scholarly journals Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3716 ◽  
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Pollution Monitoring ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Calibration Transfer ◽  
Good Efficiency ◽  
Electronic Noses ◽  
Air Pollution Monitoring ◽  
Direct Standardization

Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.

scholarly journals Air Pollution Monitoring and Prediction System for Sustainable Metropolitan Cities using Iot

2020 ◽  
Vol 9 (3) ◽  
pp. 3308-3312
Keyword(s):  
Air Pollution ◽  
Pollution Monitoring ◽  
Support Vector ◽  
Air Pollution Monitoring ◽  
Counter Measures ◽  
Metropolitan Cities ◽  
Successful Prediction ◽  
Prediction Mechanism

In today’s world, the temperature of the environment is gradually rising. One of the main reasons for it is decreasing the quality of air mainly caused due to air pollution. There are many harmful substances present in the environment that is the cause of the declining quality of air. These pollutants get mixed along with the air and pollute the environment. The two air pollutants are considered here, CO2 and NO, to reduce air pollution, there is a need to know the number of pollutants, with the help of sensors in this experiment the level of pollutants are to be monitored and based on that a prediction mechanism is developed to determine the level of pollutants in the future. There are some machine learning concepts involved, K means clustering for classification of pollutants along with the S.V.M. (Support Vector Machine). With the successful prediction of the level of pollutants, the necessary counter measures can be adaopted.

Air Pollution Evaluation by Combining Stationary, Smart Mobile Pollution Monitoring and Data-Driven Modelling

2021 ◽  
pp. 222-227
Author(s):  
A. Shifa ◽  
Dr. S. Rathi
Keyword(s):  
Air Pollution ◽  
Atmospheric Condition ◽  
Vital Role ◽  
Pollution Monitoring ◽  
Data Driven ◽  
Support Vector ◽  
Pollution Dispersion ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
Air Quality Prediction

Air pollution has become a major issue in large cities because increasing traffic, industrialization and it becomes more difficult to manage due to its hazardous effects on the human health and many air pollution-triggering factors. This paper puts forth a machine learning approach to evaluate the accuracy and potential of such mobile generated information for prediction of air pollution. Temperature, wind, humidity play a vital role in influencing the pollution dispersion and accumulation, majorly influencing the prediction of pollution levels. Thus, this paper includes the atmospheric condition information registered throughout the study period in order to understand the influence of these factors on air pollution monitoring. Data driven modelling is an efficient way of extracting valuable information from generated data sets, however it is less efficient when the data is incomplete or contains inaccuracies. This modelling approach has true potential for real time operations because it can detect non-linear spatial relationships between sensing units and could aggregate results for regional investigation. Neural networks comparatively showed good capability in air quality prediction than support vector regression.

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

Atmosphere ◽  
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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