scholarly journals The effect of national protest in Ecuador on PM pollution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rasa Zalakeviciute ◽  
Katiuska Alexandrino ◽  
Danilo Mejia ◽  
Marco G. Bastidas ◽  
Nora H. Oleas ◽  
...  
Keyword(s):  
Air Quality ◽  
Positive Impact ◽  
Capital City ◽  
Machine Learning Techniques ◽  
Particulate Pollution ◽  
Premature Deaths ◽  
Learning Techniques ◽  
Regional Air Quality ◽  
Short And Long Term ◽  
Business As Usual

AbstractParticulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall − 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128325-128338 ◽  
Author(s):  
Saba Ameer ◽  
Munam Ali Shah ◽  
Abid Khan ◽  
Houbing Song ◽  
Carsten Maple ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Air Quality ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals An Economic Evaluation of the Health Effects of Reducing Fine Particulate Pollution in Chinese Cities

2018 ◽  
Vol 35 (2) ◽  
pp. 58-84 ◽  
Author(s):  
Yana Jin ◽  
Shiqiu Zhang
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Control Process ◽  
Significant Heterogeneity ◽  
Mortality Reduction ◽  
Republic Of China ◽  
Particulate Pollution ◽  
Premature Deaths ◽  
Fine Particulate ◽  
Welfare Implications

Fine particulate pollution (PM2.5) is a leading mortality risk factor in the People's Republic of China (PRC) and many Asian countries. Current studies of PM2.5 mortality have been conducted at the national and provincial levels, or at the grid-based micro level, and report only the exposure index or attributable premature deaths. Little is known about the welfare implications of PM2.5 mortality for urban areas. In this study, we estimate the total cost of PM2.5 mortality, the benefit of its reduction achieved through meeting various air quality targets, and the benefit of mortality reduction achieved through a uniform 10 micrograms per cubic meter decrease in PM2.5 concentration in the urban areas of 300 major cities in the PRC. Significant heterogeneity exists in welfare indicators across rich versus poor and clean versus dirty cities. The results indicate that cities in the PRC should accelerate the fine particulate pollution control process and implement more stringent air quality targets to achieve much greater mortality reduction benefits.

Quantifying COVID-19’s Silver Lining: Avoided Deaths from Air Quality Improvements in Bogotá

2021 ◽  
Author(s):  
Allen Blackman ◽  
Jorge Bonilla ◽  
Laura Villalobos
Keyword(s):  
Air Quality ◽  
First Year ◽  
Quality Improvements ◽  
Short Term ◽  
Particulate Pollution ◽  
Premature Deaths ◽  
Fine Particulate ◽  
Short Term Mortality ◽  
Silver Lining

In cities around the world, Covid-19 lockdowns have improved outdoor air quality, in some cases dramatically. Even if only temporary, these improvements could have longer-lasting effects on policy by making chronic air pollution more salient and boosting political pressure for change. To that end, it is important to develop objective estimates of both the air quality improvements associated with Covid-19 lockdowns and the benefits these improvements generate. We use panel data econometric models to estimate the effect of Bogotás lockdown on fine particulate pollution, epidemiological models to simulate the effect of reductions in that pollution on long-term and short-term mortality, and benefit transfer methods to estimate the monetary value of the avoided mortality. We find that in its first year of implementation, on average, Bogotás lockdown cut fine particulate pollution by more than one-fifth. However, the magnitude of that effect varied considerably over the course of the year and across the citys neighborhoods. Equivalent permanent reductions in fine particulate pollution would reduce long-term premature deaths by more than one-quarter each year, a benefit valued at $670 million per year. Finally, we estimate that in 2020-2021, the lockdown reduced short-term deaths by 31 percent, a benefit valued at $180 million.

scholarly journals Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3113 ◽  
Author(s):  
Silvia Liberata Ullo ◽  
G. R. Sinha
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Sustainable Growth ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Smart Environment ◽  
Environment Monitoring ◽  
Learning Techniques ◽  
Quality Water ◽  
Major Factors

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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