Low cost HAB platform to measure particulate matter in the troposphere

2016 ◽  
Author(s):  
Mark J. Potosnak ◽  
Bernhard Beck-Winchatz ◽  
Paul Ritter ◽  
Emily Dawson
Keyword(s):  
Particulate Matter ◽  
Low Cost

scholarly journals Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wan-Sik Won ◽  
Rosy Oh ◽  
Woojoo Lee ◽  
Sungkwan Ku ◽  
Pei-Chen Su ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Light Scattering ◽  
Low Cost ◽  
Weather Type ◽  
Hygroscopic Growth ◽  
Weather Factors ◽  
Mass Growth ◽  
Hygroscopic Properties ◽  
Weather Observations ◽  
Censored Regression Model

AbstractThe hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, $$f\left( {RH} \right)$$ f RH , and the hygroscopic mass growth, $$GM_{VIS}$$ G M VIS , which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled $$f\left( {RH} \right)$$ f RH agreed well with the observed $$f\left( {RH} \right)$$ f RH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.

Characterization of the emission behavior of pulse-jet cleaned filters using a low-cost particulate matter sensor/Charakterisierung der Emission von druckstoßgereinigten Oberflächenfiltern mit einem Low-Cost-Feinstaubsensor

Gefahrstoffe ◽  
2019 ◽  
Vol 79 (11-12) ◽  
pp. 443-450
Author(s):  
P. Bächler ◽  
J. Meyer ◽  
A. Dittler
Keyword(s):  
Particle Size ◽  
Particulate Matter ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Low Cost ◽  
Particle Emission ◽  
Filter Medium ◽  
Emission Measurement ◽  
Pm2.5 Concentration ◽  
Pulse Jet

The reduction of fine dust emissions with pulse-jet cleaned filters plays an important role in industrial gas cleaning to meet emission standards and protect the environment. The dust emission of technical facilities is typically measured “end of pipe”, so that no information about the local emission contribution of individual filter elements exists. Cheap and compact low-cost sensors for the detection of particulate matter (PM) concentrations, which have been prominently applied for immission monitoring in recent years have the potential for emission measurement of filters to improve process monitoring. This publication discusses the suitability of a low-cost PM-sensor, the model SPS30 from the manufacturer Sensirion, in terms of the potential for particle emission measurement of surface filters in a filter test rig based on DIN ISO 11057. A Promo® 2000 in combination with a Welas® 2100 sensor serves as the optical reference device for the evaluation of the detected PM2.5 concentration and particle size distribution of the emission measured by the low-cost sensor. The Sensirion sensor shows qualitatively similar results of the detected PM2.5 emission as the low-cost sensor SDS011 from the manufacturer Nova Fitness, which was investigated by Schwarz et al. in a former study. The typical emission peak after jet-pulse cleaning of the filter, due to the penetration of particles through the filter medium, is detected during Δp-controlled operation. The particle size distribution calculated from the size resolved number concentrations of the low-cost sensor yields a distinct distribution for three different employed filter media and qualitatively fits the size distribution detected by the Palas® reference. The emission of these three different types of filter media can be distinguished clearly by the measured PM2.5 concentration and the emitted mass per cycle and filter area, demonstrating the potential for PM emission monitoring by the low-cost PM-sensor. During the period of Δt-controlled filter aging, a decreasing emission, caused by an increasing amount of stored particles in the filter medium, is detected. Due to the reduced particle emission after filter aging, the specified maximum concentration of the low-cost sensor is not exceeded so that coincidence is unlikely to affect the measurement results of the sensor for all but the very first stage of filter life.

Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors

2019 ◽  
Vol 245 ◽  
pp. 932-940 ◽  
Author(s):  
T. Sayahi ◽  
A. Butterfield ◽  
K.E. Kelly
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Field Evaluation ◽  
Term Field

scholarly journals Optical particulate matter sensors in PM2.5 measurements in atmospheric air

2018 ◽  
Vol 44 ◽  
pp. 00006 ◽  
Author(s):  
Marek Badura ◽  
Piotr Batog ◽  
Anetta Drzeniecka-Osiadacz ◽  
Piotr Modzel
Keyword(s):  
Particulate Matter ◽  
Temporal Resolution ◽  
Optical Sensors ◽  
Low Cost ◽  
Monitoring Systems ◽  
Atmospheric Air ◽  
The Public ◽  
Good Operation

Monitoring systems are needed to obtain information about particulate matter (PM) concentrations and to make such information accessible to the public. Small, low-cost, optical sensors could be used to improve the spatial and temporal resolution of PM data. The paper presents results of collocated comparison of four low-cost PM sensors and TEOM analyser, conducted from 20-08-2017 to 24-12-2017 in Wrocław, Poland. Plantower PMS7003 and Nova Fitness SDS011 sensors proved to be the best in terms of precision and were linearly correlated with TEOM data. Alphasense OPC-N2 sensors exhibited only moderate precision and linearity. Winsen ZH03A sensors had low repeatability between units and only one copy demonstrated good operation possibilities. All tested sensors had a bias in relation to PM2.5 concentrations obtained from TEOM.

scholarly journals A study on the development of a light scattering particulate matter sensor and monitoring system

2020 ◽  
Vol 17 (3) ◽  
pp. 867-890
Author(s):  
Jun-Hee Choi ◽  
Hyun-Sug Cho
Keyword(s):  
Big Data ◽  
Particulate Matter ◽  
Light Scattering ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Gravimetric Method ◽  
Correction Method ◽  
Measurement Methods ◽  
Linear Correction

The gravimetric method, which is mainly used among particulate matter (PM) measurement methods, includes the disadvantages that it cannot measure PM in real time and it requires expensive equipment. To overcome these disadvantages, we have developed a light scattering type PM sensor that can be manufactured at low cost and can measure PM in real time. We have built a big data system that can systematically store and analyze the data collected through the developed sensor, as well as an environment where PM states can be monitored mobile in real time using such data. In addition, additional studies were conducted to analyze and correct the collected big data to overcome the problem of low accuracy, which is a disadvantage of the light scattering type PM sensor. We used a linear correction method and proceeded to adopt the most suitable value based on error and accuracy.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Assessment of a Low-Cost Portable Proportional Exhaust Sampling System for Gravimetric Particulate Matter Emissions Measurement

2013 ◽  
Vol 11 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Michal Vojtíšek ◽  
Martin Pechout
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Sampling System ◽  
Particulate Matter Emissions

Shrnutí Částice obsažené ve výfukových plynech spalovacích motorů jsou jejich pro lidské zdraví nejvíce škodlivou složkou. Se snižující se celkovou hmotností emitovaných částic se zvyšují nároky na její měření, které vyžaduje plnoprůtočný ředicí tunnel nebo proporcionální vzorkovač s ředěním části toku s rychlou odezvou. Pro umožnění takových měření během jízdy vozidla a v méně vybavených laboratořích bylo vytvořeno nízkonákladové zařízení pro proporcionální vzorkování výfukových plynů. Zařízení využívá dvojici regulátorů hmotnostního průtoku, z nichž jeden dodává proměnlivé množství ředicího vzduchu do miniaturního ředicího tunelu, a druhý udržuje konstantní průtok směsi ředicího vzduchu a výfukových plynů přes filtr, na který jsou částice vzorkovány. Výsledky naměřené tímto systémem během dynamických jízdních cyklů jsou, po korekci systematického rozdílu, v rozmezí faktoru dvou od výsledků gravimetrické analýzy vzorků odebraných z klasického plnoprůtočného ředicího tunelu.

scholarly journals Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors

2019 ◽  
Vol 255 ◽  
pp. 113131 ◽  
Author(s):  
T. Sayahi ◽  
D. Kaufman ◽  
T. Becnel ◽  
K. Kaur ◽  
A.E. Butterfield ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Calibration Chamber

scholarly journals Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1406
Author(s):  
Rok Novak ◽  
David Kocman ◽  
Johanna Amalia Robinson ◽  
Tjaša Kanduč ◽  
Dimosthenis Sarigiannis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Particulate Matter ◽  
Low Cost ◽  
Minute Ventilation ◽  
Dose Assessment ◽  
Sensor Data ◽  
Activity Data ◽  
Time Activity ◽  
Activity Monitors ◽  
Coarse Information

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.

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