Low-cost proximal sensors for assessing organic carbon and potentially toxic metals in highly weathered soils: A systematic review

In the last decade, many low-cost monitoring sensors and sensor-networks have been used as an alternative air quality assessment method. It is also well known that these low cost monitors have calibration, accuracy and long term variation problems which require various calibration techniques. In this work PM2.5and PM10low cost sensors (Plantower and Nova Fitness) have been tested in five cities under different environmental conditions and compared with collocated standard instruments. Simultaneously, particle composition (organic and black carbon, sulfate, nitrate, chloride, ammonium, and chemical elements) has been measured in the same places to study its influence on the accuracy. The results show a very large variability in the correlation between the low cost sensors and collocated standard instruments depending on the composition and size of particles present in the site. The PM10correlation coefficient (R2) between the low cost sensor and a collocated regulatory instrument varied from to 0.95 in Temuco to 0.04 in Los Caleos. PM2.5correlation varied from 0.97 to 0.68 in the same places. It was found that sites that had higher proportion of large particles had lower correlation between the low cost sensor and the regulatory instrument. Sites that had higher relative concentration of organic and black carbon had better correlation because these species are mostly below the 1 μm size range. Sites that had higher sulfate, nitrate or SiO2concentrations in PM2.5or PM10had low correlation most likely because these particles have a scattering coefficients that depends on its size or composition, thus they can be classified incorrectly.

In this paper we present our method of detecting elderly patient's activities using a low-cost mobile sensor network. The activities are visualized using a 3D virtual environment. The sensor network consists of two wearable accelerometer/gyro sensors and 4-6 proximity sensors. A pattern recognition system detects the activities and a 3D virtual environment platform visualizes 19 actions and the approximate location of the patient, who is represented as a humanoid avatar. We evaluate our system with healthcare professionals using the focus group interview method and present the results as guidelines for using 3D virtual environments with elderly patient monitoring.

Air quality is a global challenge issue, and many regions of the world, such as India, are experiencing daunting challenges. An important aspect is to identify and then control the emissions from major contributing sources. To advance this aspect, this paper describes an air quality network that has been set up in the National Capital Territory of Delhi (NCT-Delhi) to identify major contributing source categories in real-time. The various components include an innovative cloud-based dashboard to compile the data in real-time from a series of PM instruments (Beta Attenuation Monitors (BAM)) and a low-cost sensor network (22 APT- MAXIMA sensors). Furthermore, at one of the locations (urban site), three real-time chemical speciation monitors are installed to provide elemental speciation (30 elements), elemental carbon (EC), and organic carbon (OC) data. PM2.5 concentrations at different sites (urban, industrial, and background) were compared to the BAM measurements over an 8-month period from May 2019 to February 2020; spanning the summer, monsoon, autumn, and winter seasons in Delhi. The APT sensor measurements were well correlated to the BAM measurements, with R2 values ranging between 0.84 and 0.95 for all sites. This validated that the APT-MAXIMA low-cost sensors can be a useful tool for distributed monitoring of PM2.5 levels. The mean PM2.5 concentrations showed a trend with winter (Dec, Jan, Feb: 205.2 ± 95.1 µg m−3) and autumn (Oct, Nov: 171.7 ± 128.3 µg m−3) highs and summer (May, Jun: 64.6 ± 57.2 µg m−3) and monsoon (Jul, Aug, Sep: 27.6 ± 16.7 µg m−3) lows. The bivariate polar plot reveals high PM2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM2.5 episodes are encountered during the festival season and other smog episodes. Implications: Low-cost sensors are useful for distributed monitoring under both low and high pollution conditions. A cloud-based dashboard system provided real-time, remote access to the data and in the visualization of air quality in the entire region. The real-time data availability on the cloud enabled establishing hot-spot regions of air pollution, spatial variation of PM2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.

The waste of food presents a challenge for achieving a sustainable world. In Germany alone, over 10 million tonnes of food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

Technology in transportation becomes important nowadays and must be developed overtime. In the era of development, there are so many roads extensions to balance the significant additions of motorized vehicles. The increasing number of vehicles caused problems such as damaged roads and lack of maintenance to the road itself. Lack of awareness to repair the damaged roads, especially potholes, make it more dangerous for riders to drive safely. This issues are more concerning right now because the increasing number of accident and mortality. To prevent accident to happen, the pothole detection sensor can be used to on car system. The development of pothole detection sensor in this research is adopted from the proximity sensor system where in that system they use camera and digital imaging process. The advantages from our system that we research and develop are more user friendly from the feasibility and the financial aspect. Low cost sensor with the same quality of the existing system is devlop in this work. Despite the use of low cost sensor, the maintenance also on the lower cost and easier to do. As the result of this research and study, an error was obtained between the distance that detected the pothole should be in less than 4% distance range from the sensor.

