[Plankton Community Stability and Drive Factor Identification Guizhou Plateau Reservoir].

In order to understand the stability of the zooplankton and phytoplankton communities in the Guizhou plateau reservoir environment, the process of reservoir water quality change affecting the stability of plankton was studied. The changes in the plankton community and water quality in three different nutrient reservoirs （Huaxi Reservoir, Goupitan Reservoir, and Hailong Reservoir） were studied from October 2020 to August 2021. The stability of the zooplankton and phytoplankton communities was studied using time-lag analysis （TLA）. Variance decomposition analysis （VPA） was used to explore the response of the two communities to environmental changes. The driving factors of plankton community changes in reservoirs were also revealed. The results showed that Huaxi Reservoir and Goupitan Reservoir were mesotrophic reservoirs, and Hailong Reservoir was a eutrophic reservoir. The average comprehensive nutrition indices of the three reservoirs were 44.07, 44.68, and 50.25. A total of 51 species of zooplankton rotifers, 39 species of rotifers, three species of copepods, and nine species of cladocera were identified. Among them, the abundance of rotifers was the highest, accounting for 85.96%. A total of seven phyla and 73 species of phytoplankton were identified, including 16 species in the phylum Cyanophyta, 32 species in the phylum Chlorophyta, 16 species in the phylum Diatoma, three species in the phylum Chlorophyta, four species in the phylum Euglenophyta, and one species each in the phyla Cryptophyta and Chrysophyta. Among them, the abundance of cyanobacteria and diatoms was the highest, accounting for 66.2% and 27.35%, respectively. The median absolute deviation （MAD） of the Bray-Curtis distance of zooplankton and phytoplankton community in the three reservoirs were 0.67 and 0.65 in Huaxi Reservoir, 0.80 and 0.69 in Goupitan Reservoir, and 0.85 and 0.47 in Hailong Reservoir, respectively. The larger the value, the greater the variation in the community. The absolute value of the slope of zooplankton was greater than that of phytoplankton in the TLA results, and the absolute values of the slopes were 0.018 and 0.004, respectively. The larger the absolute value of the slope, the faster the community variability. The zooplankton community in the three reservoirs was less stable than the phytoplankton community and more sensitive to environmental changes, and the degree of variation was greater. The higher the degree of eutrophication of the reservoir, the more obvious this phenomenon. VPA showed that the changes in plankton communities in Huaxi Reservoir and Hailong Reservoir were mainly influenced by water temperature and eutrophication factors. The changes in planktonic community in Goupitan Reservoir were mainly influenced by water temperature and chemical factors. The driving factors of Huaxi Reservoir were water temperature, TP, permanganate index, and SD. The driving factors of Goupitan Reservoir were water temperature, NO3-- N, and pH. The driving factors of Hailong Reservoir were water temperature and TP. Nutrients and water temperature were the main factors affecting the stability of plankton communities in reservoirs.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Internal transformation and damming regulate the longitudinal variation of DOM bioavailability in a large river.

Understanding the spatial patterns of dissolved organic matter (DOM) and factors that influence them is crucial for maintaining river ecosystem functions and riverine health, considering the significant role of DOM in water quality and aquatic ecosystems. Nevertheless, there is limited knowledge regarding the spatial variation of DOM bioavailability and the factors driving them in large river systems. This study involved 39 sampling locations along the main stem of the Changjiang River, spanning its entire length (> 5000 km) during a dry season. Spatial patterns of DOM were assessed by measurements of DOC concentrations and eight fluorescence DOM indices, namely fluorescence index (FI-A and FI-B), Trytophan/Tyrosine, Humic A, Humic C, humification indices (HIX-A and HIX-B), and Freshness index (ß/α). The results revealed that the water DOM in the main stem of the Changjiang River primarily originated from terrestrial sources. A decline in DOM bioavailability was observed from the upper to the lower basin, aligning with the carbon processing prediction rather than the river continuum concept (RCC). The pure effect of physicochemical factors (25.30%) was greater than that of geographic factors (9.40%). The internal transformation processes determined the significant longitudinal decreases of DOM bioavailability. While no significant difference in DOM bioavailability was observed between reaches before and after the dams, the construction of dams was found to improve DOM bioavailability at the subsection scale and reduce the spatial autocorrelation of DOM bioavailability across the entire basin.

