scholarly journals Association between COVID-19 lockdown policies and air pollution with associated mortality reduction in Europe

2021 ◽  
Author(s):  
Rochelle Schneider ◽  
Pierre Masselot ◽  
Ana Maria Vicedo-Cabrera ◽  
Francesco Sera ◽  
Marta Blangiardo ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollutant ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Pollution Levels ◽  
European Cities ◽  
Linear Relationships ◽  
Wide Range ◽  
Non Linear ◽  
Policy Measures

<p>Governments were enforced to respond to SARS-CoV-2 virus spread by taking a wide range of policy measures. Several studies have reported a decrease in air pollution following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference, and did not assess the role of different policy interventions. These responses offered an unprecedented opportunity to assess the effectiveness of several interventions to reduce air pollution levels worldwide. Using an accurate representation of business-as-usual and lockdown air pollution scenarios, provided by Copernicus Atmosphere Monitoring Service (CAMS), we quantitatively evaluated the association between policies responses to the COVID-19 pandemic with changes in NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub> levels in 47 European cities. We also estimated the short-term mortality in the period of February-July 2020. An advanced spatio-temporal Bayesian non-linear mixed effect model was performed to determine the association between air pollutant levels and stringency indices as well as individual policy measures. The results indicate non-linear relationships, with a stronger decrease in NO<sub>2</sub> and to a lesser extent PM<sub>10</sub> and PM<sub>2.5</sub> at very strict policy levels. Differences across interventions were also identified, actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements had strong effects, while restrictions on internal movement and international travels showed little impact. The observed decrease in pollution potentially resulted in hundreds of avoided deaths across the European cities. This project provides information that can help inform future policies on air pollution reduction.</p>

A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network

2016 ◽  
Vol 13 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Lídice García Ríos ◽  
José Alberto Incera Diéguez
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Sensor Network ◽  
Proof Of Concept ◽  
Test Bed ◽  
Pollution Levels ◽  
Wide Range ◽  
Military Applications

Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.

Visualization of uncertainty in air quality ensemble forecasts

2021 ◽  
Author(s):  
Angelika Heil ◽  
Augustin Colette
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Mobile Apps ◽  
Predictive Performance ◽  
User Preferences ◽  
Ensemble Forecasts ◽  
The Public ◽  
Pollution Levels ◽  
Wide Range

<p>Air quality forecasts help decision-makers to respond to air pollution episodes and to improve air quality management. In recent years, the public increasingly uses mobile apps to check forecasted air pollution levels and then adjusts outdoor activities accordingly. For Europe, state-of-the-art daily air quality forecasts are provided by the regional Copernicus Atmosphere Monitoring System (CAMS). The system integrates forecasts from 9 individual models. This ensemble approach not only achieves better predictive performance compared to a single model, but also allows a better quantification of forecast uncertainty. How to best communicate this uncertainty to a broad audience is by no means a trivial task, but yet essential to maintain trust in the forecasts.</p><p>We developed innovative visualizations to convey CAMS forecast uncertainties in time series and maps. The development is strongly user-driven and involves iterative consultation with a wide range of expert and non-expert users. We investigate the feasibility of different bivariate techniques to communicate the ensemble's best estimate and its uncertainty in a single map. We explore user preferences for a variety of time-series graphs, including boxplots, violinplots, and fancharts. Whilst preferences are largely driven by the data and visualization literacy of the users, we identify some generally valid best practices in terms of graph types, choices of colors and labels, and accompanying textual explanations. Finally, we present our candidate designs for the public display of air quality forecasts on the regional CAMS webpage.</p>

scholarly journals A Comparative Analysis of Air Pollution Levels During Times of International Uncertainty: The Financial Crisis of 2008 and the COVID-19 Pandemic of 2020

2021 ◽  
pp. 1-4
Author(s):  
Ammar Vora ◽  
Hillary Hale
Keyword(s):  
Air Pollution ◽  
Financial Crisis ◽  
Global Economy ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Pollution Levels ◽  
System 2 ◽  
Financial Crisis Of 2008 ◽  
Severe Recession ◽  
National Economies

During a crisis, economies stagnate as uncertainty grows about the future state of the world. The financial crisis of 2008 led to a severe recession where the global economy halted for approximately two years, causing unemployment and poverty [1]. Coronavirus disease 2019 (COVID-19), which attacks the respiratory system [2], was first identified in Wuhan, China, in late December of 2019. Within a matter of months, it spread globally causing economies to shut down. As distinct as the financial crisis of 2008 may seem from the COVID-19 pandemic lockdowns, both have had devastating effects on national economies and industrial production, resulting in an overall decrease in air pollutant emissions such as carbon dioxide (CO2) and nitrogen oxides (NOx). Therefore, parallels can be made between air pollution levels during each crisis. Given air pollution rates increased after the financial crisis of 2008 [3], it is likely air pollution will also rise in the aftermath of the COVID-19 pandemic. This study aims to support this argument by analyzing air pollution trends outlined in the results of several published papers.

