Photonic Sensor–Based Quantification of Harmful Gas Uptake Efficiency in Roadside Air-Purifying Plant

Abstract: Urban roadside areas experience high concentrations of nitrogen oxides (NOx), sulphur dioxide (SO₂), carbon monoxide (CO), volatile organic compounds (VOCs), and particulate matter (PM), all of which negatively affect human health and environmental quality. Certain plant species are known to naturally absorb, metabolize, and reduce these pollutants, but accurate, real-time quantification of their uptake efficiency remains limited due to a lack of precise and non-invasive monitoring tools. This study proposes a photonic sensor–based system to measure pollutant concentrations before and after interaction with selected roadside air-purifying plants. Using optical absorption and fibre-optic sensing techniques, the system detects wavelength-specific signatures of NO₂, SO₂, and VOCs, enabling continuous, high-sensitivity, and in-situ monitoring of pollutant reduction.

A spatial network of photonic sensors will be installed around targeted plants to generate real-time pollutant concentration gradients, from which plant-specific gas uptake rates will be calculated. Machine learning models will further analyze the data to identify diurnal variations, environmental influences, and species-wise differences in pollutant removal capacity. The study aims to identify the most efficient roadside plant species for air purification and develop a scalable, non-destructive framework for evaluating phytoremediation performance. Overall, the system supports smart, plant-integrated air quality monitoring and offers new insights for sustainable urban green-belt planning.

Keywords: Photonic sensors, Fibre-optic gas sensing, Air quality monitoring, Phytoremediation, Roadside plants, Gas uptake efficiency, NOx and VOC detection, Optical spectroscopy