The days we never forget: Flashbulb memories across the life span in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by autobiographical memory deficits, with the ability to retrieve episodic-rich memories being particularly affected. Here, we investigated the influence of AD on a specific subtype of episodic memories known as flashbulb memories (i.e., the ability to remember the personal circumstances for the reception of important news events). We examined the frequency, characteristics, and the temporal distribution of flashbulb memories across the life span. To this aim, 28 older adults diagnosed with AD and a matched sample of 29 healthy older controls were probed for flashbulb memories for two historical events from each decade of their lives. They also estimated the subjective degree of reexperiencing for the memories reported. AD participants showed impaired access to flashbulb memories, the frequency of reported memories being lower than for healthy older adults. However, qualitative aspects of AD participants' flashbulb memories were quite similar to those of the controls, as no group differences were obtained with respect to the canonical categories or degree of reexperience. AD participants' flashbulb memories clustered during the early years of their life, consistent with a reminiscence bump, whereas healthy controls also reported memories dated to later lifetime periods. Our results suggest that probing for personal memories of important public events may serve as a powerful cue for detailed episodic memories in AD.

Biogas upgrading and membrane anti-fouling mechanisms in electrochemical anaerobic membrane bioreactor (EC-AnMBR): Focusing on spatio-temporal distribution of metabolic functionality of microorganisms.

Electrochemical anaerobic membrane bioreactor (EC-AnMBR) by integrating a composite anodic membrane (CAM), represents an effective method for promoting methanogenic performance and mitigating membrane fouling. However, the development and formation of electroactive biofilm on CAM, and the spatio-temporal distribution of key functional microorganisms, especially the degradation mechanism of organic pollutants in metabolic pathways were not well documented. In this work, two AnMBR systems (EC-AnMBR and traditional AnMBR) were constructed and operated to identify the role of CAM in metabolic pathway on biogas upgrading and mitigation of membrane fouling. The methane yield of EC-AnMBR at HRT of 20 days was 217.1 ± 25.6 mL-CH4/g COD, about 32.1 % higher compared to the traditional AnMBR. The 16S rRNA analysis revealed that the EC-AnMBR significantly promoted the growth of hydrolysis bacteria (Lactobacillus and SJA-15) and methanogenic archaea (Methanosaeta and Methanobacterium). Metagenomic analysis revealed that the EC-AnMBR promotes the upregulation of functional genes involved in carbohydrate metabolism (gap and kor) and methane metabolism (mtr, mcr, and hdr), improving the degradation of soluble microbial products (SMPs)/extracellular polymeric substances (EPS) on the CAM and enhancing the methanogens activity on the cathode. Moreover, CAM biofilm exhibits heterogeneity in the degradation of organic pollutants along its vertical depth. The bacteria with high hydrolyzing ability accumulated in the upper part, driving the feedstock degradation for higher starch, sucrose and galactose metabolism. A three-dimensional mesh-like cake structure with larger pores was formed as a biofilter in the middle and lower part of CAM, where the electroactive Geobacter sulfurreducens had high capabilities to directly store and transfer electrons for the degradation of organic pollutants. This outcome will further contribute to the comprehension of the metabolic mechanisms of CAM module on membrane fouling control and organic solid waste treatment and disposal.

Distribution, seasonal variation and influencing factors of total dissolved inorganic arsenic in the middle and lower reaches of the Yellow River.

The concentrations of dissolved arsenate in natural water has an important impact on human health. The distributions, seasonal variation and major influencing factors of total dissolved inorganic arsenic (TDIAs) were studied in the Yellow River. The concentrations of TDIAs in the middle and lower reaches of the Yellow River ranged from 4.3 to 42.4 nmol/L, which met the standards for drinking water of WHO. The seasonal variation of TDIAs concentration in the middle and lower reaches of the Yellow River was highest in summer, followed by autumn and winter, and lowest in spring. The influencing factors of TDIAs concentration in the middle and lower reaches of the Yellow River mainly include the hydrological conditions, topographical variation, the adsorption and desorption of suspended particulate matter (SPM) and the intervention of human activities. The absorption of TDIAs by phytoplankton in the Xiaolangdi Reservoir (XLD) is an important factor affecting its distributions and seasonal variation. The annual flux of TDIAs transported from the Yellow River into the Bohai Sea ranged from 1.1 × 105 to 4.5 × 105 mol from 2016 to 2018, which is lower than the flux in 1985 and 2009. The carcinogenic risks (CR) of TDIAs for children and adults were all within acceptable levels (<10-6).

