Basal Temperature of Snow (BTS) measurements were used as the primary inputs to a high resolution (30 x 30 m grid cells) empirical-statistical regional permafrost probability model for the southern and central Yukon, and northernmost British Columbia (59° - 65°N). Data from seven individual study areas distributed across the region were combined using a blended distance decay technique, with an eighth area used for validation. The model predictions are reasonably consistent with previous permafrost maps for the area with some notable differences and a much higher level of detail. The modelling gives an overall permafrost probability of 52%. North of 62°N, permafrost becomes more extensive in the lowland areas whereas farther south permafrost is typically common only above treeline. Significant differences exist between the mountain environments of the Yukon and the Swiss Alps where the BTS method originated and as a result different modelling approaches had to be developed. This work therefore: (1) develops additional explanatory variables for permafrost probability modelling, the most notable of which is equivalent elevation, (2) confirms the use of ground truthing as a requirement for empirical-statistical modelling in the Yukon and (3) uses a combination of models for the region in order to spatially predict between study areas. The results of this thesis will be of use to linear infrastructure route-planning, geohazard assessment and climate change adaptation strategies. Future work employing the model will allow the effects of scenario-based climate warming to be examined.

Basal Temperature of Snow (BTS) measurements were used as the primary inputs to a high resolution (30 x 30 m grid cells) empirical-statistical regional permafrost probability model for the southern and central Yukon, and northernmost British Columbia (59° - 65°N). Data from seven individual study areas distributed across the region were combined using a blended distance decay technique, with an eighth area used for validation. The model predictions are reasonably consistent with previous permafrost maps for the area with some notable differences and a much higher level of detail. The modelling gives an overall permafrost probability of 52%. North of 62°N, permafrost becomes more extensive in the lowland areas whereas farther south permafrost is typically common only above treeline. Significant differences exist between the mountain environments of the Yukon and the Swiss Alps where the BTS method originated and as a result different modelling approaches had to be developed. This work therefore: (1) develops additional explanatory variables for permafrost probability modelling, the most notable of which is equivalent elevation, (2) confirms the use of ground truthing as a requirement for empirical-statistical modelling in the Yukon and (3) uses a combination of models for the region in order to spatially predict between study areas. The results of this thesis will be of use to linear infrastructure route-planning, geohazard assessment and climate change adaptation strategies. Future work employing the model will allow the effects of scenario-based climate warming to be examined.

Basal Temperature of Snow (BTS) measurements were used as the primary inputs to a high resolution (30 x 30 m grid cells) empirical-statistical regional permafrost probability model for the southern and central Yukon, and northernmost British Columbia (59° - 65°N). Data from seven individual study areas distributed across the region were combined using a blended distance decay technique, with an eighth area used for validation. The model predictions are reasonably consistent with previous permafrost maps for the area with some notable differences and a much higher level of detail. The modelling gives an overall permafrost probability of 52%. North of 62°N, permafrost becomes more extensive in the lowland areas whereas farther south permafrost is typically common only above treeline. Significant differences exist between the mountain environments of the Yukon and the Swiss Alps where the BTS method originated and as a result different modelling approaches had to be developed. This work therefore: (1) develops additional explanatory variables for permafrost probability modelling, the most notable of which is equivalent elevation, (2) confirms the use of ground truthing as a requirement for empirical-statistical modelling in the Yukon and (3) uses a combination of models for the region in order to spatially predict between study areas. The results of this thesis will be of use to linear infrastructure route-planning, geohazard assessment and climate change adaptation strategies. Future work employing the model will allow the effects of scenario-based climate warming to be examined.

Basal Temperature of Snow (BTS) measurements were used as the primary inputs to a high resolution (30 x 30 m grid cells) empirical-statistical regional permafrost probability model for the southern and central Yukon, and northernmost British Columbia (59° - 65°N). Data from seven individual study areas distributed across the region were combined using a blended distance decay technique, with an eighth area used for validation. The model predictions are reasonably consistent with previous permafrost maps for the area with some notable differences and a much higher level of detail. The modelling gives an overall permafrost probability of 52%. North of 62°N, permafrost becomes more extensive in the lowland areas whereas farther south permafrost is typically common only above treeline. Significant differences exist between the mountain environments of the Yukon and the Swiss Alps where the BTS method originated and as a result different modelling approaches had to be developed. This work therefore: (1) develops additional explanatory variables for permafrost probability modelling, the most notable of which is equivalent elevation, (2) confirms the use of ground truthing as a requirement for empirical-statistical modelling in the Yukon and (3) uses a combination of models for the region in order to spatially predict between study areas. The results of this thesis will be of use to linear infrastructure route-planning, geohazard assessment and climate change adaptation strategies. Future work employing the model will allow the effects of scenario-based climate warming to be examined.

