Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Comparative Study on the Use of Some Low-Cost Optical Particulate Sensors for Rapid Assessment of Local Air Quality Changes

Official air quality (AQ) stations are sporadically located in cities to monitor the anthropogenic pollutant levels. Consequently, their data cannot be used for further locations to estimate hidden changes in AQ and local emissions. Low-cost sensors (LCSs) of particulate matter (PM) in a network can help in solving this problem. However, the applicability of LCSs in terms of analytical performance requires careful evaluation. In this study, two types of pocket-size LCSs were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min). These devices utilize the method of laser irradiation and multi-angle light scattering on air-suspended particulates. A research-grade AQ monitor was applied as a reference. The LCSs showed acceptable accuracy for PM species in indoor/outdoor air even without calibration. Low PM readings (<10 μg/m3) were generally handicapped by higher bias, even between sensors of the same type. The relative humidity (RH) slightly affected the PM readings of LCSs at RHs higher than 85%, necessitating field calibration. The air quality index was calculated to classify the extent of air pollution and to make predictions for human health effects. The LCSs were useful for detecting peaks stemming from emissions of motor vehicular traffic and residential cooking/heating activities.

Noise and air pollution during Covid-19 lockdown easing around a school site

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM₁, PM_2.5, and PM₁₀ (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO₂) and ground-level ozone (O₃) (-0.68 < r < -0.78) as NO₂ is a precursor of O₃. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases.