Differences in Airborne Particulate Matter Concentration in Urban Green Spaces with Different Spatial Structures in Xi’an, China

With the acceleration of urbanization and industrialization, air pollution is becoming one of the most serious problems in cities. Urban green spaces, as “green infrastructure”, are an important part of urban ecosystems for air purification. Therefore, 10 typical green spaces of urban parks in the city of Xi’an, China, were selected as study areas according to vegetation structure and species composition. Considering meteorological factors and time changes, the effects of the selected green spaces with different vegetation structures of different heights on the reduction in airborne particulate matter concentration were explored. The results showed that the following: (1) Temperature, relative humidity, wind speed, and air pressure had significant correlation with the concentration of airborne particulate matter at the different heights, and the correlations were the same at 1.5 and 5 m. (2) After heating in winter, the concentration of airborne particulate matter with different particle sizes increased significantly. The concentration of airborne particulate matter showed different trends throughout the day, and the small particles (PM1 and PM2.5) had a trend of “lower in the morning and evening, and higher at noon”, while the large particles (PM10 and TSP) gradually decreased over time. (3) In the selected green spaces with different vegetation structure types, the concentration of airborne particulate matter below the canopy (1.5 m) was generally higher than that in the middle of the canopy (5 m), but the effects of reducing the concentration of airborne particulate matter were consistent at the different heights. (4) The adsorption capacity of PM1 and PM2.5 concentration was strong in the partially closed broad-leaved one-layered forest (PBO), and poor in the partially closed broad-leaved multi-layered forest (PBM). Partially closed broad-leaved multi-layered forest (PBM) and partially closed coniferous and broad-leaved mixed multi-layered forest (PMM) also had strong dust-retention effect on PM10 and TSP, while closed broad-leaved one-layered forest (CBO) had a poor dust-retention effect. The results showed that the reduction effects of urban green spaces with different spatial structures on air particles were different, and were restricted by various environmental factors, which could provide a theoretical basis for the optimization of urban green space structure and the improvement of urban air quality.

APPLICATION OF LOW-COST PARTICULATE MATTER SENSORS FOR MEASUREMENT OF POLLUTANTS GENERATED DURING 3D PRINTING

This work presents measurement results of pollutants generated during 3D printing. The measure of pollutants is the concentration of particulate matter with a diameter of up to 2.5 μm (PM2,5). Materials acrylonitrile-butadiene-styrene (ABS), polyactide (PLA) for a 3D printer and low-cost particulate matter concentration sensors PMS3003, PMS7003 were used in the research. Research results show that low-cost sensors can be useful for monitoring pollution during 3D printing in offices, laboratories or private homes.

Environmental Factors Related to Non-compliant Health Behaviors in Urban-Dwelling Elderly

Purpose: The purpose of this study was to explore non-compliant health behaviors among urban-dwelling elderly and identify related environmental factors.Methods: This study analyzed integrated data from the 2017 Korean National Survey on the elderly and 2017 Annual Report of Air Quality in Korea (N=3,198). In this study, health behaviors included seven recommendations for promoting health. Social and physical environmental factors were selected based on Bronfenbrenner’s ecosystem theory, including air quality as an environmental factor. Hierarchical multiple regression analysis was used to analyze the data while controlling for general characteristics of the elderly.Results: The average score for non-compliant health behaviors was 3.05±1.03 among seven health behaviors. Hierarchical multiple regression showed the environmental factors related to non-compliant health behaviors were social activity participation (β=.15, p＜.001); interactions with friends, neighbors, and acquaintances (β=.06, p=.002); access to institutions and facilities (β=.06, p=.001); and particulate matter concentration (β=-.10, p＜.001).Conclusion: Our study findings emphasized that social activity participation, interactions with significant others, access to institutions and facilities, and particulate matter concentration should be considered when developing ecological interventions to improve health behaviors among the urban-dwelling elderly.

Methods for calculating the average particulate matter concentrations in the Krasnoyarsk city ground layer

The paper considers a method based on the Voronoi diagram for calculating the average particulate matter concentration in the surface layer of the Krasnoyarsk city air environment. We have used two methods to achieve this goal. The first method relied on buffer zones built in the immediate vicinity around monitoring posts. The second one uses the boundaries of the city of Krasnoyarsk.

Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China

The threat of fine particulate matter concentration (PM2.5) is increasing globally, Tackling this issue requires an accurate understanding of its trends and drivers. The article investigates the PM2.5 characteristics of 285 prefecture-level cities in China from 2000-2018 based on multiscale geographically weighted regression(MGWR), and the results show that（1）previous studies based on classical MGWR models may be somewhat unstable, while MGWR can reflect the scale of influence of different variables on the dependent variable, and its regression results are more reliable.（2）PM2.5 is very sensitive to carbon emission(CE) factors, and there is a high degree of spatial heterogeneity, and the influence scale of location is the smallest among all variables, close to the municipal scale.（3）In 2000, the constant term all, IS, OFT, CE, and LT positively affect PM2.5, while GDP (jurisdiction) and UR negatively affect PM2.5; in 2010, the constant term all, GDP (jurisdiction), IS, OFT and LT positively affect PM2.5, while UR and CE negatively affect PM2.5; in 2018 the constant term all, IS, OFT and CE factors positively affect PM2.5, and GDP (jurisdiction), UR and LT negatively affect PM2.5.

The Impact of The Pico Y Placa Policy On Fine Particulate Matter In Lima, Peru

Lima has been ranked among the top most polluted cities in the Americas. Vehicular emissions are the dominant source of pollution in the city. In order to reduce congestion and pollution levels during the XVIII Pan- and Parapan-American Games, Lima government officials enacted the pico y placa policy to restrict the number of vehicles on certain heavily trafficked roads in the city at rush hours between Monday to Thursday based on the last digit of their license plates. This policy was retained after the Games. In this paper we evaluate the impact of this policy on fine particulate matter concentration levels (PM2.5) at a background site in the city using a difference-in-difference approach. We find that the policy resulted in increases on PM2.5 levels on Monday-Thursday compared to Friday-Sunday levels after the policy was enacted, compared to previous years. However, such an increase was not significant. These results suggest the need for additional policies to reduce pollution due to traffic in Lima. It also suggests the need to track the response to this policy over time to evaluate its efficacy.