Crucial Role for Outdoor Chemistry in Ultrafine Particle Formation in Modern Office Buildings

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

Background

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.

Methods

In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.

Results

The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.

Conclusion

In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Indoor gaseous air pollutants determinants in office buildings-The OFFICAIR project

The aim of this study was to identify determinants of aldehyde and volatile organic compound (VOC) indoor air concentrations in a sample of more than 140 office rooms, in the framework of the European OFFICAIR research project. A large field campaign was performed, which included (a) the air sampling of aldehydes and VOCs in 37 newly built or recently retrofitted office buildings across 8 European countries in summer and winter and (b) the collection of information on building and offices' characteristics using checklists. Linear mixed models for repeated measurements were applied to identify the main factors affecting the measured concentrations of selected indoor air pollutants (IAPs). Several associations between aldehydes and VOCs concentrations and buildings' structural characteristic or occupants' activity patterns were identified. The aldehyde and VOC determinants in office buildings include building and furnishing materials, indoor climate characteristics (room temperature and relative humidity), the use of consumer products (eg, cleaning and personal care products, office equipment), as well as the presence of outdoor sources in the proximity of the buildings (ie, vehicular traffic). Results also showed that determinants of indoor air concentrations varied considerably among different type of pollutants.

Modelling consortium for chemistry of indoor environments (MOCCIE): integrating chemical processes from molecular to room scales

We report on the development of a modelling consortium for chemistry in indoor environments that connects models over a range of spatial and temporal scales, from molecular to room scales and from sub-nanosecond to days, respectively. Our modeling approaches include molecular dynamics (MD) simulations, kinetic process modeling, gas-phase chemistry modeling, organic aerosol modeling, and computational fluid dynamics (CFD) simulations. These models are applied to investigate ozone reactions with skin and clothing, oxidation of volatile organic compounds and formation of secondary organic aerosols, and mass transport and partitioning of indoor species to surfaces. MD simulations provide molecular pictures of limonene adsorption on SiO2 and ozone interactions with the skin lipid squalene, providing kinetic parameters such as surface accommodation coefficient, desorption lifetime, and bulk diffusivity. These parameters then constrain kinetic process models, which resolve mass transport and chemical reactions in gas and condensed phases for analysis of experimental data. A detailed indoor chemical box model is applied to simulate α-pinene ozonolysis with improved representation of gas-particle partitioning. Application of 2D-volatility basis set reveals that OH-induced aging sometimes drives increases in indoor organic aerosol concentrations, due to organic mass functionalization and enhanced partitioning. CFD simulations show that concentrations of ozone and primary product change near the human surface rapidly, indicating non-uniform spatial distributions from the occupant surface to ambient air, while secondary ozone product is relatively well-mixed throughout the room. This development establishes a framework to integrate different modeling tools and experimental measurements, opening up an avenue for development of comprehensive and integrated models with representations of various chemistry in indoor environments.

How do breath and skin emissions impact indoor air chemistry?

People are an important source of pollution indoors, through activities such as cleaning, and also from "natural" emissions from breath and skin. This paper investigates natural emissions in high-occupancy environments. Model simulations are performed for a school classroom during a typical summer in a polluted urban area. The results show that classroom occupants have a significant impact on indoor ozone, which increases from ~9 to ~20 ppb when the pupils leave for lunch and decreases to ~14 ppb when they return. The concentrations of 4-OPA, formic acid, and acetic acid formed as oxidation products following skin emissions attained maximum concentrations of 0.8, 0.5, and 0.1 ppb, respectively, when pupils were present, increasing from near-zero concentrations in their absence. For acetone, methanol, and ethanol from breath emissions, maximum concentrations were ~22.3, 6.6, and 21.5 ppb, respectively, compared to 7.4, 2.1, and 16.9 ppb in their absence. A rate of production analysis showed that occupancy reduced oxidant concentrations, while enhancing formation of nitrated organic compounds, owing to the chemistry that follows from increased aldehyde production. Occupancy also changes the peroxy radical composition, with those formed through isoprene oxidation becoming relatively more important, which also has consequences for subsequent oxidant concentrations.

Reactive indoor air chemistry and health-A workshop summary

The chemical composition of indoor air changes due to the reactive nature of the indoor environment. Historically, only the stable parent compounds were investigated due to their ease of measurement by conventional methods. Today, however, scientists can better characterize oxidation products (gas and particulate-phase) formed by indoor chemistry. An understanding of occupant exposure can be developed through the investigation of indoor oxidants, the use of derivatization techniques, atmospheric pressure detection, the development of real-time technologies, and improved complex modeling techniques. Moreover, the connection between exposure and health effects is now receiving more attention from the research community. Nevertheless, a need still exists for improved understanding of the possible link between indoor air chemistry and observed acute or chronic health effects and long-term effects such as work-related asthma.

