Journal of Exposure Science and Environmental Epidemiology (2015), 1–8 © 2015 Nature America, Inc. All rights reserved 1559-0631/15
www.nature.com/jes
ORIGINAL ARTICLE
Effects of climate change on residential infiltration and air pollution exposure Vito Ilacqua, John Dawson, Michael Breen, Sarany Singer and Ashley Berg Air exchange through infiltration is driven partly by indoor/outdoor temperature differences, and as climate change increases ambient temperatures, such differences could vary considerably even with small ambient temperature increments, altering patterns of exposures to both indoor and outdoor pollutants. We calculated changes in air fluxes through infiltration for prototypical detached homes in nine metropolitan areas in the United States (Atlanta, Boston, Chicago, Houston, Los Angeles, Minneapolis, New York, Phoenix, and Seattle) from 1970–2000 to 2040–2070. The Lawrence Berkeley National Laboratory model of infiltration was used in combination with climate data from eight regionally downscaled climate models from the North American Regional Climate Change Assessment Program. Averaged over all study locations, seasons, and climate models, air exchange through infiltration would decrease by ~ 5%. Localized increased infiltration is expected during the summer months, up to 20–30%. Seasonal and daily variability in infiltration are also expected to increase, particularly during the summer months. Diminished infiltration in future climate scenarios may be expected to increase exposure to indoor sources of air pollution, unless these ventilation reductions are otherwise compensated. Exposure to ambient air pollution, conversely, could be mitigated by lower infiltration, although peak exposure increases during summer months should be considered, as well as other mechanisms. Journal of Exposure Science and Environmental Epidemiology advance online publication, 27 May 2015; doi:10.1038/jes.2015.38 Keywords: air exchange; climate change; indoor exposure; infiltration
INTRODUCTION Long-term trends in outdoor temperatures brought about by climate change1 can be expected to alter the air exchange between indoor and outdoor environments based on simple physical considerations. These effects on air exchange could, in turn, modify patterns of exposure to both indoor and outdoor air pollutants and the resulting risks. Although such changes can be easily understood qualitatively, their magnitude and significance from a building operation and public health perspective remain to be addressed. The goal of this paper is to examine the effects on exposure to indoor and outdoor air pollution as changes in climate alter air exchange between indoor and outdoor environments. Average annual temperatures in the contiguous Unites States are currently below the human thermal comfort range (~20–26 °C, depending on humidity and clothing), in every state with the exception of Florida, at the very margin of that range.2,3 That is, on average, indoor environments must be heated to achieve thermal comfort. The projected increase in ambient temperatures as a result of climate change will therefore reduce the yearly average thermal differential between indoor and outdoor environments. Such reduction, if not compensated by other factors, could then reduce average passive ventilation, albeit with substantial geographic differences. To determine how meaningful such reduction might be, as well as its timing and variability, we modeled the infiltration process under current and future climate scenarios. The need for an explicit quantitative analysis depends on the observation that when the indoor/outdoor temperature difference
is modest, even small changes in temperature could potentially result in large relative changes in ventilation through infiltration. The terms infiltration and leakage are used as synonyms throughout this article to mean the unintended air flows between indoor and outdoor environment through the building envelope, and include air flows into and out of the building. Knowledge of the intrinsic changes in infiltration brought about by climate change, if meaningful, could be useful to develop guidelines and recommendations about adapting building design and use, particularly with respect to measures that attempt to achieve higher energy efficiency by controlling infiltration. In addition, planning for the health impact of climate change, as related to air pollution, should take into account the modulation of exposure resulting from climate change interaction with buildings, where people spend the vast majority of their time. Model Exposure to indoor and outdoor sources depends on their separate contributions to indoor concentrations. At steady state, and assuming perfect mixing, the mass balance equation for a single compartment indoor environment yields:4 C in ¼
Pa S C out þ aþd Vða þ dÞ
ð1Þ
Where Cin and Cout are the indoor and outdoor concentrations, respectively, P is a penetration factor specific to each air pollutant (often close to 1) that describes the effect of the building envelope, a is the air exchange rate (AER), d is a first order decay
United States Environmental Protection Agency, Washington, District of Columbia, USA. Correspondence: Dr. Vito Ilacqua, United States Environmental Protection Agency, 1200 Pennsylvania Avenue NW (8726P), Washington, DC 20460, USA. Tel.: +1 703 347 0261. Fax: 703 347 8140. E-mail: [email protected] Received 27 October 2014; revised 16 March 2015; accepted 24 March 2015
Climate change and infiltration Ilacqua et al
2 rate constant (combining all sink processes), S is the indoor source strength (or mass emission rate), and V is the volume of the environment. The first term on the right expresses the contributions of outdoor sources through ventilation, whereas the second term represents the contributions of indoor sources. Both terms depend on the AER, and it is through its impact on the AER that climate change has the potential to alter exposures to emissions from both indoor and outdoor sources. The AER for an indoor environment is simply the ratio of the ventilation rate (volume per unit time) to the volume of that environment: a = Q/V. Ventilation results from both the deliberate use of mechanical or natural ventilation, and infiltration through the building envelope. Although mechanical ventilation is less common in residential environment, and natural ventilation is generally restricted outside periods of milder weather, infiltration operates in every type of building and under every type of climate and weather, and this analysis will focus on this component of AER, by considering situations where deliberate ventilation is negligible. Various AER models have been described in the literature.5 In this study, we used the Lawrence Berkeley National Laboratory (LBL) model to predict residential leakage. The LBL model is one of the most widely used physics-based model to predict residential leakage rates. Evaluations of the LBL model showed mean absolute error of 43% for 31 detached homes across four seasons.6 The LBL model describes leakage (or infiltration) from differential air pressure across the building envelope owing to the combination of (1) the indoor–outdoor temperature differential (stack effect) and (2) a wind effect. The leakage air flux φ described by the LBL model is qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2Þ φ ¼ k jT i T o j þ hW 2 where φ is the volume of air per unit time per unit area subject to infiltration, k (stack coefficient) and h (wind coefficient) are empirical parameters determined by the building height and level of wind sheltering respectively, Ti and To are the indoor and outdoor temperatures, and W is the wind speed, over the time interval of calculation. We use the ratio R of future to reference (present) air flux as a relative measure of the change in infiltration, resulting from climate change impacts on both outdoor temperature and wind speeds: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k T F;i T F;o þ hW 2F φF ð3Þ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R¼ φP k T T þ hW 2 F;i
F;o
F
where the subscripts P and F refer to the reference (present) and projected (future) periods. The future-to-present ratios of ventilation rates through infiltration, infiltration air fluxes, and AERs are all identical for the same building, assuming air exchange is driven only by infiltration. So, it is possible to express the change in concentrations contributed by indoor and outdoor sources at steady state in the mass balance Eq. (1) as a function of R. QF φF aF ¼ ¼ ¼R QP φP aP
ð4Þ
From Eq. (4), changes in indoor exposure concentrations in Eq. (1) can be calculated for specific air exchange and decay rates in the reference period. MATERIALS AND METHODS Climate Data Climatic data were derived from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate intercomparison data set.7,8 In the NARCCAP exercise, climatic outputs from several Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Table 1. GCM-RCM combinations from the NARCCAP modeling exercise included in this analysis. GCM
RCMs
CCSM CGCM3 GFDL HadCM3
CRCM, WRF CRCM, RegCM3, WRF HadRM3, RegCM3 HadRM3
Abbreviations: CCSM, Community Climate System Model, Version 3.0 (http:// www.pcmdi.llnl.gov/ipcc/model_documentation/CCSM3.htm); CGCM3, Third Generation Coupled Global Climate Model (http://www.ec.gc.ca/ccmaccccma/default.asp?lang = En&n = 1299529F-1); CRCM, Canadian Regional Climate Model, Version 4.2 (http://www.ouranos.ca/fr/programmationscientifique/science-du-climat/simulations-climatiques/MRCC/eng/crcm.html# crcm42); GFDL, Geophysical Fluid Dynamics Laboratory GCM (http://www. pcmdi.llnl.gov/ipcc/model_documentation/GFDL-cm2.htm); HadCM3, Hadley Centre Couple Model, Version 3 (http://www.pcmdi.llnl.gov/ipcc/model_ documentation/HadCM3.htm); HadRM3, Hadley Regional Model 3/PRECIS (http://www.metoffice.gov.uk/precis/); RegCM3, Regional Climate Model, Version 3 (http://users.ictp.it/RegCNET/model.html); WRF, Weather Research & Forecasting model (http://www.wrf-model.org/index.php).
global-scale general circulation models (GCMs) for both present and future conditions were downscaled using several regional climate models (RCMs). Many GCM-RCM combinations are included in the NARCCAP data set; the combinations available for this study are summarized in Table 1. All of the NARCCAP model combinations for which the complete set of necessary meteorological information for Eqs. (2) and (3) (above) were available were included in this analysis. The RCM modeling domains covered the entire contiguous United States. Nine large metropolitan areas in the continental United States were selected to represent the spatial variability of climates (Table 2). The RCM grid cell closest to each city was chosen to represent that city; however, the RCMs used different map projections and spatial resolutions. Therefore, there may not be perfect alignment between city locations and model grid cells. The reference period for present climate is the three decades 1970– 2000; the mid-21st century future scenario spans the years 2040–2070 for the Intergovernmental Panel on Climate Change A2 scenario.9 The A2 scenario represents a rather high amount of continued greenhouse gas emissions through the century. This is the only future scenario explored in the NARCCAP simulations. Both reference and future climates are simulated by models in 3-h increments. Air flux rates were calculated at this time resolution, and then averaged (mean or median) by month for each location, and for each climate model, using R version 2.14.10 Using temperature and wind speed from the climate models with a 3-h resolution, instead of daily or monthly averages, allows consideration of the diurnal temperature cycle, so that air flux rates are more accurately calculated even when mean (for example, daily or monthly) outdoor temperatures are very close to indoor temperatures. Without consideration of the diurnal cycle, infiltration would be greatly underestimated.
