Determinants of carbon sequestration in thinned forests.

Given global climate change and the projected increases in the greenhouse effect, enhancing the carbon storage capacity of forest ecosystems is especially critical. To fully realize the potential carbon sequestration, it is imperative to understand the drivers affecting carbon storage in forest ecosystems, particularly with disturbances that disrupt existing balance. In this study, we explored the effects of stem-only harvest at various thinning intensities on forest structure and carbon density in middle-aged natural secondary forests, located in the northern temperate zone. Carbon density included aboveground carbon density (ACD), soil organic carbon stocks (SOCD), and total carbon density (TCD), which was the sum of ACD and SOCD. We employed the random forest analysis method to identify significant variables influencing changes in carbon density. Structural equation modelling (SEM) was then used to determine the drivers of changes in forest carbon density. The results showed that moderate thinning (20 %-35 % trees removed), is an effective management practice for increasing the TCD in forests. Although heavy thinning (35.1 %-59.9 % trees removed) accelerated individual growth, it did not fully offset the carbon removed due to thinning. It is noteworthy that light thinning (0-19.9 % trees removed) not only reduced the species richness but also caused a significant number of tree deaths. Large live trees were an important direct determining factor of ACD, but not the only one. In addition, thinning indirectly influenced ACD by reducing canopy density and deformed tree density. The increase in dead tree density had an adverse impact on SOCD, and this phenomenon increased with the passage of recovery time. Conversely, greater thinning intensity enhanced SOCD. Moreover, TCD was directly influenced by tree height, large live trees, and stand density. Furthermore, thinning altered the conifer ratio, thereby influencing tree growth and indirectly controlling the TCD. We believe that this knowledge will be highly beneficial for successful forest management and enhancing the carbon sequestration capacity of forest ecosystems.

Spatiotemporal evolution of runoff and sediment and their dominant driving factors in the Lower Jinsha River basin.

The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

Understanding air pollution reduction from the perspective of synergy with carbon mitigation in China from 2008 to 2017.

In response to the dual challenges of air pollution control and carbon mitigation, China has strategically shifted its focus towards the synergistic reduction of air pollutants and CO2 emissions. This study identifies the potential areas and specific air pollutant species (including CO, NOx, and SO2) for co-reduction with carbon mitigation. We also reveal the driving forces behind the emissions of each air pollutant at both the national and regional scales. Our findings are as follows: (1) The potential for synergistic reduction of CO and SO2 with CO2 emissions has diminished in economically developed areas. There is a significant opportunity for co-reduction of SO2 and CO2 in the western and northern regions of China, particularly within Heilongjiang Province. (2) NOx is the key species for synergistic reduction with CO2 emissions across China, especially in the Chengyu Plain. (3) Cleaner production and the synergistic reduction effect are the primary contributors to national air pollutant reduction in China from 2008 to 2017. Conversely, efforts in economic development and energy efficiency have led to emission increases. Energy and industrial structures have only made limited contributions to emission reductions, and carbon mitigation shows an inhibition effect on emission reductions. These results offer valuable insights for developing targeted regional strategies for deeper air pollution control, considering the specific characteristics and needs of each region. Additionally, our findings highlight the importance of addressing policy misalignments and strengthening mutual-influence mechanisms between air pollution control and carbon mitigation, ensuring that policies for carbon reduction also effectively contribute to air quality improvements.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Heterogenetic mechanism in multidimensional pit mud with different fermentation years: From microbial structure dynamic succession to metabolism phenotypes.

Pit mud (PM) is fermenting agents in the strong-flavor baijiu (SFB) production. In this paper, the discrepancies in fermentation parameters, microbial community succession patterns and metabolic phenotypes were compared in multidimensional PMs. The results showed that pyruvic acid, succinic acid, S-Acetyldihydrolipoamide-E, glycerol and glyceric acid were the key metabolites responsible for the metabolic differences between the 2-, 30-,100- and 300-year multidimensional PMs, while the butanoic acid, heptyl, heptanoic acid, heptanoic acid ethyl ester, hexanoic acid and octanoic acid were the key differential flavor compounds in the 2-, 30-,100- and 300-year multidimensional PMs. Concurrently, the diversity and abundance of microbial community also exhibited significant differences between the new and old multidimensional PMs, the assembly pattern of bacterial communities changed from deterministic to stochasticity from lower (bottom of the pit and under the huangshui fluid) to upper PM (up the huangshui fluid and top of the pit). Key microorganisms related to the succession process of the lower PM were Clostridium, Methanobacterium, Petrimonas, Lactobacillus, Methanobrevibacter, Bellilinea, Longilinea, Bacillus. In contrast, the upper PM were Caproicibacter, Longilinea, Lactobacillus, Proteinphilum, Methanobrevibacter, Methanobacterium, Methanobacteriaceae, Petrimonas, Bellilinea and Atopobium. Redundancy analysis (RDA) indicated that the key environmental factors regulating the succession of microbial in upper PM were lactic acid, moisture, pH and available phosphorus. In contrast, the lower was lactic acid, acetic acid and ammonia N. Based on these results, heterogeneous mechanisms between new and old multidimensional PMs were explored, providing a theoretical support for improving the quality of new PM.

Antibiotic resistome dynamics in agricultural river systems: Elucidating transmission mechanisms and associated risk to water security.

