Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 254-277 |
| Seitenumfang | 24 |
| Fachzeitschrift | Geographical analysis |
| Jahrgang | 52 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 12 Apr. 2020 |
Abstract
This paper investigates the effect of daily wind direction and speed on the spatio-temporal distribution of particulate matter, (Formula presented.). Interdependencies between the (Formula presented.) values of different monitoring sites are characterized by incorporating time-varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible deviations between wind direction and bearing. The empirical analysis is based on daily (Formula presented.) values recorded by monitoring sites located across the eastern United States in 2015 as well as several meteorological regressors. More precisely, we propose a space-time dynamic panel data model with different spatial autoregressive, temporal and exogenous dependencies. All model parameters are estimated by the quasi-maximum likelihood approach. The estimation procedure, including the identification of the range and spatial parameters, is verified by Monte Carlo simulations. We show that part of the spatial dependency of (Formula presented.) values is explained by wind direction.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Erdkunde und Planetologie (insg.)
- Erdoberflächenprozesse
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in: Geographical analysis, Jahrgang 52, Nr. 2, 12.04.2020, S. 254-277.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Estimation of Anisotropic, Time-Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction
AU - Merk, Miryam S.
AU - Otto, Philipp
PY - 2020/4/12
Y1 - 2020/4/12
N2 - This paper investigates the effect of daily wind direction and speed on the spatio-temporal distribution of particulate matter, (Formula presented.). Interdependencies between the (Formula presented.) values of different monitoring sites are characterized by incorporating time-varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible deviations between wind direction and bearing. The empirical analysis is based on daily (Formula presented.) values recorded by monitoring sites located across the eastern United States in 2015 as well as several meteorological regressors. More precisely, we propose a space-time dynamic panel data model with different spatial autoregressive, temporal and exogenous dependencies. All model parameters are estimated by the quasi-maximum likelihood approach. The estimation procedure, including the identification of the range and spatial parameters, is verified by Monte Carlo simulations. We show that part of the spatial dependency of (Formula presented.) values is explained by wind direction.
AB - This paper investigates the effect of daily wind direction and speed on the spatio-temporal distribution of particulate matter, (Formula presented.). Interdependencies between the (Formula presented.) values of different monitoring sites are characterized by incorporating time-varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible deviations between wind direction and bearing. The empirical analysis is based on daily (Formula presented.) values recorded by monitoring sites located across the eastern United States in 2015 as well as several meteorological regressors. More precisely, we propose a space-time dynamic panel data model with different spatial autoregressive, temporal and exogenous dependencies. All model parameters are estimated by the quasi-maximum likelihood approach. The estimation procedure, including the identification of the range and spatial parameters, is verified by Monte Carlo simulations. We show that part of the spatial dependency of (Formula presented.) values is explained by wind direction.
UR - http://www.scopus.com/inward/record.url?scp=85066468573&partnerID=8YFLogxK
U2 - 10.1111/gean.12205
DO - 10.1111/gean.12205
M3 - Article
AN - SCOPUS:85066468573
VL - 52
SP - 254
EP - 277
JO - Geographical analysis
JF - Geographical analysis
SN - 0016-7363
IS - 2
ER -