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Experimental setup: (a) colocated air sampler and Stevenson screen; (b) devices under test inside of the Stevenson screen: 11-D with dryer, OPC-N2 with SPI adapter and (white-orange) enclosure, MAQS (white enclosure with grey front panel), and (c) indoor SMPS with dryer.
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In this paper we evaluate characteristics of three optical particulate matter sensors/sizers (OPS): high-end spectrometer 11-D (Grimm, Germany), low-cost sensor OPC-N2 (Alphasense, United Kingdom) and in-house developed MAQS (Mobile Air Quality System), which is based on another low-cost sensor – PMS5003 (Plantower, China), under realistic conditio...
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... The Grimm 11-D is a portable, robust and reliable instrument, suitable for indoor and outdoor in situ air quality measurements. While it is not a reference grade instrument, it complies with the ISO 21501-1:2009 standard (ISO 2009), and several studies have found its performance comparable to that of various reference instruments under diverse atmospheric and laboratory conditions when properly calibrated (Masic et al. 2020;Vasilatou et al. 2021;Wu et al. 2022). It has been widely used in various indoor (Zheng et al. 2022) and outdoor atmospheric studies (Masic et al. 2020;Stavroulas et al. 2020). ...
... While it is not a reference grade instrument, it complies with the ISO 21501-1:2009 standard (ISO 2009), and several studies have found its performance comparable to that of various reference instruments under diverse atmospheric and laboratory conditions when properly calibrated (Masic et al. 2020;Vasilatou et al. 2021;Wu et al. 2022). It has been widely used in various indoor (Zheng et al. 2022) and outdoor atmospheric studies (Masic et al. 2020;Stavroulas et al. 2020). ...
Good quality of the environment in indoor spaces is an important prerequisite for the health, well-being and productivity of office workers. In cases where poor indoor environmental quality threatens the health and well-being of employees, it is important for the employer to undertake the necessary steps to address these issues. However, very often the employer is not well informed on the proper way to implement the required changes, which can result in suboptimal results and waste of resources. In this work we discuss these issues starting from a case study of the actions taken by the occupational safety and health services at a university situated in the city of Gdańsk, Poland, after complaints regarding health problems and poor well-being due to inadequate indoor air quality, lodged by office workers of the university human resources department. Particular focus is given to the legislative and regulatory aspects of indoor air quality in the European Union and Poland, as well as the impact of remediation. Finally, a suggested general course of action is presented aimed at presenting employers with a procedure for positive resolution of the reported issues using effective methods.
... …where m is the mass (µg/m 3 ), ρ is the density (g/cm 3 ), V is the volume, d is the arithmetic mean diameter of the bin (µm), and N is the PNC for the respective bin (#/dl). Based on the literature, the average density is assumed to be 1.65 g cm − 3 (Crilley et al. 2020; Masic et al. 2020). The objective ...
Low-cost sensors (LCS) have gained significant attention in recent years due to their application in urban air quality mapping, community monitoring networks, indoor air quality monitoring, personal exposure monitoring, and citizen science initiatives. This study has developed an integrated approach combining measurements from LCS and existing source apportionment (SA) results with machine learning (ML) algorithms to achieve real-time SA. Source contributions apportioned by Chemical Mass Balance (CMB) model and PM2.5 as well as particle number concentration (PNC) in size bins (0–0.3 μm, 0.3–0.5 μm, 0.5–1 μm, and 1–2.5 μm) from LCS are acquired from May 2019 to February 2020 at Major Dhyan Chand National Stadium (NS), Delhi. The PNC in size bins was converted to mass (PM0 − 0.3, PM0.3 − 0.5, PM0.5 − 1, PM1 − 2.5) for respective sizes. The objective function is {S1, S2, S3, …. S8} = f {PM0 − 0.3, PM0.3 − 0.5, PM0.5 − 1, PM1 − 2.5, PM2.5} where S1, S2, S3, …. S8 are the sources. Four ML algorithms, namely support vector regression (SVR), k-nearest neighbour (kNN), random forest (RF) and gradient boosting (GB), are applied for SA. GB performs the best among all algorithms with a train and test score (R2) of 0.82 and 0.75. The R2 (in parentheses) between actual and predicted PM2.5 for sources of biomass burning (0.92), dust (0.83), gasoline vehicle (0.75), diesel vehicle (0.78), coal combustion (0.70), waste burning (0.76), industrial (0.77) and secondary aerosol (0.89) indicate the acceptable performance of the model. The statistical t-test comparing the PM2.5 contributions obtained from CMB and ML for each source indicates no significant difference (p > 0.05) except for dust and waste burning. This study demonstrated the ability of LCS to perform real-time SA with the help of an existing dataset. This cost-effective approach will provide rough estimations of the sources to regulatory agencies and policymakers for immediate action.
