Average Time Series Research Articles

Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data

Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5–32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for “bad” segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker.

Dust Storms Are Associated with an Increase in Outpatient Visits for Rheumatoid Arthritis

Our study is the first to illuminate a previously underexplored dimension in the intricate interplay between environmental factors, specifically dust storms (DSs), and rheumatoid arthritis (RA) outcomes. An Auto Regressive Moving Average (ARMA) time-series estimation model was employed to analyze rheumatoid arthritis outpatient visits, dust storms, air pollution, and meteorology data in Taiwan from 2006 to 2012. The results show that females are three times more likely to experience RA-related issues and seek medical attention. Percentage analysis revealed a 10–15% increase in daily RA outpatient visits on post-event days 1 and 2 compared to non-DS days. However, the time-series estimation indicated a delayed and statistically significant (p < 0.05) increase in RA outpatient visits on post-event day 1 for males but not females. Additionally, a significant increase in RA outpatient visits (p < 0.05) was observed on post-event day 1 among individuals aged 61 and above. Environmental factors such as temperature and SO2 showed strong significance across all genders and age groups (p < 0.001). The findings highlight distinct gender and age disparities regarding the impacts of DS on RA outpatient visits, emphasizing the heightened sensitivity of males to environmental pollutants and the vulnerability of the elderly population.

Prophet modeling for oil production forecasting in an enhanced oil recovery field

Accurate daily oil production forecasting is essential for efficient reservoir management and investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and conformance-dominated fields is a complex process due to the nonlinear, voluminous, and often uncertain nature of reservoir parameters and hidden factors. As a result, conventional tools such as decline curve analysis frequently fail to accurately predict daily oil production in conformance-controlled areas. In contrast, machine learning works efficiently for large datasets, even if the parameter values are unknown. The current study employs a Prophet time series forecasting method for five oil production wells in an EOR applied field, but it fails to achieve the desired sweep efficiency. This study compares the results of conventional decline curve analysis (DCA) and popular autoregressive integrated moving average time series forecasting methods with the Prophet model. This is the first attempt to use Prophet for oil well production forecasting, where polymer flooding is used. In all, 60% of the data are used for training, and the remaining 40% are used for testing. The Prophet shows the best performance for all the wells. This study is also the first to handle shut-in data using the Prophet model for oil production. Well-2 achieves the highest accuracy after incorporating shut-in results, with an R2 score of 92%. The result shows that though the DCA performs reasonably well with higher linearity and trend stationary data, Prophet modeling shows superior results than conventional DCA for all EOR applied producing wells.

Influence of air temperature and interrelationship with greenhouse gases (CO2 and CH4) over Iraq using AIRS data

Global and regional observations of air temperature (AT) and specific atmospheric greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2) are required for a variety of applications, including constraining global or regional estimates of their significant impacts on the climate system. The present study employs Atmospheric Infrared Sounder (AIRS) -Level3 monthly products for AT, CH4, and CO2, at two standard pressure levels (925 and 500 hPa) over Iraq during 2010–2016. Both CO2 and CH4 shows significant seasonal variation, with maximum (minimum) CO2 observed in June (October), while CH4 recorded three maximum peaks during April, August, and November, and a minimum in February. CH4 shows a negative correlation during winter (DJF), spring (MAM), summer (JJA), and autumn (SON) with correlation coefficients (R) −0.627, −0.734, −0.491, and −0.688, respectively. The P-value is below 0.05 (4.14 × 10−15, 2.13 × 10−22, 1.1 × 10−8, and 5.2 × 10−19) for the four seasons, indicating a negative linear relationship. CO2 shows a low negative correlation in DJF and SON, and a low positive correlation in MAM and JJA seasons, with R values equal to −0.315, −0.221, 0.059, and 0.079, for DJF, SON, MAM and JJA seasons, respectively. The P-value was greater than 0.05 (0.061, 0.728, 0.647, and 0.195) for the four seasons, respectively, indicating a nonlinear relationship with AT. The monthly averaged time-series for CH4 and CO2 shows an evident increase, with an annual average increase of 1.81% (4.75) ppbv/year and 3.31% (1.84) ppm/year, respectively. Analysis reveals that the major sink and sources for CH4 are the presence of hydroxyl (OH) radicals and vegetation, whereas the major sources for CO2 are anthropogenic emissions, burning fossil fuels, and land-use change. The satellite observations of AIRS can efficiently show the spatiotemporal variations of air temperature versus CH4 and CO2 for the study area.

