Search Results (3)

Air Quality (AQ) and the management of low-emission zones are critical issues in densely populated urban areas. In such environments, human activity significantly impacts AQ, prompting increased efforts to monitor it using a range of devices. Traditional Air Quality monitoring relies on regulated stations, which are often scarce due to high costs, leaving many areas unmonitored. Low-cost sensors offer a promising solution by enabling the higher-spatial-resolution monitoring of pollution levels. In this article, we present the results of a case study conducted in an urban setting where AQ is affected by human activity, particularly during Las Fallas, Valencia’s most renowned festival, which has been declared an Intangible Cultural Heritage of Humanity by UNESCO. The festival features widespread bonfires, firecrackers and large crowds, all of which contribute to worsening air pollution. In this context, we evaluate the performance of the off-the-shelf, low-cost ZPHS01B multisensor module in a real deployment. This module is capable of monitoring Temperature (T), Relative Humidity (RH), Particulate Matter (PM), CO, ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, ${\mathrm{O}}_3$, ${\mathrm{CH}}_2$O and Volatile Organic Compounds. We analyze the features and properties of these sensors. In our deployments, the ZPHS01B module is connected to an ESP32 microcontroller and assembled into an AQ Internet of Things (IoT) node. We present AQ monitoring results from the festival and compare the measurements with those from regulated AQ monitoring stations, used as a reference. Additionally, we evaluate the power consumption of this AQ IoT node, providing its electrical operating characteristics and considering the use of duty cycles to reduce consumption while maintaining sensor stability. We conclude that this module offers promising capabilities for identifying pollution risk zones and opens the door to new research opportunities, particularly in efficient sensor calibration and AQ parameter prediction. Full article

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort. Full article

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R² values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R² values lower than 0.5 are observed. Full article