Humidity Measurements Research Articles

A Polyimide-Coated Fiber Bragg Grating Sensor for Multidepth Soil Humidity Measurement

A Polyimide-Coated Fiber Bragg Grating Sensor for Multidepth Soil Humidity Measurement

Leveraging artificial neural networks for air humidity measurement in air conditioning systems

Accurate measurement of air humidity serves as the foundation for the air conditioning system to achieve and maintain optimal indoor thermal environments. The current humidity sensors, due to their complexity, high cost, and susceptibility to contamination, are deemed unsuitable for humidity testing in the practical deployment of air conditioning systems. This underscores the need for the evolution of novel humidity measurement techniques. This study presented the development of an indirect method for measuring air relative humidity by evaluating certain readily available parameters. The underlying principle is the high degree of coupling in the heat and mass transfer occurring on the evaporator. This coupling facilitates the derivation of the relative humidity of the air at the air conditioning system's inlet from parameters such as temperature and air volume. In an effort to swiftly and accurately derive the relative humidity of the air from numerous parameters, this study employed the artificial neural networks (ANN) methodology and constructed an ANN model. Upon comparison with experimental data, it was discerned that the maximum absolute error in all prediction results was below 4.5% RH, with a substantial proportion falling below 2.5% RH. Further research corroborated that the inclusion of the degree of refrigerant superheat as a training parameter for the model exerted negligible influence on prediction accuracy. The prediction results yielded RMSE values of 0.72% and 0.71% respectively. These findings suggest that the proposed method exhibits a high degree of accuracy, thereby demonstrating its potential applicability in the field testing of air conditioning systems.

Design, fabrication, automation, and scaleup of anaerobic reactors for waste management and bioenergy recovery

AbstractDigitally controlled reactors can optimize biological reactions and process control through a neural network system. This study reports on the design, fabrication, and automation of a laboratory‐scale anaerobic reactor for the management of agrifood byproducts and bioenergy recovery. The process described here can digitally control the operational parameters, which is beneficial for stable methane production. The proposed process comprises the digital measurement of temperature, pH, humidity, biogas volume, and methane composition by integrating the data in a processor module. The proposed automated reactor can assist significantly in controlling and monitoring the anaerobic digestion process, providing decision making during waste management and bioenergy recovery. A case study is described with the application of automated reactors in a pilot‐scale plant, operated with the flow of 8 m3 slaughterhouse wastewater per day and a biogas production of 10 m3 h−1. The automated pilot‐scale process presents many advantages, including a continuous mode of operation and a faster adaptation of the microorganisms to the substrate, improving biogas production.

Development and Testing of a Rocket-Based Sensor for Atmospheric Sensing Using an Unmanned Aerial System.

Measurements of the vertical structure of the lower atmosphere are important to the understanding of air quality. Unmanned Aerial Systems (UASs, drones) can provide low cost, repeatable measurements of the temperature, pressure, and relative humidity. A set of inexpensive sensors controlled with an Arduino microprocessor board were tested on a UAS against a meteorology grade sensor. Two modes of operation for sampling were tested: a forward moving sampler and a vertical ascent sampler. A small particle sensor (Sensiron SPS30) was integrated and was capable of retrieving vertical aerosol distributions during an inversion event. The thermocouple-based temperature probe and the relative humidity measurement on the Bosch BME280 sensor correlated well with the meteorological sensor. The temperature and relative humidity sensors were then deployed on a rocket sounding platform. The rocket sounding system performed well up to a height of 400 m. The inexpensive sensors were found to perform adequately for low-cost development and uses in education and research.

Influence of Anodic Aluminum Oxide Nanostructures on Resistive Humidity Sensing

Humidity nanosensors play a vital role in modern technology industries, including weather forecasts, industrial manufacturing, agriculture, food and chemistry storage. In recent years, research on humidity sensors has focused on different materials such as ceramics, polymers, carbon-based materials, semiconductors, MXenes or triboelectric nanogenerators, each with their own advantages and disadvantages. Among them, anodic aluminum oxide (AAO) is a well-known ceramic humidity sensor material with a long history of research and development. AAO humidity sensors offer advantages such as simple manufacturing processes, controllable nanostructures, high thermal stability and biocompatibility. However, traditional AAO fabrication still has disadvantages like high costs and longer process times. Hence, finding a low-cost and efficient method to fabricate AAO for controlling different nanostructures to meet the requirements is consistently a major research topic. From our previous studies, we have studied the relationship between the AAO capacitive humidity sensor and its nanostructures. In this paper, we explore the effect of an AAO nanoporous structure controlled by an anodization voltage of 20–40 V on the resistive-type humidity sensor performance instead of a capacitive one. We efficiently apply one-step hybrid pulse anodization at 25 °C to significantly reduce the processing time compared to the traditional two-step process under 0–10 °C. The AAO nanostructures and their impact on sensor measurements of humidity at 20–80 RH% will be discussed in detail. An electrical resistive sensing mechanism is established for further performance improvement by controlling anodization voltage.