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces.

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage (FVC), Wetness (WET), Evapotranspiration (ET), Land Surface Temperature (LST) and Normalized Difference Bare Soil Index (NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of "high in the east and low in the west, multi-point development". (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73 % in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020].

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

[Spatial and temporal distribution characteristics of extreme wind and its effect on wind erosion in Northeast China]. / ä¸œåŒ—åœ°åŒºæžç«¯å¤§é£Žæ—¶ç©ºåˆ†å¸ƒç‰¹å¾åŠå ¶å¯¹é£Žèš€é‡çš„å½±å“.

Under the background of climate change, extreme wind events occur frequently in Northeast China, and the soil erosion caused by these extreme wind events has attracted progressively more attention from scholars. We used the methods of linear analysis, Sen+Mann-Kendal trend analysis, and Kriging interpolation to analyze the spatial and temporal characteristics of extreme wind in Northeast China from 2005 to 2020, and used the RWEQ wind erosion estimation model to calculate the annual soil wind erosion of typical wind erosion sites and wind erosion under extreme wind conditions. The results showed that the extreme wind frequency in Northeast China presented a significant upward trend from 2005 to 2020, with an increase of 2.9 times·a-1. The annual average extreme wind frequency in Northeast China ranged from 1 to 49 times·a-1, and the high frequency areas were distributed in the northwest of Xilin Gol, the west of the Hulunbuir Plateau, and the northeast of Changbai Mountain. The average contribution rate of extreme wind to soil wind erosion in four typical sites (Xilinhot, New Barhu Right Banner, Nenjiang, and Tongyu) was 31%.

[Spatial and Temporal Distribution and Risk Assessment of Heavy Metals in Surface Water of Changshou Lake Reservoir, Chongqing].

To understand the water pollution status and environmental risks of Changshou Lake, the concentrations of heavy metals (Cr, Cu, Zn, As, Cd, and Pb) in the water were collected and analyzed during different seasons. The study investigated temporal and spatial variations, distribution characteristics, pollution levels, and health risks associated with heavy metals in Changshou Lake. The results showed that all six heavy metals were below than the Class Ⅰ standard of the Surface Water Environmental Quality Standard (GB 3838-2002), but recent years have witnessed an increasing trend, with Cu, As, and Pb showing a significant increase (P<0.05). The temporal and spatial distributions of these heavy metals were different. Temporally, Cr and Cd concentrations in surface water were higher in summer, As and Zn were higher in spring, and Pb and Cu were higher in autumn and winter. Spatially, the concentrations of Cr, As, Cu, Zn, and Pb showed higher concentrations in the southern outlet of the reservoir, the northwestern Longxi River inlet, and the central part of the reservoir, whereas Cd was higher in the northern stagnant area. The overall levels of heavy metals in the water body of Changshou Lake were low, with Cr and Cu slightly polluted, while other heavy metals were identified as having an insignificant pollution level. Drinking water was the primary exposure pathway to carcinogenic and non-carcinogenic heavy metals in surface water bodies. The health risk values of Cr and As in water bodies were high, ranging from 6.2×10-10 to 3.0×10-4 and 5.1×10-8 to 3.9×10-5, respectively. The corresponding contribution rates for children and adults to the total health risk were high, with Cr accounting for 87.18% and 87.20%, respectively, while As accounted for 12.73% and 12.71%, respectively. Therefore, it is crucial to prioritize environmental risks associated with Cr and Cu, as well as the health risks associated with Cr and As in Changshou Lake These findings provide a scientific foundation for water pollution control and environmental quality improvement in Changshou Lake, and rational development and utilization of water resources.

[Distribution of Typical Resistant Bacteria and Resistance Genes in Source Water of the Middle and Lower Reaches of the Yellow River].

The Yellow River water of an urban area located in the middle and lower reaches of the Yellow River was taken as the research object， in which the seasonal and along-range distribution of total culturable bacteria， typical antibiotic resistant bacteria （amoxicillin resistant bacteria and sulfamethoxazole-resistant bacteria）， and their corresponding typical resistance genes ï¼»ß-lactam resistance gene （blaCTX-M） and sulfamamide resistance genes （sulI and sulⅡ）， as well as intⅠ1 were investigated. The results showed that the total culturable bacteria， ß-lactam-resistant bacteria and sulfonamide-resistant bacteria in the Yellow River Basin were significantly affected by temperature and human activities. The composition and quantity of their genera had obvious spatiotemporal distribution characteristics， in which Bacillus and Pseudomonas were dominant in the composition and number of bacteria. The abundance of resistance genes decreased with the decrease in temperature. The proportion of ß-lactam resistance genes in the total genes was higher than that of sulfanilamide genes， and sulI was the dominant gene in sulfanilamide genes. Correlation analysis showed that class Ⅰ integron played an important role in accelerating the spread of resistance genes. This study offers insight into the status quo of water resistance pollution in the Yellow River and provides theoretical support for the risk assessment of resistance genes in the middle and lower reaches of the Yellow River Basin.

