Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones

Abstract

Air pollution has impacted people's lives in urban China, and the analysis of the distribution and driving factors behind air quality has become a current research focus. In this study, the temporal heterogeneity of air quality (AQ) and the dominant air pollutants across the four seasons were analyzed based on the Kruskal-Wallis rank-sum test method. Then, the spatial heterogeneity of AQ and the dominant air pollutants across four sites were analyzed based on the Wilcoxon signed-rank test method. Finally, the copula model was introduced to analyze the effect of relative factors on dominant air pollutants. The results show that AQ and dominant air pollutants present significant spatiotemporal heterogeneity in the study area. AQ is worst in winter and best in summer. PM 10 , O 3 , and PM 2.5 are the dominant air pollutants in spring, summer, and winter, respectively. The average concentration of dominant air pollutants presents significant and diverse daily peaks and troughs across the four sites. The main driving factors are pollutants such as SO 2 , NO 2 , and CO, so pollutant emission reduction is the key to improving air quality. Corresponding pollution control measures should account for this heterogeneity in terms of AQ and the dominant air pollutants among different urban zones.

Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China

“…Then, we discovered that not all data of air pollutants comply with the normal distribution by carrying out the Kolmogorov-Smirnov test. Given the fact that the Kruskal-Wallis rank-sum test is a widely used nonparametric approach to examine differences between multi-independent group samples with unknown distributions [16], it was introduced in our study after a Chi-square test confirming the seven air pollutants are independent from each other. Subsequently, we conducted the Kruskal-Wallis test at the significant level of 5% and the null hypothesis (H 0 ) is no significant differences of distributions of air pollutants during holidays and non-holiday period.…”

“…Due to unevenly distributed ground monitoring stations, most investigations were carried out in key cities or environment protection mode cities [12][13][14], albeit with limited studies on fine particulate matters and air quality index (AQI) in prefecture-level cities [15,16]. Equivalent findings in temporal variabilities of air pollutants have shown interannual, seasonal, day-of-week, diurnal and specific timescale variability characteristics (i.e., haze episodes and national holidays) [15][16][17][18][19][20][21][22][23]. Nevertheless, TSP has not popularly been studied since 2001 when TSP was excluded from the China Environment Bulletin [23].…”

“…Nevertheless, TSP has not popularly been studied since 2001 when TSP was excluded from the China Environment Bulletin [23]. A distinct seasonal pattern for "worse winter and better summer" and traffic-related diurnal characteristics were captured by an array of studies [15][16][17][18][19][20][21][22]. Although day-of-week variations in different cities follow different rules, mean concentrations of air pollutants during weekdays are usually greater than those during weekends.…”

Spatial and Temporal Distribution of Aerosol Optical Depth and Its Relationship with Urbanization in Shandong Province

“…High-intensity human activities also cause the release of a large amount of harmful particulate matter into the atmosphere, seriously affecting air quality and human health. Studying the relationship between AOD and urbanization is of great significance for maintaining and improving the quality of the atmospheric environment [6,[8][9][10]17,[33][34][35][36].…”

Life Cycle Assessment of Integrated Exploitation Technology for Tailings in Bayan Obo Mine, China

“…However, using the Life Cycle Assessment Method to analyze environmental problems produced in the process of integrated exploitation of tailings, can calculate their environmental pollution values in advance from the beginning of technology designing and achieve the goal of green production from the origin. There are a lot of assessment methods at present (Ni et al, 2018;Zhai et al, 2018;Zhao et al, 2018;Zhou et al, 2018;Li et al, 2017;Yang et al, 2017;Jin et al, 2015;Li et al, 2015b;Song et al, 2015;Yan et al, 2014). Most of the methods are based on the current situation, and only the Life Cycle Assessment (LCA) starts from the source.…”