Meteorological Variables Research Articles

Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method

The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites.

Spatiotemporal Variability and Trend of Ozone Pollution from Satellite Data (2003- 2021) in Iraq

Ozone (O3) occurs naturally in the Earth's upper atmosphere, and at ground level is a dangerous pollutant hurts plants and lung tissue and it’s a major component of smog. The purpose of this study to analyze the time series, trend, and spatial-temporal changes of monthly Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and O3 in Iraq using the Atmospheric Infrared Sounder (AIRS) ascending AIRS3STM data during 2003 - 2021. The time series over six stations (Mosul, Sulaymaniyah, Khanaqin, Rutba, Baghdad, and Basra) have been analyzed and showed similar changes and fluctuation in O3 and OLR. The results of the study obtained were: minimum (decreasing, January–December) and maximum (increasing, May–August), and the mean and standard deviation were (0.044±0.003 ppmv) for O3 and (298± 22 Wm-2) for OLR during the study period. During December to February, the highest RH values were recorded, while the lowest RH values occurred from June to August. The monthly mean and standard deviation of RH was (28.66±29.54 g/kg). Further O3 trends revealed negative results in their annual series over all stations, except for Khanaqin and Basra, which had a positive trend. The O3 concentrations were consistently connected with other meteorological variables (the O3 has a negative correlation with RH and a beneficial correlation with temperature). The O3 spatiotemporal maps showed recorded the highest values recorded in April and May over Mosul and Sulaymaniyah (0.055–0.057), and in December, at middle and southern regions (Baghdad and Basra), there were the lowest O3 value (0.033). These results were due to meteorological and geographical factors. The results showed the efficient use of AIRS data to analyze the variations and distributions of atmospheric factors over different regions. In addition, AIRS observations can be used to obtain atmospheric, climatic, and environmental scientific reports and analyses for large, regional, and global regions and for long, continuous periods.

Seasonal forecasts have sufficient skill to inform some agricultural decisions

Abstract Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American Multi-Model Ensemble (NMME) hindcasts for the time period 1982 - 2020 over the Pacific Northwest US to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire Pacific Northwest region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

A 4 km daily gridded meteorological dataset for China from 2000 to 2020.

Multi-variate gridded meteorological data with high spatial resolution play a key role in studies related to climate change. This study constructed a 4 km daily gridded meteorological dataset for mainland of China (China Daily Meteorological Dataset; CDMet) from 2000 to 2020. The dataset includes nine meteorological variables: 2-meter air temperature (maximum, minimum, and mean temperatures), total precipitation, skin temperature, 10-meter wind speed, relative humidity, surface pressure, and sunshine duration. CDMet was generated using an adaptive interpolation scheme, which employed thin-plate spline and random forest methods to construct the interpolation model. Six combinations of location and terrain information were designed and used as covariates in the model together with reanalysis data. Validation with independent observation stations and existing datasets showed that CDMet has acceptable accuracy, reasonable seasonal variability, and precise spatial distribution, and its accuracy is comparable to that of other datasets. Due to its comprehensive variables and high resolution, CDMet can be used as input data for hydrological, agricultural, and ecological models.

Scaling Correlation Analysis of Particulate Matter Concentrations of Three South Indian Cities

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques.

Estimation of Understory Fine Dead Fuel Moisture Content in Subtropical Forests of Southern China Based on Landsat Images

The understory fine dead fuel moisture content (DFMC) is an important reference indicator for regional forest fire warnings and risk assessments, and determining it on a large scale is a critical goal. It is difficult to estimate understory fine DFMC directly from satellite images due to canopy shading. To address this issue, we used canopy meteorology estimated by Landsat images in combination with explanatory variables to construct random forest models of in-forest meteorology, and then construct random forest models by combining the meteorological factors and explanatory variables with understory fine DFMC obtained from the monitoring device to (1) investigate the feasibility of Landsat images for estimating in-forest meteorology; (2) explore the feasibility of canopy or in-forest meteorology and explanatory variables for estimating understory fine DFMC; and (3) compare the effects of each factor on model accuracy and its effect on understory fine DFMC. The results showed that random forest models improved in-forest meteorology estimation, enhancing in-forest relative humidity, vapor pressure deficit, and temperature by 50%, 34%, and 2.2%, respectively, after adding a topography factor. For estimating understory fine DFMC, models using vapor pressure deficit improved fit by 10.2% over those using relative humidity. Using in-forest meteorology improved fits by 36.2% compared to canopy meteorology. Including topographic factors improved the average fit of understory fine DFMC models by 123.1%. The most accurate model utilized in-forest vapor pressure deficit, temperature, topographic factors, vegetation index, precipitation data, and seasonal factors. Correlations indicated that slope, in-forest vapor pressure deficit, and slope direction were most closely related to understory fine DFMC. The regional understory fine-grained DFMC distribution mapped according to our method can provide important decision support for forest fire risk early warning and fire management.

