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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-136
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-136
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Apr 2020

27 Apr 2020

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A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications

Priyanka deSouza1, Ralph A. Kahn2, James A. Limbacher2, Eloise A. Marais3,a, Fábio Duarte1,4, and Carlo Ratti1 Priyanka deSouza et al.
  • 1Senseable City Lab, Massachusetts Institute of Technology, Cambridge MA, United States
  • 2Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
  • 3School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
  • 4Pontifícia Universidade Católica do Paraná, Brazil
  • anow at: School of Physics and Astronomy, University of Leicester, Leicester, UK

Abstract. Poor air quality is the world’s single largest environmental health risk, and air quality monitoring is crucial for developing informed air quality policies. Efforts to monitor air pollution in different countries are uneven, largely due to the high capital costs of reference air quality monitors (AQMs), especially for airborne particulate matter (PM). In sub-Saharan Africa, for example, few cities operate AQM systems. It is thus important to examine the potential of alternative monitoring approaches. Although PM measurements can be obtained from low-cost optical particle counters (OPCs), data quality can be an issue.

This paper develops a new method using raw aerosol size distributions from multiple, surface-based low-cost Optical Particle Counters (OPCs) to calibrate the Multi-angle Imaging SpectroRadiometer (MISR) component-specific, column aerosol optical depth (AOD) data, that contain some particle-size-resolved information. The combination allows us to derive surface aerosol concentrations for particles as small as ~ 0.1 μm in diameter that MISR detects but are below the OPC detection limit of ~ 0.5 μm. As such, we obtain better constraints on the near-surface particulate matter (PM) concentration, especially as the smaller particles tend to dominate urban pollution.

We test our method using data from five low-cost OPCs deployed in the city of Nairobi, Kenya, from May 1 2016 to March 2 2017. As MISR passes over Nairobi only once in about eight days, we use the size-resolved MISR AODs to scale the more frequent Moderate Resolution Imaging Spectrometer (MODIS)-derived AODs over our sites. The size distribution derived from MISR and MODIS agrees well with that from the OPCs in the size range where the data overlap (adjusted-R2 ~ 0.80). We then calculate surface-PM concentration from the combined data. The situation for this first demonstration of the technique had significant limitations. We thus identify factors that will reduce the uncertainty in this approach for future experiments. Within these constraints, the approach has the potential to greatly expand the range of cities that can afford to monitor long-term air quality trends and help inform public policy.

Priyanka deSouza et al.

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Priyanka deSouza et al.

Priyanka deSouza et al.

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Latest update: 21 Sep 2020
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Short summary
This paper presents a novel method to calibrate satellite data using low-cost optical particle counters (OPCs) to develop higher quality particulate matter (PM) estimates. This method could enable cities that do not have access to expensive reference air quality monitors, many of which are in the global South, to develop locally calibrated PM estimates from satellite data. Such information can be crucial for the development of effective air quality management plans.
This paper presents a novel method to calibrate satellite data using low-cost optical particle...
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