air quality and population growth: an analytic approach

A Qualitative Analytic Approach to Interpreting

Modern Population Growth in terms of Air

Quality

Auston LiNorth Carolina School of Science and Mathematics

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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

Introduction Regulation Analysis Figures Conclusion

Air Quality and Population Auston Li, North Carolina School of Science and Mathematics, Sigma Xi 2014

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US Air Quality Index TableAir 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



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Air Quality Algorithm

Air Quality IndexIndex breakpoint corresponding to Index breakpoint corresponding to concentration breakpoint for concentration breakpoint for pollutant concentration



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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



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General Methods



Selection of CountiesCook County (Illinois)Los Angeles County (California)New York County (New York)Travis County (Texas) Wake County (North Carolina)Wayne County (Michigan)

Data CollectionEPA AirData Query for pollutant and air quality dataUS Census for the population dataData range from 1980 to 2012

Analysis and Refinement

Population data: .rtf to .csv to .xslxAir Quality data: .csv to .xslxRemoval of unnecessary and irrelevant data columnsAverage/ Median take from air quality

Extrapolation and Visualization

Correlation tests between time, air quality, and populationScatter plots of the raw data to picture trendsForming conditional statements from data

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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

international air pollution• 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



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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



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Long-Term Effects



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

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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



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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



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Recent Trends

SO2 Content Decrease NO2 Content Decrease



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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



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Analytic Approach

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



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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 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



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Graph 1Population valuesper county from1980 to 2012The initial valueshave all beenreduced to 300,000for comparability

1975 1980 1985 1990 1995 2000 2005 2010 20150

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

Scalar Population Over Time

Cook Los Angeles New York Travis Wake Wayne

17Air Quality and Population Auston Li, North Carolina School of Science and Mathematics, Sigma Xi 2014


1975 1980 1985 1990 1995 2000 2005 2010 20150

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10

15

20

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Scalar Air Quality Index Values Over Time

Avg Cook Avg LA Avg NYAvg Travis Avg Wake Average Wayne

Graph 2Air quality valuesper county from1980 to 2012The initial valueshave all beenreduced to 15for comparability

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Fig. 1-Pearson’s Correlation Coefficient Formula

• =the x variable• =the y variable• =the initial



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



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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



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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



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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



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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 international air pollution• Breaking down the causes of population change to show a causal link

of population growth and better air quality



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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



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Acknowledgements

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

air quality and population growth: an analytic approach

Documents

air quality extrapolation

air quality algorithm

xslx air quality data

air quality data us

severity of air pollution

population auston li

population data data

refinement population