A Study of Reducing Policy of Air Pollution and Characteristics of Air Pollutants Using an Air Pollution monitoring Network in Mexico city

2014 ◽  
Vol 17 (4) ◽  
pp. 121
Author(s):  
Yu Woon Jang ◽  
Sang Sub Ha ◽  
Il Soo Park ◽  
Gang Woong Lee ◽  
Kyung Won Chung
Keyword(s):  
Air Pollution ◽  
Mexico City ◽  
Air Pollutants ◽  
Monitoring Network ◽  
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

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 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.

scholarly journals Magnetic Biomonitoring as a Tool for Assessment of Air Pollution Patterns in a Tropical Valley Using Tillandsia sp.

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 283 ◽  
Author(s):  
Daniela Mejía-Echeverry ◽  
Marcos Chaparro ◽  
José Duque-Trujillo ◽  
Mauro Chaparro ◽  
Ana Castañeda Miranda
Keyword(s):  
Air Pollution ◽  
Toxic Elements ◽  
Environmental Magnetism ◽  
Monitoring Network ◽  
Potentially Toxic Elements ◽  
Pollution Monitoring ◽  
Residential Areas ◽  
Concentrated Mass ◽  
Air Pollution Monitoring ◽  
Magnetic Parameters

Recently, air pollution alerts were issued in the Metropolitan Area of Aburrá Valley (AVMA) due to the highest recorded levels of particulate matter (PM2.5 and PM10) ever measured. We propose a novel methodology based on magnetic parameters and an epiphytic biomonitor of air pollution in order to improve the air pollution monitoring network at low cost. This methodology relies on environmental magnetism along with chemical methods on 185 Tillandsia recurvata specimens collected along the valley (290 km2). The highest magnetic particle concentrations were found at the bottom of the valley, where most human activities are concentrated. Mass-specific magnetic susceptibility (χ) reaches mean (and s.d.) values of 93.5 (81.0) and 100.8 (64.9) × 10−8 m3 kg−1 in areas with high vehicular traffic and industrial activity, while lower χ values of 27.3 (21.0) × 10−8 m3 kg−1 were found at residential areas. Most magnetite particles are breathable in size (0.2–5 μm), and can host potentially toxic elements. The calculated pollution load index (PLI, based on potentially toxic elements) shows significant correlations with the concentration-dependent magnetic parameters (R = 0.88–0.93; p < 0.01), allowing us to validate the magnetic biomonitoring methodology in high-precipitation tropical cities and identify the most polluted areas in the AVMA.

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