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Final Report of Areas of Poor air Quality in Camden

Paper Type: Free Assignment Study Level: University / Undergraduate
Wordcount: 2637 words Published: 29th Nov 2021

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1. Background

Camden Borough is one of crowded commuter flows in London after Westminster, City of London, in which Camden Borough received over 100,000 commuters per day (Aether, 2017).

In Camden Borough, transports from TFL contributed 14.2 % of pollutants, diesel car and Heavy Good Vehicle (HGV) are 8 % (Camden Council, 2016). In 2013, London Atmospheric Emission Inventory was established and defined 183 Air quality Focus Areas where are the location of air quality exceeded mean value of NO2 set by the EU regulation (GLA, 2013)

The air pollution dynamic and relationship of public transport and road concentration where there are exceeding limit values can be captured by GIS tool which plays a crucial role in urban planning and modelling. It can be used to monitor pollution and its distribution, variation, and characteristic of spatial pattern and its relationship (Kumar, 2015).

This study objectives are, the first is to estimate the number of people and areas where are affecting by pollution based on the London Air Quality Focus Areas. The second is to analyze geographic relationship between the Focus Areas, and spatial variation, such as underground stations and train lines, roads and natural geographic features.

2. Analysis Scheme

2.1. Data introduction

The data used in this study is as the following:

  • LSOA (Lower Layer Super Out Areas) is polygon obtained from Office for National Statistics (ONS, 2011).
  • LAEI (London Atmospheric Emission Inventory) is polygon that contains 187 air quality focus areas in London Borough (GLA, 2016).
  • LAE (London Atmospheric Emission 2016 is raster data that computes pollutants emission of NO2 covering 33 London Boroughs (GLA, 2016).
  • Overground dataset is point data of overground stations and polyline of train lines.
  • Road network is polyline data in Camden Borough obtained from Digimap (Digimap, 2016).
  • Natural geographic features contain green spaces and water polygons in Camden Borough from Digimap site (Digimap, 2016).

2.2. Analysis procedure

2.2.1. Air pollution impact

Estimate Focus Area in Camden Borough

Adding new fields as "area" and "population_density" in LSOA. This shapefile has geometry and population, then calculate geometry by hectare in area field which obtained area in hectare.

The Focus Areas shapfile was selected by location where the Focus Areas contained within Camden Borough. Then use "Feature Clip" to crop the selected areas. The new feature contained only the Focus Areas in Camden Borough. Adding new field named area and calculating the Focus Areas by square hectare and using statistics to summarize the total size of the Focus areas in Camden Borough.

Estimate People in the Focus Area

Applying "Select by Location" that selects locations of the clipped Focus Areas from the previous process where were intersected with areas in Camden Borough. Several wards of Camden Borough intersected with the Focus areas were selected and export to a new shapefile. These wards contain their code and name, area and population, using a statistic to summarize the total number of people who are in the Focus Areas and affecting by air pollution.

2.2.2. Geographic relationship

This analysis stage aims to find the spatial pattern of transport connections. Traffic is main emitter (Whitelegg, 2003). While air pollution can be affected by the natural geographic features, for instance the proximity to water and vegetated areas affecting airflow and concentration in city center (Ross, 2006; Kumar, 2015; Kashima, 2009; Zupancic, 2015).

Dataset preparation

Air pollution is dynamic and has no boundary coverage of a certain location. The winds and turbulence are critical and control air pollution dispersion. The buffer zones with 500 m around the Focus areas of Camden Borough were generated. Then select overground stations and train lines, roads, green spaces, and water where intersected within buffer zones of the Focus Areas in Camden Borough by using "Select by Location" and then exported to new shapefiles (See Figure 1 and 2)

Figure 2: Transport and natural geographic features in Camden.

Kernel density

The Kernel density was used to analyze the spatial hotspot of geographic features where there is high or low cluster of overground stations, train lines, roads, green spaces, and water. Especially for visualizing geographic incidence of air pollution to generate hotspot map as smooth patthern (ESRI, 2015; Francis Tuluri, 2007). Kernel density only computes point and polygon, overground stations and train lines and roads were directly calculated the Kernel density in Arcemap. Green space and water features were converted to point features and ran the Kernel density. The results of Kernel density indicate in continuous values where an area is large relative to other features, the output is very dense while a small relative, the output is distance (see Figure 3) (Francis Tuluri, 2007)

Figure 3: Kernel Density of transport network and natural geographic features.

Fuzzy Overlay

This tool is to analyze raster dataset that a cell value is likelihood belong to other cell values in multi-criteria overlay analysis and the relationship of members of the multi-set (ESRI, 2016). The Fuzzy Overlay was used to aggregate raster of Kernel density in the previous process and compute the values of the raster inputs belonging to a particular membership.

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The Fuzzy Overlay was applied two times separately, the first was aggregating Kernel density raster of overground stations, train lines, and roads in order to analyze the pattern of transport congestion where there are high and low hotspots. The second Fuzzy Overlay was computing natural geographic features including the Kernel density of green space and water where are a high and low density of these natural features.

3. Result and limitation

3.1. Result and conclusion

3.1.1. Air pollution map

LAE indicates air pollution concentration of NO2 in London with 16 different levels. The Camden Borough is also one of the highest level of the air polluted Boroughs in London where there were 8 the air quality focus areas were defined (see Figure 4 and 5).

Figure 4: NO2 in Focus Areas of London 2016.

3.1.2. Air pollution impact

There were 55 wards in and around the Focus Areas in Camden Borough had affected by air pollution and the total number of people in these wards is 84,260 out of a total number of 203,000 people living in Camden Borough (see Figure 6 and Table 1). This is similar to the study of Aether (2017) found that there were 78,664 around of Camden residents living in the polluted areas of NO2.

In terms of the extent location, the 8 Focus areas in Camden Borough cover 225.138 hectares (10.32% of Camden Borough) and the 55 affected wards have 852.722 hectares (39.12% of Camden Borough) (see Figure 6 and Table 1)

Figure 6: Wards and population density in Focus area of Camden

Table 1: Wards and Focus Areas in Camden Borough

3.1.3. Geographic relationship

Transport relationship

There were 12 overground stations out of 17 stations where are located in 500 m buffer zones of the Focus Areas. Eight train lines running between these stations and through the Focus Areas were selected. There were 18,791 roads out of 33,413 roads selected. The highest congested roads were at the southern and western Camden Borough (see Table 2 and Figure 7-8).

These can be input for transport management to improve the air quality for Camden Borough. The emission from transport accounted for approximately 50% of Camden's NO2 (Camden Council, 2018). More sustainable transport is needed, such as walking, cycling and increasing more cycle lands, walking, and low emission vehicles.

Table 2: Transport network located in and out of buffer areas

Figure 7: Transport Hotspot in Camden.

Figure 8: Transport network located in and out of buffer zones

Natural Geographic relationship

In figure 9 indicates the hotspots of green spaces and water in the red color are in the northern areas where there are dense green spaces and water in Hampstead Heath Park and a low air pollution level compared to the other areas of Camden Borough. The western areas also have the hotspots of green spaces and water where are near to Regent park location. A total area of green spaces is 164.13 hectares where are 34.20 of 130.13 hectares located in buffer areas of the Forest Areas. Water has 7 hectares located in the buffer areas of the total water area 20.8 hectares (see Table 3 and Figure 10)

Figure 9: Green spaces and water hotspot in Camden

Figure 10: Green spaces and water located in and out of buffer zones

Table 3. Green spaces and water located in and out of buffer areas (hectare)

In conclusion, the transport and natural geographic relationship is highly correlated for air quality improvement, for instance, the high congestion of public transport and road network with low natural figures are within 500 m buffer zone of the Focus Areas should be considered for the air quality improvement by emission sub-charge for polluting vehicles entering congested areas, implementing the central London Ultra-Low Emission Zone (ULEZ), proposing diesel scrappage scheme for London, bringing clean bus corridor for dirtiest air areas in London, increasing more green spaces in a low natural figures (Aether, 2017; Camden Council, 2018). While the dense natural figure areas should be promoted for recreational areas.

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Recently, the Camden Council set the Clean Air Action Plan 2030 including monitoring air quality, reducing emission from building, new development, transportation, raising awareness of air quality and working with partners. However, the high emissions from the transports, which the local council does not have direct control of these emission sources (Camden Council, 2016), The air quality improvement plans must be revised and implemented annually together with Transport for London (TFL), Greater London Authority, and Camden's Cabinet Members for Sustainability and Environment (Camden Council, 2016; Aether, 2017)

3.2. Limitation

The result provides a preliminary study which is incomprehensive content and data analysis, which there are some limitations:

3.2.1. Data limitation

The Air Quality Focus Areas and London Atmospheric Emission are based on 2016 data which could not present the current air pollution in Camden and London. The LSOA data is in 2011 data collection and has not been updated since that. The different time of these versions might cause inaccuracy estimation of the number of population affected by air pollution in 2016.

3.2.2. Analysis limitation

The analysis process only focuses on the geographic relationship in terms of spatial intersection between air pollution and geographic features that represent in a hotspot of geographic factors. This does not provide much evidence of the spatial correlation between air pollution and geographic factors in order to test different hypotheses with the spatial variation such as correlated geographic features and model prediction of the spatial relationship. This method requires a larger dataset and is quite complex, which is considered not suitable for this study as the scope and data availability, but it is recommended for the future study.

Bibliography

Kumar, A. M. (215). IS Application in Urban Traffic Air Pollution Exposure Study: A Research Review. Suan Sunandha Science and Technology Journal.

Whitelegg, J. &. (2003). The global trans- port problem: some issues but a different Place (in John World Transport, Policy and Practice). Earth scan publications limited.

GLA,. (2013). LAEI2013 Review of Focus Areas. Greater London Authority.

ONS. (2011). Office for National Statistic. Retrieved from https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeography

GLA. (2016). London Datastore. (G. L. Authority, Producer) Retrieved 11 30, 2019, from https://data.london.gov.uk/dataset?q=laei

Digimap. (2016). Digimap. (U. o. Edinburgh, Producer) Retrieved from https://digimap.edina.ac.uk/

ESRI. (2015). Kernel Density. Retrieved 11 30, 2019, from ESRI: https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-kernel-densityworks.htm

Zupancic, T. (2015). THE IMPACT OF GREEN SPACE ON HEAT AND AIR POLLUTION IN URBAN COMMUNITIES: A META-NARRATIVE SYSTEMATIC REVIEW. David Suzuki Foundation.

Aether. (2017). Updated Analysis of Air Pollution Exposure in London for Greater London Authority. Aether.

Camden Council. (2018). Camden Clean Air Action Plan 2019-2022. Camden Council.

Francis Tuluri, F. G. (2007). Hotspot Analysis For Examining The Association Between Spatial Air Pollutants And Asthma In New York State, USA Using Kernel Density Estimation (KDE). Jackson State University.

TFL. (2015, 05 04). Record passenger numbers on London's transport network. Retrieved 12 07, 2019, from Transport for London: https://tfl.gov.uk/info-for/media/pressreleases/2015/june/record-passenger-numbers-on-london-s-transport-network

Kashima, S. Y. (2009). Application of land use regression to regulatory air quality data in Japan. (Vol. 407). Sci Total Environ.

Ross, Z. E. (2006). Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses. (Vol. 16). J Expo Sci Environ Epidemiol.

Camden Council. (2016). Clean Air Action Plan. Camden Council.

ESRI. (2016). How Fuzzy Overlay works. Retrieved 11 30, 2019, from ESRI: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-fuzzy-overlayworks.htm

 

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