Air Quality and Population Growth: An Analytic Approach

A Qualitative Analytic Approach to
Interpreting Modern Population
Growth in terms of Air Quality
Auston Li
North Carolina School of Science and Mathematics

Air Quality
• A quantifiable measure of the severity of air pollution
• Provides an idea of the impact of human health
• United States scales from 0 (Best) to 500 (Worst)
• Linearizes the pollution standards to 100
• Uses the criteria pollutants of: ground-level ozone, particulate matter,
carbon monoxide, sulfur dioxide, and nitrogen dioxide
2
Introduction Regulation Analysis Figures Conclusion
Air Quality and Population Auston Li, North Carolina School of Science and Mathematics, Sigma Xi 2014

US Air Quality Index Table
Air Quality Index (AQI) Values Levels of Health Concern Colors
0 to 50 Good Green
51 to 100 Moderate Yellow
101 to 150 Unhealthy for Sensitive Groups Orange
151 to 200 Unhealthy Red
201 to 300 Very Unhealthy Purple
301 to 500 Hazardous Maroon
3

Air Quality Algorithm
𝐼 =
𝐼ℎ𝑖𝑔ℎ − 𝐼𝑙𝑜𝑤
𝐶ℎ𝑖𝑔ℎ − 𝐶𝑙𝑜𝑤
𝐶 − 𝐶𝐿𝑜𝑤 + 𝐼𝐿𝑜𝑤
𝐼 =Air Quality Index
𝐼ℎ𝑖𝑔ℎ =Index breakpoint corresponding to 𝐶ℎ𝑖𝑔ℎ
𝐼𝑙𝑜𝑤 =Index breakpoint corresponding to 𝐶𝑙𝑜𝑤
𝐶ℎ𝑖𝑔ℎ =concentration breakpoint for ≥ 𝐶
𝐶𝑙𝑜𝑤 =concentration breakpoint for ≤ 𝐶
𝐶 =pollutant concentration
4

Research Goals
• 1. Understand current changes in air quality
• 2. Compare changes in population to air quality
• 3. Formulate conditional statements to describe population and air
quality in terms of each other
Air Quality and Population Auston Li, North Carolina School of Science and Mathematics, Sigma Xi 2014 5

General Methods
6Air Quality and Population Auston Li, North Carolina School of Science and Mathematics, Sigma Xi 2014
Selection of Counties
Cook County (Illinois)
Los Angeles County (California)
New York County (New York)
Travis County (Texas)
Wake County (North Carolina)
Wayne County (Michigan)
Data Collection
EPA AirData Query for pollutant and
air quality data
US Census for the population data
Data range from 1980 to 2012
Analysis and
Refinement
Population data: .rtf to .csv to .xslx
Air Quality data: .csv to .xslx
Removal of unnecessary and
irrelevant data columns
Average/ Median take from air
quality
Extrapolation and
Visualization
Correlation tests between time, air
quality, and population
Scatter plots of the raw data to
picture trends
Forming conditional statements
from data

Reasoning for Counties
• An attempt to recreate a holistic representation of the United States
• Los Angeles County: populous and coastal, susceptible to Asian
international air pollution
• New York County: populous and coastal, susceptible to European to
• Travis County: populous, industrial, Southern
• Cook County: populous, industrial, Northern
• Wake County: local area
• Wayne County: formerly industrious, shows effects of prolonged poor air
quality
7

EPA
• EPA stands for Environmental Protection Agency
• Government agency that regulates and stipulates air pollution
criterion
• Six Criteria Pollutants: ground-level ozone, particulate matter, carbon
monoxide, sulfur dioxide, and nitrogen dioxide
• Other High-Risk Pollutants: volatile organic compounds, persistent
free radicals, toxic metals, and chlorofluorocarbons
• Enforces air monitoring
• Released the Clean Air Act
8

Long-Term Effects
9
Health Impact
-aggravates respiratory
issues
-increases susceptibility of
cardiovascular diseases
-increased lung sensitivity
Environmental Impact
-adverse effects on
vegetation
-leads to acid rain
-lowers crop output
Economic Impact
-illnesses lead to worker
loss
-loss in forestry and crops
lead to increased prices

Air Monitors
• Machines which measure specific pollutant concentration and
transmit data to a computer
• Three categories: continuous emissions monitoring system (O2, CO,
CO2), particulate matter sampler (PM10), and portable emissions
measurement system (mobile source pollution)
• Improving technology has enhanced the quality of data with shorter
measurement intervals and more precise sampling
• Data is centralized at the EPA
10

Clean Air Act
• Created the National Ambient Air Quality Standards (NAAQS) for the
six criteria pollutants
• Generated incentives and initiatives to utilize and innovate clean,
efficient “green” technologies
• Examples: alternate mass transportation systems, renewable energy
programs, and waste reduction
• 13 million workdays recovered in the US, as a result
• Catalyst for making 200,000 new jobs
11

Recent Trends
SO2 Content Decrease NO2 Content Decrease
12

Hypothesis
• The hypothesis is if the net air quality within an urban city in the
United States increases, then the population’s growth rate will
decrease because of the higher risk of air quality related diseases,
poorer quality living environment, and altered mentality for
immigration/emigration.
• While it is difficult to gauge if the listed factors are indeed the cause,
a noticeable trend should manifest
13

Analytic Approach
• Scientific Visualization Approach
-Form scatterplots
-Observe the tendencies
-Describe the relations between different sets of variables
14

Correlation Test
• Data sets arranged as an array
• X component variable as Population of County
• Y component variable as median/average AQI value
• Coefficient ranges from -1 to 1, with a high 𝑟 value near 1 showing a
strong relation ship.
• A positive relationship corresponds to 1
• A negative relationship corresponds to -1
• No relationship corresponds to 0

