Humidity Measurements Research Articles

Titanium Dioxide/Graphene Oxide Nanocomposite-Based Humidity Sensors with Improved Performance

Accurate relative humidity (RH) measurement is critical in many applications, from process control and material preservation to ensuring human comfort and well-being. This study presents high-performance humidity sensors based on titanium oxide nanoparticles/graphene oxide (TiO2/GO) composites, which demonstrate excellent sensing capabilities compared to pure GO-based sensors. The multilayer structure of the TiO2/GO composites enables the enhanced adsorption of water molecules and improved dynamic properties while providing dual-mode sensing capability through both resistive and capacitive measurements. Sensors with different TiO2/GO ratios were systematically investigated to optimize performance over different humidity ranges. The TiO2/GO sensor achieved remarkable sensitivity (8.66 × 104 Ω/%RH), a fast response time (0.61 s), and fast recovery (0.87 s) with minimal hysteresis (4.09%). In particular, the sensors demonstrated excellent mechanical stability, maintaining reliable performance under bending conditions, together with excellent cyclic stability and long-term durability. Temperature dependence studies showed consistent performance under controlled temperature conditions, with the potential for temperature-compensated measurements. These results highlight TiO2/GO nanocomposites as promising candidates for next-generation humidity sensing applications, offering enhanced sensitivity, mechanical flexibility, and operational stability. The dual-mode sensing capability combined with mechanical durability opens up new possibilities for flexible and wearable humidity-sensing devices.

Temperature Compensation of Low-cost Sensors for Accurate Temperature Measurement

Evapotranspiration (ET) is a critical process in agricultural system. ET states the combined water loss from the soil and plant surfaces. Predicting ET is vital for effective water resource management. Accurate temperature and humidity measurements is instrumental in calculating Et. Temperature measurement plays a key role in various industrial, scientific, and agricultural applications. The study investigates the performance of three temperature humidity sensors. DHT11, DHT22 and LM35. The sensors are analyzed under different conditions, including compensation, and with a temperature compensation algorithm, and with proportional P and proportional-integral-derivative PID controllers. Sensors are interfaced with a Raspberry Pi 4B model. The sensors were tested in controlled environments with temperatures ranging from 10 degrees to 100 degrees C, and their accuracy was evaluated by comparing their outputs with the reference values. Results revealed that the error percentages for all sensors exceeded permissible limits when used outside their specified ranges. Incorporating temperature compensation algorithm and P and PID controllers significantly improved measurement accuracy, reducing error percentages and root mean square errors (RMSE) across all sensors. Out of the three sensors, LM35 demonstrated a 5-fold reduction in error percentage at higher temperatures compared to other methods. The improved sensor accuracy has significant implications for agricultural applications such as ET calculation, where precise temperature data is necessary. Reducing error enhances better agricultural water management strategies and overall productivity.

One-Point Calibration of Low-Cost Sensors for Particulate Air Matter (PM) Concentration Measurement

The use of low-cost sensors has dramatically increased in recent years in all engineering sectors. In the buildings and automotive field, low-cost sensors open very interesting perspectives, because they allow one to monitor temperature and humidity distributions together with air quality in a widespread and punctual way and allow for the control of all energy parameters. The main issue remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems were set to measure Particulate Air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity, and GPS position. These systems were calibrated for PM concentration measurement by comparison with standard and certified sensors used by the regional authority of the Emilia-Romagna region (ARPAE, Italy) for characterizing air quality. The duration of the analysis, three days, is not representative of the diverse environmental conditions that occur across different seasons. However, the innovation of this approach lies in both the in-field comparison of low-cost and high-quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors’ performance is given, with a focus on the effects of time granularity, relative humidity, mass conversion from particle counts, and size detection response. The results show that the low-cost sensors’ measurements of air temperature, relative humidity, and particle number concentration are in good agreement with high-quality sensors’ measurements, with a strong impact of relative humidity on performance indicators. Overall, good quality and consistency of the data among the sensors were achieved.

