• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br In Table the amount of the different


    In Table 1 the amount of the different pollutants yearly emitted by the three sectors (residential, industrial, traffic) in Cassino are reported; data are also expressed per capita and per unit area. Indeed, such per capita and per unit area emissions allowed to compare the emissions of the city under investigation to the national emissions obtained by the Institute for Environmental Protection and Research (2017). As an example, specific PM10 and CO2 emissions obtained through the current inventory carried out for Cassino (Table LLY507 1) resulted quite similar to corresponding national values that are equal to 2.98 × 100 kg yr−1 pers.−1 (or 5.94 × 102 kg yr−1 km−2) and 5.71 × 103 kg yr−1 pers.−1 (or 1.14 × 106 kg yr−1 km−2) for PM10 and CO2, respectively. Therefore, considering the large uncertainty typically related to emission invento-ries, the pollutant emission in Cassino (normalized to the LLY507 and area) can be considered roughly similar to the national emissions, i.e. Cassino can be a representative Italian case-study. This similarity can be also noticed in terms of particle number, indeed, even if the ultra-fine particle metric is not yet routinely included in national emission in-ventories, Kumar et al. (2014) and Keogh et al. (2009) estimated the particle number emission for European Countries and for the Brisbane
    spectively, which are quite comparable to the data here obtained for Cassino.
    Despite the overall and per-capita emissions of the city, which repre-sent local information not easily transferable to wider study area, a more interesting information obtained from the data here shown is the emission source apportionment, pollutant by pollutant, amongst the different sources. Such analysis clearly highlights that some of the pollutants are mostly emitted by the traffic sector, others by the resi-dential sector, whereas a minor contribution can be addressed to the in-dustrial sector. In particular, PM10, Cd, B(a)P, Dioxins, CO and SO2 are
    Table 1
    Yearly emission of the investigated pollutants in the city of Cassino source by source (data are reported as tons per year, with the exception of ultrafine particles, UFPs, which are reported in particles per year).
    Residential sector
    Industrial sector
    mostly emitted by residential combustions for heating purposes, whereas As, Ni, NOx and UFPs are mainly related to the traffic sector. Even though it is already well recognized in the scientific community that Cd, B(a)P, Dioxins, and SO2 are tracers of the biomass combustion processes and As, Ni, NOx are tracers of vehicular traffic sector (Carslaw, 2005; Talebi and Abedi, 2005), the different behavior of the two airborne particle metrics (PM10 and UFPs) is remarkable. Indeed, the contribution of the PM10 to the residential sector is equal to 79% (just 20% to the traffic sector), and the contribution of the UFPs to the traffic sector is equal to 86% (just 7% to the residential sector). Such a completely different behavior is related to several reasons: a) the differ-ent combustion phenomena involving the different fuels (diesel/gaso-line vs. wood/biomass, (Stabile et al., 2018; Zhang et al., 2014; Zosima et al., 2016)), b) the reduction of particle mass emission due to the in-troduction of new and more strict European emission standards for ve-hicles (Crippa et al., 2016; Vouitsis et al., 2017), c) the imprudent approach of the European governments providing incentives to the bio-mass heating systems (just taking into account their competitive run-ning costs and the carbon neutrality but ignoring their high overall emissions of airborne contaminants (European Environmental Agency, 2016; Longhin et al., 2016; World Health Organization, 2015)). The au-thors point out that the different behavior of such particle metrics is a key aspect since, as mentioned above (Eq. 7), the overall lung cancer risk emitted by the sources is obtained by summing of PM10 and UFP contributions, therefore a larger risk is expected to be related to the sources mainly emitting UFPs.