Waste Management: Where does our waste go?

Posted on Oct 24, 2019
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

flickr Image by Jeffrey Beall; Creative Commons Licence: Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0)


The issue of waste management is of increasing interest as we grow aware of the problems posed by the amount of waste that is currently produced and how it is disposed of. What happens to our household waste once it is picked up by our local council? How much does household or industry waste contribute to our overall waste production as a society?

I wanted to look into some of these questions and visualise the types of waste we produce, the routes waste takes to where it is processed, and how it is processed.

For this analysis I chose the Waste Data Interrogator 2018 published by the UK Government, Department for Environment, Food and Rural Affairs, as a resource. This data set contains comprehensive information reported by ~6,000 operators of regulated waste management facilities in England on the quantities and types of waste received as well as on waste sent on to other facilities.

The database is restricted to waste management facilities in England as environmental regulation responsibilities for Wales and Scotland are being held by Natural Resources Wales (NRW) and the Scottish Environment Protection Agency (SEPA), respectively.


Waste types are categorised based on the European Waste Catalogue (EWC), a hierarchical list of waste descriptions established by the European Commission. Reporting for the Waste Data Interrogator 2018 resource uses two hierarchical levels: a basic system with three main categories – (1) inert/construction and demolition, (2) household/industrial and commercial, and (3) hazardous. In addition there is a system of higher granularity with specific EWC descriptors.

Looking at the composition of waste received across waste management facilities, 54.2% is Household/Industrial/Commercial, 42.7% Inert/Construction and Demolition waste, and 3.1% hazardous waste, which is defined as waste that is harmful to humans or the environment.

At a more detailed level, the five largest waste classes are construction and demolition waste (41%), municipal waste (25.7%), waste and water treatment (24.3%), a category of unspecified waste (2.6%), and agriculture and food processing waste (2.0%). To help users understand the waste numbers in tonnes behind these percentages, the app provides additional tables for all categories.

Panel 1. Contribution of different basic waste classes (top) and detailed EWC classes (bottom) as percentages of the total waste received by waste management facilities in England.


A comparison of the three basic waste categories shows not all UK regions handle comparable waste numbers. Essex handled the largest amount of Inert/Construction and Demolition Waste. Great Manchester and Meyerside received more Household/Industrial/Commercial Waste (Panel 2). Tees Valley Unitary Authorities and West Midlands Met Districts processed the largest numbers for hazardous waste.

Panel 2. Household/Industrial/Commercial waste in tonnes received by different regions.


Waste Disposal Choices

In addition to location differences, waste disposal choices differ for the 3 waste categories. Most, 53.7%, of Inert/Construction and Demolition Waste was recovered, 30.4% went to Landfill and 10.1% was transferred for recovery or disposal (combined: 94% of all Inert/Construction and Demolition Waste). In comparison a lower proportion of Household/Industrial/Commercial Waste went to Landfill (13.5%), while 13.7% required treatment prior to disposal (Panel 3). As expected, hazardous waste required the largest processing of waste prior to disposal (24.7%).

Interestingly, a significant proportion of waste is being transferred from the receiving waste management facilities for disposal or recovery, encompassing 20% of all Household/Industrial/Commercial and Hazardous Waste and 12% of Inert/ Construction and Demolition Waste.

Panel 3. Breakdown of waste disposal procedures for Household/Industrial/Commercial waste.


To gain a better understanding of the waste routes, information on waste origin and respective receiving waste disposal sites can be reviewed as both - general waste categories and at a more detailed level. Here visualised the amount of Household/ Industrial/Commercial waste (top) and more specifically of municipal waste (bottom) from Aberdeenshire received by various waste disposal regions (Panel 4).

Panel 4. Breakdown of waste disposal procedures for Household/Industrial/Commercial waste.


My app also includes a map for a more intuitive visualisation of the distances and routes waste travels from its origin to its site of disposal.

Panel 5. Visualisation of waste origin (green dot) and respective waste disposal sites (red dots) across all waste types.



Overall, waste travels large distances prior to disposal. Additionally, over 20% of household and hazardous waste is currently being transferred on from the receiving waste disposal site for disposal or recovery elsewhere. It would be interesting to understand if this is due to certain features of a specific waste subgroup and could be identified at the site of origin, due to capacities of waste disposal sites, or other factors.

Please feel free to go to my shiny app to explore UK waste information details or locations of your interest.


Future DirectionsΒ 

I would like to integrate waste data information from the NRW, SEPA and the Northern Ireland Environment Agency (NIEA) to gain a view on the UK-wide waste management. To obtain an understanding of how waste management has changed over the years, waste information from previous years for which information is provided by the UK government could be included. Enriching the data underlying my Shiny app with information on capacity and possible specialisation of UK waste disposal sites could provide useful insights for future waste management decisions.

About Author

Bettina Meier

Bettina Meier is a NYC Data Science Fellow with a PhD in biochemistry/molecular biology and experience in Cancer research, Genetics, and NGS data analysis.
View all posts by Bettina Meier >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI