Mid-IR spectrometer for mobile, real-time urban NO<sub>2</sub> measurements

Abstract

Abstract. Detailed knowledge about the urban NO2 concentration field is a key element for obtaining accurate pollution maps and individual exposure estimates. These are required for improving the understanding of the impact of ambient NO2 on human health and for related air quality measures. However, city-scale NO2 concentration maps with high spatio-temporal resolution are still lacking, mainly due to the difficulty of accurate measurement of NO2 at the required sub-ppb level precision. We contribute to close this gap through the development of a compact instrument based on mid-infrared laser absorption spectroscopy. Leveraging recent advances in infrared laser and detection technology and a novel circular absorption cell, we demonstrate the feasibility and robustness of this technique for demanding mobile applications. A fully autonomous quantum cascade laser absorption spectrometer (QCLAS) has been successfully deployed on a tram, performing long-term and real-time concentration measurements of NO2 in the city of Zurich (Switzerland). For ambient NO2 concentrations, the instrument demonstrated a precision of 0.23 ppb at one second time resolution and of 0.03 ppb after 200 s averaging. Whilst the combined uncertainty estimated for the retrieved spectroscopic values was less than 5 %, laboratory intercomparison measurements with standard CLD instruments revealed a systematic NO2 wall loss of about 10 % within the laser spectrometer. For the field campaign, the QCLAS has been referenced to a CLD using urban atmospheric air, despite the potential cross sensitivity of CLD to other nitrogen containing compounds. However, this approach allowed a direct comparison and continuous validation of the spectroscopic data to measurements at regulatory air quality monitoring (AQM) stations along the tram-line. The analysis of the recorded high-resolution time series allowed us to gain more detailed insights into the spatio-temporal concentration distribution of NO2 in an urban environment. Furthermore, our results demonstrate that for reliable city-scale concentration maps a larger data set and better spatial coverage is needed, e.g., by deploying more mobile and stationary instruments to account for mainly two shortcomings of the current approach: (i) limited residence time close to sources with large short-term NO2 variations, and (ii) insufficient representativeness of the tram tracks for the complex urban environment.

On-Line Analysis of Exhaled Breath

“…The dark blue and light blue bars represent the theoretical line strengths of NO 2 and H 2 O bands, which lie in a tiny fraction, 6.2477–6.2508 μm, of the IR spectrum. Despite being in the range of strong water absorption, the very high spectral resolution allows identification of NO (Adapted with permission from ref under CC BY 3.0 license).…”

Advances in High-Precision NO2 Measurement by Quantum Cascade Laser Absorption Spectroscopy

“…The second QCLAS (NO 2 Analyzer, MIRO Analytical AG, Dübendorf, Switzerland; hereafter, QCL-MIRO) is a newly available commercial instrument, which is based on the same technique as our custom-made QCLAS (hereafter, QCL-Empa). Furthermore, it also deploys the same laser-driving scheme, DAQ processes and spectral evaluation software [46], but it uses an NO 2 absorption line around 1630 cm´1. The entire spectrometer is built into a 19"/4U rack and uses an AMAC-76 multipass cell (OPL = 76 m; Aerodyne Research Inc, Billerica, MA, USA).…”

Mapping the spatial distribution of NO<sub>2</sub> with in situ and remote sensing instruments during the Munich NO<sub>2</sub> imaging campaign

“…NO 2 maps can be created in different ways, each with its specific advantages and disadvantages: in situ ground measurements can be combined with geostatistical methods such as land-use-regression models to generate city-wide air pollution maps (e.g., Mueller et al, 2015), but urban air quality monitoring networks are typically very sparse, limiting the accuracy of such maps, although mobile sensors on public buses or trams could increase the measurement density (e.g., Hagemann et al, 2014;Hundt et al, 2018). Dense networks of low-cost sensors have also been proposed as a complement to the traditional networks, but issues with precision, stability, or specificity of existing sensors remain an obstacle to their widespread deployment (Heimann et al, 2015;Bigi et al, 2018;Karagulian et al, 2019).…”