scholarly journals AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting

2021 ◽  
Vol 2115 (1) ◽  
pp. 012016
Author(s):  
Geetha Mani ◽  
Joshi Kumar Viswanadhapalli ◽  
P Sriramalakshmi
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Moving Average ◽  
Pollution Monitoring ◽  
Model Parameters ◽  
Air Pollution Monitoring ◽  
Moving Average Model ◽  
Monitoring And Forecasting ◽  
Life On Earth ◽  
Auto Regression

Abstract Air is one of the most fundamental constituents for the sustenance of life on earth. The consumption of non-renewable energy sources and industrial parameters steadily increases air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of Air Quality in our environment needs to be monitored continuously. This paper presents the execution and plan of Internet-of-Things (IoT) based Air Pollution Monitoring and Forecasting utilising Artificial Intelligent (AI) methods. Also, Online Dashboard was created for real-time monitoring of Air pollutants (both live and forecasted data) through ‘firebase’ from the Google cloud server. The air pollutants like Carbon Mono Oxide (CO), Ammonia (NH3), and Ozone (O3) layer information are collected from IoT-based sensor nodes in Vijayawada Region. Time Series modelling techniques like the Naive Bayes Model, Auto Regression Model (AR), Auto Regression Moving Average Model (ARMA), and Auto-Regression Integrating Moving Average Model (ARIMA) used to forecast the individual air pollutants aforementioned. The data collected from the IoT sensor node with a time frame is fed as input features for training the model, and optimised model parameters are obtained. The obtained model parameters are again verified with new unseen data for time. The performances of various Time Series models are validated with the help of performance indices like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The machine learning algorithm flashed in Raspberry Pi-3. It acts as an edge computing device. The current air pollutants data and forecasted data are monitored for the next 4 hours through an online dashboard created in an open-source firebase from Google cloud service.

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

2021 ◽  
Vol 30 (04) ◽  
pp. 2150018
Author(s):  
Anindita Borah ◽  
Bhabesh Nath
Keyword(s):  
Air Pollution ◽  
Association Rules ◽  
Air Pollutants ◽  
Pattern Mining ◽  
Environmental Parameters ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rare Association ◽  
Support Threshold ◽  
Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

scholarly journals Biological monitoring used in assessment of the air pollutants

2019 ◽  
pp. 152-159
Author(s):  
Andreea Cozea ◽  
Elena Bucur
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Environmental Problem ◽  
Anthropogenic Activities ◽  
Pollution Monitoring ◽  
Biological Models ◽  
Monitoring And Control ◽  
Pollution Load ◽  
Air Pollution Monitoring ◽  
And Control

Nowadays, the air pollution has become a major environmental problem due to rapid increase of industrialization and anthropogenic activities which led to climate change. Air pollution is considered as a harmful agent for human health. Different classes of pollutants like gaseous (SOx, NOx) are continuously released in air and perceived/recognized as pollutants. Among the biological models, plants could indicate pollution load in a particular area via alterations in physiological parameters so, there is a need for reliable and sustainable air pollution monitoring and control methods.

scholarly journals Using deep learning to extend the range of air pollution monitoring and forecasting

2020 ◽  
Vol 408 ◽  
pp. 109278 ◽  
Author(s):  
Philipp Hähnel ◽  
Jakub Mareček ◽  
Julien Monteil ◽  
Fearghal O'Donncha
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Monitoring And Forecasting

scholarly journals Short Communication: Effect of urban greening and land use on air pollution in Chelyabinsk, Russia

2020 ◽  
Vol 21 (6) ◽  
Author(s):  
TATYANA G. KRUPNOVA ◽  
OLGA V. RAKOVA ◽  
ANNA L. PLAKSINA ◽  
SVETLANA V. GAVRILKINA ◽  
EVGENY O. BARANOV ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Air Pollutants ◽  
Thermal Power ◽  
Coniferous Forest ◽  
Pollution Monitoring ◽  
Dominant Factor ◽  
Urban Greening ◽  
Air Pollution Monitoring ◽  
Power Stations

Abstract. Krupnova TG, Rakova OV, Plaksina AL, Gavrilkina SV, Baranov EO, Abramyan AD. 2020. Effect of urban greening and land use on air pollution in Chelyabinsk, Russia. Biodiversitas 21: 2716-2720. Chelyabinsk is a major industrial Russian city that faces diverse environmental issues, the most important of which is air emissions. The primary sources of air pollution in Chelyabinsk are industry (concrete product plants, ferrous and nonferrous metallurgy such as zinc production plants, and pulp production), thermal power stations, and transport. People have known that trees can help to reduce air pollutants for a long time. We studied 8 zones within a radius of one kilometer from state air pollution monitoring stations. Eight land-use types such as industrial category, residential category, natural and semi-natural broadleaved vegetation, natural and semi-natural coniferous vegetation, broadleaved forest, coniferous forest, artificial broadleaved vegetation, and artificial coniferous vegetation, were obtained. The response of air pollution to land-use and urban greening was analyzed. Analysis results showed that there was no correlation between industrial and residential categories of land-using and concentrations of the most dangerous air pollutants in Chelyabinsk (formaldehyde, hydrogen fluoride, and nitrogen dioxide). The dominant factor affecting urban air quality was urban greening.

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