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

Elucidating biogeochemical characterization of nitrogen in the vadose zone integrating geochemistry, microorganism, and numerical simulation.

A thorough comprehension of nitrogen biogeochemical processes in the vadose zone is crucial for the effective prevention and remediation of soil-groundwater system contamination. Despite the growing research on this subject, the full scope of nitrogen biogeochemical characterization in different geological environments remains poorly understood. This study addresses this knowledge gap by integrating geochemical, microbiological and numerical simulation approaches to gain a deeper insight into nitrogen biogeochemistry in agriculture. Our findings indicate the biogeochemical behavior of nitrogen in the vadose zone is mediated by microorganisms, driven by hydraulics, influenced by geological conditions and environmental factors. Along the groundwater flow, NH4+-N was found to be heavily accumulated in the topsoil of 0-40 cm, while NO3--N was transported and driven by hydrodynamics from both vertical and horizontal directions. Microbial diversity, species composition and functional microorganisms were significantly influenced by soil depth, rather than geomorphological types. Oxidation-reduction potential (ORP), total organic carbon (TOC), soil moisture (MOI), bicarbonate (HCO3-), and ferrous (Fe2+) were identified as the principal environmental factors that regulate nitrogen metabolism and the dominant biochemical processes, encompassing nitrogen fixation, nitrification, and denitrification. Driven by hydrodynamics, NH4+-N, NO2--N and NO3--N tend to form distinct biochemical reaction zones in the vertical vadose zone. These areas are dynamic and subject to geomorphologies. It should be noted that NO3--N can migrate towards groundwater from the clayey sand in the Alluvial Plain, which presents a potential risk of groundwater contamination. The fissure structure of loess may serve as the major transport pathway for groundwater nitrogen contamination in the Loess Tableland. This finding highlights the importance of integrating microbiology, geochemistry and hydraulics to elucidate the biogeochemical processes of nitrogen in the vadose zone with a dynamic mindset.

Defending against environmental threats: Unveiling household adaptation strategies and population heterogeneity.

Humanity faces a variety of risks from pollution and environmental degradation. Societal advancement has equipped the public with numerous self-protection measures to mitigate these threats. However, the ways in which individuals deploy and balance self-defence mechanisms within this complex risk landscape and the resulting consequences remain largely unexplored. Drawing on a detailed survey of households' self-defence practices, this study rigorously analyses the heterogeneity and driving factors behind household-level self-defence strategies. Through exploratory latent class modelling, we identified four distinct defence patterns: inaction, water-sensitive, air-sensitive, and multifaceted. These patterns reveal varied defence capabilities among the population. By integrating frameworks from economics and social psychology, significant disparities were found in the driving factors behind these patterns. Practices aimed at combating air pollution are primarily driven by the actual severity of pollution and perceived coping capabilities, whereas measures to enhance water quality are influenced more by perceived threats. This disparity arises from variations in information availability and health awareness. The study also highlights a misalignment between the distribution of defence capabilities and the levels of pollution. Given that income restricts self-defence options, this mismatch indicates that economically disadvantaged groups are disproportionately affected by severe health inequalities.

Species pool and soil properties in mangrove habitats influence the species-immigration process of diazotrophic communities across southern China.

Microbial immigration is an ecological process in natural environments; however, the ecological trade-off mechanisms that govern the balance between species extinction and migration are still lacking. In this study, we investigated the mechanisms underlying the migration of diazotrophic communities from soil to leaves across six natural mangrove habitats in southern China. The results showed that the diazotrophic alpha and beta diversity exhibited significant regional and locational variations. The diazotrophic species pool gradually increased from the leaves to nonrhizosphere soil at each site, exhibiting a vertical distribution pattern. Mantel test analyses suggested that climate factors, particularly mean annual temperature, significantly influenced the structure of the diazotrophic community. The diazotrophic community assembly was mainly governed by dispersal limitation in soil and root samples, whereas dispersal limitation and ecological drift were dominant in leaves. Partial least squares path modeling revealed that the species pool and soil properties, particularly the oxidation-reduction potential and pH, were closely linked to the species-immigration ratio of diazotrophic communities. Our study provides novel insights for understanding the ecological trait diversity patterns and spread pathways of functional microbial communities between below- and aboveground habitats in natural ecosystems.IMPORTANCEEnvironmental selection plays key roles in microbial transmission. In this study, we have provided a comprehensive framework to elucidate the driving patterns of the ecological trade-offs in diazotrophic communities across large-scale mangrove habitats. Our research revealed that Bradyrhizobium japonicum, Marinobacterium lutimaris, and Agrobacterium tumefaciens were more abundant in root-associated soil than in leaves by internal and external pathways. The nonrhizospheric and rhizospheric soil samples harbored the most core amplicon sequence variants, indicating that these dominant diazotrophs could adapt to broader ecological niches. Correlation analysis indicated that the diversities of the diazotrophic community were regulated by biotic and abiotic factors. Furthermore, this study found a lower species immigration ratio in the soil than in the leaves. Both species pool and soil properties regulate the species-immigration mechanisms of the diazotrophic community. These results suggest that substantial species immigration is a widespread ecological process, leading to alterations in local community diversity across diverse host environments.

The shift in the spatiotemporal relationship between supply and demand of ecosystem services and its drivers in China.

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010-2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development.

Marsh decrease was much faster than the water increase among the Yellow River Source wetlands during 1986-2022.

Wetlands are valuable and sensitive ecosystems that make them imperative to tracking the dynamics in their extent for sustainable management under global warming. Here we focused on the Yellow River Source (YRS) wetlands, which is renowned for hosting one of the world's largest plateau peat bog, unfortunately, it had experienced sharp degradation, threatening the safety of water supply for approximately 110 million people of the lower Yellow River basin. However, the lack of long-term, dense time-series data makes it challenging to assess its evolution trends and driving factors. Therefore, we developed a decision tree sample migration method based on Euclidean distance and Land Surface Water Index, and successfully generated annual wetland mapping of YRS from 1986 to 2022 by utilizing the Landsat 5/7/8 datasets and Random Forest method. The average sample migration rate was 89.21 %, with an average overall accuracy of 95.49 %. We observed that the marsh area decreased by 2031 km2, marking a decline of 12.98 %, while the water area increased by 710 km2 (31.24 %) compared to 1986. Spatially, 10.96 % of marsh composition presents significant (P < 0.05) decline trend, which are mainly converted to grass (86 %), followed by impervious (10 %). There were 6.69 % of water composition showing significant (P < 0.05) increase trend, which are mainly sourced from impervious (82 %) and marsh (12 %). Grazing activities were more important driving forces than climate change for marsh degradation, while the water expansion was associated with recent rising temperature in YRS. The sample migration method is proved to be feasible, robust, and effective for long-term wetland mapping. We suggest that wetland decision-makers need to focus on marsh degradation and reduce grazing intensity, so that fostering the sustainable and healthy wetlands in the Qinghai-Tibetan Plateau.

Spatio-temporal pattern evolution of green development efficiency in Northeastern China and its driving factors.

Scientific analysis of green development efficiency is crucial for promoting healthy green development at home. The subjects of this study were 181 counties in three provinces in Northeast China. As a first step, the Super-SBM model is utilized to estimate the efficiency of 181 counties from 2006 to 2020; in addition, spatial autocorrelation analysis is applied to assess green development efficiency, spatially and temporally, of 181counties; and to examine the driving factors and spillover effects associated with the efficiency of 181 counties, the Spatial Durbin model (SDM) is utilized. The conclusions show that 181 counties have low levels of green development efficiency and are all on a downward trajectory. Liaoning Province has the highest level, Jilin Province has the second highest level, and Heilongjiang Province has the lowest level. According to the geographical distribution, the locations with high and very high green development efficiency are roughly located in Mohe City, Huma County, Xunke County, Daqing Municipal District, Harbin Municipal District, Changchun Municipal District, Wafangdian City, Dalian Municipal District, and Zhuanghe City. There is a favorable spatial connection of efficiency across regions, but the positive spatial agglomeration trend first diminishes and then gradually increases. Economic development, industrial structure, policy regulations, and environmental protection play significant roles in economic development, industrial structure, policy regulations, and environmental protection. The contribution of this essay is of paramount importance for understanding the status quo and potential for green development in different counties in Northeast China and for realizing coordinated regional green development.

A new multivariate index for ecological security assessment in the China-Myanmar border region.

