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In this paper, the wind direction (WD) behaviour with respect to the variability of other meteorological parameters (i.e., rainfall (R), temperature (T), relative humidity (Rh), solar radiation (SR) and wind speed (U)) was studied in a multi-scale way. To carry out this study, the Hilbert–Huang transform (HHT) framework was applied to a Guadeloupe archipelago dataset from 2016 to 2021. Thus, the time-dependent intrinsic correlation (TDIC) analysis based on multivariate empirical mode decomposition (MEMD) was performed. For time scales between ∼3 days and ∼7 months, the localized positive and negative correlations between WD and the meteorological parameters have been identified. The alternation between these correlations was more significant for T and Rh. With regard to SR and U, there was a dominance of a negative correlation with WD. We assumed that the micro-climate previously identified in the literature for the study area plays a key role in these behaviours. A strong positive correlation between WD and R was found from ∼7 months to ∼2.5 years. At the annual scale, the relationships between WD and all meteorological parameters were long range and no significant transition in correlation was observed showing the impact of the Earth’s annual cycle on climatic variables. All these results clearly show the influence of R-T-Rh-SR-U on WD over different time scales.

The Caribbean basin is a geographical area with a high prevalence of asthma due to mineral dust. As such, it is crucial to analyze the dynamic behavior of particulate pollutants in this region. The aim of this study was to investigate the relationships between particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) using Hilbert–Huang transform (HHT)-based approaches, including the time-dependent intrinsic correlation (TDIC) and time-dependent intrinsic cross-correlation (TDICC) frames. The study utilized datasets from Puerto Rico from between 2007 and 2010 to demonstrate the relationships between two primary particulate matter concentration datasets of air pollution across multiple time scales. The method first decomposes both time series using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to obtain the periodic scales. The Hilbert spectral analysis identified two dominant peaks at a weekly scale for both PM types. High amplitude contributions were sustained for long and continuous time periods at seasonal to intra-seasonal scales, with similar trends in spectral amplitude observed for both types of PM except for monthly and intra-seasonal scales of six months. The TDIC method was used to analyze the resulting modes with similar periodic scales, revealing the strongest and most stable correlation pattern at quarterly and annual cycles. Subsequently, lagged correlations at each time scale were analyzed using the TDICC method. For high-frequency PM10 intrinsic mode functions (IMFs) less than a seasonal scale, the value of the IMF at a given time scale was found to be dependent on multiple antecedent values of PM2.5. However, from the quarterly scale onward, the correlation pattern of the PM2.5-PM10 relationship was stable, and IMFs of PM10 at these scales could be modeled by the lag 1 IMF of PM2.5. These results demonstrate that PM2.5 and PM10 concentrations are dynamically linked during the passage of African dust storms. [ABSTRACT FROM AUTHOR]

In this paper, the wind direction ( W D ) behaviour with respect to the variability of other meteorological parameters (i.e., rainfall (R), temperature (T), relative humidity ( R h ), solar radiation ( S R ) and wind speed (U)) was studied in a multi-scale way. To carry out this study, the Hilbert–Huang transform (HHT) framework was applied to a Guadeloupe archipelago dataset from 2016 to 2021. Thus, the time-dependent intrinsic correlation (TDIC) analysis based on multivariate empirical mode decomposition (MEMD) was performed. For time scales between ∼3 days and ∼7 months, the localized positive and negative correlations between W D and the meteorological parameters have been identified. The alternation between these correlations was more significant for T and R h . With regard to S R and U, there was a dominance of a negative correlation with W D . We assumed that the micro-climate previously identified in the literature for the study area plays a key role in these behaviours. A strong positive correlation between W D and R was found from ∼7 months to ∼2.5 years. At the annual scale, the relationships between W D and all meteorological parameters were long range and no significant transition in correlation was observed showing the impact of the Earth's annual cycle on climatic variables. All these results clearly show the influence of R-T- R h - S R -U on W D over different time scales. [ABSTRACT FROM AUTHOR]

In the Caribbean basin, particulate matter lower or equal to 10 μm in diameter (PM10) has a huge impact on human mortality and morbidity due to the African dust. For the first time in this geographical area, the theoretical framework of artificial intelligence is applied to forecast PM10 concentrations. The aim of this study is to forecast PM10 concentrations using six machine learning (ML) models: support vector regression (SVR), k-nearest neighbor regression (kNN), random forest regression (RFR), gradient boosting regression (GBR), Tweedie regression (TR), and Bayesian ridge regression (BRR). Overall, with MBEmax = −2.8139, the results showed that all the models tend to slightly underestimate PM10 empirical data. GBR is the model that gives the best performance (r = 0.7831, R2 = 0.6132, MAE = 6.8479, RMSE = 10.4400, and IOA = 0.7368). By comparing our results to other PM10 ML studies in megacities, we found similar performance using only three input variables, whereas previous studies use many input variables with Artificial Neural Network (ANN) models. All these results showed the features of PM10 concentrations in the Caribbean area. [ABSTRACT FROM AUTHOR]

