# Assessment of the Air Pollution Level in the City of Rome (Italy)

^{*}

## Abstract

**:**

_{3}] that has high values in summer. It can be clearly concluded that Rome has a strongly unsteady behaviour in terms of a family of pollutant concentration, which fluctuate significantly. It is worth noticing that there is a strong linear dependence between [C

_{6}H

_{6}] and [NO] and a more complex interdependence of [O

_{3}] and [C

_{6}H

_{6}]. Qualitatively is provided that, to a reduction of [C

_{6}H

_{6}] under a certain threshold level corresponds an increase of [O

_{3}].

## 1. Introduction

_{2.5}can be taken as indicator of population exposure to outdoor air pollution. In 2010, 3.2 million of people died because of cardiovascular disease, caused by the exposure to ambient fine particles (PM

_{2.5}) and 22,300 people died because of lung cancer. China and East Asia show the largest number of people who lost their life [5,6].

_{2}, SO

_{2}and (PMs) there is general agreement in the scientific literature that they are the main agents responsible for the damage encountered on monuments and historical buildings in urban areas [7]. Atmospheric composition is of unquestionable importance in the study of the damage produced on building materials of artistic interest, since it directly influences the species characteristics and entity of the degradation mechanism occurring on the cultural heritage.

_{2}], [NOx], [NO], [NO

_{2}], [C

_{6}H

_{6}], [PM

_{10}], [PM

_{2.5}] and [O

_{3}] generated in Rome during 2015 have been analysed. These pollutants were taken from the Directive 2008/50/EC, the main legislation about ambient air quality. In these analyses we applied different advanced post-processing techniques. Statistic and cross-statistic have been computed in time and Fourier domain. In particular, probability distribution, Kurtosis, Skewness, Poincaré sections and cross-correlation of the different pollutants were analysed in order to assess the air pollution level in the city of Rome and the correlation of anthropogenic sources with the pollutant emission. The extreme value theory was applied to the experimental data. Especially using the generalized extreme value (GEV) distribution, several fittings of the experiment probability density functions were calculated. GEV distribution introduced by Fisher and Tippett [32] is commonly applied in environmental science to model a wide variety of natural extremes, including floods, rainfall, wind speeds, wave height, snow depths, earthquake, and other maxima. For the present research activity, probability density functions fitting of the pollutant concentration were calculated. The GEV distribution turned out to be very attractive mathematical tool since its inverse has an analytical form, and its parameters are can be easily estimated [33,34,35,36,37,38]. This last feature allows us to compute the return period: the likelihood of an event to occur. This post-processing strategy is common applied to pollution database to develop model. Shen et al. developed a statistical model using extreme value theory to estimate changes in ozone episodes [39].

## 2. Materials and Methods

#### 2.1. Characteristics of the Study Area

^{2}. Considering the metropolitan area of Rome, the population reaches up to 4.3 million residents in 5352 km

^{2}. With a history of more than two and half thousand years, Rome is called the Eternal City because of the number of open-air museums. It is a mixture of a modern city and a plethora of monuments, piazzas, villas, museums, churches, Egyptian obelisks, the Forum and Vatican City. Its climate is typically Mediterranean: winters are cool and humid and summers are hot and dry. In the coldest month (January) the temperature can reach about 0 °C, and in the warmest months (July and August) the temperature can reach 36 °C.

#### 2.2. Pollutant Legislation

#### 2.3. Post Processing Techniques

**Probability distribution**describes the possible values that a random variable can take within a given range.**Kurtosis**is a measure of whether the data have a flattening or elongation from the normal distribution. High kurtosis indicates a flattering distribution, while low values indicate an elongation distribution.**Skewness**is a measure of the asymmetry of the distribution. A data set is symmetric if it looks the same to the left and right of the center point.**Poincaré sections**are a way to represent a dynamical system. The surface of section presents a trajectory in n-dimensional phase space in an (n-1)-dimensional space. By picking one phase element constant and plotting the values of the other elements each time the selected element has the desired value, an intersection surface is obtained. The phase space is a surface that describes all the possible states of a system.**Cross-correlation**is a measure of similarity of two data series as a function of the lag of one relative to the other.**Coefficient of variation**normalizes the standard deviation with the mean of a data. This index gives information about the variability of a data set.**Generalized Extreme Value**distribution is often applied to analyse a large set of data characterized by small or large value. In this approach three simpler distributions into a single form are combined, allowing a continuous range of possible shapes.

