Decoding Air Quality💨💨💨
A Data Science Approach to Environmental Health Insights.
@kaggle.vijayveersingh_decoding_air_quality
About this Dataset
Decoding Air Quality💨💨💨
Description:
This dataset features air pollution data from the Environmental Protection Agency's (EPA) Air Quality System (AQS), recognized as the "gold standard" for outdoor air pollution measurements. The AQS collects data on ozone and particulate matter (PM2.5) from approximately 4,000 monitoring stations, primarily located in urban areas across the United States.
The dataset provides comprehensive air quality measures across the United States by offering detailed information about air quality metrics, geographic locations, and temporal trends.
The dataset is structured with the following attributes:
Measure Information:
Includes unique identifiers, descriptive names, and types of air quality measures to help users understand the nature and format of the data.
Geographic Information:
Provides standardized Federal Information Processing Standards (FIPS) codes for states and counties, alongside their human-readable names, allowing for geographic-level analyses.
Temporal Data:
Contains "Report Year", enabling trend analyses over time.
Quantitative Metrics:
Features numerical values and associated units for air quality measures, indicating the magnitude and context of environmental conditions.
Data Source Details:
Includes information about data origins and a binary "Monitor Only" field to identify whether the data is exclusively derived from monitoring stations.
This dataset is ideal for studying environmental patterns, assessing public health impacts, and informing policy decisions related to air quality and environmental health.
##Attribution:
####This work uses data from the Air Quality Measures on the National Environmental Health Tracking Network, originally created and maintained by the Centers for Disease Control and Prevention (CDC).
####LICENSE: The Data is available under the Open Database License (ODbL).
####NO ENDORSEMENT: The CDC does not endorse this work or its conclusions.
DISCLAIMER: Please independently verify the data and it's derivation(s) before applying it to research or decision-making.