Dispersion models and air quality data for population exposure assessment to air pollution

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

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Developing Web-Based 3D Health-Aware Routing for Pedestrians and Cyclists

E3S Web of Conferences ◽

10.1051/e3sconf/202017102009 ◽

2020 ◽

Vol 171 ◽

pp. 02009

Author(s):

Rosanny Sihombing ◽

Sabo Kwada Sini ◽

Matthias Fitzky

Keyword(s):

Air Pollution ◽

Air Quality ◽

Health Concern ◽

Quality Data ◽

Quality Level ◽

3D Navigation ◽

Index Numbers ◽

Web Based ◽

Air Quality Data ◽

Monitoring Stations

As the population of people migrating to cities keeps increasing, concerns have been raised about air quality in cities and how it impacts everyday life. Thus, it is important to demonstrate ways of avoiding polluted areas. The approach described in this paper is intended to draw attention to polluted areas and help pedestrians and cyclists to achieve the lowest possible level of air pollution when planning daily routes. We utilise real-time air quality data which is obtained from monitoring stations across the world. The data consist of the geolocation of monitoring stations as well as index numbers to scale the air quality level in every corresponding monitoring stations. When the air quality level is considered having a moderate health concern for people with respiratory disease, such as asthma, an alternative route that avoid air pollution will be calculated so that pedestrians and cyclists can be informed. The implementation can visualize air quality level in several areas in 3D map as well as informs health-aware route for pedestrian and cyclist. It automatically adjusts the observed air quality areas based on the availability of monitoring stations. The proposed approach results in a prototype of a health-aware 3D navigation system for pedestrian and cyclist.

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Impacts of the COVID-19 lockdown on air quality and its association with human mortality trends in megapolis Mexico City

10.21203/rs.3.rs-90079/v1 ◽

2020 ◽

Author(s):

Gurusamy Kutralam-Muniasamy ◽

Fermín Pérez-Guevara ◽

Priyadarsi D. Roy ◽

I. Elizalde-Martínez ◽

V.C. Shruti

Keyword(s):

Air Pollution ◽

Latin America ◽

Air Quality ◽

Mexico City ◽

Air Pollutants ◽

Mortality Rates ◽

Quality Data ◽

Mortality Trends ◽

Human Mortality ◽

Air Quality Data

Abstract Mexico City is the second most populated city in Latin America, and it went through two partial lockdowns between April 1 and May 31, 2020 for reducing the COVID-19 propagation. The present study assessed air quality and its association with human mortality rates during the lockdown by estimating changes observed in air pollutants (CO, NO2, O3, SO2, PM10 and PM2.5) between the lockdown (April 1 - May 31) and pre-lockdown (January 1 – March 31) periods, as well as by comparing the air quality data of lockdown period with the same interval of previous five-years (2015-2019). Concentrations of NO2 (-29%), SO2 (-55%) and PM10 (-11%) declined and the contents of CO (+1.1%), PM2.5 (+19%) and O3 (+63%) increased during the lockdown compared to the pre-lockdown period. This study also estimated that NO2, SO2, CO, PM10 and PM2.5 reduced by 19-36%, and O3 enhanced by 14% compared to the average of 2015-2019. Reduction in traffic as well as less emission from vehicle exhausts led to remarkable decline in NO2, SO2 and PM10. The significant positive associations of PM2.5, CO and O3 with the numbers of COVID-19 infections and deaths, however, underscored the necessity to enforce air pollution regulations to protect human health in one of the important cities of the northern hemisphere.

