scholarly journals Inline Infrared Chemical Identification of Particulate Matter

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4193 ◽  
Author(s):  
Javier Núñez ◽  
Yunqi Wang ◽  
Stefan Bäumer ◽  
Arjen Boersma
Keyword(s):  
Particle Size ◽  
Particulate Matter ◽  
Chemical Nature ◽  
State Of The Art ◽  
Low Cost ◽  
Infrared Light ◽  
Hollow Waveguide ◽  
Health And Environmental Effects ◽  
Clear Differentiation ◽  
Pm2.5 And Pm10

The health and environmental effects of particulate matter (PM) in the air depend on several parameters. Besides particle size, shape, and concentration, the chemical nature of the PM is also of great importance. State-of-the-art PM sensors only detect the particle size and concentration. Small, low-cost sensors only identify PM according to PM2.5 and PM10 standards. Larger detectors measure the complete particle size distribution. However, the chemical composition of PM is not often assessed. The current paper presents the initial stages of the development of an infrared-based detector for the inline assessment of the chemistry of PM in the air. By combining a mini cyclone that is able to concentrate the particles at least a thousand fold and a hollow waveguide that aligns the flow of particles with infrared light, the feasibility of the concept was shown in this study. A clear differentiation between amorphous and crystalline silica was demonstrated at outdoor PM levels of lower than 1 mg per cubic meter.

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.

scholarly journals Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7290
Author(s):  
Miron Kaliszewski ◽  
Maksymilian Włodarski ◽  
Jarosław Młyńczak ◽  
Krzysztof Kopczyński
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Air Monitoring ◽  
Concentration Estimation ◽  
Air Purifier ◽  
Pm2.5 And Pm10 ◽  
Indoor Conditions ◽  
The Relationship ◽  
Beijing China ◽  
Matter Concentration

This study shows the results of air monitoring in high- and low-occupancy rooms using two combinations of sensors, AeroTrak8220(TSI)/OPC-N3 (AlphaSense, Great Notley, UK) and OPC-N3/PMS5003 (Plantower, Beijing, China), respectively. The tests were conducted in a flat in Warsaw during the restrictions imposed due to the COVID-19 lockdown. The results showed that OPC-N3 underestimates the PN (particle number concentration) by about 2–3 times compared to the AeroTrak8220. Subsequently, the OPC-N3 was compared with another low-cost sensor, the PMS5003. Both devices showed similar efficiency in PN estimation, whereas PM (particulate matter) concentration estimation differed significantly. Moreover, the relationship among the PM1–PM2.5–PM10 readings obtained with the PMS5003 appeared improbably linear regarding the natural indoor conditions. The correlation of PM concentrations obtained with the PMS5003 suggests an oversimplified calculation method of PM. The studies also demonstrated that PM1, PM2.5, and PM10 concentrations in the high- to low-occupancy rooms were about 3, 2, and 1.5 times, respectively. On the other hand, the use of an air purifier considerably reduced the PM concentrations to similar levels in both rooms. All the sensors showed that frying and toast-making were the major sources of particulate matter, about 10 times higher compared to average levels. Considerably lower particle levels were measured in the low-occupancy room.

scholarly journals Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2790 ◽  
Author(s):  
Andrea Di Antonio ◽  
Olalekan Popoola ◽  
Bin Ouyang ◽  
John Saffell ◽  
Roderic Jones
Keyword(s):  
Particle Size ◽  
Particulate Matter ◽  
Relative Humidity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Low Cost ◽  
Monitoring Networks ◽  
Ambient Particulate Matter ◽  
Correction Algorithm ◽  
Ambient Conditions

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

scholarly journals Laboratory evaluation of particle size-selectivity of optical low-cost particulate matter sensors

