Assessing the Impact of Spatial and Temporal Variability in Fine Particulate Matter Pollution on Respiratory Health Outcomes in Asthma and COPD Patients.

Ambient air pollution's health impacts are well documented, yet the domestic environment remains underexplored. We aimed to compare indoor versus outdoor (I/O) air quality and estimate the association between indoor/ambient fine particulate matter (PM2.5) exposure and lung function in asthma and chronic obstructive pulmonary disease (COPD) patients. The study involved 24 h monitoring of PM2.5 levels indoors and outdoors, daily peak expiratory flow (PEF), and biweekly symptoms collection from five patients with asthma and COPD (average age of 50 years, 40% male) over a whole year. Data analysis was performed with linear mixed effect models for PEF and generalized estimating equations (GEE) for exacerbations. More than 5 million PM2.5 exposure and meteorological data were collected, demonstrating significant I/O PM2.5 ratio variability with an average ratio of 2.20 (±2.10). Identified indoor PM2.5 sources included tobacco use, open fireplaces, and cooking, resulting in average indoor PM2.5 concentrations of 63.89 µg/m3 (±68.41), significantly exceeding revised World Health Organization (WHO) guidelines. Analysis indicated a correlation between ambient PM2.5 levels and decreased PEF over 0-to-3-day lag, with autumn indoor exposure significantly impacting PEF and wheezing. The study underscores the need to incorporate domestic air quality into public health research and policy-making. A personalized approach is required depending on the living conditions, taking into account the exposure to particulate pollution.

Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province.

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 µg/m3 and 9.0 µg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

Exploring the environmental influences and community assembly processes of bacterioplankton in a subtropical coastal system: Insights from the Beibu Gulf in China.

Due to rapid urbanization, the Beibu Gulf, a semi-closed gulf in the northwestern South China Sea, faces escalating ecological and environmental threats. Understanding the assembly mechanisms and driving factors of bacterioplankton in the Beibu Gulf is crucial for preserving its ecological functions and services. In the present study, we investigated the spatiotemporal dynamics of bacterioplankton communities and their assembly mechanisms in the Beibu Gulf based on the high-throughput sequencing of the bacterial 16 S rRNA gene. Results showed significantly higher bacterioplankton diversity during the wet season compared to the dry season. Additionally, distinct seasonal variations in bacterioplankton composition were observed, characterized by an increase in Cyanobacteria and Thermoplasmatota and a decrease in Proteobacteria and Bacteroidota during the wet season. Null model analysis revealed that stochastic processes governed bacterioplankton community assembly in the Beibu Gulf, with drift and homogenizing dispersal dominating during the dry and wet seasons, respectively. Enhanced deterministic assembly of bacterioplankton was also observed during the wet season. Redundancy and random forest model analyses identified the physical properties (e.g., temperature) and nutrient content (e.g., nitrate) of water as primary environmental drivers influencing bacterioplankton dynamics. Moreover, variation partitioning and distance-decay of similarity revealed that environmental filtering played a significant role in shaping bacterioplankton variations in this rapidly developed coastal ecosystem. These findings advance our understanding of bacterioplankton assembly in coastal ecosystems and establish a theoretical basis for effective ecological health management amidst ongoing global changes.

Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales.

Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

River dust-induced air pollution in a changing climate: A study of Taiwan's Choshui and Kaoping Rivers.

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Infestation prevalence, spatio-temporal distribution, phylogenetic positioning, and pathogen investigation of Argas persicus ticks in domestic hens (Gallus gallus domesticus) from Pakistan.

Soft ticks pose significant health risks as vectors of various pathogens. This study explored the spatio-temporal distribution and genetic relationships of the soft tick species Argas persicus infesting domestic hens (Gallus gallus domesticus) across different districts in Pakistan. An examination of 778 hens revealed a notable tick infestation prevalence of 70.82%, with a total of 1299 ticks collected from 551 hens. The overall mean intensity was 2.19 soft ticks per infested chicken, and the overall mean abundance was 1.61 soft ticks per examined hen. Morphological identification confirmed all collected ticks (n = 1210) as A. persicus, comprising 719 males, 333 females, 121 nymphs, and 38 larvae. The Haveli, Muzaffarabad, and Kotli districts had the highest infestation rates, while Bagh had the lowest. Molecular analyses of tick DNA, focusing on 16S rDNA and 12S rDNA sequences, revealed genetic similarities among A. persicus soft ticks from Pakistan and other regions, providing insights into their evolutionary history. Importantly, no Babesia, Rickettsia, or Anaplasma infections were detected in the examined samples. These findings enhance the understanding of soft tick infestation patterns and the genetic diversity of A. persicus in the studied region.

