DOI: 10.5061/dryad.hhmgqnkrr
title: Patterns discovery dataset for particulate matter (pm2.5) pollution trends in Japan

description: Air pollution presents a significant environmental risk, impacting human
 health, accelerating climate change, and disrupting ecosystems. The main
 aim of air pollution research is to pinpoint the most harmful pollutants
 identified in previous studies and to map regions exposed to high
 pollution levels. This study introduces a large-scale, high-quality
 dataset to advance the analysis of PM2.5 pollution and reveal hidden
 patterns through pattern mining techniques. The dataset covers five years
 of hourly PM2.5 measurements collected from approximately 1,900 sensors
 across Japan, sourced from the Ministry of the Environment's Soramame
 platform. This platform offers hourly pollutant records, downloadable as
 monthly raw data files. The unorganised raw data files are systematically
 organised and stored in database tables using an Entity-Relationship (ER)
 schema. The primary objective of this dataset is to aid in developing and
 validating pattern mining models, enabling the accurate detection of
 frequent patterns within the PM2.5 dataset under diverse conditions. The
 dataset collection includes the "FINAL_DATASET" CSV file
 containing timestamps, sensor location IDs, and recorded PM2.5 values. Due
 to storage limitations, raw data files are excluded from the compressed
 ZIP (AEROS) file but can be accessed directly via the link provided in the
 README (Data). By revealing complex patterns, this dataset is a valuable
 resource for researchers employing pattern mining techniques in PM2.5
 analysis. Publicly sharing this dataset promotes collaboration and
 advances efforts to identify frequently polluted sensors or regions.
 Researchers are invited to use and contribute to the dataset, broadening
 its relevance and potential impact.
description: The air pollution data was collected from Japan’s Soramame
 platform, which provides hourly updates on pollutant levels nationwide.
 The data files were collected from January 1, 2018, 01:00:00, to April 25,
 2023, 22:00:00, covering records from approximately 1,900 sensors
 stationed in various locations across Japan. These files are initially
 unorganised in CSV format and require systematic organisation by year,
 month, time, sensor, and pollutant type. To maintain data integrity, we
 structured the dataset using an Entity-Relationship (ER) schema within a
 PostgreSQL database, comprising two main tables: the Sensor table (storing
 sensor name, ID, address, and location) and the Observations table
 (recording pollutant types and their values). A detailed step-by-step
 process is provided in the README, and this organization created a
 consolidated CSV file containing PM2.5 levels, timestamps, and sensor
 details.
description: # AEROS PM2.5 Dataset ## Overview The **AEROS PM2.5 Dataset** provides a
 comprehensive collection of hourly PM2.5 measurements recorded over a
 period of five years from sensors located across Japan. This dataset is a
 valuable resource for studying air quality trends, pollution patterns, and
 environmental health impacts. --- ## Dataset Description ### File
 Information * **File Name:** `FINAL_DATASET.csv` * **Content:** Hourly
 PM2.5 measurements collected from sensors located in Japan over five
 years. ### Structure The dataset includes the following columns: 1.
 **Timestamps**: The date and time when the measurement was recorded. 2.
 **Sensor Location IDs**: Unique identifiers for the sensor locations. 3.
 **PM2.5 Values (µg/m³)**: The recorded PM2.5 concentration at a specific
 timestamp and location. ### Units * **PM2.5 Values:** Measured in
 micrograms per cubic meter (µg/m³). --- ## Notes on Data * **Empty
 Cells**: Represent instances where no PM2.5 data was recorded by the
 sensors at the corresponding timestamp. * **Value Interpretation**: *
 **Lowest Values**: Indicate less pollution at the specific timestamp and
 location. * **Highest Values**: Indicate high pollution levels at the
 specific timestamp and location. --- ## Use Cases This dataset can be used
 for: * Analyzing air quality trends over time. * Investigating regional
 variations in PM2.5 pollution. * Developing predictive models for air
 pollution. * Studying the correlation between pollution and environmental
 or health-related factors. --- ## User Interest For users interested in a
 deeper analysis or in understanding the creation process of this dataset,
 please refer to the following GitHub repository: [AEROS PM2.5 Dataset -
 GitHub Repository](https://github.com/UdayLab/AEROS)