[Spatial-temporal Distribution and Source Analysis of Polycyclic Aromatic Hydrocarbons in the Sediments of Poyang Lake].

The concentrations, spatial-temporal distribution, and influencing factors of 16 polycyclic aromatic hydrocarbons (PAHs) in the sediments of Poyang Lake were studied, and a quantitative source analysis of PAHs in different areas of the lake was conducted. PAHs were widespread within the sediments. The concentrations of ∑16PAHs in the surface sediments of all sites ranged from 203 to 2 318 µg·kg-1. The concentrations of PAHs in the surface sediments of the lake body were higher than those in the surface sediments of the inlet rivers. The ratio of PAHs in Poyang Lake was 4 rings > 5 rings > 6 rings > 3 rings > 2 rings; the composition of 4-ring PAHs was dominant, and its content accounted for 86.11% of ∑16PAHs. The 2- and 3-ring and some 4-ring PAHs, including Flua and Pyr, were more susceptible to SOM, and the 4 through 6-ring PAHs were more susceptible to ORP and heavy metals and other environmental factors. Spatially, the higher concentration of ∑16PAHs occurred in the area of the lake adjacent to Duchang County and Poyang County, where the terrain was relatively closed, and the water exchange with the surrounding area was less than that in other sections, which was not conducive to the migration, transformation, and degradation of pollutants. In the temporal distribution, the changes in PAHs concentration level and the development of GDP in Jiangxi Province showed high consistency, and the influence of economic development and human activities might have been the main reason for the increasing PAHs concentration level. The main sources of PAHs in surface sediments of Poyang Lake included petroleum pollution and oil and coal and biomass combustion sources, and there were some spatial differences in PAHs sources in different regions. This study can provide a reference for PAHs pollution in surface sediments of Poyang Lake, which is important for the ecological environmental protection and management of Poyang Lake.

Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things.

Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China.

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Monitoring Off-Shore Fishing in the Northern Indian Ocean Based on Satellite Automatic Identification System and Remote Sensing Data.

Satellite-derived Sea Surface Temperature (SST) and sea-surface Chlorophyll a concentration (Chl-a), along with Automatic Identification System (AIS) data of fishing vessels, were used in the examination of the correlation between fishing operations and oceanographic factors within the northern Indian Ocean from March 2020 to February 2023. Frequency analysis and the empirical cumulative distribution function (ECDF) were used to calculate the optimum ranges of two oceanographic factors for fishing operations. The results revealed a substantial influence of the northeast and southwest monsoons significantly impacting fishing operations in the northern Indian Ocean, with extensive and active operations during the period from October to March and a notable reduction from April to September. Spatially, fishing vessels were mainly concentrated between 20° N and 6° S, extending from west of 90° E to the eastern coast of Africa. Observable seasonal variations in the distribution of fishing vessels were observed in the central and southeastern Arabian Sea, along with its adjacent high sea of the Indian Ocean. Concerning the marine environment, it was observed that during the northeast monsoon, the suitable SST contributed to high CPUEs in fishing operation areas. Fishing vessels were widely distributed in the areas with both mid-range and low-range Chl-a concentrations, with a small part distributed in high-concentration areas. Moreover, the monthly numbers of fishing vessels showed seasonal fluctuations between March 2020 and February 2023, displaying a periodic pattern with an overall increasing trend. The total number of fishing vessels decreased due to the impact of the COVID-19 pandemic in 2020, but this was followed by a gradual recovery in the subsequent two years. For fishing operations in the northern Indian Ocean, the optimum ranges for SST and Chl-a concentration were 27.96 to 29.47 °C and 0.03 to 1.81 mg/m3, respectively. The preliminary findings of this study revealed the spatial-temporal distribution characteristics of fishing vessels in the northern Indian Ocean and the suitable ranges of SST and Chl-a concentration for fishing operations. These results can serve as theoretical references for the production and resource management of off-shore fishing operations in the northern Indian Ocean.

