Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants' comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
The need for environmental protection and water pollution control has led to the development of different sensors for determining many kinds of pollutants in water. Ammonia nitrogen presence is an important indicator of water quality in environmental monitoring applications. In this paper, a high sensitivity sensor for monitoring ammonia nitrogen concentration in water using a tapered microfiber interferometer (MFI) as a sensor platform and a broad supercontinuum laser as the light source is realized. The MFI is fabricated to the waist diameter of 8 µm producing a strong interference pattern due to the coupling of the fundamental mode with the cladding mode. The MFI sensor is investigated for a low concentration of ammonia nitrogen in water in the wide wavelength range from 1500-1800 nm with a high-power signal provided by the supercontinuum source. The broad source allows optical sensing characteristics of the MFI to be evaluated at four different wavelengths (1505, 1605, 1705, and 1785 nm) upon exposure towards various ammonia nitrogen concentrations. The highest sensitivity of 0.099 nm/ppm that indicates the wavelength shift is observed at 1785 nm operating wavelength. The response is linear in the ammonia nitrogen range of 5-30 ppm with the best measurement resolution calculated to be 0.5 ppm. The low concentration ammonia nitrogen detected by the MFI in the unique infrared region reveals the potential application of this optical fiber-based sensor for rivers and drinking water monitoring.
Recent research in underwater wireless sensor networks (UWSNs) has gained the attention of researchers in academia and industry for a number of applications. They include disaster and earthquake prediction, water quality and environment monitoring, leakage and mine detection, military surveillance and underwater navigation. However, the aquatic medium is associated with a number of limitations and challenges: long multipath delay, high interference and noise, harsh environment, low bandwidth and limited battery life of the sensor nodes. These challenges demand research techniques and strategies to be overcome in an efficient and effective fashion. The design of routing protocols for UWSNs is one of the promising solutions to cope with these challenges. This paper presents a survey of the routing protocols for UWSNs. For the ease of description, the addressed routing protocols are classified into two groups: localization-based and localization-free protocols. These groups are further subdivided according to the problems they address or the major parameters they consider during routing. Unlike the existing surveys, this survey considers only the latest and state-of-the-art routing protocols. In addition, every protocol is described in terms of its routing strategy and the problem it addresses and solves. The merit(s) of each protocol is (are) highlighted along with the cost. A description of the protocols in this fashion has a number of advantages for researchers, as compared to the existing surveys. Firstly, the description of the routing strategy of each protocol makes its routing operation easily understandable. Secondly, the demerit(s) of a protocol provides (provide) insight into overcoming its flaw(s) in future investigation. This, in turn, leads to the foundation of new protocols that are more intelligent, robust and efficient with respect to the desired parameters. Thirdly, a protocol can be selected for the appropriate application based on its described merit(s). Finally, open challenges and research directions are presented for future investigation.
Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.
In this article a luminescence fiber optic biosensor for the microdetection of heavy metal toxicity in waters based on the marine bacterium Aliivibrio fischeri (A. fischeri) encapsulated in alginate microspheres is described. Cu(II), Cd(II), Pb(II), Zn(II), Cr(VI), Co(II), Ni(II), Ag(I) and Fe(II) were selected as sample toxic heavy metal ions for evaluation of the performance of this toxicity microbiosensor. The loss of bioluminescence response from immobilized A. fischeri bacterial cells corresponds to changes in the toxicity levels. The inhibition of the luminescent biosensor response collected at excitation and emission wavelengths of 287 ± 2 nm and 487 ± 2 nm, respectively, was found to be reproducible and repeatable within the relative standard deviation (RSD) range of 2.4-5.7% (n = 8). The toxicity biosensor based on alginate micropsheres exhibited a lower limit of detection (LOD) for Cu(II) (6.40 μg/L), Cd(II) (1.56 μg/L), Pb(II) (47 μg/L), Ag(I) (18 μg/L) than Zn(II) (320 μg/L), Cr(VI) (1,000 μg/L), Co(II) (1700 μg/L), Ni(II) (2800 μg/L), and Fe(III) (3100 μg/L). Such LOD values are lower when compared with other previous reported whole cell toxicity biosensors using agar gel, agarose gel and cellulose membrane biomatrices used for the immobilization of bacterial cells. The A. fischeri bacteria microencapsulated in alginate biopolymer could maintain their metabolic activity for a prolonged period of up to six weeks without any noticeable changes in the bioluminescence response. The bioluminescent biosensor could also be used for the determination of antagonistic toxicity levels for toxicant mixtures. A comparison of the results obtained by atomic absorption spectroscopy (AAS) and using the proposed luminescent A. fischeri-based biosensor suggests that the optical toxicity biosensor can be used for quantitative microdetermination of heavy metal toxicity in environmental water samples.
