Transplanted Epiphytic Lichens as Biomonitors of Air-Contamination by Natural Radionuclides Around the Žirovski VRH Uranium Mine, Slovenia

1995 ◽  
Vol 27 (5) ◽  
pp. 375-385 ◽  
Author(s):  
Z. Jeran ◽  
A. R. Byrne ◽  
F. Batič
Keyword(s):  
Natural Radionuclides ◽  
Pollution Monitoring ◽  
Uranium Mine ◽  
Production Plant ◽  
Active Biomonitoring ◽  
Air Pollution Monitoring ◽  
Air Contamination ◽  
Site Monitoring ◽  
Sampling Locations

AbstractSamples of Hypogymnia physodes were transplanted to the environment of the former uranium mine at Žirovski vrh, Slovenia for two exposure experiments. The levels of the long-lived radionuclides, 238U, 226Ra and 210Pb in lichen material were measured after 4 and 7 months in the first experiment, and 4, 8 and 12 months in the second, and compared with the levels in lichens growing in-situ from the same sampling locations. They were also compared with the nuclide levels found in air particulates by gamma spectrometry obtained at the regular site monitoring stations. The results showed that each of the radionuclides had its own distribution pattern in this environment. The highest 226Ra levels were found in lichens in the near vicinity of the dry-tailings pile, while U concentrations were high in the valley of the confluence of the Todraščica and Brebovščica streams close to the former yellow-cake production plant in Todraž, and then decreased downstream. 210Pb was the most uniformly distributed radionuclide and exhibited the highest level. The results also confirm that active biomonitoring with transplanted lichens can be a useful and cheap supplement to instrumental air pollution monitoring.

Design of Real Time Air Pollution Monitoring System Based on Web Application and Main-Memory Database

2020 ◽  
Vol 48 (3) ◽  
pp. 01-02
Author(s):  
Matthew Loginov
Keyword(s):  
Air Quality ◽  
Web Application ◽  
Main Memory ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Air Contamination ◽  
Negative Effect ◽  
Information Stream ◽  
World Information ◽  
The Impact

Air contamination issues are firmly connected to geographic areas. The current ecological observing frameworks have little spotlight on geographic data handling and continuous perception. Air quality checking framework. Sensor constituent on this scaffold depends on nano organized zinc oxide slender film blended utilizing soakedstufftrack. In this magazine, a spatial augmentation to fundamental reminiscencerecord is executed to help the capacity and record of virtual-world information. In the interim, noteworthy information is put away in disk-based spatial catalogue, giving the capacity of questioning and breaking down. A continuous air quality checking framework is structured and executed which can route and show information stream on constant.Natural checking is a methodical methodology for watching and contemplating the state of condition. For the sound person require taking in a spotless air yet because of expanding the transportation framework natural air get contaminated. Transport framework has the effect on condition where we live. Increment in vehicle offers ascend to expanding traffic related poison discharge. In this way, to follow the impact of this contamination on condition and soundness of individual it is important to follow the degree of contamination in urban and sub urban zones. Numerous wellbeing related issues are emerging from air contamination. In this manner, air quality observing is required so as to give valuable data about the contamination and can take proper measures to alleviate the negative effect at whatever point it is vital.

scholarly journals Air pollution monitoring with mosses in Western Rhodopes, Bulgaria

2019 ◽  
Vol 51 (2) ◽  
pp. 256-260
Author(s):  
N. P. Gribacheva ◽  
G. M. Gecheva ◽  
V. M. Stefanova
Keyword(s):  
Inductively Coupled Plasma ◽  
Pollution Monitoring ◽  
Uranium Mine ◽  
Radioactive Elements ◽  
Air Pollution Monitoring ◽  
Plasma Spectrometry ◽  
Inductively Coupled ◽  
Inductively Coupled Plasma Spectrometry ◽  
Icp Ms ◽  
First Time

