USA Air Pollution

So what is Air data?

The United States Environmental Protection Agency (EPA) provides access to air quality data (primarily from Air quality system (AQS) database) collected at outdoor monitors across the United Sates, Puerto Rico, and the U.S Virgin Islands. The data can be used to view, create visuals and graphical displays, investigate monitor locations, and so on. It is accessible to the public and can be downloaded by hourly, daily, and annual concentration data, AQI data, and spectated particle pollution data.

How does the four criteria gases air pollution impact us?

Ozone

• Coughing and pain when taking a deep breath

• Lung and throat irritation

• Wheezing and trouble breathing during exercise or outdoor activities

• Can effect everyone, but specifically people with asthma or other lung diseases, older adults, babies and children

Carbon monoxide

• Breathing air with a high concentration of CO reduces the amount of oxygen that can be transported in the blood stream to critical organs like the heart and brain.

• Can cause dizziness, confusion, unconsciousness and death.

• Very high levels of CO are not likely to occur outdoors. However, when CO levels are elevated outdoors, they can be of particular concern for people with some types of heart disease.

Nitrogen dioxide

• Increased inflammation of the airways

• Worsened cough and wheezing

• Reduced lung function

• Increased asthma attacks

• Greater likelihood of emergency department and hospital admissions.

Sulfur dioxide

• Irritates the skin and mucous membranes of the eyes, nose, throat, and lungs.

• Can cause inflammation and irritation of the respiratory system.

• Pain when taking a deep breath, coughing, throat irritation, and breathing difficulties. High concentrations of SO₂ can affect lung function, worsen asthma attacks, and worsen existing heart disease in sensitive groups.

Literature Review

•European ozone measurements examined through a cluster analysis (CA) of 4 years of 3-hourly ozone data from 1492 European surface monitoring stations in the Airbase database

•Propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method, based in China.

•Novel hybrid learning method, carried out to forecast urban air quality index (AQI). Wavelet packet decomposition (WPD) was firstly performed to decompose the original AQI data into lower-frequency subseries. (China)

1.Cluster analysis of European surface ozone observations for evaluation of MACC reanalysis data

Authors: Olga Lyapina, Martin G. Schultz, and Andreas Hens

2. K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

Authors: Victor Chang; Pin Ni; Yuming L

3. A clustering-based ensemble approach with improved pigeon-inspired optimization and extreme learning machine for air quality prediction

Author: Feng Jiang a,, Jiaqi He a, Tianhai Tian b

Data Set Overview

The Data set is retrieved from United States Environmental Protection Agency (EPA).

EPA has annual datasets from 1980 - 2021 (as of 2021-05-18) categorized by concentration by monitor, Air Quality Index (AQI) by Core Based Statistical Areas (CBSA), and AQI by County.

Each daily summary file contains data for every monitor (sampled parameter) in our database for each day. These files are separated by parameter (or parameter group) to make the sizes more manageable.

This file will contain a daily summary record that is:

1) The aggregate of all sub-daily measurements taken at the monitor.

2) The single sample value if the monitor takes a single, daily sample (e.g., there is only one sample with a 24-hour duration). In this case, the mean and max daily sample will have the same value.

The daily summary files contain (at least) one record for each monitor that reported data for the given day. There may be multiple records for the monitor if:

• There are calculated sample durations for the pollutant. For example, PM2.5 is sometimes reported as 1-hour samples and EPA calculates 24-hour averages.

• There are multiple standards for the pollutant (q.v. pollutant standards).

• There were exceptional events associated with some measurements that the monitoring agency has or may request be excluded from comparison to the standard.

(Information taken from website)

Target variable: AQI

Unit of measure

Ozone, carbon monoxide: Parts per million

Nitrogen dioxide (NO2), Sulfur dioxide :Parts per billion

Daily Summary dataset for Ozone (44201) 2020 contains 391,923 Rows, 24 Columns, and is 3,127 KB. It contains numerical and categorical data.

Daily Summary dataset for Sulfur dioxide (SO2 (42401)) 2020 contains 324,817 Rows, 24 Columns, and is 4,357 KB. It contains numerical and categorical data.

Daily Summary dataset for Carbon Monoxide (CO (42101)) 2020 contains 178,789 Rows, 24 Columns, and is 1,784 KB. It contains numerical and categorical data.

Daily Summary dataset for Nitrogen dioxide (NO2 (42602)) 2020 contains 157,726 Rows, 24 Columns, and is 2,159 KBv. It contains numerical and categorical data.

Hypothesis / Research Question(s)

1. How is air pollution different by state/county?

2. Clustering Analysis to see how the pollution is distributed geographically.

3. What are some solutions for the different clusters?

Datasets for all four types of pollution (ozone, sulfur dioxide, carbon monoxide, and nitrogen dioxide) will be used to find answers to the research questions, along with comparing the AQI for the different types of pollution.

4. Sulfur Dioxide

Geographical Visualization

Implementation (Model)

K-Means Clustering

In order to find the best analyzation for our dataset, the K-Means clustering model was ran to see how many clusters, and where the clusters were. The four data sets (four types of criteria gases) were ran separately, with the elements that had an AQI over the limit of a good-moderate pollution rate (In other words, the clusters showed the states/regions that had air pollution for each type of gas).

In order to find the number of clusters, the elbow method was used and generated. Below shows the generated model for the elbow method.

Results

Findings/ Solutions

-Sulfur Dioxide’s air pollution is the most severe out of the 4 gases

-Carbon Monoxide’s air pollution is the least sever out of the 4 gases

-Central States have the best AQI overall

The clusters seem to be generated by regions (West, Central, East)

1.Ozone

-Limiting driving

-Maintain vehicles/tools well

-Try to use tools without motors.

2. Carbon Monoxide

-Regular inspections, maintenance

-Proper storage in house

3. Nitrogen Dioxide

-Manage and Reduce Emissions

-Conserve energy, Less driving

4. Sulfur Dioxide

- Set regulations by State

Moving Forward

• Find more details for the clusters.

• Find a detailed dataset that has the indicators for pollution.

• Generate clusters using time (See when the pollution is more severe).

• Think of other analysis/ machine learning that I can do with the dataset.

References

1. United States Environmental Protection Agency. (n.d.). Download files | AirData | US EPA. Retrieved from https://aqs.epa.gov/aqsweb/airdata/download_files.html#Daily

2. https://www.cdc.gov/air/ozone.html

3. https://www.nps.gov/subjects/air/humanhealth-sulfur.htm

4. https://www.epa.gov/co-pollution/basic-information-about-carbon-monoxide-co-outdoor-air-pollution

5. https://www.lung.org/clean-air/outdoors/what-makes-air-unhealthy/nitrogen-dioxide

6. https://swachhindia.ndtv.com/air-pollution-what-is-air-quality-index-how-is-it-measured-and-its-health-impact-40387/