Air pollution is a silent killer, claiming approximately 7 million lives each year globally. World Health Organization (WHO) data reveals that nearly everyone breathes air exceeding WHO thresholds. People living in low and middle-income countries are typically more vulnerable to air pollution due to the higher levels of contamination they are exposed to daily. Poor air quality is one of the main contributors to the global disease burden.

Air quality monitoring helps identify pollution sources, assess exposure levels, and inform decision-making to mitigate impact. Additionally, air quality data supports regulatory compliance, urban planning, and policy development to reduce emissions and improve overall air quality. With growing concerns about low air quality and its adverse effects, accurate and reliable monitoring is essential for ensuring a safe and healthy living environment.

Indeed, various technologies exist for monitoring air quality, each offering unique advantages and applications. From traditional reference-grade monitors and ground-based sensors to state-of-the-art satellite monitoring technology, these diverse approaches collectively contribute to a thorough understanding of air pollution dynamics.

Ground-based monitoring networks form the backbone of air quality assessment but are limited by their spatial coverage, potentially overlooking the full spectrum of pollution variability across expansive regions. Satellite technologies promise to revolutionise air quality observation, providing extensive coverage across vast geographical areas.

The satellite-based remote sensing approach offers distinct advantages, including comprehensive spatial coverage and consistent data, facilitating cross-country air quality comparisons. However, due to its broad spectral bands, traditional multispectral imagery has limitations in accurately discerning between various pollutants.

In contrast, hyperspectral imaging technologies offer a unique blend of spatial and spectral information, surpassing the limitations of traditional sensors. While a conventional sensor commonly captures visible light through three channels (red, green, and blue, or RGB), hyperspectral imaging expands this spectrum, revealing hidden details. Instead of simply recreating colours, hyperspectral imaging delves into the spectral composition of incoming light, generating a rich dataset known as a hypercube. This multidimensional data, comprising 30 or more wavelength bands per pixel, goes beyond human perception, unlocking a trove of insights applicable across diverse industries, including environmental monitoring.

RGB (red, green and blue) imaging records three bands of visible light, while multispectral imaging expands on this by capturing additional non-visible components of the electromagnetic spectrum. In contrast, hyperspectral imaging goes further by gathering and analysing data across the electromagnetic spectrum for every pixel within an image. Image credit: Pixxel

Understanding Hyperspectral Imaging Satellites

Like several other application areas, hyperspectral imagery has emerged as a game-changer in air quality monitoring. Hyperspectral imagery, unlike its traditional counterparts, captures intricate spectral information across the electromagnetic spectrum, enabling precise identification and characterisation of atmospheric substances.

For instance, NASA's Earth Surface Mineral Dust Source Investigation (EMIT), a hyperspectral sensor aboard the International Space Station, has pinpointed over 50 "super-emitters" across Central Asia, the Middle East, and the Southwestern United States. These super-emitters are often found in sectors like fossil fuel extraction, waste management, and agriculture and include facilities or equipment that emit methane at exceptionally high rates.

The image captures a methane plume spanning 2 miles (3 kilometres), identified by NASA's Earth Surface Mineral Dust Source Investigation mission southeast of Carlsbad, New Mexico. Image credits: NASA/JPL-Caltech

Methane plumes (depicted in purple, orange, and yellow) are visible over Turkmenistan (left). The rainbow colours represent the spectral fingerprints captured from corresponding spots in the front image. The graph (right) displays the methane fingerprint detected by EMIT (blue line), juxtaposed with the expected fingerprint derived from an atmospheric simulation (red line). Image credits: NASA/JPL-Caltech

Several research studies have noted the value of hyperspectral imaging (HSI) in air quality monitoring. One study showcased the technology's effectiveness by employing hyperspectral ground-based and satellite measurements to monitor tropospheric NO2 and HCHO levels across Eastern China. Similarly, another study utilised a Chinese satellite-based ultraviolet-visible spectrometer to evaluate global air quality trends during the COVID-19 pandemic in early 2020.

Despite these advancements, existing methods often relied on costly and cumbersome sensors and instruments, limiting widespread adoption.

However, recent strides in sensor technology and the expanding commercial space sector are democratising access to hyperspectral imaging. For instance, the upcoming constellation of hyperspectral Earth imaging satellites developed by Pixxel promises to revolutionise global coverage, offering unparalleled spatial and spectral resolution at regular intervals.