HYBRID MODEL OF SINGULAR SPECTRUM ANALYSIS WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND FUZZY TIME SERIES FOR INDONESIAN CRUDE PRICE FORECASTING

This study discusses a hybrid model of Singular Spectrum Analysis (SSA) with Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS) for forecasting the Indonesian Crude Price (ICP). SSA is considered to capture the deterministic component of the data while the ARIMA and FTS are to represent the stochastics one. The data that used in this study are ICP per month from January 2017 to May 2023. The data from January 2017 to December 2022 are used as insample data, while the data from January to May 2023 are used as outsample data. The insample data is firstly modeled by SSA and the residuals are then modeled by ARIMA, referred to as the hybrid SSA-ARIMA. By the same procedure, the hybrid SSA-FTS model is also constructed to the insample data. Based on the experiment, the hybrid SSA-ARIMA produces Mean Absolute Percentage Error values 8.08% for an insample and 7.10% for an outsample data. These values are less than those obtained by hybrid SSA-FTS. Therefore, the hybrid SSA-ARIMA is recommended for forecasting the monthly ICP.

Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost

Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.

Analysis of streamflow and rainfall trends and variability over the Lake Kariba catchment, Upper Zambezi Basin

ABSTRACT Hydro-meteorological trend analysis is critical for assessing climate change and variability at basin and regional levels. This study examined the long- and short-term trends from stream discharge and rainfall data in the Lake Kariba catchment. A trend and change point analysis was carried out on the mean, minimum and maximum monthly average time series for 14 gauging stations that are located within the Kariba catchment. The Mann–Kendall and the Pettitt tests were used to determine the trend and any changes in the long-term average of the time series. The magnitude of the trend was determined by Sen's slope method. The results indicate that generally there has been a decreasing trend in river and rainfall long-term mean values across the catchment. A statistically significant trend (p ≤ 0.05) was observed at Zambezi River at Lukulu, Senanga and Victoria Falls, with a positive correlation in Pearsons's coefficient of water levels and rainfall at Lukulu (0.312) and Senanga (0.365). The decrease in the time series trend and the change point observed have been attributed to anthropogenic activities, climate change and variability impact on the catchment. The findings are critical for climate risk management and reduction decisions for near- and long-term timescales.

PREDIKSI NILAI PENGADAAN BARANG DAN JASA PADA SEBUAH PERUSAHAAN PARIWISATA MENGGUNAKAN METODE ARIMA DAN FUZZY TIME SERIES

PT XYZ does not have a tested evaluation metric to measure the accuracy of procurement prediction models. This research aims to find a suitable metric by comparing two methods: Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS). Both methods were chosen based on the ability of ARIMA to handle time patterns and trends, and the flexibility of FTS in dealing with uncertainty in procurement values. The research uses Root Mean Squared Error (RMSE) values to measure prediction accuracy. The ARIMA implementation analyzes historical data to forecast patterns, while FTS accommodates fluctuations and uncertainties, allowing for more adaptive and accurate predictions. The analysis results show that the ARIMA model has an AIC value of 3953.57 and a residual value of 3351745.26, while FTS has a residual of -224.79. The RMSE evaluation shows the ARIMA value of 3351745.30 and FTS of 224793895.00. The predicted value of ARIMA is 440,326,255, while FTS is 668,471,895. Based on these results, FTS shows superior prediction accuracy compared to ARIMA. FTS is recommended to be implemented at PT XYZ due to its ability to effectively manage uncertainty and fluctuations in procurement values, enabling more accurate strategic decisions. Further research is needed to understand the factors that influence the performance difference between these two methods.

