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.

scholarly journals Research on an Iot Based Air Pollution Monitoring System

2019 ◽  
Vol 8 (9S2) ◽  
pp. 553-558
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Life Forms ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Pollution Level ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
Different Sources

Humankind, moving to a period centered upon improvement has overlooked the significance of supportability and has been the real guilty party behind the rising Pollution levels in the world's air among all other living life forms. The Pollution levels at certain spots have come to such high degrees that they have begun hurting our very own It will being. An IoT based Air Pollution observing framework incorporates a MQ Series sensor interfaced to a Node MCU outfitted with an ESP8266 WLAN connector to send the sensor perusing to a Thing Speak cloud. Further extent of this work incorporates an appropriate AI model to foresee the air Pollution level and an anticipating model, which is fundamentally a subset of prescient displaying. As age of poisonous gases from ventures, vehicles and different sources is immensely expanding step by step, it winds up hard to control the dangerous gases from dirtying the unadulterated air. In this paper a practical air Pollution observing framework is proposed. This framework can be utilized for observing Pollutions in demeanor of specific territory and to discover the air peculiarity or property examination. The obligated framework will concentrate on the checking of air poisons concentrate with the assistance of mix of Internet of things with wireless sensor systems. The investigation of air quality should be possible by figuring air quality index (AQI)

Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study

2021 ◽  
Author(s):  
Paul D Rosero-Montalvo ◽  
Vivian F López-Batista ◽  
Ricardo Arciniega-Rocha ◽  
Diego H Peluffo-Ordóñez
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Classification Performance ◽  
Pollution Monitoring ◽  
Machine Learning Techniques ◽  
Related Factors ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
Learning Techniques ◽  
High Pollution

Abstract Air pollution is a current concern of people and government entities. Therefore, in urban scenarios, its monitoring and subsequent analysis is a remarkable and challenging issue due mainly to the variability of polluting-related factors. For this reason, the present work shows the development of a wireless sensor network that, through machine learning techniques, can be classified into three different types of environments: high pollution levels, medium pollution and no noticeable contamination into the Ibarra City. To achieve this goal, signal smoothing stages, prototype selection, feature analysis and a comparison of classification algorithms are performed. As relevant results, there is a classification performance of 95% with a significant noisy data reduction.

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.

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.

IoT-Based Real Time Air Pollution Monitoring System

2019 ◽  
Vol 11 (4) ◽  
pp. 28-41 ◽  
Author(s):  
Cynthia J. ◽  
Saroja M.N. ◽  
Parveen Sultana ◽  
J. Senthil
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Monitoring System ◽  
Geographical Area ◽  
Ambient Air ◽  
Pollution Monitoring ◽  
Pollution Level ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
Major Factors

Humans can be adversely affected by exposure to air pollutants in ambient air. Hence, health-based standards and objectives for a number of pollutants in the air are set by each country. Detection and measurement of contents of the atmosphere are becoming increasingly important. Careful planning of measurements is essential. One of the major factors that influence the representativeness of data collected is the location of monitoring stations. The planning and setting up of a monitoring station are complex and incurs a huge expenditure. An IoT-based real time air pollution monitoring system is proposed to monitor the pollution levels of various pollutants in Coimbatore city. The geographical area is classified as industrial, residential and traffic zones. This article proposes an IoT system that could be deployed at any location and store the measured value in a cloud database, perform pollution analysis, and display the pollution level at any given location.

scholarly journals Intelligent Air Pollution Monitoring System for Smart Cities Using IoT and Machine Learning

2021 ◽  
Vol 10 (2) ◽  
pp. 1399-1407
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Control Measures ◽  
Pollution Monitoring ◽  
World Health ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
The World

Today, almost everything is going under automation. Air pollution has become one of the major crises across the globe. According to the report of the World Health Organization (WHO), around 580,000 people died due to air pollution. This document deals with the effective monitoring of air pollution systems. The proposed technique uses machine learning algorithms for the intelligent monitoring of air pollution. The concept of the Internet of Things (IoT) is implemented in the system to make it more reliable and accessible from anywhere throughout the world. ESP32 is used as a microprocessor for decision making purposes. The system uses Arduino software to build an algorithm. The DHT11 module is used to sense the humidity as well as temperature. MQ-2, MQ-7 and MQ-135 are used for sensing the level of methane, carbon monoxide and for measuring air quality, respectively. A buzzer is used to identify any unusual condition. Our work considers pollution caused by vehicles and provides an in-the-moment solution that does not directly monitor pollution levels, as well as control measures for reducing traffic in extremely polluted areas. This system will undoubtedly be on humans' behalf in such a way that a smart city will have much less time for spending, and there will undoubtedly be other industries, and the air will undoubtedly be extra polluted, and this device will undoubtedlyallow people to understand how safe the air is.

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
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