Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements (original) (raw)
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Sensors and Materials, 2023
Air quality (AQ) monitoring is crucial for maintaining human health and well-being, whether outdoors or indoors. Particulate matter (PM) is among the most critical parameters that must be routinely monitored. Traditional reference particulate analyzers are expensive and difficult to deploy on a large scale, leading to poor spatial and temporal AQ information. However, the reliability and accuracy of these sensors are yet to be established. This study is aimed at assessing the performance of five low-cost sensors by comparing them with a particulate reference analyzer for AQ monitoring in accordance with the United States Environmental Protection Agency (US EPA)-recommended guidelines. The sensors were tested for indoor and outdoor environments using simple linear regression (SLR) models. The results indicate that low-cost sensors are unreliable for accurately measuring AQ in indoor environments. The correlation between the sensors and the reference analyzer was poor, with coefficient of determination (R2) values ranging from 0.2 to 0.58 during the three-week analysis period for a 1-h average. However, after increasing the average time interval, the sensor (HPMA115) satisfied the US EPA-recommended guideline with an R2 value of 0.72. Root-mean-square error (RMSE) values for some sensors exceeded the US EPA guideline of less than 7 μgm-3 for PM sensors. The concentration of PM2.5, indoor relative humidity (RH), and temperature were identified as potential factors contributing to sensor behavior. The air conditioning system also affected the sensor performance, with variations in RH and temperature observed between tests with and without occupants. The results showed that low-cost sensors could be utilized for outdoor environments, with Honeywell's HPMA115 performing well. However, the calibration process must be performed for each specific environment. Our findings highlighted the limitations of low-cost sensors for AQ monitoring and the need for further research to develop reliable sensors.
Development and On-Field Testing of Low-Cost Portable System for Monitoring PM2.5 Concentrations
Sensors, 2018
Recent developments in the field of low-cost sensors enable the design and implementation of compact, inexpensive and portable sensing units for air pollution monitoring with fine-detailed spatial and temporal resolution, in order to support applications of wider interest in the area of intelligent transportation systems (ITS). In this context, the present work advances the concept of developing a low-cost portable air pollution monitoring system (APMS) for measuring the concentrations of particulate matter (PM), in particular fine particles with a diameter of 2.5 µm or less (PM2.5). Specifically, this paper presents the on-field testing of the proposed low-cost APMS implementation using roadside measurements from a mobile laboratory equipped with a calibrated instrument as the basis of comparison and showcases its accuracy on characterizing the PM2.5 concentrations on 1 min resolution in an on-road trial. Moreover, it demonstrates the intended application of collecting fine-grained spatio-temporal PM2.5 profiles by mounting the developed APMS on an electric bike as a case study in the city of Mons, Belgium.
Sensors, 2022
This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, inc...
Review of the Performance of Low-Cost Sensors for Air Quality Monitoring
Atmosphere, 2019
A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from pe...
