Mapping of dispersion of urban air pollution using remote sensing and in-situ monitoring data

2005 ◽  
Author(s):  
M. A. Zoran ◽  
L. F. V. Zoran
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Urban Air Pollution ◽  
In Situ Monitoring ◽  
Monitoring Data ◽  
Urban Air

scholarly journals ASSESSING POPULATION SENSITIVITY TO URBAN AIR POLLUTION USING GOOGLE TRENDS AND REMOTE SENSING DATASETS

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 93-100
Author(s):  
P. Misra ◽  
W. Takeuchi
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Air Pollution ◽  
Aerosol Concentration ◽  
Google Trends ◽  
Urban Air ◽  
Indirect Measure ◽  
Search Volume ◽  
Fine Aerosol

Abstract. This study demonstrates relationship between remote sensing satellite retrieved fien aerosol concentration and web-based search volumes of air quality related keywords. People’s perception of urban air pollution can verify policy effectiveness and gauge acceptability of policies. As a serious health issue in Asian cities, population may express concern or uncertainty for air pollution risk by performing search on the web to seek answers. A ‘social sensing’ approach that monitors such search queries, may assess people’ perception about air pollution as a risk. We hypothesize that trend and volume of searches show impact of air pollution on general population. The objectives of this research are to identify those atmospheric conditions under which relative search volume (RSV) obtained from Google Trends shows correlation with measured fine aerosol concentration, and to compare search volume sensitivity to rise in aerosol concentration in seven Asian megacities. We considered weekly relative search volumes from Google Trends (GT) for a four year period from January, 2015 to December, 2018 representing diverse PM2.5 concentrations. Search volumes for keywords corresponding to perception of air quality (‘air pollution’) and health effects (‘cough’ and ‘asthma’) were considered. To represent PM2.5 we used fine aerosol indicator developed in an earlier research. The results suggest that tendency to search for ‘air pollution’ and ‘cough’ occurs when AirRGB R is in excess and temperature is below the baseline values. Consistent with this, in cities with high baseline concentrations, sensitivity to rise in AirRGB R is also comparatively lower. The result of this study can used as an indirect measure of awareness in the form of perception and sensitivity of population to air quality. Such an analysis could be useful for forecasting health risks specially in cities lacking dedicated services.

Mice exposed in situ to urban air pollution exhibit pulmonary alterations in gene expression in the lipid droplet synthesis pathways

2013 ◽  
Vol 54 (4) ◽  
pp. 240-249 ◽  
Author(s):  
Andrea Rowan-Carroll ◽  
Sabina Halappanavar ◽  
Andrew Williams ◽  
Christophers M. Somers ◽  
Carole L. Yauk
Keyword(s):  
Gene Expression ◽  
Air Pollution ◽  
Lipid Droplet ◽  
Urban Air Pollution ◽  
Urban Air

scholarly journals Estimation of Dust Downfall Time in Dusty Days using the Correlation between PM<sub>10</sub> and Sunphotometer Data

2017 ◽  
Author(s):  
Samira Zolfaghari Nikanjam ◽  
Hamid Reza Khalesifard ◽  
Yousefali Abedini
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Correlation Coefficient ◽  
Aerosol Optical Depth ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Aerosol Sampling ◽  
Measurement Instruments ◽  
Sampling Device ◽  
Vertical Column

Abstract. Urban air pollution has become a serious challenge in all societies. Nowadays, pollutants are measured by various tools and devices, including local particulate measurement instruments and ground-based remote sensing devices. This paper aims to estimate dust downfall time in dusty days of Zanjan city using the data obtained from ground-based aerosol sampling device and sunphotometer. The particulates with the size of less than 10 micrometers (PM10) were measured by ground-based measurement device in the height of 3 meters above the ground, while aerosol optical depth (AOD), which represents the amount of aerosols in vertical column of atmosphere, was measured by sunphotometer. According to correlation coefficient diagram, PM10 and AOD reached a maximum of 2, 6, 3 and 3 hours in dusty days of 9, 10, 24 and 25 June 2010. In other words, dust downfall took 2, 6, 3 and 3 hours respectively in the aforementioned days. Keywords: Downfall, dust, sunphotometer, correlation

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

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