Water scarcity in the Mediterranean significantly affects the sustainability of citrus cultivation in eastern Sicily, a key production area in Italy. Innovative monitoring approaches are crucial for assessing citrus water status and applying precise irrigation strategies. This study evaluates the potential of low-cost proximal sensors based on thermal infrared (TIR) (e.g., canopy temperature, Tc; ΔT; crop water stress index, CWSI) and visible near-infrared (VNIR) (e.g., normalized difference vegetation index, NDVI) data, combined with stem water potential (SWP), for determining citrus water status proxies across four fields under different water regimes (full irrigation, FI, and deficit irrigation, DI) and cultivar/rootstock combinations. Temporal and spatial differences were detected for most variables during the irrigation season. A 6% decrease in NDVI corresponded to higher Tc values in July (up to 37.6 °C). CWSI highlighted cumulative water deficits, reaching 0.65 ± 0.15 in September. More negative SWP values (−1.91 ± 0.38 MPa) were found under DI compared to FI (−1.70 ± 0.17 MPa) conditions. Microclimatic differences influenced ΔT, with lower values in fields 3–4, despite site-specific SWP, NDVI, and Tc variations. The use of VNIR and TIR tools provided valuable insights for describing the spatial and temporal variability of citrus water status indicators under Mediterranean conditions, supporting their sustainable irrigation management.

BACKGROUND AND AIM: We tested in India and Mongolia an ultra-low-cost passive sampling method for long term average light-absorbing carbon (LAC) air pollution. METHODS: The passive LAC method estimates the change in reflectance based on digital images of a passively-exposed paper filter. Previous tests in polluted indoor environments in India suggested that the passive LAC method has robust reproducibility and high precision; the present research aimed to calibrate the method and thereafter test its accuracy. We deployed three methods (five monitoring devices) to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir (PA); two quartz filters for EC-OC (elemental carbon; organic carbon) using a UPAS; and two passive LAC samplers. We compare multiple rounds of 3-week-average measurements from the three methods. The EC- OC filter analysis is the “gold standard” / reference; PA and LAC are proxy-measurements for PM2.5 and BC, respectively. RESULTS:In pilot testing in Mongolia, average concentrations were 266 µg of PM2.5 /m³ (PA; uncalibrated) and, 47 µg of BC /m³ (UPAS, EC). LAC measurements in their native, uncorrected, units reflect the rate of change in filter color (pixel intensity/month), which depends on LAC deposition per time; the average value was 2590 PI/month. Preliminary analyses calibrating the LAC to the EC measurement suggest, as a conversion factor, that 1 µg of BC /m³ corresponds to 22 PI/month, on average. Applying that conversion to all measurements, to predict BC concentrations from passive LAC measurements, yields a root- mean-square-error of 16 µg of BC /m³, or ~25% of the average BC concentration. CONCLUSIONS:Measurements are continuing past this initial pilot stage and likely will shed improved light on the accuracy and precision of the novel passive LAC method employed here. KEYWORDS: low cost sensors, black carbon, passive black carbon sensor, light absorbing carbon

The usage of proximity sensor for marker detections such as color line detection is the easiest technique in robotic competition. Most of the contestants use simple photo-reflective circuit as proximity sensor with constant light source and voltage-based threshold to deduce the presence of marker line. By using this technique, noises such as various changes in indoor lighting exposure and camera flash may cause negative effect against system performance. To overcome this situation, a proximity sensor model is presented utilizing photo-reflective circuit with modulated light source. Goertzel algorithm is then used to calculate the power spectrum value of the received signal from photo-reflective circuit. The presence of marker line is deduced by simple thresholding technique based on the previous calibrated power spectrum value. Using this model, marker line detection method based on proximity sensor becomes more robust against noises and disturbances.

Weather and soil water dictate farm operations such as irrigation scheduling. Low-cost and open-source agricultural monitoring stations are an emerging alternative to commercially available monitoring stations because they are often built from components using open-source, do-it-yourself (DIY) platforms and technologies. For irrigation management in an experimental vineyard located in Quiroga (Lugo, Spain), we faced the challenge of installing a low-cost environmental and soil parameter monitoring station composed of several nodes measuring air temperature and relative humidity, soil temperature, soil matric potential, and soil water content. Commercial solutions were either too expensive or did not meet our needs. This challenge led us to design the low-cost sensor system that fulfilled our requirements. This node is based on the ESP32 chip, and communication between the nodes and the gateway is carried out by LoRa technology. The gateway is also based on the ESP32 chip. The gateway uploads the data to an FTP server using a Wi-Fi connection with a 4G router while simultaneously storing the data on a memory card. The programming of the code for the nodes and the gateway is performed using the Arduino IDE. The equipment developed is proven to be effective and for managing vineyard irrigation based on the built-in sensors, with replicable results. It is, however, essential to calibrate the capacitive sensors for measuring soil water content in each soil type in order to enhance their ability to produce reliable results. In addition, the limits marking the beginning and end of irrigation tasks must be adjusted to local conditions and according to the producer's specific vineyard objectives.

