Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets and Preprocessing
2.3. Geostatistical Modelling
3. Results
- The proximity of monitoring stations of different types, especially background and traffic, causing high differences in the loo-cv procedure in the case where one of the two stations was left out,
- The recurrence of single events in the vicinity of the measurement station with strong impacts on PM concentrations at this station, causing very high measured values, which the model was not able to reproduce accurately, and
- A false prediction based on the value used in the re-trending process due to discrepancies between the CORINE land cover classification and the real-world conditions.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Siting-Type | Area | Buffer Radius in m | Buffer Area in km² |
---|---|---|---|
Traffic | 100 | 0.03 | |
Industry | 150 | 0.07 | |
Background | Urban | 1000 | 3.14 |
Background | Suburban | 2500 | 19.63 |
Background | Rural | 5000 | 78.53 |
Run | R² | MAE | RMSE | Parameter Adjustment (Details Described in the Text) |
---|---|---|---|---|
1 | 0.68 | 9.87 | 15.17 | Initial run |
2 | 0.68 | 9.84 | 15.18 | New: no point sources (PRTR) in GRETA |
3 | 0.68 | 9.84 | 15.18 | New: emissions from GRETA capped to the 99.99% percentile |
4 | 0.75 | 8.12 | 12.86 | New: buffer size set according to directive 2008/50/EC (Table 1) |
5 | 0.75 | 8.04 | 12.82 | New: 9 additional CORINE land cover classes (traffic routes) |
6 | 0.76 | 7.75 | 12.32 | New: ln() transformation in trend functions |
7 | 0.80 | 7.68 | 11.20 | New: sampling from 99.99% of measured PM10 values |
8 | 0.80 | 7.69 | 11.20 | New: constant increment of all measured values of 10 |
Dataset | Metrics | Residuals (Observed-Modelled), * Quantile | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
R² | MAE | RMSE | min | 0.01 * | 0.25 * | median | mean | 0.75 * | 0.99 * | max | |
(1) 1500 random hours of 2011 | 0.80 | 7.68 | 11.20 | −166.72 | −28.76 | −5.53 | 0.09 | 0.12 | 5.55 | 31.02 | 233.31 |
n = 566,326 | |||||||||||
(2) all hours of February 2011 | 0.83 | 9.77 | 14.36 | −190.53 | −39.41 | −6.88 | 0.09 | −0.04 | 6.74 | 39.12 | 216.79 |
n = 256,506 | |||||||||||
(3) mean 2011 | 0.42 | 4.82 | 6.08 | −18.93 | −15.84 | −3.93 | 0.43 | 0.01 | 3.96 | 12.66 | 18.92 |
n = 413 |
Siting-Type | Type of Data | MAE | RMSE | Median |
---|---|---|---|---|
Background | Measured | 7.63 | 10.87 | −1.75 |
Industry | Measured | 7.19 | 11.39 | 0.64 |
Traffic | Measured | 7.91 | 11.69 | 2.34 |
Traffic | Synthesised | 7.61 | 11.48 | 3.24 |
Study | Period | Area | Temporal Res. | Spatial Res. | R² | MAE | RMSE |
---|---|---|---|---|---|---|---|
this study—dataset (1) | 2011 | Germany | hourly mean | 100 m × 100 m | 0.80 | 7.68 | 11.20 |
this study—dataset (2) | February 2011 | Germany | hourly mean | 100 m × 100 m | 0.83 | 9.77 | 14.36 |
this study—dataset (3) | 2011 | Germany | annual mean | 100 m × 100 m | 0.42 | 4.82 | 6.08 |
Janssen et al. [37] | 2006 | Belgium | daily mean | 4 km × 4 km | 6.98 | 9.89 | |
Diaz-de Quijano et al. [50] | 2008 | Central Europe | annual mean | 200 m × 200 m | 0.49 | 5.38 | |
Son et al. [51] | 2011–2014 | Mexico City M. A. | hourly mean | 30 m × 30 m | 0.38 | 27.23 | |
Chen et al. [52] | 2014–2016 | China | annual mean | 10 km × 10 km | 0.81 | 14.40 |
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Wallek, S.; Langner, M.; Schubert, S.; Schneider, C. Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data. Atmosphere 2022, 13, 1282. https://doi.org/10.3390/atmos13081282
Wallek S, Langner M, Schubert S, Schneider C. Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data. Atmosphere. 2022; 13(8):1282. https://doi.org/10.3390/atmos13081282
Chicago/Turabian StyleWallek, Stefan, Marcel Langner, Sebastian Schubert, and Christoph Schneider. 2022. "Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data" Atmosphere 13, no. 8: 1282. https://doi.org/10.3390/atmos13081282
APA StyleWallek, S., Langner, M., Schubert, S., & Schneider, C. (2022). Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data. Atmosphere, 13(8), 1282. https://doi.org/10.3390/atmos13081282