Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

To cite this case study please use:

Wright, Carrie and Meng, Qier and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2020). https://github.com//opencasestudies/ocs-bp-air-pollution. Predicting Annual Air Pollution (Version v1.0.0).

To access the GitHub Repository for this case study see here: https://github.com/opencasestudies/ocs-bp-air-pollution.

You may also access and download the data using our OCSdata package. To learn more about this package including examples, see this link. Here is how you would install this package:

install.packages("OCSdata")

This case study is part of a series of public health case studies for the Bloomberg American Health Initiative.

The total reading time for this case study is calculated via koRpus and shown below:

Reading Time Method
107 minutes koRpus

Readability Score:

A readability index estimates the reading difficulty level of a particular text. Flesch-Kincaid, FORCAST, and SMOG are three common readability indices that were calculated for this case study via koRpus. These indices provide an estimation of the minimum reading level required to comprehend this case study by grade and age.

Text language: en
index grade age
Flesch-Kincaid 10 15
FORCAST 10 15
SMOG 13 18

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Motivation

A variety of different sources contribute different types of pollutants to what we call air pollution.

Some sources are natural while others are anthropogenic (human derived):

Major types of air pollutants

  1. Gaseous - Carbon Monoxide (CO), Ozone (O3), Nitrogen Oxides(NO, NO2), Sulfur Dioxide (SO2)
  2. Particulate - small liquids and solids suspended in the air (includes lead- can include certain types of dust)
  3. Dust - small solids (larger than particulates) that can be suspended in the air for some time but eventually settle
  4. Biological - pollen, bacteria, viruses, mold spores

See here for more detail on the types of pollutants in the air.

Particulate pollution

Air pollution particulates are generally described by their size.

There are 3 major categories:

  1. Large Coarse Particulate Matter - has diameter of >10 micrometers (10 µm)

  2. Coarse Particulate Matter (called PM10-2.5) - has diameter of between 2.5 µm and 10 µm

  3. Fine Particulate Matter (called PM2.5) - has diameter of < 2.5 µm

PM10 includes any particulate matter <10 µm (both coarse and fine particulate matter)

Here you can see how these sizes compare with a human hair:

[source]

The following plot shows the relative sizes of these different pollutants in micrometers (µm):

[source]

This table shows how deeply some of the smaller fine particles can penetrate within the human body:

Negative impact of particulate exposure on health

Exposure to air pollution is associated with higher rates of mortality in older adults and is known to be a risk factor for many diseases and conditions including but not limited to:

  1. Asthma - fine particle exposure (PM2.5) was found to be associated with higher rates of asthma in children
  2. Inflammation in type 1 diabetes - fine particle exposure (PM2.5) from traffic-related air pollution was associated with increased measures of inflammatory markers in youths with Type 1 diabetes
  3. Lung function and emphysema - higher concentrations of ozone (O3), nitrogen oxides (NOx), black carbon, and fine particle exposure PM2.5 , at study baseline were significantly associated with greater increases in percent emphysema per 10 years
  4. Low birthweight - fine particle exposure(PM2.5) was associated with lower birth weight in full-term live births
  5. Viral Infection - higher rates of infection and increased severity of infection are associated with higher exposures to pollution levels including fine particle exposure (PM2.5)

See this review article for more information about sources of air pollution and the influence of air pollution on health.

Sparse monitoring is problematic for Public Health

Historically, epidemiological studies would assess the influence of air pollution on health outcomes by relying on a number of monitors located around the country.

However, as can be seen in the following figure, these monitors are relatively sparse in certain regions of the country and are not necessarily located near pollution sources. We will see later when we evaluate the data, that even in certain relatively large cities there is only one monitor!

Furthermore, dramatic differences in pollution rates can be seen even within the same city. In fact, the term micro-environments describes environments within cities or counties which may vary greatly from one block to another.

[source]

This lack of granularity in air pollution monitoring has hindered our ability to discern the full impact of air pollution on health and to identify at-risk locations.

Machine learning offers a solution

An article published in the Environmental Health journal dealt with this issue by using data, including population density and road density, among other features, to model or predict air pollution levels at a more localized scale using machine learning (ML) methods.

Yanosky, J. D. et al. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors. Environ Health 13, 63 (2014).

The authors of this article state that:

“Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.”

[source]

The article above demonstrates that machine learning methods can be used to predict air pollution levels when traditional monitoring systems are not available in a particular area or when there is not enough spatial granularity with current monitoring systems. We will use similar methods to predict annual air pollution levels spatially within the US.

