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This paper introduces an integrated spatial and temporal modeling system developed mathematically for assessing microbial contaminants on animal-grazed farmlands. The model uses fecal coliform, specifically Escherichia coli, as an indicator of fecal contamination and describes the sources, sinks, transport processes, and fate of E. coli contaminants in catchments and associated streams. Spatial features include grazing location, land topography, distance to a nearby stream, and distance through the stream network to the outlet. Temporal features are population dynamics on the land surface, in flow, and on streambeds. The model applies the principles of conservation of mass balance on two different types of pools: grid cells on land surfaces and networked stream segments. The model aims to improve the prediction of the effects of different land management strategies on the fecal contamination of waterways. This is achieved by characterizing the movement of fecal contaminants from land to streams and in-stream mobilization. Processes of attenuation, diffusion, and transport govern the movement. Our study site is a hill land catchment with an area of 140 ha and is used exclusively for animal grazing. The model was calibrated with previous research results, and then tested using the data collected at the outlet of the catchment. The sensitivity of the model predictions was analyzed for different scenarios: effect of stock rate, attenuation rate, and flow volumes. The similar pattern between monitored and predicted E. coli concentration proved that the model captures the key features that control the population dynamics of fecal contaminants. Further experiments are required to expand the model's functionality for covering more mitigation options.

The impact of urban form on residential space-conditioning energy use has been controversial in recent planning literature. This study empirically evaluates the association between urban form and residential energy use, focusing particularly on residential electricity use for space cooling in the City of Sacramento, California. We characterize urban form, property conditions, and demographic and socioeconomic characteristics by applying spatial metrics embedded within a geographic information system where LiDAR data effectively include each building and the surrounding vegetation. A statistical model is applied to assess the relationship between these explanatory variables and the estimated summer air-conditioning energy use. Controlling for other variables, higher population density, east-west street orientation, higher green space density, larger vegetation on the east, south, and especially the west sides of houses, appears to have statistically significant effects on reducing summer cooling energy use. This study quantifies the built environment impact on the energy demand of air conditioning and informs planners as they craft urban planning and design policies for energy conservation.Keywords: building energy use, passive solar community design, tree planting, GIS 1 Introduction Does urban form have an impact on the energy use of buildings? Conventionally, the energy consumption of buildings has been explained as a function of 'internal' determinants such as building design, energy system efficiency, and occupant behavior. For example, Baker and Steemers (2000) reported that building design can affect energy consumption by a factor of up to 2.5, system efficiency by a factor of up to 2, and occupant behavior by a factor of 2. If a building is poorly designed, equipped with inefficient mechanical systems, and occupied by energy-wasting occupants, it could consume up to ten times more energy than when looking at the best possible scenario (Baker and Steemers, 2000).Later Ratti et al (2005) added 'external' urban geometry to this formula when they compared three different building layouts, and reported that urban geometry leads to a variation in energy use with a range of approximately 10%. Although urban form may appear to have relatively little impact on energy use in buildings, its long-term impact across thousands of buildings can bring about a substantial difference due to the inertia of the built environment. Despite this long-term macroscale impact of urban form and the considerable contribution (40.36%) of buildings to total energy consumption (US Energy Information Administration, 2011) in the United States, the impact of the external physical environment on buildings' energy use has not