The coordination of development efforts and ecological conservation in China's border regions is a significant challenge due to the overlap of biodiversity hotspots, ecologically fragile zones, and impoverished areas. Achieving the harmonious integration of ecological preservation and economic development relies on the fundamental assessment of ecological security (ES). However, comprehensive assessments of ES in border regions remain limited. This study introduces a new index, the multivariate ecological security index (MESI), which integrates ecosystem vigor, organization, elasticity, services and risk. Here, the MESI was utilized to assess the temporal and spatial changes in ES and its associated impact factors in the China-Myanmar border region (CMBR) from 2000 to 2020. The MESI provides a clear representation of the actual ES status in the CMBR, exhibiting a significant correlation with the eco-environmental quality index (EEQI; p < 0.01). The ES status exhibited notable spatial heterogeneity in the CMBR, consisting primarily of both relatively safe and safe levels, which accounted for approximately 85% of the total area. From 2000 to 2020, the CMBR experienced a gradual improvement in ES status, with the area experiencing an increase in the ES level accounting for 23.41% of the total area, which exceeded the proportion of the area experiencing a decrease in the ES level (4.71%). The combined impact of multiple factors exerted a greater influence on ES than did individual factors alone. Notably, human factors increasingly influenced the ES status during the study period. The results of this study provide valuable insights for ecological preservation and sustainable management in the CMBR, and the MESI can be extended to assess the ES of other regions.

[Driving Factors Analysis for Spatial-Temporal Distribution of Organic Pollutants in the Yangtze River Delta Based on the Optimal-scale Geographical Detector].

Site contamination has caused serious harm to human health and the ecological environment, so understanding its spatial and temporal distribution patterns is the basis for contamination assessment and site remediation. For this reason, this study analyzed the spatial-temporal distribution patterns of organic pollutants and their driving factors in the Yangtze River Delta based on site sampling data using the optimal-scale geographical detector. The analysis results showed that:① There was a significant scale effect in the spatial distribution of organic pollutants in the Yangtze River Delta, and its optimal geographic detection scale grid was 8 000 meters. ② The main control factor of the spatial distribution of pollutants in the Yangtze River Delta originated mostly from the biological field, followed by the chemical field. ③ At the depth of 0-20 cm of soil, the explanatory power of sucrase content, urease content, microbial nitrogen amount, total nitrogen content, and cation exchange amount were stronger for the spatial distribution of organic pollutants. At the soil depth of 20-40 cm, the factors with stronger explanatory power on the spatial distribution of organic pollutants were soil moisture, population, and total nitrogen content. With the deepening of soil depth, the explanatory power of the factors of the hydrodynamic field increased. ④ Population, total nitrogen content, and polyphenol oxidase content had stronger explanatory power for the spatial distribution of organic pollutants in the spring. The spatial distribution of organic pollutants was more complex in autumn, and the factors showed stronger enhanced-nonlinear and enhanced-bi phenomena.

Deciphering the key factors affecting pesticide residue risk in vegetable ecosystem.

Soil contamination, particularly from pesticide residues, presents a significant challenge to the sustainable development of agricultural ecosystems. Identifying the key factors influencing soil pesticide residue risk and implementing effective measures to mitigate their risks at the source are essential. Here, we collected soil samples and conducted a comprehensive survey among local farmers in the Three Gorges Reserve Area, a major agricultural production region in Southwest China. Subsequently, employing a dual analytical approach combining structural equation modeling (SEM) and random forest modeling (RFM), we examined the effects of various factors on pesticide residue accumulation in vegetable ecosystems. Our SEM analysis revealed that soil characteristics (path coefficient 0.85) and cultivation factor (path coefficient 0.84) had the most significant effect on pesticide residue risk, while the farmer factors indirectly influenced pesticide residues by impacting both cultivation factors and soil characteristics. Further exploration using RFM identified the three most influential factors contributing to pesticide residue risk as cation exchange capacity (CEC) (account for 18.84%), cultivation area (account for 14.12%), and clay content (account for 13.01%). Based on these findings, we carried out experimental trials utilizing Integrated Pest Management (IPM) technology, resulting in a significant reduction in soil pesticide residues and notable improvements in crop yields. Therefore, it is recommended that governmental efforts should prioritize enhanced training for vegetable farmers, promotion of eco-friendly plant protection methods, and regulation of agricultural environments to ensure sustainable development.

