aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

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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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Game-Like 3D Visualisation of Air Quality Data

Multimodal Technologies and Interaction ◽

10.3390/mti4030054 ◽

2020 ◽

Vol 4 (3) ◽

pp. 54

Author(s):

Bruno Teles ◽

Pedro Mariano ◽

Pedro Santana

Keyword(s):

Air Quality ◽

Visual Cues ◽

Empirical Support ◽

Quality Monitoring ◽

Quality Data ◽

Urban Air ◽

Air Quality Monitoring ◽

Usability Tests ◽

3D Game ◽

Air Quality Data

The data produced by sensor networks for urban air quality monitoring is becoming a valuable asset for informed health-aware human activity planning. However, in order to properly explore and exploit these data, citizens need intuitive and effective ways of interacting with it. This paper presents CityOnStats, a visualisation tool developed to provide users, mainly adults and young adults, with a game-like 3D environment populated with air quality sensing data, as an alternative to the traditionally passive visualisation techniques. CityOnStats provides several visual cues of pollution presence with the purpose of meeting each user’s preferences. Usability tests with a sample of 30 participants have shown the value of air quality 3D game-based visualisation and have provided empirical support for which visual cues are most adequate for the task at hand.

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Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules

Applied Sciences ◽

10.3390/app9245445 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5445 ◽

Cited By ~ 1

Author(s):

Chou-Yuan Lee ◽

Zne-Jung Lee ◽

Jian-Qiong Huang ◽

Fu-Lan Ye ◽

Zheng-Yuan Ning ◽

...

Keyword(s):

Air Quality ◽

Parameter Optimization ◽

Decision Rules ◽

Urban Air Quality ◽

Quality Analysis ◽

Quality Data ◽

Urban Air ◽

Key Factors ◽

Intelligent Algorithm ◽

Best Parameter

Air pollution has an ongoing devastating impact on the planet, damaging ecosystems, depleting natural resources, and endangering human health. This paper proposes a new intelligent algorithm that includes parameter optimization and decision rules to forecast and analyze of urban air quality. Through analysis of 24-h daily air quality data provided by the Beijing Air Quality Monitoring Station, simulated annealing (SA) and a decision tree (DT) emerge as the key factors. We prove that in the investigated algorithm, SA and DT can be used to make decision rules and achieve better accuracy for classification. We find that SA can be used to adjust the best parameter settings for the DT. Simulation results show that the accuracy of the proposed algorithm for classification is far better than other existing approaches.

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High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

Atmospheric Measurement Techniques ◽

10.5194/amt-13-4601-2020 ◽

2020 ◽

Vol 13 (8) ◽

pp. 4601-4617

Author(s):

Bas Mijling

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Urban Air Pollution ◽

Urban Air Quality ◽

Quality Data ◽

City Centre ◽

Urban Air ◽

Reference Measurements ◽

Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks or unrealistic assumptions being made in urban-air-quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution and the large uncertainties associated with measurements of low-cost sensors. This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open-source atmospheric-dispersion model and emission proxies from open-data sources, and (2) a practical spatial-interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy (defined as the ratio between the root mean square error and the mean of the observations) of 39 % within 2 km of an observation location and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. It is shown that during the summer holiday period, NO2 concentrations drop about 10 %. The reduction is less in the historic city centre, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, it is shown that Retina can be applied for an enhanced understanding of reference measurements and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

Download Full-text