Assessment of Low-Cost Particulate Matter Sensor Systems against Optical and Gravimetric Methods in a Field Co-Location in Norway

Implementation of fuzzy logic control algorithm for temperature control in robusta rotary dryer coffee bean dryer

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%.

Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors

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.

Indication Variability of the Particulate Matter Sensors Dependent on Their Location

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.

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning

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.

An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring

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.

Particulate air pollution in the Copenhagen metro part 1: Mass concentrations and ventilation

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 (PM_2.5) and carbon dioxide (CO₂) concentrations in stations and in trains across the entire system. In partially underground lines, high PM_2.5 concentrations with an average of 109 μg m^-3 are found in below-ground stations. The observed correlation between PM_2.5 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 PM_2.5 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.

Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification

In this study fine particulate matter (PM_2.5) 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 PM_2.5 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.

Exposure to fine particulate matter (PM2.5) from non-tobacco sources in homes within high-income countries: a systematic review

The health impacts associated with exposure to elevated concentrations of fine particulate matter (PM_2.5) are well recognised. There is a substantial number of studies characterising PM_2.5 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 PM_2.5 relating to smoking, but the evidence on exposure to PM_2.5 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 PM_2.5 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 PM_2.5 concentrations. On occasion, other sources were also reported to be responsible for high PM_2.5 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 PM_2.5 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.

A Real-Time Approach to Detect PM2.5 in a Seriously Polluted Environment Based on Pressure Drop

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.

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland

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.

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

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%).

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

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.