La prévision en temps réel des charges de polluants dans un réseau d'assainissement urbain

Le présent travail vise le développement des méthodologies de prévision et de validation, en temps réel, des charges de polluants dans un réseau d'assainissement urbain. La méthodologie de prévision préconisée s'est basée sur le modèle de "rating curve". Le modèle a été modifié afin de surmonter une de ses faiblesses. Le filtre de Kalman a été utilisé pour identifier les paramètres du modèle en temps réel. L'approche de validation développée se base sur le principe de la redondance de l'information. Un modèle autoregressif a été utilisé comme indicateur de la tendance de variation à court terme. Le modèle de "rating curve" a été également utilisé pour simuler les charges de polluants en temps réel. Entre la valeur mesurée et simulée, celle qui se rapproche le plus de la valeur prévue par le modèle autoregressif est retenue. Les méthodologies développées ont été testées avec succès sur le bassin du secteur I de la ville de Verdun (Québec)

Utilisation de la télédétection pour l'estimation de la réserve hydrique au bassin du Mackenzie au nord ouest canadien

La présente recherche qui est appliquée au bassin du fleuve Mackenzie, vise l'estimation de l'humidité du sol en utilisant des données de télédétection captées dans le domaine des micro-ondes passives. Compte tenu de l'étendue et l'hétérogénéité du bassin du Mackenzie, un intérêt particulier a été réservé à des approches globales. Un indice d'humidité a été estimé à partir des images SSM/I, en utilisant des températures de brillance verticalement polarisées et mesurées à 19, 37 et 85 GHz. La comparaison des fractions des plans d'eau obtenues aux débits observés a montré l'existence d'une intéressante corrélation. Les micro-ondes passives sont capables de "voir" les plans d'eau et l'humidité du sol. Dans le domaine du visible, seuls les plans d'eau sont captés. Un nouvel indice d'humidité a été donc proposé en se basant sur la différence de ces sensitivités. L'indice a montré une concordance satisfaisante avec les précipitations et les températures observées

Trends and variability in methane concentrations over the Southeastern Arabian Peninsula

Methane (CH4) is a potent greenhouse gas with an important contribution to global warming. While national and international efforts have been put in place to reduce methane emissions, little is known about its variability, especially in hotspot regions where natural and anthropogenic emissions are compounded. In this study, the current state of CH4 concentrations and their trends over the United Arab Emirates (UAE) and surrounding region are investigated with satellite and reanalysis data. CH4 concentrations have increased over the last 5 years, with a trend in the satellite-derived column values (XCH4) of about 9 ppb/year. A clear annual cycle is detected in XCH4, with an amplitude of up to 75 ppb and peak values in the warmer months. The largest concentrations are found in coastal sites, where sabkhas and landfills are present, and along the Al Hajar mountains, where agricultural activities and microhabitats that may host CH4-producing microbes occur and where advection by the background flow is likely an important contributor. The reanalysis data shows a good agreement with the satellite-derived estimates in terms of the spatial pattern, but the magnitudes are smaller by up to 50 ppb, due to deficiencies in the data assimilated. Surface CH4 concentrations in the reanalysis data account for more than 50% of the corresponding XCH4 values, and exhibit a seasonal cycle with the opposite phase due to uncertainties in the emissions inventory. Our findings provide an overview of the state of CH4 concentration in the UAE and surrounding region, and may aid local authorities to propose the appropriate emission reduction strategies in order to meet the proposed net-zero greenhouse gas emission target by 2050. This study highlights the need for the establishment in the Arabian Peninsula region of a ground-based observational network for greenhouse gas concentrations which is still lacking to date

Application of a Nighttime Fog Detection Method Using SEVIRI Over an Arid Environment

Fog degrades horizontal visibility causing significant adverse impacts on transport systems. The detection of fog from satellite data remains challenging especially in the presence of higher clouds, dust, mist, or unknown underlying soil conditions. Observations from Meteosat second generation Spinning-Enhanced Visible and Infrared Imager (MSG SEVIRI) over the United Arab Emirates (UAE), an arid area on the Arabian Peninsula, from 2016 to 2018 (two fog seasons) are used in this study. We implement an adaptive threshold-based technique using pseudo-emissivity values to detect nocturnal fog from SEVIRI. The method allows the threshold to vary spatially and temporally. Low clouds are detected with the analysis of the vertical temperature gradient. Fog classification was verified against four stations in the UAE, namely Abu Dhabi, Dubai, Al Ain, and Al Maktoum, where visibility and meteorological observations are available. The probability of detection (POD) (false alarm ratio (FAR)) was 0.81 (0.40), 0.83 (0.50), 0.83 (0.33), and 0.77 (0.44) at Abu Dhabi, Dubai, Al Ain, and Al Maktoum, respectively. In addition, the spatial frequency of fog is presented, which provides new insights into the fog dynamics in the region

Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics

The goal of this study is to investigate the variability of poor visibility events occurring hourly in the UAE and their relationship to climate dynamics. Hourly visibility observation data spanning more than three decades from ten stations across the country were used. Four intervals of low visibility, between 0.10 km and 5.0 km, were considered. Poor visibility records were analyzed under wet and dry weather conditions. The Mann–Kendall test was used to assess the inferred trends of low visibility records. The relationships between poor visibility measurements and associated meteorological variables and climate oscillations were also investigated. Results show that Fujairah city has the highest average visibility values under wet weather conditions, while Abu Dhabi city has the lowest average visibility values under both wet and dry conditions. Wet weather conditions had a greater impact than dry weather conditions on visibility deterioration in seven out of the ten stations. Results confirm that fog and dust contribute significantly to the deterioration of visibility in the UAE and that Abu Dhabi has been more impacted by those events than Dubai. Furthermore, the numbers of fog and dust events show steep increasing trends for both cities. A change point in poor visibility records triggered by fog and dust events was detected around the year 1999 at Abu Dhabi and Dubai stations after the application of the cumulative sum method. Increasing shifts in the means and the variances were noticed in the total annual fog events when Student’s t-test and Levene’s test were applied. In Abu Dhabi, the mean annual number of dust events was approximately 112.5 before 1999, increasing to 337 dust events after 1999. In Dubai, the number of dust events increased from around 85.5 to 315.6 events. The inferred fog and dust trends were compared to four climate indices. Results showed a significant correlation (positive and negative) between four climate indices and the occurrence of fog and dust events in the UAE