Humidity Sensor Based on a Hollow Core Fiber Anti-Resonant Reflection Optical Waveguide

In this paper, a graphene oxide (GO) composite film-coated humidity sensor is proposed based on the hollow core fiber (HCF). A segment of the HCF is spliced between two segments of the single-mode fiber (SMF). The relative humidity (RH) sensing characteristics of the sensor are experimentally investigated by observing the intensity shift of resonant dips in the transmission spectrum, which shows the GO composite film-coated HCF has the good stability in the measurement of humidity. The maximum humidity sensitivity of 0.12 dB/%RH is obtained in the RH range of 30%–78%. The proposed sensor has the advantages of the simple structure, easy fabrication, good stability, and high performance, which can be applied to marine climate detection, tunnel air humidity detection, agricultural testing, and other fields.

Comparison of temperature and humidity measurements from two separate weather stations at Camden Airport, Australia

Comparison of temperature and humidity measurements from two separate weather stations at Camden Airport, Australia

A flux tower site attribute dataset intended for land surface modeling

Abstract. Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.12596218 (Shi et al., 2024).

SPR fiber optic sensor based on MMF-NCF doped with MgF2 for dual-parameter measurement of temperature and humidity.

Dual-parameter temperature and humidity sensors based on optical fiber sensing have wide applications. Among various optical fiber sensors, surface plasmon resonance (SPR) sensors exhibit excellent sensing sensitivity. To address the bandwidth issue and expand the sensitivity, this paper proposes a multimode fiber-no core fiber (MMF-NCF) SPR sensor. The humidity channel covers an Ag/PVA composite film, while the temperature channel covers an Ag/MgF2/PDMS composite film. The MgF2 modulates the shape of the resonance curve, shifts the resonance curve towards the infrared direction and enhancing the sensitivity by 0.245 nm/oC. Compared with previous temperature and humidity detection ranges up to 20°C ∼ 100°C and 40% ∼ 100%, this has obvious improvement. The maximum sensitivities are -0.5683 nm/%RH and -3.259 nm/oC. The mean temperature sensitivity is -1.88877 nm/oC and the mean humidity sensitivity is -0.51249 nm/%RH. The polynomial fitting degrees reach 0.99914 and 0.99875, and FOM values are 2.715 × 10-2/oC and 4.735 × 10-3/%RH, which is expected to be in agriculture, food industry, such as biological implementation is widely used in production activities.

Heat Stroke Warning System Prototype for Athletes: A Pilot Study.

This research has developed a heat stroke warning system prototype for athletes utilizing the following sensors: DHT22, GY-906-BAA MLX90614, MAX30102. The device calculates the heat stroke risk and notifies users. The data is recorded, stored, displayed on a free-access website which graphs body temperature, ambient temperature, humidity, heart rate and heat stroke risk, and provides notifications for athletes engaged in outdoor activities. The researchers recorded sensors data (n = 1) for two sessions (12 min/session) in a closed room, at the sixth-minute marker, with an air conditioner activated to observe the changes observed by the sensors. For accuracy, the researchers employed Criterion-Related Validity, comparing sensor against standard equipment measurement. For reliability, we utilized Test-Retest Reliability, comparing sensor data from the first and second measurements. Accuracy and reliability were evaluated using the Pearson Correlation Coefficient, with significance set at p < 0.01. The DHT22 sensor demonstrates very high accuracy (r = 0.923) in ambient temperature and (r = 0.774) humidity measurements. It showed no significant reliability (r = 0.489) in temperature and (r = 0.185) humidity measurements. The GY-906-BAA MLX90614 sensor exhibited very high accuracy (r = 0.923) and reliability (r = 0.866) in body temperature measurements. The MAX30102 sensor lacked significant accuracy (r = 0.179) and reliability (r = 0.171) in heart rate measurements. The development of accuracy and reliability of sensors are important for preventing heat stroke in future applications.

