Modeling spatial patterns of traffic-related air pollutants in complex urban terrain.
BACKGROUND
The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion.


OBJECTIVE
We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time.


METHODS
In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources.


RESULTS
We observed spatial heterogeneity of both UFP and PM2.5 concentrations. A variety of statistical methods consistently found a 15-20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For PM2.5, temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways.


CONCLUSIONS
The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.
Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts 02215, USA. lzwack@post.harvard.edu
Leonard M
LM
Zwack
Christopher J
CJ
Paciorek
John D
JD
Spengler
Jonathan I
JI
Levy
D000393:Air Pollutants / Q000032:analysis* / Q000145:classification; D004784:Environmental Monitoring; D015233:Models, Statistical; D009519:New York City; D052638:Particulate Matter / Q000032:analysis* / Q000145:classification; D011084:Polycyclic Aromatic Hydrocarbons / Q000032:analysis* / Q000145:classification; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D052638:Particulate Matter; D011084:Polycyclic Aromatic Hydrocarbons; D001335:Vehicle Emissions
Environ Health Perspect. 2003 Sep;111(12):1512-8
12948892
Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91
22838153
Stat Sci. 2010 Feb;25(1):107-125
21528104
Biometrics. 2006 Dec;62(4):1197-206
17156295
BMC Public Health. 2007 May 22;7:89
17519039
Am J Respir Crit Care Med. 2004 Sep 1;170(5):520-6
15184208
Epidemiology. 2008 Jan;19(1):121-8
18091421
Am J Public Health. 2009 Nov;99 Suppl 3:S629-35
19890168
J Expo Sci Environ Epidemiol. 2008 Nov;18(6):551-63
18183044
J Air Waste Manag Assoc. 2004 May;54(5):540-54
15149042
J Air Waste Manag Assoc. 2002 Sep;52(9):1032-42
12269664
J Air Waste Manag Assoc. 2001 Oct;51(10):1414-22
11686245