Predicting plant-pollinator interactions: concepts, methods, and challenges.

Plant-pollinator interactions are ecologically and economically important, and, as a result, their prediction is a crucial theoretical and applied goal for ecologists. Although various analytical methods are available, we still have a limited ability to predict plant-pollinator interactions. The predictive ability of different plant-pollinator interaction models depends on the specific definitions used to conceptualize and quantify species attributes (e.g., morphological traits), sampling effects (e.g., detection probabilities), and data resolution and availability. Progress in the study of plant-pollinator interactions requires conceptual and methodological advances concerning the mechanisms and species attributes governing interactions as well as improved modeling approaches to predict interactions. Current methods to predict plant-pollinator interactions present ample opportunities for improvement and spark new horizons for basic and applied research.

Potential and benefits of biochar production: crop straw management and carbon emission mitigation in Shaanxi Province, China.

Shaanxi Province is an important agricultural province in western China. Its profit-oriented management of crop residues remains a concern in the agriculture sector. Aiming to accelerate the valorization of agricultural straw and offer potential solutions for profit-oriented use of crop residues in Shaanxi, this study estimated the quantity of resources and collectable amount of crop straw by using the grain-to-straw ratio, analyzed the carbon emission reduction potential considering biochar energy and soil uses with the help of a life cycle assessment (LCA) model, and calculated the economic benefits of biochar production using waste and abandoned straw in Weinan (a city of Shaanxi). The theoretical resources and collectible amount of crop straw in Shaanxi showed an overall growth trend from 1949 to 2021, reaching 1.47 × 107 and 1.26 × 107 t in 2021 respectively. In 2021, straw from corn, wheat, and other grains accounted for 94.32% of the total straw. Among the 11 cities in Shaanxi, Weinan had the largest straw resources of 2.82 × 106 t, Yulin had the largest per capita straw resources of 0.72 t/person, and Yangling had the highest resource density of 7.60 t/hm2. The total carbon emission reduction was 3.11 × 104 t under scenario A with crop straw used for power generation. The emission reduction ranged from 1.25 × 107 to 1.27 × 107 CO2e t under scenario B with biochar production for energy and soil use. By using waste and abandoned straw in Weinan for biochar production, carbon emissions could be reduced by up to 2.07 × 105 t CO2e. In terms of the economic benefit from straw pyrolysis, the actual income was estimated to range from 0.67 × 108 to 1.33 × 108 ¥/a with different carbon prices. This study sheds light on the economic and environmental benefits of agricultural straw valorization through pyrolysis in Shaanxi, and provided an important basis for promoting the agricultural straw utilization in view of its potential for carbon emission reduction.

Bovine anaplasmosis in Zimbabwe: spatio-temporal distribution and environmental drivers.

Understanding the spatial and temporal distribution of Bovine anaplasmosis is crucial for identifying areas of high prevalence for targeted disease control. This research was aimed at modelling and mapping the B. anaplasmosis potential distribution, and identify hotspots as well as significant variables explaining the occurrence of the disease. The Getis Ord Gi* statistic for Hotspot analysis was used as well as MaxEnt ecological niche modelling. The effects of time, land-use, and agro-ecological regions on B. anaplasmosis occurrence were tested using Analysis of Variance (ANOVA). Results showed that several districts in Zimbabwe are suitable for the occurence of the disease for example Binga, Seke, Buhera, Kwekwe, Gweru, Mhondoro, Chegutu, Sanyati, and in the North: Mbire, Muzarabani, Mt Darwin, Shamva, Bindura, Zvimba and Makonde. Morbidity and mortality hotspots were detected in Gokwe-south, Kwekwe, and Chirumhanzu districts. Binga, Gokwe-south, Gutu, Hurungwe, Mazoe, Nkayi, Shamva, and Kwekwe districts also experienced high disease incidences. Temperature seasonality, precipitation seasonality, mean diurnal range, and isothermality were the most important variables in explaining 93% of B. anaplasmosis distribution. Unlike land-use and agro-ecological regions, time (months) had a significant effect on B. anaplasmosis occurrence with July and September having significantly (p < 0.05) higher cases and deaths than the rest of the months. The results of this study provide insights into the management strategies and control of B. anaplasmosis in Zimbabwe. It is thus concluded that geo-spatial techniques, combined with ecological niche modelling can provide useful insights into disease prevalence and distribution and hence can contribute to effective management and control of B. anaplasmosis in Zimbabwe.