Indoor Temperatures Indoor temperatures used in Eq. (3) were taken as the median indoor temperature measured by Breen et al.6 in 31 homes in Raleigh-Durham, NC, USA for each season: spring 22.5 °C; summer 24.9 °C; fall 23.5 °C; winter 22.5 °C. The choice of these values reflects a preference for contemporary empirical data on indoor temperatures over models based for example on thermal comfort, which introduce greater complexity and require additional unavailable information.11 In general, there is limited recent information about residential indoor temperatures, and heating and cooling set points from large-scale surveys in the literature, and the basis for comparability of indoor temperatures used here to those in other parts of the country is largely inferred. Nevertheless, despite individual preferences and acclimatization, the range of comfortable temperatures for humans has been shown to vary little between studies in different climates,12,13 as it is based on shared biological characteristics and use of seasonal clothing.2 Actual indoor temperatures generally reflect comfort preferences14 and do not differ remarkably from the range and © 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
3 Table 2.
3
Climate normals for 1981–2010 for the cities in this analysis. Koeppen classification
Atlanta, GA, USA Boston, MA, USA Chicago, IL, USA Houston, TX, USA Los Angeles, CA, USA Minneapolis- St Paul, MN, USA New York, NY, USA Phoenix, AZ, USA Seattle, WA, USA
Humid subtropical (Cfa) Transition Humid subtropical/humid continental (Cfa/Dfa) Humid continental (Dfa) Humid subtropical (Cfa) Subtropical Mediterranean (Csb) Humid continental (Dfa) Humid subtropical (Cfa) Subtropical desert (BWh) Subtropical Mediterranean (Csb)
approximate relationship to seasonal patterns of ambient temperatures adopted here.14–16 Indoor temperatures were considered fixed for the entire season. This simplification can introduce a bias, as the temperature of indoor environments varies in response to outdoor temperatures, within the range of temperatures acceptable to its residents, with a lag dependent on the building radiative insulation and air exchange. This variability, both within and between days, is not captured by this analysis, so that the indoor–outdoor temperature gradient is likely to be overestimated by this approximation. This short-term association of indoor and outdoor temperatures has received limited characterization, particularly through empirical studies. To explore the effect of this simplification we performed a sensitivity analysis using an empirical regression model developed for a small number of residences in Boston17 that relates indoor to outdoor temperature. Although there are limitations owing to the number and selection of those residences, the comparison can shed some light on the accuracy of the approximation. As this alternative model is not necessarily more accurate, we limited the comparison with the possible over- or underestimation of the change in infiltration: we calculated the differences of the absolute distance from R = 1 for the two approaches, using the RCM3 HadCM3 climate model.
Model Parameters Air fluxes were calculated using the LBL model (Eq. (2)) for a prototypical two-storey house under sheltered conditions (class 5): the stack coefficient was 2.9 × 10 − 4 (L/s cm2)2, and the wind coefficient 4.2 × 10 − 5 (L/s cm2)2. The choice of this particular set of conditions was an attempt to represent population exposure within the applicability range of the LBL model, by focusing on single-family detached houses surrounded by similar buildings, which describes over 60% of dwellings in the United States.18 Other types of buildings (e.g., high-rise or attached row-houses) are certainly important to capture population exposure in denser urban areas and so are office and school buildings everywhere, but infiltration in these may not be as accurately described by the LBL model, which was not developed for them. Furthermore, this choice of model parameters places more emphasis on temperature changes (expected to change more in a dynamic climate) and less on wind changes (expected to change less;19 Supplementary Information), therefore focusing on the housing locations more sensitive to an effect of climate change on infiltration. Monthly mean air fluxes averaged over the entire reference and future periods (e.g., all 3-h values in January for 1970–2000 or 2040–2070) were then compared calculating the ratio R of future-to-present air flux, which (from Eq. (4)) is also equivalent to the ratio of ventilation and AERs for the same building. Monthly SDs of air fluxes were similarly calculated and compared.
Treatment of Uncertainty Several sources of uncertainty contribute to the overall uncertainty of this analysis. Uncertainty about future climate as represented by climate model predictions is especially prominent. Each model provides its best approximation of climate averages based on its assumptions about the relevant physical processes and initial conditions. These assumptions are uncertain, as indeed the mere existence of several models of comparable credibility attests. We refer to the current uncertainty about such assumptions as modeling uncertainty, and choose to represent it by applying the same analysis to every model and reporting all their results © 2015 Nature America, Inc.
January mean high and low temperatures (°C) 11.3 2.1 − 0.3 17.2 20.1 − 4.6 3.5 19.6 8.4
1.3 − 5.4 − 7.7 6.2 8.8 − 13.6 0.3 7.6 2.7
July mean high and low temperatures (°C) 31.7 27.4 29.0 34.3 28.4 28.6 28.9 40.2 24.3
21.8 18.6 19.7 23.9 17.6 17.8 20.5 28.2 13.1
with equal weight, summarizing results from all models only when necessary through their median predictions. This choice means that the modeling uncertainty in this study is represented by the range of predictions of all climate models. Other choices and assumptions contribute to our uncertainty, such as modeling and parameter uncertainty in the LBL infiltration model, and uncertainty about the distribution of indoor air temperatures. These additional sources of uncertainty are not captured by our uncertainty analysis, which is likely dominated by modeling uncertainty.
RESULTS Rates of air infiltration to indoor environments for the middle of the 21st century are estimated to decrease overall by ~ 5%, averaged over study locations, seasons, and climate models. Individual cities differ little, with mean yearly ratios of future-topresent infiltration rates (R) ranging from 0.93 for Boston to 0.98 for Phoenix. Monthly mean ratios of infiltration rates vary more, ranging from 0.86 to 1.25, considering all climate models and cities, representing the range of climate model predictions by their median for each month and city, the range is reduced to 0.89–1.09. The median, maximum, and minimum predictions for each city, by month, are shown in Figure 1. Although infiltration rates decrease for most cities and months (ratio R below 1), increased infiltration is predicted by the models median for the warmer locales (Atlanta, Houston, and Phoenix) for all or part of the summer months, and by at least one model for most others. Estimates of change in infiltration using different climate models vary by 8.0%, averaged across all cities and months, and their range can be observed in Figure 1. Models differ the least about decreased infiltration, with relative range (i.e., Max-Min/ Median) as low as 1.4% for February in New York. For winter months, when decreases are predicted for all cities, the average relative range across cities is just 4.1%. Modeling uncertainty increases considerably for the summer months, especially where infiltration is predicted to increase the most: the highest relative range between models is 30.0% for June in Houston, and the overall summer relative range is 14.4%. Variability in infiltration rate is estimated to increase by most models in many cases, as measured by the ratio of future to reference SD of air flow (Figure 2). The increase is particularly apparent during summer months in warmer locales. For example, the median of all models for Houston and Atlanta shows 20–30% variability increases for July and August, whereas the models' median for Chicago shows a 5–10% increase in variability through most of the year. Variability is predicted to remain essentially unchanged at other locations, such as Boston or Minneapolis. It should be noted that the simple increase in mean temperature and wind speed implies corresponding increases in their SD (i.e., if μ’ = 2μ then σ’ = 2σ), without indicating an increase in relative variability. The predicted variability increase, however, is usually larger than would be explained by this merely statistical effect, which would actually reduce the ratio of SDs for the majority of cases when the ratio of the means is below 1. The comparisons of Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
4 ratios in Figures 1 and 2 readily bear out this observation. In fact, accounting for this effect, even ratios of SD close to 1 may indicate increased relative variability (e.g., Boston or Seattle).
The possible bias of using a fixed seasonal indoor temperature was explored comparing the differences of the absolute distance from R = 1 for (a) the fixed seasonal temperature and (b) the
Figure 1. Ratio of future (2040–2070) to reference (1970–2000) monthly means of infiltration rates to indoor environments. The range represents the climate modeling uncertainty: the median, minimum, and maximum predictions of eight climate models. Values below 1 indicate a decrease in infiltration and imply lower exposure to outdoor sources, but higher exposure to indoor sources. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
Figure 2. Ratio of future (2040–2070) to reference (1970–2000) monthly SD of infiltration to indoor environments. The range represents the climate modeling uncertainty: the median, minimum, and maximum predictions of eight climate models. Values above 1 indicate an increase in variability of infiltration, driven by increased temperature or wind speed variability. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online. Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
© 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
5
Figure 3. Effect of using a seasonal fixed indoor air temperature vs a model dependent on outdoor temperature developed for Boston,17 calculated for the HRM3 HadCM3 climate model. Values are the differences of the absolute distance from R = 1 for the two approaches. Negative values indicate that using a fixed indoor temperature might underestimate the change in the ratio of future-to-present infiltration (whether increasing or decreasing); positive values indicate the change in air flux would be overstated. Mean effect across all cities and months is − 0.001; for Boston only, 0.009. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
outdoor-dependent model by Nguyen et al.17 (Figure 3). Negative values indicate that using a fixed indoor temperature underestimates the change (either increase or decrease) in the ratio of future-to-present air flux; positive values indicate the change in air flux would be overstated. The mean difference across all cities and months is − 5.3 × 10 − 4, about 2 orders of magnitude below the mean estimated change in ratio. It is not known how the model by Nguyen et al. would perform outside Boston, however, so the mean difference limited to that city was 8.9 × 10 − 3, about 1 order of magnitude below the changes estimated. The small discrepancies using a fixed indoor temperature understate the magnitude of the change for more than half of the year. For the summer months, however, the decrease in infiltration rate appears to be exaggerated in comparison with using a variable indoor temperature, by 0.03–0.05, making the change during summer months in Boston similar to that during the rest of the year. DISCUSSION Implications for Exposure and Risk To interpret the meaning of changing infiltration rates in terms of risk from exposure to both indoor and outdoor sources of air pollution, Figure 4 relates the changes in the infiltration ratio to changes in exposure, when air exchange is solely driven by infiltration, for pollutants of varying decay rates generated indoors (a) and outdoors (b). For the reference (present) period, an AER of 0.7 per hour is used, a fairly typical residential value.20 First order decay rates (in Eq. (1)) are assumed not to vary between reference and future scenarios. The relationships for both indoor and outdoor sources are very nearly linear, within the range of expected changes in infiltration, but their slopes vary greatly depending on decay rates. For reference, Table 3 shows the indoor decay rates for a variety of agents. Considering indoor sources first (Figure 4a), a 5% decrease in infiltration rate, for example, would result in an approximately equivalent (5%) increase in exposure to the more chemically stable agents (d ≤ 0.05 per hour) emitted indoors, such as radon. Note that, from Eqs. (1) and (4) the change in exposure asymptotically approaches 1/R-1 as the decay rate decreases. Indoor exposure to highly reactive species, on the other hand, © 2015 Nature America, Inc.