Microfluidics-based lab-on-chip (LOC) systems are an active research area that is revolutionising high-throughput sequencing for the fast, sensitive and accurate detection of a variety of pathogens. LOCs also serve as portable diagnostic tools. The devices provide optimum control of nanolitre volumes of fluids and integrate various bioassay operations that allow the devices to rapidly sense pathogenic threat agents for environmental monitoring. LOC systems, such as microfluidic biochips, offer advantages compared to conventional identification procedures that are tedious, expensive and time consuming. This paper aims to provide a broad overview of the need for devices that are easy to operate, sensitive, fast, portable and sufficiently reliable to be used as complementary tools for the control of pathogenic agents that damage the environment.
The humidity sensing characteristics of different sensing materials are important properties in order to monitor different products or events in a wide range of industrial sectors, research and development laboratories as well as daily life. The primary aim of this study is to compare the sensing characteristics, including impedance or resistance, capacitance, hysteresis, recovery and response times, and stability with respect to relative humidity, frequency, and temperature, of different materials. Various materials, including ceramics, semiconductors, and polymers, used for sensing relative humidity have been reviewed. Correlations of the different electrical characteristics of different doped sensor materials as the most unique feature of a material have been noted. The electrical properties of different sensor materials are found to change significantly with the morphological changes, doping concentration of different materials and film thickness of the substrate. Various applications and scopes are pointed out in the review article. We extensively reviewed almost all main kinds of relative humidity sensors and how their electrical characteristics vary with different doping concentrations, film thickness and basic sensing materials. Based on statistical tests, the zinc oxide-based sensing material is best for humidity sensor design since it shows extremely low hysteresis loss, minimum response and recovery times and excellent stability.
The rapid spread of the SARS-CoV-2 in the COVID-19 pandemic had raised questions on the route of transmission of this disease. Initial understanding was that transmission originated from respiratory droplets from an infected host to a susceptible host. However, indirect contact transmission of viable virus by fomites and through aerosols has also been suggested. Herein, we report the involvement of fine indoor air particulates with a diameter of ≤ 2.5 µm (PM2.5) as the virus's transport agent. PM2.5 was collected over four weeks during 48-h measurement intervals in four separate hospital wards containing different infected clusters in a teaching hospital in Kuala Lumpur, Malaysia. Our results indicated the highest SARS-CoV-2 RNA on PM2.5 in the ward with number of occupants. We suggest a link between the virus-laden PM2.5 and the ward's design. Patients' symptoms and numbers influence the number of airborne SARS-CoV-2 RNA with PM2.5 in an enclosed environment.
This work investigates the metals concentration in the tissues of Asian swamp eel, Monopterus albus. Five selected tissues, including liver, gill, bone, skin, and muscle were examined for the concentration of Zn, Cu, Cd, Pb, and Ni. The concentrations of Cd and Pb were found high in the muscle tissues of the eels. Additionally, high amounts of Zn and Cu metals were observed in the liver, whereas the Cd, Pb, and Ni metals were highly detected in gill. The accumulation of Zn, Cu, Cd, Pb, and Ni in both skin and bone of the eel seems to vary between seasons. Low levels of Zn, Cu, and Ni were identified in the muscle tissues of the eels. This study revealed that the concentration of Cd and Pb in the muscle tissues of Asian swamp eels exceeded the permissible limits by the US EPA, suggesting the consumption of the muscle may be hazardous and can severely affect one's health.
Contamination by endocrine disrupting compounds (EDCs) concerns the security and sustainability of a drinking water supply system and human exposure via water consumption. This study analyzed the selected EDCs in source (river water, n = 10) and supply (tap water, n = 155) points and the associated risks. A total of 14 multiclass EDCs was detected in the drinking water supply system in Malaysia. Triclosan (an antimicrobial agent) and 4-octylphenol (a plasticizer) were only detected in the tap water (up to 9.74 and 0.44 ng/L, respectively). Meanwhile, chloramphenicol and 4-nonylphenol in the system were below the method detection limits. Bisphenol A was observed to be highest in tap water at 66.40 ng/L (detection: 100%; median concentration: 0.28 ng/L). There was a significant difference in triclosan contamination between the river and tap water (p
Coral cover on reefs is declining globally due to coastal development, overfishing and climate change. Reefs isolated from direct human influence can recover from natural acute disturbances, but little is known about long term recovery of reefs experiencing chronic human disturbances. Here we investigate responses to acute bleaching disturbances on turbid reefs off Singapore, at two depths over a period of 27 years. Coral cover declined and there were marked changes in coral and benthic community structure during the first decade of monitoring at both depths. At shallower reef crest sites (3-4 m), benthic community structure recovered towards pre-disturbance states within a decade. In contrast, there was a net decline in coral cover and continuing shifts in community structure at deeper reef slope sites (6-7 m). There was no evidence of phase shifts to macroalgal dominance but coral habitats at deeper sites were replaced by unstable substrata such as fine sediments and rubble. The persistence of coral dominance at chronically disturbed shallow sites is likely due to an abundance of coral taxa which are tolerant to environmental stress. In addition, high turbidity may interact antagonistically with other disturbances to reduce the impact of thermal stress and limit macroalgal growth rates.
There is a paucity of information about the occurrence of microplastics (MPs) in edible fish tissues. Here, we investigated the potential presence of MPs in the excised organs (viscera and gills) and eviscerated flesh (whole fish excluding the viscera and gills) of four commonly consumed dried fish species (n = 30 per species). The MP chemical composition was then determined using micro-Raman spectroscopy and elemental analysis with energy-dispersive X-ray spectroscopy (EDX). Out of 61 isolated particles, 59.0% were plastic polymers, 21.3% were pigment particles, 6.55% were non-plastic items (i.e. cellulose or actinolite), while 13.1% remained unidentified. The level of heavy metals on MPs or pigment particles were below the detection limit. Surprisingly, in two species, the eviscerated flesh contained higher MP loads than the excised organs, which highlights that evisceration does not necessarily eliminate the risk of MP intake by consumers. Future studies are encouraged to quantify anthropogenic particle loads in edible fish tissues.
Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.
Although coastal water marine algae have been popularly used by others as indicators of heavy metal pollution, data within the Bay of Bengal for the estuarine Cox's Bazar region and Saint Martin's Island has remained scarce. Using marine algae, the study herein forms an effort in biomonitoring of metal contamination in the aforementioned Bangladesh areas. A total of 10 seaweed species were collected, including edible varieties, analyzed for metal levels through the use of the technique of EDXRF. From greatest to least, measured mean metal concentrations in descending order have been found to be K > Fe > Zr > Br > Sr > Zn > Mn > Rb > Cu > As > Pb > Cr > Co. Potential toxic heavy metals such as Pb, As, and Cr appear at lower concentration values compared to that found for essential mineral elements. However, the presence of Pb in Sargassum oligocystum species has been observed to exceed the maximum international guidance level. Given that some of the algae species are cultivated for human consumption, the non-carcinogenic and carcinogenic indices were calculated, shown to be slightly lower than the maxima recommended by the international organizations. Overall, the present results are consistent with literature data suggesting that heavy metal macroalgae biomonitoring may be species-specific. To the best of our knowledge, this study represents the first comprehensive macroalgae biomonitoring study of metal contamination from the coastal waters of Cox's Bazar and beyond.
