Abstract
Air quality is a common cause for respiratory health problems. It shows high temporal and spatial variability within urban areas and currently sensors are installed to monitor air quality. The objective of this paper is to investigate its spatial variability during peak hours. Spatial statistical methods are based upon copula theory, integrating distributions from different pollutants and at different locations. In this paper attention focused on PM 1 as one of the neglected components of air quality so far. Using observations from the Netherlands, we compared two hours: the first hour of the New Year, and an hour with a high traffic congestion. We investigated the size of the sensor network by analyzing observations from a city with 35 sensors with a city with four sensors and a city with one sensor. In the absence of an environmental standard for PM 1, the paper defined a threshold related to existing thresholds of PM 2.5 and PM 10. Results showed the adequacy of the large network, generating a varying pattern during the high peak hour, whereas in cities with less sensors both the spatial spread and possibly large values are missed. In particular the first hour of the New Year showed large concentrations and high probabilities that a threshold was exceeded, whereas the second peak hour showed values well below the threshold. We conclude that the mapping of PM 1 concentrations can well be done by copula interpolation. Using one or three sensors may save expenses, but this is at the cost of missing extreme values as well as spatial patterns. Both are potentially important for public health measures.
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An Analysis of the Data on November 20th, 2017, Between 7:30 and 8:30Â am. This Time Step Is Reportedly a Moment with a High Traffic Density (Figs. 7, 8, 9, 10, 11, 12 and Table 4)
An Analysis of the Data on November 20th, 2017, Between 7:30 and 8:30Â am. This Time Step Is Reportedly a Moment with a High Traffic Density (Figs. 7, 8, 9, 10, 11, 12 and Table 4)
Probability distribution function (PDF) and cumulative distribution function (CDF) on 2017 11 20. (a) the Eindhoven data, (b) four locations with relatively large values, (c) the city of Breda (three observations), (d) the city of Helmond (one observation) and (e) all data
Probability distribution function (PDF) and cumulative distribution function (CDF). (a) the Eindhoven data, (b) four locations with relatively large values, (c) the city of Breda (three observations), (d) the city of Helmond (one observation) and (e) all data
Four locations in the city with relatively high values. Note that these lcations are different from the ones at January 1st.
Interpolated PM 1 data for the six time steps: 7:40, 7:50, 8:00, 8:10, 8:20 and 8:30Â am. The predictions are the results of applying the mixed copula interpolator
Interpolated hourly PM 1 data taken as the average concentrations over the six time steps
Predictions with copulas using the spatial copula interpolator including different covariates
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Stein, A., Alidoost, F., van Zoest, V. (2020). Spatial Interpolation of Extreme PM1 Values Using Copulas. In: Bekker, A., Chen, (.DG., Ferreira, J.T. (eds) Computational and Methodological Statistics and Biostatistics. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-42196-0_13
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