Building and Environment 176 (2020) 106865 Available online 9 April 2020 0360-1323/© 2020 Elsevier Ltd. All rights reserved. Assessment of the environmental impact of road construction: Modelling and prediction of fne particulate matter emissions Marinella Giunta Department of Civil, Energy, Environmental, Materials Engineering, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89100, Reggio Calabria, Italy A R T I C L E INFO Keywords: Air pollution Environmental impact PM 10 emission Road construction Sustainability ABSTRACT The increasing importance of the environmental and social impacts of air pollution calls for the prediction of the PM 10 emissions in construction projects to prevent conficts with population and workers. The PM 10 generated by road constructions produces signifcant adverse effects on human health and environment. The signifcance of the concern relies on the great amount of roads that are expected to be built in the near future, especially in developed countries. The reliable estimation of these emissions, the assessment of the admissibility of their concentration, the identifcation of measures aimed at their lowering, the constant control during the works are indispensable actions in making road construction more sustainable. In this paper a procedure for predicting the PM 10 emissions and propagation due to the road construction is presented and applied to a case study of a motorway project. The daily and annual mean PM 10 concentration in the area near the worksites have been estimated, based on the gathered data on all the construction sites, the construction processes, the type and operation time of the equipment used in each site and applying the emission factor and the equations proposed by United States Environmental Protection Agency. Simulations showed that daily and annual mean limit are generally verifed. The overruns occur, as expected, within the emission sources and are exhausted in few meters. Results allowed recognizing the worksites and the inherent activities characterized by the highest level of the emission toward which to direct the most effective mitigation measures able to reduce the particulate concen- tration in atmosphere. 1. Introduction Roads construction produces signifcant adverse effects on environ- ment and human health due to the discharge in atmosphere of pollutants released during the execution of the various construction processes (earthmovings, trucks transit on unpaved roads, crushing, material production) and the operation of diesel-powered equipment [1–4]. Among the pollutants, the concentration of particular matter (PM) is an increasing concern for the negative effects on human health of workers [5–7], inhabitants [8–10] and environment [11]. PM is a complex mixture of extremely small particles and liquid droplets responsible for widespread air pollution [12]. The PM comprises two main components PM 10 , particles with diameter <10 μm, and PM 2.5 , particle with diam- eter <2.5 μm, respectively. Off-road diesel equipment used in road construction is the second largest source of PM emissions and represents the 24.3% of total PM emissions from mobile fonts [13]. Researches deal with the acquisition of data concerning the exhaust emissions of devices and means used during construction. Wang et al. [14] highlights the importance of knowing how the equipment is used and operate in order to quantify its emissions. They sustain that an extensive data collection of the real-work activity, including on-site survey activity and emissions measurements when the equipment is on duty, is fundamental for defning a suitable emission inventory. Pirjola et al. [15], carried out a wide study on exhaust emissions of a tractor in real-world and labora- tory conditions and determined emission factors for NOx, PM and non-volatile particles. They found that type of fuel and the driving conditions affect the emissions. Another signifcant activity, which requires a specifc attention in terms of carefully estimation of PM 10 emissions, control and mitigation, is the transit of trucks on unpaved or dirty roads [16–18]. The relevance of the particulate emissions for unpaved road led, recently, Liu and Yoon [19] to propose a method, based on the employment of artifcial neural networks (ANN), aimed at improving the quality of the emission factor equations provided by the US Environmental Protection Agency (U.S. EPA) [20] for paved and unpaved roads. The method, by considering all the affecting factors, allows increasing the predictability of PM emission and helping the agencies and the enterprises to establish reliable PM E-mail address: marinella.giunta@unirc.it. Contents lists available at ScienceDirect Building and Environment journal homepage: http://www.elsevier.com/locate/buildenv https://doi.org/10.1016/j.buildenv.2020.106865 Received 6 March 2020; Received in revised form 23 March 2020; Accepted 28 March 2020