scholarly journals IOT based Air Quality Monitoring System Using MQ135 and MQ7 with Machine Learning Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 599-606 ◽  
Author(s):  
Kinnera Bharath Kumar Sai ◽  
Somula Rama Subbareddy ◽  
Ashish Kumar Luhach
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
State Of The Art ◽  
New Delhi ◽  
Air Quality Monitoring ◽  
High Pollution ◽  
Iot Platforms ◽  
The Cost ◽  
Learning Analysis ◽  
Stroke Engine

This paper deals with measuring the Air Quality using MQ135 sensor along with Carbon Monoxide CO using MQ7 sensor. Measuring Air Quality is an important element for bringing awareness to take care of the future generations and for a healthier life. Based on this, Government of India has already taken certain measures to ban Single Stroke and Two Stroke Engine based motorcycles which are emitting high pollution. We are trying to implement a system using IoT platforms like Thingspeak or Cayenne in order to bring awareness to every individual about the harm we are doing to our environment. Already, New Delhi is remarked as the most pollution city in the world recording Air Quality above 300 PPM. We have used easiest platform like Thingspeak and set the dashboard to public such that everyone can come to know the Air Quality at the location where the system is installed. Machine Learning analysis brings us a lot of depth in understanding the information that we obtained from the data. Moreover, we are proviing a reducement of the cost of components versus the state of the art.

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

Sensors ◽  
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.

Unwatched Pollution: The Effect of Intermittent Monitoring on Air Quality

2021 ◽  
Vol 111 (7) ◽  
pp. 2101-2126
Author(s):  
Eric Yongchen Zou
Keyword(s):  
Air Quality ◽  
Local Governments ◽  
Clean Air Act ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Environmental Standards ◽  
Strategic Responses ◽  
Short Term ◽  
Clean Air ◽  
High Pollution

Intermittent monitoring of environmental standards may induce strategic changes in polluting activities. This paper documents local strategic responses to a cyclical, once-every-six-day air quality monitoring schedule under the federal Clean Air Act. Using satellite data of monitored areas, I show that air quality is significantly worse on unmonitored days. This effect is explained by short-term suppression of pollution on monitored days, especially during high-pollution periods when the city’s noncompliance risk is high. Cities’ use of air quality warnings increases on monitored days, which suggests local governments’ role in coordinating emission reductions. (JEL K32, Q35, Q58, R11)

scholarly journals Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

2017 ◽  
Vol 7 (8) ◽  
pp. 823 ◽  
Author(s):  
Shaharil Mad Saad ◽  
Allan Andrew ◽  
Ali Md Shakaff ◽  
Mohd Mat Dzahir ◽  
Mohamed Hussein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Supervised Machine Learning ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring

scholarly journals Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

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.

scholarly journals Ulixes: Facial Recognition Privacy with Adversarial Machine Learning

2021 ◽  
Vol 2022 (1) ◽  
pp. 148-165
Author(s):  
Thomas Cilloni ◽  
Wei Wang ◽  
Charles Walter ◽  
Charles Fleming
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Facial Recognition ◽  
Clustering Methods ◽  
Non Invasive ◽  
Current State ◽  
Social Media Platforms ◽  
Adversarial Examples ◽  
The Cost ◽  
Photo Management

Abstract Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.

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