Patterns discovery dataset for particulate matter (pm2.5) pollution trends in Japan|空气污染监测数据集|模式挖掘数据集

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.