Machine Learning Techniques for Air Quality Forecasting and Study on Real-Time Air Quality Monitoring

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

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A Survey on Different Machine Learning Techniques for Air Quality Forecasting for Urban Air Pollution

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2019.4395 ◽

2019 ◽

Vol 7 (4) ◽

pp. 2185-2194

Author(s):

Sayali Nemade

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Air Quality ◽

Urban Air Pollution ◽

Machine Learning Techniques ◽

Urban Air ◽

Learning Techniques ◽

Air Quality Forecasting

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Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

ACM Transactions on Sensor Networks ◽

10.1145/3446005 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-44

Author(s):

Francesco Concas ◽

Julien Mineraud ◽

Eemil Lagerspetz ◽

Samu Varjonen ◽

Xiaoli Liu ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Environmental Monitoring ◽

Low Cost ◽

Quality Monitoring ◽

Machine Learning Techniques ◽

Future Research ◽

Great Promise ◽

Air Quality Monitoring ◽

Monitoring Stations

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

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Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1014.0785s319 ◽

2019 ◽

Vol 8 (5S3) ◽

pp. 53-57

Keyword(s):

Machine Learning ◽

Water Quality ◽

Random Forest ◽

Real Time ◽

Multilayer Perceptron ◽

Low Cost ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Machine Learning Techniques ◽

Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

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Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Sensors ◽

10.3390/s21154956 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4956

Author(s):

Chew Cheik Goh ◽

Latifah Munirah Kamarudin ◽

Ammar Zakaria ◽

Hiromitsu Nishizaki ◽

Nuraminah Ramli ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Support Vector Regression ◽

Real Time ◽

Monitoring System ◽

Machine Learning Algorithms ◽

Quality Monitoring ◽

Prediction Algorithm ◽

Support Vector ◽

Air Quality Monitoring

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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Machine Learning Methods for Air Quality Monitoring

Proceedings of the 3rd International Conference on Networking, Information Systems & Security ◽

10.1145/3386723.3387835 ◽

2020 ◽

Author(s):

Mohamed Akram Zaytar ◽

Chaker El Amrani

Keyword(s):

Machine Learning ◽

Air Quality ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Learning Methods ◽

Machine Learning Methods

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Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽

10.3390/s21103338 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3338

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Nenad Gligoric ◽

Ilija Radovanovic ◽

Ivan Popovic

Keyword(s):

Machine Learning ◽

Air Quality ◽

Relative Humidity ◽

Low Cost ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lower Accuracy ◽

Wide Range ◽

The Impact ◽

Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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