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.

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%.
气候变化背景下东北地区极端大风事件频发,由极端风蚀事件造成的水土流失逐渐引起了相关学者的广泛关注。本研究使用线性分析、Sen+Mann-Kendal趋势分析、Kriging插值法对2005—2020年东北地区极端大风时空特征进行分析,并使用RWEQ风蚀估算模型计算典型风蚀站点的全年土壤风蚀量和极端大风条件下的风蚀量。结果表明: 2005—2020年间,东北地区极端大风频次呈显著上升趋势,上升幅度为2.9次·a-1。东北地区极端大风频次的年均值在1～49次·a-1,频次高值区分布在锡林郭勒西北部、呼伦贝尔高原西部及长白山东北部等地。4个典型风蚀点(锡林浩特、新巴尔虎右旗、嫩江、通榆)极端大风对土壤风蚀量的平均贡献率为31%。.

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 ［β-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， β-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 β-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.

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.

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.

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.

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.

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.

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.

Wolbachia and Rickettsia are bacterial endosymbionts that can induce a number of reproductive abnormalities in their arthropod hosts. We screened and established the co-infection of Wolbachia and Rickettsia in Bemisia tabaci and compared the spatial and temporal distribution of Wolbachia and Rickettsia in eggs (3-120 h after spawning), nymphs, and adults of B. tabaci by qPCR quantification and fluorescent in situ hybridization (FISH). The results show that the titer of Wolbachia and Rickettsia in the 3-120 h old eggs showed a \"w\" patterned fluctuation, while the titers of Wolbachia and Rickettsia had a \"descending-ascending descending-ascending\" change process. The titers of Rickettsia and Wolbachia nymphal and the adult life stages of Asia II1 B. tabaci generally increased with the development of whiteflies. However, the location of Wolbachia and Rickettsia in the egg changed from egg stalk to egg base, and then from egg base to egg posterior, and finally back to the middle of the egg. These results will provide basic information on the quantity and localization of Wolbachia and Rickettsia within different life stages of B. tabaci. These findings help to understand the dynamics of the vertical transmission of symbiotic bacteria.