Significant Temporal Variation Research Articles

Temporal and spatial variations in squid jigging catch efficiency in the Oyashio Extension region

AbstractThe abundance of fishery resources significantly impacts the ratio of fishing effort to harvest. However, traditional statistics of fishery catches or commonly used catch per unit effort (CPUE) metrics cannot accurately capture the complexity of resource abundance in the ocean. To address this issue, we propose here a novel approach that integrates the actual fishery catch from vessel logs with fishing duration obtained through automatic identification system (AIS) positioning. This combined analysis eliminates confounding factors and introduces a novel metric called “catch efficiency (CE)” to evaluate fishing operations more accurately, thereby reflecting resource abundance in a more reasonable way. In this study, we focus on the CE of squid in the Oyashio Extension region in the Northwestern Pacific. Our analysis reveals significant temporal and spatial variations of CE, manifesting in both intensity and distribution patterns. Moreover, our findings establish a close relationship between CE and background water mass distribution, chlorophyll‐a concentration, and micronekton biomass. This implies that the resource abundance of squid can be inferred by considering the varying environmental factors within the fishing area.

Overview of microbial communities in the surface water of the Seine River to understand their response to climate change and human activities

AbstractDiverse microbial communities play a crucial role in maintaining the proper functioning of river ecosystems and are considered important indicators of river water quality. Although the Seine River being heavily impacted by human activities, little is known about the microbial communities in its surface waters. In order to monitor water quality and promote sustainable use, we studied its microbial diversity using high-throughput sequencing technology, and explored their relationships with physico-chemical properties. The dominant phyla identified were Proteobacteria, Actinobacteriota, Bacteriodota, and Cyanobacteria. The presence of the Alphaproteobacteria and Gammaproteobacteria indicates that the Seine River water nutrient profile is mainly determined by the recalcitrant organic compounds present in WWTP effluents. Bacterial diversity showed significant temporal variability with a highly significant difference in bacterial composition between 2020 and 2021, probably due to variations in water flow favoring Cyanobacteria growth. Summer displayed higher microbial activity and abundance than autumn, attributed to temperature and orthophosphate content. Spatial variation in bacterial composition was observed between sites upstream and downstream of Paris, as well as before and after of the Seine Valenton-WWTP, subject to an accumulation phenomenon and impacted by wastewater treatment. Further assessment of emerging contaminants and other pollutants is required to better understand these variations. These results provide a basic understanding of the microbial community in the Seine River, serving as a reference for assessing the impact of implementing new wastewater disinfection techniques in the near future. Graphical abstract

Remote Sensing Assessment of Water Resources, Vegetation, and Land Surface Temperature in Eastern Saudi Arabia: Identification, Variability, and Trends

Saudi Arabia has one of the biggest water shortages and the least vegetation in the world, which is presumed to provoke this problem further due to climate change. Therefore, the present study investigates the water, vegetation, and temperature over Al-Asfar Lake region, Al Ahsa, Eastern province of Saudi Arabia using the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST: °C) from Landsat-8 based operational land imager (OLI) measurements for the period 2013 to 2023. This study presented annual and seasonal (dry months: June–September and wet months: December–April) spatiotemporal distribution and variations, calculated their absolute change and trends, and examined their relationship. Results showed positive NDWI values over Al-Asfar Lake, indicating waterbodies; while positive NDVI values on the lake's bank, signifying vegetation. Notably, there were significant temporal variations in water and vegetation observed on annual, seasonal, and monthly scales. The study also found an overall decrease in vegetation areas of 5.36 km2 in 2023 compared to 2013, while waterbodies increased by 8.83 km2. The trend analysis using area-averaged data demonstrated that NDWI increased on annual (0.0075/year) and seasonal (dry: 0.0083/year and wet: 0.0049/year) scales, while NDVI decreased (annual: 0.0066/year, dry: 0.0083/year, and wet: 0.0009/year). Moreover, LST was recorded least amount over waterbodies (28.23 °C) and vegetation (32.45 °C) covered areas compared to the entire lake region (38.43 °C), respectively. Remark, LST displayed decreasing trends over waterbodies (−0.05/year), followed by vegetation (−0.17/year), and the entire lake region (−0.0001/year), signifying that water and vegetation are vital components to controlling land surface temperature in this region. Finally, the LST showed a positive correlation with NDVI and negative correlation with NDWI. There may be a direct and indirect impact of climate change upon NDVI, LST, and NDWI as shown by the decreases in NDVI and LST and an increase in NDWI. This study can be considered as a base document to monitor waterbodies, vegetation cover, and temperature changes using remote sensing measurements of NDWI, NDVI, and LST, which will assist policymakers in developing water resource management, irrigation planning, and environmental monitoring strategies.