Rapid industrialization and urbanization have resulted in environmental pollution and unsustainable development of cities. The concentration of 12 potentially toxic metal(loid)s in windowsill dust samples (n = 50) were investigated from different functional areas of Qom city with the highest level of urbanization in Iran. Spatial analyses (ArcGIS 10.3) and multivariate statistics including Principal Component Analysis and Spearman correlation (using STATISTICA-V.12) were adopted to scrutinize the possible sources of pollution. The windowsill dust was very highly enriched with Sb (50 mg/kg) and Pb (1686 mg/kg). Modified degree of contamination (mCd) and the pollution load indices (PLIzone) indicate that windowsill dust in all functional areas was polluted in the order of industrial > commercial > residential > green space. Arsenic, Cd, Mo, Pb, Sb, Cu, and Zn were sourced from a mixture of traffic and industrial activities, while Mn in the dust mainly stemmed from mining activities. Non-carcinogenic health risk (HI) showed chronic exposure of Pb for children in the industrial zone (HI = 1.73). The estimations suggest the possible carcinogenic risk of As, Pb, and Cr in the dust. The findings of this study reveal poor environmental management of the city. Emergency plans should be developed to minimize the health risks of dust to residents.
The tropical estuarine ecosystem is fascinating for studying the dynamics of water quality and phytoplankton diversity due to its frequently changing hydrological conditions. Most importantly, phytoplankton is the main supplier of ω3 polyunsaturated fatty acids (PUFA) in the coastal food web for fish as they could not synthesize PUFA. This study evaluated seasonal variations of water quality parameters in the Meghna River estuary (MRE), explored how phytoplankton diversity changes according to hydro-chemical parameters, and identified the major phytoplankton groups as the main source of PUFA for hilsa fish. Ten water quality indicators including temperature, dissolved oxygen, pH, salinity, dissolved inorganic nitrogen (DIN = nitrate, nitrite, ammonia) and phosphorus, dissolved silica and chlorophyll-a were evaluated. In addition, phytoplankton diversity was assessed in the water and hilsa fish gut. Principal component analysis (PCA) was used to analyze the spatio-temporal changes in the water quality conditions, and the driving factors in the MRE. Four main components were extracted and explained 75.4% variability of water quality parameters. The most relevant driving factors were dissolved oxygen, salinity, temperature, and DIN (nitrate, nitrite and ammonia). These variabilities in physicochemical parameters and dissolved inorganic nutrients caused seasonal variations in two major groups of phytoplankton. Peak abundance of Chlorophyta (green algae) occurred in water in nutrient-rich environments (nitrogen and phosphorus) during the wet (36%) season, while Bacillariophyta (diatoms) were dominant during the dry (32%) season that depleted dissolved silica. Thus, the decrease of green algae and the increase of diatoms in the dry season indicated the potential link to seasonal changes of hydro-chemical parameters. The green algae (53.7%) were the dominant phytoplankton group in the hilsa gut content followed by diatoms (22.6%) and both are contributing as the major source of PUFAs for hilsa fish according to the electivity index as they contain the highest amounts of PUFAs (60 and 28% respectively).
Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.
Most components of petroleum oily sludge (POS) are toxic, mutagenic and cancer-causing. Often bioremediation using microorganisms is hindered by the toxicity of POS. Under this circumstance, phytoremediation is the main option as it can overcome the toxicity of POS. Cajanus cajan a legume plant, was evaluated as a phyto-remediating agent for petroleum oily sludge-spiked soil. Culture dependent and independent methods were used to determine the rhizosphere microorganisms' composition. Degradation rates were estimated gravimetrically. The population of total heterotrophic bacteria (THRB) was significantly higher in the uncontaminated soil compared to the contaminated rhizosphere soil with C. cajan, but the population of hydrocarbon-utilizing bacteria (HUB) was higher in the contaminated rhizosphere soil. The results show that for 1 to 3% oily sludge concentrations, an increase in microbial counts for all treatments from day 0 to 90 d was observed with the contaminated rhizosphere CR showing the highest significant increase (p
Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.