Impact of the organ-system integrated curriculum on the research productivity of a Philippine government medical school: An ARIMA-based bibliometric analysis

The organ-system integrated (OSI) curriculum was pioneered in the country by the U.P. College of Medicine (UPCM) in 2004. Data on its impact on research productivity was lacking. This study aims to describe the trends in research productivity of UPCM in a time series and the impact of OSI implementation on publication trends. Relevant electronic databases (PubMed, Scopus, and WPRIM) were searched from the earliest indexed record until December 2023. The auto-regressive integrated moving average (ARIMA) time series analysis was used to compare the actual and predicted trends during the OSI implementation using the pre-OSI trend. A total of 2,705 articles were included. Acta Medica Philippina is the journal with the highest number of publications (n = 420, 20.24%). Although there is a considerable rise in the number of publications utilizing evidence synthesis and qualitative design, observational studies remain as the most common design (n = 293, 2021-2023 period). The leading clinical departments are Neurosciences and Medicine. The departments of Biochemistry and Clinical Epidemiology take the lead among basic sciences departments. Despite the glaring uptrend in publications concurrent with OSI implementation, the actual trend is not significantly different from the predicted trend (LR chi2(1) = 0.37; Prob > chi2 = 0.5432). The OSI curriculum did not significantly change the trend of productivity. Medical curriculum designers should revisit strategies dedicated for research competencies. It is imperative to include activities for qualitative and mixed-methods designs. Future studies should consider relevant critical events in the time series forecasting models and the scientific outputs in the grey literature.

Global warming’s grip on agriculture: Strategies for sustainable production amidst climate change using regression based prediction

The intersection of climate change and food production is emerging as a critical area of research, focusing on both the potential benefits and the significant challenges posed by changing climate conditions. Elevated levels of carbon dioxide alongside rising global temperatures could theoretically boost crop yields, benefiting both human and animal consumption. This study examines the impact of various climate variables—temperature, humidity, precipitation, and soil moisture—on the primary production of essential foods such as rice, wheat, livestock, milk, eggs, vegetables, and fruits. Utilizing data from different countries spanning from 2000 to 2020, drawn from world development indicators, this research employs econometric analysis coupled with deep learning-based cluster analysis. Additionally, it projects future production trends up to 2100 using the moving average time series forecasting method. The findings reveal a direct correlation between climate variables and the production levels of vegetables and other food items, highlighting the immediate effects of climatic changes on agriculture. The study also points out the uneven distribution of these climate impacts, with developing countries facing more severe challenges due to their limited resources and adaptive capacities. This uneven impact contributes to increased uncertainty in food supply and affects market stability. Furthermore, concerns about food safety are intensifying under the influence of climate change, although some regions have implemented effective food conservation and control measures to mitigate these risks. This research underscores a complex landscape where the risks and benefits of climate change on food production are not uniformly distributed, but rather are influenced by a myriad of factors including geographic location, economic conditions, and the level of technological advancement in food safety practices. The nuanced understanding of these dynamics is crucial for developing targeted strategies to enhance food security in the face of a changing climate.

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Assessing the Differentiated Impacts of COVID-19 on the Immigration Flows to Europe

