Readme of CUPI-G data (ver. 1)

August 23, 2023, CUPI task teamIntroduction
Under the framework of Research Project named: “An Interdisciplinary Study toward Clean Air, Public
Health and Sustainable Agriculture: The Case of Crop Residue Burning in North India” (hereafter “Aakash”)
promoted by Research Institute for Humanity and Nature, air pollutant observation campaign was held overnorthwestern India including Delhi NCR, from September to November 2022. (Singh et al, 2023) Thisdataset consists of daily mean concentration of PM2.5, daily mean temperature & relative humidity obtained
during the campaign.
Data Processing
1

Time correction
Observation time is referenced to the Real Time Clock of the Raspberry Pi. If the RTC time is notaccurate, the observation time is corrected by using the GPS time, if available. Neither RTC ofRaspberry Pi nor GPS time is available, the sensor data is not used.

2

Screening of PM2.5 sensor data
If the measurement is suspended for more than 4 minutes (mainly due to power shortage), data fromthe first 5 minutes after resumption is not used.

3Daily average
3.1

Observation value is recorded in the interval indicated in the data file. After the time correction
and screening the data, daily average is calculated.

3.2"Date" indicates the end-of-day in IST. Data of 2022/09/01 0:00 indicates average over2022/08/31 0:00:01 - 2022/09/01 0:00:00

3.3

Daily average is calculated if more than 50% of data is valid.

3.4

The number “-99999” indicates that the data is missing.

CautionPM2.5 mass concentrations are potentially overestimated due to hygroscopic growth when RH in thebox were >70%.
(Nakayama et al. 2018, https://doi.org/10.1080/02786826.2017.1375078).
Data Citation:Tanbir Singh, Yutaka Matsumi, Tomoki Nakayama, Sachiko Hayashida, Prabir K. Patra, Natsuko Yasutomi,
Mizuo Kajino, Kazuyo Yamaji, Pradeep Khatri, Masayuki Takigawa, Hikaru Araki, Yuki Kurogi, MakotoKuji, Kanako Muramatsu, Ryoichi Imasu, Anamika Ananda, Ardhi A. Arbain, Khaiwal Ravindra, Sanjeev
Bhardwaj, Sahil Kumar, Sahil Mor, Surendra K. Dhaka, A. P. Dimri, Aka Sharma, Narendra Singh,Manpreet S. Bhatti, Rekha Yadav, Kamal Vatta & Suman Mor (2023): Very high particulate pollution overnorthwest India captured by a high-density in situ sensor network, Scientific Reports 13, 13201 (2023),
doi:10.1038/s41598-023-39471-1
Contact: Prabir Patra (prabir@chikyu.ac.jp)
Aakash project leader, Research Institute for Humanity and Nature

Data format of Daily averaged data：
DateDateinterval

temperaturetemperaturetemperaturehumidity

humidity

Humidity

pm2.5

pm2.5

pm2.5

UTCsecmeanStdNmeanstdNmeanstdN

date:

Local time (IST)date UTC:

UTC timeinterval sec:

observation time intervals (seconds)

temperature mean:

mean value of temperature(ºC)

temperature std:

standard deviation of temperaturetemperature N:

number of data used for averagehumidity mean:

mean value of humidity(%)

humidity std:

standard deviation of humidity

humidity N:

number of data used for average

pm2.5 mean:

mean value of PM2.5(μg/㎥)

pm2.5 std:

standard deviation of PM2.5

pm2.5 N:

number of data used for average

Filename convention:“CUPIG_Site01_PM26.csv”
Mission name, always “CUPIG”
Site No., “Site”nn ; n indicates 2 character grid number.
Instruments No., “PM”mm ; mm indicates instruments number.

＊The file “CUPIG_Locations.pdf" is a list of site locations with instrument numbers.
CUPI-G Datasets are available at https://aakash-rihn.org/en/data-set
About Aakash project: https://aakash-rihn.org/en/
RIHN homepage: https://www.chikyu.ac.jp/en