Informatics Publishing Limited

Yang Lei ¹, Ran Wei ², Shiyuan Wang ¹, Yan Wang ¹, Liuliu Du ³, Henggen Shen ⁴

Affiliations
1 School of Energy and Environment Engineering, Zhongyuan University of Technology, Zhengzhou 450007, CN
2 School of Economics & Management, Zhongyuan University of Technology, Zhengzhou 450007, CN
3 National Institute for Health and Welfare, Helsinki 00271, FI
4 School of Environmental Science and Engineering, Donghua University, Shanghai 201620, CN

Abstract

During the winter season from November 4, 2014 to November 16, 2014, one-hour average fine samples (PM2.5) of airborne particulate matter were collected at a busy roadside of the No. 107 national road located in a suburb of Zhengzhou City, China. For a convenient comparative analysis, two sets of data were tested in two different locations (No. 1 and No. 2) at a distance of 200m beside the road. Following previous research results, we considered traffic flow, ambient air temperature, relative humidity, wind speed, wind direction, atmospheric pressure and haze condition as factors to analyse their potential influence on the mass concentration of PM2.5. Same day data from the China National Environmental Monitoring Centre were also compared with the data of the study. Results showed that the average ambient PM2.5 concentrations were significantly higher at the tested suburban roadside than in the city region. However, the t-test result disclosed that the two sets of data had no significant difference (p = 0.001). The difference between the roadside data and the background concentration could be attributed to certain factors that were only sampled at the suburban roadside. Pearson correlation analysis was performed to identify the source contribution of the ambient PM2.5 concentration at the study locations. The correlation values indicated that the major factors with relatively significant influence on the PM2.5 data during the events under study were temperature (0.310 at the No. 1 site and 0.268 at the No. 2 site), relative humidity (0.532 at the No. 1 site and 0.303 at the No. 2 site), traffic flow (0.393 at the No. 1 site and 0.379 at the No. 2 site), wind speed ("0.264 at the No. 1 site and "0.187 at the No. 2 site), and wind direction (0.262 at the No. 2 site). Among all the impact parameters considered in the study, traffic flow contributed most to the PM2.5 mass concentration (correlation values were 0.393 and 0.379 for the No. 1 and No. 2 sites, respectively). Relative humidity (correlation value = 0.532 for the No. 1 site and correlation value = 0.303 for the No. 2 site) and wind speed contributed to the reduction of the PM2.5 mass concentration.

Keywords

PM2.5, Mass Concentration, Factor Analysis, Suburban Roadside.

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Yang Lei ¹, Ran Wei ², Yue Liu ¹, Wang Yan ¹, Liuliu ³, Henggen Shen ⁴

Affiliations
1 School of Energy and Environment, Zhongyuan University of Technology, Zhengzhou, 450007, P.R., CN
2 School of Economics and Management, Zhongyuan University of Technology, Zhengzhou, 450007, P.R., CN
3 National Institute for Health and Welfare, Helsinki 00271, FI
4 School of Environmental Science and Engineering, Donghua University, Shanghai 201620, CN

Abstract

Studying the impacts of PM2.5 concentrations is critical due to health risks associated with PM2.5. This study analyses 2001-2010 provincial panel data of population-weighted PM2.5 exposure and its main sources in China to identify any correlations that may exist. The results show that energy consumption, highway length, and construction positively affect population-weighted PM2.5 exposure, but vehicle possession has a negative effect. Increasing energy consumption, highway length, and construction areas by 1% resulted in 0.11%, 0.12%, and 0.06% increases to PM2.5 population exposure, respectively. However, when vehicle possession increased by 1%, population exposure to PM2.5 decreased by 0.20%. Highway length may be a very important factor for the increased PM2.5 concentrations in China. Therefore, China should consider national and provincial factors when developing policies to control PM2.5 emissions.

Keywords

Population-Weighted, PM2.5 Exposure, Energy Consumption, Highway Length, Construction Area, Panel Data Analysis.

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