in

Spatio-temporal visualization and forecasting of $${text {PM}}_{10}$$ PM 10 in the Brazilian state of Minas Gerais

  • Martins, L. C. et al. Poluição atmosférica e atendimentos por pneumonia e gripe em São Paulo, Brasil. Revista de Saúde Pública 36, 88–94 (2002).

    Article 
    PubMed 

    Google Scholar 

  • Goudarzi, G. et al. Health risk assessment on human exposed to heavy metals in the ambient air PM10 in Ahvaz, Southwest Iran. Int. J. Biometeorol. 62, 1075–1083 (2018).

    Article 
    ADS 
    PubMed 

    Google Scholar 

  • Makri, A. & Stilianakis, N. I. Vulnerability to air pollution health effects. Int. J. Hygiene Environ. Health 211, 326–336 (2008).

    Article 

    Google Scholar 

  • Idani, E. et al. Characteristics, sources, and health risks of atmospheric PM10-bound heavy metals in a populated Middle Eastern City. Toxin Rev. 39, 266–274 (2020).

    Article 

    Google Scholar 

  • Wang, J., Hu, Z., Chen, Y., Chen, Z. & Xu, S. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229 (2013).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383, 1581–1592 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Anderson, J. O., Thundiyil, J. G. & Stolbach, A. Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Roy, D., Seo, Y.-C., Kim, S. & Oh, J. Human health risks assessment for airborne PM10-bound metals in Seoul, Korea. Environ. Sci. Pollut. Res. 26, 24247–24261 (2019).

    Article 
    CAS 

    Google Scholar 

  • Maesano, C. et al. Impacts on human mortality due to reductions in PM10 concentrations through different traffic scenarios in Paris, France. Sci. The Total. Environ. 698, 134257 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Maleki, H., Sorooshian, A., Goudarzi, G., Nikfal, A. & Baneshi, M. M. Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res. 22, 135–140 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Medina, S., Le Tertre, A. & Saklad, M. The Apheis project: Air pollution and health—A European information system. Air Qual. Atmos. Heal. 2, 185–198 (2009).

    Article 

    Google Scholar 

  • Medina, S., Plasencia, A., Ballester, F., Mücke, H. & Schwartz, J. Apheis: Public health impact of PM10 in 19 European cities. J. Epidemiol. Community Heal. 58, 831–836 (2004).

    Article 
    CAS 

    Google Scholar 

  • Pérez-Martínez, P. J., de Fátima Andrade, M. & de Miranda, R. M. Traffic-related air quality trends in São Paulo, Brazil. J. Geophys. Res. Atmos. 120, 6290–6304 (2015).

    Article 
    ADS 

    Google Scholar 

  • Sánchez-Ccoyllo, O. R. et al. Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ. Monitoring Assess. 149, 241–249 (2009).

    Article 

    Google Scholar 

  • Ribeiro, H. & de Assunção, J. V. Historical overview of air pollution in São Paulo Metropolitan Area, Brazil: Influence of mobile sources and related health effects. WIT Trans. Built Environ. 52,10 (2001).

  • Bravo, M. A. & Bell, M. L. Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies. J. Air Waste Manag. Assoc. 61, 69–77 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • De Freitas, E. D., Martins, L. D., da Silva Dias, P. L. & de Fátima Andrade, M. A simple photochemical module implemented in rams for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation. Atmos. Environ. 39, 6352–6361 (2005).

    Article 
    ADS 

    Google Scholar 

  • Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P. C., Salas, R. & López-Gonzales, J. L. A spatio-temporal visualization approach of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609 (2021).

    Article 
    ADS 

    Google Scholar 

  • do Meio Ambiente, C. N. Institutes the national air quality control programee. Tech. Rep., Official Journal of the Federative Republic of Brazil (1989).

  • do Meio Ambiente, C. N. Sets standards of primary and secondary air quality and even the criteria for acute episodes of air pollution. Tech. Rep., Official Journal of the Federative Republic of Brazil (1990).

  • Artaxo, P. O estado da qualidade do ar no brasil. Work. Pap. WRI Brasil 32 (2021).

  • Costa, A. F., Hoek, G., Brunekreef, B. & Ponce de Leon, A. C. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: An analysis of mortality displacement. Environ. Health Perspectives 125, 349–354 (2017).

    Article 
    CAS 

    Google Scholar 

  • Bravo, M. A., Son, J., De Freitas, C. U., Gouveia, N. & Bell, M. L. Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. J. Exposure Sci. Environ. Epidemiol. 26, 150–161 (2016).

    Article 
    CAS 

    Google Scholar 

  • Chiarelli, P. S. et al. The association between air pollution and blood pressure in traffic controllers in Santo André, São Paulo, Brazil. Environ. Res. 111, 650–655 (2011).

    Article 
    CAS 

    Google Scholar 

  • Ventura, L. M. B., de Oliveira Pinto, F., Soares, L. M., Luna, A. S. & Gioda, A. Forecast of daily PM2.5 concentrations applying artificial neural networks and holt-winters models. Air Qual. Atmos. Heal. 12, 317–325 (2019).