Air pollution is a multifaceted issue affecting people’s health, environment, and biodiversity. Gaining comprehension of the interactions between natural and anthropocentric pollutant concentrations and local climate is challenging. This study aims to address the following two questions: (1) What is the influential mechanism of climatic and anthropogenic factors on the ground-level ozone (O3) concentrations in an urban environment during different seasons? (2) Can the ozone weekend effect be observed in a medium-sized city like Craiova, and under which conditions? In order to answer these questions, ozone interactions with meteorological parameters (temperature, pressure, relative humidity) and pollutant concentrations (particulate matter, carbon dioxide, volatile organic compounds, formaldehyde, nitrogen dioxide, nitric oxide and carbon monoxide) is evaluated based on a one-year dataset given by a low-cost sensor and one-year dataset provided by the National Environment Agency. Using two statistical analysis programs, Python and SPSS, a good understanding of the correlations between these variables and ozone concentration is obtained. The SPSS analysis underscores the significant impact of three meteorological factors and nine other pollutants on the ozone level. A positive correlation is noticed in the summer when sunlight is intense and photochemical reactions are elevated. The relationship between temperature and ozone concentration is strong and positive, as confirmed by Spearman’s rho correlation coefficient (r = 0.880). A significant negative correlation is found between relative humidity and ozone (r = -0.590). Moreover, the analysis shows that particulate matter concentrations exhibit a significant negative correlation with ozone (r ≈ -0.542), indicating that higher particulate matter concentrations reduce ozone levels. Volatile organic compounds show a significant negative correlation with ozone (r = -0.156). A negative relationship between ozone and carbon dioxide (r = -0.343), indicates that elevated carbon dioxide levels might also suppress ozone concentrations. A significant positive correlation between nitrogen dioxide and ozone (r = 0.060), highlights the role of nitrogen dioxide in the production of ozone through photochemical reactions. However, nitric oxide shows a negative correlation with ozone (r = -0.055) due to its role in ozone formation. Carbon monoxide has no statistically significant effect on ozone concentration. To observe the differences between weekdays and weekends, T-Test was used. Even though significant differences were observed in temperature, humidity, carbon dioxide, volatile organic compounds, nitrogen dioxide, nitric oxide and carbon monoxide levels between weekdays and weekends, the T-Test did not highlight a significant weekend ozone effect in a mid-sized city as Craiova. Using Python, the daily values were calculated and compared with the limit values recommended by the World Health Organization (WHO) and European Environment Agency (EEA). The WHO O3 recommended levels were exceeded for 13 times in one year. This study offers a comprehensive understanding of ozone pollution in a mid-sized city as Craiova, serving as a valuable reference for local decision-makers. It provides critical insights into the seasonal dynamics of ozone levels, emphasizing the significant role of temperature in ozone formation and the complex interactions between various pollutants and meteorological factors.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-024-77989-0.

