Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor.
Ontology highlight
ABSTRACT:
SUBMITTER: Wahlborg D
PROVIDER: S-EPMC8032644 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The performance of a low cost ozone monitor (Aeroqual Series 500 portable gas monitors using a metal oxide sensor for ozone; model OZL) was assessed under field conditions. Ten ozone monitors were calibrated under clean-air laboratory conditions and controlled ozone concentrations of 5 to 100 ppb. Good linearity and response were obtained relative to a research-grade ozone monitor. One monitor was co-located at a regulatory air quality monitoring station that uses a U.S. federal equivalent method (FEM) ozone analyzer. Raw data from the Aeroqual monitor collected over 4 months (June-October) at a 10-minute time-resolution, showed good agreement (r2=0.83) with the FEM values but with an overestimation of ~12%. Data were averaged to different time resolutions; 1 h time averaged concentrations showed the best fit with the FEM results (r2=0.87). Data analyses suggested the potential of interferences due to temperature, relative humidity, nitrogen oxides, and volatile organic compounds. Correction models using temperature, humidity, and nitrogen dioxide (NO2) were tested to relate the monitor concentrations to the FEM values. Temperature and humidity were two readily available variables. The model (#3) that added NO2 did not provide a substantial improvement in the fit. Thus, the models with only temperature and humidity can be easily developed by any user. The best model explained 91% of the variance and showed statistically significant improvement of the goodness of fits as well as decreased influence of the interfering variables on the diurnal and weekly patterns. The correction models were also able to lower the effect of seasonal temperature changes, allowing the use of the monitors over long-term sampling campaigns. Thus, the Aeroqual ozone monitor can return "FEM-like" concentrations after appropriate corrections. Data provided by a network of monitors could provide intra-urban spatial variations in ozone concentrations and provide more accurate human exposure assessments by reducing exposure misclassification.
| S-EPMC7518407 | biostudies-literature
Project description:Soil volumetric water content ( V W C ) is a vital parameter to understand several ecohydrological and environmental processes. Its cost-effective measurement can potentially drive various technological tools to promote data-driven sustainable agriculture through supplemental irrigation solutions, the lack of which has contributed to severe agricultural distress, particularly for smallholder farmers. The cost of commercially available V W C sensors varies over four orders of magnitude. A laboratory study characterizing and testing sensors from this wide range of cost categories, which is a prerequisite to explore their applicability for irrigation management, has not been conducted. Within this context, two low-cost capacitive sensors-SMEC300 and SM100-manufactured by Spectrum Technologies Inc. (Aurora, IL, USA), and two very low-cost resistive sensors-the Soil Hygrometer Detection Module Soil Moisture Sensor (YL100) by Electronicfans and the Generic Soil Moisture Sensor Module (YL69) by KitsGuru-were tested for performance in laboratory conditions. Each sensor was calibrated in different repacked soils, and tested to evaluate accuracy, precision and sensitivity to variations in temperature and salinity. The capacitive sensors were additionally tested for their performance in liquids of known dielectric constants, and a comparative analysis of the calibration equations developed in-house and provided by the manufacturer was carried out. The value for money of the sensors is reflected in their precision performance, i.e., the precision performance largely follows sensor costs. The other aspects of sensor performance do not necessarily follow sensor costs. The low-cost capacitive sensors were more accurate than manufacturer specifications, and could match the performance of the secondary standard sensor, after soil specific calibration. SMEC300 is accurate ( M A E , R M S E , and R A E of 2.12%, 2.88% and 0.28 respectively), precise, and performed well considering its price as well as multi-purpose sensing capabilities. The less-expensive SM100 sensor had a better accuracy ( M A E , R M S E , and R A E of 1.67%, 2.36% and 0.21 respectively) but poorer precision than the SMEC300. However, it was established as a robust, field ready, low-cost sensor due to its more consistent performance in soils (particularly the field soil) and superior performance in fluids. Both the capacitive sensors responded reasonably to variations in temperature and salinity conditions. Though the resistive sensors were less accurate and precise compared to the capacitive sensors, they performed well considering their cost category. The YL100 was more accurate ( M A E , R M S E , and R A E of 3.51%, 5.21% and 0.37 respectively) than YL69 ( M A E , R M S E , and R A E of 4.13%, 5.54%, and 0.41, respectively). However, YL69 outperformed YL100 in terms of precision, and response to temperature and salinity variations, to emerge as a more robust resistive sensor. These very low-cost sensors may be used in combination with more accurate sensors to better characterize the spatiotemporal variability of field scale soil moisture. The laboratory characterization conducted in this study is a prerequisite to estimate the effect of low- and very low-cost sensor measurements on the efficiency of soil moisture based irrigation scheduling systems.
| S-EPMC7014303 | biostudies-literature
Project description:Air pollution is an important source of morbidity and mortality. It is essential to understand to what levels of air pollution citizens are exposed, especially in urban areas. Low-cost sensors are an easy-to-use option to obtain real-time air quality (AQ) data, provided that they go through specific quality control procedures. This paper evaluates the reliability of the ExpoLIS system. This system is composed of sensor nodes installed in buses, and a Health Optimal Routing Service App to inform the commuters about their exposure, dose, and the transport's emissions. A sensor node, including a particulate matter (PM) sensor (Alphasense OPC-N3), was evaluated in laboratory conditions and at an AQ monitoring station. In laboratory conditions (approximately constant temperature and humidity conditions), the PM sensor obtained excellent correlations (R2≈1) against the reference equipment. At the monitoring station, the OPC-N3 showed considerable data dispersion. After several corrections based on the k-Köhler theory and Multiple Regression Analysis, the deviation was reduced and the correlation with the reference improved. Finally, the ExpoLIS system was installed, leading to the production of AQ maps with high spatial and temporal resolution, and to the demonstration of the Health Optimal Routing Service App as a valuable tool.
| S-EPMC10002067 | biostudies-literature
Project description:Apex carnivores are wide-ranging, low-density, hard to detect, and declining throughout most of their range, making population monitoring both critical and challenging. Rapid and inexpensive index calibration survey (ICS) methods have been developed to monitor large African carnivores. ICS methods assume constant detection probability and a predictable relationship between the index and the actual population of interest. The precision and utility of the resulting estimates from ICS methods have been questioned. We assessed the performance of one ICS method for large carnivores-track counts-with data from two long-term studies of African lion populations. We conducted Monte Carlo simulation of intersections between transects (road segments) and lion movement paths (from GPS collar data) at varying survey intensities. Then, using the track count method we estimated population size and its confidence limits. We found that estimates either overstate precision or are too imprecise to be meaningful. Overstated precision stemmed from discarding the variance from population estimates when developing the method and from treating the conversion from tracks counts to population density as a back-transformation, rather than applying the equation for the variance of a linear function. To effectively assess the status of species, the IUCN has set guidelines, and these should be integrated in survey designs. We propose reporting the half relative confidence interval width (HRCIW) as an easily calculable and interpretable measure of precision. We show that track counts do not adhere to IUCN criteria, and we argue that ICS methods for wide-ranging low-density species are unlikely to meet those criteria. Established, intensive methods lead to precise estimates, but some new approaches, like short, intensive, (spatial) capture-mark-recapture (CMR/SECR) studies, aided by camera trapping and/or genetic identification of individuals, hold promise. A handbook of best practices in monitoring populations of apex carnivores is strongly recommended.
| S-EPMC7141012 | biostudies-literature
Project description:Evaluation of the semi-continuous Monitor for AeRosols and GAses in ambient air (MARGA, Metrohm Ap-plikon B.V.) was conducted with an emphasis on examination of accuracy and precision associated with processing of chromatograms. Using laboratory standards and atmospheric measurements, analytical accuracy, precision and method detection limits derived using the commercial MARGA software were compared to an alternative chromatography procedure consisting of a custom Java script to reformat raw MARGA conductivity data and Chromeleon (Thermo Scientific Dionex) software for peak integration. Our analysis revealed issues with accuracy and precision resulting from misidentification and misintegration of chromatograph peaks by the MARGA automated software as well as a systematic bias at low concentrations for anions. Reprocessing and calibration of raw MARGA data using the alternative chromatography method lowered method detection limits and re-duced variability (precision) between parallel sampler boxes. Instrument performance was further evaluated during a 1-month intensive field campaign in the fall of 2014, including analysis of diurnal patterns of gaseous and particulate water-soluble species (NH3, SO2, HNO3, NH4+ , SO42- and NO3- , gas-to-particle partitioning and particle neutralization state. At ambient concentrations below ~ 1 µg m-3, concentrations determined using the MARGA software are biased +30 and +10 % for NO3- and SO42- , respectively, compared to concentrations determined using the alternative chromatography procedure. Differences between the two methods increase at lower concentrations. We demonstrate that positively biased NO3- and SO42- measurements result in overestimation of aerosol acidity and introduce nontrivial errors to ion balances of inorganic aerosol. Though the source of the bias is uncertain, it is not corrected by the MARGA online single-point internal LiBr standard. Our results show that calibra-tion and verification of instrument accuracy by multilevel external standards is required to adequately control analytical accuracy. During the field intensive, the MARGA was able to capture rapid compositional changes in PM2.5 due to changes in meteorology and air mass history relative to known source regions of PM precursors, including a fine NO3- aerosol event associated with intrusion of Arctic air into the southeastern US.