Spatial versus spatio-temporal approaches for studying metacommunities: a multi-taxon analysis in Mediterranean and tropical temporary ponds.

Prior research on metacommunities has largely focused on snapshot surveys, often overlooking temporal dynamics. In this study, our aim was to compare the insights obtained from metacommunity analyses based on a spatial approach repeated over time, with a spatio-temporal approach that consolidates all data into a single model. We empirically assessed the influence of temporal variation in the environment and spatial connectivity on the structure of metacommunities in tropical and Mediterranean temporary ponds. Employing a standardized methodology across both regions, we surveyed multiple freshwater taxa in three time periods within the same hydrological year from multiple temporary ponds in each region. To evaluate how environmental, spatial and temporal influences vary between the two approaches, we used nonlinear variation partitioning analyses based on generalized additive models. Overall, this study underscores the importance of adopting spatio-temporal analytics to better understand the processes shaping metacommunities. While the spatial approach suggested that environmental factors had a greater influence, our spatio-temporal analysis revealed that spatial connectivity was the primary driver influencing metacommunity structure in both regions. Temporal effects were equally important as environmental effects, suggesting a significant role of ecological succession in metacommunity structure.

[Change and Prediction of Water Purification Function in the South Bank of Hangzhou Bay in the Past 20 Years].

The investigation of regional water purification functionality and its influencing factors holds significant pragmatic implications in understanding the potential of regional water purification, guiding context-specific regional comprehensive planning schemes, and environmental conservation measures. The study site, situated along the southern coast of Hangzhou Bay, represents a prototypical region characterized by intricate land-sea interactions that bear substantial economic and ecological functions. By assimilating a meticulously collected topographical and land-use dataset, in conjunction with site-specific meteorological records, the water purification model embedded within the integrated valuation of ecosystem services and trade-offs (InVEST) framework was employed to scrutinize the spatiotemporal dynamics of nitrogen (N) and phosphorus (P) loads, discharges, and removals within the southern coast of Hangzhou Bay. The prime objective of this study was to unravel the differentials in water purification functionality under diverse developmental scenarios. The investigation unearthed distinct temporal discrepancies in N and P discharges and removals over two temporal dimensions. Relative to the benchmark year 2000, the total N load experienced a reduction of 276.72 t, whereas the N discharge and removals decreased by 140.86 and 137.86 t, respectively, in the year 2020. In contrast, the total P load observed an increase of 93.65 t, accompanied by a surge in P discharge and removals by 28.91 and 64.74 t, respectively. Spatially, the distribution pattern of N and P discharges exhibited a general inclination of elevated values in the northern region and subdued values in the southern region, with certain pockets in the southern region exhibiting pronounced peaks, intimately associated with land-use typologies. Simulation analyses conducted under distinct scenarios unveiled that under the natural development priority scenario, the N and P discharges within the study area amounted to 1 682.36 and 115.50 t, respectively. Conversely, under the scenario emphasizing economic development, the regional N and P discharges showed an approximate escalation of 83.02% and 79.93%, correspondingly. In contrast, under the scenario emphasizing environmental conservation, the regional N and P discharges exhibited a notable decline of approximately 79.96% and 56.44%, respectively. Hence, the scenario prioritizing the amalgamation of environmental conservation and development effectively reduced the N and P discharges within the region, bolstering the water purification functionality. The results derived from this study furnish a solid theoretical foundation for effectuating region-specific planning schemes fostering coordinated economic and ecological advancement within the study area.

[Spatio-temporal Variation in Net Primary Productivity of Different Vegetation Types and Its Influencing Factors Exploration in Southwest China].