The aim of this study was to model the behavior of particles with aerodynamic diameter lower or equal to 10 μ m (PM10) in the Caribbean area according to African dust seasonality. To carry out this study, PM10 measurement from Guadeloupe (GPE) and Puerto Rico (PR) between 2006 and 2010 were used. Firstly, the missing data issues were addressed using algorithms that we elaborated. Thereafter, the coupled SARIMA-GARCH (Seasonal Autoregressive Integrated Moving Average and Generalized Autoregressive Conditional Heteroscedastic) model was developed and compared to PM10 empirical data. The SARIMA process is representative of the main PM10 sources, while the heteroskedasticity is also taken into account by the G A R C H process. In this framework, PM10 data from GPE and PR are decomposed into the sum of the background atmosphere ( B t = anthropogenic activities + marine aerosol), African dust seasonality ( S t = mineral dust), and extreme events processes ( C t ). Akaike's information criterion (AIC) helped us to choose the best model. Forecast evaluation indexes such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Scale Error (MASE), and Theil's U statistic provided significant results. Specifically, the MASE and U values were found to be almost zero. Thus, these indexes validated the forecasts of the coupled SARIMA-GARCH model. To sum up, the SARIMA-GARCH combination is an efficient tool to forecast PM10 behavior in the Caribbean area. [ABSTRACT FROM AUTHOR]

Due to African dust, the Caribbean area is known to have one of the highest incidences of asthma on the planet. Consequently, it is crucial to dissociate the impact of local sources from large scale sources in this region. The aim of this study was to estimate the PM10 detection threshold for dusty events using a statistical approach and a dynamic approach. To carry out this analysis, PM10 time series from Martinique (MAR), Guadeloupe (GPE) and Puerto-Rico (PR) were used between 2006 and 2016. The statistical analysis highlighted that the distance from the African coast is a key feature for PM10 concentrations distribution with the highest at MAR (26.52 μg/m3) and the lowest at PR (24.42 μg/m3). The probability density function analysis showed that MAR-GPE-PR distributions converge towards a same point between the first and the second maximum probability value at 28 μg/m3. The dynamical analysis with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Improved CEEMDAN (ICEEMDAN) validated the 28 μg/m3 found with the statistical analysis. The analysis of HYSPLIT back trajectories confirmed this threshold. Thus, our results indicated that 28 μg/m3 is the PM10 detection threshold for African dust in the Caribbean basin. It will therefore be a good indicator allowing the competent authorities to take the appropriate decisions to protect vulnerable populations during dusty events.

The Caribbean basin is a geographical area with a high prevalence of asthma due to mineral dust. As such, it is crucial to analyze the dynamic behavior of particulate pollutants in this region. The aim of this study was to investigate the relationships between particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) using Hilbert–Huang transform (HHT)-based approaches, including the time-dependent intrinsic correlation (TDIC) and time-dependent intrinsic cross-correlation (TDICC) frames. The study utilized datasets from Puerto Rico from between 2007 and 2010 to demonstrate the relationships between two primary particulate matter concentration datasets of air pollution across multiple time scales. The method first decomposes both time series using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to obtain the periodic scales. The Hilbert spectral analysis identified two dominant peaks at a weekly scale for both PM types. High amplitude contributions were sustained for long and continuous time periods at seasonal to intra-seasonal scales, with similar trends in spectral amplitude observed for both types of PM except for monthly and intra-seasonal scales of six months. The TDIC method was used to analyze the resulting modes with similar periodic scales, revealing the strongest and most stable correlation pattern at quarterly and annual cycles. Subsequently, lagged correlations at each time scale were analyzed using the TDICC method. For high-frequency PM10 intrinsic mode functions (IMFs) less than a seasonal scale, the value of the IMF at a given time scale was found to be dependent on multiple antecedent values of PM2.5. However, from the quarterly scale onward, the correlation pattern of the PM2.5-PM10 relationship was stable, and IMFs of PM10 at these scales could be modeled by the lag 1 IMF of PM2.5. These results demonstrate that PM2.5 and PM10 concentrations are dynamically linked during the passage of African dust storms.

In the Caribbean basin, particulate matter lower or equal to 10 μm in diameter (PM10) has a huge impact on human mortality and morbidity due to the African dust. For the first time in this geographical area, the theoretical framework of artificial intelligence is applied to forecast PM10 concentrations. The aim of this study is to forecast PM10 concentrations using six machine learning (ML) models: support vector regression (SVR), k-nearest neighbor regression (kNN), random forest regression (RFR), gradient boosting regression (GBR), Tweedie regression (TR), and Bayesian ridge regression (BRR). Overall, with MBEmax = −2.8139, the results showed that all the models tend to slightly underestimate PM10 empirical data. GBR is the model that gives the best performance (r = 0.7831, R2 = 0.6132, MAE = 6.8479, RMSE = 10.4400, and IOA = 0.7368). By comparing our results to other PM10 ML studies in megacities, we found similar performance using only three input variables, whereas previous studies use many input variables with Artificial Neural Network (ANN) models. All these results showed the features of PM10 concentrations in the Caribbean area.