#### 2.4. Monitoring Station Network

_{2}], [NOx], [NO], [NO

_{2}], [C

_{6}H

_{6}], [PM

_{10}], [PM

_{2.5}] and [O

_{3}] that are shown in Figure 1. The monitoring network acquires concentration data every hour and every stations is set to monitor different type of concentrations. All the stations taken into account are placed inside high density urban areas that are characterized by traffic and domestic heating system pollutant sources.

## 3. Results and Discussion

_{10}], there is only one station that indicates a different Kurtosis value than the others. Despite this consideration, in all the concentrations the Kurtosis and Skewness values of the different stations are similar and it suggest that the mean of the network is possible.

_{2}] and [C

_{6}H

_{6}] have similar trend during the year suggesting the presence of pollutant source simultaneously.

_{3}] is recorded to be higher in the summer and during the hottest hours, when the solar radiation is highest. As a matter of fact, the presence of solar radiation allows the reaction of nitrogen dioxide (NO

_{2}) in the formation of ozone (O

_{3}).

_{2}] have values up to 8 µg/m

^{3}compared with the limit of 125 µg/m

^{3}reported in Table 1. That is foreseeable because the [SO

_{2}] is mainly caused by the combustion of fuel at power plants and other industrial facilities. As a matter of fact, in the center of Rome there is not this kind of system.

_{2.5}and PM

_{10}has high values in the winter season and reach the maximum values in the last two months of 2015, with values close to 50 µg/m

^{3}of PM

_{2.5}and values close to 70 µg/m

^{3}of PM

_{10}.

_{2}] and [C

_{6}H

_{6}] have a narrow distribution around the most frequent value and significant positive tail. The latter aspect can be ascribed to intermittent energetic events embedded in concentration time histories. To better clarify this aspect all pdf are fitted using the Generalized Extreme Value (GEV) function. By means of the GEV function, the return level and period were calculated in order to evaluate the occurrence time of these events as reported in Figure 13. With reference to the return period of [NO], every two days a high energetic peak in [NO] concentration can be detected. On the other hand, in [NO

_{2}] time history intermittent events are rarely observed. This aspect is a footprint of chaotic behavior of the Rome in terms of pollutant generation and could be taken into account during the modelling stage of this process.

_{2}], [PM

_{2.5}], [PM

_{10}] and [O

_{3}] have a distribution with a more flat trend. In particular, [NO

_{2}] pdf is well fitted by the Gaussian pdf except for a small region over 3σ where a departure of the positive tail can be clearly observed. In the latter case, we can consider, in a first approximation, equally probable concentrations in a certain range.

_{2}], [PM

_{2.5}], [PM

_{10}], [C

_{6}H

_{6}] and [O

_{3}] exceeds the limit of legislation during the year. In particular, 64% of time the [NO

_{2}] was over the limit of 40 µg/m

^{3}, 23% of time the [PM

_{2.5}] was over the limit of 25 µg/m

^{3}, and 21% of time [PM

_{10}] was over the limit of 40 µg/m

^{3}. Regarding the concentration of [C

_{6}H

_{6}] and [O

_{3}] the time in which the concentration is over the limit of legislation is less than previous pollutant: 4% of time the [C

_{6}H

_{6}] was over the limit of 40 µg/m

^{3}and 3% of time the [O

_{3}] was over the limit of 120 µg/m

^{3}.

