Deriving high-resolution urban air pollution maps using mobile sensor nodes

2015 ◽  
Vol 16 ◽  
pp. 268-285 ◽  
Author(s):  
David Hasenfratz ◽  
Olga Saukh ◽  
Christoph Walser ◽  
Christoph Hueglin ◽  
Martin Fierz ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Resolution ◽  
Urban Air Pollution ◽  
Sensor Nodes ◽  
Urban Air ◽  
Mobile Sensor ◽  
Mobile Sensor Nodes

Influences of Season and Geography on Urban Air Quality Monitoring Stations (AQMS) Representativeness Areas by High Resolution Air Quality Monitoring

2020 ◽  
Author(s):  
Tilman Leo Hohenberger
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Air Quality ◽  
High Resolution ◽  
Urban Air Pollution ◽  
Quality Monitoring ◽  
Urban Air ◽  
Air Quality Monitoring ◽  
Air Quality Monitoring Stations ◽  
Better Than

<p>Urban air pollution remains a key pressure on public health. With the megatrend of urbanization and its forcing on emissions and exposure, effective monitoring tools in cities are at the center of prevention efforts.</p><p>Air Quality Monitoring Stations (AQMS) are traditionally used for regulatory efforts and, increasingly, as publicly available information sources. Facing high levels of air pollution heterogeneity in complex urban environments, a simple spatial approach is often misleading when choosing an AQMS that represents local street-level conditions the best. Model-based calculation of representativeness areas are rare for the urban scale (e.g. Rodriguez et al., 2019), and suffer from short model times, low model correlations and a lack of external validation by observation data. Moreover, as both health impacts and air-pollution episodes are influenced by environmental factors, the sensitivity of representativeness areas to wind impacts and during different seasons are a further point of interest not covered well by previous literature.</p><p>For the high-density environment of geographically complex Hong Kong, we used a full year (2019) of high-resolution air quality modelling (ADMS-Urban) data to establish representativeness areas for the territory’s 16 AQMS. We constructed representativeness areas for key air-pollutants for the full period and based on season and wind speed. We parameterized the effects of wind and geography on the size and shape of the representativeness areas. Furthermore, we validated our findings by a series of week-long outdoor measurements aimed to cover the whole territory of Hong Kong.</p><p>Our results show that Hong Kong’s AQMS network covering the territory well for a PM<sub>2.5</sub>, PM<sub>10</sub> and O<sub>3</sub>, where the mean CSF (hourly Concentration Similarity Frequency with a target of ±20%) of each grid-cell to the best matching AQMS lies at around 60%. Both NO<sub>2</sub> and SO<sub>2</sub> are less well represented, with a CSF of around 30%. Moreover, we show that representativeness areas calculated from similarity-based metrices as CSF and percentage difference represent the impact of geographical features on pollution dispersion better than correlation-based metrices (R<sup>2</sup> and ioa). It was further found that AQMS represent upwind areas better than downwind areas, especially in areas exposed to open wind-flow, and that the represented areas change strongly over the course of a year.</p><p>In this study, we showcase the ability of high-resolution urban air-pollution modelling to guide the public with information on AQMS representativeness. Furthermore, we report that representativeness areas are non-static, changing with seasons and under the influence of wind. High-resolution urban modelling can further be used to gauge the quality of AQMS networks and assess the need and location of additions to an existing network.</p><p> </p><p>Rodriguez, D., Valari, M., Payan, S., & Eymard, L. (2019). On the spatial representativeness of NOX and PM10 monitoring-sites in Paris, France. Atmospheric Environment: X, 1, 100010.</p>

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.

scholarly journals Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

2020 ◽  
Vol 1 (3) ◽  
pp. 100047 ◽  
Author(s):  
Donghai Liang ◽  
Liuhua Shi ◽  
Jingxuan Zhao ◽  
Pengfei Liu ◽  
Jeremy A. Sarnat ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Urban Air Pollution ◽  
Case Fatality ◽  
The United States ◽  
Mortality Rates ◽  
Urban Air

The impact of COVID-19 pandemic in Mediterranean urban air pollution and mobility

2021 ◽  
pp. 1-16
Author(s):  
Nikolaos Sifakis ◽  
Maria Aryblia ◽  
Tryfon Daras ◽  
Stavroula Tournaki ◽  
Theocharis Tsoutsos
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Urban Air ◽  
The Impact
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