Spectral Observations of PM10 Fluctuations in the Hilbert Space

During the last 20 years, many megacities have experienced air pollution leading to negative impacts on human health. In the Caribbean region, air quality is widely affected by African dust which causes several diseases, particularly, respiratory diseases. This is why it is crucial to improve the understanding of PM10 fluctuations in order to elaborate strategies and construct tools to predict dust events. A first step consists to characterize the dynamical properties of PM10 fluctuations, for instance, to highlight possible scaling in PM10 density power spectrum. For that, the scale-invariant properties of PM10 daily time series during 6 years are investigated through the theoretical Hilbert frame. Thereafter, the Hilbert spectrum in time-frequency domain is considered. The choice of theoretical frame must be relevant. A comparative analysis is also provided between the results achieved in the Hilbert and Fourier spaces

Wet scavenging process of particulate matter (PM10): A multivariate complex network approach

Datos de investigación disponibles en: http://www.gwadair.frThis paper reports the results of research on PM10 wet scavenging by rainfall using a new multilayer complex networks called Multiplex Visibility Graphs (MVG). To the best of our knowledge, this work is the first to assess PM10 wet deposition using multivariate time series according to African dust seasonality. We considered 11 years of daily PM10 and rainfall data from the Guadeloupe archipelago. To analyse the impact of rainfall on PM10 behaviour, two MVG parameters were computed: the average edge overlap (ω) and the interlayer mutual information (IPM). On the 1-d scale, the ω results showed that the wet scavenging process was higher during the second half of the year when the high dust season and the rainy season are juxtaposed. This highlights a greater correlation between the microscopic structure of the signal, and the impact of rainfall on PM10 concentrations is more significant when the atmosphere is loaded with dust. The joint probability computed between the PM10 and rainfall nodes confirmed this trend. The IPM results indicated a correlation between PM10 and rainfall structures throughout the year. Furthermore, IPM values were higher during the transition periods between winter and summer (and vice versa). Our study showed that MVG is a powerful technique for investigating the relationship between at least two nonlinear time series using a multivariate time series

Background PM10 atmosphere: In the seek of a multifractal characterization using complex networks

In the literature, several epidemiological studies have already associated respiratory and cardiovascular diseases to acute exposure of mineral dust. However, frail people are also sensitive to chronic exposure to particulate matter with an aerodynamic diameter 10μm or less (PM10). Consequently, it is crucial to better understand PM10 fluctuations at all scales. This study investigates PM10 background atmosphere in the Caribbean area according to African dust seasonality with complex network framework. For that purpose, the regular Visibility Graph (VG) and the new Upside-Down Visibility Graph (UDVG) are used for a multifractal analysis. Firstly, concentration vs degree (v-k) plots highlighted that high degree values (hubs behavior) are related to the highest PM10 concentrations in VG while hubs is associated to the lowest concentrations in UDVG, i.e. probably the background atmosphere. Then, the degree distribution analysis showed that VG and UDVG difference is reduced for high dust season contrary to the low one. As regards the multifractal analysis, the multifractal degree is higher for the low season in VG while it is higher for the high season in UDVG. The degree distribution behavior and the opposite trend in multifractal degree for UDVG are due to the increase of PM10 background atmosphere during the high season, i.e. from May to September. To sum up, UDGV is an efficient tool to perform noise fluctuations analysis in environmental time series where low concentrations play an important role as wel

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Datos de investigación disponibles en: http://www.gwadair.frA good knowledge of pollutant time series behavior is fundamental to elaborate strategies and construct tools to protect human health. In Caribbean area, air quality is frequently deteriorated by the transport of African dust. In the literature, it is well known that exposure to particulate matter with an aerodynamic diameter of 10 μm or less (PM10) have many adverse health effects as respiratory and cardiovascular diseases. To our knowledge, no study has yet performed an analysis of PM10 time series using complex network framework. In this study, the so-called Visibility Graph (VG) method is used to describe PM10 dynamics in Guadeloupe archipelago with a database of 11 years. Firstly, the fractal nature of PM10 time series is highlighted using degree distribution for all data, low dust season (October to April) and high dust season (May to September). Thereafter, a profound description of PM10 time series dynamics is made using multifractal analysis through two approaches, i.e. Rényi and singularity spectra. Achieved results are consistent with PM10 behavior in the Caribbean basin. Both methods showed a higher multifractality degree during the low dust season. In addition, multifractal parameters exhibited that the low dust season has the higher recurrence and the lower uniformity degrees. Lastly, centrality measures (degree, closeness and betweenness) highlighted PM10 dynamics through the year with a decay of centrality values during the high dust season. To conclude, all these results clearly showed that VG is a robust tool to describe times series properties

