High-Resolution Mapping of Air Pollution in Delhi Using Detrended Kriging Model
Abstract Air quality information is not captured adequately due to limited numbers of air quality monitoring stations across many cities worldwide. Limited studies apply advanced spatial mapping techniques to predict pollutant concentrations in highly polluted regions with high spatial variability. This paper demonstrates an advanced detrending spatial mapping technique to assess the variations of particulate matter concentrations across different land use categories in a highly polluted city—Delhi—and estimate population-weighted average concentrations in the city. The “Detrended Kriging” method uses the city’s monitored datasets and land use information to predict pollutant concentrations. Concentrations are detrended based on high-resolution local land use characteristics and then interpolated using ordinary kriging before retrending again. The model estimates population-weighted concentrations (more important for health exposures) of $ PM_{2.5} $ (113 µg/$ m^{3} $) and $ PM_{10} $ (248 µg/$ m^{3} $) for Delhi and finds them to be 21–36% higher than the monitored values in the crucial winter season of 2018. The model demonstrates satisfactory performance on both spatial and temporal scales in Delhi and shows high index of agreement (d = 0.86 for $ PM_{10} $ and 0.81 for $ PM_{2.5} $), low RMSE (27.3 µg/$ m^{3} $ for $ PM_{10} $ and 11.8 µg/$ m^{3} $ for $ PM_{2.5} $), and low bias (− 1.6 µg/$ m^{3} $ for $ PM_{10} $ and − 0.5 µg/$ m^{3} $ for $ PM_{2.5} $) for the detrended kriging model, in comparison to ordinary kriging ($ PM_{2.5} $ (d = 0.54, RMSE = 13.81, bias = − 0.86) and $ PM_{10} $ (d = 0.33, RMSE = 41.73, bias = − 4.7)) and inverse distance weighting method ($ PM_{2.5} $ (d = 0.65, RMSE = 16.08, bias = 2.93) and $ PM_{10} $ (d = 0.55, RMSE = 46.10, bias = 7.8)). Statistical measure “d” varies between 0 (no agreement) and 1 (perfect match)..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Environmental modeling & assessment - 28(2022), 1 vom: 27. Juli, Seite 39-54 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rahman, Md H. [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Air pollution |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
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| doi: |
10.1007/s10666-022-09842-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC2133507892 |
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| 520 | |a Abstract Air quality information is not captured adequately due to limited numbers of air quality monitoring stations across many cities worldwide. Limited studies apply advanced spatial mapping techniques to predict pollutant concentrations in highly polluted regions with high spatial variability. This paper demonstrates an advanced detrending spatial mapping technique to assess the variations of particulate matter concentrations across different land use categories in a highly polluted city—Delhi—and estimate population-weighted average concentrations in the city. The “Detrended Kriging” method uses the city’s monitored datasets and land use information to predict pollutant concentrations. Concentrations are detrended based on high-resolution local land use characteristics and then interpolated using ordinary kriging before retrending again. The model estimates population-weighted concentrations (more important for health exposures) of $ PM_{2.5} $ (113 µg/$ m^{3} $) and $ PM_{10} $ (248 µg/$ m^{3} $) for Delhi and finds them to be 21–36% higher than the monitored values in the crucial winter season of 2018. The model demonstrates satisfactory performance on both spatial and temporal scales in Delhi and shows high index of agreement (d = 0.86 for $ PM_{10} $ and 0.81 for $ PM_{2.5} $), low RMSE (27.3 µg/$ m^{3} $ for $ PM_{10} $ and 11.8 µg/$ m^{3} $ for $ PM_{2.5} $), and low bias (− 1.6 µg/$ m^{3} $ for $ PM_{10} $ and − 0.5 µg/$ m^{3} $ for $ PM_{2.5} $) for the detrended kriging model, in comparison to ordinary kriging ($ PM_{2.5} $ (d = 0.54, RMSE = 13.81, bias = − 0.86) and $ PM_{10} $ (d = 0.33, RMSE = 41.73, bias = − 4.7)) and inverse distance weighting method ($ PM_{2.5} $ (d = 0.65, RMSE = 16.08, bias = 2.93) and $ PM_{10} $ (d = 0.55, RMSE = 46.10, bias = 7.8)). Statistical measure “d” varies between 0 (no agreement) and 1 (perfect match). | ||
| 650 | 4 | |a Spatial mapping | |
| 650 | 4 | |a Air pollution | |
| 650 | 4 | |a Land-use regression | |
| 650 | 4 | |a Hotspot identification | |
| 650 | 4 | |a Urban air quality | |
| 700 | 1 | |a Agarwal, Shivang |4 aut | |
| 700 | 1 | |a Sharma, Sumit |4 aut | |
| 700 | 1 | |a Suresh, R. |4 aut | |
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| 700 | 1 | |a Vranckx, Stijn |4 aut | |
| 700 | 1 | |a Maiheu, Bino |4 aut | |
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| 700 | 1 | |a Shukla, V. K. |4 aut | |
| 700 | 1 | |a Batra, Sakshi |4 aut | |
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