Experimental Techniques and Numerical Models to Detect Pollutant Emission in the Transport Sector

Abstract

In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain

“…They also exert direct (absorption and scattering) and indirect impacts (cloud interaction) on the climate through changing the Earth's energy balance (IPCC, 2021). Submicron particles (PM1) contribute the most to these effects because their sizes are closer to the wavelength of visible light and they can penetrate deep into the respiration system (Costa et al, 2015;Marseglia et al, 2019;Pope and Dockery, 2006).…”

Impacts of winter heating on the population mobility of northern China's prefectural cities: evidence from a regression discontinuity design

“…Sustainable development has become the central issue of human survival and development, which is also now pretty much the agenda for countries around the globe. China as the world's second largest economy and largest energy consumer have also paid great attentions on sustainable developments; China not only has witnessed world-shaking economic achievement since the reform and opening up in 1978 (Chen et al , 2017) but also experienced widespread notorious atmospheric pollution problems due to rapid industrialization expansion and urbanization, especially, the fine particulate matter (PM 2.5 ) pollution (Cotana et al , 2019; Marseglia et al , 2019a, b, 2020; Pigliautile et al , 2020) has already become an extremely stern challenge in urban China (Cheng et al , 2017; Zhang et al , 2019). According to the 2018 China Environmental Bulletin, 217 cities have exceeded the air quality standard among 338 cities, accounting for 64.2% of total cities.…”

“…First, we investigate the relationship between winter heating and population mobility using regression discontinuity design, which is helpful to avoid the estimation error caused by endogeneity. Then, in the era of big data, with the rapid development of big data technology, big data application has been integrated into all walks of life to solve problems in the society, such as business, medical health, traffic congestion, urban management, tourism forecast, air pollution and industrial development (An and Wu, 2020; Babar et al , 2019; Grover et al , 2018; Santoro et al , 2019; Carrese et al , 2019; Marseglia et al , 2019a, b). We use the passenger travel “big data” based on the website of Tencent Location Big Data, which can effectively capture the daily characteristics of China's population mobility.…”