According to the United Nations Educational, Scientific and Cultural Organization (2024), the quality and quantity of water available for all our uses will become the major challenge of the 21st century. Increased temperatures, problems of oxygenation, (micro)pollutants concentrations are some of the problems to be dealt with, whose consequences are many, not only for livestock farming, fishing and aquatic biodiversity, but also for drinking water production. In Europe the Water Framework Directive (WFD) aims to assess the ecological and chemical status of surface waters at the level of the river basin district (i.e., “an area of land and sea, comprising one or more river basins and associated groundwater and coastal waters, identified as the main unit for the purposes of river basin management”). Small streams, typical of watershed headwaters, are not monitored in the WFD. However, they are estimated to account for up to 80% of the total hydrographic length in a river watershed, making a major contribution to the water supply of downstream ecosystems (MacDonald and Coe 2007). Since 2010, the LTSER Zone Atelier du Bassin de la Moselle has been involved on the long-term monthly monitoring, with the help of forest rangers, of an initial set of 16 pristine headwater streams in the Vosges Mountains (https://acev.otelo.univ-lorraine.fr/ , https://deims.org/22915474-7c50-47c1-8239-6c59fa924a1b). These streams are running on granite or sandstone soils in forests dotted with wetlands of various size. The monitoring stations are upstream of any anthropogenic activity, except forestry and extensive tourism. This initial set has been progressively expanded with other nearby streams. However, all of them belong to the same mountainous typology. With this in mind, a participatory research project, O'CitEaux (https://ociteaux.fr/), has been set up to monitor the quality of small rivers in a wider context, using new low-cost sensors. We believe in the importance of such monitoring in the face of climate change (Whyte et al. 2024, von Gönner et al. 2024). Together with an increase of temperature, longer periods of drought interspersed with episodes of heavy rainfall are expected in the coming decades. The flow of small rivers and the quality of their water are therefore likely to be significantly altered. The O'CitEaux participants are: fishing association members (A), primary and secondary school teachers and their students (B), or just people interested in quality of the aquatic environment (C). fishing association members (A), primary and secondary school teachers and their students (B), or just people interested in quality of the aquatic environment (C). Participants A and B are equipped with a low-cost water case which enables them to measure pH, conductivity and temperature in-situ and in the future dissolved organic matter (Ritson et al. 2014). Participants A measure the water level and the width of the watercourse, which can be used for estimation of the discharge rate after proper calibration. All participants collect water samples (one-shot or on a monthly basis, depending upon their level of implication), filtrate them and send them immediately to the research laboratory. Additional information about the location of the station and its immediate surroundings, as well as on biodiversity (odonata, fish, etc.), is collected. Samples collected either by researchers or by O'CitEaux participants follow the same analysis process: filtration at 0.45 µm, analysis of dissolved organic and inorganic carbon, dissolved total nitrogen and major anions and cations, DOM spectral characteristics by UV-visible spectroscopy (aromaticity and molecular weight scoring) and fluorescence spectroscopy (DOM humification, etc.). To date, 150 streams (mainly in France, UK and Scandinavia) have been sampled (i.e., 400 samples) in addition to the 40 streams monitored directly by the researchers on a monthly basis (Fig. 1). The variety of their typology is shown in Fig. 2: high dissolved inorganic carbon concentrations reflect rivers running on calcareous soils, when high dissolved organic carbon concentrations characterized rivers influenced by forests and peatlands. The presentation will discuss the water quality results in function of geology, land use and season and compare them to WFD data collected at a larger scale. An example of data analysis is shown in Fig. 3 for dissolved nitrogen, with a gradient between the forested Vosges Mountains and the western zone where agriculture is more intensive. Citizen involvement (motivation, effectiveness, fear of doing the wrong thing, etc.) will be discussed as well as the best ways for feedback (database, website, counseling).

Distributed fluorescent optical fiber proximity sensor: Towards a proof of concept

Abstract. Traditional calibration models for low-cost air quality sensors have demonstrated a tendency to underpredict peak concentrations. We assessed the utility of adding data weights to low-cost sensor colocation data to improve the quantification of peak concentrations when the majority of colocation data is at a baseline concentration and varies due to intermittent, transient events. Specifically, we explore the effects of data weighting on three different pollutant colocation datasets: total volatile organic compounds (VOCs), carbon monoxide (CO), and methane (CH4). Leveraging two different weighting functions, a sigmoidal and a piecewise weighting regime, we explored the impacts of the base model choice (multilinear regression, MLR, vs. random forest, RF, models), the sensitivity of weighting functions, and the ability of data weighting to improve high-concentration pollution measurements. When compared to unweighted colocation data, we demonstrate significant reductions in both error (root mean square error, RMSE) and bias (mean bias error, MBE) for pollutant peaks across all three datasets when data weighting is employed. For the top percentile of data, we observe an average of 23 % reduction in RMSE and a 35 % reduction in MBE when optimal weights are employed. More significant reductions occurred in the 95th–99th percentile of data, where MBE was reduced by an average of 70 %. RMSE in the 95th-99th percentile was reduced by an average of 26 %. However, data weighting can also generate larger errors at baseline pollutant concentrations. Data weighting regimes were sensitive to input parameters, and input weighting functions may be tuned to better predict peak concentration data without significant reductions in the fidelity of baseline pollutant predictions.

To improve the accuracy of real time vehicle surveillance, utilize the advances in wireless sensor networks to develop a magnetic signature and length estimation based vehicle classification methodology with binary proximity magnetic sensor networks and intelligent neuron classifier. In this algorithm, we use the low cost and high sensitive magnetic sensors to measure the magnetic field distortion when vehicle crosses the sensors and detect vehicle via an adaptive threshold. The vehicle length is estimated with the geometrical characteristics of the proximity sensor networks, and finally identifies vehicle type from an intelligent neural network classifier. Simulation and on-road experiment obtains high recognition rate over 90%. It verified that this algorithm enhances the vehicle surveillance with high accuracy and solid robustness.