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"Air sampling apparatus."
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Real-time AIoT platform for monitoring and prediction of air quality in Southwestern Morocco
Urbanization and industrialization have led to a significant increase in air pollution, posing a severe environmental and public health threat. Accurate forecasting of air quality is crucial for policymakers to implement effective interventions. This study presents a novel AIoT platform specifically designed for PM
2.5
monitoring in Southwestern Morocco. The platform utilizes low-cost sensors to collect air quality data, transmitted via WiFi/3G for analysis and prediction on a central server. We focused on identifying optimal features for PM
2.5
prediction using Minimum Redundancy Maximum Relevance (mRMR) and LightGBM Recursive Feature Elimination (LightGBM-RFE) techniques. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters of popular machine learning models for the most accurate PM
2.5
concentration forecasts. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R
2
). Our results demonstrate that the LightGBM model achieved superior performance in PM
2.5
prediction, with a significant reduction in RMSE compared to other evaluated models. This study highlights the potential of AIoT platforms coupled with advanced feature selection and hyperparameter optimization for effective air quality monitoring and forecasting.
Journal Article
Key Concerns and Drivers of Low-Cost Air Quality Sensor Use
Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.
Journal Article
Design and Evaluation of ACFC—An Automatic Cloud/Fog Collector
Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain.
Journal Article
Influence of Field Sampling Methods on Measuring Volatile Organic Compounds in a Swine Facility Using SUMMA Canisters
Volatile organic compounds (VOCs) play a crucial role in emission control, being one of the most important sources of odor while also serving as significant precursors to secondary organic aerosols and ozone formation. Appropriate sampling methods are essential for accurately assessing the concentration and composition of VOCs within swine barns. In this study, the effects of both passive air sampling and active air sampling on VOCs were evaluated, and the influence of storage time on the VOC stability in sampling canisters for both methods was investigated. SUMMA canisters, which are electropolished and passivated with silanization, offer excellent corrosion protection and resistance to high pressure and temperature and were used in this study. The predominant component categories prevailing within the pig house were found to be oxygenated VOCs (OVOCs) and volatile sulfur compounds (VSCs), with ethanol emerging as the most abundant component of VOCs detected. Notably, the statistical analysis results revealed no significant differences between passive and active sampling regarding the impact of storage time on substance concentration. Changes in canister pressure also did not significantly affect substance stability. The results showed that the C2–C3 compounds remained relatively stable, especially within 3 days, with recoveries above 80% within 20 days. Methyl sulfide, dimethyl disulfide, and ethanol were more stable within the first week, but their recoveries significantly dropped by day 20, with methyl sulfide and dimethyl disulfide at 62.3% and 65.3%, respectively. This study contributes to the development of a foundation for selecting appropriate VOC sampling methods in swine facilities for conducting a rational analysis of VOC samples.
Journal Article
Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models
Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R2 improved from 0.22 to 0.86. Similar results were obtained for O3, wherein the mean error was reduced from 13 to 4.3 ppb and R2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants.
Journal Article
Evaluation of four sampling devices for Burkholderia pseudomallei laboratory aerosol studies
Previous field and laboratory studies investigating airborne
Burkholderia pseudomallei
have used a variety of different aerosol samplers to detect and quantify concentrations of the bacteria in aerosols. However, the performance of aerosol samplers can vary in their ability to preserve the viability of collected microorganisms, depending on the resistance of the organisms to impaction, desiccation, or other stresses associated with the sampling process. Consequently, sampler selection is critical to maximizing the probability of detecting viable microorganisms in collected air samples in field studies and for accurate determination of aerosol concentrations in laboratory studies. To inform such decisions, the present study assessed the performance of four laboratory aerosol samplers, specifically the all-glass impinger (AGI), gelatin filter, midget impinger, and Mercer cascade impactor, for collecting aerosols containing
B
.
pseudomallei
generated from suspensions in two types of culture media. The results suggest that the relative performance of the sampling devices is dependent on the suspension medium utilized for aerosolization. Performance across the four samplers was similar for aerosols generated from suspensions supplemented with 4% glycerol. However, for aerosols generated from suspensions without glycerol, use of the filter sampler or an impactor resulted in significantly lower estimates of the viable aerosol concentration than those obtained with either the AGI or midget impinger. These results demonstrate that sampler selection has the potential to affect estimation of doses in inhalational animal models of melioidosis, as well as the likelihood of detection of viable
B
.
pseudomallei
in the environment, and will be useful to inform design of future laboratory and field studies.
Journal Article
Airborne Pollen, Allergens, and Proteins: A Comparative Study of Three Sampling Methods
Nowadays, there is a wide range of different methods available for the monitoring of pollen and allergens, but their relative efficiency is sometimes unclear, as conventional pollen monitoring does not thoroughly describe pollen allergenicity. This study aims to evaluate airborne pollen, allergen, and protein levels, associating them with meteorological and chemical parameters. The sampling was performed in Bologna (Italy) during the grass flowering period, with three different devices: a Cyclone sampler (CS), a Dicothomous sampler (DS), and a Berner impactor (BI). Total proteins were extracted from aerosol samples, and grass allergens Phl p 1 and Phl p 5 were quantified by ELISA. Airborne Poaceae pollen concentrations were also evaluated, using a Hirst-type trap. Proteins and allergens collected by CS resulted about ten times higher than those collected by the other two instruments, possibly due to their different cut-offs, while DS and BI results appeared consistent only for the total proteins collected in the fine fraction (1.3 vs. 1.6 μg/m3). Airborne proteins correlated neither with Poaceae pollen nor with its aeroallergens, while aeroallergens correlated with pollen only in the coarse particulate, indicating the presence of pollen-independent aeroallergens in the fine particulate, promoted by high wind speed.
Journal Article
Locating Low-Cost Air Quality Monitoring Devices in Low-Resource Regions Is Not Enough to Acquire Robust Air Quality Data Usable for Policy Decisions
Air quality monitoring (AQM) is key to maintaining healthy air in cities. This is crucial in low- and middle-income countries due to increasing evidence of poor air quality but lack of monitors to consistently collect evaluate air quality data and effect policy changes, mainly because of the costs of monitoring devices. In participating in a challenge for the development of low-cost AQM devices in low-resource regions, an Arduino-based device with sensors for particulate matter size, temperature, and humidity data acquisition was developed for deployment in Port Harcourt, a city in Nigeria’s Niger Delta region, exposed to poor air quality partly due to gas and oil production activities. During the project, challenges to AQM were encountered, including inadequate awareness of air quality issues, lack of necessary AQM device components, unavailability of trained manpower and partnerships, and lack of funding. However, lack of a means of calibrating the device was a major hindrance, as no reference AQM instrument was available, rendering the data acquired largely qualitative, educational, and useless for regulatory purposes. There is an urgent need for AQM in such cities. However, a robust AQM strategy must be designed and used to address these constraints, especially whilst using low-cost devices, for significant progress in acquiring robust air quality data in such low-resource regions to be made.
Journal Article