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In recent years, the high concentration of air pollutants has seriously affected people's daily life. So, it is important and necessary to know the spreading PM2.5 concentration and its correlation with other pollutants. The data presented in this paper was received from 13 monitoring stations by a typical northern city of Xi'an in China for the whole year of 2017. The concentration distribution of PM2.5 and its variations with the season, month and day were analyzed. Also the average correlation between PM2.5 and CO, SO2, NO2, O3 and PM10 was studied. The results showed concentrating PM2.5 was consistent with the seasonal, monthly and daily trends. The concentration of PM2.5 showed a low in summer and autumn. While the concentration of PM2.5 was higher in spring and winter, and the highest concentration was in winter. The daily variation distribution of PM2.5 showed a double-peak variation characteristic. The concentration of PM2.5 reached a peak between 8:00-9:00 in the morning and 22:00-23:00 in the evening. Particles of Xi'an in atmospheric were mainly fine particles. There was a strong correlation between PM2.5 and CO, SO2, NO2, PM10, but a negative correlation between PM2.5 and O3. A multivariate linear regression model with the pollutant concentration of CO, SO2, NO2 and PM10 was established. This study helps to understand the distribution of PM2.5 and its correlation with other pollutants of Xi'an, and further supplying important support for the targeted controlling approach of fog and haze. [ABSTRACT FROM AUTHOR]

With the widespread promotion of underground buildings, it is necessary to know the status of indoor air quality of underground buildings. In this study, indoor air quality was tested from 9 sites of three styles underground buildings in Xi'an. Simultaneously, a questionnaire survey was conducted with indoor staff satisfaction. The results showed the concentrations of pollutants in 9 sites except for CO were all exceeding the standard. The concentrations of formaldehyde, PM10, CO2, and TVOC all exceeded the standard of 1.26, 1.62, 1.28, and 1.66 times. Pollutions were the most serious in underground shopping malls, followed by underground snack cities and underground electronic cities. PM10 and TVOC were the most prominent pollutants, followed by formaldehyde. Indoor air quality of underground shopping malls was the worst satisfaction with people. Indoor air quality of underground electronic cities was satisfied. Satisfaction limit of formaldehyde was lower than the standard value. Satisfaction limit of TVOC was higher than the standard value. Satisfaction limit of PM10 was slightly higher than the standard value, and satisfaction limit of CO2 was close in line with the standard value. It would contribute to the development and application of underground buildings. [ABSTRACT FROM AUTHOR]

With the increase of particulate pollution in the atmosphere, it becomes extremely significant to understand the overall distribution characteristics of particulates and their adsorption of toxic gases for the source analysis and precise controlling of atmospheric particulate matters. The fractal theory was adopted to analyze particle sizes distribution characteristics in Xi'an city, China. Results showed the fractal dimension of particulate matters distribution ranged from 4.32-4.83, with an average fractal dimension of 4.54. A higher fractal dimension predicts a higher concentration of fine particles. Additionally the effects of outdoor temperature, humidity, and wind speed on the fractal dimension were also studied experimentally. [ABSTRACT FROM AUTHOR]

With speeding up urbanization in China, it is important to understand the vertical distribution characteristics of outdoor particulate matter concentrations of high-rise buildings. A high-rise building at different heights (1st, 7th, 11th, 17th, 23th, and 27th floors) in a university in Xi'an was tested and analyzed in this paper. Test time was 8:00, 12:00, 15:00, 15:00 and 22:00, respectively. The results showed that the concentration of each particulate matter changed the same trend roughly with the increase of vertical height at 8:00 and 22:00, as well as at 15:00 and 18:00. While the mass concentration of each particle showed a trend of first increased, then decreased and next then increased with the height increased when the time was 12:00. It caused by the inverse temperature. The particles between 0 and 1.0 μm accounted for more than 99.4% during the testing. There were mainly small particles in the atmosphere. PM1.0/PM10 and PM2.5/PM10 all showed a trend of first decreased and then increased at different vertical height. The highest points of PM2.5 /PM10 and PM1.0 /PM10 increased by 9.63% and 8.2% respectively from the lowest points, and the ratio was the highest at 15:00. At the same time, outdoor meteorological conditions would also affect the concentration distribution of particulate matters. This paper provides a reference for the development of high-rise buildings and the effective control of particulate matter concentration in high-rise buildings. [ABSTRACT FROM AUTHOR]

High concentration of PM22.5 has seriously affected people's daily lives in recent years. It is necessary to analyze PM22.5 and the correlations with other pollutants in winter. Data presented in this paper were obtained from monitoring stations from 2016 to 2018. Attention was fixed on PM22.5 and its monthly and daily variations in winter. Furthermore, the correlations between PM22.5 and CO, SO2, NO2, O3 and PM10 were studied. The results showed concentrating PM22.5 was roughly consistent with the monthly and daily trends. It was January>December>February>November>March. The mass concentration ranges of PM22.5 before and after the adjustment of heating energy structures were 64.5-184.1 μg/m³, and 86.4-140.1 μg/m³, respectively. The average concentrations of PM22.5 were 135.5 μg/m³, and 109.1 μg/m³, decreased by 26.4 μg/m³. PM22.5/PM10 was changed from 64.6% to 62.6%, reduced by 2%. The linear correlation analysis revealed a strong correlation between PM22.5 and CO, SO2, NO2, and PM10, but a negative correlation between PM22.5 and O3. Two multiple linear regression models on the pollutants were established, respectively. This study helps understand the concentrating distribution of PM22.5 and other pollutants in winter. It will provide some useful references to control air pollution for some cities, which have a similar type of heating energy structure. [ABSTRACT FROM AUTHOR]