Modelling and mapping carbon capture potential of farmed blue mussels in the Baltic Sea region

This study applies a regional Dynamic Energy Budget (DEB) model, enhanced to include biocalcification processes, to evaluate the carbon capture potential of farmed blue mussels (Mytilus edulis/trossulus) in the Baltic Sea. The research emphasises the long-term capture of carbon associated with shell formation, crucial for mitigating global warming effects. The model was built using a comprehensive pan-Baltic dataset that includes information on mussel growth, filtration and biodeposition rates, and nutrient content. The study also examined salinity, temperature, and chlorophyll a as key environmental factors influencing carbon capture in farmed mussels. Our findings revealed significant spatial and temporal variability in carbon dynamics under current and future environmental conditions. The tested future predictions are grounded in current scientific understanding and projections of climate change effects on the Baltic Sea. Notably, the outer Baltic Sea subbasins exhibited the highest carbon capture capacity with an average of 55 t (in the present scenario) and 65 t (under future environmental conditions) of carbon sequestrated per farm (0.25 ha) over a cultivation cycle – 17 months. Salinity was the main driver of predicted regional changes in carbon capture, while temperature and chlorophyll a had more pronounced local effects. This research advances our understanding of the role low trophic aquaculture plays in mitigating climate change. It highlights the importance of developing location-specific strategies for mussel farming that consider both local and regional environmental conditions. The results contribute to the wider discourse on sustainable aquaculture development and environmental conservation.

Overexpression of LAG-3: a potential indicator of low immune function in tuberculosis.

Tuberculosis (TB) persists as a global health challenge, with its treatment hampered by the side effects of long-term combination drug therapies and the growing issue of drug resistance. Therefore, the development of novel therapeutic strategies is critical. This study focuses on the role of immune checkpoint molecules (ICs) and functions of CD8+ T cells in the search for new potential targets against TB. We conducted differential expression genes analysis and CD8+ T cell functional gene analysis on 92 TB samples and 61 healthy individual (HI) samples from TB database GSE83456, which contains data on 34,603 genes. The GSE54992 dataset was used to validated the findings. Additionally, a cluster analysis on single-cell data from primates infected with mycobacterium tuberculosis and those vaccinated with BCG was performed. The overexpression of LAG-3 gene was found as a potentially important characteristic of both pulmonary TB (PTB) and extrapulmonary TB (EPTB). Further correlation analysis showed that LAG-3 gene was correlated with GZMB, perforin, IL-2 and IL-12. A significant temporal and spatial variation in LAG-3 expression was observed in T cells and macrophages during TB infection and after BCG vaccination. LAG-3 was overexpressed in TB samples. Targeting LAG-3 may represent a potential therapeutic target for tuberculosis.

The diversity and risk of potential pathogenic bacteria on the surface of glaciers in the southeastern Tibetan Plateau

Glaciers, which constitute the world's largest global freshwater reservoir, are also natural microbial repositories. The frequent pandemic in recent years underscored the potential biosafety risks associated with the release of microorganisms from the accelerated melting of glaciers due to global warming. However, the characteristics of pathogenic microorganisms in glaciers are not well understood. The glacier surface is the primary area where glacier melting occurs that is often the main subject of research on the dynamics of pathogenic microbial communities in efforts to assess glacier biosafety risks and devise preventive measures. In this study, high-throughput sequencing and quantitative polymerase chain reaction methods were employed in analyses of the composition and quantities of potential pathogenic bacteria on the surfaces of glaciers in the southeastern Tibetan Plateau. The study identified 441 potential pathogenic species ranging from 215 to 4.39 × 1011 copies/g, with notable seasonal and environmental variations being found in the composition and quantity of potential pathogens. The highest level of diversity was observed in April and snow, while the highest quantities were observed in October and cryoconite. Host analysis revealed that >70 % of the species were pathogens affecting animals, with the highest proportion of zoonotic pathogens being observed in April. Analysis of aerosols and glacial meltwater dispersion suggested that these microbes originated from West Asia, primarily affecting the central and southern regions of China. Null model analysis indicated that the assembly of potential pathogenic microbial communities on glacier surfaces was largely governed by deterministic processes. In conclusion, potential pathogenic bacteria on glacier surfaces mainly originated from the snow and exhibited significant temporal and spatial variation patterns. These findings can be used to enhance researchers' ability to predict potential biosafety risks associated with pathogenic bacteria in glaciers and to prevent their negative impact on populations and ecological systems.