Embedded Wireless Sensor for In Situ Concrete Internal Relative Humidity Monitoring.

The moisture content within the concrete pore network significantly influences the mechanical, thermal, and durability characteristics of concrete structures. This paper introduces a novel fully embedded wireless temperature and relative humidity sensor connected to an automatic acquisition system designed for continuous concrete monitoring. Relative humidity measurements from this new sensor are compared with those obtained by a commercial system based on the borehole method at different depths (2.5 and 4.0 cm) and exposure conditions (oven drying and humid chamber). The results allow for proving that both systems provide consistent internal relative humidity measurements aligned with the exposure conditions and highlight the capability of fully embedded wireless sensors as a practical and reliable alternative to the conventional borehole method. Additionally, the continuous monitoring of the wireless cast-in sensor exhibits reliability during unintended temperature fluctuations, emphasizing the effectiveness of permanently installed sensors in promptly detecting unintended curing variations in real time. The continuous real-time information provided combined with the practicality of these sensors might assist construction managers to improve the quality control of the concrete curing process and shrinkage behavior, and ensure the integrity of concrete surface finishing.

Prediksi Kenaikan Awan Di Wisata Lolai Berbasis Machine Learning

The increase in cloud cover is an important indicator in predicting upcoming weather. However, manual observations of cloud cover are still limited and time-consuming. Therefore, this research aims to develop a cloud cover classification model based on measurement data in Lolai using the Naive Bayes machine learning method. In this study, data on cloud cover, temperature, and humidity measurements were collected directly in Lolai for 30 days and using online BMKG data. Then, the data was processed and divided into training and testing datasets. The Naive Bayes model was applied to the training data and its accuracy was tested on the testing data. The research results show that the cloud cover classification model based on Naive Bayes has varying accuracy levels depending on the data source. For direct measurement data, the model achieved an accuracy rate of 63%, while for online BMKG data, the model achieved an accuracy rate of 80%. In testing on the testing data, the model successfully classified cloud cover based on temperature and humidity data. This research contributes to identifying the relationship between temperature, humidity, and cloud conditions and evaluates the performance of the Naive Bayes model in determining the influence of air temperature and humidity on cloud conditions. It is expected that this research can serve as a basis for the development of weather prediction systems in the future.

Troposphere delay modeling in SLR based on PMF, VMF3o, and meteorological data

Satellite laser ranging (SLR) requires accurate troposphere delay models to properly correct the observed distances to satellites and derive fundamental geodetic and geodynamic parameters. The currently used models for the tropospheric delay employ in situ meteorological data collected simultaneously with laser measurements. However, the standard models assume full symmetry of the atmosphere above the SLR stations because all meteorological data come from one sensor. In this study, we evaluate various methods of troposphere delay modeling based on numerical weather models, such as the Potsdam Mapping Function (PMF) and Vienna Mapping Function for optical frequencies (VMF3o), in situ measurements, the Wrocław gradient model (WGM) and the combination of different models. We found large discrepancies between pressure, temperature, and humidity records between in situ measurements and numerical models. The best results for the zenith delays are obtained when using in situ meteorological data with the estimation of tropospheric biases. For stations with some deficiencies in proper humidity measurements, e.g., Zimmerwald in Switzerland, the best results are obtained when using hydrostatic zenith delays based on in situ data and wet delays based on numerical weather models. Finally, we found that using horizontal gradients of the tropospheric delay is indispensable to avoid biases in the SLR-based Earth rotation parameters of approximately 20 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}as for polar motion. The horizontal gradients successfully account for the asymmetry of the troposphere above SLR stations and can be derived from PMF, VMF3o, or a parameterized WGM model with similar accuracies.

Raspberry-Pi Based Smart Environmental Monitoring and Controlling System

This study presents the design and implementation of an Internet of Things (IoT) based framework for continuous monitoring and control of air quality parameters. The system incorporates various sensors, including the DHT11 for temperature and humidity measurement, the MQ-135 for gas detection, an LM393 comparator sensor for rainfall detection, and an additional 12V fan controlled by a relay. These sensors seamlessly interface with a Raspberry Pi, serving as the central control unit. The collected data is transmitted to the ThingSpeak cloud platform for real-time visualization and analysis. The system's architecture allows for remote monitoring and control of air quality parameters, facilitating proactive decision-making for environmental management. Experimental results demonstrate the system's effectiveness in providing accurate and timely information about temperature, humidity, rainfall, and gas concentrations in the air, making it a valuable tool for environmental monitoring and control applications.

Humidity sensors based on doped ZnO: An overview

ZnO is widely studied due to its fascinating properties useful for variety of application, specifically for the measurement of humidity. Humidity measurement is of great importance in a wide variety of commercial and industrial applications. Sensor based on ZnO depict prominence, further, doping with various elements results in sensing enhancement. A thorough review of doped ZnO for humidity sensing objective is carried out. A considerable part of the review is focused on the different structures of ZnO, further, emphasis on the kind dopant used in ZnO humidity sensing. The performance based on structure and dopant has detailed.

3D hierarchical microcubic morphological composite and its application for Bi-functional Humidity/Pressure sensing in TENG architecture

Harnessing the power of contact electrification, triboelectric nanogenerators (TENGs) are a cutting-edge energy technology, where output performance is greatly influenced by the micro/nano scale architectures of triboelectric layers. We present a study on the development of TENG by introducing an advanced 3D hierarchical microcubic morphological structure prepared by simple solution casting method, employing a renewable material, lignin, in combination with eco-friendly polyvinyl alcohol (PVA) matrix. The strategic incorporation of epichlorohydrin (EPCH) as a crosslinker enhances thermal stability, and crosslinking within the lignin/PVA composite (LPC) film. A remarkable outcome of this integration is the emergence of a 3D hierarchical microcubic structure within the LPC film. This structure, with its intricate 3D arrangement, emerges as the cornerstone that underpins the development of LPC-TENGs devices distinguished by their simple fabrication process, elevated output performance, and multifaceted applications. The LPC-TENG, adorned with its unique 3D hierarchical microcubic architecture, establishes new standards in energy harvesting and sensing capabilities with a maximum output power density of 135.4 mWm−2, a short-circuit current of 6.44 μA, and a maximum output voltage of 65 V (5 times higher than PVA-based TENGs). Additionally, the LPC-TENG showed exceptional performance as a self-powered bifunctional sensing system, providing a steady output voltage range of 0–10 V across various humidity levels (20–90 % RH) and a wide usable pressure range (0–162 kPa) with a low detection limit of 1 kPa. We demonstrated the application of the LPC-TENG in energy harvesting from human walking, enabling simultaneous measurements of environmental relative humidity, pressure, and illumination of 27 commercial LEDs. The unique 3D hierarchical microcubic architecture offers an unparalleled foundation for accommodating overall performance and diverse sensing functionalities.

Indoor Environmental Quality In Preschool Buildings In an Andean City In Ecuador

ABSTRACT Indoor environmental quality has been associated with the health and wellbeing of building occupants; nevertheless, there is limited evidence in this regard for Latin American schools. This research aimed to characterize indoor environmental quality in public and private preschools in an Andean city in Ecuador. Data collection comprised onsite monitoring for the thermal-humidity microclimate of 90 classrooms in 30 preschools in Cuenca-Ecuador (March-August 2018). Infrared thermography and direct observation were applied to determine dampness. Classrooms seemed to be inadequate thermal-humidity microclimates; only a few maintained a comfortable temperature (6%) and relative humidity (11%) throughout the shift. When comparing public and private schools, in private schools, temperatures below the comfort range (61.3% in private schools vs 31.4% in public schools, p<0.001) and relative humidity measures above the comfort range were more frequent (74.3% in private schools vs. 58.6% in public schools, p<0.001). Hollow blocks were the primary construction material in private and public schools. Sixty-four per cent of private schools operated in adaptive, reused buildings, vs 19% in public schools (p<0.05). Infrared thermography confirmed dampness in 26% (n=23) of the classrooms in the covering structures indoors (15% in public vs 33% in private schools, p<0.05). This research reveals the urgent need to develop specific regulations and control mechanisms for building sustainable and healthy environments for preschools in Ecuador.

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

Greenhouses, often composed of plastic or glass structures, play a fundamental role in optimizing the environmental conditions essential for successful plant cultivation, ultimately resulting in higher-quality crop yields. The study aims to develop a real-time monitoring and control system for a smart mini greenhouse utilizing the Internet of Things (IoT), with a specific focus on cultivating spinach. The monitoring system is constructed around an ESP32 microcontroller, complemented by a DHT22 sensor for accurate air temperature and humidity measurements, and an RTC DS3231 timer for scheduling tasks. The DHT22 sensor's set point values trigger the fan and misting system operations. The fan activates when the air temperature reaches 32°C, while the misting system turns on when humidity levels (RH) reach 55%. The ESP32 is the central processing unit, enabling internet connectivity through Wi-Fi for real-time data monitoring via the Blynk application. Sensor calibration confirms the system's precision, with regression analyses producing impressive R2 values of 0.989 and 0.952. The shading net control system operates four hours daily, from 11:00 to 15:00 WIB (Western Indonesian Time), optimizing the greenhouse environment. This well-executed system leads to the robust growth of spinach plants, reaching a height of 23.02 cm, sprouting nine leaves, and attaining a weight of 10 grams. The findings from this study highlight the efficiency and effectiveness of the smart mini greenhouse's monitoring and control system, offering promising applications in broader greenhouse management and crop cultivation practices.

Passive Cooling Performance in Vernakular Javanese Architecture

Through the development of passive design solutions, architecture has to deal with issues relating to the subject of the energy crisis and building comfort. Vernacular architecture contains design principles that optimize natural systems for the creation of habitable comfort. This needs to be proven through the study of natural design for thermal environments in vernacular architecture, especially in Joglo buildings. This paper aims to evaluate the performance of passive cooling in Javanese vernacular houses through observation techniques and field measurements. Passive cooling performance is limited to visually appropriate aspects of building elements as well as air temperature measurement results that fall within the comfortable temperature limit along with the magnitude of the decrease in air temperature in the building. Visual methods are used to determine the suitability of passive design parameters, while field measurements are carried out to assess comfort levels and air temperature reductions. The object of this research is Joglo House in Karangjati, Ngawi, East Java. The air temperature and humidity measurement tool used is the RC 4H Data Logger with a measurement duration of seventeen days. The results showed that the main elements of passive cooling based on visual observation techniques were orientation, roof volume and porous walls. Passive cooling performance results include two things, namely: first, the average air temperature (27.5°C) in the building is within the comfortable temperature limit; second, the rate of decrease in air temperature in buildings during the day reaches 3.3°C

The Impact of ZIF-8 Particle Size Control on Low-Humidity Sensor Performance

An accurate humidity measurement is essential in various industries, including product stability, pharmaceutical and food preservation, environmental control, and precise humidity management in experiments and industrial processes. Crafting effective humidity sensors through precise material selection is crucial for detecting minute humidity levels across various fields, ultimately enhancing productivity and maintaining product quality. Metal–organic frameworks (MOFs), particularly zeolitic imidazolate frameworks (ZIFs), exhibit remarkable properties and offer a wide range of applications in catalysis, sensing, and gas storage due to their structural stability, which resembles zeolites. The previous research on MOF-based humidity sensors have primarily used electrical resistance-based methods. Recently, however, interest has shifted to capacitive-based sensors using MOFs due to the need for humidity sensors at low humidity and the resulting high sensitivity. Nevertheless, further studies are required to optimize particle structure and size. This study analyzes ZIF-8, a stable MOF synthesized in varying particle sizes, to evaluate its performance as a humidity sensor. The structural, chemical, and sensing properties of synthesized ZIF-8 particles ranging from 50 to 200 nanometers were examined through electron microscopy, spectroscopic, and electrochemical analyses. The fabricated copper electrodes combined with these particles demonstrated stable and linear humidity sensing capabilities within the range of 3% to 30% relative humidity (RH).

Water vapor measurements inside clouds and storms using a differential absorption radar

Abstract. NASA's Vapor In-cloud Profiling Radar (VIPR) is a tunable G-band radar designed for in-cloud and precipitation humidity remote sensing. VIPR estimates humidity using the differential absorption radar (DAR) technique. This technique exploits the difference between atmospheric attenuation at different frequencies (“on” and “off” an absorption line) and combines it with the ranging capabilities of the radar to estimate the absorbing gas concentration along the radar path. We analyze the VIPR humidity measurements during two NASA field campaigns: (1) the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign, with the objective of studying wintertime snowstorms focusing on east coast cyclones; and (2) the Synergies Of Active optical and Active microwave Remote Sensing Experiment (SOA2RSE) campaign, which studied the synergy between DAR (VIPR) and differential absorption lidar (DIAL, the High altitude Lidar Observatory – HALO) measurements. We discuss a comparison with dropsondes launched during these campaigns as well as an intercomparison against the ERA5 reanalysis fields. Thus, this study serves as an additional evaluation of ERA5 lower tropospheric humidity fields. Overall, in-cloud and in-snowstorm comparisons suggest that ERA5 and VIPR agree within 20 % or better against the dropsondes. The exception is during SOA2RSE (i.e., in fair weather), where ERA5 exhibits up to a 50 % underestimation above 4 km. We also show a smooth transition in water vapor profiles between the in-cloud and clear-sky measurements obtained from VIPR and HALO respectively, which highlights the complementary nature of these two measurement techniques for future airborne and space-based missions.