    Article 
    CAS 

    Google Scholar 

  • Leão, M. L. P., Zhang, L. & da Silva Júnior, F. M. R. Effect of particulate matter (PM2.5 and PM10) on health indicators: Climate change scenarios in a Brazilian Metropolis. Environ. Geochem. Heal. 44, 1–12 (2022).

  • Habermann, M. & Gouveia, N. Application of land use regression to predict the concentration of inhalable particular matter in São Paulo City, Brazil. Engenharia Sanit. e Ambiental 17, 155–162 (2012).

    Article 

    Google Scholar 

  • Braga, A. L. F., Pereira, L. A. A., Procópio, M., André, P. A. D. & Saldiva, P. H. D. N. Association between air pollution and respiratory and cardiovascular diseases in Itabira, Minas Gerais State. Brazil. Cadernos de Saúde Pública 23, S570–S578 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Pinto, W. D. P., Reisen, V. A. & Monte, E. Z. Previsão da concentração de material particulado inalável, na região da grande vitória, ES, Brasil, utilizando o modelo sarimax. Engenharia Sanitária e Ambiental 23, 307–318 (2018).

    Article 

    Google Scholar 

  • Schornobay-Lui, E. et al. Prediction of short and medium term PM10 concentration using artificial neural networks. Manag. Environ. Qual. An Int. J. 30, 414–436 (2018).

  • Neto, P. S. D. M. et al. Neural-based ensembles for particulate matter forecasting. IEEE Access 9, 14470–14490 (2021).

    Article 

    Google Scholar 

  • Albuquerque Filho, F. S. D., Madeiro, F., Fernandes, S. M., de Mattos Neto, P. S. & Ferreira, T. A. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks. Química Nova 36, 783–789 (2013).

  • Lei, T. M., Siu, S. W., Monjardino, J., Mendes, L. & Ferreira, F. Using machine learning methods to forecast air quality: A case study in Macao. Atmosphere 13, 1412 (2022).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Yu, T. et al. Study on the regional prediction model of PM2.5 concentrations based on multi-source observations. Atmos. Pollut. Res. 13, 101363 (2022).

    Article 
    CAS 

    Google Scholar 

  • Li, J., Xu, G. & Cheng, X. Combining spatial pyramid pooling and long short-term memory network to predict PM2.5 concentration. Atmos. Pollut. Res. 13, 101309 (2022).

    Article 
    CAS 

    Google Scholar 

  • Cordova, C. H. et al. Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci. Rep. 11, 1–19 (2021).

    Article 

    Google Scholar 

  • Plocoste, T., Calif, R. & Jacoby-Koaly, S. Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in caribbean region. Atmos. Environ. 169, 22–35 (2017).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Calif, R. & Schmitt, F. G. Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Process. Geophys. 21, 379–392 (2014).

    Article 
    ADS 

    Google Scholar 

  • Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for r. J. Stat. Softw. 27, 1–22 (2008).

    Article 

    Google Scholar 

  • Harvey, A. C. Forecasting, structural time series models and the Kalman filter (Cambridge University Press, 1990).

    Book 
    MATH 

    Google Scholar 

  • Zhang, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003).

    Article 
    MATH 

    Google Scholar 

  • Liao, T. W. Clustering of time series data—A survey. Pattern Recognit. 38, 1857–1874 (2005).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Bell, M. L., Samet, J. M. & Dominici, F. Time-series studies of particulate matter. Annu. Rev. Public Heal. 25, 247–280 (2004).

    Article 

    Google Scholar 

  • Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).

    Google Scholar 

  • Box, G. E., Hillmer, S. C. & Tiao, G. C. Analysis and modeling of seasonal time series. in Seasonal analysis of economic time series, 309–344 (NBER, 1978).

  • Sulandari, W., Suhartono, Subanar & Rodrigues, P. C. Exponential smoothing on modeling and forecasting multiple seasonal time series: An overview. Fluctuation Noise Lett. 20, 2130003 (2021).

    Article 
    ADS 

    Google Scholar 

  • Rodrigues, P. C., Awe, O. O., Pimentel, J. S. & Mahmoudvand, R. Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3, 137–157 (2020).

    Article 

    Google Scholar 

  • Sako, K., Mpinda, B. N. & Rodrigues, P. C. Neural networks for financial time series forecasting. Entropy 24, 657 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Coelho, Leite et al. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 15, 588 (2022).

    Article 

    Google Scholar 

  • Sulandari, W., Subanar, S., Lee, M. H. & Rodrigues, P. C. Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX 7, 101015 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sulandari, W. et al. Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy 190, 116408 (2020).

    Article 

    Google Scholar 

  • Rodrigues, P. C. & Mahmoudvand, R. The benefits of multivariate singular spectrum analysis over the univariate version. J. Frankl. Inst. 355, 544–564 (2018).

    Article 
    MathSciNet 
    MATH 

    Google Scholar 


  • Source: Ecology - nature.com

    Two wild carnivores selectively forage for prey but not amino acids

    Development of an array of molecular tools for the identification of khapra beetle (Trogoderma granarium), a destructive beetle of stored food products