Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Perturbations alter community convergence, divergence, and formation of multiple community states

Environmental perturbations (e.g., disturbance, fertilization) commonly shift communities to a new mean state, but much less is known about their effects on the variability (dispersion) of communities around the mean, particularly when perturbations are combined. Community dispersion may increase or decrease (representing a divergence or convergence among communities) if changing environmental conditions alter species interactions or magnify small initial differences that develop during community assembly. We used data from an experimental study of disturbance and fertilization in a low-productivity grassland to test how these two perturbations affect patterns of species composition and abundance. We found that a one-time biomass reduction decreased community dispersion, which persisted over four growing seasons. Conversely, continuous fertilization increased community dispersion and, when combined with disturbance, led to the formation of three distinct community states. These results illustrate that perturbations can have differing effects on community dispersion. Attention to the variance in community responses to perturbations lends insight into how ecological interactions determine community structure, which may be missed when focusing only on mean responses. Furthermore, multiple perturbations may have complex effects on community dispersion, yielding convergence or divergence patterns that are difficult to predict based on analysis of single factors.     

An analysis of continuous black carbon concentrations in proximity to an airport and major roadways

Black carbon (BC), a constituent of particulate matter, is emitted from multiple combustion sources, complicating determination of contributions from individual sources or source categories from monitoring data. In close proximity to an airport, this may include aircraft emissions, other emissions on the airport grounds, and nearby major roadways, and it would be valuable to determine the factors most strongly related to measured BC concentrations. In this study, continuous BC concentrations were measured at five monitoring sites in proximity to a small regional airport in Warwick, Rhode Island from July 2005 to August 2006. Regression was used to model the relative contributions of aircraft and related sources, using real-time flight activity (departures and arrivals) and meteorological data, including mixing height, wind speed and direction. The latter two were included as a nonparametric smooth spatial term using thin-plate splines applied to wind velocity vectors and fit in a linear mixed model framework. Standard errors were computed using a moving-block bootstrap to account for temporal autocorrelation. Results suggest significant positive associations between hourly departures and arrivals at the airport and BC concentrations within the community, with departures having a more substantial impact. Generalized Additive Models for wind speed and direction were consistent with significant contributions from the airport, major highway, and multiple local roads. Additionally, inverse mixing height, temperature, precipitation, and at one location relative humidity, were associated with BC concentrations. Median contribution estimates indicate that aircraft departures and arrivals (and other sources coincident in space and time) contribute to approximately 24–28% of the BC concentrations at the monitoring sites in the community. Our analysis demonstrated that a regression-based approach with detailed meteorological and source characterization can provide insights about source contributions, which could be used to devise control strategies or to provide monitor-based comparisons with source-specific atmospheric dispersion models.