how a steel plant affects air quality of a nearby urban area a study on metals and pah concentrations

Taranto (in the Apulia Region of southern Italy) has been included in a list of the most polluted sites of national interest because of its large industrial area that is situated near the urban centre. The impact of this on urban air quality has been evaluated by monitoring PM2.5 and PM10 at the industrial site of 'via Orsini' and the urban station of 'via Dante'. At both sites, the temporal distribution and chemical composition of PM, in terms of PAHs and element concentrations, were used to characterize the air quality in the urban area and to deduce the possible and theoretical carcinogenic indices, and thus the impact on human health. High PM concentrations were found to be caused by wind coming from the north (industrial area), and during days when the wind was from this direction the PAH and elemental concentrations (such as iron, manganese and zinc) were the highest of the sampling period. These data confirm the impact of this industrial area, in particular its steel plant activities, on urban air quality in Taranto. In order to determine the source contributions to PM levels at the two investigated sites, Principal Component Analysis was applied to the collected data. Statistical investigations also included PAH and elemental concentrations determined at two other sites in Apulia Region, characterized by traffic and biomass burning sources. These investigations made it possible to distinguish the samples collected in via Dante and via Orsini from those collected at the two other sites, confirming the effects of industrial activities on urban air quality in Taranto

PM2.5 in indoor air of a bakery: Chemical characterization and size distribution

In current literature, studies on indoor air quality mostly concern environments such as hospitals, schools and homes, and less so on spaces producing food, such as bakeries. However, small-and medium-sized bakeries are typical and very common food production spaces, mostly in Southern Italy. Considering this, the present study investigated size trends of the aerosol particles during bakery working activities and the indoor particulate matter PM2.5 chemical speciation at the same time, in order to characterize the aerosol particulate matter emissions. In particular, indoor air monitoring was performed using a silent sequential sampler and an optical particle counter monitor during 7-19 April 2013. For each daily sampling, four PM2.5 samples were collected. In each sample, OC (organic carbon), EC (elemental carbon), LG (levoglucosan) Cl- (chloride), NO2- (nitrite), NO3- (nitrate), SO42- (sulfate), C2O42- (oxalate), Na+ (sodium), NH4+ (ammonium), K+ (potassium), Mg2+ (magnesium) and Ca2+ (calcium) concentrations were determined. The main sources of particles were wood burning, the cleaning of ovens (ash removal) and the baking of bread. While levoglucosan was associated with the source wood burning, potassium in this case can be considered as a marker of the contribution of the bakery activities. This work represents the second part of indoor research activities performed in the bakery. The first part was published in Ielpo et al. (2018)

Application of receptor models to airborne particulate matter

The human activities in their various aspects cause a change in the natural air quality. This change results more marked in very populated and in high industrialized areas. Some pollutants emitted are typical of a particular activity. Each source of pollution is identified by its profile in the composition of the emissions in the environment. Multivariate receptor models can be used in order to apportion pollutants to the different sources assessing the contribution of each source to the total pollution. This paper deals with the application of Absolute Principal Component Scores (APCS) receptor model to data obtained from the automatic network of air quality monitoring in the city of Bari (South Italy). The parameters monitored by automatic networks, as bihourly values, are PM10, NOx, CO, Benzene, Toluene, Xilene. The data shown in this paper concerning 1 month almost of sampling in different monitoring stations of Bari Municipality during the period of time from January 2005 to April 2006. Moreover preliminary results obtained applying the APCS model to daily PM2.5 samples collected during SITECOS PRIN project are shown. The results concerning data collected in corso Cavour (Bari) during the month of October 2005. The results obtained by APCS receptor model seem to suggest a poor contribution of the "vehicular traffic source" and a relevant contribution of the "secondary particulate source" to particulate matter concentrations