Usage of antibiotics in agriculture has increased dramatically recently, significantly raising the influx of antibiotic resistance genes (ARGs) into river systems through organic manure runoff, seriously threatening water security. However, the dynamics, transmission mechanisms, and potential water security risk of ARGs, as well as their response to land use spatial scale and seasonal variations in agricultural river systems remain unclear. To address these challenges, this work employed metagenomic technique to systematically evaluate the pollution and dissemination of ARGs in overlying water and sediment within a typical agricultural catchment in China. The results demonstrated significant differences between overlying water and sediment ARGs. Overlying water dominated by multidrug ARGs exhibited higher diversity, whereas sediment predominantly containing sulfonamide ARGs had higher abundance. The dynamics of ARGs in overlying water were more responsive to seasonal variations compared to sediment due to greater changes in hydrodynamics and nutrient conditions. The profiles of ARGs in overlying water were largely regulated by microbiota, whereas mobile genetic elements (MGEs) were the main forces driving the dissemination of ARGs in sediment. The variation in dissemination mechanisms led to different resistance risks, with sediment presenting a higher resistance risk than overlying water. Furthermore, Mantel test was applied to discover the impact of land use spatial scale and composition on the transmission of ARGs in river systems. The findings showed that cultivated land within 5 km of the riverbank was the key influencing factor. Cultivated land exacerbated ARGs spread by increasing MGEs abundance and nutrient concentrations, resulting in the abundance of ARGs in high-cultivated sites being twice that in low-cultivated sites, and raising the regional water security risk, with a more pronounced effect in sediment. These findings contribute to a better understanding of ARGs dissemination in agricultural watersheds, providing a basis for implementing effective resistance control measures and ensuring water security.

Analysis of dynamic evolution characteristics and driving factors of regional synergistic effect of reducing carbon and saving water: the case of Yangtze River Delta Urban Agglomerations, China.

Carbon emissions and water consumption are both important factors affecting sustainable development. Therefore, it is necessary to put them in the same research framework and investigate the synergy. In this study, the dynamic evolution characteristics of the synergistic effect of reducing carbon and saving water (RCSW) were analyzed. Then, taking the Yangtze River Delta Urban Agglomerations (YRDUA) as the research object, the influencing factors and specific paths of the synergistic effect were clarified. The results showed that the low-carbon emission efficiency (LCEE) had a stable synergy with the intensive utilization efficiency of water resources (IUEWR) in the YRDUA. Government financial expenditure, actual use of foreign capital, and population density were the most significant driving forces for the synergistic effect of RCSW, with q values of 0.561, 0.363, and 0.240, respectively. In addition, most of the interactions of the driving factors were nonlinear enhancement and double-factor enhancement.

Drivers and collaborative governance of public health emergency response in the context of digital city.

Introduction: With the frequent occurrence of public health events, the government inevitably makes many mistakes in emergency management. In modern emergency management, it is particularly important to promote the diversification of emergency management subjects and improve the government's emergency management ability. Methods: In order to make up for the deficiency of government's participation in public health emergency management, this paper analyzes the driving factors and driving effects of enterprises' participation in public health emergency response under the background of digital city. A fully explained structural model is used to analyze the relationship between the different drivers. In addition, the spatial and temporal distribution characteristics of public health events were analyzed through spatial auto-correlation. On this basis, the government cooperative governance strategy is discussed. Results and discussion: The results show that in the context of digital cities, there are 14 driving factors for enterprises to participate in public health emergency response. The most important factors are the company's own development needs, relative technical advantages and so on. The driving efficiency is mainly concentrated in three aspects: psychology, resources and structure. Public health events have periodicity in time distribution and regional differences in spatial distribution. The significance of this study is to help the government improve the emergency management ability from different angles.

Impact of elevated CO2 on soil microbiota: A meta-analytical review of carbon and nitrogen metabolism.

In the face of 21st-century challenges driven by population growth and resource depletion, understanding the intricacies of climate change is crucial for environmental sustainability. This review systematically explores the interaction between rising atmospheric CO2 concentrations and soil microbial populations, with possible feedback effects on climate change and terrestrial carbon (C) cycling through a meta-analytical approach. Furthermore, it investigates the enzymatic activities related to carbon acquisition, gene expression patterns governing carbon and nitrogen metabolism, and metagenomic and meta-transcriptomic dynamics in response to elevated CO2 levels. The study reveals that elevated CO2 levels substantially influence soil microbial communities, increasing microbial biomass C and respiration rate by 15 % and upregulating genes involved in carbon and nitrogen metabolism by 12 %. Despite a 14 % increase in C-acquiring enzyme activity, there is a 5 % decrease in N-acquiring enzyme activity, indicating complex microbial responses to CO2 changes. Additionally, fungal marker ratios increase by 14 % compared to bacterial markers, indicating potential ecosystem changes. However, the current inadequacy of data on metagenomic and meta-transcriptomic processes underscores the need for further research. Understanding soil microbial feedback mechanisms is crucial for elucidating the role of rising CO2 levels in carbon sequestration and climate regulation. Consequently, future research should prioritize a comprehensive elucidation of soil microbial carbon cycling, greenhouse gas emission dynamics, and their underlying drivers.

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.

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.

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.

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.

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.

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.

Improvements in the InVEST water purification model for detecting spatio-temporal changesintotal nitrogen and total phosphorus discharges in the Huai river watershed, China.

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980-2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980-2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP.

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.

[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.

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.