Dynamics of CO2 fluxes and environmental responses in a Poplar plantation

Forest plantations cover a large percentage of global forest landscapes contributing significantly to carbon sequestration. By using continuous eddy covariance technique, we observed net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (ER), and meteorological variables from August 2018 to December 2019 in a Poplar plantation. The Poplar plantation ecosystem was a carbon sink overall, with high carbon uptake in growing season and limited uptake/emission in non-growing season. The annual cumulative NEE, GEP, and ER were −763.61, 1542.19, and 778.58 g C m−2 yr−1, respectively. Photosynthetically active radiation (PAR) significantly influenced NEE both at half-hourly and daily scale (P < 0.01 for both), while relative humidity (RH) and vapor pressure deficit (VPD) only significantly affected NEE at half-hourly scale (P < 0.01). The prevailing wind direction throughout 2019 was southeast and it varied between seasons. Southeast wind was the prevailing wind direction in summer and winter, while southwest and northeast wind were the dominant wind direction in spring and autumn, respectively. Our results highlight that polar plantations play an important role in storing carbon, and that understanding meteorological conditions is crucial in investigating ecosystem-atmosphere interactions and their impacts on carbon cycling.

Eelgrass (Zostera marina) Trait Variation Across Varying Temperature-Light Regimes

Seagrass trait variation, which results from both local genetic adaptation and phenotypic plasticity, has important effects on ecosystems. Physical drivers underlie these processes and are important determinants of trait variation. Despite this, few studies examine multivariate predictive relationships between sets of physical drivers and sets of seagrass traits. Here, we use redundancy analysis to define this relationship for eelgrass Zostera marina, using traits that represent bed structure, morphology, and physiology and physical drivers that emphasize light conditions and temperature variability on different time scales. We found a relationship between plant size (i.e., leaf length and width, rhizome width, number of leaves) and shoot density that dominated the trait variation. Specifically, as temperatures became warmer, more variable, and light was less limiting, plants became smaller (shorter, narrower, and fewer leaves, thinner rhizomes) but beds became denser. Plant biomass (leaf area index), which increased with decreasing temperature variability and bottom light, further refined this relationship. Overall, temperature variability (i.e., daily temperature range, heat accumulation, time in the optimal temperature range, and tidal and meteorological variability), as well as bottom light, were important predictors of eelgrass traits. We further identified three distinct temperature-light regimes across which traits differed; these included the cool low variability temperature regime with low light, the warm high variability temperature regime with high light, and the intermediate case between these endpoints. Our study identifies specific temperature and light drivers that define certain eelgrass traits and provides a multivariate statistical model that can be used to predict eelgrass trait values from known physical conditions.

Pollen season variations among anemophilous species in an Atlantic-influenced mediterranean environment: a long term study (1993-2022).

Numerous studies have shown that climate change is impacting the airborne pollen concentration of various anemophilous plant species, both herbaceous and woody. The aim of this study was to determine whether this trend, highly marked in Europe, is also occurring in the city of Huelva (south-western Spain), an area with a milder Mediterranean climate. The factors influencing the main pollen season were studied for all local taxa accounting for more than 1% of the total pollen collected, over the period 1993-2022, using a volumetric Hirst type trap to determine concentration. The study included a trend analysis of meteorological variables susceptible to climate change, specifically maximum, minimum and average temperature, precipitation and the number of hours of insolation per day. Although some of the variables analysed do not show significant trends for most taxa, such as start date or peak date, the overall results indicate a trend in the airborne pollen dynamics for herbaceous and woody taxa. Six of the eight woody taxa studied showed a significant trend towards increased pollen concentration, some of which are associated with temperature in previous seasons. In contrast, three of the five herbaceous taxa, which are more sensitive to short term meteorological variables, show a significant acceleration in the end date of the main pollen season, correlated negatively with temperature and insolation. In conclusion, some incipient changes are occurring, which may be indicative of future consequences on biodiversity in the Mediterranean area influenced by the Atlantic Ocean.

Environmental factors influencing the development and spread of resistance in erythromycin-resistant streptococcus pneumoniae.

Bacterial drug resistance is becoming increasingly serious, this study aims to investigate the relationship between the resistance rate of erythromycin-resistant Streptococcus pneumoniae (SP) and reasons for the epidemic under complex geographical and climatic factors in China. Data spanning from 2014 to 2021, including drug resistance rates, isolate rates, meteorological variables, and demographic statistics, were collected from the China Antimicrobial Resistance Monitoring System, the China Statistical Yearbook and China Meteorological Website. Our analysis involved nonparametric tests and the construction of multifaceted regression models for rigorous multivariate analysis. Single-factor analysis revealed significant differences in the resistance rate and isolate rate of erythromycin-resistant SP across different regions characterized by Hu Huanyong lines or different climate types. Multivariate regression analysis indicated positive correlations between the drug resistance rate and temperature, Subtropical climate, Gross Domestic Product (GDP), Hu Huanyong line, and the highest temperature in the past period (Tm); the isolate rate showed a positive correlation with regional GDP and a negative correlation with monsoon climate. The model developed in this study provides valuable insights into the resistance rate and potential relationships of erythromycin-resistant SP under complex meteorological conditions in China.

Heat Index: An Alternative Indicator for Measuring the Impacts of Meteorological Factors on Diarrhoea in the Climate Change Era: A Time Series Study in Dhaka, Bangladesh

Heat index (HI) is a biometeorological indicator that combines temperature and relative humidity. This study aimed to investigate the relationship between the Heat Index and daily counts of diarrhoea hospitalisation in Dhaka, Bangladesh. Data on daily diarrhoea hospitalisations and meteorological variables from 1981 to 2010 were collected. We categorised the Heat Index of >94.3 °F (>34.6 °C), >100.7 °F (>38.2 °C) and >105 °F (>40.6 °C) as high, very high and extremely high Heat Index, respectively. We applied a time series adjusted generalised linear model (GLM) with negative binomial distribution to investigate the effects of the Heat Index and extreme Heat Index on hospitalisations for diarrhoea. Effects were assessed for all ages, children under 5 years old and by gender. A unit higher HI and high, very high and extremely high HI were associated with 0.8%, 8%, 7% and 9% increase in diarrhoea hospitalisations in all ages, respectively. The effects varied slightly by gender and were most pronounced in children under 5 years old with a rise of 1°F in high, very high and extremely high HI associated with a 14.1% (95% CI: 11.3–17.0%), 18.3% (95% CI: 13.4–23.5%) and 18.1% (95% CI: 8.4–28.6%) increase of diarrhoea, respectively. This suggests that the Heat Index may serve as an alternative indicator for measuring the combined effects of temperature and humidity on diarrhoea.

Investigation of meteorological variables and associated extreme events over North-East India and its adjoining areas using high-resolution IMDAA reanalysis

Investigation of meteorological variables and associated extreme events over North-East India and its adjoining areas using high-resolution IMDAA reanalysis

Unveiling the Dynamics of Canopy Transpiration: A Novel Model Integrating Stomatal and Aerodynamic Resistance in Semi-Humid Forests

Canopy–atmospheric water vapor output resistance (gs) is a key parameter in researching forest canopy transpiration. It is important for quantifying the water vapor exchange in forest ecosystems. However, the method by which gs is determined has been controversial, and it cannot precisely represent water vapor exchange. This study aimed to develop a model to quantify the water vapor resistance between the canopy and the atmosphere in Platycladus orientalis (P. orientalis) forests using sap flow and meteorological factors monitoring data. The resistance model was constructed using the relationship between canopy stomatal resistance (gc) and aerodynamic resistance (ga) from the mechanism perspective, and sap flow data and measurements of meteorological variables were used to model the stomatal and aerodynamic resistance of the canopy. The results indicate that the canopy-atmospheric water vapor output resistance was closer to the measured values and showed a unimodal curve in the diurnal scale, and this change could provide more accurate measurements of tree transpiration. At the same time, the canopy-atmospheric water vapor output resistance was strongly influenced by wind speed and PAR when 0.2 m/s < u < 0.4 m/s (R2 = 0.871, p < 0.01). The stomatal and aerodynamic resistance were also both strongly influenced by wind speed, with the proposed model achieving a high degree of fit (R2 = 0.949, p < 0.01), providing a new tool for analyzing forest transpiration. This research provides a new perspective and technical reference for clarifying the mechanism of forest canopy water output.

Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

The source of volatile organic compounds pollution and its effect on ozone in high-altitude areas

Volatile organic compounds (VOCs) are crucial precursors in the formation of ozone (O3). The sources of pollution are complex and significantly impact O3 generation. Long-term exposure to high-concentration O3 environments causes serious damage to organisms. High-altitude areas experience continuous high temperatures and strong solar radiation, which can easily produce O3 through photochemical reactions, making these areas prone to frequent air pollution. This study utilizes the National Positioning Station of the Yinchuan Urban Ecosystem in Ningxia to conduct field synchronous observations, gathering data on VOCs, meteorological variables, and O3. By employing machine learning algorithms, we examined the seasonal distribution characteristics of VOCs, their pollution sources, and their impact on O3. The results show that the seasonal distribution of VOCs areas is low in spring and autumn and high in summer and winter. The components with higher volume fraction contribution are m-tolualdehyde in spring and summer, ethane in autumn, and acetylene in winter. The main emission sources of VOCs pollution are hydrocarbon volatile emission in spring and winter, solvent volatilization emission in summer, and industrial sources in autumn. VOCs have a negative effect on O3, with a greater impact in winter and spring, evidenced by standardized effect values of −0.26 and −0.24. The key components affecting O3 in VOCs vary by season. Aromatic hydrocarbons are the key components in spring and winter, with contribution rates of 22 % and 21.3 %. Alkenes are the key components in summer and autumn (24.5 % and 26.8 %). Among meteorological variables, temperature is the key factor affecting O3, while wind speed is the key factor affecting O3 only in winter. This study aims to clarify the sources of VOCs pollution in different seasons and their impact on O3, providing a theoretical basis and technical support for the prevention and control of VOCs and O3 pollution in high-altitude areas.

Capsular attention Conv-LSTM network (CACN): A deep learning structure for crop yield estimation based on multispectral imagery

Precise prediction of agricultural production output is crucial for farmers, policymakers, and the Farming-related industry. This article introduces a novel methodology to crop yield forecasting using a capsular neural network equipped with Conv-LSTM and attention mechanism. Our model combines the strengths of 3DCNN, and Conv-LSTM, which can capture the temporal dependencies and 3D features of crop yield data, and attention mechanism, which Can prioritize the most significant characteristics for making predictions. We evaluated CACN on a sizable collection of data of soybean crop yield in the United States from 2003 to 2019 and evaluated against various cutting-edge deep learning models. The outcomes indicate that our suggested approach surpasses other models in performance in terms of RMSE, correlation coefficient, and prediction error map. Specifically, our model achieved approximately 14 % improvement in terms of RMSE, compared to the state-of-the-art model Deep-Yield. Our model also demonstrated the ability to extract more meaningful features and capture the complex relationships between crop yield data and meteorological variables. Overall, our proposed method shows great potential for accurate and efficient crop yield forecasting and can be applied to other crops and regions.

Characterization of Airborne Microbial Communities in Northern Thailand: Impacts of Smoke Haze Versus Non-Haze Conditions.

