Urban Air Pollution Monitoring System With Forecasting Models

Shaban, Khaled; Kadri, Abdullah; Rezk, Eman

doi:10.1109/jsen.2016.2514378

Cited by 213 publications

(49 citation statements)

References 23 publications

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“…One of the areas in which these tools are more and more frequently used is the problem of air pollution, described in this paper. In this case, they are capable of contamination forecasting [11], [12], [6], [5], [13], [10], [7], [4].…”

Section: Using Ann To Monitor and Predict Air Pollutantsmentioning

confidence: 99%

“…In another work [6] a multilayer perceptron (MLP) has been used to forecast the impact of pollution caused by road traffic (exhaust fumes) on the health of residents. In [13] three different machine learning algorithms are presented, including, among others, a neural network for the monitoring and forecasting of pollutants, such as ground-level ozone O 3 , nitrogen dioxide NO 2 and dioxide sulfur SO 2 . Another work in this area [10] presents the concept of self-organizing networks [7] used for the classification of data on sulfur dioxide hazards SO 2 is presented.…”

Section: Using Ann To Monitor and Predict Air Pollutantsmentioning

confidence: 99%

See 1 more Smart Citation

Novel Solutions for Smart Cities—Creating Air Pollution Maps Based on Intelligent Sensors

Banach

Talaśka

Długosz

2018

Annals of Computer Science and Information Systems

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The paper presents novel solutions for systems used to create air pollutions maps in smart cities. Ability to record pollution levels with a function of a short-term prediction of their fluctuations may be useful for cyclists and pedestrians moving through the city. Based on such data they can choose their route through the city in such a way, as to avoid the most polluted areas. Systems of this type are in the range of solutions characteristic for smart cities. Their effectiveness requires a relatively dense wireless sensor network (WSN) composed of miniaturized and cheap intelligent pollution sensors, capable not only of data recording and transmitting, but also of some data processing with the prediction abilities. Sensors of this type require a development of various circuit components that feature small sizes and ultra-low energy consumption. One of the main blocks, in this case, should be an artificial neural network (ANN) implemented at the transistor level. In this work, we present prototype circuits designed by us for the described purposes. The realized blocks include a finite impulse response (FIR) filter, programmable analog-to-digital converters (ADCs) with internal controlling clock generators and main building blocks of a parallel ANN. The specialized chips (ASIC -application specific integrated circuit) with the described components were implemented in the CMOS technology in the full custom style.

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Section: Using Ann To Monitor and Predict Air Pollutantsmentioning

confidence: 99%

Section: Using Ann To Monitor and Predict Air Pollutantsmentioning

confidence: 99%

Novel Solutions for Smart Cities—Creating Air Pollution Maps Based on Intelligent Sensors

Banach

Talaśka

Długosz

2018

Annals of Computer Science and Information Systems

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“…Real time indoor sensors are being used in most of the references for indoor in some cases such as [4], [5], [8], and [12] whereas [6] designed for urban Air Quality monitoring. Most of them uses an Arduino and auto calibration sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Most of them uses an Arduino and auto calibration sensors. Reference [3], [4] produces a feedback alert for any abrupt change of values, whereas reference [5], [6] takes real time action based on the values from the sensors. [4] Has a problem of issues such as spectrum availability detection, interference mitigation and spectrum sharing.…”

Section: Related Workmentioning

confidence: 99%

Indoor Air Quality Management Using Raspberry Pi and Graphlab

Acharyya¹,

Hazra²,

Pal³

et al. 2017

IJRET

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“…These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving [4] …”

Section: Urban Air Pollution Monitoring System With Forecasting Momentioning

confidence: 99%

IOT-Enabled Air Pollution Meter with Digital Dashboard on Smart-Phone for Vehicles

Taskar¹

2017

IJRASET

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Air pollution is one of the main environmental issues nowadays. Airpollution is a most serious problem of the current time all over the world especially in the large cities because of industrialization and huge number of vehicles. The release of gaseous pollutants from burning fuel of motor vehicles is one major reason of air pollution. Vehicles release harmful gaseous like Carbon Monoxide (CO), Carbon Dioxide (CO2) which accounts for 50% of the total atmospheric pollutants. This project focuses on the monitoring and controlling the air pollution generated by vehicles. It is an real-time embedded system in which, a device will be attached to Silencer of vehicle. When pollution generated by vehicles goes beyond the threshold value, sensors will detect and then device will give intimation to the owner of vehicle through android app. If owner doesn't repair his/her vehicle and vehicle again generates pollution above threshold, then second intimation will be given to owner. And if the same repeats for third time, then device will lock the vehicle after 10 kilometers and send the details of the vehicles to RTO.

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Urban Air Pollution Monitoring System With Forecasting Models

Cited by 213 publications

References 23 publications

Novel Solutions for Smart Cities—Creating Air Pollution Maps Based on Intelligent Sensors

Novel Solutions for Smart Cities—Creating Air Pollution Maps Based on Intelligent Sensors

Indoor Air Quality Management Using Raspberry Pi and Graphlab

IOT-Enabled Air Pollution Meter with Digital Dashboard on Smart-Phone for Vehicles

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