Laser-based remote sensing has long promised wide-area environmental monitoring, but atmospheric turbulence and extreme power attenuation have kept the goal out of reach – until now. A team from the University of Science and Technology of China and The Chinese University of Hong Kong has developed a photon-counting dual-comb spectroscopy (DCS) system capable of measuring atmospheric gases at just 4 attowatts per comb line, without sacrificing spectral resolution or stability.
The setup uses a common-mode triggering protocol to reconstruct broadband interference patterns from sparse photon arrivals – treating each photon as a data point, not a limitation. The approach compensates for optical path fluctuations caused by fiber wandering or turbulence, maintaining kHz-level resolution and shot-noise-limited sensitivity across tens of nanometers of bandwidth. “We’ve achieved 15-minute time-resolution open-path spectral detection for multiple greenhouse gases and their isotopes,” said the researchers in a press release.
In field tests, the compact all-fiber platform measured CO₂, H₂O, and HDO over a 3.3 km open-air path through dense urban traffic, without the need for a retroreflector. Despite three nearby earthquakes during the month-long trial, the system remained functional with only minor adjustments.
Using InGaAs single-photon avalanche diodes (SPADs), the team achieved detection speeds that outpaced traditional DCS methods by orders of magnitude. A segmented parallel detection scheme further increased throughput, making real-time, eye-safe, and power-efficient monitoring feasible even under extreme attenuation (~93 dB). With future upgrades – including SPAD arrays and broader parallelization – the method could support multi-kilometer, multi-gas monitoring for industrial leaks, climate studies, and even ground-to-satellite sensing.
“This breakthrough not only enhances our capability to monitor atmospheric gases with unprecedented sensitivity,” the authors concluded, “but also paves the way for next-generation optical sensing networks that are robust, low-power, and scalable.”
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