Main Question

Our main question:

  1. Can we predict annual average air pollution concentrations at the granularity of zip code regional levels using predictors such as data about population density, urbanization, road density, as well as, satellite pollution data and chemical modeling data?

Learning Objectives

In this case study, we will walk you through importing data from CSV files and performing machine learning methods to predict our outcome variable of interest (in this case annual fine particle air pollution estimates).

We will especially focus on using packages and functions from the tidyverse, and more specifically the tidymodels package/ecosystem primarily developed and maintained by Max Kuhn and Davis Vaughan. This package loads more modeling related packages like rsample, recipes, parsnip, yardstick, workflows, and tune packages.

The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially legible and intuitive.

The skills, methods, and concepts that students will be familiar with by the end of this case study are:

Data Science Learning Objectives:

  1. Familiarity with the tidymodels ecosystem.
  2. Ability to evaluate correlation among predictor variables (corrplot and GGally).
  3. Ability to implement tidymodels packages such as rsample to split the data into training and testing sets as well as cross validation sets.
  4. Ability to use the recipes, parsnip, and workflows to train and test a linear regression model and random forest model.
  5. Demonstrate how to visualize geo-spatial data using ggplot2.

Statistical Learning Objectives:

  1. Basic understanding the utility of machine learning for prediction and classification
  2. Understanding of the need for training and test sets
  3. Understanding of the utility of cross validation
  4. Understanding of random forest
  5. How to interpret root mean squared error (rmse) to assess performance for prediction

We will begin by loading the packages that we will need:

# Load packages for data import and data wrangling
library(here)
library(readr)
library(dplyr)
library(skimr)
library(summarytools)
library(magrittr)
# Load packages for making correlation plots
library(corrplot)
library(RColorBrewer)
library(GGally)
# Load packages for building machine learning algorithm
library(tidymodels)
library(workflows)
library(vip)
library(tune)
library(randomForest)
library(doParallel)
# Load packages for data visualization/creating map
library(ggplot2)
library(stringr)
library(tidyr)
library(lwgeom)
library(proxy)# needed for lwgeom
library(sf)
library(maps)
library(rnaturalearth)
library(rnaturalearthdata) # needed for rnaturalearth
library(rgeos)
library(patchwork)
# Load package for downloading the case study data files
library(OCSdata)

Packages used in this case study:

Package Use in this case study
here to easily load and save data
readr to import CSV files
dplyr to view/arrange/filter/select/compare specific subsets of data
skimr to get an overview of data
summarytools to get an overview of data in a different style
magrittr to use the %<>% pipping operator
corrplot to make large correlation plots
GGally to make smaller correlation plots
tidymodels to load in a set of packages (broom, dials, infer, parsnip, purrr, recipes, rsample, tibble, yardstick)
rsample to split the data into testing and training sets; to split the training set for cross-validation
recipes to pre-process data for modeling in a tidy and reproducible way and to extract pre-processed data (major functions are recipe(), prep() and various transformation step_*() functions, as well as bake which extracts pre-processed training data (used to require juice()) and applies recipe preprocessing steps to testing data). See here for more info.
parsnip an interface to create models (major functions are fit(), set_engine())
yardstick to evaluate the performance of models
broom to get tidy output for our model fit and performance
ggplot2 to make visualizations with multiple layers
dials to specify hyper-parameter tuning
tune to perform cross validation, tune hyper-parameters, and get performance metrics
workflows to create modeling workflow to streamline the modeling process
vip to create variable importance plots
randomForest to perform the random forest analysis
doParallel to fit cross validation samples in parallel
stringr to manipulate the text the map data
tidyr to separate data within a column into multiple columns
rnaturalearth to get the geometry data for the earth to plot the US
maps to get map database data about counties to draw them on our US map
sf to convert the map data into a data frame
lwgeom to use the sf function to convert map geographical data
rgeos to use geometry data
patchwork to allow plots to be combined
OCSdata to access and download OCS data files

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

Context

The State of Global Air is a report released every year to communicate the impact of air pollution on public health.

The State of Global Air 2019 report which uses data from 2017 stated that:

Air pollution is the fifth leading risk factor for mortality worldwide. It is responsible for more deaths than many better-known risk factors such as malnutrition, alcohol use, and physical inactivity. Each year, more people die from air pollution–related disease than from road traffic injuries or malaria.

[source]

The report also stated that:

In 2017, air pollution is estimated to have contributed to close to 5 million deaths globally — nearly 1 in every 10 deaths.