The Neighborhood Scale Variability of Airborne Particulates

Abstract

Airborne particulates play a central role in both the earth's radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities globally. Typically these provide at best a few recording locations per city. However, large spatial variability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 -32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km 2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM 2.5 concentration was 14.1 ± 5.7 μg•m −3 corresponding to periods when the wind was out of the south. The lowest PM 2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only "cleanse" the air, leaving a mean PM 2.5 concentration as low as 3.0 ± 0.5 μg•m −3 , but also leave the region with a more uniform PM 2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.

Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States

“…It suggests that adjacent locations as far as 10 km also affect the PM2.5 concentration in addition to local factors, indicating the broader scale at which spatial processes driving PM2.5 are operating. Harrison et al (2015) collected street-level PM2.5 data in our study region's 10 km by 10 km area. Their study found that depending on weather conditions, the spatial scale of PM2.5 variation in the area varied between 0.8 and 5.2 km.…”

Transformational IoT sensing for air pollution and thermal exposures

“…However, traditional monitoring of urban air quality faces several shortcomings. First and foremost, the conventional monitoring of urban air is stationary, sparse, and typically remote from human activities, and as such, it poorly resembles air inhaled by people (Miller et al, 2007;McKone et al, 2009;Goldman et al, 2010;Harrison et al, 2015;Pearce et al, 2016). This has been confirmed in studies that deployed wearable monitors and samplers for measuring PM 10 (Jenkins et al, 1996;Scapellato et al, 2009;Broich et al, 2011), PM 2.5 (Andresen et al, 2005;Crist et al, 2008;Steinle et al, 2015), PM 1 (Williams et al, 2000;Johannesson et al, 2007;Velasco and Tan, 2016), CO 2 (Gall et al, 2016), CO (Huang et al, 2012), NO x (Xu et al, 2017) and various volatile organic compounds (Rotko et al, 2000;O'Connell et al, 2014;Manzano et al, 2018).…”

How May Building Morphology Influence Pedestrians’ Exposure to PM2.5?

“…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].…”

“…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].…”