Unwanted Indoor Air Quality Effects from Using Ultraviolet C Lamps for Disinfection

Ultraviolet germicidal irradiation (UVGI) is known to inactivate various viruses and bacteria, including SARS-CoV-2, and is widely applied especially in medical facilities. This inactivation results from the high photon energies causing molecular bonds to break, but when nonpathogen molecules are affected, unwanted effects may occur. Here, we explored the effect of a commercial high intensity (similar to 2 kW) UVC disinfection device on the composition and concentration of gases and particles in indoor air. We find that the UVC (254 nm) caused dramatic increases in particle number concentrations, and nearly all (similar to 1000) monitored gas phase species also increased. These responses were unsurprising when considering the typical impacts of UVC on atmospheric chemistry. High particle concentrations are associated with adverse health effects, suggesting that the impact of UVGI devices on indoor air quality (IAQ) should be studied in much more detail. The high-intensity device in this study was intended for short durations in unoccupied rooms, but lower-intensity devices for continuous use in occupied rooms are also widely applied. This makes further studies even more urgent, as the potential IAQ effects of these approaches remain largely unexplored.Peer reviewe

Fragmentation patterns of particulate organic nitrates in an Aerosol Mass Spectrometer

Atmospheric aerosols affect the Earth's radiative balance, visibility and human health. Therefore the formation processes and growth of these particles are important and should be studied to understand how human and natural processes affects these processes. One poorly understood and relatively little studied part of aerosols is particulate organic nitrates (pONs). These pONs are mostly formed during nighttime when NOx, mainly emitted from fossil fuel combustion and industrial processes, and volatile organic compounds (VOCs), from both natural and anthropogenic sources, reacts in the atmosphere. The quantification of these pONs is still hard due to instrumental restrictions, although much improvement has happened during recent years. One main reason for these challenges is the difficulty to separate inorganic nitrates from organic nitrates with real-time instruments. During this work, we generated pure pON in well controlled laboratory conditions and sampled it with an Aerosol Mass Spectrometer (AMS), an instrument widely used for measuring the chemical composition of atmospheric aerosols. We used four different pON precursors to generate pON. I investigated the fragmentation patterns of pON detected by the AMS, utilizing the high resolution of the newest model of the AMS. As older versions of the AMS has difficulties to separate nitrate-containing organic fragments due to lower resolution than the AMS I used, I was able to study pON mass spectrum with better resolution than anyone before me. I found mass spectral differences for the different pON precursors, and was able to find unique fragments for some of the pON precursors that possibly can be used as marker fragments

Oxidation product characterization from ozonolysis of the diterpene ent-kaurene

Diterpenes (C20H32) are biogenically emitted volatile compounds that only recently have been observed in ambient air. They are expected to be highly reactive, and their oxidation is likely to form condensable vapors. However, until now, no studies have investigated gas-phase diterpene oxidation. In this paper, we explored the ozonolysis of a diterpene, ent-kaurene, in a simulation chamber. Using state-of-the-art mass spectrometry, we characterized diterpene oxidation products for the first time, and we identified several products with varying oxidation levels, including highly oxygenated organic molecules (HOM), monomers, and dimers. The most abundant monomers measured using a nitrate chemical ionization mass spectrometer were C19H28O8 and C20H30O5, and the most abundant dimers were C38H60O6 and C39H62O6. The exact molar yield of HOM from kaurene ozonolysis was hard to quantify due to uncertainties in both the kaurene and HOM concentrations, but our best estimate was a few percent, which is similar to values reported earlier for many monoterpenes. We also monitored the decrease in the gas-phase oxidation products in response to an increased condensation sink in the chamber to deduce their affinity to condense. The oxygen content was a critical parameter affecting the volatility of products, with four to five O atoms needed for the main monomeric species to condense onto 80 nm particles. Finally, we report on the observed fragmentation and clustering patterns of kaurene in a Vocus proton-transfer-reaction time-of-flight mass spectrometer. Our findings highlight similarities and differences between diterpenes and smaller terpenes during their atmospheric oxidation, but more studies on different diterpenes are needed for a broader view of their role in atmospheric chemistry.Peer reviewe

Detecting and Characterizing Particulate Organic Nitrates with an Aerodyne Long-ToF Aerosol Mass Spectrometer

Particulate organic nitrate (pON) can be a major part of secondary organic aerosol (SOA) and is commonly quantified by indirect means from aerosol mass spectrometer (AMS) data. However, pON quantification remains challenging. Here, we set out to quantify and characterize pON in the boreal forest, through direct field observations at Station for Measuring Ecosystem Atmosphere Relationships (SMEAR) II in Hyytia''la'', Finland, and targeted single precursor laboratory studies. We utilized a long time-of-flight AMS (LToF-AMS) for aerosol chemical characterization, with a particular focus to identify CxHyOzN+ ("CHON+") fragments. We estimate that during springtime at SMEAR II, pON (including both the organic and nitrate part) accounts for similar to 10% of the particle mass concentration (calculated by the NO+/NO2+ method) and originates mainly from the NO3 radical oxidation of biogenic volatile organic compounds. The majority of the background nitrate aerosol measured is organic. The CHON+ fragment analysis was largely unsuccessful at SMEAR II, mainly due to low concentrations of the few detected fragments. However, our findings may be useful at other sites as we identified 80 unique CHON+ fragments from the laboratory measurements of SOA formed from NO3 radical oxidation of three pON precursors (beta-pinene, limonene, and guaiacol). Finally, we noted a significant effect on ion identification during the LToF-AMS high-resolution data processing, resulting in too many ions being fit, depending on whether tungsten ions (W+) were used in the peak width determination. Although this phenomenon may be instrument-specific, we encourage all (LTOF-) AMS users to investigate this effect on their instrument to reduce the possibility of incorrect identifications.Peer reviewe

Non-adiabatic transitions in a non-symmetric optical lattice

We study Landau-Zener interband transitions for a non-symmetric optical lattice in the presence of an external force. We show that gain and losses of the light beam, as well as the relative occupation probabilities of the bands involved in the transitions can be accurately managed upon tuning the amplitude of the non-Hermitian component of the lattice. Exact expressions for the transition and non-transition probabilities for a non-symmetric system obtained within a two-mode approximation are provided. These equations successfully account for the main features of the transitions in the optical lattice. We also interpret the non-conventional Bloch oscillations at criticality studied in Phys. Rev. Lett. 103, 123601 (2009) as a series of a Landau-Zener transitions.Comment: 6 pages, 5 figure

Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

The Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth-atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012-2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 mu gm(-3)) into three sub-categories - low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) - proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ngm 3 h 1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-yearlong time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.Peer reviewe