A spatial-and-temporal-based method for rapid particle concentration estimations in an urban environment

The increasing construction of buildings and infrastructure in cities heavily influences pollutant dispersion and causes a spread of increased particle concentrations. Real-time data and information on local pollution levels are highly desired by residents, urban planners and policy-makers. Such information is scarce due to the high cost of real-time measurement. To fill the gap, the aim of this research is to develop a model that can rapidly estimate particulate pollution based on a data-driven artificial neural network modelling approach. The key influential factors such as background pollution level, weather conditions, urban morphology and local pollution sources are embedded in the model in association with local emission sources of pollution relating to construction activities and traffic flows. The data for urban spatial-variables (building and road) and traffic information is processed with the aid of the Geographic Information System using self-developed Python scripts. The geographic dataset containing the required information for each grid is integrated with the artificial neural network model to perform forecasting of particle concentrations. The model has been verified with measurements from a case study with 20 sample locations in Chongqing, China, showing that the average relative error of particle concentration estimation compared to measurement is 17.56% for PM10 and 16.04% for PM2.5. A map of a time-specific spatial interpolation of particle concentrations which visualises real-time pollution is consequently produced based on the method. The method can be used as a tool for real-time air quality forecasting with suitable adaptations for any other dense urban area with minimum information from local observation stations

Estimation of the contribution of atmospheric deposition to coastal water eutrophication

Ph.DDOCTOR OF PHILOSOPH

Prévision statistique de la qualité de l'air et d'épisodes de pollution atmosphérique en Corse

The objective of this doctoral work is to develop a forecasting model able to correctly predict next day pollutant concentrations in Corsica. We focused on PM10 and ozone, the two most problematic pollutants in the island. The model had to correspond to the constraints of an operational use in a small structure like Qualitair Corse, the local air quality monitoring network. The prediction was performed using artificial neural networks. These statistical models offer a great precision while requiring few computing resources. We chose the MultiLayer Perceptron (MLP), with input data coming from pollutants measurements, meteorological measurements, chemical transport model (CHIMERE via AIRES platform) and numerical weather predictionmodel (AROME). The configuration of the MLP was optimized prior to machine learning, in accordance with the principle of parsimony. To improve forecasting performances, we led a feature selection study. We compared the use of genetic algorithms, simulated annealing and principal componentanalysis to optimize the choice of input variables. The pruning of the MLP was also implemented. Then we proposed a new type of hybrid model, combination of a classification model and various MLPs, each specialized on a specific weather pattern. These models, which need largelearning datasets, allow an improvement of the forecasting for extreme and rare values, corresponding to pollution peaks. We led unsupervised classification with self organizing maps coupled with k-means algorithm, and with hierarchical ascendant classification. Sensitivity analysis wasled with ROC curves. We developed the application “Aria Base” running with Matlab and its Neural Network Toolbox, able to manage our datasets, to lead rigorously the experiments and to create operational models.We also developed the application “Aria Web” to be used daily by Qualitair Corse. It is able to lead automatically the prevision with MLP, and to synthesize forecasting information provided by other organizations and available on the Internet.L’objectif de ces travaux de doctorat est de développer un modèle prédictif capable de prévoir correctement les concentrations en polluants du jour pour le lendemain en Corse. Nous nous sommes intéressés aux PM10 et à l’ozone, les deux polluants les plus problématiques sur l’île.Le modèle devait correspondre aux contraintes d’un usage opérationnel au sein d’une petite structure, comme Qualitair Corse, l’association locale de surveillance de la qualité de l’air. La prévision a été réalisée à l’aide de réseaux de neurones artificiels. Ces modèles statistiquesoffrent une grande précision tout en nécessitant peu de ressources informatiques. Nous avons choisi le Perceptron MultiCouche (PMC), avec en entrée à la fois des mesures de polluants, des mesures météorologiques, et des sorties de modèles de chimie-transport (CHIMERE via laplate-forme AIRES) et de modèles météorologiques (AROME). La configuration des PMC a été optimisée avant leur apprentissage automatique, en conformité avec le principe de parcimonie. Pour en améliorer les performances, une étude de selection de variables a été au préalable menée. Nous avons comparé l’usage d’algorithmes génétiques, de recuits simulés et d’analyse en composantes principales afin d’optimiser le choix des variablesd’entrées. L’élagage du PMC a été également mis en oeuvre. Nous avons ensuite proposé un nouveau type de modèle hybride, combinaison d’un classifieur et de plusieurs PMC, chacun spécialisé sur un régime météorologique particulier. Ces modèles, qui demandent un large historique de données d’apprentissage, permettent d’améliorer la prévision des valeurs extrêmes et rares, correspondant aux pics de pollution. La classificationnon-supervisée a été menée avec des cartes auto-organisatrices couplées à l’algorithme des kmeans, ainsi que par classification hiérarchique ascendante. L’analyse de sensibilité à été menée grâce à l’usage de courbes ROC.Afin de gérer les jeux de données utilisés, de mener les expérimentations de manière rigoureuse et de créer les modèles destinés à l’usage opérationnel, nous avons développé l’application « Aria Base », fonctionnant sous Matlab à l’aide de la Neural Network Toolbox.Nous avons également développé l’application « Aria Web » destinée à l’usage quotidian à Qualitair Corse. Elle est capable de mener automatiquement les prévisions par PMC et de synthétiser les différentes informations qui aident la prévision rendues disponibles sur internetpar d’autres organismes

Do bacteria thrive when the ocean acidifies? Results from an off-­shore mesocosm study

Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 µatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean