Global economic development and urbanization during the past two decades have driven the increases in demand of personal and commercial vehicle fleets, especially in developing countries, which has likely resulted in changes in year-to-year vehicle tailpipe emissions associated with aerosols and trace gases. However, long-term trends of impacts of global gasoline and diesel emissions on air quality and human health are not clear. In this study, we employ the Community Earth System Model (CESM) in conjunction with the newly developed Community Emissions Data System (CEDS) as anthropogenic emission inventory to quantify the long-term trends of impacts of global gasoline and diesel emissions on ambient air quality and human health for the period of 2000-2015. Global gasoline and diesel emissions contributed to regional increases in annual mean surface PM2.5 (particulate matter with aerodynamic diameters ≦ 2.5 µm) concentrations by up to 17.5 and 13.7 µg/m3, and surface ozone (O3) concentrations by up to 7.1 and 7.2 ppbv, respectively, for 2000-2015. However, we also found substantial declines of surface PM2.5 and O3 concentrations over Europe, the US, Canada, and China for the same period, which suggested the co-benefits of air quality and human health from improving gasoline and diesel fuel quality and tightening vehicle emissions standards. Globally, we estimate the mean annual total PM2.5- and O3-induced premature deaths are 139,700-170,700 for gasoline and 205,200-309,300 for diesel, with the corresponding years of life lost of 2.74-3.47 and 4.56-6.52 million years, respectively. Diesel and gasoline emissions create health-effect disparities between the developed and developing countries, which are likely to aggravate afterwards.

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Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.