Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient

Jui-Hung Chang, Chien Yuan Tseng, Hung Hsi Chiang, Ren Hung Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There are many influential factors in PM2.5, reducing the emission of PM2.5 is one of international subjects. In recent years, it is indicated that one of the sources of secondary PM2.5 is the complex chemical reaction between NH3 and air pollutants (VOCs, particulate matter, NOx, SOx). The Committee on Agriculture of FAO indicates that 64% of NH3 emission on the earth surface is derived from stock raising which motivates this study to discuss following two subjects based on Open Government Data. Subject 1 calculates the effect of the controlled air pollutants (VOCs, particulate matter, NOx, SOx) and the quantity of livestock (e.g. pigs, chickens and so on) nearby the air monitoring stations on the annual mean of PM2.5. Subject 2 uses Apache Spark as Cloud computing platform, the air monitoring stations are geographically clustered by K-medoids to calculate the Spearman's correlation coefficient of pollution source and PM2.5 of each cluster. The experimental results show that the monitoring station with more air pollutants and livestock raised nearby has higher annual mean PM2.5 concentration. The results are expected to provide the government bodies to make environmental decisions and the plants and livestock farms to install air monitors to analyze the air quality data. Our ultimate goals are to improve the environment and reduce both the emission of PM2.5 and the probability of getting cardiovascular disease.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages177-182
Number of pages6
Volume2018-January
ISBN (Electronic)9780769563282
DOIs
Publication statusPublished - 2018 Mar 13
Event7th IEEE International Symposium on Cloud and Service Computing, SC2 2017 - Kanazawa, Japan
Duration: 2017 Nov 222017 Nov 25

Other

Other7th IEEE International Symposium on Cloud and Service Computing, SC2 2017
CountryJapan
CityKanazawa
Period17-11-2217-11-25

Fingerprint

Farms
Air
Volatile organic compounds
Monitoring
Cloud computing
Electric sparks
Air quality
Agriculture
Livestock
Correlation coefficient
Air pollutants
Influential factors
Chemical reactions
Pollution
Earth (planet)
Particulate matter
Government
Chicken
Pig
Cardiovascular disease

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Information Systems and Management

Cite this

Chang, J-H., Tseng, C. Y., Chiang, H. H., & Hwang, R. H. (2018). Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient. In Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017 (Vol. 2018-January, pp. 177-182). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SC2.2017.34
Chang, Jui-Hung ; Tseng, Chien Yuan ; Chiang, Hung Hsi ; Hwang, Ren Hung. / Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient. Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 177-182
@inproceedings{33379678d3a3463fb99c7d47e651eb15,
title = "Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient",
abstract = "There are many influential factors in PM2.5, reducing the emission of PM2.5 is one of international subjects. In recent years, it is indicated that one of the sources of secondary PM2.5 is the complex chemical reaction between NH3 and air pollutants (VOCs, particulate matter, NOx, SOx). The Committee on Agriculture of FAO indicates that 64{\%} of NH3 emission on the earth surface is derived from stock raising which motivates this study to discuss following two subjects based on Open Government Data. Subject 1 calculates the effect of the controlled air pollutants (VOCs, particulate matter, NOx, SOx) and the quantity of livestock (e.g. pigs, chickens and so on) nearby the air monitoring stations on the annual mean of PM2.5. Subject 2 uses Apache Spark as Cloud computing platform, the air monitoring stations are geographically clustered by K-medoids to calculate the Spearman's correlation coefficient of pollution source and PM2.5 of each cluster. The experimental results show that the monitoring station with more air pollutants and livestock raised nearby has higher annual mean PM2.5 concentration. The results are expected to provide the government bodies to make environmental decisions and the plants and livestock farms to install air monitors to analyze the air quality data. Our ultimate goals are to improve the environment and reduce both the emission of PM2.5 and the probability of getting cardiovascular disease.",
author = "Jui-Hung Chang and Tseng, {Chien Yuan} and Chiang, {Hung Hsi} and Hwang, {Ren Hung}",
year = "2018",
month = "3",
day = "13",
doi = "10.1109/SC2.2017.34",
language = "English",
volume = "2018-January",
pages = "177--182",
booktitle = "Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Chang, J-H, Tseng, CY, Chiang, HH & Hwang, RH 2018, Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient. in Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 177-182, 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017, Kanazawa, Japan, 17-11-22. https://doi.org/10.1109/SC2.2017.34

Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient. / Chang, Jui-Hung; Tseng, Chien Yuan; Chiang, Hung Hsi; Hwang, Ren Hung.

Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 177-182.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient

AU - Chang, Jui-Hung

AU - Tseng, Chien Yuan

AU - Chiang, Hung Hsi

AU - Hwang, Ren Hung

PY - 2018/3/13

Y1 - 2018/3/13

N2 - There are many influential factors in PM2.5, reducing the emission of PM2.5 is one of international subjects. In recent years, it is indicated that one of the sources of secondary PM2.5 is the complex chemical reaction between NH3 and air pollutants (VOCs, particulate matter, NOx, SOx). The Committee on Agriculture of FAO indicates that 64% of NH3 emission on the earth surface is derived from stock raising which motivates this study to discuss following two subjects based on Open Government Data. Subject 1 calculates the effect of the controlled air pollutants (VOCs, particulate matter, NOx, SOx) and the quantity of livestock (e.g. pigs, chickens and so on) nearby the air monitoring stations on the annual mean of PM2.5. Subject 2 uses Apache Spark as Cloud computing platform, the air monitoring stations are geographically clustered by K-medoids to calculate the Spearman's correlation coefficient of pollution source and PM2.5 of each cluster. The experimental results show that the monitoring station with more air pollutants and livestock raised nearby has higher annual mean PM2.5 concentration. The results are expected to provide the government bodies to make environmental decisions and the plants and livestock farms to install air monitors to analyze the air quality data. Our ultimate goals are to improve the environment and reduce both the emission of PM2.5 and the probability of getting cardiovascular disease.

AB - There are many influential factors in PM2.5, reducing the emission of PM2.5 is one of international subjects. In recent years, it is indicated that one of the sources of secondary PM2.5 is the complex chemical reaction between NH3 and air pollutants (VOCs, particulate matter, NOx, SOx). The Committee on Agriculture of FAO indicates that 64% of NH3 emission on the earth surface is derived from stock raising which motivates this study to discuss following two subjects based on Open Government Data. Subject 1 calculates the effect of the controlled air pollutants (VOCs, particulate matter, NOx, SOx) and the quantity of livestock (e.g. pigs, chickens and so on) nearby the air monitoring stations on the annual mean of PM2.5. Subject 2 uses Apache Spark as Cloud computing platform, the air monitoring stations are geographically clustered by K-medoids to calculate the Spearman's correlation coefficient of pollution source and PM2.5 of each cluster. The experimental results show that the monitoring station with more air pollutants and livestock raised nearby has higher annual mean PM2.5 concentration. The results are expected to provide the government bodies to make environmental decisions and the plants and livestock farms to install air monitors to analyze the air quality data. Our ultimate goals are to improve the environment and reduce both the emission of PM2.5 and the probability of getting cardiovascular disease.

UR - http://www.scopus.com/inward/record.url?scp=85050791608&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050791608&partnerID=8YFLogxK

U2 - 10.1109/SC2.2017.34

DO - 10.1109/SC2.2017.34

M3 - Conference contribution

AN - SCOPUS:85050791608

VL - 2018-January

SP - 177

EP - 182

BT - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chang J-H, Tseng CY, Chiang HH, Hwang RH. Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient. In Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 177-182 https://doi.org/10.1109/SC2.2017.34