Application of inverse variance weighting method to temperature and humidity measurement robots

Application of inverse variance weighting method to temperature and humidity measurement robots

Graphene, GO, and Borophene: Innovations in QCM-Based Humidity Sensors for Enhanced Sensitivity

Humidity measurements are crucial in daily life as they influence human comfort, health, safety, and product quality. Quartz Crystal Microbalance (QCM) sensors, known for their fast response times and high sensitivity, offer a significant advantage in humidity sensing due to their ability to provide highly linear and accurate measurements. These sensors are particularly valuable because they enable real-time, precise humidity detection with minimal calibration, making them ideal for various applications. This mini-review highlights the significance of QCM sensors, focusing on the sensing layers made from nanomaterial fillers integrated into composite matrices. Typical QCM sensor surfaces are could be coated with highly conductive materials such as graphene, graphene oxide (GO), and borophene, which offer excellent humidity-sensing capabilities due to their two-dimensional allotrope structure and unique properties of carbon and boron. This review begins with a brief overview of humidity measurement principles and QCM sensor characteristics. It then explores a variety of materials used for preparing QCM sensing layers, discussing their advantages and disadvantages for humidity sensor applications. Finally, the review presents future perspectives on the development of layer-by-layer self-assembled conductive polymeric films, novel GO-based composite QCM humidity sensors, and borophene-based humidity sensors, illustrating their potential for multifunctional composites.

Operational Capability of Drone‐Based Meteorological Profiling in an Urban Area

AbstractDuring the Uncrewed Aircraft Systems Demonstration Campaign (UAS‐DC), led by the World Meteorological Organization (WMO), twice‐daily meteorological profiling was conducted for 2 months at the Meteorological Research Institute in Tsukuba City (Japan), which is identified as a densely inhabited district. This campaign was instigated to assess the feasibility of obtaining continuous daily measurements for the long term (over a period of more than 1 month), to distribute the data in a format designated for numerical weather prediction, and to evaluate data quality compared to conventional meteorological data. Three types of uncrewed aircraft systems were utilized, that is, a meteorological medium‐sized hexacopter and medium‐ and small‐sized commercial drones with meteorological sensors attached. The maximum flight height was limited to 900 m above ground level owing to airspace regulations around the observation site. Compared with routine radiosonde data, the bias of air temperature, relative humidity, and wind speed measurements was less than 0.3 K, 1.5%, and 0.6 m , respectively, thereby meeting WMO requirements. Moreover, data transfer to the WMO‐prepared repository was completed within 30 min after measurement acquisition. Based on user experience, several aspects regarding the UAS‐DC campaign were discussed from the perspective of sustainable operation and atmospheric boundary layer research.

Seasonal Trend Analysis of Humidity using Multi-Method Detection Techniques of Silchar, Assam (1986-2022)

This study explores the extended form of methodologies for trend detection in meteorological time series, focusing on one of the critical climatic variables, Humidity, which is collected from Silchar station of Assam, India. To assess the presence and magnitude of trends, a comprehensive approach is adopted, employing the Mann-Kendall (M-K) test, its modified version, and Sen’s slope estimation, alongside the Innovative Trend Analysis (ITA) tech-nique. These methods together offer a robust framework for detecting and quantifying trends in the dataset. The monthly data of 37 years from 1986-2022 at 3 pm and at12 pm has been processed to find out the variability of humidity for which M-K test, modified M-K test and ITA method with trend magnitude and slopes is adopted to check the seasonal variation of humidity at 3 pm and 12 pm. Based on the statistical outcomes derived from the three trend detection methods and their corresponding visualizations, the analysis reveals both increasing and decreasing trends in humidity across different seasons includes a significant trend in humidity measurements has been identified during the winter, monsoon, and postmonsoon seasons at 3 PM, with a corresponding pattern similarly apparent in the monsoon season 12 PM observations. These variations are observed in the humidity values recorded at both 3 pm and 12 pm, highlighting a seasonal divergence in humidity pat-terns, with certain periods exhibiting rising rates, while others demonstrate declining or no trends.