An evaluation tool kit of air quality micro-sensing units
Science of The Total Environment, 2016
Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) Micro-Sensing Units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website. 1 Introduction Air pollution is recognized as a contributing factor to various health outcomes, and has been associated with public health risks [1, 2]. Accurately assessing ambient concentrations of different air pollutants is necessary in any study on the impact of air quality (AQ) on different health endpoints. To date, ambient pollutant concentrations are obtained from either short time-period measurement campaigns using a large number of sensing devices (e.g. [3]), or from measurements reported by standard Air Quality Monitoring (AQM) stations over extended time periods (e.g. [4]). While the former is limited in temporal representativeness (e.g. due to inter-seasonal variation), the latter is limited in spatial representativeness (e.g. due to dispersion patterns) and typically measures only a limited number of criteria pollutants [5]. Further, regulatory AQM stations require certified instrumentation meeting measurement accuracy requirements, and an extensive set of procedures to ensure that data quality remains satisfactory. These requirements, typically required by laws and regulations, ensure that measurements are comparable across all networks with similar requirements, but limit the AQM spatial deployment due to their high investment and operational cost. As a result, the AQM network has limited ability to account for spatial variability of pollution levels in heterogeneous regions such as urban areas, which in return, renders exposure assessment a very difficult task [6]. Moreover, the air-inlets of AQM stations are typically located on rooftops or way above the ground [7], thus misrepresenting the true exposure of any individual at head height. Recent developments in sensory and communication technologies have made the deployment of portable and relatively low-cost Micro Sensing Units (MSUs) possible. These MSUs can operate as a set of individual nodes, or may be interconnected to form a Wireless Distributed Environmental Sensor Network (WDESN) to measure air pollution over large spatial scales. WDESNs gather high-resolution spatial and temporal data from numerous individual nodes allowing for a better interpolation and the generation of dense pollution maps, which are closer to real-life pollution dispersion scenarios [8]. The gaseous sensors mounted on these MSUs are low-power and low-cost, and are based on widely understood amperometric sensor methodologies designed for sensing selected gases at the parts-per-million (ppm) level [9, 10, 11]. Electronic circuitry, which applies signal processing, allows for the detection at the part-per-billion level [11]. Recent miniaturization of Optical Particles Counters (OPCs) [12, 13] and solid state [14, 15] sensors allows to extend the MSUs capabilities to measure Particulate Matter (PM) as well. The small size and low power-consumption of MSUs lay the path for many new applications that require AQ data, such as exposure analyses [16, 17], education [18], hot-spot identification and characterization [19], supplementary network monitoring [20, 21], and citizen science [22, 23, 24]. In particular, the essence of citizen science requires active participation of citizens in the scientific research process [22]. Within the context of air-quality research, MSUs may be deployed at citizen's homes, monitoring either ambient or indoor air quality in their local environment. An example is the CITI-SENSE project, which aims at developing sensor-based citizen observatories for improving the quality of life in cities [25]. Seminal studies that evaluate MSUs in pre-field and field trials show that these units indeed can capture air pollution spatio-temporal variation [26, 27, 11, 28, 21, 29, 30]. However, these studies have shown that the MSUs' main limitation is their low accuracy relative to laboratory equipment [26, 27, 11, 28, 30] or an AQM station [11, 28, 21].
Air Quality Measurement Using Low-Cost Sensors—A Review
Lecture Notes in Networks and Systems, 2021
Rapid urbanization and a consumption centric economy have created an enormous pressure on the environment. Air, water and soil pollution are a global problem. The affects of pollution are more apparent in developing countries such as China and India, because of various economic and demographic factors. In major cities, such as New Delhi, Beijing, the air pollution reaches hazardous levels, especially during winters. Air quality measurement is the first step toward mitigating the effects of air pollution; hence, there has been an effort to set up air quality measurement stations all over the world. However, the availability of these measurement stations is sparser in developing countries, where the air quality is lower. Hence, there is a need for low-cost air quality measurement devices. The following work presents a brief overview of various low-cost approaches to measuring air quality.
Development of a low-cost portable device for the monitoring of air pollution
Acta Scientific Computer Sciences, 2023
The Air Quality is an ever-growing environmental issue and over time it is seen as getting worse. In this paper we will look at a case study example where we integrated a low-cost and portable air quality monitoring system in order to perform some local measurements of pollution in the Liverpool area, UK. The system is presented in its set of components and then the preliminary set of measurements and results are shown. Even if a more in depth validation should be considered, these initial results provide a good benchmark for the development of low cost and portable system for the air quality monitoring.
