Another casualty of our modern lives is poor air quality, particularly in urban centers. One of the biggest culprits is nitrogen dioxide (NO2), a typical by-product of mostly diesel powered vehicles, although factories and other sources also play an important role in emissions.
While health experts have learned more about the detrimental effects of NO2, planners are now busy trying to better measure and mitigate the effects of this gas in our dense urban zones.
Fortunately, in many countries, NO2 has been in (slow) decline since the 1990s. Concentrations in particular areas, often in high dense traffic areas, still makes the levels present potentially hazardous for many of us.
Using Satellite Data and Ground Measurements to Map out Nitrogen Dioxide Levels
Worldwide, many countries are still facing elevated levels of overall NO2. In an article discussing China’s problem with NO2 prior to the pandemic, the authors in a recently published study demonstrated the use of ground-based measures and satellite data using a cokriging interpolation technique, a method which interpolates uses different sources of data to estimate NO2 at a given area.
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The authors were able to produce a nationwide map of NO2. Satellite systems, including Global Ozone Monitoring Experiment-2 (GOME-2), the Ozone Monitoring Instrument (OMI), and the Scanning Imaging Spectrometer for Atmospheric Chartography (SCIAMACHY), were used for remote observation. These systems utilizes optical spectrometer and hyperspectral imaging to record and visualize different gases including NO2.[1]
Citizen Science and Machine Learning to Map Nitrogen Dioxide
Another study combined machine learning and citizen science techniques to better capture the spatial variation of NO2. The method combines 20,000 ground-level measurements with aerial images and deep neural networks, with the case study applied in Belgium. The approach uses mass mobilization of volunteers to measure NO2.
The combined data were then integrated into convolutional neural networks to train the approach to better predict annual NO2 concentrations. The authors suggest the method could then be applied to regions with less data and less monitoring tools, which could help predict NO2 levels in these areas.
For instance, in areas where only aerial or satellite data are obtained, the full spatial extent and spatial variation of the pollutants levels could be better estimated using the machine rained model. This could address the fact that not all regions can afford to have an extensive cover of ground-based sensors and deep learning/machine learning methods may be needed to facilitate estimates.[2]
Advances in Remotely Sensed Data for Mapping Nitrogen Dioxide Levels
More recent forms of satellite data also help with the spatial variability problem in estimating and knowing different concentrations of NO2.
The TROPOspheric Monitoring Instrument (TROPOMI) system aboard Sentinel-5 Precursor (S-5P) is a recent tool that should enable better troposphere measures. This tool provides measures of ultraviolet and visible light in the troposphere, giving unprecedented detail.
In one of the first studies to look at NO2 concentrations, researchers used a 1 km × 1 km spatial resolution grid to understand NO2 in the troposphere. Not surprisingly, many major urban areas such as the Ruhr valley show high concentrations of annual tropospheric NO2.
What was surprising in the study is the demonstration of dispersal and spread of NO2 in the atmosphere that likely led to concentrations of NO2 in even less urban regions. In fact, two of the top ten NO2 areas were in less urban zones, demonstrating that emission concentrations are also affected by such factors as concentration of secondary roads, low seasonal vegetation concentration, and distances to airports.[3]
Improving Techniques for Measuring Nitrous Dioxide in Urban Areas
Given the importance of measuring nitrogen oxides in general, researchers are also trying to create accurate and cheaper instruments that can be deployed across urban areas. Such tools could better measure nitrogen oxides at ground level, indicating the immediate exposure people might have to pollutants.
What is also important is to gain not only spatial variability and understanding but also temporal variability within urban settings. For instance, in periods of high traffic, such as rush hour, concentrations of NO2 become far higher, particularly damaging to pedestrians and cyclists at this time.
The new AQMesh multisensor pods, equipped with electrochemical sensors, were able to demonstrate accurately the effects of concentrated traffic on NO2 levels. The pods are cheap and portable, which means they could be potentially easier to use and could be distributed to many different cities.[4]
New instruments from satellites and ground-level measures mean that we can better monitor NO2 levels. While this is critical, there are still regions where we know little about NO2. Potentially, machine learning methods could help with such locations, but we do need to better monitor a variety of urban locations if we are to better understand the levels in which exposure is happening to this gas.
Even less urban regions appear to be affect by high levels of NO2. Finding ways to remove NO2 and diminish exposure will be critical in improving global health.
References
[1] For more on the China study looking at nitrogen dioxide levels from land-based and satellite instruments, see: Ryu, J.; Park, C.; Jeon, S.W. Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation. Sustainability 2019, 11, 3809, doi:10.3390/su11143809.
[2] For more on the deep learning and citizen science technique measuring nitrogen dioxide, see: Weichenthal, S.; Dons, E.; Hong, K.Y.; Pinheiro, P.O.; Meysman, F.J.R. Combining Citizen Science and Deep Learning for Large-Scale Estimation of Outdoor Nitrogen Dioxide Concentrations. Environmental Research 2021, 196, 110389, doi:10.1016/j.envres.2020.110389.
[3] For more on measuring tropospheric nitrogen dioxide in Germany using higher resolution instrumentation, see: 10.1016/j.rse.2021.112839.
[4] For more on the AQMesh multisensor pods, see: Mohammed, W.; Shantz, N.; Neil, L.; Townend, T.; Adamescu, A.; Al-Abadleh, H.A. Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations. Atmosphere 2022, 13, 83, doi:10.3390/atmos13010083.