Figure 4. Relative change in exposure to indoor (a) and outdoor (b) sources resulting from changes in infiltration rate, expressed as ratio (R) of predicted vs reference rate, and for various decay rates d. The relationships are calculated for a reference air exchange rate a = 0.7. Higher values of a increase the slope of the relationship for indoor sources, and decrease it for outdoor sources. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
such as ultrafine particles, would hardly be modified, with the change asymptotically approaching 0 for fast-reacting species. Radon and its progeny are major indoor pollutants from a public health perspective,21–23 and exposures are dependent on weather-driven AERs.24,25 The linear association between radon exposure and lung cancer at residential level shown in large-scale studies26,27 and the comparatively slow decay rate could result in Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
6 Table 3.
Examples of single agent first order decay rates from the
literature. Agent
Decay rate (per hour)
Reference
Radon PM2.5
0.008 0.2–0.8 1.8–3 0.15–0.64 2.8 ± 1.3 0.99 ± 0.19 4.5 54–65
46 37
Carbonyls Ozone NO2 HNO3 UF
47 48 49 50 51
risk increase approximately equivalent to the decrease in air flux predicted for most scenarios, based only on the change in infiltration rate. The indoor–outdoor temperature difference may also affect infiltration of soil gas,28 however, and the quantitative implications for risk from radon require a more extensive analysis than is possible here. For similar reasons, elevated exposures to VOCs from indoor sources may also increase risks in some situations that are already of concern,23,29–32 but specific risk analyses need to consider all determinants of exposure. Although changes in infiltration in a dynamic climate appear to raise risks from exposures to indoor sources of air pollution, the same mechanism may mitigate some of the risk from exposure to ambient air pollutants. For example (Figure 4b), a 5% decrease in infiltration rate corresponds to a 4% exposure reduction for more reactive agents, like ozone or nitric acid (see Table 3), asymptotically approaching R-1. However, in southern locations with predicted increase in infiltration during summer months, the reverse effect may increase peak exposures to photochemical agents (by up to 25%) just during periods when ambient concentrations are highest. It is also important not to overstate the protective effects from ambient pollution by considering that in addition to altering infiltration rates, climate change is expected to intrinsically bring about worse air quality, an effect referred to as climate penalty.33–35 An estimated change in ozone level of 2.2 ppb/°C 36 would result in a 6% or greater increase in ambient concentrations, assuming a 2 °C warming and scenarios near the 75 ppb 8-h standard. Fine particles have sources in both indoor and outdoor environments37,38 and important public health impacts,39–41 so they deserve special attention. Decay rates for particles in indoor environments show considerable variability based on measurement methods,37 but have been most commonly reported in the in the 0.2–0.8 range (reference 37), with values as high as 1.8–3. The mean overall 5% decrease in infiltration rates would translate into a 2.4–4.0% increase in exposure to indoor generated particles, and 1.2–2.7% lower exposure to particles generated outdoor. The possible public health significance varies based on relative indoor/ outdoor concentrations and particle composition, so that no generalized recommendation should be made. Specific recommendations should consider the spatial and seasonal variability of PM sources. In particular, exposure to combustion byproducts from space heating and other sources may show a seasonal pattern42 that would be enhanced by changing infiltration. Changes in infiltration may also have implications for indoor moisture management. The complexity of this topic goes beyond the scope of this analysis, but these results suggest that it will be more difficult to keep indoor humidity at desirable levels in the summer in the hot humid climates of the Southeast. Conversely, maintenance of wintertime indoor humidity in cold climates may be facilitated. The results of this analysis also show a general increase in infiltration variability. The implication of increased variability during the summer months is that infiltration of outdoor air Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
pollutants may markedly increase above the average during particular days, especially in places such as Houston or Atlanta, leading to more frequent episodes of acute exposures to outdoor air pollution. Although the outdoor air quality so far into the future may only be speculated at the moment, this decreased ability of buildings to protect their residents from spikes in outdoor air pollution should be factored into exposure and risk scenarios. Uncertainty and Sensitivity Analyses The sensitivity analysis to explore the cost of using a simpler approximation of indoor temperature (seasonal fixed) vs a more realistic one dependent on outdoor temperature was limited by the scarcity of research relating indoor and outdoor temperature fluctuations in representative home samples. From Figure 3, it is also apparent that the difference between using the fixed and variable indoor temperature is greatest in the summer months, and drops by an order of magnitude or more in the other seasons. On the basis of the limited information available, this sensitivity analysis suggests that the fixed seasonal indoor temperature is a reasonable approximation, overall, and our results would not change substantially with a variable indoor temperature model. The implications for exposure and risk appear robust with respect to assumptions about the reference AER used. If the current median AER of 0.7 is replaced by a much lower value of 0.2, the change in exposure to the less reactive indoor sources with R = 0.95 barely changes, from a 4.9% increase to 4.17%, whereas exposure to the most reactive outdoor agents goes from a 4.1% drop to 4.7%. Similarly, minor responses are observed with increased AER. The relative modeling uncertainty associated with predictions on changes of infiltration rates is modest for much of the year, but not uniform. The greatest modeling uncertainty is around predictions for summer months in warmer locations (Atlanta, Houston, Phoenix), where predictions are so scattered that models do not even agree on the direction of the change. In general, predictions for summer months are less reliable than for the rest of the year, accumulating not only the greatest modeling uncertainty, but also the greatest daily and annual variability and, possibly, the most bias (in either direction) from the use of a fixed indoor temperature. Strengths and Weaknesses of the Analysis A strength of this analysis is its wide applicability to similar climates in United States, as both infiltration and climate models are based on general physical principles. Another strength is the inclusion of several different climate models, representing the range of modeling uncertainty about future climate scenarios. The results of our sensitivity analysis show that the analysis is robust to assumptions about the reference AER, as well assumptions about indoor air temperature fluctuations. Furthermore, as the flux analysis is independent of leakage area, these results are also robust to assumptions about initial building condition, within the type analyzed. This implicit or practical insensitivity to assumptions allows for generalizability of the results. Among the limitations, the range of adaptation responses is not a part of the analysis and might markedly modify some conclusions. In particular, adaptations intended to reduce the leakage area of buildings would diminish the effect of increased infiltration and exposure to outdoor contaminants predicted for the summer months in the warmer locations. This same adaptation, however, would enhance the effects of decreased infiltration and increased exposure to indoor sources that is anticipated in most locations for all or part of the year. Although the current efforts encouraging more energy-efficient buildings would favor this adaptation,43,44 the extent of its future implementation is not known. Another adaptation that could affect these conclusions is a change in the use of natural or © 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
7 mechanical ventilation, which may be presumably increased in response to milder indoor/outdoor temperature differences. To put our findings in the rights perspective, it is important to observe that these probable future adaptations (more energyefficient buildings and changes in use of natural ventilation) can completely overshadow the effect described in this paper. For example, as shift to buildings with AERs as low as 0.2 per hour, which is already being observed in some locations,45 could change exposure to indoor and outdoor air pollution by 1–2 orders of magnitude more than the effect shown in Figure 4. Conversely, doubling AER through increased natural ventilation (e.g., through window opening) could change exposures by 50% or more under the same conditions. Although both these competing trends may develop concurrently, their relative importance in future scenarios remains unexplored. Perhaps less significantly, changes in the proportion of time spent indoors and outdoors would also affect the balance of exposure to indoor and outdoor sources, but the direction and magnitude of such changes remain speculative. The degree to which building use and natural ventilation may change deserves some exploration for its implications for exposure and risk assessments, perhaps by examining the current variability across geographical climate differences that would be comparable to temporal ones in a changing climate. Another limitation is the focus on a subset of buildings typical of densely populated regions, a choice determined by considerations of both significance for population exposures, and magnitude of effects. Consideration of specific risks to rural populations would require broadening this analysis to different classes of wind sheltering and vertical structure. Similarly, examining the effects on the denser urban areas with residential or commercial high-rise buildings would require a completely different analysis using different infiltration models or sets of parameters appropriate to such buildings. Without inappropriately extending the model applicability, however, based on the choice of infiltration model parameters, we expect that consequences for exposure would be less meaningful for lower and more isolated buildings. Exposure to indoor sources would be less affected in buildings with lower AER, but changes would be more significant for outdoor sources (Figure 4). CONCLUSIONS Diminished air exchange in future climate scenarios may be expected to increase exposure to indoor sources of air pollution, unless these air infiltration reductions are otherwise compensated. Exposure to ambient air pollution, conversely, would be mitigated by lower infiltration, although peak exposure increases during summer months should be considered. Although this analysis elucidates one of the processes through which climate change affects exposure, the net effect of climate change on exposure to both indoor and outdoor sources also depends on other mechanisms, including behavioral responses, potential building modifications, and the climate change penalty on ambient air pollution, which need to be considered in further analyses. ABBREVIATIONS AER, air exchange rate; GCM, global climate model; LBL, Lawrence Berkley National Laboratory; NARCCAP; North American Regional Climate Change Assessment Program; RCM, regional climate model. CONFLICT OF INTEREST The authors declare no conflict of interest.
