USE OF SELF-ORGANIZING MAPS TO CATEGORIZE EXPOSURE PATTERNS IN AIR POLLUTION MONITORING DATA

The paper reveals the essence and features of the proposed approach to detecting anomalies in a self-organizing decentralized wireless sensor network (WSN). As a basis for detecting anomalies, the used WSN is intended to monitor atmospheric air pollution near industrial facilities and human life objects. The distinctive features of such a network are the decentralized nature of its structure and services, the autonomy and mobility of the network nodes, as well as the possibility of non-deterministic physical movement of nodes in space. The spontaneous nature of the dynamic formation of the network topology as well as the assignment of roles and private monitoring functions between the available network nodes determines such networks are subject to attacks that exploit the properties of network decentralization and its self-organization. The proposed approach to detecting anomalies is based on the collection and analysis of data from sensors and is designed to increase the security of self-organizing decentralized WSN by identifying anomalies that are critical in the context of the monitoring purposes.

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Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

Environmental Science & Technology ◽

10.1021/es1013328 ◽

2010 ◽

Vol 44 (17) ◽

pp. 6738-6744 ◽

Cited By ~ 24

Author(s):

Alexander Kolovos ◽

André Skupin ◽

Michael Jerrett ◽

George Christakos

Keyword(s):

Air Pollution ◽

Pollution Monitoring ◽

Monitoring Data ◽

Air Pollution Monitoring ◽

Spatiotemporal Mapping ◽

Perspective Analysis

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A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

i-manager’s Journal on Embedded Systems ◽

10.26634/jes.5.1.8249 ◽

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

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

Data in Brief ◽

10.1016/j.dib.2021.107127 ◽

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

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Research on air pollution system based on neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189464 ◽

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.

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IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 5th International Conference on Information Technology Research (ICITR) ◽

10.1109/icitr51448.2020.9310838 ◽

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

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COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽

10.3390/atmos12030290 ◽

2021 ◽

Vol 12 (3) ◽

pp. 290

Author(s):

Akvilė Feiferytė Skirienė ◽

Žaneta Stasiškienė

Keyword(s):

Air Pollution ◽

Air Quality ◽

Social Services ◽

Pollution Monitoring ◽

Air Pollution Monitoring ◽

Rapid Spread ◽

Air Quality Improvement ◽

Environment Agency ◽

Pollution Emissions ◽

Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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