16
Graph 1
Population values
per county from
1980 to 2012
The initial values
have all been
reduced to 300,000
for comparability
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
1975 1980 1985 1990 1995 2000 2005 2010 2015
Scalar Population Over Time
Cook Los Angeles New York Travis Wake Wayne

0
5
10
15
20
25
30
1975 1980 1985 1990 1995 2000 2005 2010 2015
Scalar Air Quality Index Values Over Time
Avg Cook Avg LA Avg NY Avg Travis Avg Wake Average Wayne
Graph 2
Air quality values
per county from
1980 to 2012
The initial values
have all been
reduced to 15
for comparability

Fig. 1-Pearson’s Correlation Coefficient Formula
• 𝑟 = 𝑖
𝑥 𝑖− 𝑥 𝑦 𝑖− 𝑦
𝑖 𝑥 𝑖− 𝑥 2
𝑖 𝑦 𝑖− 𝑦 2
• 𝑥=the x variable
• 𝑦=the y variable
• 𝑖=the initial
18

Table 1- Correlation Test Results
Correlation to Population
Average AQI of Cook 0.2
Average AQI of Los Angeles -0.95
Average AQI of New York -0.81
Average AQI of Travis 0.03
Average AQI of Wake -0.73
Average AQI of Wayne 0.1
Median AQI of Cook 0.1
Median AQI of Los Angeles -0.89
Median AQI of New York -0.72
Median AQI of Travis 0.22
Median AQI of Wake -0.74
Median AQI of Wayne -0.25

Data Interpretation
• Graph 1: Cook and Travis County show significant growth, Los Angeles
and New York County show minor growth, Wake County shows
stagnated growth, and Wayne County shows population loss
• Graph 2: Wake, New York, and Los Angeles County show somewhat
lowered average AQI; Cook, Travis, and Wayne County show minimal
lowered average AQI
• Table 1: Los Angeles, New York, and Wake, in descending order show
strong negative correlations, and Cook, Travis, and Wayne County
show no real correlation
20

Conclusion
• Partially supported, but largely inconclusive
• Only the converse was shown to be true, where in Wake, New York,
and Los Angeles County the AQI values decreased, but population
maintained growth for a strong negative correlation
• In addition, no supporting example has been given to support the
scenario for poor air quality leading to lowered population growth
rates
• Only exception is Wayne County, because it shows the result of having
excessive air pollution leading to decreasing population size
21

Limitations
• The quality of the data, time frame of the data, and the scope of the
study
• Quality of the data is hindered through inconsistencies from the
different types of monitors
• Time frame of the data is restricted due to public access and the air
monitor regulations date
• Scope of the study was limited due to experimental parameters and
time
22

Future Improvements
• Conducting similar experiments on wider variety of counties and a
larger pool of counties
• Focusing on specific cases of counties, such as those similar to Wayne
County, which have lost population from excessive pollution, or
coastal counties compared to inland counties to realize the extent of
• Breaking down the causes of population change to show a causal link
of population growth and better air quality
23

Assumptions
• The Clean Air Act has produced beneficial results and provided the
majority of the United States with a better environment to live in,
shown through the overall lowered AQI values
• Mature counties similar to Los Angeles have lowered population
growth, therefore are better able to manage its air pollution, as it has
the second highest change in initial and final median/average AQI
values, about 14/ 27.647
• Los Angeles County and New York County, the two larger counties,
both have extremely high correlation to air quality, leading to the
conclusion that larger cities are more greatly impacted by air quality
24

References
Bolod E., Linares C., Argaones N.,Lumbreras J., Borge R., de la Paz D., Perez-Gomez B., Fernandez-Navarro P., Garcia-Perez J., Pollan M., Ramis R., Moreno T., Angeliki K., Lopez-Abente G. (2013). Air quality modeling and
mortality impact of fine particles reduction policies in Spain, Elsevier.
Breitner S., Stölzel M., Cyrys J., Pitz M., Wölke G., Kreyling W., Küchenhoff H., Heinrich J., Wichmann H.-Erich, Peters A. (2009). Short-Term Mortality Rates during a Decade of Improved Air Quality in Erfurt, Germany, Brogan
& Partners.
Buzelli M. (2008.) A Political Ecology of Scale in Urban Air Pollution Monitoring, Wiley.
Cramer J.C. (1998.) Population Growth and Air Quality in California, Demography.
Environmental Protection Agency, AQS Team. (2010).Protection of the environment (40). Retrieved from e-CFR website: http://www.ecfr.gov/cgi-bin/text-
idx?SID=ac3309c73b21208b37af70f108fcf10c&node=40:6.0.1.1.6.7.1.3.40&rgn=div9
G. Beig, D.M. Chate, Sachi, D. Ghude., K. Ali., Trutpi Sapute, S.K. Sahu, Neha Parkhi, H.K. Trimbake (2012). Evaluating population exposure to environmental pollutants during Deepavali fireworks displays using air quality
measurements of the SAFAR network, Elsevier.
Hansen C., Luben T.J., Sacks J.D., Olshan A., Jeffay S., Strader L., Perreault S.D. (2010.)The Effect of Ambient Air Pollution on Sperm Quality, Brogan & Partners.
McCarthy M.C., O’Brien, T.E., Charrier J. G., Hafner H. R. (2009.)Characterization of the Chronic Risk and Hazard of Hazardous Air Pollutants in the United States Using Ambient Monitoring Data, Brogan & Partners.
Walton D., Murrary S.J., Thomas J.A. (2008.)Relationships between Population Density and the Perceived Quality of Neighbourhood, Springer.
25

Acknowledgements
• Mr. Robert Gotwals- research supervisor
• Mr. Nick Mangus- EPA contact
• NCSSM Science Department- resources, ideas
26

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