Comprehensive assessment of refined greenhouse gas emissions from China's livestock sector.

Livestock and poultry products are an essential human food source. However, the rapid development of the livestock sector (LS) has caused it to become a significant source of greenhouse gas (GHG) emissions. Consequently, investigating the spatio-temporal characteristics and evolution of GHG emissions is crucial to facilitate the green development of the LS and achieve "peak carbon and carbon neutrality". This study combined life cycle assessment (LCA) with the IPCC Tier II method to construct a novel GHG emissions inventory. The GHG emissions of 31 provinces in China from 2000 to 2021 were calculated, and their spatio-temporal characteristics were revealed. Then, the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model was used to identify the main driving factors of GHG emissions in six regions of China and explore the emission reduction potential. The results showed that GHG emissions increased and then decreased from 2000 to 2021, following a gradual and steady trend. The peak of 628.55 Mt CO2-eq was reached in 2006. The main GHG-producing segments were enteric fermentation, slaughtering and processing, and manure management, accounting for 45.39 %, 26.34 %, and 23.08 % of total GHG emissions, respectively. Overall, the center of gravity of GHG emissions in China migrated northward, with spatial aggregation observed since 2016. The high emission intensity regions were mainly located west of the "Hu Huanyong line". Economic efficiency and emissions intensity were the main drivers of GHG emissions. Under the baseline scenario, GHG emissions are not projected to peak until 2050. Therefore, urgent action is needed to promote the low-carbon green development of the LS in China. The results can serve as scientific references for the macro-prevention and control of GHG emissions, aiding strategic decision-making. Additionally, they can provide new ideas for GHG accounting in China and other countries around the world.

Understanding visible light and microbe-driven degradation mechanisms of polyurethane plastics: Pathways, property changes, and product analysis.

The accumulation of polyurethane plastics (PU-PS) in the environment is on the rise, posing potential risks to the health and function of ecosystems. However, little is known about the degradation behavior of PU-PS in the environment, especially water environment. To address this knowledge gap, we investigated and isolated a degrading strain of Streptomyces sp. B2 from the surface of polyurethane coatings. Subsequently, a photoreactor was employed to simulate the degradation process of bio-based polyurethane (BPU) and petroleum-based polyurethane (PPU) under three conditions, including single microorganism (SM), single light exposure (SL), and combined light exposure/microorganism action (ML) in aqueous solution. The results indicated that PU-PS mainly relies on biodegradation, with the highest degradation rate observed after 28 d under SM condition (BPU 5.69 %; PPU 5.25 %). SL inhibited microbial growth and degradation, with the least impact on plastic degradation. Microorganisms colonized the plastic surface, secreting relevant hydrolytic enzymes and organic acids into the culture medium, providing a negative charge. The carbon chains were broken and aged through hydrogen peroxide induction or attack by oxygen free radicals. This process promoted the formation of oxidized functional groups such as OH and CO, disrupting the polymer's structure. Consequently, localized fragmentation and erosion of the microstructure occurred, resulting in the generation of secondary microplastic (MPs) particles, weight loss of the original plastic, increased surface roughness, and enhanced hydrophilicity. Additionally, BPU exhibited greater degradability than PPU, as microorganisms could utilize the produced fatty acids, which promoted their reproduction. In contrast, PPU degradation generated a large amount of isocyanate, potentially toxic to cells and inhibiting biodegradation. This study unveils the significant role of microorganisms in plastic degradation and the underlying degradation mechanisms of BPU, providing a novel strategy for polyurethane degradation and valuable information for comprehensive assessment of the behavior and fate of MPs in the environment.

Metal-containing nanoparticles in road dust from a Chinese megacity over the last decade: Spatiotemporal variation and driving factors.