Characterization of physicochemical parameters and insect composition in a reservoir in Northern Akwa Ibom State, Nigeria

This study was conducted in a reservoir located in Mbiabet Ikot Udo community, Northern Akwa Ibom State, Nigeria, to assess the physicochemical status, composition and abundance of aquatic insects. Samples were collected from June 2022 to January 2023 and all the parameters were evaluated using standard methods. The results revealed that temperature ranged from 22.7 to 30.6°C, TDS (153 – 238 mgL-1), EC (153 – 238μScm-1), TDS (97.9 –152.3mgL-1), pH (6.3 – 8.1), DO (2.22 – 5.14 mgL-1), TSS (1.62 – 6.34 mgL-1), NO-3 (1.53 – 5.68 mgL-1), PO-4 (2.18 – 4.16 mgL-1), Mg2+ (3.92 – 5.36 mgL-1), Ca2+ (5.39 – 8.13 mgL-1), Na+ (0.72 – 2.37 mgL-1), K+ (0.68 – 1.48 mgL-1), BOD (1.34 – 6.46mgL-1), Ni (0.001 – 0.08 mgL-1), Fe (0.1 – 0.3 mgL-1), Pb (0.003 – 0.008 mgL-1), Cu (0.01 – 1.3 mgL-1) and Cd (0.002 – 0.008). There were significant temporal variations in some of these parameters. However, all the parameters were within the acceptable limits, except for TSS, BOD, DO, Ni, Cu and Fe. A total of 185 species of aquatic insects, in five taxonomic orders and 10 families, were recorded. Odonata had the highest percentage (40.5%), followed by Hemiptera (25.0%), and Diptera had the lowest percentage (8.6%). The dominant family was Aeshnidae (18.9%) while the least percentage was from Pleidae (1.1%). The analysis revealed that parameters such as temperature, DO, pH, Ca2+, EC, TDS and Cu negatively influenced the abundance of aquatic insects coupled with seasonal influence.

Air quality challenges in Central Asian urban areas: a PM2.5 source apportionment analysis in Dushanbe, Tajikistan.

This work presents the first comprehensive assessment of PM pollution sources in Dushanbe, Tajikistan. A total of 138 PM2.5 samples were collected during 2015-2016 and 2018-2019 and were analyzed through gravimetric, ED-XRF, and multi-wavelength absorption techniques. The results show that PM2.5 concentrations were substantially higher than the European annual limit value and WHO Air Quality Guidelines annual average value, with an average of 90.9 ± 68.5μgm-3. The PMF application identified eight sources of pollution that influenced PM2.5 concentration levels in the area. Coal burning (21.3%) and biomass burning (22.3%) were the dominant sources during the winter, while vehicular traffic (7.7%) contributed more during the warm season. Power plant emissions (17.5%) showed enhanced contributions during the warm months, likely due to high energy demand. Cement industry emissions (6.9%) exhibited significant contribution during the cold period of 2018-2019, while soil dust (11.3%) and secondary sulphates (11.5%) displayed increased contribution during the warm and cold months, respectively. Finally, waste burning (1.5%) displayed the lowest contribution, with no significant temporal variation. Our results highlight the significant impact of anthropogenic activities, and especially the use of coal burning for energy production (both in power plants and for residential heating), and the significant contribution of biomass burning during both warm and cold seasons.

