Analysis for the Relationship Between Concentrations of Air Pollutants and Meteorological Parameters in Xi'an, China

Abstract

The current study was based on annual ambient air quality monitoring data and corresponding meteorological observation data of Xi'an in 2011. Distribution models on hourly concentrations of PM10, SO2, and NO2 were studied, and the results showed that statistical distribution functions varied from seasons and from pollutants. The optimal distribution models of PM10 concentrations in the four seasons (spring, summer, autumn, and winter) were generalized extreme value distribution (GEVD), Weibull, Weibull, and GEVD, respectively; those of SO2 were lognormal, log-logistic, log-logistic, and GEVD, respectively; and those of NO2 were Weibull, lognormal, GEVD, and GEVD, respectively. The concentrations ranges were 0.03 ∼ 0.20 mg/m3 for PM10, 0.008 ∼ 0.17 mg/m3 for SO2, and 0.01 ∼ 0.12 mg/m3 for NO2, and the probabilities of concentrations in the ranges for accordingly pollutants were up to 85 %. Effects of the meteorological parameters on concentrations of PM10, SO2, and NO2 were studied with linear correlation analysis method for Xi'an city. The results indicated that pollutant concentrations had a negative correlation with wind speed, temperature, and mixing height (MH), whereas it had a positive correlation with atmospheric pressure and atmospheric stability. Both temperature and atmospheric pressure were the most obvious correlation with SO2 concentration with r value of −0.7916 and 0.7032, respectively. Wind speed and MH had the most obvious correlation with NO2 concentration with r value of −0.4423 and −0.3997, respectively. SO2 had the best correlation with meteorological parameters. Analyzing the statistical characteristics of urban air pollution concentration and their relationships with meteorological parameters are of great importance to study urban air pollution problems and corresponding prevention measure.

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China

“…It is easy to see that the simulated values are generally lower than the observed values during various periods. One major reason might be due to the overestimated wind speed (Gao et al, 2015;Tasić et al, 2013), as shown in Figure 6. Another major reason can also account for this phenomenon: the emission Figure 7.…”

Technological and Economic Design of Asphalt Mixture Composition Based on Optimization Methods

“…The range of r value = -1 to 1. In general, 0.4 r < suggests poor linear correlation, 0.4 0.7 r ≤ < indicates significant correlation, and 0.7 1.0 r ≤ < stands for highly linear correlation (Gao et al 2016). The lowest value of correlation coefficient r min is calculated according to equation 14and is as follows (Podvezko, Sivilevičius 2013):…”