The immediate effects of COVID-19 on mortality, fertility, and internal and international migration have been widely studied. Particularly, immigration to high-income countries declined in 2020. However, the persistence of these declines and the extent to which they have impacted different migration flows are yet to be established. Drawing on immigration flows from Eurostat and Autoregressive Integrated Moving Average (ARIMA) time-series models, we assess the impact of COVID-19 on different immigration streams to seven European countries. We forecast counterfactual levels of immigration in 2020 and 2021 assuming no pandemic, and compare these estimates with actual immigration counts. We use regression modeling to explore the role of immigrants’ origin, distance, stringency measures, and gross domestic product (GDP) trends at origins and destinations as potential driving forces of changes in immigration during COVID-19. Our results show that, while there was a general decline in immigration during 2020, inflows returned to expected levels in 2021, except for Spain. However, drops in immigration flows from countries outside the Schengen Area to Europe persisted in 2021. Immigrants’ origin emerged as the main factor modulating immigration changes during the pandemic, and to a lesser extent stringency measures and GDP trends in destination countries. Contextual factors at origin seem to have been less important.

Twitter's capacity to forecast tourism demand: the case of way of Saint James

PurposeRadical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.Design/methodology/approachThis study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.FindingsThe results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.Originality/valueThis study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

Impact of the COVID-19 pandemic on hospital episodes for falls and fractures associated with new-onset disability and frailty in England: a national cohort study.

Older people with frailty are at risk of harm from immobility or isolation, yet data about how COVID-19 lockdowns affected them are limited. Falls and fractures are easily measurable adverse outcomes correlated with frailty. We investigated whether English hospital admission rates for falls and fractures varied from the expected trajectory during the COVID-19 pandemic, and how these varied by frailty status. NHS England Hospital Episode Statistics Admitted Patient Care data were analysed for observed versus predicted outcome rates for 24 January 2020 to 31 December 2021. An auto-regressive integrated moving average time-series model was trained using falls and fracture incidence data from 2013 to 2018 and validated using data from 2019. Models included national and age-, sex- and region-stratified forecasts. Outcome measures were hospital admissions for falls, fractures, and falls and fractures combined. Frailty was defined using the Hospital Frailty Risk Score. 144,148,915 pre-pandemic hospital admissions were compared with 42,267,318 admissions after pandemic onset. For the whole population, falls and fracture rates were below predicted for the first period of national lockdown, followed by a rapid return to rates close to predicted. Thereafter, rates followed expected trends. For people living with frailty, however, falls and fractures increased above expected rates during periods of national lockdown and remained elevated throughout the study period. Effects of frailty were independent of age. People living with frailty experienced increased fall and fracture rates above expected during and following periods of national lockdown. These remained persistently elevated throughout the study period.

Comparative Analysis of ARIMA and BSTS Models for Electricity Price Forecasting in Major West Coast Cities

This study presents a detailed comparative analysis of two prominent statistical models, the AutoRegressive Integrated Moving Average (ARIMA) and Bayesian Structural Time Series (BSTS), in the context of forecasting electricity prices in major West Coast cities of the United States, namely Los Angeles, San Francisco, and Seattle. Utilizing historical electricity price data obtained from the Bureau of Labor Statistics, the research meticulously trains and tests both models, aiming to evaluate their predictive accuracy and reliability in dynamic urban energy markets. The core objective of this research is to ascertain the effectiveness of ARIMA and BSTS models in the realm of electricity price forecasting, with a particular emphasis on their applicability in urban settings characterized by rapid market evolution. Through this analysis, the study offers valuable insights that could significantly influence energy market forecasting and policy formulation. The findings are particularly relevant for stakeholders in the energy sector, including policymakers and market strategists, as they navigate the complexities and challenges of modern energy landscapes. This research contributes to a more comprehensive understanding of statistical modeling techniques in energy market analysis and provides a foundation for more informed decision-making in energy policy and strategy development.

