scholarly journals The interplay between environmental exposures and COVID-19 risks in the health of children

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Peter D. Sly ◽  
Brittany A. Trottier ◽  
Catherine M. Bulka ◽  
Stephania A. Cormier ◽  
Julius Fobil ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Disease Severity ◽  
Research Effort ◽  
Severe Disease ◽  
Environmental Exposures ◽  
Research Strategy ◽  
Lessons Learned ◽  
Research Questions ◽  
The Impact

Abstract Background An unusual feature of SARS-Cov-2 infection and the COVID-19 pandemic is that children are less severely affected than adults. This is especially paradoxical given the epidemiological links between poor air quality and increased COVID-19 severity in adults and that children are generally more vulnerable than adults to the adverse consequences of air pollution. Objectives To identify gaps in knowledge about the factors that protect children from severe SARS-Cov-2 infection even in the face of air pollution, and to develop a transdisciplinary research strategy to address these gaps. Methods An international group of researchers interested in children’s environmental health was invited to identify knowledge gaps and to develop research questions to close these gaps. Discussion Key research questions identified include: what are the effects of SAR-Cov-2 infection during pregnancy on the developing fetus and child; what is the impact of age at infection and genetic susceptibility on disease severity; why do some children with COVID-19 infection develop toxic shock and Kawasaki-like symptoms; what are the impacts of toxic environmental exposures including poor air quality, chemical and metal exposures on innate immunity, especially in the respiratory epithelium; what is the possible role of a “dirty” environment in conveying protection – an example of the “hygiene hypothesis”; and what are the long term health effects of SARS-Cov-2 infection in early life. Conclusion A concerted research effort by a multidisciplinary team of scientists is needed to understand the links between environmental exposures, especially air pollution and COVID-19. We call for specific research funding to encourage basic and clinical research to understand if/why exposure to environmental factors is associated with more severe disease, why children appear to be protected, and how innate immune responses may be involved. Lessons learned about SARS-Cov-2 infection in our children will help us to understand and reduce disease severity in adults, the opposite of the usual scenario.

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

scholarly journals Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

2020 ◽  
Vol 9 (8) ◽  
pp. 2351
Author(s):  
Łukasz Kuźma ◽  
Krzysztof Struniawski ◽  
Szymon Pogorzelski ◽  
Hanna Bachórzewska-Gajewska ◽  
Sławomir Dobrzycki
Keyword(s):  
Public Health ◽  
Gender Differences ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutant ◽  
World Health ◽  
The European Union ◽  
Daily Mortality ◽  
Male Population ◽  
The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

Assessing the impact of the New Athens airport to urban air quality with contemporary air pollution models

1997 ◽  
Vol 31 (10) ◽  
pp. 1497-1511 ◽  
Author(s):  
N. Moussiopoulos ◽  
P. Sahm ◽  
K. Karatzas ◽  
S. Papalexiou ◽  
A. Karagiannidis
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
The Impact ◽  
Air Pollution Models

The impact of multiple sclerosis severity on health state utility values: Evidence from Australia

2016 ◽  
Vol 23 (8) ◽  
pp. 1157-1166 ◽  
Author(s):  
Hasnat Ahmad ◽  
Bruce V Taylor ◽  
Ingrid van der Mei ◽  
Sam Colman ◽  
Beth A O’Leary ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Disease Severity ◽  
Severe Disease ◽  
Health Utility ◽  
Health State Utility ◽  
Health State ◽  
Utility Values ◽  
Health State Utility Values ◽  
Economic Analyses ◽  
The Impact

Background: The measurement of health state utility values (HSUVs) for a representative sample of Australian people with multiple sclerosis (MS) has not previously been performed. Objectives: Our main aim was to quantify the HSUVs for different levels of disease severities in Australian people with MS. Method: HSUVs were calculated by employing a ‘judgement-based’ method that essentially creates EQ-5D-3L profiles based on WHOQOL-100 responses and then applying utility weights to each level in each dimension. A stepwise linear regression was used to evaluate the relationship between HSUVs and disease severity, classified as mild (Expanded Disability Status Scale (EDSS) levels: 0–3.5), moderate (EDSS levels: 4–6) and severe (EDSS levels: 6.5–9.5). Results: Mean HSUV for all people with MS was 0.53 (95% confidence interval (CI): 0.52–0.54). Utility decreased with increasing disease severity: 0.61 (95% CI: 0.60–0.62), 0.51 (95% CI: 0.50–0.52) and 0.40 (95% CI: 0.38–0.43) for mild, moderate and severe disease, respectively. Adjusted differences in mean HSUV between the three severity groups were statistically significant. Conclusion: For the first time in Australia, we have quantified the impact of increasing severity of MS on health utility of people with MS. The HSUVs we have generated will be useful in further health economic analyses of interventions that slow progression of MS.

scholarly journals THE IMPACT OF COVID-19 QUARANTINE MEASURES ON THE LEVEL OF AIR POLLUTION IN THE SHCHUCHINSK-BOROVSKAYA RESORT AREA

2021 ◽  
Author(s):  
Maikanov Balgabay ◽  
Auteleeva Laura
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Maximum Permissible Concentration ◽  
Atmospheric Air ◽  
Standard Index ◽  
High Degree ◽  
The Impact ◽  
Very High

In this study, changes in air quality were quantified before and during the introduction of COVID-19 quarantine measures in the Shchuchinsk-Borovskaya resort area. During 2020, there were only 49 resolutions "On strengthening restrictive quarantine measures in the territory of the Akmola region"on the territory of the resort zone. The maximum permissible concentration of sulfur dioxide in the atmospheric air has been exceeded. We have revealed that in the entire territory of the resort area for 2018-2019. atmospheric air pollution, according to the standard index, was elevated and high (3.38 to 6.4), according to the highest frequency (16.6 to 100%), there was a very high degree of pollution, and in 2020, the indicators of the standard index and the highest frequency were within the norm.

scholarly journals Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Environments ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 114
Author(s):  
Jiří Bílek ◽  
Ondřej Bílek ◽  
Petr Maršolek ◽  
Pavel Buček
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Sensor Network ◽  
Electrochemical Sensors ◽  
Reference Method ◽  
Ambient Air ◽  
Sensor Data ◽  
Dust Particles ◽  
Concentration Changes ◽  
The Impact

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

scholarly journals The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

scholarly journals City Logistics and Air Quality

2018 ◽  
Vol 6 (1) ◽  
pp. 26-29
Author(s):  
Radovan Slávik ◽  
◽  
Dominika Beňová ◽  
Jozef Gnap ◽  
Ondrej Stopka
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Greenhouse Gases ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Slovak Republic ◽  
Road Transport ◽  
City Logistics ◽  
Harmful Emissions ◽  
The Impact

The paper focuses on the impact of city logistics on air quality. The first chapter focuses on the EU's transport policy for 2030-2050 to reduce greenhouse gas emissions. The second chapter focuses on air quality in the Slovak Republic and the amount of greenhouse gases in the air. The aim of the contribution is to highlight the impact of road transport on air quality and air pollution as well as the need to reduce these harmful emissions.

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