1
|
Nafisah N, Syamsiana IN, Putri RI, Kusuma W, Sumari ADW. Implementation of fuzzy logic control algorithm for temperature control in robusta rotary dryer coffee bean dryer. MethodsX 2024; 12:102580. [PMID: 38322137 PMCID: PMC10844861 DOI: 10.1016/j.mex.2024.102580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Indonesia is one of the coffee producers ranked third in the world in the supply of coffee beans. To maintain competitiveness international market, it is necessary to maintain and improve the quality of coffee beans. OBJECTIVE One crucial aspect of maintaining the quality of coffee beans is maintaining the moisture content of green coffee beans. One of the water content settings is using the drying method. While traditional drying methods often experience weather and long-time constraints. RESULTS This study designed an innovative coffee bean dryer based on fuzzy logic to overcome the problem. This system uses temperature control with Mamdani's fuzzy logic control interference algorithm, input and delta errors, and output percentage valve opening. This method achieved a moisture content following SNI standards of 12% and a 0.00015% / s drying rate for each coffee bean mass increased by 1kg. This method is also more efficient and stable in maintaining the temperature at a value of 50°C. METHODS The drying equipment also estimates the drying time by considering variations in the mass of coffee beans. This dryer can provide an effective solution to maintain optimal coffee bean quality. CONCLUSION The second semi-wash method of drying coffee beans using a fuzzy logic-based coffee bean drier has proven successful for drying coffee beans to a moisture content of 12% in a period of 90 min to 195.65 min with a drying capacity of 1 kilogram to 10kg at 50°C.•The coffee beans utilized in the studies are robusta coffee beans from plantations on Mount Kawi's slopes in East Java, Indonesia.•The trial sample was 1 kilogram of green coffee beans removed from the horn skin.•According to SNI standards, the drying performed is the second in the postharvest semi-wash procedure to achieve a moisture content of 12%.
Collapse
Affiliation(s)
- Nihayatun Nafisah
- Department of Electrical Engineering, State Polytechnic of Malang, Malang, 65141, Indonesia
| | - Ika Noer Syamsiana
- Department of Electrical Engineering, State Polytechnic of Malang, Malang, 65141, Indonesia
| | - Ratna Ika Putri
- Department of Electrical Engineering, State Polytechnic of Malang, Malang, 65141, Indonesia
| | - Wijaya Kusuma
- Department of Electrical Engineering, State Polytechnic of Malang, Malang, 65141, Indonesia
| | - Arwin Datumaya Wahyudi Sumari
- Department of Electrical Engineering, State Polytechnic of Malang, Malang, 65141, Indonesia
- Faculty of Industrial Technology, Adisutjipto Institute of Aerospace Technology, Yogyakarta 55198, Indonesia
| |
Collapse
|
2
|
Song Y, Chen N, Jiang Q, Mukhopadhyay T, Wondmagegn W, Klausen RS, Katz HE. Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310527. [PMID: 38050933 DOI: 10.1002/smll.202310527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Indexed: 12/07/2023]
Abstract
This paper reports a new mechanism for particulate matter detection and identification. Three types of carbon particles are synthesized with different functional groups to mimic the real particulates in atmospheric aerosol. After exposing polymer-based organic devices in organic field effect transistor (OFET) architectures to the particle mist, the sensitivity and selectivity of the detection of different types of particles are shown by the current changes extracted from the transfer curves. The results indicate that the sensitivity of the devices is related to the structure and functional groups of the organic semiconducting layers, as well as the morphology. The predominant response is simulated by a model that yielded values of charge carrier density increase and charge carriers delivered per unit mass of particles. The research points out that polymer semiconductor devices have the ability to selectively detect particles with multiple functional groups, which reveals a future direction for selective detection of particulate matter.
Collapse
Affiliation(s)
- Yunjia Song
- Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Nan Chen
- Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Qifeng Jiang
- Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Tushita Mukhopadhyay
- Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Wudyalew Wondmagegn
- Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ, 08628, USA
| | - Rebekka S Klausen
- Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Howard E Katz
- Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218, USA
- Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| |
Collapse
|
3
|
Wiora A, Wiora J, Kasprzyk J. Indication Variability of the Particulate Matter Sensors Dependent on Their Location. SENSORS (BASEL, SWITZERLAND) 2024; 24:1683. [PMID: 38475219 DOI: 10.3390/s24051683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/20/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.
Collapse
Affiliation(s)
- Alicja Wiora
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| | - Józef Wiora
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| | - Jerzy Kasprzyk
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| |
Collapse
|
4
|
Hassani A, Schneider P, Vogt M, Castell N. Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15162-15172. [PMID: 37756014 PMCID: PMC10569052 DOI: 10.1021/acs.est.3c03661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.
Collapse
Affiliation(s)
- Amirhossein Hassani
- The Climate and Environmental
Research Institute NILU, P.O. Box 100, Kjeller 2027, Norway
| | - Philipp Schneider
- The Climate and Environmental
Research Institute NILU, P.O. Box 100, Kjeller 2027, Norway
| | - Matthias Vogt
- The Climate and Environmental
Research Institute NILU, P.O. Box 100, Kjeller 2027, Norway
| | - Núria Castell
- The Climate and Environmental
Research Institute NILU, P.O. Box 100, Kjeller 2027, Norway
| |
Collapse
|
5
|
Suriano D, Prato M. An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3976. [PMID: 37112317 PMCID: PMC10143454 DOI: 10.3390/s23083976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/11/2023] [Indexed: 06/19/2023]
Abstract
In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R2, RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R2) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m3 to 12.09 µg/m3. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.
Collapse
|
6
|
Kappelt N, Russell HS, Fessa D, Ryswyk KV, Hertel O, Johnson MS. Particulate air pollution in the Copenhagen metro part 1: Mass concentrations and ventilation. ENVIRONMENT INTERNATIONAL 2023; 171:107621. [PMID: 36493608 DOI: 10.1016/j.envint.2022.107621] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/12/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
The Copenhagen Metro comprises four lines, the M1, M2, M3 and M4, with 25 subterranean stations and an additional 14 stations above ground, serving ca. 80 million passengers annually. In this study we measure fine particulate matter (PM2.5) and carbon dioxide (CO2) concentrations in stations and in trains across the entire system. In partially underground lines, high PM2.5 concentrations with an average of 109 μg m-3 are found in below-ground stations. The observed correlation between PM2.5 concentration and distance between a station and a tunnel exit is attributed to ventilation via the piston effect. The piston effect via tunnel draught relief shafts was therefore found to be relatively limited. Filter samples of particulate matter are analysed using particle-induced X-ray emission and show an iron content of 88.6 % by mass which is quite different from above-ground particulate matter and consistent with particle production by train wheels, rails and brakes. The average concentration measured at the stations of a recently opened (2019) fully underground M3 closed loop line is 168 μg m-3, further demonstrating that while piston effect-driven ventilation is effective in close proximity to tunnel openings, it is relatively limited via tunnel draught relief shafts. Measurements onboard trains show even higher PM2.5 concentrations and the patterns in CO2 concentrations suggest carriage ventilation by tunnel air. Ventilation via doors during platform stops caused a drop in observed PM (and CO2) at stations, but the system is surprisingly polluted despite its recent construction. CO2 mixing ratios ranged from ambient to around 600 ppm. Measures should be taken to control PM levels using a combination of source control and increased clean air supply of the Copenhagen and other similar metro systems.
Collapse
Affiliation(s)
- Niklas Kappelt
- Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark; Airlabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
| | - Hugo S Russell
- Airlabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark; Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Dafni Fessa
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Keith Van Ryswyk
- Air Health Science Division, Health Canada, Ottawa K1A 0K9, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto M5S 3E5, Canada
| | - Ole Hertel
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark; Department of Ecoscience, Aarhus University, DK-4000 Roskilde, Denmark
| | - Matthew S Johnson
- Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark; Airlabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark.
| |
Collapse
|
7
|
Russell HS, Kappelt N, Fessa D, Frederickson LB, Bagkis E, Apostolidis P, Karatzas K, Schmidt JA, Hertel O, Johnson MS. Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification. ENVIRONMENT INTERNATIONAL 2022; 170:107645. [PMID: 36434885 DOI: 10.1016/j.envint.2022.107645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/12/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
In this study fine particulate matter (PM2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R2 values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.
Collapse
Affiliation(s)
- Hugo S Russell
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark; AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Niklas Kappelt
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark
| | - Dafni Fessa
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Louise B Frederickson
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark; AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Evangelos Bagkis
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | - Pantelis Apostolidis
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | - Kostas Karatzas
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | | | - Ole Hertel
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark; Department of Ecoscience, Aarhus University, DK-4000 Roskilde, Denmark
| | - Matthew S Johnson
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark.
| |
Collapse
|
8
|
Wei S, Semple S. Exposure to fine particulate matter (PM 2.5) from non-tobacco sources in homes within high-income countries: a systematic review. AIR QUALITY, ATMOSPHERE, & HEALTH 2022; 16:553-566. [PMID: 36467893 PMCID: PMC9703437 DOI: 10.1007/s11869-022-01288-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 11/19/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED The health impacts associated with exposure to elevated concentrations of fine particulate matter (PM2.5) are well recognised. There is a substantial number of studies characterising PM2.5 concentrations outdoors, as well as in homes within low- and middle-income countries. In high-income countries (HICs), there is a sizeable literature on indoor PM2.5 relating to smoking, but the evidence on exposure to PM2.5 generated from non-tobacco sources in homes is sparse. This is especially relevant as people living in HICs spend the majority of their time at home, and in the northern hemisphere households often have low air exchange rates for energy efficiency. This review identified 49 studies that described indoor PM2.5 concentrations generated from a variety of common household sources in real-life home settings in HICs. These included wood/solid fuel burning appliances, cooking, candles, incense, cleaning and humidifiers. The reported concentrations varied widely, both between sources and within groups of the same source. The burning of solid fuels was found to generate the highest indoor PM2.5 concentrations. On occasion, other sources were also reported to be responsible for high PM2.5 concentrations; however, this was only in a few select examples. This review also highlights the many inconsistencies in the ways data are collected and reported. The variable methods of measurement and reporting make comparison and interpretation of data difficult. There is a need for standardisation of methods and agreed contextual data to make household PM2.5 data more useful in epidemiological studies and aid comparison of the impact of different interventions and policies. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11869-022-01288-8.
Collapse
Affiliation(s)
- Shuying Wei
- Faculty of Health Sciences and Sport, University of Stirling, Stirling, FK9 4LA UK
| | - Sean Semple
- Institute for Social Marketing and Health, University of Stirling, Stirling, FK9 4LA UK
| |
Collapse
|
9
|
A Real-Time Approach to Detect PM2.5 in a Seriously Polluted Environment Based on Pressure Drop. ATMOSPHERE 2022. [DOI: 10.3390/atmos13081237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
A differential pressure-based low-cost PM2.5 detection system was developed for particulate matter measurement in polluted environments. The PM2.5 monitor consists of a sampling device, a pump, a pressure sensor, and a control circuit. Two sampling devices including a foam penetration-filter tube and a cyclone-filter holder were applied. Tests were conducted in a haze environment and laboratory particle chambers with varying PM2.5 concentration. The pressure data were related to the PM2.5 concentration recorded by Dusttrak to show the calibration process and the performance of this instrument. Results showed the concentration information given by the instrument was consistent with the actual concentration in the experiment, and this instrument was more suitable for seriously polluted environment detection. Concentration oscillation of the pressure-based PM2.5 monitor caused by turbulent flow could be reduced by a longer calculation interval and data averaging in the calculation process. As a low-cost sensor, the pressure-based PM2.5 monitor still has good performance and application value for detecting high-concentration PM2.5 in atmospheric environments or workplaces.
Collapse
|
10
|
Danek T, Weglinska E, Zareba M. The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland. Sci Rep 2022; 12:11050. [PMID: 35773386 PMCID: PMC9244891 DOI: 10.1038/s41598-022-15160-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/20/2022] [Indexed: 11/10/2022] Open
Abstract
Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.
Collapse
Affiliation(s)
- Tomasz Danek
- Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059, Kraków, Malopolska, Poland
| | - Elzbieta Weglinska
- Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059, Kraków, Malopolska, Poland.
| | - Mateusz Zareba
- Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059, Kraków, Malopolska, Poland
| |
Collapse
|
11
|
Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor. ENERGIES 2022. [DOI: 10.3390/en15061962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).
Collapse
|
12
|
Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System. ATMOSPHERE 2021. [DOI: 10.3390/atmos12101306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms.
Collapse
|