OPEN ACCESS

Articles online

A Mathematical Approach to Merging Data from Different Trace Gas/Particulate Sensors Having Dissimilar (T90) Response Times: Application to Fire Emission Factor Determination

Category: Aerosol Physics and Instrumentation

Article In Press
DOI: 10.4209/aaqr.2019.02.0061
PDF

Export Citation:  RIS | BibTeX

Tianran Zhang 1,2, Martin Wooster1,2, David C. Green3, Bruce Main1

  • 1 Department of Geography, King’s College London, Strand, London, WC2R 2LS, UK
  • 2 National Centre for Earth Observation (NCEO), King’s College London, Strand, London, WC2R 2LS, UK
  • 3 Environmental Research Group, MRC-PHE Centre for Environment and Health, King's College London, London, SE1 9NH, UK

Highlights

  • A post-processing model is developed for mismatched data from different T90 sensors.
  • This model can create unbiased emission ratio for biomass burning plumes.
  • An optimised linear fitting approach can be used for sensors with unknown T90.

Abstract

Low cost atmospheric composition sensors are increasingly used in both air quality research and in air pollution monitoring. Those focused on particulates have generally rather rapid (T90) response times due to their reliance on light scattering methods. However, those electrochemical and NDIR sensors targeting trace gases such as, respectively, CO and CO2 typically respond very much slower, with T90 response times that can be further lengthened by the rate at which sample exchange occurs in the measurement cell. Data from these types of low cost sensors are often used to derive emission ratios [ERs] of two simultaneously measured atmospheric species, yet ER derivations made using data from sensors having dissimilar T90 values can be problematic, because any rapid change in the pollutant concentration can mean one sensor responding faster to the changing conditions than the other. Such situations are typical within biomass burning plumes, yet where such ER assessments are routinely required to generate the emissions factors (EFs) needed for biomass burning emissions calculations. Here we confirm that the ERs coming from such analyses can be strongly biased if differential sensor T90 effects remain unadjusted for, and we demonstrate a simple mathematical approach for undertaking this adjustment. The method is able to take data from a sensor taken with a particular T90 value and simulate that which would be collected from a sensor targeting the same species but with a different T90 value, and if the output T90 is selected to match that of the companion sensor measuring the second species then the two measures with the same effective T90 values can be used to generate unbiased emissions ratios and emissions factors. We demonstrate our approach on simulated data with known T90 response times, and then apply it to real data from low-cost sensors collected within biomass burning plumes, including those installed in a sampling system that introduced further – and unknown – additional measurement lag. In the latter case we find that our mathematical approach to the post-measurement T90 adjustment is still able to derive ERs from the low-cost sensor data that are comparable to those derived from data collected with a very high precision laser absorption spectrometer that measures the two species exactly simultaneously. Our methodology is thus well suited to the problem of deriving emissions ratios and emission factors from data collected by low-cost sensing systems deployed in rapidly changing pollutant plumes.

Keywords

Low-cost sensor Carbon dioxide Carbon monoxide Biomass burning


Related Article

Estimation of Surface Particulate Matter (PM2.5 and PM10) Mass Concentration from the Ceilometer Backscattered Profiles

Avinash N. Parde, Sachin D. Ghude , Prakash Pithani, Narendra G Dhangar, Sandip Nivdange, Gopal Krishna, D.M. Lal, R. Jenamani, Pankaj Singh, Chinmay Jena, Ramakrishna Karumuri, P.D. Safai, D.M. Chate
Accepted Manuscripts
DOI: 10.4209/aaqr.2019.08.0371
PDF
;