Seasonal Variation Research Articles

Trihalomethanes monitoring and their seasonal variation in urban municipal water supply system in North India

Trihalomethanes monitoring and their seasonal variation in urban municipal water supply system in North India

Seasonal variation in D2/3 dopamine receptor availability in the human brain.

Brain functional and physiological plasticity is essential to combat dynamic environmental challenges. The rhythmic dopamine signaling pathway, which regulates emotion, reward and learning, shows seasonal patterns with higher capacity of dopamine synthesis and lower number of dopamine transporters during dark seasons. However, seasonal variation of the dopamine receptor signaling remains to be characterized. Based on a historical database of healthy human brain [11C]raclopride PET scans (n = 291, 224 males and 67 females), we investigated the seasonal patterns of D2/3 dopamine receptor signaling. Daylength at the time of scanning was used as a predictor for brain regional non-displaceable binding of the radiotracer, while controlling for age and sex. Daylength was negatively correlated with availability of D2/3 dopamine receptors in the striatum. The largest effect was found in the left caudate, and based on the primary sample, every 4.26h (i.e., one standard deviation) increase of daylength was associated with a mean 2.8% drop (95% CI -0.042 to -0.014) of the receptor availability. Seasonally varying D2/3 receptor signaling may also underlie the seasonality of mood, feeding, and motivational processes. Our finding suggests that in future studies of brain dopamine signaling, especially in high-latitude regions, the effect of seasonality should be considered.

Determination of the Radioactive Nature of the Sediments of the Bays of the Ebrié Lagoon of Abidjan, Ivory Coast

In Ivory Coast, the lagoons represent the transit point for the majority of continental inputs. They are thus increasingly vulnerable and their ecological balance is rapidly modified under the influence of certain natural or anthropogenic factors. The objective of this study is to evaluate the level of presence of lead 210 in sediments. The sediments were sampled during the campaigns of the National Water Quality Observation Network on the bays spread over thirteen study sites. Analysis by alpha spectrometry was carried out in three stages (mineralization with concentrated acids, self-deposition on a silver disk, counting in an alpha detector with a silicone surface barrier). The highest lead-210 activities were obtained in the bays of Koumassi 1 (228.57 ± 12.21 Bq/kg) and Marcory (198.91 ± 9.46 Bq/kg). However, no seasonal variation in lead-210 contamination of sediments in the bays of Abidjan was observed. We wish to extend this study to other aquatic matrices.

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Evaluation of soil temperature in CMIP6 multimodel simulations

Soil temperature plays a crucial role in understanding land-atmosphere interactions. The Sixth Phase of Coupled Model Intercomparison Project (CMIP6) provides valuable information on soil temperature, however, the performance of these models in capturing soil temperature variations remains unclear. To comprehensively assess the performance of CMIP6 in simulating soil temperature, we employed a set of in-situ observation data, observation-derived gridded data (TS-GCB) and reanalysis data (ERA5L) to build various evaluating metrics for the surface (0–5 cm) and subsurface (5–15 cm) soils. At the global scale, the multimodel ensemble mean (MME) of CMIP6 generally captured the spatial, annual and seasonal variations of soil temperature, but overestimated measured soil temperature by 1.86 °C (vs. TS-GCB, surface) and 2.16 °C (vs. TS-GCB, subsurface), indicating more heat accumulated in soils than the reality. Surface soil temperature was better represented by MME compared to the subsurface soil layer (vs. TS-GCB). In addition, we found the largest simulation bias in the tropical zone, and both positive and negative bias in the arid regions. The large model spread of the individual models in representing soil temperatures in cold regions or periods highlights the needs of improved understanding of how snow and freeze-thaw affect soil thermal dynamics. Overall, the performance of MME is superior to that of the majority of individual models (vs. TS-GCB). Locally, the large discrepancy among observation-derived data, reanalysis data and CMIP6 simulations suggested that it is imperative to acquire more ground-truth soil temperature to better inform model simulation and forecasting.

Seasonal changes in ecosystem health and their spatial relationship with landscape structure in china's Loess plateau

