Determination of an Atmospheric Condition Index with Evidential Annotated Paraconsistent Logic Tools

Authors

DOI:

https://doi.org/10.33448/rsd-v11i16.38428

Keywords:

Atmospheric condition index; Expert system; Paraconsistent logic; Degree of certainty; Degree of uncertainty.

Abstract

The objective of this article is to propose the creation of an atmospheric condition index using Artificial Intelligence tools. The atmospheric condition can be obtained from parameters that the scientific communities – environment, health, labor safety – adopt for the different pollutants in the atmosphere. Parameters were obtained from the scientific literature and specialists, who were consulted about the objective data to allow an analysis through an expert system. As the data are from highly imprecise sources, classic-logic (binary) systems do not fit. We opted to use the Evidential Annotated Paraconsistent Logic. This non-classical logic naturally accommodates imprecisions, contradictions, and paracompleteness without the danger of trivialization. Thus, the Evidential Annotated Paraconsistent Logic can be ideally adopted as a valuable tool for controlling atmospheric conditions, especially in large metropolises.

References

Abe, J. M. (1992). Fundamentos da lógica anotada (Doctoral dissertation, Universidade de São Paulo).

Abe, J. M. (1997). Some aspects of paraconsistent systems and applications. Logique et Analyse, 157.

Da Silva Filho, Ji, J M Abe, Paraconsistent analyzer module, International Journal of Computing Anticipatory Systems, vol. 9, ISSN 1373-5411, ISBN 2-9600262-1-7, 346-352, 2001.

Alyousifi, Y., Kıral, E., Uzun, B., & Ibrahim, K. (2021). New Application of Fuzzy Markov Chain Modeling for Air Pollution Index Estimation. Water, Air, & Soil Pollution, 232(7), 1-13.

Asif, Z., & Chen, Z. (2019). An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine. Frontiers of Environmental Science & Engineering, 13(5), 1-14.

Bonilla, S. H., Papalardo, F., Tassinari, C. A., Sacomano, J. B., & de Carvalho, F. R. (2019). Contribution of the Paraconsistent Tri-Annotated Logic to emergy accounting and decision making. Ecological Modelling, 393, 98-106.

Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V. D., & Papaleonidas, A. (2018). FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens. Neural Computing and Applications, 29(7), 375-388.

CETESB, (2020a). Companhia Ambiental do Estado de São Paulo (Environmental Company of the State of São Paulo, in Portuguese). Air Quality - Pollutants. Brazil.

CETESB, (2020b). Companhia Ambiental do Estado de São Paulo (Environmental Company of the State of São Paulo, in Portuguese). Air Quality Index - IOAr. Brazil.

CHINA-AQS. (2016). Environmental Quality Standards in China: Air Quality Standards. https://www.transportpolicy.net/standard/china-air-quality-standards/#

Chung, C. J., Hsieh, Y. Y., & Lin, H. C. (2019). Fuzzy inference system for modeling the environmental risk map of air pollutants in Taiwan. Journal of environmental management, 246, 808-820.

CONAMA. (2019). Air Quality Indicators. An overview about the pollutants of the air. Brazil.

Da Costa, N. C. A., & Vago, C. (1991). VS Subrahmanian-The Paraconsistent Logics P¿. Zeitschr. f. math. Logik und Grundlagen d. Math, Bd, 37, 139-148.

Costa, N. A. C., Abe, J. M., & Subrahmanian, U. S. (1991). Remarks on annotated logic.

Da Silva Filho, J. I., Torres, G. L., & Abe, J. M. (2010). Uncertainty treatment using paraconsistent logic: Introducing paraconsistent artificial neural networks (Vol. 211). IOS Press.

De Carvalho, F. R. (2002). Paraconsistent Logic Applied in Decision Making: an approach to university administration (in Portuguese). São Paulo-SP: Editora Aleph.

Carvalho, F. R. D. (2006). Aplicação de lógica paraconsistente anotada em tomadas de decisão na engenharia de produção.

De Carvalho, F. R., & Abe J. M. (2011). Decision Making with Annotated Paraconsistent Logic Tools (in portuguese). São Paulo-SP: Ed. Blucher. ISBN: 9788521206071.

Carvalho, F. R. D., & Abe, J. M. (2018). Decision Rules. In A Paraconsistent Decision-Making Method (pp. 37-40). Springer, Cham.

Dursun, S., Kunt, F., & Taylan, O. (2015). Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors. International journal of environmental science and technology, 12(12), 3915-3928.

Du, J. L., Liu, Y., & Forrest, J. Y. L. (2019). An interactive group decision model for selecting treatment schemes for mitigating air pollution. Environmental Science and Pollution Research, 26(18), 18687-18707.

EPA-USA. (2019). Environmental Protection Agency, United States. Environmental Quality Index, Overview Report. EPA/600/R-14/305. https://www.epa.gov/criteria-air-pollutants/naaqs-table

European Commission. (2019). Air Quality Standards. https://ec.europa.eu/environment/ air/quality/standards.htm

FEAM (2021). State Environmental Foundation Bulletin - Air Quality. Brazil

Giannetti, B. F., Bonilla, S. H., Silva, C. C., & Almeida, C. M. V. B. (2009). The reliability of experts' opinions in constructing a composite environmental index: The case of ESI 2005. Journal of Environmental Management, 90(8), 2448-2459.

House of Lords. (2017). Library Briefing. Impact of Air and Water Pollution on the Environment and Public Health. Debate on 26 October 2017.

IEMA. (2016). State Institute of Environment and Water Resources. Air quality, IQA. http://iema.es.gov.br.

IEMA. (2017). State Institute of Environment and Water Resources. Air quality, IQA.

INECC. (2014). Mexico’s Ecology Climate Change Institute. Mexico: Air Quality Standards. https://www.transportpolity.net/standard/mexico-air-quality-standards/#links

JAPAN-EQS. (1999). Environmental Quality Stasndards in Japan. Ministry of the Environment, Government of Japan. https://www.env.go.jp/en/moemail/

Jiang, P., Dong, Q., & Li, P. (2017). A novel hybrid strategy for PM2. 5 concentration analysis and prediction. Journal of environmental management, 196, 443-457.

Langa, E. S., Agostinho, F., Liu, G., Almeida, C. M., & Giannetti, B. F. (2021). Journal of Environmental Accounting and Management. Journal of Environmental Accounting and Management, 9(3), 299-318.

Li, Y., Jiang, P., She, Q., & Lin, G. (2018). Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine. Environmental Pollution, 241, 1115-1127.

Liu, H. X., Li, Y. P., & Yu, L. (2019). Urban agglomeration (Guangzhou-Foshan-Zhaoqing) ecosystem management under uncertainty: A factorial fuzzy chance-constrained programming method. Environmental research, 173, 97-111.

MEXICO-AQS (1994). Air Quality Mexican Official Standards, Secretary of Health.

NAAQS. (1997). India’s National Ambient Air Quality Standards. Recuperado em https://cpcb.nic.in/National-Air-Quality-Index/

Oliveira, M. L. (2016). Air quality policies to protect the health of the population. Pan American Health Organization. World Health Organization.

Ostro, B. D. (1994). Estimating the health effects of air pollutants: a method with an application to Jakarta (Vol. 1301). World Bank Publications.

Silva, L. T. (2015). Environmental quality health index for cities. Habitat International, 45, 29-35.

Shimizu, T. (2006). Decisão nas Organizações. (2ª edição). Editora Atlas.

Stossel, Z., Kissinger, M., & Meir, A. (2015). Assessing the state of environmental quality in cities–a multi-component urban performance (EMCUP) index. Environmental pollution, 206, 679-687.

Wang, J., Li, H., & Lu, H. (2018). Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Applied Soft Computing, 71, 783-799.

Who, E. (2005). WHORO: Air Quality Guidelines global update. In Report on a Working Group meeting. In. Bonn, Germany.

Xu, Y., Du, P., & Wang, J. (2017). Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China. Environmental pollution, 223, 435-448.

Yang, Z., & Wang, J. (2017). A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction. Environmental research, 158, 105-117.

Zeinalnezhad, M., Chofreh, A. G., Goni, F. A., Klemeš, J. J., Darvishvand, A. M., & Vashaghi, K. (2019, June). Forecasting air pollution by adaptive neuro fuzzy inference system. In 2019 4th international conference on smart and sustainable technologies (SpliTech) (pp. 1-3). IEEE.

Zeng, X. T., Tong, Y. F., Cui, L., Kong, X. M., Sheng, Y. N., Chen, L., & Li, Y. P. (2017). Population-production-pollution nexus based air pollution management model for alleviating the atmospheric crisis in Beijing, China. Journal of environmental management, 197, 507-521.

Downloads

Published

14/12/2022

How to Cite

CARVALHO, F. R. de .; ABE, J. M.; BONILLA, S. H.; ALMEIDA, C. M. V. B. de .; GIANNETTI, B. F. . Determination of an Atmospheric Condition Index with Evidential Annotated Paraconsistent Logic Tools. Research, Society and Development, [S. l.], v. 11, n. 16, p. e442111638428, 2022. DOI: 10.33448/rsd-v11i16.38428. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/38428. Acesso em: 25 nov. 2024.

Issue

Section

Engineerings