... In recent decades, gas sensor technology has made great advances and the prices of low-cost gas sensors (LCSs) have registered limited increases. Low-cost air quality monitors (LCAQMs) based on LCSs are used in many fields and applications, such as air pollution monitoring in urban areas [1][2][3][4][5][6][7][8], malodor control or detection [9,10], and air quality monitoring in outdoor and indoor environments [11][12][13][14][15][16][17][18][19][20][21]. ...
Agricultural production or rural activities can involve the emission of unpleasant gases, malodors, or most commonly, greenhouse gases. In any case, the control and monitoring of such
emissions in rural, unattended, and remote locations represent an issue in need of addressing. In
this article, the monitoring of gases produced by a poultry manure depot and performed by devices based on low-cost gas sensors in the context of the POREM (poultry-manure-based bio-activator for better soil management through bioremediation) project is reported. This experience has shown that the continuous and real-time monitoring of gas emissions in an unattended, remote, and rural area, where it is unfeasible to employ expensive, professional instruments, can be successfully performed
by low-cost technologies. Two portable monitoring units developed in the laboratory and based on low-cost gas sensors were used to provide indications about the concentrations of NH3, CH4, H2S,
and CO2. During this experiment, two monitors were deployed: the first one was placed in the manure storage depot, while the second one was deployed out of the storage site to compare the gas concentrations related to the outdoor environment with the gas emissions coming from the manure. Both devices were wirelessly linked to the Internet, even though the radio signal was weak and unstable in that area. This situation provided us with the opportunity to test a particular protocol based on sending and receiving e-mails containing commands for the remote machines. This experiment proved the effectiveness of the use of low-cost devices for gas emission monitoring in such particular environments
... 32,33 Laboratory and eld studies that have co-located LCS for PM measurements along reference or research grade instruments have identied the following factors as sources of bias: (1) the averaging time, 34,35 (2) relative humidity, 33,34,[36][37][38][39][40][41] (3) the ambient temperature, 33,38,42 (4) the nature, composition and size, of the aerosol 33,34,36,[38][39][40][41][42][43] and (5) its concentration. 27,34,36,39,44,45 The ambient temperature has also been reported as a source of bias, albeit mostly in ambient studies 33,38 and less in laboratory studies, 39,42 demonstrating its possible nature as a confounder. The instrumentation, both the design of the LCS and the measuring principle of the instrument it is calibrated against, is a further factor identied as a source of bias. ...
The use of low-cost sensors (LCS) for the evaluation of the ambient pollution by particulate matter (PM) has grown and become significant for the scientific community in the past few years. However promising this novel technology is, the characterization of their limitations is still not satisfactory. Reports in the scientific literature rely on calibration, which implies the physical (or geographical) co-location of the LCS with reference in situ (or remote, e.g. onboard satellite platforms) instrumentation. However, calibration is not always feasible, and even when feasible, the validity of the developed relationship, even in similar settings, is subject to large uncertainties. In the present work, the performance of a popular LCS for PM, the Plantower PMS5003, is investigated. The LCS performs particle counts, which is the physical quantity that is input to the black-box model of the manufacturer to compute the ambient PM mass, which is output to the operator. The particle counts of LCS Plantower PMS5003 units were compared to those of the co-located research-grade Grimm EDM-164 monitor. The results show that humidity possibly has a reduced influence on the performance, but the performance can better be constrained, however spanning more than one order of magnitude in terms of agreement ratio, by functions of the actual particle count itself. In view of these results, further development in the field of LCS for PM monitoring should focus on improvements of the physical design of the devices, in order to enhance the sizing of the particles. The use of the actual Plantower PMS5003 models should be limited to the monitoring of PM mass in the smaller size bins.
... Optical particle counters (OPCs) have emerged as a cost-effective tool for monitoring particle size distributions [5][6][7]. Recent advancements have improved the accuracy and reliability of OPCs [6], especially for measuring particle mass concentrations [7,8], making ...
... Optical particle counters (OPCs) have emerged as a cost-effective tool for monitoring particle size distributions [5][6][7]. Recent advancements have improved the accuracy and reliability of OPCs [6], especially for measuring particle mass concentrations [7,8], making ...
Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution.