Environmental managers have sought to optimize landscape structure to maintain regional ecosystem health (EH). However, we still lack a full understanding of the effects of changing landscape structure on EH, especially in terms of shorter time scales (e.g., monthly or seasonal scales) and spatial heterogeneity. To address this gap, this study used the vigor-organization-resilience-services (VORS) model to assess the annual and seasonal variation of EH in the Loess Plateau (LP) during 2000–2015, and then applied the geographically weighted regression (GWR) model to investigate the spatial effects of different aspects (i.e., landscape composition and configuration) of the landscape structure change on EH over time. Our results showed that the EH level in the LP from 2000 to 2015 displayed temporal non-stationarity at annual and seasonal scales, and a spatial pattern of decreasing from southeast to northwest. The effects of landscape structure on EH had obvious spatial and temporal heterogeneity, depending on the local landscape composition and configuration. In general, landscape configuration exerted a greater impact on EH than landscape composition apart from Shannon’s diversity index and percentage of forestland area, but the intensity and direction of the impact displayed higher temporally instability and therefore should receive more attention. Furthermore, regulation of the dominant patch size, landscape connectivity and forest spatial spacing is the optimal choice for regional ecosystem management. The results suggest that EH responses to changes in landscape structure in the LP vary by time and place, and that the integrating temporal variation and spatial characteristics can help decision-makers make precise spatial arrangements and temporal adjustments to keep the negative ecological impacts within reasonable limits.

Teleconnection of the Quasi-biennial oscillation with boreal winter surface climate in Eurasia and North America

An improved understanding of dynamical coupling from regional to global scales via tropospheric or stratospheric region can be helpful in improving seasonal forecasts for a given region of interest. Here we investigate dynamical coupling between the equatorial stratospheric Quasi-biennial oscillation and the boreal winter surface climate of the Northern Hemisphere mid and high latitudes using 42 years of data (1979–2020). For neutral El Niño Southern Oscillation periods, the Quasi-biennial oscillation westerly phase at 70 hPa favors high sea level pressure in the polar region, colder conditions and deeper snow over Eurasia and North America, and the opposite effects for the easterly phase. When Quasi-biennial oscillation anomalies arrive near the tropopause, it is observed that planetary wave activity is enhanced towards to extratropical region during westerly phase and reduced during easterly phase. This teleconnection pathway via the upper troposphere and lower stratosphere to the high latitude surface is independent of the “stratospheric pathway” (Holton-Tan mechanism). Diagnosis of this pathway can help to improve understanding of sub-seasonal to seasonal variations, and long-range forecasting over Eurasia and North America.

Seasonality of Birth Weight in Singleton Full-term Births in Finland

Seasonal birth weight has been identified across different climate zones. Finland has wide seasonal and regional variation in climate within the country. We explore the potential seasonality of birth weight in singleton full-term births in Finland, and its variations across time and within the country. We apply descriptive time-series graphs, linear regression across regions, and linear regression with mother fixed effects using Finnish register data consisting of more than 1,800,000 infants born from 1987 to 2021. The descriptive findings indicate a decline in birth weight from 1987 to 2021. A monthly seasonal trend with peaks in spring and autumn and troughs in summer and winter is observed. The pattern gets less distinct with time and shows within-country variations. The seasonal pattern is also present when applying mother fixed effects. We suggest that the seasonal variation is more related to variations in climate than stable characteristics at the family level.

Seasonal Variation of Groundwater Quality in Bonny Island, Rivers State Nigeria

This study investigated the seasonal variations in physicochemical parameters, heavy metals, and petroleum hydrocarbons in groundwater samples collected in nine locations in Bonny Island, Rivers State Nigeria. The water samples for the dry season were collected in December and January while for the wet season, water samples were collected in July and October. A quantitative approach was employed, involving the collection of quantitative data through field sampling and laboratory analysis. Physicochemical parameters, including pH, electrical conductivity, dissolved oxygen, and nutrient levels, were measured using standard analytical techniques. Heavy metal concentrations (Fe, Cd, etc.) were determined using atomic absorption spectrophotometry, while petroleum hydrocarbons (total hydrocarbons and polycyclic aromatic hydrocarbons) were analyzed through solvent extraction and gas chromatography techniques. The study revealed significant seasonal variations in several parameters, with notable significant decreases in phosphate during the wet season and significant increases in chromium and cadmium levels during the dry season (p-value < 0.05). Petroleum hydrocarbon concentrations also exhibited seasonal fluctuations, potentially influenced by precipitation patterns, oil and gas activities, and accidental spills or leaks which were observed during the dry season.

Meteorological effects on particulate matter PM10, PM2.5 concentrations with diurnal and seasonal variations in cities neighboring desert lands

Meteorological effects on particulate matter PM10, PM2.5 concentrations with diurnal and seasonal variations in cities neighboring desert lands

Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing

Abstract. The Chlorophyll-a (Chla) concentration is an important parameter characterizing the water quality of rivers and lakes. Satellite remote sensing provides new opportunities for the quantitative monitoring of Chla concentration in large-scale water bodies. In this study, Sentinel-2 satellite remote sensing data integrated on the Google Earth Engine (GEE) remote sensing big data platform are employed, along with hourly measured water quality site data, to establish a quantitative inversion model for Chla concentration water quality parameters in Jiujiang City within Jiangxi Province, China. The Chla concentration is estimated for each quarter from 2020 to 2022, and the spatial distribution is analyzed, revealing the changing trend of Chla concentration over the past three years. The key findings are as follows: (1) The quantitative inversion model for Chla concentration has been validated with measured data, achieving a model accuracy of 0.53; (2) The spatial inversion results of Chla concentration exhibit an increasing trend that is consistent with actual measurement site results; (3) Owing to the influence of human activities and the peak-low water level of rivers and lakes, Chla concentration shows a discernible seasonal variation pattern. This methodology offers a new perspective for analyzing the seasonal variation characteristics of Chla concentration in rivers and lakes, providing valuable insights for the sustainable management of river and lake water quality.

