Abstract
Atmospheric visibility is an important parameter of the environment which is dependent on meteorological and air quality conditions. Forecasting of visibility is a complex task due to the multitude of parameters and nonlinear relations between these parameters. In this study, meteorological, air quality, and atmospheric visibility data were analyzed together to demonstrate the capabilities of the multidimensional logistic regression model for visibility prediction. This approach allowed determining independent variables and their significance to the value of the atmospheric visibility in four ranges (i.e., 0–10, 10–20, 20–30, and ≥ 30 km). We proved that the Iman–Conover (IC) method can be used to simulate a time series of meteorological and air quality parameters. The visibility in Warsaw (Poland) is dependent mainly on air temperature and humidity, precipitation, and ambient concentration of PM10. Three logistic models of visibility allowed us to determine precisely the number of days in a month with visibility in a specific range. The sensitivity of the models was between 75.53 and 90.21%, and the specificity 78.51 and 96.65%. The comparison of the theoretical (modeled) with empirical (measured) distribution with the Kolmogorov–Smirnov test yieldedp-values always above 0.27 and, in half of the cases, above 0.52.
Highlights
The effects of anthropogenic air pollution are discussed for years (Schedling, 1967; Saikawa et al, 2017; Kinney, 2018; Grewling et al, 2019; Makra, 2019)
1+exp α·x+α0 where p is the probability that Vis is greater than the specific threshold value Vislim, x is the vector of independent variables— air quality data or meteorological conditions, and α is the vector of the coefficients determined using the maximum likelihood estimation method (Hosmer et al, 2013)
We present the performance and possibilities of the logistic regression model with three limit values
Summary
The effects of anthropogenic air pollution are discussed for years (Schedling, 1967; Saikawa et al, 2017; Kinney, 2018; Grewling et al, 2019; Makra, 2019). Predicting Number of Days With Visibility its background is just equal to the contrast threshold of an observer” (WMO, 2015). It can be defined as the clearness with which objects stand out from their backgrounds or other objects and how far people can see and how well they can identify objects (Bennett, 1930; Wooten and Hammond, 2002). We presented an original model with the consistent methodological approach and assumptions This approach to the visibility forecast can be successful in application to the analysis of publicly available data concerning many sites around the world
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