This paper describes the in situ monitoring of indoor air quality (IAQ) in two dwellings, using low-cost IAQ sensors to provide high-density temporal and spatial data. IAQ measurements were conducted over 2-week periods in the kitchen and bedroom of each home during the winter, spring, and summer seasons, characterized by different outside parameters, that were simultaneously measured. The mean indoor PM2.5 concentrations were about 15 μg m−3 in winter, they dropped to values close to 10 μg m−3 in spring and increased to levels of about 13 μg m−3 in summer. During the winter campaign, indoor PM2.5 was found mainly associated with particle penetration inside the rooms from outdoors, because of the high outdoor PM2.5 levels in the season. Such pollution winter episodes occur frequently in the study region, due to the combined contributions of strong anthropogenic emissions and stable atmospheric conditions. The concentrations of indoor volatile organic compounds (VOCs) and CO2 increased with the number of occupants (humans and pets), as likely associated with consequent higher emissions through breathing and metabolic processes. They also varied with occupants’ daily activities, like cooking and cleaning. Critic CO2 levels above the limit of 1000 ppm were observed in spring campaign, in the weeks close to the end of the COVID-19 quarantine, likely associated with the increased time that the occupants spent at home.

Oxidative potential (OP) of particulate matter (PM) is gaining strong interest as a promising health exposure metric. This study investigated OP of a large set of PM10 and PM2.5 samples collected at five urban and background sites near Milan (Italy), one of the largest and most polluted urban areas in Europe, afflicted with high particle levels. OP responses from two acellular assays, based on ascorbic acid (AA) and dithiothreitol (DTT), were combined with atmospheric detailed composition to examine any possible feature in OP with PM size fraction, spatial and seasonal variations. A general association of volume-normalized OP with PM mass was found; this association may be related to the clear seasonality observed, whereby there was higher OP activity in wintertime at all investigated sites. Univariate correlations were used to link OP with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, AA was particularly sensitive towards transition metals in coarse particles released from vehicular traffic. The results obtained confirm that the responses from the two assays and their relationship with atmospheric pollutants are assay- and location-dependent, and that their combination is therefore helpful to singling out the PM redox-active compounds driving its oxidative properties.

The concentrations of polycyclic aromatic hydrocarbons (PAHs) and quinones, a subgroup of oxygenated PAHs (oxy-PAHs), were measured in PM2.5 samples collected during warm (May–June 2019) and cold (February–March 2020) seasons in the city of Bologna, Italy. Total PAHs concentration was nearly double in winter (6.58 ± 1.03 ng m−3) compared with spring (3.16 ± 0.53 ng m−3), following the trend of the PM2.5 mass concentration. Molecular diagnostic ratios suggested that, together with traffic, biomass burning was the dominant emission source contributing to the peaks of concentration of PM2.5 registered in the cold season. Quinone level was constant in both seasons, being 1.44 ± 0.24 ng m−3, that may be related to the increased secondary formation during warm season, as confirmed by the higher Σoxy-PAHs/ΣPAHs ratio in spring than in winter. The oxidative potential (OP) of the PM2.5 samples was assessed using acellular dithiothreitol (DTT) and ascorbic acid (AA) assays. The obtained responses showed a strong seasonality, with higher volume-normalized (OPV) values in winter than in spring, i.e., OPVDTT: 0.32 ± 0.15 nmol min−1 m−3 vs. 0.08 ± 0.03 nmol min−1 m−3 and OPVAA: 0.72 ± 0.36 nmol min−1 m−3 vs. 0.28 ± 0.21 nmol min−1 m−3. Both OPVDTT and OPVAA responses were significantly associated with total PAHs, as a general descriptor of redox-active PAH derivatives, associated with co-emission from burning sources or secondary atmospheric oxidation of parent PAHs. Otherwise, only winter OPVDTT responses showed a significant correlation with total Ʃoxy-PAHs concentration.

No abstract

Abstract. Primary biological aerosol particles (PBAPs) play an important role in the interaction between biosphere, atmosphere, and climate, affecting cloud and precipitation formation processes. The presence of pollen, plant fragments, spores, bacteria, algae, and viruses in PBAPs is well known. In order to explore the complex interrelationships between airborne and particulate chemical tracers (amino acids, saccharides), gene copy numbers (16S and 18S for bacteria and fungi, respectively), gas phase chemistry, and the particle size distribution, 84 size-segregated aerosol samples from four particle size fractions (< 1.0, 1.0–2.5, 2.5–10, and > 10 µm) were collected at the SMEAR II station, Finland, in autumn 2017. The gene copy numbers and size distributions of bacteria, Pseudomonas, and fungi in biogenic aerosols were determined by DNA extraction and amplification. In addition, free amino acids (19) and saccharides (8) were analysed in aerosol samples by hydrophilic interaction liquid chromatography–mass spectrometry (HILIC-MS). Different machine learning (ML) approaches, such as cluster analysis, discriminant analysis, neural network analysis, and multiple linear regression (MLR), were used for the clarification of several aspects related to the composition of biogenic aerosols. Clear variations in composition as a function of the particle size were observed. In most cases, the highest concentration values and gene copy numbers (in the case of microbes) were observed for 2.5–10 µm particles, followed by > 10, 1–2.5, and < 1.0 µm particles. In addition, different variables related to the air and soil temperature, the UV radiation, and the amount of water in the soil affected the composition of biogenic aerosols. In terms of interpreting the results, MLR provided the greatest improvement over classical statistical approaches such as Pearson correlation among the ML approaches considered. In all cases, the explained variance was over 91 %. The great variability of the samples hindered the clarification of common patterns when evaluating the relation between the presence of microbes and the chemical composition of biogenic aerosols. Finally, positive correlations were observed between gas-phase VOCs (such as acetone, toluene, methanol, and 2-methyl-3-buten-2-ol) and the gene copy numbers of microbes in biogenic aerosols.