Studying the spatiotemporal variation in vegetation net primary productivity (NPP) and exploring its influencing factors are of considerable practical significance for understanding the spatiotemporal variation in vegetation and for guiding ecological restoration and management projects based on local conditions. Based on MODIS NPP data, combined with in situ meteorological data, land use data, and vegetation type data, this study explores the spatiotemporal variation in different types of vegetation NPP in southwest China via the Mann-Kendall significance test and Theil-Sen Median slope estimator. It reveals the influencing factors of spatial differentiation of different types of vegetation NPP and the interaction between influencing factors in combination with stability analysis and Geo Detectors. The results revealed that on the temporal scale, from 2000 to 2021, vegetation NPP, NPPPre (vegetation NPP exclusively under the influence of climate change), and NPPRes (vegetation NPP exclusively under the influence of human activities) in southwest China showed a fluctuating upward trend. Among different vegetation types, NPP, NPPPre, and NPPRes exhibited an upward trend, except for a minor decline in NPPRes of tree vegetation at a rate of -0.183 g·(m2·a)-1. Among them, NPP, NPPPre, and NPPRes of economic vegetation showed the most significant upward rates, 5.96, 3.09, and 2.94 g·(m2·a)-1, respectively. On the spatial scale, the tree vegetation NPP with the most significant downward trend was mainly distributed in Tibet and southern Yunnan, while the economic vegetation NPP with the highest upward trend was primarily distributed in eastern Sichuan Province. The stability of vegetation NPP in southwest China presented a spatial distribution pattern of "low in the south and high in the north," and the average value of the correlation coefficient increased in the ascending order of arbor vegetation (0.101), shrub vegetation (0.105), herb vegetation (0.110), and economic vegetation (0.114). The interaction between surface temperature and relative humidity was the main influencing factor for spatial differentiation of vegetation NPP, while the interaction between sunshine duration and warmth index had the most significant impact on vegetation in southwest China, with an increasing percentage of 30.91%. Different types of vegetation had different requirements for different climatic factors, but their requirements for surface temperature and warmth index were significantly consistent. When the surface temperature was 21.03-28.49℃, and the warmth index was 106.46-167.2, the NPP of different vegetation types peaked. Under natural succession, the impact of climate change on vegetation was inversely proportional to the stability of the vegetation community. The arbor vegetation community with high stability was less affected, while the herb vegetation community with low stability was highly affected by climate. In contrast, the stability of economic vegetation was directly proportional to the impact of climate due to the influence of human activities. This study establishes a theoretical foundation for evaluating the impact of regional climate on the growth of different vegetation types and can be crucial for formulating ecological restoration and management strategies in southwest China that are adapted to the local conditions.

Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI).

Wildfires are among the most destructive natural disasters globally. Understanding the drivers behind wildfires is a crucial aspect of preventing and managing them. Machine learning methods have gained popularity in wildfire modeling in recent years, but their algorithms are usually complex and challenging to interpret. In this study, we employed the SHapley Additive exPlanations (SHAP) value, an Explainable Artificial Intelligence method, to interpret the model and thus generate spatio-temporal feature attributions. Our research focuses on the forest, shrub and herbaceous vegetated areas of Europe during the summers from 2018 to 2022. Using burned areas, meteorology, vegetation, topography, and anthropogenic activity data, we established a wildfire occurrence model using random forest classification. The model was highly accurate, with an Area Under the Receiver Operating Characteristic Curve of 0.940. The SHAP results revealed six features that significantly influence wildfire occurrences: land surface temperature (LST), solar radiation (SR), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), precipitation (Prep), and soil moisture (SM). The tipping points for the positive or negative shifts in contributions are around 30 °C (LST), 2.20e7 J/m^2 (SR), 0.2 (TCI), 0.78 (NDVI), 2 mm/h (Prep), and 0.18 (SM). These predictors display strong spatial variability in their contribution levels. In Southern Europe, LST and SR emerge as the primary contributors to wildfires, making substantial impacts. Conversely, in regions at mid and high latitudes in Europe, NDVI, Prep, and SM assume a more prominent role in promoting wildfires, albeit with relatively smaller contributions. Furthermore, the disparities in SHAP values for TCI and SMCI across different years provide valuable insights into the effects of variation in regional meteorological conditions on wildfires. Our study provides a new approach to uncovering the spatio-temporal variations of feature contributions, which will help to better understand the mechanism of wildfire occurrence and enhance prevention and mitigation.

Prediction and countermeasures of heavy metal copper pollution accident in the Three Gorges Reservoir Area.

