PM2.5 Data of Five Chinese Cities
Donated on 7/17/2017
This hourly data set contains the PM2.5 data in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang. Meanwhile, meteorological data for each city are also included.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Physics and Chemistry
Associated Tasks
Regression
Feature Type
Integer, Real
# Instances
52854
# Features
-
Dataset Information
Additional Information
The time period is between Jan 1st, 2010 to Dec 31st, 2015. Missing data are denoted as NA.
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 86
Additional Variable Information
No: row number year: year of data in this row month: month of data in this row day: day of data in this row hour: hour of data in this row season: season of data in this row PM: PM2.5 concentration (ug/m^3) DEWP: Dew Point (Celsius Degree) TEMP: Temperature (Celsius Degree) HUMI: Humidity (%) PRES: Pressure (hPa) cbwd: Combined wind direction Iws: Cumulated wind speed (m/s) precipitation: hourly precipitation (mm) Iprec: Cumulated precipitation (mm)
Dataset Files
| File | Size |
|---|---|
| FiveCitiePMData.rar | 3.2 MB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset pm2_5_data_of_five_chinese_cities = fetch_ucirepo(id=394) # data (as pandas dataframes) X = pm2_5_data_of_five_chinese_cities.data.features y = pm2_5_data_of_five_chinese_cities.data.targets # metadata print(pm2_5_data_of_five_chinese_cities.metadata) # variable information print(pm2_5_data_of_five_chinese_cities.variables)
Chen, S. (2016). PM2.5 Data of Five Chinese Cities [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52K58.
Creators
Song Chen
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.