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Monitoring urban environmental pollution by bivariate control charts: New methodology and case study in Santiago, Chile

Author

Listed:
  • Carolina Marchant
  • Víctor Leiva
  • George Christakos
  • M. Fernanda Cavieres

Abstract

Particulate matter (PM) pollution is a serious environmental problem. Santiago of Chile is one of the most polluted cities in the world in terms of PM2.5 and PM10. Monitoring of environmental risk is useful for detecting and preventing adverse effects on human health in highly polluted cities. In this paper, we propose a methodology for monitoring PM pollution in Santiago based on bivariate quality control charts and an asymmetric distribution. A simulation study is carried out to evaluate performance of the proposed methodology. A case study with PM pollution real‐world data from Santiago is provided, which shows that the methodology is suitable to alert early episodes of extreme air pollution. The results are in agreement with the critical episodes reported with the current model used by the Chilean health authority.

Suggested Citation

  • Carolina Marchant & Víctor Leiva & George Christakos & M. Fernanda Cavieres, 2019. "Monitoring urban environmental pollution by bivariate control charts: New methodology and case study in Santiago, Chile," Environmetrics, John Wiley & Sons, Ltd., vol. 30(5), August.
    • Handle: RePEc:wly:envmet:v:30:y:2019:i:5:n:e2551
    DOI: 10.1002/env.2551
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    Cited by:

    1. Gonzálo Carreño & Xaviera A. López-Cortés & Carolina Marchant, 2022. "Machine Learning Models to Predict Critical Episodes of Environmental Pollution for PM2.5 and PM10 in Talca, Chile," Mathematics, MDPI, vol. 10(3), pages 1-17, January.
    2. Alejandra Tapia & Viviana Giampaoli & Víctor Leiva & Yuhlong Lio, 2020. "Data-Influence Analytics in Predictive Models Applied to Asthma Disease," Mathematics, MDPI, vol. 8(9), pages 1-19, September.
    3. Łukasz Warguła & Mateusz Kukla & Piotr Krawiec & Bartosz Wieczorek, 2020. "Reduction in Operating Costs and Environmental Impact Consisting in the Modernization of the Low-Power Cylindrical Wood Chipper Power Unit by Using Alternative Fuel," Energies, MDPI, vol. 13(11), pages 1-16, June.
    4. Rodrigo Puentes & Carolina Marchant & Víctor Leiva & Jorge I. Figueroa-Zúñiga & Fabrizio Ruggeri, 2021. "Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model," Mathematics, MDPI, vol. 9(6), pages 1-24, March.
    5. Łukasz Warguła & Mateusz Kukla & Piotr Lijewski & Michał Dobrzyński & Filip Markiewicz, 2020. "Influence of the Use of Liquefied Petroleum Gas (LPG) Systems in Woodchippers Powered by Small Engines on Exhaust Emissions and Operating Costs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    6. Shamsuzzaman, Mohammad & Shamsuzzoha, Ahm & Maged, Ahmed & Haridy, Salah & Bashir, Hamdi & Karim, Azharul, 2021. "Effective monitoring of carbon emissions from industrial sector using statistical process control," Applied Energy, Elsevier, vol. 300(C).
    7. Luis Sánchez & Víctor Leiva & Manuel Galea & Helton Saulo, 2021. "Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 37(1), pages 53-73, January.
    8. Hao Wu & Xinwei Gao, 2021. "Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    9. Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila, 2021. "A new BISARMA time series model for forecasting mortality using weather and particulate matter data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 346-364, March.
    10. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.

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