This article compares the relationship between Australia and the South Pacific Commonwealth states with the relationship between Britain and the Commonwealth Caribbean islands. It explores these links through strategic, economic, political, and cultural lenses, finding that while both sets of relationships contain elements of all these ties, they are characterised by fundamentally different dynamics. The Australian-South Pacific relationship revolves far more around hard-nosed calculations of security and economic advantage, while the British-Caribbean relationship exists strongly in the cultural realm, with less emphasis on strategy and politics; both cases exemplify the range of potential relationships between large and small Commonwealth states. The article also observes an increasing tendency for the small states to broaden and diversify their international relationships. [ABSTRACT FROM AUTHOR]

The aim of this study was to model the behavior of particles with aerodynamic diameter lower or equal to 10μm (PM10) in the Caribbean area according to African dust seasonality. To carry out this study, PM10 measurement from Guadeloupe (GPE) and Puerto Rico (PR) between 2006 and 2010 were used. Firstly, the missing data issues were addressed using algorithms that we elaborated. Thereafter, the coupled SARIMA-GARCH (Seasonal Autoregressive Integrated Moving Average and Generalized Autoregressive Conditional Heteroscedastic) model was developed and compared to PM10 empirical data. The SARIMA process is representative of the main PM10 sources, while the heteroskedasticity is also taken into account by the GARCH process. In this framework, PM10 data from GPE and PR are decomposed into the sum of the background atmosphere (Bt = anthropogenic activities + marine aerosol), African dust seasonality (St = mineral dust), and extreme events processes (Ct). Akaike’s information criterion (AIC) helped us to choose the best model. Forecast evaluation indexes such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Scale Error (MASE), and Theil’s U statistic provided significant results. Specifically, the MASE and U values were found to be almost zero. Thus, these indexes validated the forecasts of the coupled SARIMA-GARCH model. To sum up, the SARIMA-GARCH combination is an efficient tool to forecast PM10 behavior in the Caribbean area.

The article focuses on Caribbean popular culture and popular music, specifically the Calypso music of Antigua, and how it serves as a popular culture archive.

In this study, we investigate the interactions between particulate matter that have an aerodynamic diameter less than 10 μm diameter (PM10) and rainfall (RR) in entropy framework. Our results showed there is a bidirectional causality between PM10 concentrations and RR values. This means that PM10 concentrations influence RR values while RR induces the wet scavenging process. Rainfall seasonality has a significant impact on the wet scavenging process while African dust seasonality strongly influence RR behavior. Indeed, the wet scavenging process is 5 times higher during the wet season while PM10 impact on RR is 2.5 times higher during the first part of the high dust season. These results revealed two types of causality: a direct causality (RR to PM10) and an indirect causality (PM10 to RR). All these elements showed that entropy is an efficient way to quantify the behavior of atmospheric processes using ground-based measurements.

Over the last decades, the impact of mineral dust from African deserts on human health and climate has been of great interest to the scientific community. In this paper, the climatological analysis of dusty events of the past 20 years in the Caribbean area has been performed using a particulate approach. The focus is made on June 2020 extreme event dubbed “Godzilla”. To carry out this study, different types of data were used (ground-based, satellites, model, and soundings) on several sites in the Caribbean islands. First, the magnitude of June 2020 event was clearly highlighted using satellite imagery. During the peak of this event, the value of particulate matter with an aerodynamic diameter of less than 10 μμm (PM10) reached a value 9 times greater than the threshold recommended by the World Health Organization in one day. Thereafter, the PM10, the aerosol optical depth, and the volume particle size distribution analyses exhibited their maximum values for June 2020. We also highlighted the exceptional characteristics of the Saharan air layer in terms of thickness and wind speed for this period. Finally, our results showed that the more the proportion of particulate matter with an aerodynamic diameter of less than 2.5 μμm (PM2.5) in PM10 increases, the more the influence of sea salt aerosols is significant.

This essay seeks to interrupt the dominant discourse on race, ethnicity, gender and class (intersectionality) globally, which has focussed on the experiences of 'whites' with their others, primarily in North America and Europe and shift it to the post-colonial multi-ethnic societies of the Global South. It draws on the historical experience of Trinidad and Tobago, the multi-ethnic nation-state comprising the two most southerly islands of the Caribbean archipelago to present the analytic of competing victimhoods as a mechanism for understanding inter-ethnic tensions and conflict in multi-ethnic post-colonial societies. This has the ability to make visible different groups' interpretations of their histories of hardships and distinct colonial experiences by going beneath the surface of simple hegemony and subordination relations and to examine legitimate and/or illegitimate feelings of fear and insecurity. Such an approach moves our analysis from a focus on individual group identities to the interactions between, among and within groups and the ways in which these various levels of feeling intersect and shape behaviours, attitudes and ideologies. [ABSTRACT FROM AUTHOR]