Low-cost monitoring of atmospheric PM—development and testing

Báthory, Csongor; Dobó, Zsolt; Garami, Attila; Palotás, Árpád Bence; Tóth, Pál

doi:10.1016/j.jenvman.2021.114158

Cited by 14 publications

(5 citation statements)

References 30 publications

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“…A bemutatott összefüggések segítségével kiküszöbölhető ez a mérési hiba. Szakirodalmi áttekintés során több olyan cikket is találtunk, amelyek hasonló, olcsó, kompakt érzékelőkre épülő, használatra kész megoldásokat, eszközöket mutattak be (Báthory et al, 2022). Eredményeimet összefoglalva elmondható, hogy ezen érzékelők kalibrálása releváns kutatási terület, amelyet jelenleg nagyon aktívan tanulmányoznak.…”

Section: Bevezetésunclassified

Légminőség hosszú távú mérése különböző szenzorokkal

Kiss

Pintér

Veres

2022

MDT

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Napjainkban egyre fontosabbá válik városaink levegőjének minősége, amelyet a fűtés, a közlekedés és az iparosodás is befolyásol. Magyarországon a nemzeti levegőminőség-mérési rendszer, amely hitelesített laboratóriumi műszereket alkalmazásával –köztük részecskeérzékelővel is - méri a levegő minőségét. Ezek a műszerek költségesek, és nem rendelkezünk elegendő mérőállomással a nagy felbontású mérésekhez. Ha több mérési pont és adat állna rendelkezésre, akkor egyértelmű tájékoztatást tudnánk nyújtani a nyilvánosságnak a levegő minőségről egy online anomáliatérképen keresztül. Napjainkban egyre több olcsó részecskeérzékelő válik hozzáférhetővé a piacon. Teszteltünk néhány különböző típusú részecskeérzékelőt, amelyek közül a PMS7003-at találtuk a legalkalmasabbnak, hiszen korrelációban képes mérni a hitelesített műszerekkel, de nem volt információnk arról, hogy hogyan működik a hosszú távú szabadtéri mérések során. Kifejlesztettünk egy kis mérőállomást és egy hitelesített műszer mellé telepítettük, így már több mint egyévnyi mérési adat áll rendelkezésünkre. Ebben a cikkben szeretnénk bemutatni a mérési adatok összehasonlítását a pontos műszerrel mért adatokkal.

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Section: Bevezetésunclassified

Légminőség hosszú távú mérése különböző szenzorokkal

Kiss

Pintér

Veres

2022

MDT

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“…Recently, low-cost sensors (LCSs) have been increasingly utilized as alternatives for high-resolution PM monitoring [8,9]. LCSs are economical and small and can be densely deployed, allowing the observation of PM with high spatial resolution [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…LCSs are economical and small and can be densely deployed, allowing the observation of PM with high spatial resolution [9,10]. However, the PM LCS is relatively inaccurate compared with national observatories [8,9,11]. Calibration methodologies have been proposed to improve the accuracy of the PM LCS.…”

Section: Introductionmentioning

confidence: 99%

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Kang,

Choi

2024

Sensors

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Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

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“…Advanced regression methods such as machine learning models are expected due to their better capacity for handling large amounts of data [ 26 , 27 ]. Even within the same field as machine learning models, there is a great variety in the strengths and calculation capacities of the methods, which warrants further comparisons [ 28 , 29 , 30 , 31 ]. Integrating all these factors into the validation of sensor monitors will contribute to promoting the performance of sensor-based particle measurements.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods

Tang,

Cai,

Gao

et al. 2024

Sensors

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Objective: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. Method: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. Results: Indoor particles were measured in a range of 0.8–370.7 μg/m3 and 1.9–465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0–211.0 μg/m3 and 0.0–493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively. Conclusions: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

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Low-cost monitoring of atmospheric PM—development and testing

Cited by 14 publications

References 30 publications

Légminőség hosszú távú mérése különböző szenzorokkal

Légminőség hosszú távú mérése különböző szenzorokkal

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods

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