© 2015 Nature America, Inc.
ACKNOWLEDGEMENTS We wish to thank the NARCCAP for providing the data used in this paper. NARCCAP is funded by the National Science Foundation, the United States Department of Energy, the National Oceanic and Atmospheric Administration, and the United States Environmental Protection Agency Office of Research and Development. We also thank Bryan Bloomer, for inspiring conversations about climate change leading to this work, and about the role of the climate penalty on ambient air pollution. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the United States Environmental Protection Agency.
REFERENCES 1 Stocker TFDQG-KPMTSKAJBANYXVBaPMM. Climate change 2013: The physical science basis. New York: Intergovernmental Panel on Climate Change, 2013. 2 Ashrae. ASHRAE/ANSI Standard 55-2010: Thermal environmental conditions for human occupancy. Atlanta, GA, USA, 2010. 3 Oceanic USN, Atmospheric A. Data Tools: 1981-2010 Normals (cited 18 April 2014). Available from http://www.ncdc.noaa.gov/cdo-web/datatools/normals. 4 Koutrakis P, Briggs SLK, Leaderer BP. Source apportionment of indoor aerosols in Suffolk and Onondaga counties, New York. Environ Sci Technol 1992; 26: 521–527. 5 Breen MS, Schultz BD, Sohn MD, Long T, Langstaff J, Williams R et al. A review of air exchange rate models for air pollution exposure assessments. J Expo Sci Environ Epidemiol 2013; 24: 555–563. 6 Breen MS, Breen M, Williams RW, Schultz BD. Predicting residential air exchange rates from questionnaires and meteorology: model evaluation in central North Carolina. Environ Sci Technol 2010; 44: 9349–9356. 7 Mearns L, McGinnis S, Arritt R, Biner S, Duffy P, Gutowski W et al. The North American Regional Climate Change Assessment Program dataset (doi:10.5065/ D6RN35ST). Boulder, CO: National Center for Atmospheric Research Earth System Grid data portal 2007. 8 Mearns LO, Gutowski W, Jones R, Leung R, McGinnis S, Nunes A et al. A regional climate change assessment program for North America. Eos, Trans Amer Geophys Union 2009; 90: 311. 9 Nakicenovic N, Swart R, Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, UK, 2000. 10 Team RDC. R: A Language and Environment for Statistical Computing. Vienna, Austria: Vienna, Austria: R Foundation for Statistical Computing, 2011. 11 de Dear R, Brager GS. Developing an adaptive model of thermal comfort and preference. ASHRAE Trans 1998; 104: 145–167. 12 Humphreys M. Field studies of thermal comfort compared and applied. J Inst Heat Vent Eng 1976; 44: 5–27. 13 Nicol JH, MA Humphreys. Thermal comfort as part of a self-regulating system. Build Res Pract 1973; 6: 191–197. 14 Nicol JH, MA Humphreys. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build 2002; 34: 563–572. 15 de Dear R. A global database of thermal comfort field experiments. ASHRAE Trans 1998; 104: 1141–1152. 16 Humphreys M. Outdoor temperatures and comfort indoors. Build Res Pract 1978; 6: 92–105. 17 Nguyen JS, Schwartz J, Dockery DW. The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air 2014; 24: 103–112. 18 Bureau USC. Historical Census of Housing Tables 2011 (cited 31 December 2013). Available from http://www.census.gov/hhes/www/housing/census/historic/units. html. 19 Pryor SC, Barthelmie RJ. Climate change impacts on wind energy: a review. Renew Sust Energy Rev 2010; 14: 430–437. 20 Yamamoto NS, Shendell DG, Winer AM, Zhang J. Residential air exchange rates in three major US metropolitan areas: results from the Relationship Among Indoor, Outdoor, and Personal Air Study 1999–2001. Indoor Air 2010; 20: 85–90. 21 WHO handbook on indoor radon: a public health perspective. World Health Organization: Geneva, 2009. 22 Samet JM. Radon and lung cancer. J Natl Cancer Inst 1989; 81: 745–758. 23 Jones A. Indoor air quality and health. Atmos Environ 1999; 33: 4535–4564. 24 Postendorfer JB, Butterweck G, Reineking A. Daily variation of the radon concentration indoors and outdoors and the influence of meteorological parameters. Health Phys 1994; 67: 283–287. 25 Arvela HW, K Winqvist. A model for indoor radon variations. Environ Int 1989; 15 239–246. 26 Darby S, Hill D, Auvinen A, Barros-Dios JM, Baysson H, Bochicchio F et al. Radon in homes and risk of lung cancer: collaborative analysis of individual data from 13 European case-control studies. BMJ 2005; 330: 223.
Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
8 27 Krewski D, Lubin JH, Zielinski JM, Alavanja M, Catalan VS, Field RW et al. A combined analysis of North American case-control studies of residential radon and lung cancer. J Toxicol Environ Health A 2006; 69: 533–597. 28 Nazaroff W. Radon transport from soil to air. Revi Geophys 1992; 30: 137–160. 29 Jia C, Batterman S, Godwin C. VOCs in industrial, urban and suburban neighborhoods, Part 1: Indoor and outdoor concentrations, variation, and risk drivers. Atmos Environ 2008; 42: 2083–2100. 30 Mendell MJ. Indoor residential chemical emissions as risk factors for respiratory and allergic effects in children: a review. Indoor Air 2007; 17: 259–277. 31 Wallace LA. Comparison of risks from outdoor and indoor exposure to toxic chemicals. Environ Health Perspect 1991; 95: 7–13. 32 Weschler CJ, Nazaroff WW. Semivolatile organic compounds in indoor environments. Atmos Environ 2008; 42: 9018–9040. 33 Jacob DJ, Winner DA. Effect of climate change on air quality. Atmos Environ 2009; 43: 51–63. 34 Wu S, Mickley LJ, Leibensperger EM, Jacob DJ, Rind D, Streets DG. Effects of 2000–2050 global change on ozone air quality in the United States. J Geophys Res 2008; 113: D06302-D. 35 Zhao S, Pappin AJ, Morteza Mesbah S, Joyce Zhang JY, MacDonald NL, Hakami A. Adjoint estimation of ozone climate penalties. Geophys Res Lett 2013; 40: 5559–5563. 36 Bloomer BJ, Stehr JW, Piety CA, Salawitch RJ, Dickerson RR. Observed relationships of ozone air pollution with temperature and emissions. Geophys Res Lett 2009; 36: L09803–L09803. 37 Abt E, Suh HH, Catalano P, Koutrakis P. Relative contribution of outdoor and indoor particle sources to indoor concentrations. Environ Sci Technol 2000; 34: 3579–3587. 38 Wallace L. Indoor particles: a review. J Air Waste Manage Assoc 1996; 46: 98–126. 39 Fann N, Lamson AD, Anenberg SC, Wesson K, Risley D, Hubbell BJ. Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk Anal 2012; 32: 81–95.
40 Cohen AJ, Ross Anderson H, Ostro B, Pandey KD, Krzyzanowski M, Künzli N. The global burden of disease due to outdoor air pollution. J Toxicol Environ Health A 2005; 68: 1301–1307. 41 Morawska L, Afshari A, Bae GN, Buonanno G, Chao CY, Hänninen O et al. Indoor aerosols: from personal exposure to risk assessment. Indoor Air 2013; 23: 462–487. 42 Leaderer BP, Naeher L, Jankun T, Balenger K, Holford TR, Toth C et al. Indoor, outdoor, and regional summer and winter concentrations of PM10, PM2.5, SO4(2)-, H+, NH4+, NO3-, NH3, and nitrous acid in homes with and without kerosene space heaters. Environ Health Perspect 1999; 107: 223–231. 43 Krarti M. Weatherization and Energy Efficiency Improvement for Existing Homes: An Engineering Approach. CRC Press: Boca Raton, FL, USA, 2012. 44 Bradshaw JL, Elie B-Z, Harris RH. Comparing the effectiveness of weatherization treatments for low-income, American, urban housing stocks in different climates. Energy Build 2014; 69: 535–543. 45 Offermann FJ. IAQ applications IAQ in airtight homes. ASHRAE J 2010; 52: 58–60. 46 Bruno RC. Sources of indoor radon in houses: a review. J Air Pollut Control Assoc 1983; 33: 105–109. 47 Liu W, Zhang J, Zhang L, Turpin B, Weisel C, Morandi M et al. Estimating contributions of indoor and outdoor sources to indoor carbonyl concentrations in three urban areas of the United States. Atmos Environ 2006; 40: 2202–2214. 48 Lee K, Vallarino J, Dumyahn T, Ozkaynak H, Spengler JD. Ozone decay rates in residences. J Air Waste Manage Assoc 1999; 49: 1238–1244. 49 Yamanaka Si. Decay rates of nitrogen oxides in a typical Japanese living room. Environ Sci Technol 1984; 18: 566–570. 50 Spicer CW, Kenny DV, Ward GF, Billick IH. Transformations, lifetimes, and sources of NO 2, HONO, and HNO 3 in indoor environments. Air Waste 1993; 43: 1479–1485. 51 Géhin E, Ramalho O, Kirchner S. Size distribution and emission rate measurement of fine and ultrafine particle from indoor human activities. Atmos Environment 2008; 42: 8341–8352.
Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website (http:// www.nature.com/jes)
Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
© 2015 Nature America, Inc.
www.nature.com/jes
ORIGINAL ARTICLE
Effects of climate change on residential infiltration and air pollution exposure Vito Ilacqua, John Dawson, Michael Breen, Sarany Singer and Ashley Berg Air exchange through infiltration is driven partly by indoor/outdoor temperature differences, and as climate change increases ambient temperatures, such differences could vary considerably even with small ambient temperature increments, altering patterns of exposures to both indoor and outdoor pollutants. We calculated changes in air fluxes through infiltration for prototypical detached homes in nine metropolitan areas in the United States (Atlanta, Boston, Chicago, Houston, Los Angeles, Minneapolis, New York, Phoenix, and Seattle) from 1970–2000 to 2040–2070. The Lawrence Berkeley National Laboratory model of infiltration was used in combination with climate data from eight regionally downscaled climate models from the North American Regional Climate Change Assessment Program. Averaged over all study locations, seasons, and climate models, air exchange through infiltration would decrease by ~ 5%. Localized increased infiltration is expected during the summer months, up to 20–30%. Seasonal and daily variability in infiltration are also expected to increase, particularly during the summer months. Diminished infiltration in future climate scenarios may be expected to increase exposure to indoor sources of air pollution, unless these ventilation reductions are otherwise compensated. Exposure to ambient air pollution, conversely, could be mitigated by lower infiltration, although peak exposure increases during summer months should be considered, as well as other mechanisms. Journal of Exposure Science and Environmental Epidemiology advance online publication, 27 May 2015; doi:10.1038/jes.2015.38 Keywords: air exchange; climate change; indoor exposure; infiltration
INTRODUCTION Long-term trends in outdoor temperatures brought about by climate change1 can be expected to alter the air exchange between indoor and outdoor environments based on simple physical considerations. These effects on air exchange could, in turn, modify patterns of exposure to both indoor and outdoor air pollutants and the resulting risks. Although such changes can be easily understood qualitatively, their magnitude and significance from a building operation and public health perspective remain to be addressed. The goal of this paper is to examine the effects on exposure to indoor and outdoor air pollution as changes in climate alter air exchange between indoor and outdoor environments. Average annual temperatures in the contiguous Unites States are currently below the human thermal comfort range (~20–26 °C, depending on humidity and clothing), in every state with the exception of Florida, at the very margin of that range.2,3 That is, on average, indoor environments must be heated to achieve thermal comfort. The projected increase in ambient temperatures as a result of climate change will therefore reduce the yearly average thermal differential between indoor and outdoor environments. Such reduction, if not compensated by other factors, could then reduce average passive ventilation, albeit with substantial geographic differences. To determine how meaningful such reduction might be, as well as its timing and variability, we modeled the infiltration process under current and future climate scenarios. The need for an explicit quantitative analysis depends on the observation that when the indoor/outdoor temperature difference
is modest, even small changes in temperature could potentially result in large relative changes in ventilation through infiltration. The terms infiltration and leakage are used as synonyms throughout this article to mean the unintended air flows between indoor and outdoor environment through the building envelope, and include air flows into and out of the building. Knowledge of the intrinsic changes in infiltration brought about by climate change, if meaningful, could be useful to develop guidelines and recommendations about adapting building design and use, particularly with respect to measures that attempt to achieve higher energy efficiency by controlling infiltration. In addition, planning for the health impact of climate change, as related to air pollution, should take into account the modulation of exposure resulting from climate change interaction with buildings, where people spend the vast majority of their time. Model Exposure to indoor and outdoor sources depends on their separate contributions to indoor concentrations. At steady state, and assuming perfect mixing, the mass balance equation for a single compartment indoor environment yields:4 C in ¼
Pa S C out þ aþd Vða þ dÞ
ð1Þ
Where Cin and Cout are the indoor and outdoor concentrations, respectively, P is a penetration factor specific to each air pollutant (often close to 1) that describes the effect of the building envelope, a is the air exchange rate (AER), d is a first order decay
United States Environmental Protection Agency, Washington, District of Columbia, USA. Correspondence: Dr. Vito Ilacqua, United States Environmental Protection Agency, 1200 Pennsylvania Avenue NW (8726P), Washington, DC 20460, USA. Tel.: +1 703 347 0261. Fax: 703 347 8140. E-mail: [email protected] Received 27 October 2014; revised 16 March 2015; accepted 24 March 2015
Climate change and infiltration Ilacqua et al
2 rate constant (combining all sink processes), S is the indoor source strength (or mass emission rate), and V is the volume of the environment. The first term on the right expresses the contributions of outdoor sources through ventilation, whereas the second term represents the contributions of indoor sources. Both terms depend on the AER, and it is through its impact on the AER that climate change has the potential to alter exposures to emissions from both indoor and outdoor sources. The AER for an indoor environment is simply the ratio of the ventilation rate (volume per unit time) to the volume of that environment: a = Q/V. Ventilation results from both the deliberate use of mechanical or natural ventilation, and infiltration through the building envelope. Although mechanical ventilation is less common in residential environment, and natural ventilation is generally restricted outside periods of milder weather, infiltration operates in every type of building and under every type of climate and weather, and this analysis will focus on this component of AER, by considering situations where deliberate ventilation is negligible. Various AER models have been described in the literature.5 In this study, we used the Lawrence Berkeley National Laboratory (LBL) model to predict residential leakage. The LBL model is one of the most widely used physics-based model to predict residential leakage rates. Evaluations of the LBL model showed mean absolute error of 43% for 31 detached homes across four seasons.6 The LBL model describes leakage (or infiltration) from differential air pressure across the building envelope owing to the combination of (1) the indoor–outdoor temperature differential (stack effect) and (2) a wind effect. The leakage air flux φ described by the LBL model is qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2Þ φ ¼ k jT i T o j þ hW 2 where φ is the volume of air per unit time per unit area subject to infiltration, k (stack coefficient) and h (wind coefficient) are empirical parameters determined by the building height and level of wind sheltering respectively, Ti and To are the indoor and outdoor temperatures, and W is the wind speed, over the time interval of calculation. We use the ratio R of future to reference (present) air flux as a relative measure of the change in infiltration, resulting from climate change impacts on both outdoor temperature and wind speeds: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k T F;i T F;o þ hW 2F φF ð3Þ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R¼ φP k T T þ hW 2 F;i
F;o
F
where the subscripts P and F refer to the reference (present) and projected (future) periods. The future-to-present ratios of ventilation rates through infiltration, infiltration air fluxes, and AERs are all identical for the same building, assuming air exchange is driven only by infiltration. So, it is possible to express the change in concentrations contributed by indoor and outdoor sources at steady state in the mass balance Eq. (1) as a function of R. QF φF aF ¼ ¼ ¼R QP φP aP
ð4Þ
From Eq. (4), changes in indoor exposure concentrations in Eq. (1) can be calculated for specific air exchange and decay rates in the reference period. MATERIALS AND METHODS Climate Data Climatic data were derived from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate intercomparison data set.7,8 In the NARCCAP exercise, climatic outputs from several Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Table 1. GCM-RCM combinations from the NARCCAP modeling exercise included in this analysis. GCM
RCMs
CCSM CGCM3 GFDL HadCM3
CRCM, WRF CRCM, RegCM3, WRF HadRM3, RegCM3 HadRM3
Abbreviations: CCSM, Community Climate System Model, Version 3.0 (http:// www.pcmdi.llnl.gov/ipcc/model_documentation/CCSM3.htm); CGCM3, Third Generation Coupled Global Climate Model (http://www.ec.gc.ca/ccmaccccma/default.asp?lang = En&n = 1299529F-1); CRCM, Canadian Regional Climate Model, Version 4.2 (http://www.ouranos.ca/fr/programmationscientifique/science-du-climat/simulations-climatiques/MRCC/eng/crcm.html# crcm42); GFDL, Geophysical Fluid Dynamics Laboratory GCM (http://www. pcmdi.llnl.gov/ipcc/model_documentation/GFDL-cm2.htm); HadCM3, Hadley Centre Couple Model, Version 3 (http://www.pcmdi.llnl.gov/ipcc/model_ documentation/HadCM3.htm); HadRM3, Hadley Regional Model 3/PRECIS (http://www.metoffice.gov.uk/precis/); RegCM3, Regional Climate Model, Version 3 (http://users.ictp.it/RegCNET/model.html); WRF, Weather Research & Forecasting model (http://www.wrf-model.org/index.php).
global-scale general circulation models (GCMs) for both present and future conditions were downscaled using several regional climate models (RCMs). Many GCM-RCM combinations are included in the NARCCAP data set; the combinations available for this study are summarized in Table 1. All of the NARCCAP model combinations for which the complete set of necessary meteorological information for Eqs. (2) and (3) (above) were available were included in this analysis. The RCM modeling domains covered the entire contiguous United States. Nine large metropolitan areas in the continental United States were selected to represent the spatial variability of climates (Table 2). The RCM grid cell closest to each city was chosen to represent that city; however, the RCMs used different map projections and spatial resolutions. Therefore, there may not be perfect alignment between city locations and model grid cells. The reference period for present climate is the three decades 1970– 2000; the mid-21st century future scenario spans the years 2040–2070 for the Intergovernmental Panel on Climate Change A2 scenario.9 The A2 scenario represents a rather high amount of continued greenhouse gas emissions through the century. This is the only future scenario explored in the NARCCAP simulations. Both reference and future climates are simulated by models in 3-h increments. Air flux rates were calculated at this time resolution, and then averaged (mean or median) by month for each location, and for each climate model, using R version 2.14.10 Using temperature and wind speed from the climate models with a 3-h resolution, instead of daily or monthly averages, allows consideration of the diurnal temperature cycle, so that air flux rates are more accurately calculated even when mean (for example, daily or monthly) outdoor temperatures are very close to indoor temperatures. Without consideration of the diurnal cycle, infiltration would be greatly underestimated.
Indoor Temperatures Indoor temperatures used in Eq. (3) were taken as the median indoor temperature measured by Breen et al.6 in 31 homes in Raleigh-Durham, NC, USA for each season: spring 22.5 °C; summer 24.9 °C; fall 23.5 °C; winter 22.5 °C. The choice of these values reflects a preference for contemporary empirical data on indoor temperatures over models based for example on thermal comfort, which introduce greater complexity and require additional unavailable information.11 In general, there is limited recent information about residential indoor temperatures, and heating and cooling set points from large-scale surveys in the literature, and the basis for comparability of indoor temperatures used here to those in other parts of the country is largely inferred. Nevertheless, despite individual preferences and acclimatization, the range of comfortable temperatures for humans has been shown to vary little between studies in different climates,12,13 as it is based on shared biological characteristics and use of seasonal clothing.2 Actual indoor temperatures generally reflect comfort preferences14 and do not differ remarkably from the range and © 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
3 Table 2.
3
Climate normals for 1981–2010 for the cities in this analysis. Koeppen classification
Atlanta, GA, USA Boston, MA, USA Chicago, IL, USA Houston, TX, USA Los Angeles, CA, USA Minneapolis- St Paul, MN, USA New York, NY, USA Phoenix, AZ, USA Seattle, WA, USA
Humid subtropical (Cfa) Transition Humid subtropical/humid continental (Cfa/Dfa) Humid continental (Dfa) Humid subtropical (Cfa) Subtropical Mediterranean (Csb) Humid continental (Dfa) Humid subtropical (Cfa) Subtropical desert (BWh) Subtropical Mediterranean (Csb)
approximate relationship to seasonal patterns of ambient temperatures adopted here.14–16 Indoor temperatures were considered fixed for the entire season. This simplification can introduce a bias, as the temperature of indoor environments varies in response to outdoor temperatures, within the range of temperatures acceptable to its residents, with a lag dependent on the building radiative insulation and air exchange. This variability, both within and between days, is not captured by this analysis, so that the indoor–outdoor temperature gradient is likely to be overestimated by this approximation. This short-term association of indoor and outdoor temperatures has received limited characterization, particularly through empirical studies. To explore the effect of this simplification we performed a sensitivity analysis using an empirical regression model developed for a small number of residences in Boston17 that relates indoor to outdoor temperature. Although there are limitations owing to the number and selection of those residences, the comparison can shed some light on the accuracy of the approximation. As this alternative model is not necessarily more accurate, we limited the comparison with the possible over- or underestimation of the change in infiltration: we calculated the differences of the absolute distance from R = 1 for the two approaches, using the RCM3 HadCM3 climate model.
Model Parameters Air fluxes were calculated using the LBL model (Eq. (2)) for a prototypical two-storey house under sheltered conditions (class 5): the stack coefficient was 2.9 × 10 − 4 (L/s cm2)2, and the wind coefficient 4.2 × 10 − 5 (L/s cm2)2. The choice of this particular set of conditions was an attempt to represent population exposure within the applicability range of the LBL model, by focusing on single-family detached houses surrounded by similar buildings, which describes over 60% of dwellings in the United States.18 Other types of buildings (e.g., high-rise or attached row-houses) are certainly important to capture population exposure in denser urban areas and so are office and school buildings everywhere, but infiltration in these may not be as accurately described by the LBL model, which was not developed for them. Furthermore, this choice of model parameters places more emphasis on temperature changes (expected to change more in a dynamic climate) and less on wind changes (expected to change less;19 Supplementary Information), therefore focusing on the housing locations more sensitive to an effect of climate change on infiltration. Monthly mean air fluxes averaged over the entire reference and future periods (e.g., all 3-h values in January for 1970–2000 or 2040–2070) were then compared calculating the ratio R of future-to-present air flux, which (from Eq. (4)) is also equivalent to the ratio of ventilation and AERs for the same building. Monthly SDs of air fluxes were similarly calculated and compared.
Treatment of Uncertainty Several sources of uncertainty contribute to the overall uncertainty of this analysis. Uncertainty about future climate as represented by climate model predictions is especially prominent. Each model provides its best approximation of climate averages based on its assumptions about the relevant physical processes and initial conditions. These assumptions are uncertain, as indeed the mere existence of several models of comparable credibility attests. We refer to the current uncertainty about such assumptions as modeling uncertainty, and choose to represent it by applying the same analysis to every model and reporting all their results © 2015 Nature America, Inc.
January mean high and low temperatures (°C) 11.3 2.1 − 0.3 17.2 20.1 − 4.6 3.5 19.6 8.4
1.3 − 5.4 − 7.7 6.2 8.8 − 13.6 0.3 7.6 2.7
July mean high and low temperatures (°C) 31.7 27.4 29.0 34.3 28.4 28.6 28.9 40.2 24.3
21.8 18.6 19.7 23.9 17.6 17.8 20.5 28.2 13.1
with equal weight, summarizing results from all models only when necessary through their median predictions. This choice means that the modeling uncertainty in this study is represented by the range of predictions of all climate models. Other choices and assumptions contribute to our uncertainty, such as modeling and parameter uncertainty in the LBL infiltration model, and uncertainty about the distribution of indoor air temperatures. These additional sources of uncertainty are not captured by our uncertainty analysis, which is likely dominated by modeling uncertainty.
RESULTS Rates of air infiltration to indoor environments for the middle of the 21st century are estimated to decrease overall by ~ 5%, averaged over study locations, seasons, and climate models. Individual cities differ little, with mean yearly ratios of future-topresent infiltration rates (R) ranging from 0.93 for Boston to 0.98 for Phoenix. Monthly mean ratios of infiltration rates vary more, ranging from 0.86 to 1.25, considering all climate models and cities, representing the range of climate model predictions by their median for each month and city, the range is reduced to 0.89–1.09. The median, maximum, and minimum predictions for each city, by month, are shown in Figure 1. Although infiltration rates decrease for most cities and months (ratio R below 1), increased infiltration is predicted by the models median for the warmer locales (Atlanta, Houston, and Phoenix) for all or part of the summer months, and by at least one model for most others. Estimates of change in infiltration using different climate models vary by 8.0%, averaged across all cities and months, and their range can be observed in Figure 1. Models differ the least about decreased infiltration, with relative range (i.e., Max-Min/ Median) as low as 1.4% for February in New York. For winter months, when decreases are predicted for all cities, the average relative range across cities is just 4.1%. Modeling uncertainty increases considerably for the summer months, especially where infiltration is predicted to increase the most: the highest relative range between models is 30.0% for June in Houston, and the overall summer relative range is 14.4%. Variability in infiltration rate is estimated to increase by most models in many cases, as measured by the ratio of future to reference SD of air flow (Figure 2). The increase is particularly apparent during summer months in warmer locales. For example, the median of all models for Houston and Atlanta shows 20–30% variability increases for July and August, whereas the models' median for Chicago shows a 5–10% increase in variability through most of the year. Variability is predicted to remain essentially unchanged at other locations, such as Boston or Minneapolis. It should be noted that the simple increase in mean temperature and wind speed implies corresponding increases in their SD (i.e., if μ’ = 2μ then σ’ = 2σ), without indicating an increase in relative variability. The predicted variability increase, however, is usually larger than would be explained by this merely statistical effect, which would actually reduce the ratio of SDs for the majority of cases when the ratio of the means is below 1. The comparisons of Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
4 ratios in Figures 1 and 2 readily bear out this observation. In fact, accounting for this effect, even ratios of SD close to 1 may indicate increased relative variability (e.g., Boston or Seattle).
The possible bias of using a fixed seasonal indoor temperature was explored comparing the differences of the absolute distance from R = 1 for (a) the fixed seasonal temperature and (b) the
Figure 1. Ratio of future (2040–2070) to reference (1970–2000) monthly means of infiltration rates to indoor environments. The range represents the climate modeling uncertainty: the median, minimum, and maximum predictions of eight climate models. Values below 1 indicate a decrease in infiltration and imply lower exposure to outdoor sources, but higher exposure to indoor sources. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
Figure 2. Ratio of future (2040–2070) to reference (1970–2000) monthly SD of infiltration to indoor environments. The range represents the climate modeling uncertainty: the median, minimum, and maximum predictions of eight climate models. Values above 1 indicate an increase in variability of infiltration, driven by increased temperature or wind speed variability. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online. Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
© 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
5
Figure 3. Effect of using a seasonal fixed indoor air temperature vs a model dependent on outdoor temperature developed for Boston,17 calculated for the HRM3 HadCM3 climate model. Values are the differences of the absolute distance from R = 1 for the two approaches. Negative values indicate that using a fixed indoor temperature might underestimate the change in the ratio of future-to-present infiltration (whether increasing or decreasing); positive values indicate the change in air flux would be overstated. Mean effect across all cities and months is − 0.001; for Boston only, 0.009. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
outdoor-dependent model by Nguyen et al.17 (Figure 3). Negative values indicate that using a fixed indoor temperature underestimates the change (either increase or decrease) in the ratio of future-to-present air flux; positive values indicate the change in air flux would be overstated. The mean difference across all cities and months is − 5.3 × 10 − 4, about 2 orders of magnitude below the mean estimated change in ratio. It is not known how the model by Nguyen et al. would perform outside Boston, however, so the mean difference limited to that city was 8.9 × 10 − 3, about 1 order of magnitude below the changes estimated. The small discrepancies using a fixed indoor temperature understate the magnitude of the change for more than half of the year. For the summer months, however, the decrease in infiltration rate appears to be exaggerated in comparison with using a variable indoor temperature, by 0.03–0.05, making the change during summer months in Boston similar to that during the rest of the year. DISCUSSION Implications for Exposure and Risk To interpret the meaning of changing infiltration rates in terms of risk from exposure to both indoor and outdoor sources of air pollution, Figure 4 relates the changes in the infiltration ratio to changes in exposure, when air exchange is solely driven by infiltration, for pollutants of varying decay rates generated indoors (a) and outdoors (b). For the reference (present) period, an AER of 0.7 per hour is used, a fairly typical residential value.20 First order decay rates (in Eq. (1)) are assumed not to vary between reference and future scenarios. The relationships for both indoor and outdoor sources are very nearly linear, within the range of expected changes in infiltration, but their slopes vary greatly depending on decay rates. For reference, Table 3 shows the indoor decay rates for a variety of agents. Considering indoor sources first (Figure 4a), a 5% decrease in infiltration rate, for example, would result in an approximately equivalent (5%) increase in exposure to the more chemically stable agents (d ≤ 0.05 per hour) emitted indoors, such as radon. Note that, from Eqs. (1) and (4) the change in exposure asymptotically approaches 1/R-1 as the decay rate decreases. Indoor exposure to highly reactive species, on the other hand, © 2015 Nature America, Inc.
