scholarly journals Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors

2020 ◽  
Author(s):  
C. Rajashekar Reddy ◽  
T. Mukku ◽  
A. Dwivedi ◽  
A. Rout ◽  
S. Chaudhari ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Spatio Temporal

scholarly journals Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3617 ◽  
Author(s):  
Hoochang Lee ◽  
Jiseock Kang ◽  
Sungjung Kim ◽  
Yunseok Im ◽  
Seungsung Yoo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Sensor Nodes ◽  
Lookup Table ◽  
Calibration Model ◽  
Statistical Parameters ◽  
Sensor Platform ◽  
Calibration Algorithm ◽  
Term Evaluation ◽  
Spatio Temporal

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

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 Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

2021 ◽  
Vol 11 (5) ◽  
pp. 2093
Author(s):  
Noé Perrotin ◽  
Nicolas Gardan ◽  
Arnaud Lesprillier ◽  
Clément Le Goff ◽  
Jean-Marc Seigneur ◽  
...  
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Step Length ◽  
Measurement Unit ◽  
Ecological Conditions ◽  
Sports Science ◽  
Ground Contact Time ◽  
Spatio Temporal ◽  
Performance Results ◽  
Trail Running

The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

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.

On the Spatio-Temporal Relationship Between MODIS AOD and PM2.5 Particulate Matter Measurements

2021 ◽  
Vol 12 (2) ◽  
pp. 255-275
Author(s):  
Aaron T. Porter ◽  
Jacob J. Oleson ◽  
Charles O. Stanier
Keyword(s):  
Particulate Matter ◽  
Temporal Relationship ◽  
Modis Aod ◽  
Spatio Temporal

How low-cost air pollution sensors could make homes smarter?

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Air Pollutants ◽  
Indoor Air Pollution ◽  
Prediction Models ◽  
Disease Risk ◽  
Low Cost ◽  
Mortality And Morbidity ◽  
Spatio Temporal ◽  
Indoor Air Pollutants

<p>The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.</p>

Research on Informatization Forecasting Particulate Matter Using Spatio-Temporal Data

2021 ◽  
Author(s):  
Qide Wu ◽  
Hongli Xu ◽  
Liusheng Huang ◽  
Shixuan Guan ◽  
Chunchen Lu
Keyword(s):  
Particulate Matter ◽  
Temporal Data ◽  
Spatio Temporal
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