La combustion de la biomasse, en particulier le chauffage au bois résidentiel, constitue une source d’émissions fortement contributrice aux concentrations de PM10 en hiver, de façon globale, en Europe. Afin de réduire les émissions de cette source, un programme de large envergure de renouvellement d’appareils de chauffage au bois non-performant, le Fonds Air Bois a été lancé depuis 2013 dans la vallée de l’Arve. Cette action étant amenée à se multiplier en France, l’évaluation de son effet sur les concentrations atmosphériques constitue un enjeu pour les futures politiques de gestion de la qualité de l’air. Le programme PRIMEQUAL DECOMBIO (2013-2018) a été proposé afin de mettre en place des méthodologies permettant d’évaluer l’impact de cette opération sur les concentrations de PM10 à partir de mesures de terrains. Ce programme DECOMBIO, qui a servi de cadre à ce travail de thèse, s’est appuyé sur la confrontation durant 4 hivers des mesures en continu des PM10 issus de la combustion de la biomasse (noté PM10wb), des conditions météorologiques, et des variations d’émissions liées au renouvellement des appareils. Pour répondre à cet enjeu, les travaux de cette thèse ont eu pour objectif de développer des méthodes permettant de prendre en compte l’influence des conditions atmosphériques sur les concentrations hivernales de PM10 et d’évaluer les paramètres et facteurs d’incertitudes permettant de comparer les variations estimées d’émissions de PM10wb avec celles des concentrations mesurées de PM10wb au cours des 4 hivers.Ces travaux se sont appuyés sur d’importantes bases de données de mesures chimiques et météorologiques constitués durant 4 hivers (13-14 à 16-17) au niveau de 3 sites dans la vallée de l’Arve. Par ailleurs, les données techniques issues des dossiers de renouvellements des appareils pour bénéficier de l’aide financière du Fonds Air Bois nous ont été communiquées, nous permettant de spatialiser et temporaliser les réductions des émissions de PM10wb estimées provenant de ces remplacements d’appareils. L’état de la stabilité thermique de l’atmosphère, paramètre essentiel pour déterminer les épisodes d’inversions thermiques, a été suivi en continu grâce à un système peu onéreux de capteurs de température sous abris installés le long des pentes. Ces mesures ont permis de montrer que les conditions météorologiques locales dans ces vallées encaissées sont le facteur premier conditionnant les concentrations atmosphériques. Une classification automatique des conditions atmosphériques a été établie, permettant de s’affranchir de cette variabilité et de comparer les concentrations pour des conditions atmosphériques similaires entre les différents hivers. L’innovation de cette approche repose principalement sur la prise en compte du degré d’influence de variables météorologiques et de paramètres liés à l’intensité des émissions. Une diminution graduelle des concentrations de PM10wb au cours des hivers a ainsi pu être constatée au niveau des 3 sites pour certaines classes de conditions atmosphériques, résultat consolidé par la diminution concomitante des traceurs de la combustion de la biomasse à la masse des PM10. Afin de préciser ces résultats, nous nous sommes intéressés à estimer la réduction des émissions de PM10 des remplacements d’appareils au niveau des sites de mesures et leurs incertitudes.Au terme de ce travail et du programme DECOMBIO, nous avons été à même de développer et de valider plusieurs outils méthodologiques applicables dans l’évaluation de futurs fonds air bois (utilisation de capteurs le long des pentes, classification météorologique adaptée à la qualité de l’air, estimation des incertitudes sur les émissions de parc de dispositifs de chauffage au bois). Ces bases solides nous ont permis de mieux comprendre les mesures réalisées sur les PM atmosphériques, et leurs liens avec les estimations des émissions. / Biomass burning, particularly residential wood burning, is a source of atmospheric particles that contributes significantly to winter PM10 levels globally in Europe. In order to reduce emissions from this source, a large-scale program for the renewal of non-efficient wood-burning appliances, the “Fonds Air Bois” has been launched since 2013 in the Arve Valley. The assessment of its impact on atmospheric concentrations is an issue for future air quality management policies. The research DECOMBIO program (2013-2018) has been proposed to evaluate the impact of this operation on PM10 concentrations from measurements. This DECOMBIO program, which includes these thesis works, was based on the confrontation during 4 winters of continuous measurements of PM10 resulting from biomass burning (noted PM10wb), weather conditions, and emissions variations related to appliances renewal. To answer this challenge, the aim of this thesis was to develop methods to take into account the influence of atmospheric conditions on winter PM10 concentrations and to evaluate the parameters and uncertainties that make it possible to compare the estimated PM10wb emission variations with those of the measured PM10wb concentrations during the 4 winters.This work was based on large datasets of chemical and meteorological measurements made during 4 winters (13-14 to 16-17) at 3 sites in the Arve Valley. In addition, the technical data from wood appliances renewal files was provided to us, allowing us to spatially and temporally estimated reductions of PM10wb emissions from these appliances replacements. The state of atmospheric stability, an essential parameter for determining episodes of temperature inversions, has been continuously monitored thanks to an inexpensive system of sheltered temperature sensors installed along the slopes. These measurements have shown that local weather conditions in these valleys are the primary factor conditioning atmospheric concentrations. An automatic weather types classification has been established, making it possible to overcome this variability and to compare the concentrations for similar atmospheric conditions between the different winters. The innovation of this approach is mainly based on taking into account the degree of influence of meteorological variables and parameters related to the intensity of emissions. A gradual decrease of PM10wb concentrations during winters was thus observed at the 3 sites for certain weather types, a result consolidated by the concomitant decrease in tracer biomass combustion at the mass of PM10. To clarify these results, we were interested in estimating the reduction of PM10 emissions from device replacements at measurement sites and their uncertainties.At the end of this work and of the DECOMBIO program, we have been able to develop and validate several methodological tools applicable in the evaluation of future “fonds air bois” (use of sensors along the slopes, meteorological classification adapted to the quality of the air, estimation of uncertainties on wood appliances renewable emissions). This solid foundation has allowed us to better understand the measurements made on atmospheric PM, and their relationship to emission estimates.