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Burden of disease attributed to anthropogenic air pollution in the United Arab Emirates: Estimates based on observed air quality data

The Science of The Total Environment ◽

10.1016/j.scitotenv.2010.08.017 ◽

2010 ◽

Vol 408 (23) ◽

pp. 5784-5793 ◽

Cited By ~ 44

Author(s):

Ying Li ◽

Jacqueline MacDonald Gibson ◽

Prahlad Jat ◽

Gavino Puggioni ◽

Mejs Hasan ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

United Arab Emirates ◽

Burden Of Disease ◽

Quality Data ◽

Air Quality Data

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Influence of air quality data estimation on short-term health effect estimates of air pollution in epidemiological studies

ISEE Conference Abstracts ◽

10.1289/isee.2013.p-3-03-22 ◽

2013 ◽

Vol 2013 (1) ◽

pp. 3758

Author(s):

Martina Gandini ◽

Giorgio Cattani ◽

Massimo Stafoggia ◽

Giovanna Berti

Keyword(s):

Air Pollution ◽

Air Quality ◽

Health Effect ◽

Epidemiological Studies ◽

Quality Data ◽

Short Term ◽

Data Estimation ◽

Air Quality Data

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Fitting Statistical Distribution on Air Pollution: an Overview

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.23.17256 ◽

2018 ◽

Vol 7 (3.23) ◽

pp. 40

Author(s):

Muhammad Ismail Jaffar ◽

Hazrul Abdul Hamid ◽

Riduan Yunus ◽

Ahmad Fauzi Raffee

Keyword(s):

Air Pollution ◽

Air Quality ◽

Statistical Distribution ◽

Air Pollutant ◽

Quality Data ◽

Statistical Distributions ◽

Air Pollution Modelling ◽

High Event ◽

Air Quality Data ◽

Comprehensive Study

High event of air pollution would give adverse effect to human health and cause of instability towards environment. In order to overcome these issues, the statistical air pollution modelling is an important tool to predict the return period of high event on air pollution in future. This tool also will be useful to help the related government agencies for providing a better air quality management and it can provide significantly when air quality data been analyze appropriately. In fitting air pollutant data, statistical distribution of gamma, lognormal and Weibull distribution is widely used compared to others distributions model. In addition, the aims of this overview study are to identify which distributions is the most used for predicting the air pollution concentration thus, the accuracy for prediction future air quality is the important aspect to give the best prediction. The comprehensive study need to be conducted in statistical distribution of air pollution for fitting pollutant data. By using others statistical distributions model as main suggested in this paper.

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Particular matter prediction using synergy of multiple source urban big data in smart cities

Intelligent Decision Technologies ◽

10.3233/idt-200147 ◽

2021 ◽

pp. 1-15

Author(s):

Ali Reza Honarvar ◽

Ashkan Sami

Keyword(s):

Air Pollution ◽

Big Data ◽

Air Quality ◽

Smart Cities ◽

Data Sources ◽

Quality Data ◽

Data Set ◽

Multiple Data Sources ◽

Multiple Data ◽

Air Quality Data

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

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APPLICATION OF OPEN AIR MODEL (R PACKAGE) TO ANALYZE AIR POLLUTION DATA

INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY ◽

10.25105/urbanenvirotech.v1i1.2430 ◽

2017 ◽

Vol 1 (1) ◽

pp. 94 ◽

Cited By ~ 2

Author(s):

Intan Agustine ◽

Hernani Yulinawati ◽

Endro Suswantoro ◽

Dodo Gunawan

Keyword(s):

Air Pollution ◽

Big Data ◽

Air Quality ◽

Meteorological Data ◽

Ambient Air ◽

Quality Data ◽

Fluctuation Analysis ◽

Ambient Air Quality ◽

Long Time ◽

Air Quality Data

Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. Aims: This study aims to analyze various application of openair model, which is available in open-source, for analyzing urban air quality data. Methodology and results: Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the openair model were presented in comma separated value (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: theilSen for trend analysis, timeVariation for temporal variations, scatterPlot for linear correlation analysis, timePlot for fluctuation analysis, windRose for wind rose creation, and polarPlot for creating pollution rose. Results from these functions are discussed. Conclusion, significance and impact study: Openair model is capable of analyzing a long time air quality data. Application of openair model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet

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