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This dataset was produced using ArcGIS software by Colin Tully on 30th September 2016 to be used in official figures for the local air quality assessment for Project 7-DMRB stage 3 of the A9 dualling scheme. Receptor were found at worst-case locations where the impacts of the A9 on local pollutant concentrations are expected to be the greatest these are identified spatially in this dataset.
This dataset was produced using ArcGIS software by Colin Tully on 30th September 2016 to be used in official figures for the local air quality assessment for Project 7-DMRB stage 3 of the A9 dualling scheme. Receptor were found at worst-case locations where the impacts of the A9 on local pollutant concentrations are expected to be the greatest these are identified spatially in this dataset. Additionally, OS address point data has been used to identify the addresses that relate to the receptors and this has been added to the attribute table (11/10/2016).
This dataset was produced using ArcGIS software to be used in official figures for the local air quality assessment for Project 9-DMRB stage 3 of the A9 dualling scheme. The roads links were chosen using DMRB (HA207/07) local air quality screening criteria from traffic data shapefiles provided by SIAS Transport Planners. These represent the roads used to assess pollutant emissions in the local air quality assessment; these are identified spatially in this dataset.
This dataset was produced using ArcGIS software by Colin Tully on 9th December 2016 to be used in official figures for the local air quality assessment for Project 7-DMRB stage 3 of the A9 dualling scheme. The area was drawn for 200 m around the ARN links and covers locations where worst-case impacts on local receptors are more likely to occur. The area is identified spatially in this dataset.
This dataset was produced using ArcGIS software to be used in official figures for the local air quality assessment for Project 7-DMRB stage 3 of the A9 dualling scheme. The area was drawn for 500 m around the ARN links and covers locations where worst-case impacts on local receptors are more likely to occur. The area is identified spatially in this dataset