Figure 4. Relative change in exposure to indoor (a) and outdoor (b) sources resulting from changes in infiltration rate, expressed as ratio (R) of predicted vs reference rate, and for various decay rates d. The relationships are calculated for a reference air exchange rate a = 0.7. Higher values of a increase the slope of the relationship for indoor sources, and decrease it for outdoor sources. A full color version of this figure is available at the Journal of Exposure Science and Environmental Epidemiology journal online.
such as ultrafine particles, would hardly be modified, with the change asymptotically approaching 0 for fast-reacting species. Radon and its progeny are major indoor pollutants from a public health perspective,21–23 and exposures are dependent on weather-driven AERs.24,25 The linear association between radon exposure and lung cancer at residential level shown in large-scale studies26,27 and the comparatively slow decay rate could result in Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
6 Table 3.
Examples of single agent first order decay rates from the
literature. Agent
Decay rate (per hour)
Reference
Radon PM2.5
0.008 0.2–0.8 1.8–3 0.15–0.64 2.8 ± 1.3 0.99 ± 0.19 4.5 54–65
46 37
Carbonyls Ozone NO2 HNO3 UF
47 48 49 50 51
risk increase approximately equivalent to the decrease in air flux predicted for most scenarios, based only on the change in infiltration rate. The indoor–outdoor temperature difference may also affect infiltration of soil gas,28 however, and the quantitative implications for risk from radon require a more extensive analysis than is possible here. For similar reasons, elevated exposures to VOCs from indoor sources may also increase risks in some situations that are already of concern,23,29–32 but specific risk analyses need to consider all determinants of exposure. Although changes in infiltration in a dynamic climate appear to raise risks from exposures to indoor sources of air pollution, the same mechanism may mitigate some of the risk from exposure to ambient air pollutants. For example (Figure 4b), a 5% decrease in infiltration rate corresponds to a 4% exposure reduction for more reactive agents, like ozone or nitric acid (see Table 3), asymptotically approaching R-1. However, in southern locations with predicted increase in infiltration during summer months, the reverse effect may increase peak exposures to photochemical agents (by up to 25%) just during periods when ambient concentrations are highest. It is also important not to overstate the protective effects from ambient pollution by considering that in addition to altering infiltration rates, climate change is expected to intrinsically bring about worse air quality, an effect referred to as climate penalty.33–35 An estimated change in ozone level of 2.2 ppb/°C 36 would result in a 6% or greater increase in ambient concentrations, assuming a 2 °C warming and scenarios near the 75 ppb 8-h standard. Fine particles have sources in both indoor and outdoor environments37,38 and important public health impacts,39–41 so they deserve special attention. Decay rates for particles in indoor environments show considerable variability based on measurement methods,37 but have been most commonly reported in the in the 0.2–0.8 range (reference 37), with values as high as 1.8–3. The mean overall 5% decrease in infiltration rates would translate into a 2.4–4.0% increase in exposure to indoor generated particles, and 1.2–2.7% lower exposure to particles generated outdoor. The possible public health significance varies based on relative indoor/ outdoor concentrations and particle composition, so that no generalized recommendation should be made. Specific recommendations should consider the spatial and seasonal variability of PM sources. In particular, exposure to combustion byproducts from space heating and other sources may show a seasonal pattern42 that would be enhanced by changing infiltration. Changes in infiltration may also have implications for indoor moisture management. The complexity of this topic goes beyond the scope of this analysis, but these results suggest that it will be more difficult to keep indoor humidity at desirable levels in the summer in the hot humid climates of the Southeast. Conversely, maintenance of wintertime indoor humidity in cold climates may be facilitated. The results of this analysis also show a general increase in infiltration variability. The implication of increased variability during the summer months is that infiltration of outdoor air Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
pollutants may markedly increase above the average during particular days, especially in places such as Houston or Atlanta, leading to more frequent episodes of acute exposures to outdoor air pollution. Although the outdoor air quality so far into the future may only be speculated at the moment, this decreased ability of buildings to protect their residents from spikes in outdoor air pollution should be factored into exposure and risk scenarios. Uncertainty and Sensitivity Analyses The sensitivity analysis to explore the cost of using a simpler approximation of indoor temperature (seasonal fixed) vs a more realistic one dependent on outdoor temperature was limited by the scarcity of research relating indoor and outdoor temperature fluctuations in representative home samples. From Figure 3, it is also apparent that the difference between using the fixed and variable indoor temperature is greatest in the summer months, and drops by an order of magnitude or more in the other seasons. On the basis of the limited information available, this sensitivity analysis suggests that the fixed seasonal indoor temperature is a reasonable approximation, overall, and our results would not change substantially with a variable indoor temperature model. The implications for exposure and risk appear robust with respect to assumptions about the reference AER used. If the current median AER of 0.7 is replaced by a much lower value of 0.2, the change in exposure to the less reactive indoor sources with R = 0.95 barely changes, from a 4.9% increase to 4.17%, whereas exposure to the most reactive outdoor agents goes from a 4.1% drop to 4.7%. Similarly, minor responses are observed with increased AER. The relative modeling uncertainty associated with predictions on changes of infiltration rates is modest for much of the year, but not uniform. The greatest modeling uncertainty is around predictions for summer months in warmer locations (Atlanta, Houston, Phoenix), where predictions are so scattered that models do not even agree on the direction of the change. In general, predictions for summer months are less reliable than for the rest of the year, accumulating not only the greatest modeling uncertainty, but also the greatest daily and annual variability and, possibly, the most bias (in either direction) from the use of a fixed indoor temperature. Strengths and Weaknesses of the Analysis A strength of this analysis is its wide applicability to similar climates in United States, as both infiltration and climate models are based on general physical principles. Another strength is the inclusion of several different climate models, representing the range of modeling uncertainty about future climate scenarios. The results of our sensitivity analysis show that the analysis is robust to assumptions about the reference AER, as well assumptions about indoor air temperature fluctuations. Furthermore, as the flux analysis is independent of leakage area, these results are also robust to assumptions about initial building condition, within the type analyzed. This implicit or practical insensitivity to assumptions allows for generalizability of the results. Among the limitations, the range of adaptation responses is not a part of the analysis and might markedly modify some conclusions. In particular, adaptations intended to reduce the leakage area of buildings would diminish the effect of increased infiltration and exposure to outdoor contaminants predicted for the summer months in the warmer locations. This same adaptation, however, would enhance the effects of decreased infiltration and increased exposure to indoor sources that is anticipated in most locations for all or part of the year. Although the current efforts encouraging more energy-efficient buildings would favor this adaptation,43,44 the extent of its future implementation is not known. Another adaptation that could affect these conclusions is a change in the use of natural or © 2015 Nature America, Inc.
Climate change and infiltration Ilacqua et al
7 mechanical ventilation, which may be presumably increased in response to milder indoor/outdoor temperature differences. To put our findings in the rights perspective, it is important to observe that these probable future adaptations (more energyefficient buildings and changes in use of natural ventilation) can completely overshadow the effect described in this paper. For example, as shift to buildings with AERs as low as 0.2 per hour, which is already being observed in some locations,45 could change exposure to indoor and outdoor air pollution by 1–2 orders of magnitude more than the effect shown in Figure 4. Conversely, doubling AER through increased natural ventilation (e.g., through window opening) could change exposures by 50% or more under the same conditions. Although both these competing trends may develop concurrently, their relative importance in future scenarios remains unexplored. Perhaps less significantly, changes in the proportion of time spent indoors and outdoors would also affect the balance of exposure to indoor and outdoor sources, but the direction and magnitude of such changes remain speculative. The degree to which building use and natural ventilation may change deserves some exploration for its implications for exposure and risk assessments, perhaps by examining the current variability across geographical climate differences that would be comparable to temporal ones in a changing climate. Another limitation is the focus on a subset of buildings typical of densely populated regions, a choice determined by considerations of both significance for population exposures, and magnitude of effects. Consideration of specific risks to rural populations would require broadening this analysis to different classes of wind sheltering and vertical structure. Similarly, examining the effects on the denser urban areas with residential or commercial high-rise buildings would require a completely different analysis using different infiltration models or sets of parameters appropriate to such buildings. Without inappropriately extending the model applicability, however, based on the choice of infiltration model parameters, we expect that consequences for exposure would be less meaningful for lower and more isolated buildings. Exposure to indoor sources would be less affected in buildings with lower AER, but changes would be more significant for outdoor sources (Figure 4). CONCLUSIONS Diminished air exchange in future climate scenarios may be expected to increase exposure to indoor sources of air pollution, unless these air infiltration reductions are otherwise compensated. Exposure to ambient air pollution, conversely, would be mitigated by lower infiltration, although peak exposure increases during summer months should be considered. Although this analysis elucidates one of the processes through which climate change affects exposure, the net effect of climate change on exposure to both indoor and outdoor sources also depends on other mechanisms, including behavioral responses, potential building modifications, and the climate change penalty on ambient air pollution, which need to be considered in further analyses. ABBREVIATIONS AER, air exchange rate; GCM, global climate model; LBL, Lawrence Berkley National Laboratory; NARCCAP; North American Regional Climate Change Assessment Program; RCM, regional climate model. CONFLICT OF INTEREST The authors declare no conflict of interest.
