Study of spatiotemporal variation and annual emission of CH4 in Shaoxing Yangtze River Delta, China, Using a portable CH4 detector on the UAV.

Methane (CH4) is the second greenhouse gas and has a profound impact on global climate change due to its high global warming potential and concentration. By 2022, the CH4 concentration was approximately 1.9 ppm, which was 264% of the pre-industrial level. The spatiotemporal distribution of CH4 was investigated by a portable CH4 detector on an unmanned aerial vehicle and electric bicycles in Shaoxing, a city situated in the Yangtze River Delta, China. The vertical distribution revealed CH4 concentration generally decreased slowly with height. However, the inversion condition and low atmospheric boundary layer height (ABLH) leaded to the enhancement of CH4 with height. The highest CH4 concentration (2.2 ± 0.1 ppm, n = 1428) was observed in winter and the lowest (2.0 ± 0.2 ppm, n = 1530) in spring. Regarding the daily variation, CH4 concentration peaked at 5:00 local time (LT) and reached its lowest level at 14:00 LT, which was attributed to the daily variation of ABLH, lowest in the early morning and highest in the noon. In urban areas, CH4 concentrations showed higher levels near restaurants, natural gas stations and sewerage well, with a maximum value of 13.1 ppm, which was caused by CH4 emission and natural gas leakage from these places. The annual CH4 emission in Shaoxing were estimated to be approximately 69 ton/(km2·year) by the mass balance approach. Compared with other cities in the world, the CH4 emission is in higher level which imply some control measures should be conducted to reduce CH4 emission in Shaoxing.

An assessment of atmospheric concentrations and spatiotemporal variation of BTEX and associated pollutants in India.

Benzene, toluene, ethylbenzene, and xylene (BTEX) pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects, carcinogenic properties, and adverse effects on air quality. BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities, necessitating the assessment of atmospheric concentrations and their spatial variation. This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India. The study investigates the correlation of BTEX with other criteria pollutants and meteorological parameters, aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions. Additionally, the paper categorizes various regions in India according to the Air Quality Index (AQI) based on BTEX pollution levels. The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central, eastern, and western regions. In contrast, the southern zone experiences the least pollution with BTEX. Seasonal analysis indicates that winter and post-monsoon periods, characterized by lower temperatures, are associated with higher BTEX levels due to the accumulation of localized emissions. When comparing the different zones in India, high traffic emissions and localized activities, such as solvent use and solvent evaporation, are found to be the primary sources of BTEX. The findings of the current study aid in source characterization and identification, and better understanding of the region's air quality problems, which helps in the development of focused BTEX pollution reduction and control strategies.

Earth observation reveals the shifting patterns of China's lake colour driven by climate change and land cover.

Water colour has been recognized as one of the most important Essential Climate Variables of the lake ecosystem, as it is directly related to changes in water constituents and almost all of the lake's ecological changes could alter water colour. Given the high retrieval accuracy from existing Earth observation satellite data, water colour, in terms of Forel Ule Index (FUI), can be a realistic indicator to track the long-term changes in the lake ecosystem and further explore the lake response to environmental changes. This paper aims to comprehensively investigate the spatiotemporal variation patterns of FUI in 159 large lakes (≥25 km2) across China during 2000-2022 based on the MODIS data and detect the climatic and anthropogenic driving forces of these changes. The 23 years of MODIS records revealed an overall downward trend of lake FUI across China, indicating the lakes in China shifted to bluer colour during the past two decades. Through driving factor analyses, the complicated interplay among lake colour, lake morphology, regional climate shifts and human interference dynamics was uncovered. In the long term, it was found the pronounced change in lake colour in the western lake zones was primarily attributed to climate warming and humidification, whereas that in the eastern lake zones was mainly related to the alterations in regional land cover during the past two decades. Seasonally, lake basin's air temperature was identified as the main factor impacting the seasonal patterns of lake colour, followed by wind speed and runoff. Spatially, there was high spatial variability in lake colour across China, which was mainly associated with lake elevation and lake basin's precipitation rate, although the factors exhibited considerable divergence across different zones. Based upon the above findings, the implications for lake environment protection and management in different regions of China were further discussed.

Dynamics of Spatiotemporal Variation of Groundwater Arsenic in Central Rift Vally of Ethiopia: A Serial Cross-Sectional Study.

