electricsheepafrica/africa-sensorsafrica-airquality-archive-dar-es-salaam

sensors.AFRICA Air Quality Archive Dar es Salaam

Publisher: sensors.AFRICA · Source: OpenAfrica · License: cc-by · Updated: 2025-10-03

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Abstract

This data set contains PM (particulate matter), temperature, and humidity readings taken with low-cost sensors. These sensors measure the concentration of PM in the air, including particles with diameters less than or equal to 1 micrometer (PM1), 2.5 micrometers (PM2.5), and particles with diameters less than or equal to 10 micrometers (PM10). The data set includes information on the sensor type, date, time, and location of the readings, as well as the sensor’s specific measurement values for Temperature (C), Humidity (%), PM1, PM2.5, and PM10. The data set is ideal for researchers and individuals interested in studying air quality and low-cost sensors in PM measurement. The dataset is stored in CSV format and can be opened using editors like Microsoft Excel, Google Sheets, LibreOffice Calc, etc. Note that P0 in the data represents PM1, P2 represents PM2.5, and P1 represents PM10

Each row in this dataset represents geolocated point observations. Data was last updated on OpenAfrica on 2025-10-03. Geographic scope: SENSORSAFRICA-AIRQUALITY-ARCHIVE, TANZANIA.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Geolocated point observations
Rows (total)	132,508
Columns	3 (0 numeric, 3 categorical, 0 datetime)
Train split	106,006 rows
Test split	26,501 rows
Geographic scope	SENSORSAFRICA-AIRQUALITY-ARCHIVE, TANZANIA
Publisher	sensors.AFRICA
OpenAfrica last updated	2025-10-03

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Variables

Geographic — sensor_id_sensor_type_location_lat_lon_timestamp_value_type_value (4;MQ-7;10;-6.797;39.255;2017-10-08T10:09:44.433038+00:00;P2;9.90, 33;SDS011;11;-6.818;39.285;2017-10-24T17:44:40.314129+00:00;P1;11.20, 31;SDS011;11;-6.818;39.285;2017-10-24T17:51:19.963176+00:00;P2;3.63).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-27).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sensorsafrica-airquality-archive-dar-es-salaam")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
sensor_id_sensor_type_location_lat_lon_timestamp_value_type_value	object	0.0%	4;MQ-7;10;-6.797;39.255;2017-10-08T10:09:44.433038+00:00;P2;9.90, 33;SDS011;11;-6.818;39.285;2017-10-24T17:44:40.314129+00:00;P1;11.20, 31;SDS011;11;-6.818;39.285;2017-10-24T17:51:19.963176+00:00;P2;3.63
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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Curation

Raw data was downloaded from OpenAfrica via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from sensors.AFRICA and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_sensorsafrica_airquality_archive_dar_es_salaam,
  title     = {sensors.AFRICA Air Quality Archive Dar es Salaam},
  author    = {sensors.AFRICA},
  year      = {2025},
  url       = {https://open.africa/dataset/sensorsafrica-airquality-archive-dar-es-salaam},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.