Small sample robust approach to outliers and correlation of atmospheric pollution and health effects in Santiago de Chile

Soza, Ludy Núñez; Jordanova, Pavlina; Nicolis, Orietta; Střelec, Luboš; Stehlík, Milan

doi:10.1016/j.chemolab.2018.12.010

Cited by 10 publications

(10 citation statements)

References 21 publications

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“…Owing to this condition, it has been demonstrated that humans living in highly polluted areas have a reduced respiratory capacity to react to virus attacks [10]. In addition to these general considerations, which are confirmed by an impressive wealth of recent literature [11][12][13], more interesting is the biological phenomenon at the center of the following controversy: Can particulate matter be a carrier for To respond to this question, it would be enough to remind a recent claim of 239 experts who maintained that this virus can be airborne [14], united with the information of the presence of the COVID-19 RNA, found in the particulate matter of Bergamo (Italy) [15]. All these seem to confirm that this virus can create clusters with particulate matter, and that it can be carried by this type of microscopic pollutants.…”

Section: Introductionmentioning

confidence: 75%

Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning

2020

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The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society.

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Section: Introductionmentioning

confidence: 75%

Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning

2020

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“…The main sources of atmospheric pollutants in Santiago are road traffic and industrial activities, with additional contributions in wintertime from wood burning for residential heating (Barraza et al, 2017;Mazzeo et al, 2018). Such characteristics apply to most urban areas in central Chile, including coastal zones (Sanhueza et al, 2012;Toro et al, 2014;Marín et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The central zone of Chile investigated in this study (hereafter referred to as central Chile) comprises the six administrative regions of Coquimbo, Valparaíso, Metropolitana de Santiago, O'Higgins, Maule and Ñuble. It is home to more than 12 million people (INE, 2018) who are chronically exposed to PM 2.5 pollution, leading to respiratory and cardiovascular issues (Ilabaca et al, 1999;Soza et al, 2019). Chronic and acute exposures to PM 2.5 pollution also induce a significant economic burden (MMA, 2012;OECD, 2016).…”

Section: Introductionmentioning

confidence: 99%

Seasonal variation in atmospheric pollutants transport in central Chile: dynamics and consequences

Lapere¹,

Menut²,

Mailler³

et al. 2021

Atmos. Chem. Phys.

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Abstract. Central Chile faces atmospheric pollution issues all year long as a result of elevated concentrations of fine particulate matter during the cold months and tropospheric ozone during the warm season. In addition to public health issues, environmental problems regarding vegetation growth and water supply, as well as meteorological feedback, are at stake. Sharp spatial gradients in regional emissions, along with a complex geographical situation, make for variable and heterogeneous dynamics in the localization and long-range transport of pollutants, with seasonal differences. Based on chemistry–transport modeling with Weather Research Forecasting (WRF)–CHIMERE, this work studies the following for one winter period and one summer period: (i) the contribution of emissions from the city of Santiago to air pollution in central Chile, and (ii) the reciprocal contribution of regional pollutants transported into the Santiago basin. The underlying 3-dimensional advection patterns are investigated. We find that, on average for the winter period, 5 to 10 µg m−3 of fine particulate matter in Santiago come from regional transport, corresponding to between 13 % and 15 % of average concentrations. In turn, emissions from Santiago contribute between 5 % and 10 % of fine particulate matter pollution as far as 500 km to the north and 500 km to the south. Wintertime transport occurs mostly close to the surface. In summertime, exported precursors from Santiago, in combination with mountain–valley circulation dynamics, are found to account for most of the ozone formation in the adjacent Andes cordillera and to create a persistent plume of ozone of more than 50 ppb (parts per billion), extending along 80 km horizontally and 1.5 km vertically, and located slightly north of Santiago, several hundred meters above the ground. This work constitutes the first description of the mechanism underlying the latter phenomenon. Emissions of precursors from the capital city also affect daily maxima of surface ozone hundreds of kilometers away. In parallel, cutting emissions of precursors in the Santiago basin results in an increase in surface ozone mixing ratios in its western area.

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“…Thus, the application of statistics is important in environmental sciences for effective and innovative monitoring of environmental variables over time. To avoid mistakes at the end of the statistical analysis, it is very important to detect the actual distribution of the observed data (see [156,157]). For environment-related problems, the lack of sufficient data is a major problem.…”

Section: Discussionmentioning

confidence: 99%

Statistical Techniques for Environmental Sciences: A Review

Tomy¹,

Chesneau

Madhav

2021

MCA

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This paper reviews the interdisciplinary collaboration between Environmental Sciences and Statistics. The usage of statistical methods as a problem-solving tool for handling environmental problems is the key element of this approach. This paper enhances a clear pavement for environmental scientists as well as quantitative researchers for their further collaborative learning with an analytical base.

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Small sample robust approach to outliers and correlation of atmospheric pollution and health effects in Santiago de Chile

Cited by 10 publications

References 21 publications

Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning

Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning

Seasonal variation in atmospheric pollutants transport in central Chile: dynamics and consequences

Statistical Techniques for Environmental Sciences: A Review

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