Background: Arsenic is a well-known, highly poisonous metalloid that affects human health and ecosystems and is widely distributed in the environment. Nevertheless, data on the spatiotemporal distribution of arsenic in groundwater sources in Ethiopia are scarce. Objective: The principal aim of this study was to assess the extent of arsenic in groundwater sources and analyze the spatiotemporal variations in the central rift valley of Ethiopia. Methods: The study employed a serial cross-sectional study design and census sampling methods. The concentrations of arsenic in the groundwater samples were determined using inductively coupled plasma mass spectrometry (ICP-MS) at the Ethiopian Food and Drug Authority laboratory. Descriptive statistical analyses were performed using IBM SPSS version 29 software. Additionally, ArcGIS software was utilized to map the spatiotemporal distribution of arsenic. Furthermore, Minitab statistical software version 21.4 was employed to assess the correlation between spatiotemporal variations of arsenic concentrations in groundwater sources. Results: The mean values of arsenic in the groundwater samples were 11.2 µg/L during the dry season and 10.7 µg/L during the rainy season. The study results showed that 18 wells (42.2%) and 22 wells (48.8%) had higher arsenic concentrations (>10 µg/L) during the dry and rainy seasons, respectively. Thus, arsenic levels in 42.2% and 48.8% of the samples exceeded the maximum threshold limit set by WHO, USEPA, and Ethiopian standards (10 µg/L), respectively, during the dry and rainy seasons. Furthermore, our analysis revealed a significant positive correlation between arsenic in groundwater and well depth (r = .75, P < .001), indicating a strong association between higher arsenic concentrations and deeper wells. Similarly, we observed a substantial positive correlation between arsenic concentration in groundwater and season (r = .9, P < .001), suggesting notable variations in arsenic levels between dry and rainy seasons. Conclusions: The majority of the groundwater sources in the studied area are unfit for human consumption because they contain high amounts of arsenic, which poses a significant risk to human health. Moreover, the arsenic concentration varied spatially and temporally. Therefore, special attention is needed to reduce arsenic exposure and associated health risks.

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

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

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Spatial variations of annual net ecosystem productivity and its trend over Chinese terrestrial ecosystems based on spatial downscaling.

Annual net ecosystem productivity (NEP), the amount of net carbon sequestration during a year, serves as the basis of terrestrial carbon sink. Quantifying the spatial variations of NEP and its trend would enhance our understandings on the response and adaption of ecosystems to environmental change, which also serves for the regional carbon management targeting at carbon neutrality. Based on process-based model and data-driven model simulating NEP, we selected the optimal simulating NEP mostly representing NEP spatial variations with multiple site eddy covariance measurements to develop the spatial downscaling method and generate high resolution NEP data of China, which was used to examine the spatial variations of NEP and its trend and driving factors during 2000-2017. Compared with process-based model results, data-driven model simulating NEP could mostly represent the spatial variation of site measurements. The random forest regression based on climate, soil, and biological data combining with the simple scaling could successfully downscale NEP to a high spatial resolution. From 2000 to 2017, the total amount of NEP in China was (1.30±0.03) Pg C·a-1, showing a decreasing-increasing pattern with the inflection point in 2009. Chinese NEP decreased from southeast to northwest, showing a descending latitudinal distribution and an ascending longitudinal distribution, with the combined effects of climate and biotic factors. NEP trend decreased from east towards west, which was only accompanied with a slightly ascending longitudinal distribution, while photosynthetically active radiation and soil organic carbon content dominated the spatial variations of NEP trend. Therefore, the spatial patterns of generated NEP obviously differed from those of NEP trend, suggesting the obvious difference between the responses and adaptions of ecosystems to environmental changes.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.

BACKGROUND: From January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period. OBJECTIVES: This study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises. METHODS: Data of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics. RESULTS: The incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively. CONCLUSION: The findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It's significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

Potential impact of climate change on water productivity and water footprints of rice and wheat in the Indo-Gangetic plains of India.

Agriculture faces a significant problem as a result of the decline in per capita freshwater supply that has been observed over the past couple of decades. Better water management strategies are required in order to make agriculture water secure, environmentally sustainable, and economically attractive. The goal of this study was to develop water productivity scenarios for rice and wheat crops in India's Indo-Gangetic Plain (IGP) by analysing agricultural production from the perspective of water consumption. To pinpoint the areas that are hydrologically suitable for growing rice and wheat crops under RCP4.5 and RCP8.5 climate change scenarios, water footprints and water productivity have been mapped. The study found that changes in temperature trends under future climate scenarios are probably going to increase crop water requirements, leading to greater water footprints for IGP regions. Water risk hotspots have been identified in the IGP for the rice- and wheat-growing states of Uttar Pradesh, Punjab, and Haryana. In order to achieve food security in a sustainable way, attention should be given to increase the water productivity of both crops.

