Bayesian Ecological Regression with Latent Factors: Atmospheric Pollutants Emissions and Mortality for Lung Cancer

In this work we propose a Bayesian ecological analysis in which a latent variable summarizes data on emissions of atmospheric pollutants. We specified a hierarchical Bayesian model with spatially structured and unstructured random terms with a nested latent factor model. This can be considered a combination of the convolution spatial model of Besag et al. (1991) and an ecological regression analysis in which a latent variable plays the role of the covariate. The unified approach allows to proper account for the uncertainty in the latent score estimation in the regression analysis. The Bayesian Latent Factor model is used to summarize the information on environmental pressure derived from three stressors: Carbon Monoxide, Nitrogen Oxides and Inhalable Particles. We found evidence of positive correlation between Lung cancer mortality and environmental pressure indicators, in males, Tuscany (Italy), 1995–1999. Environmental pressure seems to be restricted to fourteen municipalities (top 5% of the Latent Factor distribution). The model identified two areas with high point source emissions.     

Case study evidence that direct regulation remains the main driver of industrial pollution avoidance and may benefit operational efficiency

Environmental regulation is required to reduce industrial pollution to levels that are socially acceptable and economically efficient, but there is debate over the most efficient form of such regulation. Typically, a limited range of environmental and economic parameters are accounted for in the econometric studies that inform that debate. This paper integrates evidence from two separate studies to assess a broad range of parameters related to the efficiency of industrial environmental regulation. Questionnaire responses from 25 Italian manufacturers of building-chemicals and 28 Irish pharmaceutical manufacturers provided evidence on the environmental effectiveness and operational impacts of different regulatory instruments. Combined with extensive emissions data from Irish pharmaceutical manufacturers, survey results enabled a quantitative estimation of pollution avoidance specifically attributable to direct regulation. The monetised social benefit of avoided pollution was found to be equal to direct compliance costs. Chemical manufacturers in Italy and Ireland stated that direct regulation is a more important driver of industrial environmental performance than economic or voluntary regulation, and their responses indicated that direct compliance costs may be countered somewhat by stimulated operational improvements. It was concluded that direct regulation is an effective approach for multi-pollutant control in an industrial context, and that the efficiency of such regulation may be underestimated in studies that do not consider the full range of pollutants and operational effects.     

Technology improvements and value changes for sustainable happiness: a cross-development analytical model

This paper develops an analytical model to calculate the amount by which individuals are expected to modify their values (the relationship between lifestyle and happiness, as measured by subjective well-being, SWB) and to adopt innovative technologies (to increase the sustainability of production and consumption, measured by the ecological footprint, EF) to allow current and future generations to achieve sustainable happiness (the pursuit of happiness that does not exploit other people, the environment, or future generations). The paper also examines the dependence of these changes on an individual’s concern for future generations and on their country’s current state of economic development. It is shown that individuals in better-off developed countries and individuals in worse-off developing countries can achieve sustainability with a reasonable level of value change (0–30 %) and a feasible degree of technological innovation (10–40 %), respectively. In contrast, individuals in better-off developing countries and individuals in worse-off developed countries must rely to an impractical degree of technological innovation (50–70 %) and to an unreasonable level of value change (40–70 %), respectively. Finally, individuals in developing countries differ from individuals in developed countries in terms of their potential to achieve sustainable happiness, by achieving sustainability at a low SWB (about 10 % of its maximum) and a high SWB (about 80 % of its maximum), respectively.     

Characterization of municipal solid waste temporary storage sites: Risks posed to surrounding areas as a consequence of fire incidents

In this study temporary storage sites of municipal solid waste were characterized based on their potential social, health and environmental impacts as a consequence of spontaneous fires, by employing Boolean as well as weighted-linear-combination approaches in connection with various fuzzy set functions of population density around the storage sites. Sweden was used as the case study and data from 105 storage sites were analysed; of these, 38 were identified to be posing high risk for downwind residing population. Furthermore, during the past 10 years, the fire frequency and the average population residing within a radius of 1, 2, and 3 km were found to be comparatively higher for storage sites owned by private companies than for those owned by municipalities. The study provided first-cut information of poorly sited temporary storage sites and can help in formalizing the comprehensive risk analysis in the future.     

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C₆H₆ concentrations. The study area was identified by Notarbartolo Road and bounded by Libertà Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were used as input variables to predict pollutant concentrations by using neural networks. Finally, after a sensitivity analysis, it was showed that queues length were the mostly correlated traffic parameters to pollutant concentrations. An erratum to this article can be found at