scholarly journals Spatial modelling of air pollution in urban areas with GIS: a case study on integrated database development

2005 ◽  
Vol 4 ◽  
pp. 63-68 ◽  
Author(s):  
L. Matejicek
Keyword(s):  
Air Pollution ◽  
Data Management ◽  
Spatial Data ◽  
Urban Areas ◽  
Satellite Images ◽  
Spatial Models ◽  
Physical Modelling ◽  
Air Pollutant ◽  
Automatic Monitoring System ◽  
Wide Range

Abstract. A wide range of data collected by monitoring systems and by mathematical and physical modelling can be managed in the frame of spatial models developed in GIS. In addition to data management and standard environmental analysis of air pollution, data from remote sensing (aerial and satellite images) can ehance all data sets. In spite of the fact that simulation of air pollutant distribution is carried out by standalone computer systems, the spatial database in the framework of the GIS is used to support decision-making processes in a more efficient way. Mostly, data are included in the map layers as attributes. Other map layers are carried out by the methods of spatial interpolation, raster algebra, and case oriented analysis. A series of extensions is built into the GIS to adapt its functionality. As examples, the spatial models of a flat urban area and a street canyon with extensive traffic polluted with NOx are constructed. Different scales of the spatial models require variable methods of construction, data management, and spatial data sources. The measurement of NOx and O3 by an automatic monitoring system and data from the differential absorption LIDAR are used for investigation of air pollution. Spatial data contain digital maps of both areas, complemented by digital elevation models. Environmental analyses represent spatial interpolations of air pollution that are displayed in horizontal and vertical planes. Case oriented analyses are mostly focused on risk assessment methods. Finally, the LIDAR monitoring results and the results obtained by modelling and spatial analyses are discussed in the context of environmental management of the urban areas. The spatial models and their extensions are developed in the framework of the ESRI's ArcGIS and ArcView programming tools. Aerial and satellite images preprocessed by the ERDAS Imagine represent areas of Prague.

scholarly journals Linear and Non-linear Relationships Between Shares of the Agri-food Industries of the Warsaw Stock Exchange. Risk Aspect

2017 ◽  
Vol 17(32) (4) ◽  
pp. 249-262 ◽  
Author(s):  
Jacek Pera
Keyword(s):  
Food Industry ◽  
Stock Exchange ◽  
Test Procedure ◽  
Listed Companies ◽  
Food Sector ◽  
Linear Relationships ◽  
Wide Range ◽  
Non Linear ◽  
Warsaw Stock Exchange ◽  
Exchange Risk

Despite a wide range of research on the agricultural market conducted so far, relatively little attention has been devoted to a comprehensive analysis of linear and non-linear causality in relation to the entire agri-food sector in Poland, in the context of risk. The objective of this study is therefore to analyze the linear and non-linear relationships between shares of WSE's agri-food industry sectors in terms of risk. The study covered three sectors of agri-food sector currently existing on the WSE (29 listed companies): Foods (21 listed companies), Agricultural Production and Fisheries (5 listed companies) and Food and Foodstuffs and fast-trafficking foodstuffs (3 listed companies). The existence of linear relationships was verified using the test procedure proposed by Hong, Liu, Wang and Łęt, while non-linear relationships were verified using the Diks-Panchenko, Orzeszko and Osińska tests’s. The study was carried out on the basis of data from companies of the agri-food industry listed on the Warsaw Stock Exchange in the period from 1 May 2010 to 1 May 2017. The chosen research methodology was dictated by the correlation with investment risk on the WSE. The strongest and most enduring dependencies have been found in the agricultural and fisheries sectors. In the foodstuff sector and the fast-marketable sector, the risk of investment in the listed companies was temporary.

scholarly journals Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Bulgansaikhan Baldorj ◽  
Munkherdene Tsagaan ◽  
Lodoysamba Sereeter ◽  
Amanjol Bulkhbai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Factors ◽  
Influencing Factors ◽  
Quality Measurement ◽  
Temporal Distribution ◽  
Range Data ◽  
Pollution Levels ◽  
Variational Autoencoder ◽  
Wide Range

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.