_{2}], [SO

_{2}] and [CO] haven’t exceeded the legislation thresholds. For [PM

_{10}] the limit of 50 µg/m

^{3}for an averaging period of 24 h was exceeded 39 times that is more than 35 permitted exceedances each year by the legislation. These exceedances are concentrated in December when there was a combination of exogenous state variable (i.e., temperature, humidity and wind velocity and direction, and traffic flow) and low rainfall. For [O

_{3}] the limit of 120 µg/m

^{3}for an averaging period of 8 h was exceeded 7 times. For this concentration, the legislation permitted 25 exceedances days over 3 years.

_{2}] and [C

_{6}H

_{6}] a high Kurtosis value is evaluated, indicating that these pollutants are present on Rome in constant quantities. On the other hand the high Kurtosis values suggests a sharped distribution around the mode. Whereas the coefficient of variation of [NO] have large value indicating that there are high energetic extreme value (see Figure 12).

_{2}] the values assumed by the Kurtosis are close to those of a Gaussian distribution as confirmed by the comparison in Figure 12, indicating the presence of a species of pollutant that fluctuate significantly around an average value.

_{6}H

_{6}] are strongly related to the concentration of pollutants species such as [CO], [NO] and [NO

_{2}], as confirmed by the cross-correlation values reported in Figure 14. This correlation is due to the fact that nitrogen oxide and carbon oxides are combustion products and that their fluctuations are due to the oscillations of the city traffic flux. It is noteworthy that [O

_{3}] is weakly related to the production of benzene but it is indeed in phase opposition with it. To further investigate this interesting aspect, the Poincaré section of [C

_{6}H

_{6}] upon [NO] and [C

_{6}H

_{6}] upon [O

_{3}] are represented in Figure 15. As noted above there is a strong linear dependence between [C

_{6}H

_{6}] and [NO] and a more complex interdependence of [O

_{3}] and [C

_{6}H

_{6}]. More specifically an inverse proportionality is clearly observed as formalized in the following:

_{6}H

_{6}] under a certain threshold level corresponds an increase of [O

_{3}]. Such behaviour could be attributed to exogenous state variable, which were not taken into account in the present work.

## 4. Conclusions

_{6}H

_{6}] are strongly related to the concentration of pollutants species such as [CO], [NO] and [NO

_{2}], as confirmed by the cross-correlation analysis. This correlation is due to the fact that nitrogen oxide prompts and carbon oxides are combustion products and that their fluctuations are caused by the oscillations of the city traffic flux. [O

_{3}] is weakly related to the production of benzene and it is also in phase opposition with it. For this reason, the Poincaré section of [C

_{6}H

_{6}] upon [NO] and [C

_{6}H

_{6}] upon [O

_{3}] was investigated. It is worth noticing that there is a strong linear dependence between [C

_{6}H

_{6}] and [NO] and a more complex interdependence of [O

_{3}] and [C

_{6}H

_{6}]. Qualitatively is provided that, to a reduction of [C

_{6}H

_{6}] under a certain threshold level which corresponds to an increase of [O

_{3}]. Such behaviour could be attributed to exogenous state variable.

_{6}H

_{6}] and [PM

_{10}] concentrations.

_{6}H

_{6}] over [NO] and [C

_{6}H

_{6}] over [O

_{3}]. These aspects are well known for small scale zero-dimensional reactor, but for very large scale problems as the city domain is not commonly investigated. Therefore, a reduction of a three dimensional large scale process to zero-dimensional phenomenon as in homogeneous volume can be consider the first step for a mathematical model development by means of an ODE system as follows:

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 2.**Mean (blue) and standard deviation (red) values calculated for all pollutant species in the different stations. Blue line is the mean of the mean values of the different stations and the red line is the mean of the standard deviation values of the different stations. (

**a**) [CO]; (

**b**) [NO]; (

**c**) [NO

_{2}]; (

**d**) [C

_{6}H

_{6}]; (

**e**) [O

_{3}]; (

**f**) [SO

_{2}]; (

**g**) [PM

_{2.5}]; (

**h**) [PM

_{10}].

