Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment

Abstract

The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist’s smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 μg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 μg m−3) and in two cities with high background concentrations (41 to 74 μg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.

AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin

“…The hyperparameter values under consideration were as follows: the number of neurons per layer spanned from [300, 500, 700], the number of consecutive records fluctuated between [6,12,20], and batch sizes covered [64,256,512]. We assessed these hyperparameter combinations across five distinct architectures, resulting in a total of 135 potential configurations, each requiring training and evaluation.…”

“…The concentrations of PM exhibit inherent spatial and temporal variations, leading to differing personal exposure levels [11]. To tackle this challenge and expand the monitored areas, various initiatives have been undertaken to create affordable PM sensor networks [12][13][14][15]. Usually, these networks employ low-cost sensors that offer advantages such as scalability, real-time data, and citizen science participation; while these widely distributed sensors cannot completely replace reference stations in terms of accuracy, they play a vital role in advancing air quality research [16][17][18].…”

A baseline study on mortality and air pollution in Kabyle village (northern Algeria) under COVID-19

“…The PM2.5 concentration was measured three times minimum in the morning (between 6:00 and 10:00 a.m.) for 2 to 25 days depending on the month, from August to October 2021 and from January to June 2022, using the experimental setup of Figure 2. The Nova sensor includes a built-in fan and use a light scattering to measure the concentration of PM2.5 within the range 0.0 to 999.9 µg/m 3 with an average accuracy of 90 % according to the relative humidity [22]. The measurements were taken at a height of about 6 m from the ground, at a fixed site located at the lowest level of the village studied.…”

Adoption of Environmental Information Chatbot Services Based on the Internet of Educational Things in Smart Schools: Structural Equation Modeling Approach

“…In addition, various environmental information management systems for low-cost sensors and real-time monitoring systems have been investigated in recent research. For environmental information applications, including water pollution source localization [10,11], air quality monitoring systems [12][13][14][15], soil moisture measurement [16], and radiation pollution [17], the framework development of sensors and wireless sensor networks is proposed.…”

“…The majority of studies have focused on air pollution monitoring, which requires extensive development and performance evaluation of smart wireless environmental sensors with cloud servers and internet networks [12][13][14][15]. It was hard to find the right research examining and comparing the capability of a smart environmental information system to be used in an educational institution, especially in the context of current health and safety issues arising from the air environment.…”