Mobile air monitoring data-processing strategies and effects on spatial air pollution trends

The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data-processing approaches. The study being reported here utilized 40 h (> 140 000 observations) of mobile monitoring data collected on a roadway network in central North Carolina to explore common data-processing strategies including local emission plume detection, background estimation, and averaging techniques for spatial trend analyses. One-second time resolution measurements of ultrafine particles (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen dioxide (NO₂) were collected on 12 unique driving routes that were each sampled repeatedly. The route with the highest number of repetitions was used to compare local exhaust plume detection and averaging methods. Analyses demonstrate that the multiple local exhaust plume detection strategies reported produce generally similar results and that utilizing a median of measurements taken within a specified route segment (as opposed to a mean) may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce similar but slightly lower estimates than a location-based method. For the complete data set the estimated contributions of the background to the mean pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%), PM_2.5-10 (45%), NO₂ (57%), PM₁₀ (60%), PM_2.5 (68%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends

Predictors of Plasma Concentrations of DDE and PCBs in a Group of U.S. Women.

We evaluated predictors of plasma concentrations of dichlorodiphenyldichloroethylene (DDE), a metabolite of dichlorodiphenyltrichloroethane (DDT), and polychlorinated biphenyls (PCBs) in a group of 240 women, controls from a breast cancer case-control study nested in the Nurses' Health Study. We considered personal attributes such as age, serum cholesterol, region of residence, adiposity, lactation, and dietary intake. DDE levels increased 0.17 ppb/year of age ($p$ = 0.0003), and PCBs increased 0.08 ppb ($p$ = 0.0001). DDE and PCBs increased 0.20 ($p$ = 0.02) and 0.13 ppb ($p$ = 0.001), respectively, per 10 mg/dl serum cholesterol. Women living in the western United States had higher levels of DDE (mean = 11.0 ppb; $p$ = 0.003), and women in the Northeast and Midwest had higher levels of PCBs (mean = 5.6 ppb; $p$ = 0.0002) as compared to women from other parts of the country (mean DDE = 6.3; mean PCBs = 4. 5 ppb). Levels of DDE could not be predicted from consumption of meat, fish, poultry, dairy products, vegetables, fruits, and grains. There was a positive association between fish consumption and PCB concentrations among women in the Northeast and Midwest. Using data from the cases in the nested case-control study to assess the predictive ability of the models, we confirmed that the most reliable predictors of DDE were age and serum cholesterol, and the most important predictors of PCBs were age, serum cholesterol, and residence in the Midwest or Northeast. The null results for the majority of the food variables suggest that specific dietary factors, other than fish, are not currently a substantial contributor to human exposure to DDE and PCBs