A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie, Xingzhe; Šemanjski, Ivana; Gautama, Sidharta; Tsiligianni, Evangelia; Deligiannis, Nikos; Rajan, Raj Thilak; Pasveer, Frank; Philips, Wilfried

doi:10.3390/ijgi6120389

Cited by 148 publications

(99 citation statements)

References 123 publications

(169 reference statements)

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“…Land-use regression models have been largely used to relate concentrations measured at monitor sites with concentration estimates at different locations across the city (Beelen et al, 2013;Cattani et al, 2017;Ryan and LeMasters, 2007). Then, space-time activity data are coupled to concentration data to provide exposure estimates (Vizcaino and Lavalle, 2018;Xu et al, 2019a). Land-use regression models provide spatial maps where urban features such as roads, buildings, and parks may be distinguished from background concentration levels.…”

Section: Introductionmentioning

confidence: 99%

EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5

et al. 2020

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Abstract. This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

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Section: Introductionmentioning

confidence: 99%

EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5

et al. 2020

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“…However, given the dynamics of air pollutants, these techniques tend to produce high estimation errors. Alternatively, one can use kriging-based variogram models to capture the variance in air pollution data with respect to the geodesic distance [12], [13]. As a purely spatial interpolation method, however, this approach does not capture the temporal correlation in the air quality data.…”

Section: A Air Quality Inferencementioning

confidence: 99%

Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Huu

Nguyen

Tsiligianni

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-ofthe-art approaches.

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“…Also many developed European countries such as Italy or Germany these values are even lower respectively 9.8% and 5.8%. The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens (Xie, 2017). Commonly used indicators describing PM that are relevant to health refer to the mass concentration of particles with a diameter of less than 10 µm (PM10) and of particles with a diameter of less than 2.5 µm (PM2.5) (WHO, 2013).…”

Section: Introductionmentioning

confidence: 99%

Dispersion Model Prospective of Air Pollution in Tirana

Hysenaj¹

2019

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Outdoor air pollution is the most hazard challenge of many governments. Strictly policies followed by continue alert thresholds are being followed. Environmental issue canalized in air quality in the capital of Albania is the prior thematic analyzed in this paper. We figured to create a filter which gradually puzzles out the leading cause to exceed the limits settled by the WHO guidelines and the EU's AAQ directives. The paper tries to create potential scenario from the replacement of the passenger's vehicle fleet from the current Euro 3 to Euro 4 and over. The opportunity to structure evaluation maps for air assessment based on the outcome creates a clear overview of the current situation. Digital maps are a potential source of solution for many environmental issues. Spatial technology is fast and reliable to estimate population exposure to outdoor pollution. Geostatistical data offer reliable solution to perceive dispersion model issues. In this paper we concentrate on air pollution data (PM10).

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A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Cited by 148 publications

References 123 publications

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Dispersion Model Prospective of Air Pollution in Tirana

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A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Cited by 148 publications

References 123 publications

EXPLUME v1.0: a model for personal exposure to ambient O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;

EXPLUME v1.0: a model for personal exposure to ambient O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;

Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Dispersion Model Prospective of Air Pollution in Tirana

Contact Info

Product

Resources

About

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>