Validation and development of eDNA metabarcoding primers for comprehensive assessment of Chinese amphibians.

Environmental DNA (eDNA) metabarcoding has emerged as a powerful, non-invasive tool for biodiversity assessments. However, the accuracy and limitations of these assessment techniques are highly dependent on the choice of primer pairs being used. Although several primer sets have been used in eDNA metabarcoding studies of amphibians, there are few comparisons of their reliability and efficiency. Here, we employed lab- and field-tested sets of publicly available and de novo-designed primers in amplifying 83 species of amphibian from all three orders (Anura, Caudata, and Gymnophiona) and 13 families present in China to evaluate the versatility and specificity of these primers sets in amphibian eDNA metabarcoding studies. Three pairs of primers were highly effective, as they could successfully amplify all the major clades of Chinese amphibians in our study. A few non-amphibian taxa were also amplified by these primers, which implies that further optimization of amphibian-specific primers is still needed. The simultaneous use of three primer sets can completely cover all the species obtained by conventional survey methods and has even effectively distinguished quite a number of species (n = 20) in the Wenshan National Nature Reserve. No single primer set could individually detect all of the species from the studied region, indicating that multiple primers might be necessary for a comprehensive survey of Chinese amphibians. Besides, seasonal variations in amphibian species composition were also revealed by eDNA metabarcoding, which was consistent with traditional survey methods. These results indicate that eDNA metabarcoding has the potential to be a powerful tool for studying spatial and temporal community changes in amphibian species richness.

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Magnesium Isotopes of Carbonate Reveal Seasonal Climate Variation in the Central East Asia During the Middle Eocene

AbstractIt is debated whether there was strong climate seasonality during the Eocene, which provides a close geological analogy for near‐future scenarios of greenhouse gas emissions. Lithological data suggest the existence of a broad arid zone centered around 30°N paleo‐latitude, while a humid climate was supported by palaeobotanic assemblages in East Asia. Here, we report the occurrence of massive primary lacustrine dolomite and magnesite in the central East Asia during the middle Eocene. We provide a novel perspective from magnesium isotopes to link the formation of Mg‐carbonates to seasonal dry‐wet cycles. Rapid magnesium input during the rainy season and intense evaporation in the dry season likely caused the formation of magnesium carbonates in an enclosed lake. These findings provide insights into hydroclimatic seasonality during the Eocene, contributing to our understanding of the hydrological cycle response to a greenhouse climate.

Human-induced intensified seasonal cycle of sea surface temperature

Changes in the seasonal cycle of sea surface temperature (SST) have far-reaching ecological and societal implications. Previous studies have found an intensified SST seasonal cycle under global warming, but whether such changes have emerged in historical records remains largely unknown. Here, we reveal that the SST seasonal cycle globally has intensified by 3.9 ± 1.6% in recent four decades (1983–2022), with hotspot regions such as the northern subpolar gyres experiencing an intensification of up to 10%. Increased greenhouse gases are the primary driver of this intensification, and decreased anthropogenic aerosols also contribute. These changes in anthropogenic emissions lead to shallower mixed layer depths, reducing the thermal inertia of upper ocean and enhancing the seasonality of SST. In addition, the direct impacts of increased ocean heat uptake and suppressed seasonal amplitude of surface heat flux also contribute in the North Pacific and North Atlantic. The temperature seasonal cycle is intensified not only at the ocean surface, but throughout the mixed layer. The ramifications of this intensified SST seasonal cycle extend to the seasonal variation in upper-ocean oxygenation, a critical factor for most ocean ecosystems.