| S-EPMC6192433 | biostudies-literature
Project description:This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.
| S-EPMC7662848 | biostudies-literature
Project description:Indoor air pollution mitigation measures are highly important due to the associated health impacts, especially on children, a risk group that spends significant time indoors. Thus, the main goal of the work here reported was the evaluation of mitigation measures implemented in nursery and primary schools to improve air quality. Continuous measurements of CO₂, CO, NO₂, O₃, CH₂O, total volatile organic compounds (VOC), PM₁, PM2.5, PM10, Total Suspended Particles (TSP) and radon, as well as temperature and relative humidity were performed in two campaigns, before and after the implementation of low-cost mitigation measures. Evaluation of those mitigation measures was performed through the comparison of the concentrations measured in both campaigns. Exceedances to the values set by the national legislation and World Health Organization (WHO) were found for PM2.5, PM10, CO₂ and CH₂O during both indoor air quality campaigns. Temperature and relative humidity values were also above the ranges recommended by American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). In general, pollutant concentrations measured after the implementation of low-cost mitigation measures were significantly lower, mainly for CO₂. However, mitigation measures were not always sufficient to decrease the pollutants' concentrations till values considered safe to protect human health.
| S-EPMC5486271 | biostudies-literature
Project description:Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R 2 = 0.74 for PM2.5 and R 2 = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.
| S-EPMC8384116 | biostudies-literature
Project description:Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data. This study presents evaluation and field-calibration of the PM2.5 data from a network of low-cost monitors currently operating in Baltimore, MD, which has only one regulatory PM2.5 monitoring site within city limits. Co-location analysis at this regulatory site in Oldtown, Baltimore revealed high variability and significant overestimation of PM2.5 levels by the raw data from these monitors. Universal laboratory corrections reduced the bias in the data, but only partially mitigated the high variability. Eight months of field co-location data at Oldtown were used to develop a gain-offset calibration model, recast as a multiple linear regression. The statistical model offered substantial improvement in prediction quality over the raw or lab-corrected data. The results were robust to the choice of the low-cost monitor used for field-calibration, as well as to different seasonal choices of training period. The raw, lab-corrected and statistically-calibrated data were evaluated for a period of two months following the training period. The statistical model had the highest agreement with the reference data, producing a 24-hour average root-mean-square-error (RMSE) of around 2 μg m -3. To assess transferability of the calibration equations to other monitors in the network, a cross-site evaluation was conducted at a second co-location site in suburban Essex, MD. The statistically calibrated data once again produced the lowest RMSE. The calibrated PM2.5 readings from the monitors in the low-cost network provided insights into the intra-urban spatiotemporal variations of PM2.5 in Baltimore.
| S-EPMC7480820 | biostudies-literature
Project description:Low-cost particulate matter (PM) air quality sensors are becoming widely available and are being increasingly deployed in ambient and home/workplace environments due to their low cost, compactness, and ability to provide more highly resolved spatiotemporal PM concentrations. However, the PM data from these sensors are often of questionable quality, and the sensors need to be characterized individually for the environmental conditions under which they will be making measurements. In this study, we designed and assessed a cost-effective (∼$700) calibration chamber capable of continuously providing a uniform PM concentration simultaneously to multiple low-cost PM sensors and robust calibration relationships that are independent of sensor position. The chamber was designed and evaluated with a Computational Fluid Dynamics (CFD) model and a rigorous experimental protocol. We then used this new chamber to calibrate 242 Plantower PMS 3003 sensors from two production lots (Batches I and II) with two aerosol types: ammonium nitrate (for Batches I and II) and alumina oxide (for Batch I). Our CFD models and experiments demonstrated that the chamber is capable of providing uniform PM concentration to 8 PM sensors at once within 6% error and with excellent reliability (intraclass correlation coefficient > 0.771). The study identified two malfunctioning sensors and showed that the remaining sensors had high linear correlations with a DustTrak monitor that was calibrated for each aerosol type (R2 > 0.978). Finally, the results revealed statistically significant differences between the responses of Batches I and II sensors to the same aerosol (P-value<0.001) and the Batch I sensors to the two different aerosol types (P-value<0.001). This chamber design and evaluation protocol can provide a useful tool for those interested in systematic laboratory characterization of low-cost PM sensors.
| S-EPMC7409587 | biostudies-literature