Air quality level assessment in Lima city using the grey clustering method

Author(s):  
Alexi Delgado ◽  
Pamela Montellanos ◽  
Janfranco Llave
2019 ◽  
Vol 8 (3) ◽  
pp. 7514-7518

Air pollution is a problem in large cities, which increases with population growth and industrialization. This work assessed air quality in four Mexican cities related to mining activities near monitoring points. The method used for this study was the Grey Clustering method, which is based on the theory of grey systems. This method uses information with a high degree of uncertainty, air quality analysis being an issue that uses data with a high degree of uncertainty. The results revealed that all four cities have good air quality based on Metropolitan Air Quality Index (IMECA) standards, even when mining activities take place near these cities. The results of this work could help the relevant authorities to implement further studies on the impact of polluting emissions from mining activities on the environment


2016 ◽  
Vol 8 (9) ◽  
pp. 881 ◽  
Author(s):  
Jungho Kang ◽  
Kwang-Il Hwang

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2507 ◽  
Author(s):  
Francisco Ramos ◽  
Sergio Trilles ◽  
Andrés Muñoz ◽  
Joaquín Huerta

Nowadays, citizens have a huge concern about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance, not only to plan our activities but also to take precautionary measures for our health. All levels of governments are concerned about it and have built their indexes to measure the air quality level in their countries, regions or cities. Taking into account the existing sensor infrastructure within smart cities, it makes possible to evaluate these indices and to know anywhere the level of pollution in real-time. In this scenario, the main objective of the current work is to foster citizens’ awareness about pollution by offering pollution-free routes. To achieve this goal, a technology-agnostic methodology is presented, which allows for creating pollution-free routes across cities depending on the level of pollution in each zone. The current work includes an extensive study of existing air quality indices, and proposes and carries forward to deployment of the defined methodology in a big city, such as Madrid (Spain).


Author(s):  
Oyunjargal D ◽  
Byambatseren Ch

The purpose of this research is to determine the impact of the environment, especially the quality of air on house price. In addition, it also includes the research of the linkage between the index of air quality and average price of residential house which located in the most crowded districts of Ulaanbaatar such as Bayangol, Bayanzurkh, Chingeltei, Sukhbaatar, Songinokhairkhan and Khan-Uul. The statistical analysis and statistics determination methods were applied to identify the relationship utilizing the air quality index, determined from the air quality measurement data recorded in 2015-2017, and the average price per square meter of newly built apartment houses in the selected districts. The research findings suggest that there is little direct link between the house prices and air quality level, and the air quality levels of Ulaanbaatar districts do not have a significant impact on the price per square meter. Therefore, the air quality index should not considered as a house price determinant.


2018 ◽  
Vol 12 ◽  
pp. 117863021879286 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Paul B Tchounwou ◽  
SS Biswal ◽  
Francis Tuluri

Rising concentration of air pollution and its associated health effects is rapidly increasing in India, and Delhi, being the capital city, has drawn our attention in recent years. This study was designed to analyze the spatial and temporal variations of particulate matter (PM2.5) concentrations in a mega city, Delhi. The daily PM2.5 concentrations monitored by the Central Pollution Control Board (CPCB), New Delhi during November 2016 to October 2017 in different locations distributed in the region of the study were used for the analysis. The descriptive statistics indicate that the spatial mean of monthly average PM2.5 concentrations ranged from 45.92 μg m−3 to 278.77 μg m−3. The maximum and minimum spatial variance observed in the months of March and September, respectively. The study also analyzed the PM2.5 air quality index (PM2.5—Air Quality Index (AQI)) for assessing the health impacts in the study area. The AQI value was determined according to the U.S. Environmental Protection Agency (EPA) system. The result suggests that most of the area had the moderate to very unhealthy category of PM2.5-AQI and that leads to severe breathing discomfort for people residing in the area. It was observed that the air quality level was worst during winter months (October to January).


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