scholarly journals Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

2021 ◽  
Vol 9 ◽  
Author(s):  
E. Gramsch ◽  
P. Oyola ◽  
F. Reyes ◽  
Y. Vásquez ◽  
M. A. Rubio ◽  
...  

In the last decade, many low-cost monitoring sensors and sensor-networks have been used as an alternative air quality assessment method. It is also well known that these low cost monitors have calibration, accuracy and long term variation problems which require various calibration techniques. In this work PM2.5 and PM10 low cost sensors (Plantower and Nova Fitness) have been tested in five cities under different environmental conditions and compared with collocated standard instruments. Simultaneously, particle composition (organic and black carbon, sulfate, nitrate, chloride, ammonium, and chemical elements) has been measured in the same places to study its influence on the accuracy. The results show a very large variability in the correlation between the low cost sensors and collocated standard instruments depending on the composition and size of particles present in the site. The PM10 correlation coefficient (R2) between the low cost sensor and a collocated regulatory instrument varied from to 0.95 in Temuco to 0.04 in Los Caleos. PM2.5 correlation varied from 0.97 to 0.68 in the same places. It was found that sites that had higher proportion of large particles had lower correlation between the low cost sensor and the regulatory instrument. Sites that had higher relative concentration of organic and black carbon had better correlation because these species are mostly below the 1 μm size range. Sites that had higher sulfate, nitrate or SiO2 concentrations in PM2.5 or PM10 had low correlation most likely because these particles have a scattering coefficients that depends on its size or composition, thus they can be classified incorrectly.

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 112 ◽  
Author(s):  
Andrzej Przybylak ◽  
Radosław Kozłowski ◽  
Ewa Osuch ◽  
Andrzej Osuch ◽  
Piotr Rybacki ◽  
...  

This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.


2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Zedong Wang ◽  
Jing Wang ◽  
Fei Wang ◽  
Chengcai Li ◽  
Zesong Fei ◽  
...  

2014 ◽  
Vol 24 (1) ◽  
pp. 213-225 ◽  
Author(s):  
Piotr Szwed ◽  
Paweł Skrzyński

Abstract For contemporary software systems, security is considered to be a key quality factor and the analysis of IT security risk becomes an indispensable stage during software deployment. However, performing risk assessment according to methodologies and standards issued for the public sector or large institutions can be too costly and time consuming. Current business practice tends to circumvent risk assessment by defining sets of standard safeguards and applying them to all developed systems. This leads to a substantial gap: threats are not re-evaluated for particular systems and the selection of security functions is not based on risk models. This paper discusses a new lightweight risk assessment method aimed at filling this gap. In this proposal, Fuzzy Cognitive Maps (FCMs) are used to capture dependencies between assets, and FCM-based reasoning is performed to calculate risks. An application of the method is studied using an example of an e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on the effectiveness of the security system.


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