Study of the Dynamical Relationships between PM2.5 and PM10 in the Caribbean Area Using a Multiscale Framework

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

Detecting the Causal Nexus between Particulate Matter (PM10) and Rainfall in the Caribbean Area

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

Study of the nocturnal dispersion of air pollutants from an open lan : evidence of an urban heat island

En 2003 des mesures au spectromètre IR à Transformée de Fourier (FTIR) ont permis d'identifier et de mesurer les COV émis par la décharge à ciel ouvert de la Gabarre, principale de l'île Guadeloupe, située entre une zone urbaine et une mangrove. Ces COV ont été retrouvés (2004) la nuit dans les cités, justifiant les plaintes des riverains. Dans le cadre de cette thèse, des mesures au spectromètre de masse portatif MS 200 ont validé ces résultats du FTIR. De nouvelles mesures au MS 200 ont été menées dans toute la zone de la décharge. Les cités concernées étant à l'opposé du flux synoptique d'Alizés-Est, les facteurs météorologiques permettant la dispersion et le transport des COV de la décharge vers la zone urbaine ont été recherchés. La diminution nocturne de l'intensité des Alizés au dessus de l'ile peut laisser place à des phénomènes locaux tels les brises. L'idée d'une brise terre-mer a été éliminée. Un maillage autou~ de la décharge (cités et mangrove) avec 8 thermomètres a révélé un îlot de chaleur urbain nocturnegénérant une brise thermique d'environ Ims- I (mesurée et calculée). Avec les radiosondages Météo France et un SODAR installé dans la décharge, une très forte stabilité dans les basses couches atmosphériques de la couche limite nocturne avec une inversion de surface d'environ 120mvv apparait. Ces facteurs expliquent la pollution des cités par les COV de la décharge, Un modèlevGaussien en tenant compte a été validé par les mesures de COV.vCette étude peut être étendue à d'autres décharges à ciel ouvert et à d'autres types de traitement de polluants de décharge.In 2003, the VOC emissions coming from "La Gabarre", the main open landfill in Guadeloupe, located in-between an urban area and a mangrove, were identified and quantified with a portable FTIR spectrometer. In 2004, COVs found at nighttime in the urban area nearby confirmed why residents complain about. As part of this thesis, portable mass spectrometer MS 200 measurements validated these FTIR figures. New systematic SM measurements have been carried on around the landfill. Since the polluted urban area stands on the opposite way of the East Trade winds synoptic flux, aIl the weather factors likely to scatter and transport the dump COVs were scrutinized. At night, the strength of the Trade winds decreases over Guadeloupe, which may give way to local phenomena such as breezes. The occurrence of land/sea breeze was eliminated. A close surveying surrounding the landfill with 8 thermometers both in the projects and in the mangrove revealed an urban heat island causing thermal breezes of about lms-l (measured and calculated). Using soundings from Meteo France, and a SODAR inside the dump, we found a great stability of the night boundary layer with a surface inversion near 120m. Pollution of the nearby urban area with landfill COVs is elucidatcd by the above factors. A transport Gaussian model is in agreement with COV measurements. This study can be extended to different open landfills and different types of polluting matters processes in dump

Is there a causal relationship between Particulate Matter ( P M 10 ) and air Temperature data? An analysis based on the Liang–Kleeman information transfer theory

International audienc

Erratum to “Temporal multiscaling characteristics of particulate matter PM 10 and ground-level ozone O 3 concentrations in Caribbean region” [Atmos. Environ. 169 (2017) 22–35]

International audienc

Forecasting <i>PM</i>₁₀ Concentrations in the Caribbean Area Using Machine Learning Models

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