© 2015 Nature America, Inc.
ACKNOWLEDGEMENTS We wish to thank the NARCCAP for providing the data used in this paper. NARCCAP is funded by the National Science Foundation, the United States Department of Energy, the National Oceanic and Atmospheric Administration, and the United States Environmental Protection Agency Office of Research and Development. We also thank Bryan Bloomer, for inspiring conversations about climate change leading to this work, and about the role of the climate penalty on ambient air pollution. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the United States Environmental Protection Agency.
REFERENCES 1 Stocker TFDQG-KPMTSKAJBANYXVBaPMM. Climate change 2013: The physical science basis. New York: Intergovernmental Panel on Climate Change, 2013. 2 Ashrae. ASHRAE/ANSI Standard 55-2010: Thermal environmental conditions for human occupancy. Atlanta, GA, USA, 2010. 3 Oceanic USN, Atmospheric A. Data Tools: 1981-2010 Normals (cited 18 April 2014). Available from http://www.ncdc.noaa.gov/cdo-web/datatools/normals. 4 Koutrakis P, Briggs SLK, Leaderer BP. Source apportionment of indoor aerosols in Suffolk and Onondaga counties, New York. Environ Sci Technol 1992; 26: 521–527. 5 Breen MS, Schultz BD, Sohn MD, Long T, Langstaff J, Williams R et al. A review of air exchange rate models for air pollution exposure assessments. J Expo Sci Environ Epidemiol 2013; 24: 555–563. 6 Breen MS, Breen M, Williams RW, Schultz BD. Predicting residential air exchange rates from questionnaires and meteorology: model evaluation in central North Carolina. Environ Sci Technol 2010; 44: 9349–9356. 7 Mearns L, McGinnis S, Arritt R, Biner S, Duffy P, Gutowski W et al. The North American Regional Climate Change Assessment Program dataset (doi:10.5065/ D6RN35ST). Boulder, CO: National Center for Atmospheric Research Earth System Grid data portal 2007. 8 Mearns LO, Gutowski W, Jones R, Leung R, McGinnis S, Nunes A et al. A regional climate change assessment program for North America. Eos, Trans Amer Geophys Union 2009; 90: 311. 9 Nakicenovic N, Swart R, Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, UK, 2000. 10 Team RDC. R: A Language and Environment for Statistical Computing. Vienna, Austria: Vienna, Austria: R Foundation for Statistical Computing, 2011. 11 de Dear R, Brager GS. Developing an adaptive model of thermal comfort and preference. ASHRAE Trans 1998; 104: 145–167. 12 Humphreys M. Field studies of thermal comfort compared and applied. J Inst Heat Vent Eng 1976; 44: 5–27. 13 Nicol JH, MA Humphreys. Thermal comfort as part of a self-regulating system. Build Res Pract 1973; 6: 191–197. 14 Nicol JH, MA Humphreys. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build 2002; 34: 563–572. 15 de Dear R. A global database of thermal comfort field experiments. ASHRAE Trans 1998; 104: 1141–1152. 16 Humphreys M. Outdoor temperatures and comfort indoors. Build Res Pract 1978; 6: 92–105. 17 Nguyen JS, Schwartz J, Dockery DW. The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air 2014; 24: 103–112. 18 Bureau USC. Historical Census of Housing Tables 2011 (cited 31 December 2013). Available from http://www.census.gov/hhes/www/housing/census/historic/units. html. 19 Pryor SC, Barthelmie RJ. Climate change impacts on wind energy: a review. Renew Sust Energy Rev 2010; 14: 430–437. 20 Yamamoto NS, Shendell DG, Winer AM, Zhang J. Residential air exchange rates in three major US metropolitan areas: results from the Relationship Among Indoor, Outdoor, and Personal Air Study 1999–2001. Indoor Air 2010; 20: 85–90. 21 WHO handbook on indoor radon: a public health perspective. World Health Organization: Geneva, 2009. 22 Samet JM. Radon and lung cancer. J Natl Cancer Inst 1989; 81: 745–758. 23 Jones A. Indoor air quality and health. Atmos Environ 1999; 33: 4535–4564. 24 Postendorfer JB, Butterweck G, Reineking A. Daily variation of the radon concentration indoors and outdoors and the influence of meteorological parameters. Health Phys 1994; 67: 283–287. 25 Arvela HW, K Winqvist. A model for indoor radon variations. Environ Int 1989; 15 239–246. 26 Darby S, Hill D, Auvinen A, Barros-Dios JM, Baysson H, Bochicchio F et al. Radon in homes and risk of lung cancer: collaborative analysis of individual data from 13 European case-control studies. BMJ 2005; 330: 223.
Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
Climate change and infiltration Ilacqua et al
8 27 Krewski D, Lubin JH, Zielinski JM, Alavanja M, Catalan VS, Field RW et al. A combined analysis of North American case-control studies of residential radon and lung cancer. J Toxicol Environ Health A 2006; 69: 533–597. 28 Nazaroff W. Radon transport from soil to air. Revi Geophys 1992; 30: 137–160. 29 Jia C, Batterman S, Godwin C. VOCs in industrial, urban and suburban neighborhoods, Part 1: Indoor and outdoor concentrations, variation, and risk drivers. Atmos Environ 2008; 42: 2083–2100. 30 Mendell MJ. Indoor residential chemical emissions as risk factors for respiratory and allergic effects in children: a review. Indoor Air 2007; 17: 259–277. 31 Wallace LA. Comparison of risks from outdoor and indoor exposure to toxic chemicals. Environ Health Perspect 1991; 95: 7–13. 32 Weschler CJ, Nazaroff WW. Semivolatile organic compounds in indoor environments. Atmos Environ 2008; 42: 9018–9040. 33 Jacob DJ, Winner DA. Effect of climate change on air quality. Atmos Environ 2009; 43: 51–63. 34 Wu S, Mickley LJ, Leibensperger EM, Jacob DJ, Rind D, Streets DG. Effects of 2000–2050 global change on ozone air quality in the United States. J Geophys Res 2008; 113: D06302-D. 35 Zhao S, Pappin AJ, Morteza Mesbah S, Joyce Zhang JY, MacDonald NL, Hakami A. Adjoint estimation of ozone climate penalties. Geophys Res Lett 2013; 40: 5559–5563. 36 Bloomer BJ, Stehr JW, Piety CA, Salawitch RJ, Dickerson RR. Observed relationships of ozone air pollution with temperature and emissions. Geophys Res Lett 2009; 36: L09803–L09803. 37 Abt E, Suh HH, Catalano P, Koutrakis P. Relative contribution of outdoor and indoor particle sources to indoor concentrations. Environ Sci Technol 2000; 34: 3579–3587. 38 Wallace L. Indoor particles: a review. J Air Waste Manage Assoc 1996; 46: 98–126. 39 Fann N, Lamson AD, Anenberg SC, Wesson K, Risley D, Hubbell BJ. Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk Anal 2012; 32: 81–95.
40 Cohen AJ, Ross Anderson H, Ostro B, Pandey KD, Krzyzanowski M, Künzli N. The global burden of disease due to outdoor air pollution. J Toxicol Environ Health A 2005; 68: 1301–1307. 41 Morawska L, Afshari A, Bae GN, Buonanno G, Chao CY, Hänninen O et al. Indoor aerosols: from personal exposure to risk assessment. Indoor Air 2013; 23: 462–487. 42 Leaderer BP, Naeher L, Jankun T, Balenger K, Holford TR, Toth C et al. Indoor, outdoor, and regional summer and winter concentrations of PM10, PM2.5, SO4(2)-, H+, NH4+, NO3-, NH3, and nitrous acid in homes with and without kerosene space heaters. Environ Health Perspect 1999; 107: 223–231. 43 Krarti M. Weatherization and Energy Efficiency Improvement for Existing Homes: An Engineering Approach. CRC Press: Boca Raton, FL, USA, 2012. 44 Bradshaw JL, Elie B-Z, Harris RH. Comparing the effectiveness of weatherization treatments for low-income, American, urban housing stocks in different climates. Energy Build 2014; 69: 535–543. 45 Offermann FJ. IAQ applications IAQ in airtight homes. ASHRAE J 2010; 52: 58–60. 46 Bruno RC. Sources of indoor radon in houses: a review. J Air Pollut Control Assoc 1983; 33: 105–109. 47 Liu W, Zhang J, Zhang L, Turpin B, Weisel C, Morandi M et al. Estimating contributions of indoor and outdoor sources to indoor carbonyl concentrations in three urban areas of the United States. Atmos Environ 2006; 40: 2202–2214. 48 Lee K, Vallarino J, Dumyahn T, Ozkaynak H, Spengler JD. Ozone decay rates in residences. J Air Waste Manage Assoc 1999; 49: 1238–1244. 49 Yamanaka Si. Decay rates of nitrogen oxides in a typical Japanese living room. Environ Sci Technol 1984; 18: 566–570. 50 Spicer CW, Kenny DV, Ward GF, Billick IH. Transformations, lifetimes, and sources of NO 2, HONO, and HNO 3 in indoor environments. Air Waste 1993; 43: 1479–1485. 51 Géhin E, Ramalho O, Kirchner S. Size distribution and emission rate measurement of fine and ultrafine particle from indoor human activities. Atmos Environment 2008; 42: 8341–8352.
Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website (http:// www.nature.com/jes)
Journal of Exposure Science and Environmental Epidemiology (2015), 1 – 8
© 2015 Nature America, Inc.
