Studies revealed long-term associations between noise exposure and cardiovascular health, but the underlying short-term mechanisms remain uncertain.To explore the concomitant and lagged short-term associations between personal exposure to noise and heart rate variability (HRV) in a real life setting in the Île-de-France region.The RECORD MultiSensor Study collected between July 2014 and June 2015 noise and heart rate data for 75 participants, aged 34–74 years, in their living environments for 7 days using a personal dosimeter and electrocardiography (ECG) sensor on the chest. HRV parameters and noise levels were calculated for 5-min windows. Short-term relationships between noise level and log-transformed HRV parameters were assessed using mixed effects models with a random intercept for participants and a temporal autocorrelation structure, adjusted for heart rate, physical activity (accelerometry), and short-term trends.An increase by one dB(A) of A-weighted equivalent sound pressure level (Leq) was associated with a 0.97% concomitant increase of the Standard deviation of normal to normal intervals (SDNN) (95% CI: 0.92, 1.02), of 2.08% of the Low frequency band power (LF) (95% CI: 1.97, 2.18), of 1.30% of the High frequency band power (HF) (95% CI: 1.17, 1.43), and of 1.16% of the LF/HF ratio (95% CI: 1.10, 1.23). The analysis of lagged exposures to noise adjusted for the concomitant exposure illustrates the dynamic of recovery of the autonomic nervous system. Non-linear associations were documented with all HRV parameters with the exception of HF. Piecewise regression revealed that the association was almost 6 times stronger below than above 65 Leq dB(A) for the SDNN and LF/HF ratio.Personal noise exposure was found to be related to a concomitant increase of the overall HRV, with evidence of imbalance of the autonomic nervous system towards sympathetic activity, a pathway to increased cardiovascular morbidity and mortality.

In recent decades, significant advances have been made in understanding the generation, fates and consequences of water quality pollutants in the Great Barrier Reef ecosystem. However, skepticism and lack of trust in water quality science by farming stakeholders has emerged as a significant challenge. The ongoing failures of both compulsory and particularly voluntary practices to improve land management and reduce diffuse agricultural pollution from the Great Barrier Reef catchment underlines the need for more effective communication of water quality issues at appropriate decision-making scales to landholders. Using recent Great Barrier Reef catchment experiences as examples, we highlight several emerging themes and opportunities in using technology to better communicate land use-water quality impacts and delivery of actionable knowledge to farmers, specifically supporting decision-making, behavior change, and the spatial identification of nutrient generation ‘hotspots’ in intensive agriculture catchments. We also make recommendations for co-designed monitoring-extension platforms involving farmers, governments, researchers, and related agencies, to cut across stakeholder skepticism, and achieve desired water quality and ecosystem outcomes.

Agriculture is a significant industry that plays a major role in a country’s sustainable environment and economic development. The global population demands increased food production with minimal losses. Nutrient deficiency is one of the major and crucial factors influencing crop production significantly. Common techniques for determining crop nutrition status are the diagnosis of plant morphology, Enzymology, chemical effects, fertilization, etc. However, the above techniques are invasive and time-consuming or infeasible while considering varied production practices in different locations, environments and climatic conditions. Computer Vision is an area of Computer Science that deals with creating Artificial Intelligence based vision systems that can use image data, process, and analyze as humans perform. Early Detection of Crop Nutrient deficiencies favors the farmers to monitor the affected crops and plan for the manure or fertilizer application, which supports to regain of the crop’s efficiency for attaining its maximum yield. Modern computer vision systems rely on Machine Learning (ML), Remote sensing, Satellite imagery, unmanned aerial vehicles (UAVs), Internet of things (IoT) based sensor devices, and Deep Learning (DL) models that use algorithms to extract required features from data. The objective of this work is to provide an overview of recent research and identify the scope of computer vision-based technologies used for identifying crop nutrient content and deficiency, find research challenges in predicting nutrient imbalance in comparison with plant diseases that show certain similar characteristics, thereby to improve crop health and production.

Ionization smoke sensors are the best smoke sensors | however, the small amount of radioactive source they include is no longer desirable since it makes recycling more complicated where the people encountered with radioactive waste. In this study, a conventional ionization type smoke sensor which contained americium-241 radioactive source modelled by Monte Carlo MCNPX code. The absorbed dose rate is calculated by MCNPX code where the human tissue meddled as a soft tissue material. The dosimetry results are comparable with the experimental safety margins. It concluded that if the ionization smoke sensors positioned at a right distance from the human body, so the radiation risk and the exposure hazard can be lower than the exposure level of the background radiation.

Spatial variations of Carbon Monoxide (CO) are examined in the urban environment of Ouagadougou, Burkina Faso. Focus is given on the variations between urban background, roadside and in-traffic measurements. Results show significant differences between the three methods where average in-traffic values were 2-3 times higher than average roadside values and 10-12 times higher than average background values. During traffic congestions these differences extended up to 6 and 20 times respectively. Results are discussed in relation to human exposure assessments and WHO guidelines.

