Air Pollution Monitoring and Exposure Assessment

Air pollution estimates at high spatial-temporal scales are critically important for enforcing air quality regulations and to protect human health. The limited coverage of air pollution monitoring and conventional methods of monitoring air pollution restrict our ability to do so. Therefore, we focus on multiple strategies to generate spatially detailed estimates of air pollution including the following:

Satellite remote sensing: satellite remote sensing has been advanced considerably in terms of spatial-temporal coverage. The moderate resolution radiospectrometer (MODIS) onboard Terra and Aqua satellite have a daily global coverageat 250m resolution. We compute aerosol optical depth (AOD) using MODIS data and relate it with the ground measurements of airborne particulates by federal regulator methods (FRM).

Figure 1: AOD and Average PM2.5 at 5km resolution in Delhi, India. Source:  Kumar et al. 2007a

Our recent work suggests a significant positive association between AOD estimates from Terra and Aqua and ground measurements of airborne particles ≤2.5μm (PM2.5) and ≤10μm  (PM10) in aerodynamic diameter (Figure 1 and 2).

There are two major challenges in using AOD as an indirect measure of air quality. First, aerosols are suspended and liquid particles in the air and can easily be influenced by weather conditions. Therefore, it is critically important to correct AOD for local weather conditions. After correcting for weather conditions, AOD is left with the columnar estimates of human induced air pollution and can be used to generate continuous surface of air pollution to estimate personal ambient exposure by linking these surface with the time-activity diaries

Figure 2: 5km AOD and Average PM10 in Delhi, India, July December 2003. Source: Kumar et al. 2007b.

There are two major challenges in using AOD as an indirect measure of air quality. First, aerosols are suspended and liquid particles in the air and can easily be influenced byweather conditions. Therefore, it is critically important to correct AOD for local weather conditions. After correcting for weather conditions, AOD is left with the columnar estimates of human induced air pollution and can be used to generate continuous surface of air pollution to estimate personal ambient exposure by linking these surface with the time-activity diaries of the subjects (Figure 3). Second, the empirical relationship between ground measurements and AOD can vary regionally, because the sources and compositions of aerosols also vary regionally. Therefore, local empirical relationship needs to be established in order to use AOD to predict air quality. This requires intensive air pollution monitoring field campaign. For example, to study the time-space dynamics of air pollution in Delhi in response to recent air quality regulations, we monitored PM2.5 and PM10 for six months (Figure 4), and using these data, we have been able to predict air quality in Delhi and its neighboring area for both pre (2000-02) and post (2004-05) regulation periods. The empirical relationship between AOD and particulate matter (PM) of different sizes

observed in Delhi, however, cannot be extrapolated to other areas. We have been exploring the possibility of standardizing these relationships by integrating satellite estimates with the chemical transportation models.

Figure 3: Continuous AOD distribution in Delhi and its surroundings, September-November 2003 (5km Spatial Resolution).  Source: Kumar et al. 2007b

Figure 4: 113 air pollution monitoring stations where PM2.5 and PM10 data were monitored from July-December 2003. Source: Kumar et al. 2007.

 Real time mobile sampling: Real time mobile sampling are useful for estimating air pollution fairly quickly and inexpensively. This type of sampling requires sample site selection, sample schedule that can capture sufficient number of samples at different times of a day and different days of a week and real time samplers mounted on a mobile lab. Real time mobile sampling was adopted for generating preliminary estimates of air pollution for Iowa City area (Figure 5). Sites were selected using  spatial random sampling design and then connected to the nearest road or street. This type of sampling can serve two important purposes. First, it can generate spatially detailed air quality surface frequently and inexpensively. Second, it can provide preliminary estimates of variability (in air pollution) needed for identifying optimal sites for regular sampling by FRM.

Optimal sampling design for exposure assessment: Recent literature suggests significant intra-city variability in air quality. Therefore, it is critically important to monitor data at a large number of locations within a city. Two goals of our research are (a) to identify the minimum number of sites required for capturing intra-city variability, and (b) to optimize site location by maximizing variability and minimizing spatial autocorrelation.

Figure 5: Real time mobile sampling in Iowa City area, August 2006. Source: Peters et al. 2007

References:

Kumar, N., A. Chu  and A. D. Foster, 2007a. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan, Atmospheric Environment 41(21): 4492–4503. doi:10.1016/j.atmosenv.2007.01.046.

Kumar, N., A. Chu  and A. D. Foster, 2007b. Remote Sensing of Ambient Particles in Delhi and its Environs: Estimation and Validation. International Journal of Remote Sensing (forthcoming).

Kumar, N. and A. D. Foster, 2007. Air Quality Interventions and Spatial Dynamics of Air Pollution in Delhi and its Surroundings. International Journal of Environment and Waste Management (forthcoming).

Ott, D., N. Kumar and T. Peters, 2007. Passive sampling to capture spatial variability in PM10-2.5. Under review