FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

Abstract

Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

“…After more than a century of oil dominance, the rapid technological advancement of alternative fuels, automation, and information technologies is creating new mobility options, business models, and policies at all levels of government (Muratori et al 2020). Carbon emission control (Contreras and Platania 2019), new approaches to urban air quality assessment (Budde et al 2019), and interoperability (Sotres García et al 2019) represent only some of the strategies for developing smart contexts. With this expression, we consider a place where the correspondence between urban environment and ICT is strong (De Santis et al 2014).…”

Review of the Newly Developed, Mobile Optical Sensors for Real-Time Measurement of the Atmospheric Particulate Matter Concentration

“…The adoption of a 2D matrix sensor instead of a single-pixel detector [ 64 ], providing an image of the detection volume’s interior, enables a precise counting of the number of particles. Accurate single particle detection is achieved when all particles in the observation volume are imaged at once.…”

“…In this case, a mirror is used instead of the optical fiber, and the 2D camera detects the scattered light from the particles after passing from a single lens (using a magnifier-based method). Besides, the use of a single large magnifying lens has a significant benefit in detecting the single scatter traces of particles instead of a sum signal [ 64 ]. Another feature of this compact sensor is that it can be easily mounted and removed from the smartphone.…”

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

“…The idea of using a smartphone camera to measure air pollution has been adopted by the HackAir project (Kosmidis et al, 2018). Furthermore, the most recent smartphone cameras and ash function-based development of a ne dust measurement system called FeinPhone (Budde et al, 2019) suggests that low-cost PM sensors may become default equipment in next-generation smartphones. Low-cost and relatively good result correlations with reference air pollution stations (Karagulian et al, 2019) allows users to set up citizen science initiatives and involve local communities into global problem solving.…”