The Role of Traffic-Related Air Pollution in Neurodegenerative Diseases in Older People: An Epidemiological Perspective

2021 ◽  
Author(s):  
Rachel Tham ◽  
Tamara Schikowski
Keyword(s):  
At Risk ◽  
Air Pollution ◽  
Environmental Factors ◽  
Neurodegenerative Diseases ◽  
Environmental Epidemiology ◽  
Risk Groups ◽  
Air Pollutant ◽  
Pollution Levels ◽  
Health Burdens

Traffic-related air pollution is ubiquitous and almost impossible to avoid. It is important to understand the role that traffic-related air pollution may play in neurodegenerative diseases, such as dementia, Alzheimer’s disease, and Parkinson’s disease, particularly among older populations and at-risk groups. There is a growing interest in this area among the environmental epidemiology literature and the body of evidence identifying this role is emerging and strengthening. This review focuses on the principal components of traffic-related air pollutants (particulate matter and nitrogen oxides) and the epidemiological evidence of their contribution to common neurodegenerative diseases. All studies reported are currently observational in nature and there are mixed findings depending on the study design, assessment of traffic-related air pollutant levels, assessment of the neurodegenerative disease outcome, time period of assessment, and the role of confounding environmental factors and at-risk genetic characteristics. All current studies have been conducted in income-rich countries where traffic-related air pollution levels are relatively low. Additional longer-term studies are needed to confirm the levels of risk, consider other contributing environmental factors and to be conducted in settings where air pollution exposures are higher and at-risk populations reside and work. Better understanding of these relationships will help inform the development of preventive measures and reduce chronic cognitive and physical health burdens (cost, quality of life) at personal and societal levels.

scholarly journals COVID-19 lockdowns cause global air pollution declines with implications for public health risk

2020 ◽  
Author(s):  
Zander S. Venter ◽  
Kristin Aunan ◽  
Sourangsu Chowdhury ◽  
Jos Lelieveld
Keyword(s):  
Air Pollution ◽  
Economic Activity ◽  
Pediatric Asthma ◽  
Ground Level ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Long Distance ◽  
Potential Health ◽  
Pollution Levels ◽  
Premature Deaths

AbstractThe lockdown response to COVID-19 has caused an unprecedented reduction in global economic activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find remarkable declines in ground-level nitrogen dioxide (NO2: −29 % with 95% confidence interval −44% to −13%), ozone (O3: −11%; −20% to −2%) and fine particulate matter (PM2.5: −9%; −28% to 10%) during the first two weeks of lockdown (n = 27 countries). These results are largely mirrored by satellite measures of the troposphere although long-distance transport of PM2.5 resulted in more heterogeneous changes relative to NO2. Pollutant anomalies were related to short-term health outcomes using empirical exposure-response functions. We estimate that there was a net total of 7400 (340 to 14600) premature deaths and 6600 (4900 to 7900) pediatric asthma cases avoided during two weeks post-lockdown. In China and India alone, the PM2.5-related avoided premature mortality was 1400 (1100 to 1700) and 5300 (1000 to 11700), respectively. Assuming that the lockdown-induced deviations in pollutant concentrations are maintained for the duration of 2020, we estimate 0.78 (0.09 to 1.5) million premature deaths and 1.6 (0.8 to 2) million pediatric asthma cases could be avoided globally. While the state of global lockdown is not sustainable, these findings illustrate the potential health benefits gained from reducing “business as usual” air pollutant emissions from economic activities. Explore trends here: www.covid-19-pollution.zsv.co.zaSignificance statementThe global response to the COVID-19 pandemic has resulted in unprecedented reductions in economic activity. We find that lockdown events have reduced air pollution levels by approximately 20% across 27 countries. The reduced air pollution levels come with a substantial health co-benefit in terms of avoided premature deaths and pediatric asthma cases that accompanied the COVID-19 containment measures.

scholarly journals Yearly and Daily Relationship Assessment between Air Pollution and Early-Stage COVID-19 Incidence: Evidence from 231 Countries and Regions

2021 ◽  
Vol 10 (6) ◽  
pp. 401
Author(s):  
Yuan Meng ◽  
Man Sing Wong ◽  
Hanfa Xing ◽  
Mei-Po Kwan ◽  
Rui Zhu
Keyword(s):  
Air Pollution ◽  
Early Stage ◽  
Additive Model ◽  
Global Scale ◽  
Linear Relationships ◽  
Non Linear ◽  
Air Pollutions ◽  
Novel Coronavirus ◽  
Relationship Assessment ◽  
Relationship Of

The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales.

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