Equivalence of low-cost PM10 concentration measuring devices with a reference method using various correction functions

The aim of the study was to build a corrective model that can be used in the analysed devices and to assess the impact of such a model on the values of the measured concentrations. The novelty of this study is the test of equivalence with the equivalent reference method for hourly data. The study used hourly data of PM10 concentrations measured in a chosen city in Poland. Data was collected from two PM10 sensors and a reference device placed in close proximity. In addition, air temperature, humidity and wind speed were also measured. Among the tested models, a linear model was selected that used primary measurements of PM10, temperature, air velocity, and humidity as the most accurate approximation of the actual PM10 concentration level. The results of the analysis showed that it is possible to build mathematical models that effectively convert PM10 concentration data from tested low-cost electronic measuring devices to concentrations obtained by the reference method.

Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities

The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH3) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption. Four identical CF rooms were utilized, each accommodating 175 hens of 77 wk of age (WOA). A Latin Square Design method was employed, with 4 treatment lengths: T1 = control (0 m), T2 = 12 ft (3.7 m), T3 = 24 ft (7.3 m), and T4 = 36 ft (11.0 m), where room and WOA are considered as blocking factors. Daily PM concentrations, temperature, and humidity measurements were conducted over 24 h, while NH3 levels, litter moisture content (LMC), and ventilation were measured twice a week in each treatment room. Statistical analysis involved ANOVA, and mean comparisons were performed using the Tukey HSD method with a significance level of P ≤ 0.05. This study found that the EPI system with longer wires reduced PM2.5 concentrations (P ≤ 0.01). Treatment T2, T3, and T4 led to reductions in PM2.5 by 12.1%, 19.3%, and 31.7%, respectively, and in small particle concentrations (particle size >0.5 μm) by 18.0%, 21.1%, and 32.4%, respectively. However, no significant differences were observed for PM10 and large particles (particle size >2.5 μm) (P < 0.10), though the data suggests potential reductions in PM10 (32.7%) and large particles (33.3%) by the T4 treatment. Similarly, there was no significant impact of treatment on NH3 reduction (P = 0.712), possibly due to low NH3 concentration (<2 ppm) and low LMC (<13%) among treatment rooms. Electricity consumption was significantly related to the length of the EPI system (P ≤ 0.01), with longer lengths leading to higher consumption rates. Overall, a longer-length EPI corona pipe is recommended for better air pollutant reduction in CF housing. Further research should focus on enhancing EPI technology, assessing cost-effectiveness, and exploring combinations with other PM reduction strategies.

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Simultaneous humidity and temperature measurement with micropillar enhanced QCM sensors

In this study, we propose using a polymethylmethacrylate (PMMA) micropillar-based quartz crystal microbalance (micropillar-QCM) sensor for simultaneous measurement of exhaled breath temperature (EBT) and relative humidity (RH). The micropillar-QCM device, fabricated by nanoimprinting lithography, exhibits enhanced sensitivity due to the unique resonance phenomena formed between the vibrating micropillars and quartz substrate. The resonance frequency of the micropillar-QCM shifts in response to changes in the mechanical property – Young’s modulus of PMMA due to the temperature and humidity variations. Experimental results demonstrate that the frequency shift is linearly dependent on the temperature change within the range of 25–35 °C and follows a power law relationship with relative humidity (RH) within the range of 50%− 90%. The sensor exhibits excellent sensitivity in temperature and humidity measurements with limit of detections (LODs) of 0.09 °C and 0.35% respectively. In addition, the temperature change has little effect on the bandwidth of the signal of micropillar-QCM while its bandwidth decreases linearly with the increasing relative humidity. As a result, the combination of the resonance frequency shift and bandwidth of the micropillar-QCM sensor can be utilized to predict the temperature and relative humidity simultaneously. The finding highlights the potential of the micropillar-QCM sensor for applications requiring accurate and real-time temperature and humidity measurements, such as breath analysis, environmental monitoring, and healthcare.