# How May Building Morphology Influence Pedestrians' Exposure to PM2.5?
## 1 Introduction  
Information about PM2.5 concentration from the nearest air-quality-monitoring station may not represent PM2.5 in a microenvironment. Most monitoring stations are in open areas to avoid the effect of obstructions such as buildings [1]. Also, because of their fixed locations, PM2.5 observations from these stations provide limited opportunities to study spatial variabilities of PM2.5 in places where most people live, and the health effects are most impactful. In contrast to data from fixed-ground monitoring stations, which are often sparsely located, mobile monitoring platforms provide flexibility to gather data at high spatial resolutions and temporal frequencies. Recently, several studies used mobile monitoring platforms to measure pollutant concentration [2; 3; 4; 5; 6]. These studies involved data collection across roads in urban areas using vehicles as a platform for PM2.5 monitoring [2; 3; 4; 5; 6]. Some studies used aerial platforms to study the horizontal and vertical profiles of PM2.5 [7; 8]. Spatial obstructions like buildings and trees affect the dispersion and concentration of PM2.5 [3; 9; 10; 11]. Land use regression models incorporating the buildings' morphological characteristics to estimate PM2.5 and PM10 during stable meteorological conditions improved the model's R\({}^{2}\) by 10% in the high-density area of downtown Hong Kong [3]. Other studies examined how urban morphology influences the flow of air pollutants in the high-density environment of Hong Kong. Some of the key variables affecting air pollution in these studies were site coverage, average building height, distance between buildings, and degree of enclosure [12; 13]. In addition to physical structures or trees that might influence dispersion, PM2.5 distributions have complex relationships with geographic features. PM2.5 refers to all particleswith an aerodynamic diameter smaller than 2.5 \(\upmu\)m. The chemical components of PM2.5 vary depending on the sources of emissions in a region. Vehicle exhaustion is one of the major sources of black carbon, and black carbon correlates better with traffic patterns than with PM2.5 concentration [14]. PM2.5 observations from near-road sites appeared to be strongly correlated with nearby sites in terms of background pollution [11], but PM2.5 averages at near-road sites appeared higher than those at other nearby sites [10; 11]. This indicates that on average, near-road areas are susceptible to relatively greater PM2.5 exposure. However, two studies conducted at near-road sites in the United States showed that traffic characteristics like traffic volume and traffic speed did not correlate well with PM2.5, but meteorological factors and their interaction with site characteristics had profound impacts on PM2.5 [10; 11]. In both prior studies, a stable meteorological condition at night showed elevated PM2.5 concentration, whereas higher wind speed reduced the PM2.5 level. A source apportionment analysis at a roadside location in Maryland showed that on-road traffic contributed only 12.5-17% of PM2.5 [10]. High PM2.5 was associated with wind nearly perpendicular to the road, since the wind swept the maximum surface area of the road towards the monitor compared to when it blew precisely perpendicular to the road segment [11]. Moreover, the monitoring station with buildings on the windward side consistently displayed high PM2.5, suggesting that the presence of buildings trapped the pollutants coming from the roads [11]. Thus, besides emission sources, geographic features, spatial forms, and their interactions with wind played a role in determining PM2.5 concentration at locations. Mobile monitoring platforms allow for studying PM2.5 distributions in different urban microenvironments, such as the effect of trees and buildings near emission sources (e.g., roads) on PM2.5 concentration [3; 15].