The influence of zonal air supply on thermal comfort in a classroom located in a hot and humid environment: a case study from Jeddah—Saudi Arabia

In hot and humid regions, many classrooms depend on air conditioning systems equipped with mixing ventilation to maintain acceptable indoor temperatures. However, this method often proves inadequate in delivering satisfactory thermal comfort due to elevated temperature and poor air distribution. This research explores the potential of zonal air supply strategies to enhance thermal comfort in a classroom situated in Jeddah, Saudi Arabia. During July 2024, field data—including measurements of airflow velocity, air temperature, relative humidity, and globe temperature—were collected to find key thermal comfort indices: Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD). In addition, a survey was administered to assess students’ thermal perceptions. Computational Fluid Dynamics (CFD) simulations were utilized to predict air temperature, velocity, and humidity distribution, evaluating the impact of zonal air supply designs on thermal comfort within the classroom. Parametric analysis was used to identify the most effective zonal air supply configuration for reducing PMV and PPD values. The findings show that, under existing ventilation conditions, PMV and PPD indices at different heights exceed the recommended limits established by ASHRAE Standard-55, indicating thermal discomfort during peak temperature periods. Further investigation demonstrated that introducing a 4-zonal air supply system could reduce PMV by 16–32% and PPD by 32–36%, thereby significantly improving thermal comfort in the classroom.

An economical tunable diode laser spectrometer for fast-response measurements of water vapor in the atmospheric boundary layer

Abstract. Water vapor in the atmospheric boundary layer poses a significant measurement challenge, with abundances varying by an order of magnitude over short spatial and temporal scales. Herein, we describe the design and characterization of an economical and flexible open-path, fast-response instrument for measurements of water vapor. The in situ method of tunable diode laser absorption spectroscopy in the shortwave infrared was chosen based on a heritage with previous instruments developed in our laboratory and flown on research aircraft. The instrument is constructed from readily available components and based on low-cost distributed feedback laser diodes that enjoy widespread use for high-speed fiber-optic telecommunications. A pair of versatile, high-speed Advanced RISC Machine-based microcontrollers drive the laser and acquire and store data. High precision and reproducibility are obtained by tight temperature regulation of the laser with a miniature commercial proportional-integral controller. The instrument is powered by two rechargeable 3.6 V lithium-ion batteries, consumes 2 W of power, weighs under 1 kg, and is constructed from hardware costing less than USD 3000. The new tunable diode laser spectrometer (TDLS) agreed to within 2 % compared to a laboratory standard and displayed a precision of 10 ppm at a sample rate of 10 Hz. The new instrument is robust and simple to use, allowing users with little previous experience in laser spectroscopy to acquire high-quality, fast-response observations of water vapor for a variety of applications. These include frequent horizontal and vertical profiling by uncrewed aerial vehicles (UAVs); long-term eddy covariance measurements from fixed and portable flux towers; and routine measurements of humidity from weather stations in remote locations such as the polar ice caps, mountains, and glaciers.

High-sensitivity whispering-gallery mode sensor for simultaneous measurement of humidity and temperature.

A novel, to the best of our knowledge, optical fiber whispering-gallery mode (WGM) sensor for simultaneously measuring humidity and temperature is proposed and investigated. The proposed sensor is realized by a polyvinyl alcohol (PVA)-coated capillary tube coupling with an optical single-mode fiber (SMF), which is integrated with a fiber Bragg grating (FBG). The as-fabricated sensor can be used not only for relative humidity (RH) sensing but also for temperature detection. The achieved humidity and temperature sensitivities are -181.2 pm/%RH and 10.5 pm/°C, respectively. The easy fabrication and very high sensitivity of the proposed sensor are attractive features for high-precision and wide-range RH sensing applications.

Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency Domain Reflectometry.

This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR.

Performance Analysis of Dynamic Building‐Integrated Photovoltaic Shading System for High Technology Readiness Level Validation

This article presents the performance analysis of a new dynamic and vertically oriented building‐integrated photovoltaic (BIPV) shading device. The work forms part of a Swiss Pilot and Demonstration project. From technology readiness levels 5–7, it aims to validate the technology's consistency and replicability in the operational building and cost‐effectiveness for marketability. The shading slats comprise two‐string PV modules realized with laminated glazing layered by an outer white satin glass pane. For the small‐scale mock‐up, two designs are compared to quantify the effectiveness of temperature reduction and energy gain: 1) optimized—each string is connected to a bypass diode, and 2) standard—two strings are connected to a bypass diode. It is demonstrated that optimized slats have consistently lower module temperatures and higher energy yield, achieving more than 20% gain during spring and summer. There are no additional risks for consideration since no extreme temperature and humidity measurements are observed for the pilot installation at the actual building. The first floor's system produces a lower specific energy yield due to partial shading. Still, it can be worsened if the standard PV is used instead, highlighting the importance of a BIPV‐specific consultancy for successfully implementing BIPV systems.

Improving relative humidity measurements on Mars: new laboratory calibration measurements

Abstract. In this paper we present new calibration measurements that have been performed with the ground reference models of the relative humidity instruments of the Mars Science Laboratory (MSL), Mars 2020 and ExoMars missions. All instruments are based on capacitive sensor head technology, and they are developed, manufactured and tested by the Finnish Meteorological Institute (FMI). Calibration of capacitive humidity sensors for the Martian environment has been a challenging task and special facilities are needed in order to create Martian conditions including all relevant environmental parameters that can be accurately controlled and measured: low pressure, low temperature, carbon dioxide environment and especially humidity. A measurement campaign was performed at the German Aerospace Center (DLR) PASLAB (Planetary Analog Simulation Laboratory) to determine relative humidity calibration datasets for REMS-H, MEDA HS and METEO-H instruments in temperatures from −30 °C down to −70 °C in low-pressure CO2. In addition to the stable point humidity calibration measurements in CO2, the instrument performance was tested with the actual Martian atmosphere composition and during long continuous measurements. The new calibration dataset has already been used in the flight calibration of the MEDA HS instrument, resulting in successful calibration and excellent accuracy. The results from this campaign will further improve relative humidity measurements on Mars by providing the means to reanalyze the current calibration of the REMS-H flight model and by allowing more accurate comparison between the two instruments currently on the Martian surface.

Pt/MOF-5 film-coated optical micro-nano fibre hydrogen sensor with humidity and temperature compensation functions

In this paper, a composite fibre structure based on thin-core fibre (TCF), Fibre Bragg grating (FBG), and micro-nano fibre (MNF) is proposed to achieve simultaneous sensing of humidity, temperature, and hydrogen concentration by coating different functional films. Based on surface plasmon resonance (SPR), a TCF and Au/PVA film are combined to achieve relative humidity (RH) measurement. Pt/MOF-5 is coated on the surface of the MNF by in-situ growth method. Its refractive index (RI) is changed by absorbing hydrogen. In temperature detection, FBG realizes the temperature compensation. Based on the above sensing mechanisms, it is shown that the fibre has a RH sensitivity of 0.38 nm/%RH, a temperature sensitivity of −0.0497 nm/°C, and a hydrogen sensitivity of 0.541 nm/%. The Bragg grating has a temperature sensing sensitivity of 0.0106 nm/°C, which accurately compensates for temperature crosstalk. This composite structure provides a good application prospect for accurate measurement of hydrogen concentration.

Modelling of Carbon Monoxide and Suspended Particulate Matter Concentrations in a Rural Area Using Artificial Neural Networks

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters.