Concentrations of VOCs and ozone in indoor environments: A case study in two Mediterranean cities during winter period

Building materials represent the largest surfaces indoors and are the major contributors of volatile organic compounds (VOCs) in the indoor environment. This study which is conducted in the frame of BUMA project (Prioritization of Building Materials Emissions), aims at assessing the human exposure to air hazards emitted by building materials. In this study, indoor and outdoor VOCs and ozone measurements from field campaigns in two Mediterranean cities (Nicosia and Athens in winter period) are presented and discussed. The field campaigns concern weekly measurements. The campaigns were conducted in four buildings in each city (1 Public building, 1 school and 2 houses) and concern weekly measurements. Passive samplers were used for collecting VOCs and ozone. Eight (8) hydrocarbons (benzene, toluene, ethylbenzene, m,p-xylene, a-pinene, o-xylene and d-limonene), five (5) carbonyl compounds (formaldehyde, acetaldehyde, proprionaldehyde, acetone and hexanaldehyde) and ozone have been measured. Additional air exchange measurements have been conducted using tracer gas techniques. Hazardous substances such as benzene, formaldehyde and acetaldehyde present indoor concentrations that range between 1.5-10.2, 5.8-43.2 and 4.5-15 μg/m3, respectively. VOC concentration data show a considerable variability due to the different indoor emission sources, ventilation rates and outdoor environment's influence. A significant contribution to indoor measured concentrations seems to come from the building materials. Ozone outdoor concentrations are reduced substantially inside, indicating relatively strong indoor ozone sinks

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Vehicle-induced fugitive particulate matter emissions in a city of arid desert climate

This study investigates and proposes emission factors (EFs) and models for vehicle-induced exhaust (VEX) and fugitive (VfPM) particulate matter emissions representative of areas with arid climates. Particle number (PNC) and mass (PMC) concentrations and their integrated samples were collected for a period of three months for both PM10 and PM2.5 next to a trafficked road in the city of Doha, Qatar. Using Positive Matrix Factorization (PMF) on the elemental data of the samples, six distinct PM sources were identified: traffic exhaust, dust resuspension, fresh and aged sea salt, secondary aerosols, and fuel oil/shipping. Dispersion modelling and regression analysis were combined to derive EFs (linear analysis) and models (non-linear analysis) for the total traffic fleet (heavy and light duty). The estimated EFs were between 620 and 730 mg VKT−1 (VKT; Vehicle Kilometer Travelled) (adj. R2 ~ 0.84) and between 1080 and 1410 mg VKT−1 (adj. R2 ~ 0.70) for VEX and VfPM, respectively. The integration of field measurements, chemical characterization, and dispersion modelling presented herein is one of the first similar studies conducted in the wider region, identifies the importance of fugitive PM (fPM), and marks the need for further studies to improve emissions modelling of VfPM in arid desert climates

Spatial and temporal variation of particulate matter characteristics within office buildings \u2014 The OFFICAIR study

In the frame of the OFFICAIR project, office buildings were investigated across Europe to assess how the office workers are exposed to different particulate matter (PM) characteristics (i.e. PM2.5 mass concentration, particulate oxidative potential (OP) based on ascorbate and reduced glutathione depletion, trace element concentration and total particle number concentration (PNC)) within the buildings. Two offices per building were investigated during the working hours (5 consecutive days; 8\ua0h per day) in two campaigns. Differences were observed for all parameters across the office buildings. Our results indicate that the monitoring of the PM2.5 mass concentration in different offices within a building might not reflect the spatial variation of the health relevant PM characteristics such as particulate OP or the concentration of certain trace elements (e.g., Cu, Fe), since larger differences were apparent within a building for these parameters compared to that obtained for the PM2.5 mass concentration in many cases. The temporal variation was larger for almost all PM characteristics (except for the concentration of Mn) than the spatial differences within the office buildings. These findings indicate that repeated or long-term monitoring campaigns are necessary to have information about the temporal variation of the PM characteristics. However, spatial variation in exposure levels within an office building may cause substantial differences in total exposure in the long term. We did not find strong associations between the investigated indoor activities such as printing or windows opening and the PNC values. This might be caused by the large number of factors affecting PNC indoors and outdoors