ABSTRACT
Plasmonic nanobiosensors have an enormous application range. It has the capacity to detect a wide variety of substances including metal, protein and even nucleic acids due to the superiority of SPR and LSPR. Plasmonic biosensors have been widely applied in the field of disease diagnosis, environmental conservation and food safety, eliminating barriers of traditional diagnosis methods and providing sensitive, quick and label-free devices. The applications of plasmonic biosensors in detection of many concerned diseases like cancer and SARS-CoV-2 are making an improvement on our medical condition. In the field of environmental protection, plasmonic-based biosensors also show great potential. They can efficiently detect two main types of contaminants, inorganic heavy metals involving Pb, Cd, As and Hg, and organic pollutants like polycyclic aromatic hydrocarbons (PAHs). Plasmonic biosensors could also overcome challenges on food allergen detection. This paper mainly focusses on SPR and LSPR-based nanobiosensors' application in environmental protection, food safety and health-care. © 2022 SPIE. Downloading of the is permitted for personal use only.
ABSTRACT
Air pollution causes several diseases like suffocation, chronic obstructive pulmonary disease (COPD), lung cancer, throat infection, and so forth. So, there is a need to monitor indoor air quality for the safety of human life. Indoor air pollution is even more dangerous than outdoor air pollution. Even, after the COVID-19 pandemic, humans are spending most of their time in indoor houses. In addition to this, air pollution is increasing day by day due to varying climate changes. In view of this fact, this research wor has designed and developed a novel system based on the latest IoT technology that monitors indoor air quality and provides a web portal for data visualization. The proposed system consists of several gas sensors integrated on a single PCB that helps in reading seven pollutants like CO2, CO, O3, NO2, VOC, and Particulate Matter along with humidity and temperature. In our work, Raspberry Pi acts as a processor as well as the communicating node to the cloud. The experimental setup is deployed in several indoor places like closed labs, classrooms, homes, etc., where humans spend more time. Raspberry Pi is having an inbuilt wi-fi functionality and the real-time data is sent to Google Firebase with help of a Jio Fi router. After visualizing the data, Indoor Air Quality Index (IAQI) is measured and generates an alarm for the safety of humans when air standard crosses a marginal value. © 2022 IEEE.
ABSTRACT
Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).
ABSTRACT
TROPOMI (TROPOspheric Monitoring Instrument) measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows for the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low but slightly above zero (±2 m s-1). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A band) wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1–4×1015 molec. cm-2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the four available periods with standard and FRESCO+ wide test retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35 %–75 %) and sharper (10 %–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 10 %–20 % during the beginning of the COVID-19 pandemic in 2020. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from automatic identification system (AIS) data on ship location, speed, and engine.