Performance of Four Consumer-grade Air Pollution Measurement Devices in Different Residences
Aerosol and Air Quality Research, 2019
There has been a proliferation of inexpensive consumer-grade devices for monitoring air pollutants, including PM 2.5 and certain gasses. This study compared the performance of four consumer-grade devices-the Air Quality Egg 2 (AQE2), BlueAir Aware, Foobot, and Speck-that utilize optical sensors to measure the PM 2.5 concentration. The devices were collocated and operated for 7 days in each of three residences, and the PM 2.5 mass concentrations were compared with those measured by established optical sensing devices, viz., the personal DataRAM and DustTrak DRX, as well as the filter-based Personal Modular Impactor (PMI). Overall, the Foobot and BlueAir displayed the strongest correlations with the direct-reading reference instruments for both the hourly and daily PM 2.5 mass concentrations. Comparing the 1-hour averages obtained with the DustTrak DRX for all of the residences with those obtained with the Foobot, BlueAir, AQE2, and Speck, the Pearson's correlation coefficients (R's) were 0.80, 0.88,-0.028, and 0.60, respectively. Overall, the strength of the correlation depended on the specific residence, likely due to the differences in aerosol composition. The correlations with the PMI measurements were moderate, with R values of 0.44 and 0.56 for the BlueAir and Foobot, respectively. The correlation coefficients for the daily values obtained with the AQE2 and Speck were-0.59 and 0.70 compared to the PMI. According to a paired t-test, the average 24-h PM 2.5 concentration data obtained using the consumer-grade monitors were statistically different (p > 0.05) from the mass values measured by the gravimetric filters. Overall, this study demonstrates the ability of consumergrade air pollution monitors to report PM 2.5 trends accurately; however, for accurate mass concentration measurements, these monitors must be calibrated for a particular location and application. Further testing is needed to determine their suitability for long-term indoor field studies.
Low-cost sensors as an alternative for long-term air quality monitoring
Environmental Research, 2020
Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM 2.5 and an Alphasense CO-B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on the performance of the monitors, the responses of the PM 2.5 sensors to different types of aerosols, and the long-term stability of the PM 2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM 2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM 2.5 (0.44 < R 2 < 0.91) and CO (0.37 < R 2 < 0.90). The monitors performed well at relative humidity < 75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM 2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM 2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.
Low-Cost Air Quality Sensor Evaluation and Calibration in Contrasting Aerosol Environments
The use of low-cost sensors (LCS) in air quality monitoring has been gaining interest across all walks of society, including community and citizen scientists, academic research groups, environmental agencies, and the private sector. Traditional air monitoring, performed by regulatory agencies, involves expensive regulatory-grade equipment and requires ongoing maintenance and quality control checks. The low-price tag, minimal operating cost, ease of use, and open data access are the primary driving factors behind the popularity of LCS. This study discusses the role and associated challenges of PM2.5 25 sensors in monitoring air quality. We present the results of evaluations of the PurpleAir (PA.) PA-II LCS against regulatorygrade PM2.5 federal equivalent methods (FEM) and the development of sensor calibration algorithms. The LCS calibration was performed for 2 to 4 weeks during December 2019-January 2020 in Raleigh, NC, and Delhi, India, to evaluate the data quality under different aerosols loadings and environmental conditions. This exercise aims to develop a robust calibration model that uses PA measured parameters (i.e., PM2.5, temperature, relative humidity) as input and provides bias-corrected 30 PM2.5 output at an hourly scale. Thus, the calibration model relies on simultaneous measurements of PM2.5 by FEM as target output during the calibration model development process. We applied various statistical and machine learning methods to achieve a regional calibration model. The results from our study indicate that, with proper calibration, we can achieve biascorrected PM2.5 data using PA sensors within 12% percentage mean absolute bias at hourly and within 6% for a daily average. Our study also suggests that pre-deployment calibrations developed at local or regional scales should be performed for the PA 35 sensors to correct data from the field for scientific data analysis. 1. Introduction Air quality monitoring is critical for managing and mitigating air pollution at varying spatiotemporal scales. However, air 40 quality monitoring is limited in many parts of the world (Martin et al., 2019) in part due to the high cost and technical experience requirements of operating regulatory-grade monitors (R.G.M.). Regulatory-grade continuous air quality monitors have high measurement accuracy under varying operating conditions. The high cost of RGM and their associated infrastructure needs and regular maintenance also limit the extensive deployment of such monitors in a region and the spatial density of the network. This is particularly true in developing countries. The lack of data affects critical decision-making by the public about 45 their day-today activities and regulatory agencies for controlling and mitigating air pollution in many regions.