Environmentally safe disposal of sulfide-rich reactive mine tailings is one of the major challenges facing the mining industry in Canada, Scandinavia, USA, and many other parts of the world. Placing tailings under a water cover is one of the effective methods to reduce the influx of oxygen to the tailings. Wind-induced turbulence and subsequent resuspension of the tailings, however, are major concerns with this approach. In this paper, a study of wind-induced resuspension at the Shebandowan tailings storage facility, northwestern Ontario, Canada, is discussed. The study compares computer modeling of required water cover depths and resuspended tailings concentrations to observed field data. The calculated minimum water cover depths required to eliminate resuspension were found to be higher than the existing implemented water cover depths in each cell. The predicted resuspended tailings concentrations for the west cell were 6-22 mg/l with an average value of 15 mg/l and, for the east cell, 1-10 mg/l, with an average of 6.0 mg/l. In comparison, optical backscatter sensors, deployed in situ, recorded average resuspended tailings concentration up to 25 mg/l, indicating that the model results were similar to the field-measured values. Results from sediment trap measurements did not show any correlation between the amount of resuspended tailings and water cover depth. Sediment traps collect not only sediments eroded and suspended at the location of deployment but also those that have been transported from elsewhere and redeposited at the trap location. The amount of resuspension occurring at Shebandowan does not raise a major concern because discharge from the tailings area is collected and managed before it reports to the final effluent.

Near-infrared diffuse reflectance sensing (NIRS) of soils has been the object of considerable interest and research in the last few years. This has been motivated by the prospect that this method seems to provide a cheap, convenient alternative to conventional, time-consuming methods for the measurement of a wide range of soil parameters. In particular, various authors have advocated that NIRS could be used to measure rapidly and non-destructively the concentration of trace metals in surface soils. Correlation analyses between NIRS spectra and trace metal concentration have yielded inconclusive results to date, suggesting that trace metal concentration may belong to a class of “tertiary” soil parameters, linked to NIRS spectra through “surrogate”, or indirect, correlations, involving some other primary or secondary parameter like clay or organic matter content, to which NIRS spectra are very sensitive. To assess the validity of this surrogate correlation hypothesis in the case of trace metals, experiments were carried out with soil samples varying only in the amount of trace metals they contain. Field-aged Hudson and Arkport soil pots spiked with Cu and Zn, freshly spiked samples of the same soils, and samples of a metalliferous peat soil from Western New York naturally rich in Cd and Zn were subjected to NIRS under laboratory conditions. Detailed analysis indicates that the NIR spectrum is sensitive to sample handling, including the orientation of the samples in the NIRS instrument, but that, at the same time, there is no discernable effect of the presence of trace metals on any part of the NIR spectrum. These results provide strong experimental support to the hypothesis of “surrogate” correlation for trace metals, and indicate that trace metals, even in severely contaminated soils, should not interfere with the NIR sensing of primary or secondary parameters, like organic matter content. Further work is needed to determine if this feature of NIR spectra extends to other soil chemical parameters.

A polyaniline-modified quartz crystal microbalance (QCM) sensor was obtained through immobilizing the polyaniline film on the silver electrode surface of quartz crystal resonator by an electrochemical method. The sensor was studied for detecting the formic acid gas of different concentrations, and the results showed that the resonant frequency of QCM decreased quickly in the beginning and tended to be constant in the end when exposed to formic acid gas. The frequency shifts decreased faster as the concentration of formic acid gas increased. And the frequency shifts of the QCM sensors were found to be linearly related to the concentration of formic acid gas, which might be used to estimate the concentration level of the formic acid gas within the range of experimental concentrations. The result of on-line monitoring test fully indicated that the QCM sensor responded effectively to the increasing concentration of formic acid and had important practical significance and broad application prospect in real-time detection of antique conservation environment in the museum.

The aim of this study was examination possibility of Azolla caroliniana Willd. to aerate its medium enriched with Cd(II) and Hg(II) and comparison of DO and ODR methods in measurement aeration status of solutions. Azolla system has been chosen to verify the validity of the measuring ODR method in water solution. Water aeration measurements, one of important environmental tests, are performed most often by measurement of dissolved oxygen with oxygen sensors. Other similar method called oxygen diffusion rate is generally used in different porous materials such as soil. Our first objective was to check if these both methods are comparable and may be use exchangeable in water solution. The both types of measurements were performed in medium saturated at different oxygen concentrations. The linear relationship and high correlation (R = 0.89) were found between values of ODR and DO. The object of the second part of our studies was A. caroliniana Willd. (Azollaceae), a floating water fern living in symbiosis with cyanobacterium Anabaena azollae Strasb. (Nostoceae) that fixes atmospheric nitrogen. Azolla plants are used for centuries as a nitrogen biofertilizer. The second aim of our work was to find out, if the fern reduces or increases oxygen concentration in water. The ODR method was used to determine the aeration status of the nutrient solution. During 12 days of the experiment at laboratory conditions, an active role of A. caroliniana in aeration of the nutrient solution containing Cd(II) and Hg(II) was stated.