Is there an association between the location of retail centres and the amount of air pollution shoppers are exposed to?
The UK recently legislated to become net-zero by 2050, by which time the aim is to end the country's contribution to global warming (Masoud Sajjadian, 2023). According to the department for transport, around 24% of the UKs total greenhouse gas emissions come from transport, with 99 MtCO2e (million tonnes of carbon dioxide equivalent) coming from private transport (Department of Transport, 2022). A reduction in this figure would therefore play a major role in reaching the 2050 target of becoming the first major economy to reach net-zero.
For this to be achieved, local land-use planning policy is central. In 1995 the UK government enacted the air quality management framework, in which the effects of air pollutants would drive changes in central government policy frameworks. In it they suggest that local authorities might implement changes that should meet the objectives of improved air quality. Land-use planning effects often run over longer timescales of about 10 years, which are consistent with the timescales for measuring the changes suggested in the AQM (Beattie et al., 2001). By combining the land-use plans with considerations of air quality improvements simultaneously, there is scope to both improve the physical retail environment and the air quality in them.
There has been an increasing amount of deregulation to allow for commercial properties to be redeveloped into residential accommodation (Dunning et al., 2023). Liveability is part of the drive for these changes and indicators of the benefits include health, accessibility to services through active travel modes (such as walking and cycling), and importantly, air quality (Ntounis et al., 2023). It will therefore be important to investigate the quality of the environment in those retail centres that may find themselves being transformed into more mixed-use accessible and liveable communities, and to make sure that the negative health impacts of air pollution are to be mitigated.
The Consumer Data Research Centre (CDRC) provides Access to Healthy Assets and Hazards (AHAH) data, which contains a multi-dimensional index of neighbourhood health, and includes data on air quality amongst other measures. Air quality data is based on the amount of particulate matter in the air of a size that is 10µm (10 microns) or less (pm10).
CDRC also provides data on retail centres, so by looking at the air quality of these, it could provide a starting point in the drive to reach net-zero, and to clean up environments that all of us have a relationship of some kind with, such as those in which we shop.
This work will look at particulate matter around retail centres in the Yorkshire and The Humber region of England.
This repository contains: -
• The GEOG5995M_201578497_Final_Assignment.ipynb file containing the code and markdown for the analysis of the data, including the final two visualisations (Fig.1. and Fig.2.).
All the data required for the analysis: -
• AHAH_V3_0.csv
• LSOA_21_Boundaries.gpkg
• LSOA11_to_LSOA21.csv
• README.md
• Retail_Boundaries_UK.gpkg
• spatial_environment.yml (The environment needed in which to run the code)
• yh_lsoa_2021.shp (including the additional files required by the .shp file: -
• yh_lsoa_2021.dbf
• yh_lsoa_2021.prj
• yh_lsoa_2021.shx
The code in the Jupyter notebook aims to follow the data science process to help understand the air quality of the physical retail environment in Yorkshire and The Humber and is aimed at policy makers so that they may be able to start to understand where policy interventions may be best placed. These policy interventions should be aimed at providing cleaner air quality in retail locations so that shoppers are not unnecessarily. The notebook is annotated throughout in markdown to explain / justify the aim or results of the next/previous section, and the code itself is commented throughout and should explain the function of each line of code.
The data is loaded at various points throughout the process, cleaned and joined together in various ways, so that there is a clear communication of the sorts of retail classification types, and the average quality of the air in each. There is a calculation of a mean value (particulate matter) that provides the main result of the study, which is plotted bot non-spatially and spatially, which should provide a snapshot of the entire retail environment in the study area and a picture of the data, so that differences and similarities between locations can be seen, and should provide policy makers enough data and information to focus efforts in certain places to improve the conditions.
So that the code can be run, the following packages will be required. Some of which may need to be installed prior to running the code: -
• pandas
• geopandas
• shapely
• statsmodels
• matplotlib
• seaborn
• contextily
To run the code, please activate the environment : spatial_env | Then open the GEOG5995M_201578497_Final_Assignment.ipynb file.
Beattie, C.I., Longhurst, J.W.S. and Wood, N.K. 2001. Air quality management: evolution of policy and practice in the UK as exemplifed by the experience of English local government. Atmospheric Environment.
Department of Transport 2022. Transport and environment statistics 2022. GOV.UK. [Online]. [Accessed 22 November 2023]. Available from: https://www.gov.uk/government/statistics/transport-and-environment-statistics-2022/transport-and-environment-statistics-2022.
Dunning, R.J., Lord, A. and Smith, M. 2023. PD games: death comes to planning In: Planning in a Failing State [Online]. Policy Press, pp.72–86. [Accessed 4 December 2023]. Available from: https://bristoluniversitypressdigital.com/display/book/9781447365075/ch005.xml.
Masoud Sajjadian, S. 2023. A critique on the UK’s net zero strategy. Sustainable Energy Technologies and Assessments. 56, p.103003.
Ntounis, N., Saga, R.S., Warnaby, G., Loroño-Leturiondo, M. and Parker, C. 2023. Reframing high street viability: A review and synthesis in the English context. Cities. 134, p.104182.