Mapping urban air quality in near real-time using observations from low-cost sensors and model information

Environ Int. 2017 Sep;106:234-247. doi: 10.1016/j.envint.2017.05.005. Epub 2017 Jun 28.

Abstract

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R2 of 0.89 and a root mean squared error of 14.3 μg m-3. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.

Keywords: Air quality; Crowdsourcing; Low-cost microsensors; Mapping; Nitrogen dioxide; Urban air quality.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / analysis*
  • Cities
  • Environmental Monitoring / methods*
  • Models, Theoretical
  • Nitrogen Dioxide / analysis*
  • Norway

Substances

  • Air Pollutants
  • Nitrogen Dioxide