Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

PM2.5 Data of Five Chinese Cities Data Set
Download: Data Folder, Data Set Description

Abstract: 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.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

52854

Area:

Physical

Attribute Characteristics:

Integer, Real

Number of Attributes:

86

Date Donated

2017-07-18

Associated Tasks:

Regression

Missing Values?

Yes

Number of Web Hits:

95053


Source:

Song Xi Chen, csx '@' gsm.pku.edu.cn, Guanghua School of Management, Center for Statistical Science, Peking University.


Data Set Information:

The time period is between Jan 1st, 2010 to Dec 31st, 2015. Missing data are denoted as NA.


Attribute 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)


Relevant Papers:

Liang, X., S. Li, S. Zhang, H. Huang, and S. X. Chen (2016), PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities, J. Geophys. Res. Atmos., 121, 10220–10236, [Web Link].



Citation Request:

Liang, X., S. Li, S. Zhang, H. Huang, and S. X. Chen (2016), PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities, J. Geophys. Res. Atmos., 121, 10220–10236, [Web Link].


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML