A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of \(\text {NO}_{x}\) in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated \(R^2\) of approximately \(0.7\) at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.     

Short- and long-term responses of nourishments: Barra-Vagueira coastal stretch,Portugal

Dredged material resulting from deepening and maintenance activities of the Aveiro Harbor inlet channel, northwestern coast of Portugal, has been used to mitigate the erosion trend recorded on nearby beaches (from Barra to Costa Nova Beach) through direct placement of sand by using standard dredge equipment. The disposal activities of dredged material have been undertaken at two main sites: between the south breakwater and the 1st groin of Costa Nova (dumping area 1, DA1) and between the 3rd and the 5th groin of Costa Nova (dumping area 2, DA2).The sand was placed in the nearshore, between the ?2 and ?7 m Chart Datum, CD, contours.In this study, short- and long-term coastal morphologic changes in the sea bottom, in response to several nourishment operations and to the incoming waves, within the dumping area boundaries are investigated based on a data set of hydrographic surveys collected annually, just before and after the nourishments, between 2009 and 2015. Preliminary results describing the main morphologic changes, evolution trends, sediment budget variations, and nourishments performance are discussed using mainly Geographic Information System techniques. Overall, the analysis demonstrates that the short-term losses in the dumping areas (one month of interval) can reach 50% of the nourished volume, revealing a significant movement of the fill material towards offshore. Seasonal variations promoting cross-shore material exchange can also prevail and misrepresent the sediment balances, if the monitoring area is not comprehensive. Furthermore, some bathymetric analysis suggested that longshore transport gradients have moved the fill material from Barra beach to downdrift areas. All the obtained results contribute to the ongoing discussion about the effectiveness of nearshore sand placements especially in context of an energetic environment.     

Impacts of biodiesel on pollutant emissions of a JP-8-fueled turbine engine

The impacts of biodiesel on gaseous and particulate matter (PM) emissions of a JP-8-fueled T63 engine were investigated. Jet fuel was blended with the soybean oil-derived methyl ester biofuel at various concentrations and combusted in the turbine engine. The engine was operated at three power settings, namely ground idle, cruise, and takeoff power, to study the impact of the biodiesel at significantly different pressure and temperature conditions. Particulate emissions were characterized by measuring the particle number density (PND; particulate concentration), the particle size distribution, and the total particulate mass. PM samples were collected for offline analysis to obtain information about the effect of the biodiesel on the polycyclic aromatic hydrocarbon (PAH) content. In addition, temperature-programmed oxidation was performed on the collected soot samples to obtain information about the carbonaceous content (elemental or organic). Major and minor gaseous emissions were quantified using a total hydrocarbon analyzer, an oxygen analyzer, and a Fourier Transform IR analyzer. Test results showed the potential of biodiesel to reduce soot emissions in the jet-fueled turbine engine without negatively impacting the engine performance. These reductions, however, were observed only at the higher power settings with relatively high concentrations of biodiesel. Specifically, reductions of approximately 15% in the PND were observed at cruise and takeoff conditions with 20% biodiesel in the jet fuel. At the idle condition, slight increases in PND were observed; however, evidence shows this increase to be the result of condensed uncombusted biodiesel. Most of the gaseous emissions were unaffected under all of the conditions. The biodiesel was observed to have minimal effect on the formation of polycyclic aromatic hydrocarbons during this study. In addition to the combustion results, discussion of the physical and chemical characteristics of the blended fuels obtained using standard American Society for Testing and Materials (ASTM) fuel specifications methods are presented.     

Designing metrics that fit: Rethinking corporate environmental performance measurement systems

If you don't measure it, you can't manage it. Over the past decade, many companies have recognized this need to track environmental performance. Failure to plan metrics systems to fit organizational needs, work flows, and culture has left many companies with costly and ineffective environmental performance measurement systems. This article presents numerous practical steps for designing effective metrics systems which avoid common pitfalls.     

Exploring the influence of outdoor recreation participation on pro-environmental behaviour in a demographically diverse population

This study used structural equation modelling to examine the effects of socio-demographic variables and outdoor recreation participation on pro-environmental behaviour (PEB) among an ethnically diverse population of adult state-park visitors near Atlanta, Georgia, USA (N?=?319). Data collected through intercept surveys at three state parks were used to examine interactions among potential predictors of PEB, including socio-demographics (ethnicity, gender, education, and income), outdoor recreation participation (during childhood and adulthood), and environmental value orientations (biocentric and anthropocentric). The best fitting model accounted for 40% of the variance in self-reported PEB and indicated the strongest positive relationship between adult outdoor recreation participation and PEB (β?=?0.54, t?=?5.70). Biocentric and anthropocentric value orientation scores were also significant antecedents of PEB (β?=?0.17, t?=?1.99, and β?=?0.19, t?=?2.56, respectively), and both were influenced by socio-demographic variables. Overall, results suggest that a diverse public's environmental ethos and propensity to engage in PEBs could be influenced by outdoor recreation opportunities.