Linking divergence in phenotypic selection on floral traits to divergence in local pollinator assemblages in a pollination-generalized plant.

Divergent patterns of phenotypic selection on floral traits can arise in response to interactions with functionally distinct pollinators. However, there are a limited number of studies that relate patterns of phenotypic selection on floral traits to variation in local pollinator assemblages in pollination-generalized plant species. We studied phenotypic selection on floral traits of Viscaria vulgaris, a plant that interacts with a broad range of diurnal and nocturnal pollinators, and related divergence in phenotypic selection on floral traits to the expected level of divergence in local pollinator assemblages. We detected phenotypic selection on floral traits involved in the attraction of pollinators and the mechanics of pollen removal and deposition, and demonstrated that floral traits are subject to spatiotemporal variation in the strength and direction of phenotypic selection. We revealed that diurnal and nocturnal pollinators, when considered in isolation, mediated divergent patterns of phenotypic selection on floral traits. Consistent with the Grant-Stebbins model, we observed that divergence in phenotypic selection on floral traits increased with the expected level of divergence in local pollinator assemblages. Thus, generalized plant-pollinator interactions can mediate phenotypic selection on floral traits, and distinct local pollinator assemblages can generate a geographic mosaic of divergent patterns of phenotypic selection. We underscore that these outcomes are not exclusive to specialized plant-pollinator interactions and can emerge at a local geographic scale.

Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland.

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0-40.6% and 20.8-42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of "hot" spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin.

[Spatiotemporal Variation Characteristics of Ozone and Identification of Key Influencing Factors Based on Random Forest Model： A Case Study of Chuzhou City].

The cause of ozone pollution is a complex scientific problem. Studying the spatiotemporal variation characteristics of O3 at different time scales and analyzing the key influencing factors of O3 concentration is of great significance for the precise formulation of urban air pollution control measures and the improvement of urban air quality. Based on the analysis of the spatiotemporal variation characteristics of O3 concentration in Chuzhou City, we studied the 12 ozone-influencing factors of meteorology and pollutants at multiple time scales using Spearman correlation analysis and a random forest model. The results showed that： ① The O3 pollution level of Chuzhou City showed an aggravating trend, and the O3 concentration distribution showed a spatial pattern of "high in the southeast and low in the northwest." ② From February to May, SO2 concentration had a strong impact on the increase in O3 concentration. From June to September, PM2.5 and PM10 were significantly positively correlated with ozone and had a greater impact. ③ Relative humidity, temperature, and wind speed had a significant impact on O3, whereas barometric pressure and hourly rainfall had a weak impact. ④ The O3 pollution mechanism in Chuzhou City changed from "pollutant-controlled" to "meteorology-controlled." ⑤ Among meteorological and pollutant factors, the three influencing factors that had the greatest influence on O3 concentration were temperature, wind speed, and relative humidity, with PM10 concentration, PM2.5 concentration, and SO2 concentration also contributing. All of the above six influencing factors had a significant nonlinear relationship with the O3 concentration.

Assessment of the Restoration Potential of Forest Vegetation Coverage in the Alxa Desert Region of China.

To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship model. This model links evapotranspiration (ET) with meteorological variables and the Enhanced Vegetation Index (EVI). Furthermore, the study estimated the recovery thresholds and potential of forest and grassland vegetation coverage in the Alxa Desert region under various precipitation scenarios. The findings reveal that ET exhibited an increasing trend in 84.17% of the Alxa Desert region, with a significant increase observed in 61.53% of the area, indicating positive outcomes from the implementation of the Three-North Shelterbelt Forest Program. Notably, however, ET in the southeastern plain region demonstrated a decreasing trend, which is strongly associated with human activities. The response relationship model demonstrated that linear relationship areas constituted 47.52%, while nonlinear relationship areas accounted for 45.51% of the total. The overall model exhibited an R2 value of 0.69, indicating a high level of predictive accuracy. Analysis of forest and grassland coverage revealed that, under wet year scenarios, the vegetation coverage showed a significant trend of recovery, with an average recovery threshold of (75.4 ± 12.5)% and an average recovery potential of (8.5 ± 3.6)%. It is noteworthy that the vegetation coverage in 31.25% of the area had already surpassed the recovery threshold. The outcomes of this study provide a theoretical foundation for the formulation of more scientifically rigorous ecological restoration strategies in the future.

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.

Both nutrients and macrophytes regulate organic carbon burial: Insights from high-resolution spatiotemporal records of a large shallow lake (Baiyangdian) in eastern China.