Evaluating the impacts of anthropogenic, climate, and land use changes on streamflow

Abstract Several factors, including natural and human-induced, can affect river discharge. This study aims to examine the influence of land use changes and climate change on the monthly average streamflow time series in the Talar River basin, in northern Iran. To investigate the impact of human factors, namely land use change and point source operations, on monthly average streamflow, the DBEST method was used to detect any breakpoint in the streamflow time series caused by gradual changes in land use and climate. The SWAT model was used to simulate the basin at Kiakola and Shirghah stations, between 2001 and 2020. The land use maps were created for the years 2001 and 2019. Calibration and validation at the Kiakola station showed that the Nash-Sutcliffe model (NSE) had an efficiency of 0.8 and 0.76, respectively, while at the Shirghah station, the same values were 0.84 and 0.75. Findings revealed that human activities, specifically the combined impact of land use change and point source operations, had a 60% influence on the monthly average streamflow of the Talar River. They further showed that the combination of land use and harvesting played the most significant role in the basin's outflow on a monthly scale.

Peramalan Harga Minyak Goreng di Provinsi Sulawesi Utara dengan Menggunakan Metode Analisis Autoregressive Integrated Moving Average (ARIMA)

Cooking oil is a basic ingredient that plays an important role in the Indonesian economy. Cooking oil is one of the nine basic commodities which plays an important role in the Indonesian economy. The data used for research is from the Agency Center for Statistics from January 2018 to December 2022 with the forecasting method used for this data, namely the Autoregressive Integrated Moving Average (ARIMA) time series method. This research aims to obtain the best time series model and to determine the results of forecasting cooking oil prices in North Sulawesi using the ARIMA method. The research results show that the best model obtained is the ARIMA model (1,2,1) with a Mean Square Error (MSE) of 350248. From the forecasting results it is found that the price of cooking oil has increased.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces.

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions. We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth. We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series. Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019–2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2–98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Research on the change of the storage volume of the Nordic Seas Overflows over the last 40 years

The Nordic Sea overflow, being a significant driver of the thermohaline circulation, exerts a substantial influence on environmental dynamics in the Arctic and globally. A better understanding about the trend of the storage volume of the Nordic Seas Overflows is of paramount importance to a realistic assessment of the North Atlantic circulation and variability. EN4.2.2 reanalysis data were utilized to acquire the monthly average time series of overflow water storage volume in the Nordic Sea from 1950 to 2022. The storage volume demonstrates seasonal variations, with fluctuations of approximately 10% around the average. Over the period from 1980 to 2022, the average volume per decade exhibited a consistent decrease. Linear fitting of the annual average data estimated the overall relative change trend in the last 43 years to be -7.2 ± 2.6%. However, the quality of the EN4.2.2 data brings the average error of about 12% in the calculation of the overflow storage volume, which implies that the downward trend requires further validation. From a spatial standpoint, the Norwegian Sea, particularly the Lofoten Basin, is the primary region where overflow water storage volume in the Nordic seas have decreased. This decrease corresponds to an increase in ocean temperatures within the upper layer (0-600m) of both the Lofoten Basin and the Norwegian Basin. The warming of these regions has directly impacted the overflow water storage volume, leading to its reduction. It is noteworthy that the ocean temperature rise in the upper layer is more influenced by the Atlantic inflow rather than air-sea flux, particularly in the Norwegian Basin. This is attributed to the substantial increase in SST in the North Atlantic, which aligns with the warm current regions in the Atlantic Ocean, and the lack of statistical significance in linear trend of the air-sea heat flux. Furthermore, there has been a more rapid reduction in the overflow storage volume in the Norwegian Sea from 2011 to 2022. Specifically, the annual average overflow volume from 2018 to 2022 dropped below the previous lowest value. Concurrently, the salinity of the upper Norwegian Sea and the Atlantic inflow decreased considerably, while there has been no significant change in ocean temperature. Therefore, this short-term fluctuation is predominantly attributed to the decrease in Atlantic inflow salinity. Interestingly, the decrease in overflow water volume in the Nordic Seas does not follow a linear pattern in relation to increasing ocean temperature; instead, it exhibits an accelerating trend. If the ocean temperature rises by 1°C, the overflow water volume in the Nordic Seas will decrease to 86% of the total volume. Overall, the overflow water storage in the Norwegian Sea undergoes complex interannual variations and is notably influenced by the influx from the Atlantic Ocean.