Spatial-temporal distribution and source analysis of atmospheric particulate-bound cadmium from 1998 to 2021 in China.

Cadmium (Cd) is one of the most serious atmospheric heavy metal pollutants in China. PM2.5, PM10, and total suspended particle (TSP) are all important media for population Cd exposure. However, no studies so far have systematically explored the spatial and temporal distribution of atmospheric Cd bound to all these media in China, and the specific industrial sectors that contribute to the airborne Cd level are still unclear at present. In this study, we constructed the spatial and temporal distribution of PM (PM2.5, PM10, and TSP) binding Cd concentrations in China. Quantitative source apportionment of atmospheric Cd was carried out by analyzing the association of 23 industrial or energy-consuming sectors with Cd concentrations. Our results showed PM2.5, PM10, and TSP binding Cd concentrations decreased by 5.8%, 5.9%, and 6.1% per year at the national level, respectively. High PM-Cd concentrations were concentrated and distributed mainly in central and northwestern China. In addition, the medians of atmospheric PM2.5, PM10, and TSP binding Cd concentrations at the national level were 0.0026 µg/m3, 0.0036 µg/m3, and 0.0042 µg/m3, respectively. The main sources of PM-Cd include nonferrous metal smelting (Zn, Pb, Al) (47%), glass production (13%), pesticide production (12%), cement production (10%), and coal consumption (9%). This study analyzes comprehensively the atmospheric PM-bound Cd pollution, identifies the major industrial sectors that affect atmospheric Cd concentrations at the macroscale for the first time, and provides a basis for further reduction in the atmospheric Cd pollution.

Assessing Derawan Island's Coral Reefs over Two Decades: A Machine Learning Classification Perspective.

This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel-2, and 78.28% with Multispectral Aerial Photos. Our findings indicate that the classification accuracy is significantly influenced by the geographic resolution and the quality of the field and satellite/aerial image data. Over the two decades, there was a notable decrease in the coral reef area from 2003 to 2011, with a reduction to 16 hectares, followed by a slight increase in area but with more heterogeneous densities between 2011 and 2021. The study underscores the dynamic nature of coral reef habitats and the efficacy of machine learning in environmental monitoring. The insights gained highlight the importance of advanced analytical methods in guiding conservation efforts and understanding ecological changes over time.

Comparative analysis of climate-induced changes in distribution of representative fish species in the Yellow Sea.

Climate changes are posing remarkable impacts on marine fish and fisheries. Although many studies have addressed the distributional effects of climate change on single fish species or taxa in recent years, comparative studies focusing on different types of fish are still lacking. In this study, we applied dynamic bioclimate envelop models (DBEM), based on three earth system models, to predict sea surface and bottom temperature, as well as the spatial and temporal distribution of nine representative fishes in the Yellow Sea, contain two habitats, i.e., continental shelf benthopelagic (CBD) and continental shelf pelagic-neritic (CPN) fishes, and two thermophilies, i.e., warm temperate (WT) and warm water (WW) fishes. Under a low emissions scenario (RCP 2.6) and a high emissions scenario (RCP 8.5) between 1970 and 2060, results reveal that: a) CPN fishes show a distinct tendency to move to higher latitudes than CBD fishes, and WW fishes show a significant tendency to migrate more widely to the north than WT fishes; b) The relative abundance of CPN fishes is expected to be higher than that of CBD fishes, while there is no apparent difference in relative abundance between WW fishes and WT fishes. The main reasons for this difference are presumed to be: variance of temperature rise between the sea surface and bottom layers, divergent adaptations of the species, and disparate degrees of anthropogenic influence.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques.

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 µg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Risk characteristics of shellfish toxins in Mytilus unguiculatus around the Zhoushan Islands, East China Sea.

The Zhoushan Islands, are an important area for Mytilus unguiculatus aquaculture, and are threatened by potentially harmful algal blooms. However, a full understanding of the risks posed by their toxin residues is still lacking. M. unguiculatus samples were collected from the area between 2020 and 2021 and analyzed for their toxin profiles to assess the contamination status of shellfish toxins. The main toxins detected were the paralytic shellfish toxins (PSTs), gymnodimine (GYM), and domoic acid (DA). Nine PSTs components were detected, the dominant ones being C1, C2, and GTX5, with an overall detection rate of 85.7 %. The detection rate of DA was 55.05 %, and GYM was detected in all samples. The toxin levels in the samples were significantly lower than the European Union regulatory limits, but toxin contamination was generally universal.