2019 ◽  
Author(s):  
Joel Kuula ◽  
Timo Mäkelä ◽  
Minna Aurela ◽  
Kimmo Teinilä ◽  
Samu Varjonen ◽  
...  
Keyword(s):  
Particle Size ◽  
Particulate Matter ◽  
Low Cost ◽  
Laboratory Evaluation ◽  
Source Analysis ◽  
Ambient Conditions ◽  
Size Selectivity ◽  
Monodisperse Particles ◽  
Aerosol Generation ◽  
On Line

Abstract. Low-cost particulate matter sensors (PM) have been under investigation due to their prospective nature regarding spatial extension of measurement coverage. While majority of the existing literature highlights that low-cost sensors can be useful in achieving this goal, it is often reminded that the risk of sensor misuse is still high, and that the data obtained from the sensors is only representative of the specific site and its ambient conditions. This implies that there are underlying reasons yet to be characterized which are causing inaccuracies in sensor measurements. The objective of this study was to investigate the particle size-selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-ld0101. The investigation of size-selectivity was carried out in laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes on-line. The results of the study showed that none of the low-cost sensors adhered exactly to the detection ranges declared by the manufacturers, and moreover, cursory comparison to a mid-cost aerosol spectrometer (GRIMM 1.108) indicated that the sensors could only achieve independent responses for 1–2 size bins whereas the spectrometer could sufficiently characterize particles with 15 different size bins. These observations provide insight and evidence to the notion that particle size-selectivity may have an essential role in the error source analysis of sensors.

Squeezing out and Characterization of Silicon from Sand by Mg-Thermite Reduction Process

2020 ◽  
Vol 12 ◽  
Author(s):  
Md. Shoriful Islam ◽  
M. A. Sattar ◽  
M. A. Halim ◽  
Md. Asadul Hoque ◽  
Abdul Quader ◽  
...  
Keyword(s):  
Particle Size ◽  
Silicon Content ◽  
Chemical Nature ◽  
Low Cost ◽  
Reduction Process ◽  
Environmentally Benign ◽  
X Ray Diffraction ◽  
X Ray ◽  
Padma River

Background: Sand is one of the efficient sources of Silicon. We get quite easily the plethora of sand from the river side, Bangladesh. Utilization of the superfluous sand can be assisted to enhance our economy. Methods: In this work, silicon is extracted from sand by metal–thermite reduction process and the sample of sand is collected from padma river Rajshahi, Bangladesh. The process is environmentally benign and low cost. The reduction of the sand was performed with Mg powder, and purification was done by leaching out with HCl and HF. We have studied the structural properties, chemical nature and physical morphology. Results and conclusion: X-ray Diffraction (XRD) confirmed that the presence of elemental Si in the samples produced by Mg-thermite reduction process and the particle size was found 25.72±1.3 nm in an average. Surface morphology has been studied using Scanning Electron Microscopy (SEM) and the particle size seemed around 30 to 40 nm which was comparable to the obtained particle size from XRD. Fourier transform infrared spectroscopy (FTIR) showed the presence of Si-Si bonding in the investigating materials. The chemical nature of the sand has been studied by X-ray Fluorescence (XRF) analysis. Silicon content of sand was found about maximum 80%.

scholarly journals Use of low-cost PM-sensors to determine the infiltration of outdoor particles into indoor environments

2019 ◽  
Vol 111 ◽  
pp. 02026
Author(s):  
Jan Drzymalla ◽  
Andreas Henne
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Indoor Environments ◽  
Research Project ◽  
Private Households ◽  
Particulate Matter Concentration ◽  
High Concentrations ◽  
Pm2.5 And Pm10 ◽  
The Relationship ◽  
Matter Concentration

Whether due to traffic, industry or private households – particulate matter enters our air every day and pollutes the air we breathe. When the term air pollution is used, hardly anyone ever thinks of the air inside their own home. However, many urban residences are located in the immediate vicinity of busy roads with high concentrations of particulate matter. Consequently, the outside concentration of fine dust has considerable influence on the indoor concentration. Given the fact that many people spend more than 90 % of their lifetime indoors, it is important to measure and understand particle transport from the outside to the inside in order to assess the effects of exposure to outdoor particles on human health. A two-room apartment near a main road in Leverkusen, North Rhine-Westphalia, Germany was used in the investigation in this research project. Particulate matter concentrations for PM2.5 and PM10 were measured simultaneously inside and outside of the building. Results are size-specific deposition rates, indoor/outdoor ratios and infiltration factors, which provide information on the relationship between internal and external concentrations and the associated health consequences. The particulate matter concentration was measured using low-cost PM-sensors, which were developed and calibrated within the scope of this research project.

scholarly journals Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler

2019 ◽  
Vol 11 (24) ◽  
pp. 7220 ◽  
Author(s):  
Sergio Trilles ◽  
Ana Belen Vicente ◽  
Pablo Juan ◽  
Francisco Ramos ◽  
Sergi Meseguer ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Linear Model ◽  
Low Cost ◽  
Additive Model ◽  
Quality Data ◽  
Outdoor Environments ◽  
Pm2.5 And Pm10 ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

A suitable and quick determination of air quality allows the population to be alerted with respect to high concentrations of pollutants. Recent advances in computer science have led to the development of a high number of low-cost sensors, improving the spatial and temporal resolution of air quality data while increasing the effectiveness of risk assessment. The main objective of this work is to perform a validation of a particulate matter (PM) sensor (HM-3301) in indoor and outdoor environments to study PM2.5 and PM10 concentrations. To date, this sensor has not been evaluated in real-world situations, and its data quality has not been documented. Here, the HM-3301 sensor is integrated into an Internet of things (IoT) platform to establish a permanent Internet connection. The validation is carried out using a reference sampler (LVS3 of Derenda) according to EN12341:2014. It is focused on statistical insight, and environmental conditions are not considered in this study. The ordinary Linear Model, the Generalized Linear Model, Locally Estimated Scatterplot Smoothing, and the Generalized Additive Model have been proposed to compare and contrast the outcomes. The low-cost sensor is highly correlated with the reference measure ( R 2 greater than 0.70), especially for PM2.5, with a very high accuracy value. In addition, there is a positive relationship between the two measurements, which can be appropriately fitted through the Locally Estimated Scatterplot Smoothing model.

scholarly journals APPLICATION OF LOW-COST OPTICAL PM SENSOR FOR MONITORING OF PARTICULATE MATTER AIR POLLUTION IN THE URBAN ENVIRONMENT: A CASE STUDY IN ESPLANĀDE HOUSING ESTATE, DAUGAVPILS CITY

2021 ◽  
Vol 1 ◽  
pp. 223-229
Author(s):  
Juris Soms ◽  
Haralds Soms
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Urban Environment ◽  
Meteorological Data ◽  
Low Cost ◽  
Average Concentration ◽  
Environmental Data ◽  
Housing Estate ◽  
Spatial Coverage ◽  
Pm2.5 And Pm10

The harmful health effects of airborne particulate matter (PM) pollutants are well-known. However, the spatial coverage of automated air quality observation stations of Latvian Environment, Geology and Meteorology Centre (LEGMC) is sparse. Therefore the capability for PM concentration detection was examined by using the low-cost optical PM sensor to improve the spatial resolution of environmental data. The aim of the study was to perform 24h/7d measurements of PM2.5 and PM10 concentrations during a period of one year and to identify air quality in Esplanāde housing estate, Daugavpils city. For data obtaining on the concentration of PM2.5 and PM10 particles measurements have been performed by optical sensor Nova SDS011; meteorological data were obtained using the database of LEGMC; for processing, analysis and visualization of obtained data statistical methods were applied. Evaluation of PM2.5 and PM10 daily average concentration variability in 2020 indicates that air quality in the urban environment could be assessed as good. A well-expressed statistical correlation between meteorological factors (t°C, relative humidity) and the average concentration of PM particles was not found. It highlights the necessity of further research.

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