Seasonal variation of Salmonella enterica prevalence in milk and cottage cheese along the dairy value chain in three regions of Ethiopia

Seasonal fluctuations influence foodborne illness transmission and affect patterns of microbial contamination of food. Previous investigations on the seasonality of Salmonella enterica prevalence in dairy products in Ethiopia have been minimal. However, such data are needed to inform strategic development of effective interventions to improve food safety, as seasonal differences may affect intervention strategies. This study was conducted to identify differences in the prevalence of Salmonella in milk and cheese samples between wet and dry seasons. A longitudinal study design was utilized with a random sampling occurring during both dry and wet seasons. A total of 448 milk and cottage cheese samples were collected from Oromia, Sidama, and Amhara regions. Samples were tested for Salmonella using the ISO 6579-1: 2008 method, followed by PCR confirmation. A chi-square test was conducted to assess the significance of differences in the prevalence of Salmonella in the samples between the two seasons. Results from this study showed a higher prevalence of Salmonella in all sample types during the dry season (P < 0.05). Moreover, when comparing raw milk, pasteurized milk, and cottage cheese samples, a significant difference was observed in Salmonella prevalence from raw milk samples (27.08%) collected in the Oromia region. Additionally, data showed a significantly higher prevalence of Salmonella in samples collected from raw milk producers (29.17%) during the wet season (P < 0.05). This study indicates that in order to enhance the safety of dairy products in Ethiopia, comprehensive, long-term awareness building on hygienic milk production and handling that consider seasonal influence is warranted.

Seasonal variation in peripheral vestibular disorders based on Korean population data

AbstractObjectivesComprehensive studies in which the seasonal variation in peripheral vestibular disorders was evaluated using data from an entire population are insufficient. The seasonal variation in peripheral vestibular disorders based on data from the entire Korean population was investigated in the present study.MethodsRetrospective data from the National Health Insurance Service of Korea from 2008 to 2020 was analyzed. Benign paroxysmal positional vertigo (BPPV), vestibular neuritis (VN), and Meniere's disease (MD) were defined based on diagnostic, treatment, or audiovestibular test codes. The seasonal incidence for each peripheral vestibular disorder was calculated among all study subjects.ResultsFor the entire study cohort, the incidence of BPPV was significantly higher in spring (odds ratio [OR] = 1.031, 95% confidence interval [CI] = 1.026–1.037), autumn (OR = 1.024, 95% CI = 1.019–1.029), and winter (OR = 1.051, 95% CI = 1.046–1.056) than in summer. The incidence of VN was significantly lower in winter (OR = 0.917, 95% CI = 0.907–0.927) than in summer. The incidence of MD was significantly higher in spring (OR = 1.027, 95% CI = 1.015–1.039) and autumn (OR = 1.029, 95% CI = 1.017–1.041) and significantly lower in winter (OR = 0.919, 95% CI = 0.908–0.931) than in summer. Differences were also observed in seasonal variation based on sex and age.ConclusionsSignificant seasonal variation occurred in peripheral vestibular disorders including BPPV, VN, and MD based on the entire Korean population data. Furthermore, seasonal variation showed differences based on sex and age.Level of Evidence4.

Insights to the Water Balance of a Boreal Watershed Using a SWAT Model.

Abstract The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal zone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHR watershed includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model's capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model's performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal zone watersheds.

Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Microelements, Fatty Acid Profile, and Selected Biomarkers in Grass Carp (Ctenopharyngodon idella) Muscle Tissue: Seasonal Variations and Health Risk Assessment

AbstractThe study assesses associations between microelement levels, fatty acid composition, and oxidative stress markers in grass carp muscle in the summer and autumn seasons. Additionally, various factors were considered, including the estimated daily intake (EDI), target hazard quotient (THQ), total THQ (TTHQ), and metal pollution index (MPI), to evaluate potential health risks for consumers. The microelements (Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, Se, Sr, and Zn) were determined using inductively coupled plasma optical emission spectrometry (ICP-OES), and total mercury was determined using cold-vapor atomic absorption spectroscopy (CV-AAS). Fatty acid profiling was realized using gas chromatography (GC) detection with a flame ionization detector (FID). The overall tendency of microelement levels was as follows: Fe &gt; Zn &gt; Al &gt; Sr &gt; Ba &gt; Ni &gt; Se &gt; Cr&gt; Cu &gt; Mn &gt; Pb &gt; As &gt; Li &gt; Hg; &lt;LOQ (below limit of quantification): Cd, and Co. The correlation analysis between concentrations of trace elements (Al, Ba, Cr, Cu, Mn, Li, Sr, Zn, and Hg) and the fatty acids (C16:0, C16:1, C18:1n9c, C18:2n6c, C20:4n6c, EPA, and DHA), as well as between Al, Ba, Fe, or Hg and oxidative stress markers (superoxide dismutase—SOD, glutathione peroxidase—GPx, or total antioxidant status—TAS), revealed statistically significant interactions in different seasons. THQ values were lower than 1; TTHQ values ranged from 0.27 to 0.76. The main toxic elements forming TTHQ were Hg and Ni (49%). The calculated health risk assessment indices indicate a low concentration of observed elements and low risks associated with the consumption of grass carp muscle from the tested location, although concentrations and THQ of mercury and nickel may be of slight concern.