... The choice of particulate matter sensor for the MAQS project was the most important decision. We previously evaluated PM sensors for mobile measurements [8] and found suitable sensor for this project: PMS5003 (Plantower, Nanchang, China). Apart from being the low-cost sensor, PMS5003 demonstrated surprisingly good correlations with reference measurements of PM 2.5 under conditions of strong urban pollution in Bosnia and Herzegovina, which is illustrated in Figure 3. ...
... The filters were carried out through a stabilization and weighing procedures strictly according to the requirements of the standard EN 12341:2014. In this campaign, PMS5003 produced coefficient of determination R 2 = 0.9827 with reference measurements (for daily average concentrations of PM 2.5 ) and performed better than more expensive OPC-N2 (Alphasense, London, UK) sensor [8]. The absolute values are overpredicted by PMS5003, mostly due to the effect of the hygroscopic growth of aerosols as we explained in detail [8]. ...
... In this campaign, PMS5003 produced coefficient of determination R 2 = 0.9827 with reference measurements (for daily average concentrations of PM 2.5 ) and performed better than more expensive OPC-N2 (Alphasense, London, UK) sensor [8]. The absolute values are overpredicted by PMS5003, mostly due to the effect of the hygroscopic growth of aerosols as we explained in detail [8]. However, this behavior is suitable for corrections, especially if take into account good linearity of the sensor and no observable time drift in the long-term use [8]. ...
In this paper, we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of the presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over Sarajevo, Bosnia and Herzegovina. The system collects, calibrates, and displays particulate matter concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including, higher resolution in space and time, lower costs, both horizontal and vertical measurements. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope, we receive vertical measurements of temperature, humidity, and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.
... The filters were carried out through a stabilization and weighing procedures strictly according to the requirements of the standard EN 12341:2014. In this campaign PMS5003 produced coefficient of determination R 2 = 0.9827 with reference measurements (for daily average concentrations of PM 2.5 ), and performed better than more expensive OPC-N2 (Alphasense, UK) sensor [9]. The absolute values are overpredicted by PMS5003 , mostly due to effect of the hygroscopic growth of aerosols as we explained in details [9]. ...
... In this campaign PMS5003 produced coefficient of determination R 2 = 0.9827 with reference measurements (for daily average concentrations of PM 2.5 ), and performed better than more expensive OPC-N2 (Alphasense, UK) sensor [9]. The absolute values are overpredicted by PMS5003 , mostly due to effect of the hygroscopic growth of aerosols as we explained in details [9]. However, this behavior is suitable for corrections, especially if take into account good linearity of the sensor and no observable time drift in the long-term use [9]. ...
... The absolute values are overpredicted by PMS5003 , mostly due to effect of the hygroscopic growth of aerosols as we explained in details [9]. However, this behavior is suitable for corrections, especially if take into account good linearity of the sensor and no observable time drift in the long-term use [9]. PMS5003 is one of the most analyzed particulate matter sensors. ...
In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.
... Scattered light intensity is converted to particle number and mass concentration. Most optical PM sensors use Mie scattering theory to determine the size and number of particles in a fixed air volume ( Mie scattering theory provides the solution to Maxwell's equation for the scattering of plane waves by spherical particles (Masic et al. 2020). Unlike Rayleigh scattering, the Mie scattering expression is not as straightforward. ...
... The low-cost PM sensors measured PM1, PM2.5, and PM10 concentrations (µg/m³) every second. PMS 5003 sensor has been shown to exhibit a strong correlation with the gravimetric method, as demonstrated by A. Masic [35], while with beta attenuation monitor, PMS 5003 shows a coefficient of determination (R²) of 0.53, as reported by C. McFarlane [36]. Correlation analyses have been conducted on these LCS alongside the DustTrak 8433 TSI instrument for over two months to assess their consistency. ...
... When sedentary, they were instructed to "have it in the same room, as close as possible", as the device needed to be recharged every six to seven hours or continuously plugged into a power source. The pms5003 sensor consistently demonstrates accuracy in both short-term and long-term evaluations, exhibiting moderate to high correlation with reference instruments in various settings [66][67][68]. While some studies suggest minimal drift over time [68,69], others recommend regular calibration, especially in high-humidity environments [67]. ...
... The pms5003 sensor consistently demonstrates accuracy in both short-term and long-term evaluations, exhibiting moderate to high correlation with reference instruments in various settings [66][67][68]. While some studies suggest minimal drift over time [68,69], others recommend regular calibration, especially in high-humidity environments [67]. Additionally, the Plantower sensor recorded ambient temperature and humidity, and the ancillary GPS component provided data on speed of movement. ...
Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML’s potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.