**Figure 3.**Kurtosis (blue) and Skewness (red) values calculated for all pollutant species in the different stations. Blue line is the mean of the Kurtosis values of the different stations and the red line is the mean of the Skewness values of the different stations. (

**a**) [CO]; (

**b**) [NO]; (

**c**) [NO

_{2}]; (

**d**) [C

_{6}H

_{6}]; (

**e**) [O

_{3}]; (

**f**) [SO

_{2}]; (

**g**) [PM

_{2.5}]; (

**h**) [PM

_{10}].

**Figure 12.**Probability density function (red triangles) and Generalized Extreme Value fitting (blue circles) of pollutant concentrations calculated for samples acquired in a year.

**Figure 14.**Cross-correlation between pollutant concentrations; benzene concentration is taken into account as a reference signal; the time axis is normalized respect to a reference time: t* = 86,400 s.

Pollutant | Concentration | Averaging Period | Permitted Excess Each Year |
---|---|---|---|

PM_{10} | 50 µg/m^{3} | 24 h | 35 |

PM_{10} | 40 µg/m^{3} | 1 year | - |

PM_{2.5} | 25 µg/m^{3} | 1 year | - |

NO_{2} | 200 µg/m^{3} | 1 h | 18 |

NO_{2} | 40 µg/m^{3} | 1 year | - |

SO_{2} | 350 µg/m^{3} | 1 h | 24 |

SO_{2} | 125 µg/m^{3} | 24 h | 3 |

O_{3} | 120 µg/m^{3} | Maximum daily 8 h mean | 25 days averaged over 3 years |

CO | 10 mg/m^{3} | Maximum daily 8 h mean | - |

C_{6}H_{6} | 5 µg/m^{3} | 1 year | - |

Pollutant | PM_{s} | NO_{x} | SO_{2} | O_{3} | CO | C_{6}H_{6} |
---|---|---|---|---|---|---|

Sensors | MP101MC | M200 A-API | TE 43i | M400E API | TE 48i | AIR Toxic |

SWAMDC FAI | ||||||

SWAM5a FAI | ||||||

M100E API | M300E API | CP 7001 | ||||

SWAM DC FAI | ||||||

TE SHARP 5030 |

Specie | Kurtosis | Skewness | Coefficient of Variation |
---|---|---|---|

[CO] | 14.3 | 2.7 | 66% |

[NO] | 17.1 | 3.3 | 155% |

[SO_{2}] | 9.6 | 2.2 | 91% |

[C_{6}H_{6}] | 9.4 | 2.1 | 72% |

[NO_{2}] | 3.9 | 0.7 | 48% |

[PM_{2.5}] | 4.2 | 1.3 | 60% |

[PM_{10}] | 3.7 | 1.0 | 46% |

[O_{3}] | 2.8 | 0.7 | 80% |

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## Share and Cite

**MDPI and ACS Style**

Battista, G.; Pagliaroli, T.; Mauri, L.; Basilicata, C.; De Lieto Vollaro, R.
Assessment of the Air Pollution Level in the City of Rome (Italy). *Sustainability* **2016**, *8*, 838.
https://doi.org/10.3390/su8090838

**AMA Style**

Battista G, Pagliaroli T, Mauri L, Basilicata C, De Lieto Vollaro R.
Assessment of the Air Pollution Level in the City of Rome (Italy). *Sustainability*. 2016; 8(9):838.
https://doi.org/10.3390/su8090838

**Chicago/Turabian Style**

Battista, Gabriele, Tiziano Pagliaroli, Luca Mauri, Carmine Basilicata, and Roberto De Lieto Vollaro.
2016. "Assessment of the Air Pollution Level in the City of Rome (Italy)" *Sustainability* 8, no. 9: 838.
https://doi.org/10.3390/su8090838