Key facts
Contents & fields
The dataset includes multiple sub-datasets: drone_data (vertical profiles of lung-deposited surface area measured by drone in summer and winter 2017), kumpula_airquality_data (basic air quality observations and DMPS aerosol size distribution from SMEAR III station), met_data (Kivenlahti mast observations and SMEAR III meteorology), sniffer_data (horizontal distribution of air pollutants measured by mobile laboratory Sniffer), supersite_data (air quality observations, DMPS aerosol size distribution, and ACSM aerosol chemical composition from Mäkelänkatu station).
- drone_data——Vertical profiles of lung-deposited surface area (LDSA) measured by drone
- kumpula_airquality_data/airquality——Basic air quality observations from SMEAR III station
- kumpula_airquality_data/dmps——DMPS aerosol size distribution from SMEAR III station
- met_data/Kivenlahti_mast_DDMMYYYY.txt——Kivenlahti mast observations
- met_data/Meteorology_YYMM_10.txt——SMEAR III meteorological observations
- sniffer_data/long/[timeofday]YYYYMMDD.dat——Horizontal distribution of air pollutants by mobile laboratory Sniffer
- supersite_data/AQdata——Air quality observations from Mäkelänkatu station
- supersite_data/DMPS——DMPS aerosol size distribution from Mäkelänkatu station
- supersite_data/ACSM——ACSM aerosol chemical composition from Mäkelänkatu station
Research uses
Suitable for urban air pollution monitoring, high-resolution air quality model validation, aerosol vertical distribution and horizontal dispersion studies, and comparison of mobile and stationary monitoring data.
This card was drafted from the source page; institution, coverage, time span, scale, fields and license are subject to the official page (pending human review).
Keywords
Why this is hard to get on your own
When obtaining high spatiotemporal resolution urban air pollution monitoring data to validate fine-scale air quality models, integrating mobile and stationary measurements is often challenging.