# Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach

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## Abstract

**:**

## 1. Introduction

_{2}emissions (g/km) target, fixed at 95 g CO

_{2}/km from 2020 for passenger cars [5].

_{2}emissions through vehicle simulation model; Kholod et al. [17] proposed a methodology for vehicle emission inventories in cities where limited data are available, estimating black carbon emissions; Fu, Kelly and Clinch [18] suggested a bottom-up methodology applicable for both nationally aggregated data and spatially disaggregated results, providing modelling parameters to support policy analyses; Ahmed et al. [19] evaluated the effectiveness of transport policies to reduce transport emissions in India, as Hasan, Chapman and Frame [20] did, analysing, with a multi-criteria study, the efficacy of the adopted policies in terms of transport emissions reduction.

^{2}, starting from its vehicle fleet composition data provided by the ACI (Automobile Club d’Italia) national database [28]. It is an official database containing all the information about the Italian fleet composition, and it classifies the vehicle fleet in accordance with the Tier 3 method of the EMEP/EEA Tier 3 approach, considering both the urban and broader context. Local and regional data can be extrapolated, allowing one to vary the extent of the chosen sample of study, in function of the fuel used, the engine power, and the emissive class. The selected area, chosen as a case study for the applied methodology, develops along the eastern coast of the Strait of Messina for about 32 km, and towards the east, from sea to mountains, for another 30 km, with mid-coast, hilly, and mountainous areas. It has also been chosen because, due to the insufficient local public transportation system, modal choice is quite constrained, and it poorly affects the features of the local urban mobility, which mostly consists of private passengers’ cars. Therefore, it can be used as a plain example to illustrate the suitability of the method, which has, on the other hand, general validity, and can be applied to different and diverse spatial contexts.

## 2. Methodology

_{E}(E) of the daily emission rates E, discharged by the road traffic in a specific spatial context.

#### 2.1. EMEP/EEA Tier 3 Method

^{−1}km

^{−1}) are defined as a function of the vehicle speed:

^{−1}) can be, hence, estimated by means of:

- ${N}_{k}$ is the number of vehicles belonging to each homogeneous emitting class of the analysed fleet;
- ${L}_{k}$ is the mileage travelled by the k-class vehicle in the considered time (km day
^{−1}).

^{−1}), is given by:

#### 2.2. The Yearly Average Vehicle

- $c$ is the category involving homogeneous vehicles from the point of view of the various factors influencing the hot exhaust emissions, involving the vehicle type (i.e., passenger cars, heavy-duty vehicles, etc.), the distance covered by each vehicle, its speed, its age (related to the legislation emissive class), its engine size and its weight;
- $YA{V}_{p,c,v}$ is the yearly average vehicle of the category c (namely concerning the vehicle type, the fuel, the engine volume or vehicle weight and the legislation emissive class), for the pollutant $p$ (g vehicle
^{−1}km^{−1}); it is a function of the vehicle speed $v$; - ${N}_{c}$ is the number of homogeneous subcategories into which the category c can be subdivided;
- ${S}_{k}$ is the share of the k
^{th}subcategory of vehicles within the category c.

#### 2.3. The Stochastic Approach

## 3. An Application

#### 3.1. Scenarios Description

_{2}, CO, NO

_{x}, VOC and PM, as presented by the European Environment Agency EMEP/EEA air pollutant emission inventory guidebook 2019—Group 1 [22]; of course, many other combustion products can be considered in the function of the approach adopted for the emissions calculation, but in this paper, the study has been focused on these five, which are the most recurrent ones referring to the urban environment. In addition, two scenarios were analysed: the first one is the reference scenario, considered as a starting point, and is referred to as the passenger car fleet actually circulating in 2019 (pre-pandemic period), whereas the second one corresponds to the hypothesis that the total number of passenger cars circulating in 2030 is derived from the linear trend of each emissive category during the years 2009 to 2019, applying the same trend to years 2019 to 2030 (Figure 1).

#### 3.2. Choice of the Statistical Functions

- ${\mu}_{v}$ is the mean of the speed distribution (km/h)
- $\sigma $ is the standard deviation of the speed distribution (km/h)

- ${\mu}_{y}$ is the mean of the y probability distribution, with $y=\mathrm{ln}L,$
- ${\sigma}_{y}$ is the standard deviation of the y probability distribution.

#### 3.3. Results

_{2}emissions, whereas the petrol average vehicle is responsible for the greatest contribution to both CO and VOC releases. On the contrary, the diesel-fuelled average vehicle is responsible for the highest NO

_{x}and PM productions. This information can be pivotal when actions changing the fleet composition are to be undertaken, so that decision-making processes can be effectively guided.

## 4. Conclusions

_{E}(E) of the emission rates E, discharged by the road traffic in a specific spatial context. This last function allows a different approach to be introduced in the analysis of air pollution: knowing the function f

_{E}(E), various statistical indicators, (e.g., percentiles, confidence intervals, etc.) can be assessed, so that an in-depth evaluation of different possible scenarios can be related to their occurrence probability.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 4.**Gaussian probability density and cumulative functions of the random variable vehicle speed.

**Figure 5.**Lognormal probability density and cumulative functions of the random variable distance travelled.

**Table 1.**Mean and standard deviation of the involved variable [19].

Variable | Mean | Standard Deviation |
---|---|---|

Speed (km/h) | 22.0 | 1.8 |

Daily travelled distance (km/day) | 32.4 | 5.4 |

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**MDPI and ACS Style**

Marino, C.; Nucara, A.; Panzera, M.F.; Pietrafesa, M.
Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach. *Sustainability* **2022**, *14*, 4127.
https://doi.org/10.3390/su14074127

**AMA Style**

Marino C, Nucara A, Panzera MF, Pietrafesa M.
Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach. *Sustainability*. 2022; 14(7):4127.
https://doi.org/10.3390/su14074127

**Chicago/Turabian Style**

Marino, Concettina, Antonino Nucara, Maria Francesca Panzera, and Matilde Pietrafesa.
2022. "Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach" *Sustainability* 14, no. 7: 4127.
https://doi.org/10.3390/su14074127