As a crucial sink of metal-containing nanoparticles (MNPs), road dust can record their spatiotemporal variations in urban environments. In this study, taking Shanghai as a representative megacity in China, a total of 272 dust samples were collected in the winter and summer of 2013 and 2021/2022 to understand the spatiotemporal variations and driving factors of MNPs. The number concentrations of Fe-, Ti-, and Zn-containing NPs were 3.8 × 106 - 8.4 × 108, 2.3 × 106-1.4 × 108, and 6.0 × 105-2.3 × 108 particles/mg, respectively, according to single particle (sp)ICP-MS analysis. These MNPs showed significantly higher number concentrations in summer than in winter. Hotspots of Fe-containing NPs were more concentrated in industrial and traffic areas, Zn-containing NPs were mainly distributed in the central urban areas, while Ti-containing NPs were abundant in areas receiving high rainfall. The structural equation model results indicates that substantial rainfall in summer can help remove MNPs from atmospheric PM2.5 into dust, while in winter industrial and traffic activities were the primary contributors for MNPs. Moreover, the contribution of traffic emissions to MNPs has surpassed industrial one over the last decade, highlighting the urgency to control traffic-sourced MNPs, especially those from non-exhaust emissions by electric vehicles.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

Climate and land use changes impact the trajectories of ecosystem service bundles in an urban agglomeration: Intricate interaction trends and driver identification under SSP-RCP scenarios.

The delivery of ecosystem services (ESs), particularly in urban agglomerations, faces substantial threats from impending future climate change and human activity. Assessing ES bundles (ESBs) is critical to understanding the spatial allocation and interactions between multiple ESs. However, dynamic projections of ESBs under various future scenarios are still lacking, and their underlying driving mechanisms have received insufficient attention. This study examined the Beijing-Tianjin-Hebei urban agglomeration and proposed a framework that integrates patch-generating land use simulation into three shared socioeconomic pathway (SSP) scenarios and clustering analysis to assess spatiotemporal variations in seven ESs and ESBs from 1990 to 2050. The spatial trajectories of ESBs were analyzed to identify fluctuating regions susceptible to SSP scenarios. The results indicated that (1) different scenarios exhibited different loss rates of regulating and supporting services, where the mitigation of degradation was most significant under SSP126. The comprehensive ES value was highest under SSP245. (2) Bundles 1 and 2 (dominated by regulating and supporting services) had the largest total proportion under SSP126 (51.92 %). The largest total proportion of Bundles 4 and 5 occurred under SSP585 (48.96 %), with the highest provisioning services. The SSP126 scenario was projected to have the least ESB fluctuation at the grid scale, while the most occurred under SSP585. (3) Notably, synergies between regulating/supporting services were weaker under SSP126 than under either SSP245 or SSP585, while trade-offs between water yield and non-provisioning services were strongest. (4) Forestland and grassland proportions significantly affected carbon sequestration and habitat quality. Climatic factors (precipitation and temperature) acted as the dominant drivers of provisioning services, particularly water yield. Our findings advocate spatial strategies for future regional ES management to address upcoming risks.

Changes in sediment load in the Lower Yellow River and its driving factors from 1919 to 2021.

Rivers are not only an essential component of the development of civilization and the carbon cycle worldwide, but also a main contributor to natural disasters, especially the Lower Yellow River (LYR). With the functional degradation of the water-sediment regulation scheme (WSRS), LYR has reached a new stage. Thus, the changes in the sediment load in the Suspended River and its driving factors have significant practical applications. In this study, the sediment load in the LYR was analyzed from 1919 to 2021 based on improved sediment identity factor decomposition, wavelet analysis, and a double cumulative curve. The results showed that the changes in discharge and sediment exhibited poor synchronicity at different timescales. The sediment load decreased significantly, with evident periodicity of 9-10 years (years denoted as 'a') since 1950, and 69-a, 32-a, and 9-a since 1919. The changes in the sediment load can be divided into four phases: 1919-1959, 1960-1979, 1980-1998, and 1999-2021. Artificial levees can effectively constrain water flow and enhance sediment transport when the levee spacing is less than 6 km. To restrain deposition of the LYR, large dams control the incoming sediment coefficient so as to not exceed 0.009 kg∙s m-6. However, the WSRS reached its limit in 2010, and the wandering reach showed a deteriorating trend. Human activities control the changes in the sediment load. The reduction in the sediment load was mainly attributed to decreases in effective water yield capacity (53 %-75 %) before 1999 and sediment concentrations (46 %-65 %) after 1999. These results can provide a reference for further management of the suspended river.

Spatial-temporal evolution characteristics of PM2.5 and its driving mechanism: spatially explicit insights from Shanxi Province, China.

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies.