Search Results (9)

Background: In response to the growing demand for the real-time, in-field characterization of odorous anthropogenic emissions, this study develops and uses a MEMS-based micropacked thermal desorption Gas Chromatography system coupled with a PhotoIonization Detector (GC/PID) for Hot Mix Asphalt (HMA) plant emissions. Methods: The innovative portable device, Pyxis GC, enables the high-sensitivity profiling of Volatile Organic Compounds (VOCs), particularly aldehydes and ketones, with sub-ppb detection limits using ambient air as the carrier gas. A comprehensive experimental design optimized the preconcentration parameters, resulting in an efficient, green analytical method evaluated via the Green Analytical Procedure Index (GAPI). Sorbent comparison showed quinoxaline-bridged cavitands outperform the conventional materials. Results and conclusions: The method was successfully deployed on site for source-specific sampling at an HMA plant, generating robust emission fingerprints. To assess environmental impact, a Generalized Additive Model (GAM) was developed, incorporating the process temperature and Sum of Odour Activity Values (SOAV) to predict odour concentrations. The model revealed a significant non-linear influence of temperature on emissions and validated its predictive capability despite the limited sample size. This integrated analytical–statistical approach demonstrates the utility of MEMS technology for real-time air quality assessment and odour dispersion modelling, offering a powerful tool for environmental monitoring and regulatory compliance. Full article

This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based sensor system can be sufficiently applied in the field. Full article

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m² meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source. Full article

Benzene, toluene and xylene (BTX) are an important part of the volatile organic compounds (VOCs) to be detected and monitored in the air, due to their toxicity towards human health. One of the most reliable technique used in BTX detection is gas chromatography (GC), which presents a high sensitivity. On the other hand, it has important drawbacks, such as high costs, the need for qualified personnel and frequent maintenance. To overcome these drawbacks, this work reports the development of a low cost and portable BTX gas detection system based on a mini chromatographic cartridge, a photo ionization detector (PID), a simple control unit (based on Arduino architecture) and a mini pump. In order to separate the BTX components, we propose the use of a cartridge 80 mm in length, composed of several commercial chromatographic column sections. To test the system performances, we have injected different amounts (from about 0.3 to 5.3 µg) of benzene, toluene and xylene and two of the most frequent possible interferents (ethanol, acetone). Experimental results have shown different retention time values (i.e., 25 ± 0.5 s, 51 ± 1.2 s and 117 ± 4 s, respectively) for benzene, toluene and xylene. Full article

Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID. Full article

A low cost portable photoionization (PID) sensor was successfully designed and manufactured. Unlike existing commercial PID sensors, our device provides two outputs, one associated with the total chemical components and a second that provides some level of compositional information. We believe that this makes this sensor system more useful than a standard PID, with a similar, if not lower, cost point. Our PID sensor was tested with gas concentrations down to 2 ppm isobutylene. These results indicate that the limit of detection will be well below 1 ppm. Further detection tests were carried out with ethanol, acetone and isobutylene, which showed similar sensitivities. Compositional measurements were also undertaken and the results presented shows our sensor can discriminate successfully between low concentration isobutylene and 2-pentanone. Full article

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented. Full article

A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use. Full article

We measured temporal changes in concentrations of total volatile organic compounds (TVOCs) and individual volatile organic compounds in a newly built daycare center. The temporal changes of the TVOC concentrations were monitored with a photo ionization detector (PID), and indoor air was sampled and analyzed by Gas Chromatography/Mass Spectrometry (GC/MS) and high performance liquid chromatography (HPLC) to determine the concentrations of the constituent VOCs. The measurements were performed just after completion of the building and again 3 months after completion. The TVOC concentration exceeded 1000 µg·m⁻³ for all the sampling locations just after completion of building, and decreased almost one tenth after 3 months, to below the guideline values of the TVOC in Japan at 400 µg·m⁻³. The concentrations of the target VOCs of which the indoor concentrations are regulated in Japan were below the guideline values for all the cases. The air-exchange rates were determined based on the temporal changes of the TVOC concentrations, and it was found that the countermeasure to increase the air exchange rate successfully decrease the TVOC concentration level in the rooms. Full article