GIS-Based Emission Inventory, Dispersion Modeling, and Assessment for Source Contributions of Particulate Matter in an Urban Environment

Water, Air, & Soil Pollution, Online 25 November 2010

Sailesh N. Behera, Mukesh Sharma, Onkar Dikshit and S. P. Shukla

“The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM10 levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM10 in each of 85 grids of 2 × 2 km. The total emission of PM10 was estimated at 11 t day−1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day−1 (26%); (b) vehicles, 2.3 t day−1 (21%); (c) domestic fuel burning, 2.1 t day−1 (19%); (d) paved and unpaved road dust, 1.6 t day−1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM10 levels were very high (89–632 μg m−3). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.”

Interoperable Processing of Sensor-data in Spatial Data Infrastructures – A Use Case for Wind Power Analysis

ISW-2011: Integrating Sensor Web and Web-based Geoprocessing, An AGILE 2011 Conference Workshop; Utrecht, The Netherlands, April 18, 2011

Sandra Lanig, Georg Walenciak, and Alexander Zipf

“Nowadays, sensor data are omnipresent and ubiquitous available. Additionally, sensor measurements were required in several domains such as disaster management or renewable energies. This paper presents a proposal how sensor data measurements can be integrated in a standardized Spatial Data Infrastructures (SDIs). Therefore we extended the SDI by the OGC Web Processing Service (WPS) in order to compute and access sensor data measurements served by a Sensor Observation Service (SOS).”

Habitat Selection by Critically Endangered Florida Panthers across the Diel Period: Implications for Land Management and Conservation

Animal ConservationAnimal Conservation, Volume 14, Issue 2, pages 196–205, April 2011

D. P. Onorato1, M. Criffield, M. Lotz, M. Cunningham, R. McBride, E. H. Leone, O. L. Bass Jr, and E. C. Hellgren

“Decisions regarding landscape management, restoration and land acquisition typically depend on land managers’ interpretation of how wildlife selects habitat. Such assessments are particularly important for umbrella species like the endangered Florida panther Puma concolor coryi, whose survival requires vast wildlands. Some interpretations of habitat selection by panthers have been criticized for using only morning locations in defining habitat use. We assessed habitat selection using a Euclidean distance analysis and location data collected throughout the diel period from GPS collars deployed on 20 independent Florida panthers. We corroborated aspects of earlier analyses by demonstrating the selection of forested habitats by panthers. We also confirmed the selection of open habitats (i.e. marsh–shrub–swamps, prairie grasslands), a novel result. Habitat selection did not vary by sex or season but varied by time of day. Panthers were located closer to wetland forests in the daytime and used prairie grasslands more at night. Our assessment of the effect of patch size on selection of forest habitat revealed that panthers were not solely reliant on large patches (>500 ha) but utilized patches of all sizes (≤1, >5–10, >1000 ha, etc.). Our results emphasize the importance of collecting panther location data throughout the diel period when assessing habitat selection. Conservation strategies for panthers should consider a mosaic of habitats, a methodology that will protect other sensitive flora and fauna in South Florida.”