Both ecological regime shifts and carbon cycling in lakes have been the subject of global debates in recent years. However, the direct linkage between them is poorly understood. Lake Baiyangdian, a representative large shallow lake with the coexistence of a macrophyte-dominated area (MDA) and an algae-dominated area (ADA) in eastern China, allowing better understanding of the relationship between regime shifts and organic carbon (OC) burial in lakes. On the basis of Bayesian isotopic mixing modelling of C/N ratios and δ13C values, the sediment OC is primarily of autochthonous origin. The mean OC burial rate (OCBR) was 39 g C m-2 yr-1 before eutrophication occurred in 1990 and increased approximately 2.7-fold to 106 g C m-2 yr-1 after eutrophication. Partial least squares path modelling revealed that this change can be largely attributed to enhanced primary productivity and rapid burial as a result of intensified human perturbation. In terms of spatial patterns, the OCBR was greater in the MDA than in the ADA, which may be related to the different burial and mineralization processes of debris from macrophytes and algae. It then deduced that a decrease in the OCBR and an increase in the mineralization rate might have occurred after a shift from a macrophyte-dominated state to an algae-dominated state. Our findings highlight that eutrophication generally increases OC burial by enhancing lake primary productivity. However, once nutrient levels reach a critical range, lake ecosystems may shift from a macrophyte-dominated state to an algae-dominated state, which can lead to a significant reduction in the carbon burial capacity of lakes. Therefore, more attention should be given to avoiding shifts in eutrophic lakes, as such shifts can alter carbon cycling.

[Antibiotic Pollution and Its Effects on the Spatiotemporal Variation in Microbial Community Structure and Functional Genes in Sediment of Baiyangdian Lake].

Microbial communities play an important role in water quality regulation and biogeochemical cycles in lakes, and their community structure and function are affected by environmental factors. Therein, antibiotics affect the abundance, diversity, composition, and function of microbial communities. In this study, Baiyangdian Lake was selected as the study area. Sediment samples of 16 sites were collected in August 2018 and April 2019, respectively. Ultra-high performance liquid chromatography-mass spectrometry （HPLC-MS/MS） was used to determine the content of typical antibiotics-quinolones （QNs） in sediment. Through high-throughput sequencing technology, the structure and function of microbial communities was analyzed in the sediments to explore the spatiotemporal variation. Thereinto, redundancy analysis （RDA） was used to identify the key influencing factors of spatiotemporal variation of microbial communities. The results showed that： ① From August to April, the average ω（QNs） showed an increasing trend, and its mean value changed from 3.91 ng·g-1 to 6.34 ng·g-1, with significant seasonal differences in oxolinic acid （OXO） and total QNs content （P < 0.05）. ② In terms of temporal variation, the dominant bacteria were Proteobacteria and Chloroflexi. The relative abundance of Proteobacteria showed a decreasing trend, whereas Chloroflexi showed an increasing trend； at the genus level, the dominant bacteria genera in August were norank_ f__Anaerolineaceae and Thiobacilus, and the dominant bacteria genera in April were Acinetobacter and norank_ f_Anaerolineaceae, and the dominant bacteria genera had significant seasonal differences （P < 0.05）. ③ In terms of temporal variation, the index of Simpson, Chao, Ace, and OTU number all showed an increasing trend, and the seasonal differences were significant （P < 0.05）. ④ In terms of spatial variation, there were no significant spatial differences among functional genes of COG. In terms of temporal variation, there were significant seasonal differences in functional genes of energy production and conversion, carbohydrate transport and metabolism, transcription, cell wall/membrane/envelope biogenesis, and signal transduction mechanisms （P < 0.01 and P < 0.05）. ⑤ Microbial community structure and functional genes were significantly correlated with QNs （P < 0.01 and P < 0.05）, and QNs were the main influencing factors. Therefore, QNs were the main factor affecting the changes in microbial community structure and functional genes in sediments of Baiyangdian Lake. Thus, comprehensive control of antibiotic pollution in sediments should be further strengthened.

Spatiotemporal variation of microplastics along the long-distance raw water pipeline.

Raw water pipelines are considered a significant pathway for human exposure to microplastics (MPs, <5 mm) in surface water. However, there is currently very limited information on the longitudinal distribution characteristics of microplastics in raw water pipelines. This study assessed the abundance and distribution characteristics of microplastics in surface water from two different water sources in Jiangsu Province during different seasons. The correlation between conventional water quality indicators and microplastics was also explored. Specifically, the longitudinal variation of microplastics in raw water pipelines was investigated. Results showed that microplastics were detected in both basins during different seasons. In Basin 1, the abundance of MPs ranged from 34 ± 1 to 58 ± 2 n/L in March and from 3 ± 1 to 6.7 ± 4 n/L in June. In Basin 2, the abundance ranged from 6.5 ± 1 to 14 ± 1 n/L in March and from 2 ± 1 to 7.7 ± 1 n/L in June. The abundance of microplastics showed a decreasing trend along the pipeline. Polymethyl methacrylate (PMMA) was the main polymer type detected in both basins. Polyethylene terephthalate (PE) and polyurethane (PU) showed higher removal rates in the pipeline due to their higher density. The predominant size ranges of microplastics in the raw water were 10-50 µm and 50-100 µm. Additionally, the average particle size of MPs increased with the transportation distance, likely due to microbial colonization. This study is the first comprehensive investigation of the distribution characteristics of microplastics in raw water pipeline systems. The removal of microplastics in raw water pipelines contributes significantly to the elimination of microplastics at the source. This research helps to fill the knowledge gap regarding the fate of microplastics in raw water pipeline systems.

Limited Impacts of Water Diversion on Micro-eukaryotic Community along the Eastern Route of China's South-to-North Water Diversion Project.

The Eastern Route of the South-to-North Water Diversion Project (ER-SNWDP) represents a crucial initiative aimed at alleviating water scarcity in China's northern region. Understanding the dynamics governing the composition and assembly processes of micro-eukaryotic communities within the canal during different water diversion periods holds paramount significance for the effective management of the ER-SNWDP. Our study systematically tracks the dynamics of the micro-eukaryotic community and its assembly processes along the 1045.4 km of canals and four impounded lakes, totaling 3455 km2, constituting the ER-SNWDP during a complete water diversion cycle, utilizing high-throughput sequencing, bioinformatics tools, and null modeling algorithms. The primary objectives of this study are to elucidate the spatial-temporal succession of micro-eukaryotic communities as the water diversion progresses, to delineate the relative importance of deterministic and stochastic processes in community assembly, and to identify the pivotal factors driving changes in micro-eukaryotic communities. Our findings indicate notable variations in the composition and diversity of micro-eukaryotic communities within the ER-SNWDP across different water diversion periods and geographic locations (P < 0.05). This variation is influenced by a confluence of temporal and environmental factors, with limited impacts from water diversion. In essence, the assembly of micro-eukaryotic communities within the ER-SNWDP primarily stemmed from heterogeneous selection driven by deterministic processes. Water diversion exhibited a tendency to decrease community beta diversity while augmenting the influence of stochastic processes in community assembly, albeit this effect attenuated over time. Furthermore, our analysis identified several pivotal environmental parameters, notably including nitrite-nitrogen, nitrate-nitrogen, orthophosphate, and water temperature, as exerting significant effects on micro-eukaryotic communities across different water diversion periods. Collectively, our study furnishes the inaugural comprehensive exploration of the dynamics, assembly processes, and influencing factors governing micro-eukaryotic communities within the ER-SNWDP, thus furnishing indispensable insights to inform the water quality management of this important project.

[Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016].

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.

Spatiotemporal variations of fossil fuel CO2 emissions in China: A sectoral allocation approach based on multi-source data.

Fossil fuel (FF) CO2 emissions account for the largest portion of human-related CO2 emissions. It is essential to accurately understand the spatial distribution of high-resolution FFCO2 emissions to formulate different carbon emission reduction policies in different regions. Therefore, a sectoral allocation approach was proposed to estimate FFCO2 emissions in China from 2000 to 2021 based on multi-source data. Furthermore, the spatiotemporal characteristics of FFCO2 emissions in different sectors were analyzed at different scales, and the spatial correlation of FFCO2 emissions in the service sector and industrial sector was also evaluated through Moran's index. The results showed that the mean R2 value of the sectoral allocation approach (0.89) exceeds that of the approach using only nighttime light (0.72). Moreover, the calculated results were utilized to analyze the Spatiotemporal variation of FFCO2 emissions. The analysis revealed that China's FFCO2 emissions increased from 3173 Mt in 2000-10662 Mt in 2021. The high emissions of FFCO2 mainly come from the industrial sectors in North China and Central China, as well as the service sectors in the eastern coastal cities and other provincial capital cities. The spatial dependence of FFCO2 emissions in the industrial sector was stronger than that in the service sector, but the spatial dependence of FFCO2 emissions in the service sector showed an increasing trend from 2000 to 2021. These results have important references and implications for region-specific carbon emission reduction policies.