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

Prolonged Multi‐Phase Magmatism Due To Plume‐Lithosphere Interaction as Applied to the High Arctic Large Igneous Province

AbstractThe widespread High Arctic Large Igneous Province (HALIP) exhibits prolonged melting over more than 50 Myr, an observation that is difficult to reconcile with the classic view that large igneous provinces (LIPs) originate from melting in plume heads. Hence, the suggested plume‐related origin and classification of HALIP as a LIP have been questioned. Here, we use numerical models that include melting and melt migration to investigate a rising plume interacting with lithosphere of variable thickness, that is, a basin‐to‐craton setting applicable to the Arctic. Models reveal that melt migration introduces significant spatial and temporal variations in melt volumes and pulses of melt production, including protracted melting for at least about 30–40 Myr, because of the dynamic feedback between migrating melt and local lithosphere thinning. For HALIP, plume material deflected from underneath the Greenland craton can re‐activate melting zones below the previously plume‐influenced Sverdrup Basin after a melt‐free period of about 10–15 Myr, even though the plume is already ∼500 km away. Hence, actively melting zones do not necessarily represent the location of the deeper plume stem at a given time, especially for secondary pulses. Additional processes such as (minor) plume flux variations or local lithospheric extension may alter the timing and volume of HALIP pulses, but are to first order not required to reproduce the long‐lived and multi‐pulse magmatism of HALIP. Since melting zones are always plume‐fed, we would expect HALIP magmatism to exhibit plume‐related trace element signatures throughout time, potentially shifting from mostly tholeiitic toward more alkalic compositions.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Greenness of moss biocrusts derived from RGB image is a reliable indicator to estimate biocrust carbon-fixation rates in drylands

Biocrusts are common living covers found across drylands worldwide, and their photosynthesis substantially contributes to carbon input in these ecosystems. However, direct monitoring of biocrusts’ photosynthetic carbon fixation is challenging due to their scattered distribution, dark pigments, and lower CO2 exchange rate. Current studies have limited monitoring frequency and the results are difficult to extend to a broader spatial scale. Greenness typically functions as an indicator of both vegetation cover and plant photosynthesis. Monitoring biocrust greenness is expected to be able to provide insights into their environmental responses and estimate carbon fixation rates, particularly for moss biocrusts. Here, we monitored greenness, soil moisture and temperature, and gross primary productivity (GPP) of moss biocrusts, as well as climatic factors over two years in the northern China’s Loess Plateau. The results indicated that moss biocrust greenness was highly sensitive to changing environmental conditions and exhibited significant temporal variability. Moss biocrust greenness exhibited a notably higher value in wet season (0.384) than in dry season (0.259). According to these greenness values, we proposed moss biocrust activity and classified it into high (>0.403), medium (0.334–0.403), low (0.271–0.333), and dormancy (<0.270). Soil moisture, relative humidity, and wind speed were the three main determinates of moss biocrust greenness. Higher soil moisture and relative humidity were linked to increased greenness, whereas elevated wind speed led to a reduction. In dry seasons, soil moisture contributed 23.4 % to the variation in moss biocrust greenness, while in wet seasons relative humidity contributed 16.1 %. A significant and positive correlation between moss biocrust greenness and GPP was found, and annual GPP prediction based on greenness using machine learning performed well. Our study highlights the significance of moss biocrust greenness and their potential as indicators for moss biocrust photosynthetic carbon fixation rates and possibly other ecological functions in drylands.

Impact of rainfall on the water quality of a tropical river: based on the Nilwala River in the southern province of Sri Lanka between March and October 2019

ABSTRACT The study addresses the pressing need for an effective monitoring system to assess aquatic water quality, given the exacerbating impact of climate change on surface water bodies. Focusing on the Nilwala River, the research pursues three main objectives: comparative analysis of the Weighted Arithmetic Water Quality Index (WAWQI) values across different river locations, exploration of correlations between monthly rainfall and various physicochemical parameters at distinct sampling sites, and examination of notable disparities in the WAWQI between dry and wet months. Data collected from eight river locations from March to October 2019 revealed significant temporal variations in pH, temperature, chemical oxygen demand (COD), electrical conductivity (EC), alkalinity, chloride (Cl−), and nitrate (NO3-) content. The WAWQI increased across all sites during the drought, with only Site 4 (Wellathota) deemed suitable for drinking. Statistical analyses using an one-way ANOVA and multiple linear regressions unveiled significant relationships between rainfall and pH, biological oxygen demand (BOD), EC, and total alkalinity. The total alkalinity is the most affected parameter by the rainfall. Rainfall positively predicted BOD, COD, and NO3- concentration while negatively predicting other parameters.

