on public health the mission of urban geography, urban and rural planning or landscape architecture are to coordinate the relationship between people and places, people and nature. The municipal park system is an important part of the urban green space system. In order to effectively play the role of the urban park system in promoting the health of urban residents. This manuscript studies the coupling relationship between the "urban park system" and the "public health system" by building a coordination model, reveals the driving mechanism of the urban park system affecting the benign development of public health, and clarifies the positive driving effect of urban parks on public health. Finally, based on the analysis results, the manuscript considers the optimal development strategy of urban parks from the macro and micro levels to promote the sustainable development of urban public health.

Analyzing factors driving of eutrophication of river-type urban landscape lakes.

The eutrophication of river-type urban landscape (RTUL) lakes is different from that of natural lakes. In this study, Xiaofu Lake, a typical RTUL lake with high anthropogenic interference, was used as the study area. Monitoring data from 2018 to 2020 were used to analyze the temporal and spatial distribution characteristics of chlorophyll a (Chl-a) concentrations with meteorological, hydrodynamic, and nutrient factors. Correlation and regression analyses were used to identify the relationship between the factors influencing eutrophication and the Chl-a. The MIKE21 model is used to simulate changes in water quality indicators. The study determined the relationship between river water quality and environmental factors and explored the causes of eutrophication in the water bodies of Xiaofu Lake. The results showed that from 2018 to 2020, the water quality showed seasonal variation and differences in spatial distribution. Except for total nitrogen, which remained at a high level (average 8.23 mg/L), other water qualities remained between classes II and IV. The proportions of mild, moderate, and severe eutrophication in the study area were 25%, 69%, and 6%, respectively. Indicators that were highly correlated with water eutrophication were turbidity, water temperature, total phosphorus, and permanganate index. The contribution of water temperature, ammonia nitrogen, and permanganate index to eutrophication was 30.5%, 22.6%, and 20.9%, respectively. The high proportion of sewage in the source of recharge water is one of the reasons for the deterioration of water quality. In addition, the change in water eutrophication was closely related to the gate operation in the region. PRACTITIONER POINTS: There are differences between river-type urban landscape (RTUL) lakes and natural lakes, and the conditions that cause eutrophication are different. RTUL are subject to strong human interference and rely on water transfer and gate scheduling to maintain water quantity. The high proportion of sewage treatment plant tailwater in upstream water is the main reason for the long-term pollution of RTUL. The indicators highly correlated with water eutrophication are turbidity, water temperature, total phosphorus, and permanganate index.