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Gas emissions from broiler production have been the subject of intensive research. However, little experimental information exists for farms under the particular management and environmental conditions of the European Mediterranean area. In this study, ammonia, carbon dioxide, methane, and nitrous oxide concentrations and emissions were measured in a commercial broiler farm located in Spain. Gas concentrations were measured using a photoacoustic gas monitor, whereas the ventilation flow was evaluated by controlling the operation status of each fan. Two rearing periods were studied, one in summer and one in winter. All gas emissions increased with bird age. Ammonia emission rates averaged 19.7 and 18.1 mg/h per bird in the summer and winter, respectively, and increased with indoor temperature (r(2) = 0.51 in summer; r(2) = 0.42 in winter). Average CO(2) emission rates were 3.84 and 4.06 g/h per bird, CH(4) emission was 0.44 and 1.87 mg/h per bird, and N(2)O emission was 1.74 and 2.13 mg/h per bird in summer and winter, respectively. A sinusoidal daily variation pattern was observed in all emissions except for CH(4). These patterns were characterized in terms of time of maximum emission and amplitude of the daily variation.

ABSTRACT. There is need to identify and quantify the contribution of different sources to airborne particulate matter (PM) arge amounts of particulate matter (PM) are emitted from animal houses, which can compromise animal and human respiratory health (Radon et al., 2001;Zuskin et al., 1995) and the environment as well. The scientific community and stakeholders (farmers and local authorities) are seeking technically feasible and economically viable solutions to reduce these emissions to comply with air quality regulations. Preventing dust release from its source not only reduces emissions from the animal house but improves the indoor climate as well. To develop such reduction techniques, it is necessary to identify and quantify the sources that contribute to PM in animal houses.A complete assessment can be achieved by quantifying PM contributions from each source according to particle numbers and mass. Knowledge of the relationship between particle number and mass contributions is essential because it gives an insight into particle size and morphology related to different particle types (sources). Moreover, particle size and morphology are related to a particle's aerodynamic behavior, which is closely related to lung deposition mechanisms in the human airways: inertial impaction, sedimentation, interception, and diffusion (Zhang, 2004). Although current European and U.S. regulations set limits to PM concentrations based on mass, a mass-only approach to reduce PM would have very little effect on the number concentrations of smaller particles found in the fine fraction. This fraction contains fine and ultra-fine particles that pose greater risks of adverse health effects because these particles can go beyond the larynx and penetrate into the unciliated respiratory system (CEN, 1993). The control of particles larger than 2.5 mm in diameter, however, is also relevant, because these particles can also cause adverse health effects through deposition in the upper respiratory airways. Furthermore, particles larger than 2 mm in diameter found in animal houses have been shown to contain high amounts of odorants (Cai et al., 2006) and micro-organisms (Lee et al., 2006). Consequently, both PM number and mass concentrations should be measured to tackle PM pollution related issues within animal houses, to develop reduction techniques, and to assess their effects.Analytical methods used to characterize PM, such as microscopic analysis, can supply useful but limited data on particle or source chemical composition and morphological characteristics. To further identify and quantify source contributions, source apportionment models can be used. These models are versatile because they can be used in different scenarios (Watson et al., 2002). L

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This paper presents a technique for land-use object-based image classification of urban environments that combines high spatial resolution multi-spectral imagery and LiDAR data. Cadastral or land registry plots are used to divide the image and define objects. A set of descriptive features is presented, describing the objects at different urban aggregation levels. Objects are characterised by means of image-based (spectral and textural), three-dimensional, and geometrical features. In addition, contextual features describing two levels of the object are defined: internal and external. Internal contextual features describe the land cover object types (buildings and vegetation) inside the object. External contextual features describe each object while considering the common properties of neighbouring objects, which in urban areas usually coincide with the urban block. The proposed descriptive features emulate human cognition by numerically quantifying the properties of the image elements that enable their discrimination. The land-use classification accuracy values show that the proposed descriptive features enable an efficient characterisation of urban environments. The complementariness between the features derived from different aggregation levels is noticeable. Image-based features are highly discriminative, and the addition of internal and external contextual features significantly increases the classification accuracy of the urban classes considered in this study.