Dynamic connectedness between FinTech and energy markets: Evidence from fat tails, serial dependence, and Bayesian approach

In a complex global environment, modeling the relationships among several markets and asset classes has become more challenging. Literature strives to provide conclusive evidence due to system complexities. Therefore, we investigate the dynamic relationship between Fintech and energy sectors using fat tails, serial dependence, and the Bayesian approach from May 1, 2019, to October 28, 2022. Results unveil significant temporal variations in network interconnections, with an evident increase in interlinkages across short, medium, and long durations due to transient market events. The COVID-19 pandemic and the onset of the Russia-Ukraine conflict have substantially altered the long-term relational dynamics within these networks. An analysis of net directional linkages reveals a pivotal shift in market roles—from being predominantly shock recipients to becoming shock transmitters—highlighted during the COVID-19 pandemic and the initial phase of the Russia-Ukraine War. Initial observations indicate that, amid the global impact of COVID-19, Fintech markets absorbed shocks through investments in green bonds and renewable energy sources like wind, solar, and clean energy. These findings suggest that while Fintech consistently acts as a shock absorber in long-term scenarios, it assumes the role of a shock transmitter during adverse economic states.

Geographical Variations in Early Onset Colorectal Cancer in the United States between 2001 and 2020.

Colorectal cancer remains the second leading cause of cancer-related death in the US. As early-onset colorectal cancer (EO-CRC) becomes more prevalent in the US, research attention has shifted towards identifying at-risk populations. Previous studies have highlighted the rising rate of early-onset adenocarcinoma (ADC) and neuroendocrine tumors (NET) in the US. However, data on geographical variations of EO-CRC are scarce. Hence, our study aims to analyze time trends in EO-CRC incidence rates across various US regions and to assess these trends by sex and histopathological subtypes (ADC and NET). We analyze data spanning from 2001 to 2020 from the United States Cancer Statistics (USCS) database, covering nearly 98% of the US population. Using SEER*Stat software version (8.4.2, NCI), we calculated EO-CRC incidence rates among adults aged 20-54 years, adjusting for the age standard 2000 US population. The rates were categorized by sex and US geographical regions into west, midwest, northeast, and south. Time trends, reported as annual percentage change (APC) and average APC (AAPC), were generated via Joinpoint Regression software (v.5.0.2, NCI) utilizing the weighted Bayesian Information Criteria "BIC" method to generate the best-fit trends with a two-sided p-value cutoff at 0.05. The rates were also stratified by histopathology into ADC and NET. Between 2001 and 2020, a total of 514,875 individuals were diagnosed with early-onset CRC in the US, with 54.78% being men. Incidence rates and trends varied across geographical regions. In the western region (comprising 106,685 patients, 54.85% men), incidence rates significantly increased in both women (AAPC = 1.37, p < 0.001) and men (AAPC = 1.34, p < 0.001). Similarly, in the midwestern region (with 110,380 patients, 55.46% men), there were significant increases in incidence rates among women (AAPC = 1.06, p < 0.001) and men (AAPC = 1.35, p < 0.001). The northeastern region (with 94,758 patients, 54.53% men) also witnessed significant increases in incidence rates for both women (AAPC = 0.71, p < 0.001) and men (AAPC = 0.84, p < 0.001). In contrast, the southern region (with 203,052 patients, 54.48% men) experienced slower increases in incidence rates among both women and men (AAPC = 0.25, p < 0.05 in women; AAPC = 0.66, p < 0.05 in men). When stratified by histopathology, incidence rates for adenocarcinomas (ADC) increased in all regions, most notably in the west (AAPC = 1.45, p < 0.05), and least in the south (AAPC = 0.46, p < 0.05). Conversely, for neuroendocrine tumors (NET), while incidence rates increased similarly across all regions, the pace was notably faster compared to ADC, particularly in the west (AAPC = 3.26, p < 0.05) and slower in the south (AAPC = 2.24, p < 0.05) Discussion: Our analysis of nationwide US data spanning two decades and encompassing over half a million early-onset CRC patients, representing nearly 98% of the US population, highlights significant temporal variation in incidence rates across various geographical regions. The most substantial increases in incidence rates were observed in the west, while the least pronounced changes were noted in the south, affecting both men and women. These trends persisted across the main CRC histopathological subtypes, with NET exhibiting a notably swifter pace of increase compared with ADC. These findings hold important implications for public health strategies and underscore the need for targeted interventions to address the rising burden of early-onset CRC across different regions in the US.

