Fungi are present everywhere in indoor and outdoor environments. Many fungi are toxigenic or pathogenic that may cause various public health concerns. Rapid detection, quantification and characterization of fungi in living and working environments are essential for exposure risk assessment to safe guard public health. Rapid and accurate detection and identification of fungi using molecular method require specific markers. In this thesis, partial mt SSU and LSU rDNA were amplified and sequenced from 31 fungal species of 16 genera. Sequence alignments showed that fungal mt SSU and LSU rDNA contained sufficient amount of variation for the development of markers that can discriminate even among closely related species. Forty-eight probes were designed and were verified as highly specific to 25 fungal species commonly detected in living and working environments. These specific probes would have potential applications in clinical diagnosis and public health-related environmental monitoring. Nested PCR is a highly sensitive and specific method. Based on the nuclear 18S rDNA sequence variation pattern, three nested PCR systems were developed to detect the conifer tree pathogen Gremmeniella abietina, an ascomycete fungus that causes stem canker and shoot dieback in many conifer species. The three nested PCR systems showed high specificity and sensitivity. These methods could have broad applications in forest protection and disease management programs. Quantitative real-time PCR offers the ability of simultaneous detection and quantification of DNA of a specific microbe in one reaction. Based on the 18S rDNA sequence, two real-time PCR assays were developed to detect and quantify Wallemia sebi, a deuteromycete fungus commonly found in agricultural environments and is suspected to be a causative agent of farmer’s lung disease. Both PCR systems proved to be highly specific and sensitive for W. sebi detection even in a high background of other fungal DNAs. Application of the real-time PCR methods in the quantification of W. sebi in the aerosols of a farm revealed a high concentration of W. sebi spores (107/m3). The study indicates that W. sebi is a dominant fungus in agriculture environments. Cladosporium spores are important aeroallergens, and prolonged exposure to elevated spore concentrations can provoke chronic allergy and asthma. A TaqMan probe and a SYBR Green I based real-time PCR assay were developed to detect and quantify Cladosporium in aerosols. The two real-time PCR systems proved to be highly specific and sensitive for Cladosporium. These methods were employed to quantify Cladosporium in aerosols of five different indoor environments. High spore concentration of Cladosporium (107/m3) was observed in a cow barn. Cladosporium spore concentration in paper and pulp factory and countryside house also exceeded threshold value for clinical significance. Prolonged exposure in these environments could impose certain health risk. Thus, monitoring Cladosporium spore concentration in indoor environments is important for indoor air quality control.

Les fuites au travers des ouvrages hydrauliques en remblai sont les signes précurseurs d'un dysfonctionnementdu dispositif d'étanchéité de l'ouvrage pouvant entraîner leur rupture. La détectionprécoce des fuites et leur quanti_cation est donc primordiale.Les méthodes géophysiques et thermométriques à grand rendement apportent des éléments deréponse pour la détection des fuites, le long des ouvrages à long linéaire, mais l'estimation de leurvitesse, nécessaire à l'évaluation de la dangerosité des fuites, n'est pas encore satisfaisante.Cette étude porte sur la détection et quanti_cation des fuites à travers les ouvrages hydrauliquesen remblai soumis à une charge d'eau permanente. Les méthodes proposées exploitent des mesures detempératures naturelles du sol à l'aide de _bres optiques placées sous le talus amont ou aval.Deux modèles de quanti_cation ont été développés et testés sur les données d'un site expérimentalcontrôlé et d'un site réel. Les résultats obtenus concordent avec les mesures de vitesse e_ectuées surles deux sites. / Leakages through embankment dams are early warning signs of a sealing malfunction and couldlead to its breakdown. Early detection of leakages and their quanti_cation is essential.High output geophysical and thermometric methods provide some answers for leakage detectionalong long linear embankment dams, but their velocity estimations necessary to assess the danger ofleakages, is not yet satisfactory.This study focuses on the detection and the quanti_cation of leakages through embankment damsunder hydraulic head. The proposed method use natural temperature measurements from the groundusing optical _ber buried under the upstream or downstream face.Two models of quanti_cation were developed and tested on data from an experimental site and a realsite. Results are consistent with velocity measurements made at both side.

Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R²) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications. / Graduation date: 2013