Opinion Article - European Journal of Applied Engineering and Scientific Research ( 2023) Volume 11, Issue 2
Received: 26-Feb-2023, Manuscript No. EJASER-23-92880;
Editor assigned: 01-Mar-2023, Pre QC No. EJASER-23-92880;
Reviewed: 16-Mar-2023, QC No. EJASER-23-92880;
Revised: 23-Mar-2023, Manuscript No. EJASER-23-92880;
Published:
31-Mar-2023
, DOI: 10.36648/2278-0041.1.11.1.011
, Citations: Petersen M. 2023. Development of an I ntelligent System for Predicting Quality of Air in Urban Area. Euro
J Appl Eng Sci Res.11:011
,
Copyright: Copyrigh: © 2023 Petersen M. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
Air pollution is a major issue in urban areas, with harmful particles and gases released from vehicles, factories, and other sources causing significant damage to human health and the environment. The development of an intelligent system for predicting the quality of air in urban areas are managing this issue effectively.
The first component of the system must gather data from a variety of sources, including air quality sensors, from a variety of sources, including air quality sensors, meteorological data, and satellite imagery. Air quality sensors can be deployed throughout urban areas to measure levels of harmful gases and particles, while meteorological data can provide information on wind patterns and temperature, which affect the movement and dispersion of pollutants. Satellite imagery can also be used to identify areas of higher pollution and track changes over time.
The second component of the system is data processing and analysis. The system must be capable of processing and analyzing the large volumes of data collected from various sources. Machine learning algorithms can be used to identify patterns and trends in the data and make predictions about future air quality levels. These algorithms can also be used to identify the sources of pollution and suggest measures to mitigate them.
The third component of the system is the user interface. The system must be designed to be accessible and easy to use for a wide range of users, including city planners, policymakers, and members of the public. The interface should provide real-time updates on air quality levels, as well as historical data and trends. It should also provide suggestions for actions that can be taken to reduce air pollution, such as reducing vehicle traffic or increasing the use of public transportation.
One of the key benefits of an intelligent air quality prediction system is its ability to inform policy decisions. City planners and policymakers can use the system’s data and analysis to make informed decisions about where to locate new development projects, how to design transportation networks, and which industries to regulate more. Members of the public can also use the system to make informed decisions about when to go outside, how to protect themselves from harmful pollutants, and how to advocate for policies that improve air quality.
Another benefit of an intelligent air quality prediction system is its potential to improve public health. High levels of air pollution have been linked to a range of health problems, including respiratory illnesses, cardiovascular disease, and cancer. By providing real-time updates on air quality levels, the system can help individuals make informed decisions about when to exercise outdoors.
The development of an intelligent system for predicting air quality in urban areas is a crucial step towards managing the issue of air pollution. By collecting and analyzing data from various sources and providing real-time updates and suggestions for action, the system can inform policy decisions, improve public health, and help to provide more livable and sustainable urban environments.
Citation: Petersen M. 2023. Development of an I ntelligent System for Predicting Quality of Air in Urban Area. Euro J Appl Eng Sci Res.11:011
Copyright: Copyrigh: © 2023 Petersen M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
