Low-Cost Air Quality Measurement System Based on Electrochemical and PM Sensors with Cloud Connection.
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ABSTRACT:
SUBMITTER: Arroyo P
PROVIDER: S-EPMC8472764 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ambient air pollution in urban cities in sub-Saharan Africa (SSA) is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM2.5). On most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24 h concentration measured across all sites was 38 µg/m3 with the highest PM2.5 period average concentration of 91 µg/m3 measured in Kampala, Uganda and lowest concentrations of 15 µg/m3 measured in Faraja, The Gambia. Kampala in Uganda and Nnewi in Nigeria recorded the longest periods with concentrations >250µg/m3. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM2.5 concentrations in SSA countries. Such data are crucially needed to raise public, societal and policymaker awareness about air pollution across SSA.
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Project description:Technology innovation in sanitation is needed for the 4.2 billion people worldwide, lacking safely managed sanitation services. A major requirement for the adoption of these technologies is the management of malodor around toilet and treatment systems. There is an unmet need for a low-cost instrumented technology for detecting the onset of sanitation malodor and triggering corrective actions. This study combines sensory data with low-cost gas sensor data on malodor emanating from feces. The response of 10 commercial electrochemical gas sensors was collected alongside olfactometric measurements. Odor from fecal specimens at different relevant dilution as well as specimens with pleasant odors as a control were evaluated for a total of 64 responses. Several of the sensors responded positively to the fecal odor, with the formaldehyde, hydrogen sulfide, and ammonia sensors featuring the highest signal to noise ratio. A positive trend was observed between the sensors' responses and the concentration of the odorant and with odor intensity, but no clear correspondence with dilution to threshold (D/T) values was found. Selected sensors were responsive both above and below the intensity values used as the cutoff for offensive odor, suggesting the possibility of using those sensors to differentiate odor offensiveness based just on the magnitude of their response. The specificity of the sensors suggested that discrimination between the selected non-fecal and fecal odors was possible. This study demonstrates that some of the evaluated sensors could be used to assemble a low-cost malodor warning system.
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Project description:Recently, the monitoring of air pollution by means of low-cost sensors has become a growing research field due to the study of techniques based on machine learning to improve the sensors' data quality. For this purpose, sensors undergo a calibration process, where these are placed in-situ nearby a regulatory reference station. The data set explained in this paper contains data from two self-built low-cost air pollution nodes deployed for four months, from January 16, 2021 to May 15, 2021, at an official air quality reference station in Barcelona, Spain. The goal of the deployment was to have five electrochemical sensors at a high sampling rate of 0.5 Hz; two NO 2 sensors, two O 3 sensors, and one NO sensor. It should be noted that the reference stations publish air pollution data every hour, thus at a rate of 2.7×10-4 Hz. In addition, the nodes have also captured temperature and relative humidity data, which are typically used as correctors in the calibration of low-cost sensors. The availability of the sensors' time series at this high resolution is important in order to be able to carry out analysis from the signal processing perspective, allowing the study of sensor sampling strategies, sensor signal filtering, and the calibration of low-cost sensors among others.
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Project description:ObjectivesTo examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors.MethodsWe compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems.FindingsWe found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index.ImplicationOur study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies.SynopsisWe used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.
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Project description:The concentration of air pollutants in underground parking garages has been found to be higher compared to ambient air. Vehicle emissions from cold starts are the main sources of air pollution in underground parking garages. Eight days of measurements, using low-cost air sensors, were conducted at one underground parking garage at the University of Minnesota, Minneapolis. The CO, NO, NO2, and PM2.5 daily average concentrations in the parking garage were measured to be higher, by up to more than an order of magnitude, compared to the ambient concentration. There is positive correlation between exit traffic flow and the air concentrations in the parking garage for lung deposited surface area (LDSA), CO2, NO, and CO. Fuel specific emission factors were calculated for CO, NO, and NOx. Ranging from 25 to 28 g/kgfuel for CO, from 1.3 to 1.7 g/kgfuel for NO, and from 2.1 to 2.7 g/kgfuel for NOx. Regulated emissions were also calculated for CO and NOx with values of 2.4 to 2.9 and 0.19 to 0.25 g/mile, respectively. These emissions are about 50% higher than the 2017 U.S. emission standards for CO and nearly an order magnitude higher for NOx.
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Project description:This article presents a dataset comprising measurements made by co-located devices, with the aim of calibrating sensors for an upcoming in-situ use. The dataset includes hourly averaged data from 9 low-cost sensors and 2 traffic monitoring stations (thereafter named QDP and SUD3) in Rouen spanning from October 20, 2021 to March 25, 2022. In addition, the dataset is enriched by covariates measured by the sensors: temperature, relative humidity, atmospheric pressure, plus Ox and CO measures. The experiment was conducted as part of TIGA's call for project, and designed to have a better understanding of sensors' drawbacks, particularly when they are moved or shut down. Knowledge about the effect of air pollution on health has gained significant attention from both the scientific community and citizens, making air quality a growing issue for urban area. As a result, the city of Rouen in Normandy, France, has prioritized air quality monitoring as a key initiative. Concurrently, several means to measure air pollutants have been made more accessible, such as the use of low-cost sensors. Those sensors offer affordability, but are known to be less accurate than monitoring stations. Thus, they need to be cautiously studied so as to be used properly.
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Project description:The PurpleAir PA-II-SD is a low-cost particulate matter (PM2.5 and PM10) sensor that is currently available on the market. It is one of many such low-cost and commercially available particulate matter sensors which are being adopted by individuals and researchers worldwide. With growing use of these sensors, there is an interest in better understanding the performance and characteristics of these devices. Data was collected from twelve of these low-cost PurpleAir PA-II-SD sensors and two high fidelity Met One E-BAM PLUS instruments installed at a single location, on the campus of the American University of Beirut, in Beirut, Lebanon over a period of time from June 28, 2020 to September 30, 2020. The data was collected with the aim of assessing inter-sensor variability for the PurpleAir sensors and the sensor accuracy of the PurpleAir when compared to a high fidelity Met One E-BAM PLUS instrument.
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