In order to mitigate the hazards of water pollution in drinking water source areas (DWSAs), developing applicable models and proposing effective solutions is of paramount significance. The study developed the Heshangshan Drinking Water Source Area (HDWSA) Hydrodynamic Model, integrating Geographic Information System (GIS) into a two-dimensional hydrodynamic water quality model using FORTRAN. TECPLOT360 software (Software Tools for Numerical Simulation with Visualization) visualized contamination transportation and diffusion. The model's relative error is less than 6%, indicating its strong stability and high reliability. The HDWSA in the Three Gorges Reservoir Area (TGRA) was used as a case study, focusing on copper (Cu) as a pollutant. By regulating the flow downstream from the Xiangjiaba Reservoir, Scheduling Group 1 and Scheduling Group 2 respectively increased the flow by 4000 m3/s and 8000 m3/s. The study analyzed the spatio-temporal variations of Cu concentration following pollution accident and flow scheduling. Under accident conditions, it took 71, 61, 49, and 56 min for the Cu concentration in the study area to decrease to below the standard value (1 mg/L) during dry, falling, flood, and storage periods. Scheduling Groups 1 and 2 reduced the pollutant exceedance duration by 19-26 min and 12-18 min across the four water periods.

Cobertura algal en dos sitios del Parque Nacional de Guanahacabibes, plataforma suroccidental de Cuba

Introducción: Las comunidades de macroalgas marinas del Parque Nacional Guanahacabibes no han sido estudiadas desde el punto de vista cuantitativo, a pesar de la influencia que la vegetación submarina tiene sobre la estructura y el funcionamiento de los ecosistemas costeros, componente que hasta ahora ha sido subestimado en la región. Objetivo: Evaluar la variación temporal y espacial de los géneros de macroalgas en dos sitios de María La Gorda, sur del Parque Nacional de Guanahacabibes, y evaluar la salud del arrecife de acuerdo con la cobertura de los diferentes morfo-tipos de macroalgas predominantes. Metodología: El muestreo fue cuatrimestral entre febrero 2014 y marzo 2017 en Yemayá y Laberinto, mediante buceo autónomo a una profundidad de 10 m. La cobertura algal fue estimada siguiendo la metodología AGRRA. Se usaron ocho transectos de 20 m por sitio de muestreo de acuerdo con las condiciones del arrecife y la forma del fondo. Cada cinco metros se colocó un cuadrante de 25 x 25 cm. Se calculó el porcentaje de cobertura que aporta cada género y de los grupos morfofuncionales a la cobertura total. Resultados: Los géneros dominantes durante el periodo de estudio fueron: Dictyota, Lobophora y Halimeda. Las algas carnosas y calcáreas mostraron mayor cubrimiento en Laberinto (carnosas= 57.8 ±15.6; calcáreas = 8.3 ± 6.8) que en Yemayá, (carnosas = 47.3 ± 23.1; calcáreas = 8.5 ± 9.3). Las formas costrosas son más abundantes en Yemayá (17.1 ± 15.1) que en Laberinto (7.7 ± 10). El índice de las carnosas fue superior en Laberinto (225.7 ± 110.2). Conclusión: La disminución en los niveles de cobertura de algas costrosas y la dominancia de formas carnosas como Dictyota y Lobophora en la zona de estudio, evidencian el deterioro en ambos sitios, a pesar de las políticas de conservación de parques nacionales.

Introduction: The macroalgal communities of the Guanahacabibes National Park have been poorly studied from the quantitative point of view, despite the influence that underwater vegetation has on the structure and functioning of coastal ecosystems, a component that until now has been underestimated in the Cuba region. Objetive: To evaluate the temporal and spatial variation of the macroalgal genera in two sites of María La Gorda, south of the Guanahacabibes National Park, and evaluate the reef health according to the coverage of the different predominant macroalgal morphotypes. Methodology: Sampling took place quarterly between February 2014 and March 2017, in Laberinto and Yemayá by SCUBA diving at a depth of 10 m. The algal coverage was estimated following the AGRRA methodology. Eight 20 m transects per sampling site were used according to reef conditions and bottom shape. Every five meters a 25 x 25 cm square was placed. The coverage (%) contributed per genus and morpho-functional group to the total coverage found was calculated. Results: The dominant genera during the study period were: Dictyota, Lobophora and Halimeda. Fleshy and calcareous algae showed greater coverage in Laberinto (fleshy= 57.8 ± 15.6; calcareous = 8.3 ± 6.8) than in Yemayá (fleshy = 47.3 ± 23.1; calcareous = 8.5 ± 9.3). Crustose algae were more abundant in Yemayá (17.1 ± 15.1) than in Laberinto (7.7 ± 10). The fleshy index was higher in Laberinto (225.7 ± 110.2). Conclusion: The decrease in the levels of crustose algal cover, as well as the dominance of fleshy forms Dictyota and Lobophora in the study area, shows the deterioration in both sites, despite the conservation policies of national parks.

Effects of long-term cultivation on spatial-temporal variation of soil nitrogen and phosphorus: a case study in Shaanxi Province, China.

Investigating the spatial-temporal variation of soil nitrogen (N) and phosphorus (P) is essential to determine the balance between increased food production and environmental protection. In this study, a total of 705 soil samples were collected at depths of 0-20 cm in 2017 and analyzed for laboratory tests of soil N and P. The results showed that from the 1980s to 2017, the total nitrogen (TN), available nitrogen (AN), and available phosphorus (AP) contents of farmland soils in Shaanxi Province increased by 33%, 17%, and 199%, respectively, while the total phosphorus (TP) content decreased by 40%. The best-fit model for spatial interpolation of soil TP and AP in Shaanxi Province was the exponential model (R2 = 0.92 and 0.95); the Gaussian model was the best-fit model for spatial interpolation of soil TN and AN (R2 = 0.98 and 0.96). The spatial distribution characteristics of soil TN, AN, TP, and AP were consistent, all being higher in southern Shaanxi than in northern Shaanxi. The value of N:P* ratio (molar ratio) of cultivated soils in Shaanxi Province is 2.9, which is lower than the Chinese average (N:P* = 5.0). Based on the spatial-temporal variations of soil N and P contents between regions, it is recommended that fertilization should be strictly controlled in central and southern Shaanxi and optimized in northern Shaanxi to improve ground strength.

[Spatio-temporal Variations in PM2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration].

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM2.5 were analyzed based on PM2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD.

Runoff, sediment, organic carbon, and nutrient loads from a Canadian prairie micro-watershed under climate variability and land management practices.

This study conducted a spatio-temporal analysis of runoff, total suspended sediment, suspended particulate carbon, nitrogen, and phosphorus loadings within the 2.06 km2 Steppler subwatershed in southern Manitoba of Canada based on 11 years of field monitoring data collected at nine stations. Results showed that the nutrient losses were very small because of the implementation of multiple BMPs in the study area. However, a high spatio-temporal variation of runoff and water quality parameters was found for the nine fields within the subwatershed. The average runoff coefficient was 0.19 at the subwatershed outlet with sediment, suspended particulate carbon, total nitrogen, and total phosphorus losses of 73.8, 6.10, 4.54, and 0.76 kg/ha respectively. Spring snowmelt runoff was about 74.5% of the annual runoff at the subwatershed outlet, while for sediment, suspended particulate carbon, total nitrogen, and total phosphorus, the proportions were 61.1%, 63.6%, 74.9%, and 81.2% respectively during the monitoring period, which suggests that BMPs designed for reducing nutrient loadings from snowmelt runoff would be more effective than BMPs designed for reducing pollutant loading from rainfall storms in the study area. Research findings from this study will benefit the enhancement of current BMPs and the development of new BMPs in the region to minimize soil and nutrient losses from agricultural fields and improve water quality in receiving water bodies.

Fish diversity, composition, and guild structure influenced by the environmental drivers in a small temporarily closed tropical estuary from the western coast of India.

Globally, small temporarily closed estuaries are the least considered for fisheries assessment and management due to a low scientific priority and regional importance. However, these ecosystems are highly exposed to anthropogenic pressures and may deteriorate without assessing its aquatic resources. In this study, fish diversity and guild structure of Chettuva-a temporarily closed tropical estuary in the western coast of India-was investigated. A total of 70 fish taxa belonging to 32 families were recorded with the quantitative predominance of families; Mugilidae, Cichlidae, and Leiognathidae. Family Carangidae had the highest species diversity (seven species), followed by Clupeidae and Mugilidae (five species). Ecological guild assessment revealed that the immigrating marine fish species (marine estuarine opportunist (34 species) and marine estuarine dependent (13 species)) dominated the guild structure over the estuarine resident species (seven species). The marine species were found all along the saline gradient and throughout the sampling period indicating the salinity ingress towards the estuarine zone throughout the year. Carnivores and omnivores were the major feeding mode functional guilds in terms of diversity, whereas detritivores dominated in numerical abundance throughout the salinity gradient. The major environmental drivers of the fish assemblages in the estuary were salinity and chlorophyll a. The distribution of freshwater fishes and estuarine resident species was connected with the rising primary productivity as the majority of fishes rely on the algae for feeding, whereas marine estuarine dependent and marine estuarine opportunist species occurred in areas with the highest salinity concentrations.

Characterizing the Spatio-Temporal Variations of Urban Growth with Multifractal Spectra.

Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about central areas characterized by relatively stable growth, while those under negative moment orders mainly reflect information about marginal areas that experience more active growth. However, effectively utilizing multifractal spectra to uncover the spatio-temporal variations of urban growth remains a challenge. To addresses this issue, this paper proposes a multifractal measurement by combining theoretical principles and empirical analysis. To capture the difference between growth stability in central areas and growth activity in marginal areas, an index based on generalized correlation dimension Dq is defined. This index takes the growth rate of Dq at extreme negative moment order as the numerator and that at extreme positive moment order as the denominator. During the stable stage of urban growth, the index demonstrates a consistent pattern over time, while during the active stage, the index may exhibit abnormal fluctuations or even jumps. This indicates that the index can reveal spatio-temporal information about urban evolution that cannot be directly observed through multifractal spectra alone. By integrating this index with multifractal spectra, we can more comprehensively characterize the evolutionary characteristics of urban spatial structure.

The impacts of spatio-temporal variation of natural and agricultural influences on the environmental water quality in a fluvial-lacustrine watershed in China.

Despite the significant impacts of natural factors such as rainfall, topography, soil type, and river network as well as agricultural activities on the environmental water quality, little is known about the influence of their temporal and spatial variations in a fluvial-lacustrine watershed. In this study, a whole process accounting method based the export coefficient model (WP-ECM) was first developed to quantify how natural factors and agricultural activities distribution influenced water quality. A case study was performed in a typical fluvial-lacustrine area - Dongting basin, China. The simulated results indicated that the natural factors can promote and inhibit the migration and transformation of agricultural pollutants generated from the watershed and the spatial distribution of the natural factors displayed high variability. It should be priority to monitor the areas with greater natural impact in the basin. Moreover, the cultivated land area and the number of pig-breeding were positively correlated with the pollutant discharge. From the perspective of the spatial distribution of comprehensive influence, the comprehensive high-impact areas are mainly distributed in the Dongting Lake district in 2005-2010 and in Xiang River watershed in 2010-2020. A key strategy for controlling or reducing the cultivated land area and the intensity of livestock breeding in these high-impacts areas is recommended to reduce the impact of the environmental water quality for the entire basin.

[Spatio-temporal Variation in PM2.5 Concentration and Its Relationship with Vegetation Landscape Patterns in Typical Economic Zones in China from 2000 to 2020].

This study explored the temporal and spatial variation in PM2.5 concentration and its relationship with the vegetation landscape pattern in three typical economic zones in China, which is of great significance for regional PM2.5pollution control and atmospheric environmental protection. In this study, the pixel binary model, Getis-Ord Gi* analysis, Theil-Sen Median analysis, Mann-Kendall significance test, Pearson correlation analysis, and multiple correlation analysis were used to explore the spatial cluster and spatio-temporal variation in PM2.5 and its correlation with the vegetation landscape index in the three economic zones of China on the basis of PM2.5 concentration data and MODIS NDVI data set. The results showed that PM2.5 in the Bohai Economic Rim was mainly dominated by the expansion of hot spots and the reduction in cold spots from 2000 to 2020. The proportion of cold spots and hot spots in the Yangtze River Delta showed insignificant changes. Both cold and hot spots in the Pearl River Delta had expanded. PM2.5 showed a downward trend in the three major economic zones from 2000 to 2020, and the magnitudes of increasing rates were higher in the Pearl River Delta, followed by those in the Yangtze River Delta and Bohai Economic Rim. From 2000 to 2020, PM2.5 exhibited a downward trend in the context of all vegetation coverage grades, and PM2.5 had most significantly improved within extremely low vegetation coverage in the three economic zones. On the landscape scale, PM2.5 values were mostly correlated with aggregation index in the Bohai Economic Rim, with the largest patch index in the Yangtze River Delta and Shannon's diversity in the Pearl River Delta, respectively. Under the context of different vegetation coverage levels, PM2.5showed the highest correlation with aggregation index in the Bohai Economic Rim, landscape shape index in the Yangtze River Delta, and percent of landscape in the Pearl River Delta, respectively. PM2.5 showed significant differences with vegetation landscape indices in the three economic zones. The combined effect of multiple vegetation landscape pattern indices on PM2.5 was stronger than that of the single vegetation landscape pattern index. The above results indicated that the spatial cluster of PM2.5 in the three major economic zones had changed, and PM2.5 showed a decreasing trend in the three economic zones during the study period. The relationship between PM2.5 and vegetation landscape indices exhibited obvious spatial heterogeneity in the three economic zones.