Wiki source code of 5.12 Mean level of exposure to ambient air pollution
Last modified by Emily West on 2022/01/19 09:58
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1.1 | 1 | = 5.12 Mean level of exposure to ambient air pollution = |
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| 3 | Project Name: URBAN GreenUP | ||
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| 6 | |=(% style="width: 237px;" %)Pillar of Sustainable Development|=(% style="width: 646px;" %)Societal challenge | ||
| 7 | |(% style="width:237px" %)Planet|(% style="width:646px" %)5 - Air Quality | ||
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| 11 | ====== **Description** ====== | ||
| 12 | )))|(% style="width:730px" %)This KPIs is calculated from ground measurements by the official Air Quality monitoring networks in cities applying a methodology defined by URBAN GreenUP Project adapted from different sources. Additionally, information on the type of the zone (road traffic, city background, industrial, etc.) has been assigned to the different areas/streets of the city to weight population | ||
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| 14 | ====== **Scale as linked to D2.2 framework** ====== | ||
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| 16 | * Street | ||
| 17 | * Household | ||
| 18 | * Object | ||
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| 21 | ====== **Data source [ link to data framework]** ====== | ||
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| 23 | * Air Quality monitoring stations | ||
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| 26 | ====== **Data needs** ====== | ||
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| 28 | * Concentrations of NO2, O3 and airborne particulate matter are measured by recording PM mass per cubic meter of air (PM2.5 and PM10). | ||
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| 31 | ====== **Expected Outputs** ====== | ||
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| 33 | * The main result of this KPI is a city map where can be shown air quality average levels for the city. This outcome can be used to define population exposition levels and to highlight buildings used by vulnerable groups such as schools or residences for the elderly. | ||
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| 36 | ====== **Calculation methods** ====== | ||
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| 38 | * **Data processing** | ||
| 39 | * Calculation of annual and monthly mean levels of NO2, O3, PM10 and PM2.5 at each station location. | ||
| 40 | * There are three main types of stations for city domains (excepting industrial sites that are no considered for this KPI). | ||
| 41 | ** Road traffic | ||
| 42 | ** Urban background | ||
| 43 | ** Peri-urban background | ||
| 44 | * According to this classification, it can be obtained average values for road traffic areas, urban areas and peri-urban areas. Then, using a GIS software, a model of the city can be built that classifies all locations/streets/areas of the city in those categories. | ||
| 45 | * **Spatial Analysis software** | ||
| 46 | * QGIS is the GIS software proposed to be used, due to it is an open source and multiplatform software and it is distributed under Creative Commons Attribution-Share Alike 3.0 licence (CC BY-SA). | ||
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| 49 | ====== **Links and references** ====== | ||
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| 51 | * URBAN GreenUP Deliverable D2.4 - Monitoring program to Valladolid. https:~/~/www.urbangreenup.eu/insights/deliverables/d2-4~-~-- monitoring-program-to-valladolid.kl | ||
| 52 | * URBAN GreenUP Deliverable D3.4 - Monitoring program to Liverpool https:~/~/www.urbangreenup.eu/insights/deliverables/d3-4~-~-- monitoring-program-to-liverpool.kl | ||
| 53 | * URBAN GreenUP Deliverable D4.4 – Monitoring program to Izmir https:~/~/www.urbangreenup.eu/insights/deliverables/d4-4~-~- monitoring-program-to-izmir.kl | ||
| 54 | * URBAN GreenUP Deliverable D5.3: City Diagnosis and Monitoring Procedures https:~/~/www.urbangreenup.eu/insights/deliverables/d5-3- city-diagnosis-and-monitoring-procedures.kl | ||
| 55 | * Air Pollution in the UK 2015. https:~/~/uk air.defra.gov.uk/library/annualreport/index | ||
| 56 | * Bottalico, F., Chirici, G., Giannetti, F., De Marco, A., Nocentini, S., Paoletti, E., Salbitano, F., Sanesi, G., Serenelli, C., Travaglini, D., 2016. Air pollution removal by green infrastructures and urban forests in the city of Florence. Agric. Agric. Sci. Procedia 8, 243–251. doi:10.1016/j.aaspro.2016.02.099. | ||
| 57 | * Mullaney, J., Lucke, T., Trueman, S.J., 2015. A review of benefits and challenges in growing street trees in paved urban environments. Landscape Urban Plan. 134, 157–166. doi:10.1016/j.landurbplan.2014.10.013. | ||
| 58 | * Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158. doi:10.1016/j.ecolind.2015.03.013. | ||
| 59 | * SDG indicator 3.9.1 https:~/~/unstats.un.org/sdgs/metadata/files/Metadata-03-09- 01.pdf | ||
| 60 | * SDG indicator 11.6.2. https:~/~/unstats.un.org/sdgs/metadata/files/Metadata-11-06- 02.pdf | ||
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| 66 | (% class="success" %)|(% style="width:68px" %)**Version**|(% style="width:179px" %)**Name**|(% style="width:112px" %)**Date**|(% style="width:636px" %)**Changes made** | ||
| 67 | (% class="active" %)|(% style="width:68px" %)**0.1**|(% style="width:179px" %)Emily (EcoWise)|(% style="width:112px" %)19/01/2022|(% style="width:636px" %)Factsheet created | ||
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