Data on airborne microorganisms, particularly in Southeast Asia, are more limited compared to chemical data. This study is the first to examine the community and diversity of microorganisms on PM2.5 in an urban area of Northern Thailand during both smoke haze and non-smoke haze periods of 2020. This study evaluated the composition of airborne bacteria and fungi and analyzed their association with the chemical composition of PM2.5 and meteorological variables. Significantly higher concentrations of PM2.5 and more chemical compounds were observed during the smoke haze period compared to the non-smoke haze period. Increased PM2.5 concentrations significantly altered both bacterial and fungal communities. The diversity and richness of airborne bacteria increased, whereas those of fungi decreased. The level of PM2.5 concentration (the carrier), the chemical composition of PM2.5 (the resources for survival), and the local meteorological conditions (relative humidity (RH)) were associated with the differences in bacterial and fungal populations. In addition, air originating from the west of the receptor site, influenced by both terrestrial and marine air mass routes, contributed to higher bacterial diversity and richness during the smoke haze period. In contrast, fungal diversity and richness were greater when the air came from the southwest, following a marine route. However, the primary health concern is pathogens, which were present in both periods (such as Clostridium, Aspergillus, and Cladosporium) and were especially abundant during smoke haze periods. This study highlights those airborne microorganisms, along with the particles and their chemical composition, are important components that can impact health, including that of humans, animals, and the environment.

Understanding heavy precipitation events in southern Israel through atmospheric electric field observations

Characterizing the interaction between meteorological variables such as humidity, wind speed, cloud cover, and precipitation with the atmospheric electric field is vital for improving the nowcast of extreme weather events such as heavy precipitation. With this aim, we provide minute-scale electric field observations in southern Israel. These were taken during low-pressure weather systems in winter, often termed ‘Cyprus Lows.’ We focus only on precipitating (‘wet’) events, where rain was measured at the surface during and after the cold front's passage. The mean |PG| values for ‘wet’ Cyprus Lows are higher (Hundreds to thousands V m−1) compared with the mean fair-weather values (~100–200 V m−1, and exhibit a sharp and rapid increase of the |PG| of up to tens of V m−1 min−1 during the arrival of the cold front and hundreds of V m−1 min−1 during precipitation. Then, we analyzed selected case studies in detail. The response of the |PG| to thunderstorm clouds, i.e., Cumulonimbus, is an increase to values of thousands of V m−1. The temporal evolution of the |PG| allowed us to identify the type of cloud and its life cycle stage. We suggest that using state-of-the-art 1 Hz measurements of the |PG| and deducing cloud patterns at strategic locations, such as in arid regions like southern Israel, may improve the nowcasting capabilities of localized heavy precipitation events.

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Dissolved PAHs in the Beibu Gulf and adjacent waters of the South China Sea: Physical and biochemical processes-driven distributional variations

Polycyclic aromatic hydrocarbons (PAHs) in semi-enclosed gulfs are influenced by physical and biochemical processes, which haven’t been well understood. This study aims to investigate the spatial distribution and vertical profiles of dissolved PAHs in the Beibu Gulf and adjacent waters of the South China Sea, along with hydrological, meteorological, and biochemical variables. Particularly relevant are the effects of atmospheric pressure, salinity, ammonium, chlorophyll-a, as well as riverine inputs (RI), sea currents, and upwelling. In surface seawater, the total concentrations of eight dissolved PAHs (∑8PAHs) were 7.76 ± 2.16ng/L, with a distribution pattern of western Guangdong waters (WGWs) > BG > Qiongzhou Strait (QS). ∑8PAHs in the northern BG (9.10 ± 2.00ng/L) was significantly higher than that in the southern BG (6.65 ± 1.54ng/L) (p < 0.01), suggesting that local anthropogenic activities and unique environmental characteristics significantly influenced PAHs distribution. In water column, PAHs in BG displayed enrichment in surface and bottom but decreased in medium water, while those in WGWs and QS decreased with increasing depth. Source apportionment concluded that PAHs in QS and WGWs were primarily from petroleum sources, and PAHs in BG were mainly from coal combustion. RI, combined with circulation, coastal current, and intrusion of SCS water influenced the surface PAHs distribution in BG, with eddy impacts observed. Specifically, regarding the surface PAHs distribution, differences in atmospheric pressure may influence the air-sea exchange of PAHs, especially positively affecting 4-ring PAHs. Salinity factors further corroborated the contribution of RI to 3-ring PAHs, followed by the regulation of PAHs through biological pumps (ammonia and chlorophyll-a). Moreover, upwelling-induced biodegradation and resuspension affected the vertical distribution of PAHs. While most PAHs posed a negligible risk, coking-generated fluorene posed a moderate risk to ecosystems due to changes in the energy structure, warranting further investigation into its toxicological impacts.