TY - JOUR
T1 - Revealing causality in the associations between meteorological variables and air pollutant concentrations
AU - Yuval,
AU - Levi, Yoav
AU - Broday, David M.
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/3/15
Y1 - 2024/3/15
N2 - Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time–lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region–dependent. No causal association was found with relative humidity, temperature and precipitation.
AB - Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time–lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region–dependent. No causal association was found with relative humidity, temperature and precipitation.
KW - Air pollution
KW - Convergent cross mapping
KW - Meteorological variables
KW - Numerical weather prediction model
UR - http://www.scopus.com/inward/record.url?scp=85187255150&partnerID=8YFLogxK
U2 - 10.1016/j.envpol.2024.123526
DO - 10.1016/j.envpol.2024.123526
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C2 - 38355085
AN - SCOPUS:85187255150
SN - 0269-7491
VL - 345
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 123526
ER -