With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi’an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM₁₀ and PM_2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM_2.5 decreased by 20.3 μg/m³, and the annual concentrations of PM₁₀ decreased by 47.3 μg/m³. The days with concentrations of PM₁₀ exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM_2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM₁₀ and PM_2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi’an and even worldwide.

With the increasing popularity of fresh-air filtration systems, the methods of determining the outdoor PM_2.5 design concentration have become more important. However, the monitoring of atmospheric fine particles in China started relatively late, and there are relatively few cities with complete data, with obvious regional differences, which led to many problems in the selection of air filters for fresh-air filtration systems. In this paper, three methods of determining outdoor PM_2.5 design concentration were analyzed using the daily average concentration of PM_2.5 in 31 provincial capital cities from 2016 to 2020. Six typical cities in different regions were also taken as examples. The advantages and disadvantages of the three existing statistical methods were compared and analyzed, as well as the corresponding differences in the selection of outdoor PM_2.5 concentration value on the filter systems. The results showed that the method of mathematical induction was more accurate and reasonable for the calculation of outdoor PM_2.5 design concentrations. The local outdoor PM_2.5 design concentration could be quickly calculated using the recommended coefficient K and annual average PM_2.5 concentration of the region, especially for small and medium-sized cities without monitoring data. However, the recommended coefficient K should be provided based on the specific region, and should be divided into values for strict conditions and normal conditions during use. This would provide a simple and effective way to select the correct air filters for practical engineering.

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.

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.

Nowadays with the development of science, many new technologies are being introduced to the fabric membrane material fields. Membrane structures have been widely used in industrial storage buildings to reduce the dust pollution from open storage depots. But the thermal environment of these buildings is poor due to its light weight and low thermal resistance. It is worth exploring appropriate measures to study this problem. Therefore, a monitoring platform was established in order to study the indoor thermal environment of the industrial storage building with membrane structure. The solar radiation heat model was established using MATLAB program based on the theory of thermal transfers. And studied the impact of the outdoor temperature and solar radiation intensity in the heat transfer characteristics of membrane material. The indoor air temperature distribution of the buildings was also analyzed. The results show that solar radiation has a significant effect on the surface temperature of the fabric membrane material. The solar radiation of different intensities incident on the surface of the fabric membrane causes its surface temperature to fluctuate rapidly and significantly. Without indoor heat source, the indoor thermal environment is mainly affected by the solar radiation heat exchange of the fabric material.