Significant OH production under surface cleaning and air cleaning conditions: Impact on indoor air quality

We report measurements of hydroxyl (OH) and hydroperoxy (HO₂ ) radicals made by laser-induced fluorescence spectroscopy in a computer classroom (i) in the absence of indoor activities (ii) during desk cleaning with a limonene-containing cleaner (iii) during operation of a commercially available "air cleaning" device. In the unmanipulated environment, the one-minute averaged OH concentration remained close to or below the limit of detection (6.5×10⁵  molecule cm^-3 ), whilst that of HO₂ was 1.3×10⁷  molecule cm^-3 . These concentrations increased to ~4×10⁶ and 4×10⁸  molecule cm^-3 , respectively during desk cleaning. During operation of the air cleaning device, OH and HO₂ concentrations reached ~2×10⁷ and ~6×10⁸  molecule cm^-3 respectively. The potential of these OH concentrations to initiate chemical processing is explored using a detailed chemical model for indoor air (the INDCM). The model can reproduce the measured OH and HO₂ concentrations to within 50% and often within a few % and demonstrates that the resulting secondary chemistry varies with the cleaning activity. Whilst terpene reaction products dominate the product composition following surface cleaning, those from aromatics and other VOCs are much more important during the use of the air cleaning device.

Impact of surface ozone interactions on indoor air chemistry: A modeling study

An INdoor air Detailed Chemical Model was developed to investigate the impact of ozone reactions with indoor surfaces (including occupants), on indoor air chemistry in simulated apartments subject to ambient air pollution. The results are consistent with experimental studies showing that approximately 80% of ozone indoors is lost through deposition to surfaces. The human body removes ozone most effectively from indoor air per square meter of surface, but the most significant surfaces for C₆ -C₁₀ aldehyde formation are soft furniture and painted walls owing to their large internal surfaces. Mixing ratios of between 8 and 11 ppb of C₆ -C₁₀ aldehydes are predicted to form in apartments in various locations in summer, the highest values are when ozone concentrations are enhanced outdoors. The most important aldehyde formed indoors is predicted to be nonanal (5-7 ppb), driven by oxidation-derived emissions from painted walls. In addition, ozone-derived emissions from human skin were estimated for a small bedroom at nighttime with concentrations of nonanal, decanal, and 4-oxopentanal predicted to be 0.5, 0.7, and 0.7 ppb, respectively. A detailed chemical analysis shows that ozone-derived surface aldehyde emissions from materials and people change chemical processing indoors, through enhanced formation of nitrated organic compounds and decreased levels of oxidants.

Effect of VOC Emissions from Vegetation on Air Quality in Berlin during a Heatwave

The potential of emissions from urban vegetation combined with anthropogenic emissions to produce ozone and particulate matter has long been recognized. This potential increases with rising temperatures and may lead to severe problems with air quality in densely populated areas during heat waves. Here, we investigate how heat waves affect emissions of volatile organic compounds from urban/suburban vegetation and corresponding ground-level ozone and particulate matter. We use the Weather Research and Forecasting Model with atmospheric chemistry (WRF-Chem) with emissions of volatile organic compounds (VOCs) from vegetation simulated with MEGAN to quantify some of these feedbacks in Berlin, Germany, during the heat wave in 2006. The highest ozone concentration observed during that period was ∼200 μg/m³ (∼101 ppb_V). The model simulations indicate that the contribution of biogenic VOC emissions to ozone formation is lower in June (9-11%) and August (6-9%) than in July (17-20%). On particular days within the analyzed heat wave period, this contribution increases up to 60%. The actual contribution is expected to be even higher as the model underestimates isoprene concentrations over urban forests and parks by 0.6-1.4 ppb_v. Our study demonstrates that biogenic VOCs can considerably enhance air pollution during heat waves. We emphasize the dual role of vegetation for air quality and human health in cities during warm seasons, which is removal and lessening versus enhancement of air pollution. The results of our study suggest that reduction of anthropogenic sources of NOx, VOCs, and PM, for example, reduction of the motorized vehicle fleet, would have to accompany urban tree planting campaigns to make them really beneficial for urban dwellers.