Quantitative Assessment and Impact Analysis of Land Surface Deformation in Wuxi Based on PS-InSAR and GARCH Model

Land surface deformation, including subsidence and uplift, has significant impacts on human life and the natural environment. In recent years, the city of Wuxi, China has experienced large-scale surface deformation following the implementation of a groundwater abstraction ban policy in 2005. To accurately measure the regional impacts and understand the underlying mechanisms, we investigated the spatiotemporal characteristics of surface deformation in Wuxi from 2015 to 2023 using 100 Sentinel-1A SAR images and the Persistent Scatterer InSAR (PS-InSAR) technique. The results revealed that surface deformation in Wuxi exhibited significant spatial and temporal variations, with some areas experiencing alternating trends of subsidence and uplift rather than consistent unidirectional change. To uncover the factors influencing this volatility, we conducted a comprehensive analysis focusing on groundwater, precipitation, and soil geology. This study found strong correlations between the groundwater level changes and surface deformation, with the soft soil geology of the area, characterized by alternating layers of sand and clay, further increasing the surface volatility. Moreover, we innovatively applied the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, typically used in financial analyses, to analyze the subsidence displacement time series in Wuxi. Based on this model, we propose a new “Amplitude Factor” index to evaluate overall surface deformation volatility in the city. Our qualitative assessment of surface stability based on the Amplitude Factor was consistent with research findings, demonstrating the accuracy and effectiveness of the proposed model. These results provide valuable insights for urban planning, construction, and safety control, highlighting the importance of continuous monitoring and analysis of surface deformation volatility for the city’s future development and safety.

Groundwater Chemical Trends Analyses in the Piedmont Po Plain (NW Italy): Comparison with Groundwater Level Variations (2000–2020)

The concentrations of chemicals in the groundwater chemical values in the Piedmont Po Plain (NW Italy) show significant temporal variability and need to be characterised due to the lack of regional-scale assessments. The aim of this study was to analyse the trends (period 2000–2020) in the main physicochemical parameters and main ions in 227 wells in the shallow aquifer and to identify the potential causes. The identification of change points (points of sudden change) and comparisons with groundwater level variations were also performed. Results highlight general increasing trends for Na, Cl and HCO3, decreasing trends for SO4 and NO3, stationary conditions for pH and heterogeneous behaviours for electrolytic conductivity, Ca and Mg. Change points occurred in at least 50% of the monitoring wells, mainly during the 2008–2011 period. The comparison between groundwater levels and chemistry highlights a direct proportionality. Superimposed processes that induce an absence of proportionality are shown. The comparison of results with those of previous studies conducted under similar conditions revealed similar variations.. In conclusion, the potential responsible factors (e.g., road-salt dissolution and agricultural practices) and the relevant role of groundwater level variation were identified.

Response of a source water quality through a heavy precipitation event: Nutrients, dissolved organic matter and their DBPs formation