Advantages of Hyperspectral Imaging for Air Quality Monitoring

Hyperspectral imaging collects data across numerous contiguous bands of the electromagnetic spectrum, ranging from ultraviolet to infrared wavelengths. They can differentiate between materials and substances that may appear similar in conventional imagery. For air quality monitoring, this level of detail allows for precisely identifying pollutants and their sources and characterising complex atmospheric processes.

Besides the above-listed pollutants, hyperspectral imaging is also capable of detecting hydrofluorocarbons (HFCs), sulphur hexafluoride (SF₆), and nitrogen trifluoride (NF₃). Image source: Pixxel 

The US Environment Protection Agency has classified six pollutants as "criteria" air pollutants due to their regulation through the development of science-based guidelines for setting permissible levels. These pollutants include carbon monoxide, lead, nitrogen oxides, ground-level ozone, particle pollution ( or particulate matter), and sulphur oxides.

Hyperspectral imagery offers a valuable tool for monitoring and detecting these pollutants in low concentrations. It makes them particularly well-suited for monitoring air quality in urban areas, industrial zones, and regions prone to pollution hotspots.

Tackling the Ticking Methane Bomb with Hyperspectral Imagery

Methane, the second most prevalent anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2), contributes to approximately 16% of global emissions and is more than 28 times as potent as CO2 in trapping atmospheric heat. Over the past two centuries, large-scale industrial activities have doubled atmospheric methane concentrations.

Identifying and addressing methane leaks and sources of emissions are essential in reducing overall GHG emissions and combating the adverse effects of climate change.

Hyperspectral satellite imagery can identify methane absorption features in the shortwave infrared (SWIR) region, enabling the detection of methane emissions ranging from approximately 100 to 500 kilograms per hour.

Targeted measures can mitigate methane releases by identifying super-emitter sites and closely monitoring their activities. Near-real-time monitoring facilitates the evaluation of emission reduction efforts, supporting informed decision-making on climate policies and regulations.

Future Directions and Implications of Hyperspectral Sensors

Future directions in air quality monitoring with hyperspectral satellites lead to exciting advancements in sensor technology and data analytics. Continued sensor design and development innovations will enhance hyperspectral satellites' spectral resolution and sensitivity, allowing for precise and timely detection and quantification of air pollutants.

Integrating hyperspectral satellite data with environmental and meteorological datasets marks a significant stride in air quality monitoring and forecasting. Similarly, incorporating meteorological variables like wind speed, temperature, and humidity into air quality models enables the prediction of pollutant movement, refining pollution control strategies and public health interventions.

Additionally, advancements in data analytics techniques, such as machine learning and artificial intelligence, promise to extract valuable insights from the vast amount of hyperspectral data generated by satellites.

Pixxel's upcoming hyperspectral imaging satellites promise groundbreaking global air quality monitoring advancements. The enhanced accessibility and decreased processing costs linked with its hyperspectral technology open new avenues for organisations to leverage its capabilities.

Explore the potential benefits of Pixxel's state-of-the-art technology for your organisation by contacting their sales team today.

FAQs

Hyperspectral imaging offers unparalleled capabilities in capturing detailed spectral information across the electromagnetic spectrum, enabling precise identification and characterisation of atmospheric substances.

2.What are some specific applications of hyperspectral imaging in identifying air pollutants?

Hyperspectral imaging can detect and quantify various air pollutants, such as methane, nitrogen dioxide, and volatile organic compounds, with high precision and accuracy.

3.How do super-emitters of methane threaten the environment, and how can hyperspectral imaging help identify them?

Super-emitters of methane are facilities or equipment emitting methane at exceptionally high rates, posing a significant environmental threat. Hyperspectral imaging helps identify these super-emitters by detecting fine methane absorption features in the shortwave infrared region.

4.What role do hyperspectral satellites play in addressing global air pollution challenges?

Hyperspectral satellites are crucial in addressing global air pollution challenges by providing extensive coverage across vast geographical areas, facilitating cross-country air quality comparisons, and enabling timely detection and mitigation of pollutant emissions.

Hyperspectral imaging provides detailed spectral information across the electromagnetic spectrum, allowing for precise identification and characterisation of pollutants, even in low concentrations. This capability is well-suited for monitoring air quality in urban areas, industrial zones, and regions prone to pollution hotspots.