For the first time a complex study of the bioaccumulation of 22 macro- and microelements, as well as of 18 rare earth and radioactive elements in mosses was applied in the territory of Western Rhodopes. The research is a part of the international program (ICP Vegetation, UNECE) that has been carried out in most of the European countries since 1987. Certain local emitters like old and open mines, serpentines, as well as cross-border transmission were identified. In addition, highest concentrations of radioactive elements were registered in the area of an old uranium mine and most elevated sites, probably due to increased wet deposition. Both heavy metals and toxic elements, as well as radioactive elements maxima were lower than measured highest levels for Bulgaria and Europe. In such studies, inductively coupled plasma spectrometry (ICP-OES, ICP-MS) could be recommended.

Based Air Pollution Monitoring System based on Artificial Neural Networks in Industrial Areas

2020 ◽  
Vol 49 (3) ◽  
pp. 03-04
Author(s):  
Renata Belyakova
Keyword(s):  
Meteorological Factors ◽  
Neural System ◽  
Primary Objective ◽  
Pollution Monitoring ◽  
Scientific Models ◽  
Control Plan ◽  
Air Pollution Monitoring ◽  
Air Contamination ◽  
Quality Issue ◽  
Auto Regressive

Since quite a few years, mechanical contamination and particularly air contamination get a significant enthusiasm because of the way that ventures are an ever increasing number of poisons. On account of the advancement of numerous expectation methods, researchers and industrials give more significance to contamination forecast. The forecast of mechanical air contaminations fixations permits taking preventive estimates, for example, lessening toxin emanation in the climate. The point of this work is to foresee the toxins fixation in mechanical destinations by the utilization of a non-linear auto-regressive representation based ANN. Scientific models for expectation of air poison echelon are considered. The primary objective of the representation is the expectation, based on both the meteorological estimating and the creation strategy, of poison levels, has been thought of. The incorporation of this model in an emanation control plan may speak to a helpful way to deal with the air quality issue. Record used to prepare the neural system relates to verifiable time arrangement of meteorological factors and convergences of poisons in the mechanical zones.

scholarly journals In-Situ Environmental Gamma Radiation Monitoring at Ramna Thana, Dhaka, Bangladesh

2019 ◽  
Vol 11 (3) ◽  
pp. 263-272
Author(s):  
M. S. Mian ◽  
M. S. Rahman ◽  
J. Islam ◽  
K. N. Sakib ◽  
M. M. Tasnim ◽  
...  
Keyword(s):  
Gamma Radiation ◽  
Global Positioning System ◽  
Annual Effective Dose ◽  
Natural Radionuclides ◽  
Outdoor Environment ◽  
Dose Rates ◽  
Gps Navigation ◽  
Sampling Locations ◽  
Global Positioning

Environmental gamma radiation dose rates were measured using a digital portable Gamma-Scout detector from April-May 2018. For this, total 22 monitoring points (MP) were selected in the outdoor environment in the area of Ramna Thana under Dhaka city. The MPs were marked-out using Global Positioning System (GPS) navigation. The GPS reading of the sampling locations were varied from E: 90o23.568' to E: 90o24.895' and from N: 23o44.031' to N: 23o45.018'. The measured dose rates due to natural radionuclides were ranged from 0.115 ± 0.042 µSv.hr-1 to 0.186 ± 0.051 µSv.h-1 with an average of 0.145 ± 0.044 µSv.h-1. The annual effective dose to the population from outdoor environmental gamma radiation was varied from 0.201 ± 0.073 mSv to 0.326 ± 0.090 mSv with an average of 0.255 ± 0.073 mSv. This kind of study is required to detect the presence of natural radionuclides and artificial radionuclides (if any) releasing from nuclear and radiological facilities in the country or from neighboring countries for normal operations or in case of accident/incident. From this study, it can be concluded that there is no radiation burden to the environment due to man-made sources.

A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Analysis ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 ◽  
Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring System ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Open Framework

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

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