abhinavsarkar/delhi_air_quality_feature_store_unprocessed.csv

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Dataset Information:

• Name: Delhi Air Quality and Weather Data

• Version: 1.0.0

• Description:
  This dataset contains 4.5M records of hourly weather and air quality data from multiple locations across Delhi, India, collected over several years. The data spans from March 2000 to November 2024 and includes measurements from all 23 AQI monitoring centers in Delhi. The dataset provides environmental parameters such as temperature, humidity, atmospheric pressure, wind speed, wind direction, and concentrations of pollutants (PM2.5, PM10, NO2, SO2, O3, CO), along with the corresponding Air Quality Index (AQI).
  It is useful for analyzing air quality, studying weather patterns, and tracking environmental trends in Delhi. The dataset covers various locations like PGDAV College, Sonia Vihar, Anand Vihar, and Pusa, among others.

• Citation:
  If you use this dataset in your research or project, please cite it as follows:
  Abhinav. (2024). "Delhi Air Quality and Weather Data". Dataset retrieved from Hugging Face.

• Size: 615 MB (Please verify and update with the actual dataset size).

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Dataset Fields:

• location_id: Integer identifier for each location.
• city: The name of the city or specific location in Delhi.
• event_timestamp: The timestamp when the data was recorded, in ISO 8601 format.
• temperature: Ambient temperature in Celsius.
• humidity: Relative humidity as a percentage.
• pressure: Atmospheric pressure in hPa.
• wind_speed: Wind speed in m/s.
• wind_direction: Wind direction in degrees.
• pm25: Concentration of particulate matter with a diameter of 2.5 micrometers (µg/m³).
• pm10: Concentration of particulate matter with a diameter of 10 micrometers (µg/m³).
• no2: Concentration of nitrogen dioxide (µg/m³).
• so2: Concentration of sulfur dioxide (µg/m³).
• o3: Concentration of ozone (µg/m³).
• co: Concentration of carbon monoxide (µg/m³).
• aqi: Air Quality Index, a measure of the level of air pollution.

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Data Quality:

• The dataset contains some missing values in columns like temperature, humidity, and others for specific rows. These missing values need to be addressed during the analysis phase.
• The dataset includes data points spanning multiple years, with timestamp values ranging from 2000 to 2024.

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Usage with load_dataset:

You can load this dataset using the Hugging Face datasets library with the following code:

from datasets import load_dataset

dataset = load_dataset("abhinavsarkar/delhi_air_quality_feature_store_unprocessed.csv")

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