To better understand the influence of heavy precipitation event on water supply security, the change of nutrients, dissolved organic matter (DOM) and their corresponding disinfection by-products (DBPs) generation in river were investigated through a heavy precipitation event. The results show that short-term sustained heavy precipitation causes additional load on the river. Significant temporal variations and increases in dissolved oxygen (DO), turbidity, dissolved organic carbon (DOC) and molecular weight of raw water were observed during the heavy precipitation, and dissolved organic nitrogen (DON) showed a remarkably decline with the continuation of precipitation. The molecular weight and aromaticity of DOM increased dramatically with the continuous superposition of precipitation and flow, and the proportion of hydrophobic fraction increased during the study period. Four different fluorescent components were identified, including humic-like (C1, C2 and C3), and protein-like fluorophore C4. Heavy precipitation events affected the mobilization of DOM, while the generation of DBPs increased by 4%–47% during the chlorination, and the elevated river level further increased the DBPs formation. In particular, the HOA fraction was the main contributor of DBPs (up to 70%). The risk of haloacetic acids (HAAs) generation caused by heavy rainfall was concentrated in the river runoff producing and regression periods for both hydrophobic and hydrophilic fractions of DOM. Humification index (HIX) was positively correlated with the yield level of TCM, DCAA, TCAA, and DCAN during chlorination. Chlorination disinfection of raw water during river runoff producing and regression period is likely to increase the haloacetonitriles formation potential (HANsFP), thus posing a vital threat to water supply safety, while chloramination disinfection may significantly relieve the threat. This study provides a theoretical basis for the direct/indirect link between short-term precipitation events and their impact on water quality.

Assessment of bioremediation potential of Calotropis procera and Nerium oleander for sustainable management of vehicular released metals in roadside soils

Land transportation is a major source of heavy metal contamination along the roadside, posing significant risks to human health through inhalation, oral ingestion, and dermal contact. Therefore, this study has been designed to determine the concentrations of vehicular released heavy metals (Cd, Pb, Ni, and Cu) in roadside soil and leaves of two commonly growing native plant species (Calotropis procera and Nerium oleander).Two busy roads i.e., Lahore-Okara road (N-5) and Okara-Faisalabad roads (OFR) in Punjab, Pakistan, were selected for the study. The data were collected from five sites along each road during four seasons. Control samples were collected ~ 50 m away from road. The metal content i.e. lead (Pb), cadmium (Cd) nickel (Ni) and copper (Cu) were determined in the plant leaves and soil by using Atomic Absorption Spectrophotometer (AAS). Significantly high amount of all studied heavy metals were observed in soil and plant leaves along both roads in contrast to control ones. The mean concentration of metals in soil ranged as Cd (2.20–6.83 mg/kg), Pb (4.53–15.29 mg/kg), Ni (29.78–101.26 mg/kg), and Cu (61.68–138.46 mg/kg) and in plant leaves Cd (0.093–0.53 mg/kg), Pb (4.31–16.34 mg/kg), Ni (4.13–16.34 mg/kg) and Cu (2.98–32.74 mg/kg). Among roads, higher metal contamination was noted along N-5 road. Significant temporal variations were also noted in metal contamination along both roads. The order of metal contamination in soil and plant leaves in different seasons was summer > autumn > spring > winter. Furthermore, the metal accumulation potential of Calotropis procera was higher than that of Nerium oleander. Therefore, for sustainable management of metal contamination, the plantation of Calotropis procera is recommended along roadsides.

Cattle, conflict, and climate variability: explaining pastoralist conflict intensity in the Karamoja region of Uganda

Conflicts between pastoralist groups in Africa are rampant and cause numerous deaths each year, but their intensity exhibits significant spatial and temporal variations. Under what conditions do some pastoralist conflicts turn more violent than others? Previous research has shown that climate variability may be a relevant explanatory factor under certain conditions, yet little is known about how specific combinations of conditions jointly affect pastoralist conflict intensity. Moreover, many studies have investigated the link between resource scarcity and conflict, but few have examined how asymmetrical distribution of resources between groups affects levels of violence. This article contributes to filling these gaps through a qualitative comparative analysis (QCA) of pastoralist conflicts in Karamoja, Uganda—a region characterized by cattle husbandry, communal conflict, and a highly variable climate. Five conditions that are related either to incentives for taking to violence or to opportunity structures are examined and three pathways to high-intensity conflict are identified. The results show that an asymmetrical distribution of resources between pastoralist groups during a drought incentivizes conflict, particularly in remote areas with nearby armed conflicts. The article highlights some implications for future research and policy. First, it shows that this type of conflict cannot be reduced to simple “resource scarcity conflicts” since climate variability only has an effect in combination with other conditions. Second, it emphasizes the need for policy approaches that consider both environmental variability and political contexts in addressing pastoralist violence.