Predicting the Number of Days With Visibility in a Specific Range in Warsaw (Poland) Based on Meteorological and Air Quality Data

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 yielded p-values always above 0.27 and, in half of the cases, above 0.52.

Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach

“…A hierarchical cluster analysis method was used to separate typical visibility periods in the annual cycle taking into account wet and dry days [22]. Cluster analysis is a set of methods used to separate homogeneous subsets.…”

“…On the basis of the results of measurements (the 2004-2013 period) of meteorological conditions (T-temperature, Rh-relative humidity, w-velocity of wind, P-rainfall depth) and air quality indices (SO 2 , NO x , O 3 , CO, PM), a dendrogram was created to determine the number of typical periods (N) per year taking into account the rainfall conditions. These analyses also focused on accounting for seasonality in the annual cycle [22].…”

“…), weather conditions (temperature, relative humidity, wind speed, rainfall), season. The analyses performed by Majewski et al [22] using HCA (Hierarchical Cluster Analysis) for the analyzed visibility measurement point, weather conditions, and air quality indicated that the year can be divided into three periods-clusters, in which the above-mentioned factors are similar. The results of calculations performed by Majewski et al [22] showed that rainfall depth is the factor that affects the variation of visibility values in the obtained clusters.…”

“…According to the literature [22], in the calculations, it is assumed that a value of p ≥ 0.5 corresponds to values of Vis > Vis m . In turn, when p < 0.5, then the Vis values obtained from Equation ( 4) are smaller than Vis m .…”

“…Sensitivity (SENS), specificity (SPEC) and counting error (Rz2) are used to evaluate the agreement between the results of calculations and measurements in the logit model. At the model development stage, the correlation of independent variables was analyzed, VIF values were determined and the variables for which it was greater than 5 were rejected [22]. In the analyzed model, the forward stepwise method-preferred for the logistic regression method-was used to identify coefficients (α i ) and thus the independent variables statistically significant at the assumed confidence level.…”