A Radiator in a Single-family Home Is Considered

Indoor Air. Author manuscript; available in PMC 2015 Feb 1.

Published in final edited course as:

PMCID: PMC3791146

NIHMSID: NIHMS485752

The relationship betwixt indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity

Jennifer L. Nguyen

¹ Department of Environmental Wellness Harvard School of Public Health 401 Park Drive Boston, MA 02215

Joel Schwartz

^one Department of Environmental Health Harvard School of Public Health 401 Park Drive Boston, MA 02215

² Department of Epidemiology Harvard School of Public Health 677 Huntington Avenue Boston, MA 02215

Douglas W. Dockery

¹ Department of Ecology Health Harvard School of Public Wellness 401 Park Drive Boston, MA 02215

² Department of Epidemiology Harvard School of Public Health 677 Huntington Avenue Boston, MA 02215

Abstract

Introduction

Many studies report an association betwixt outdoor ambient weather and health. Outdoor weather condition may exist a poor indicator of personal exposure because people spend most of their fourth dimension indoors. Few studies have examined how indoor atmospheric condition relate to outdoor ambient weather.

Methods and Results

The average indoor temperature, credible temperature, relative humidity (RH), and absolute humidity (AH) measured in 16 homes in Greater Boston, Massachusetts, from May 2011 - April 2012 was compared to measurements taken at Boston Logan drome. The relationship between indoor and outdoor temperatures is not-linear. At warmer outdoor temperatures, there is a potent correlation between indoor and outdoor temperature (Pearson correlation coefficient, r = 0.91, slope, β = 0.41), but at cooler temperatures, the clan is weak (r = 0.40, β = 0.04). Results were similar for outdoor apparent temperature. The relationships were linear for RH and AH. The correlation for RH was small (r = 0.55, β = 0.39). AH exhibited the strongest indoor-to-outdoor correlation (r = 0.96, β = 0.69).

Conclusions

Indoor and outdoor temperatures correlate well simply at warmer outdoor temperatures. Outdoor RH is a poor indicator of indoor RH, while indoor AH has a strong correlation with outdoor AH yr-round.

Keywords: temperature, humidity, exposure, indoor, outdoor, one-twelvemonth measurement

INTRODUCTION

The climate is irresolute and will proceed to for the next several decades (IPCC 2007). The range and extremes of ambience temperature are expected to change and regional weather patterns are anticipated to get more than variable and more unstable (McMichael and Lindgren, 2011). A changing climate may present significant public health problems. Adverse health effects occur at both farthermost (e.one thousand., heat waves, cold spells) and less extreme ambient temperatures (Ye et al., 2012). The relationship between weather and human health is heterogeneous; the association varies past geography and is often J-shaped or U-shaped, with varying thresholds at which both common cold-related and rut-related risks increase for unlike diseases (Bhaskaran et al., 2009; Ye et al., 2012), cardiovascular-related mortality (Medina-Ramon and Schwartz, 2007; Anderson and Bell, 2009; Braga et al., 2002), and all-crusade mortality (Medina-Ramon and Schwartz, 2007; Anderson and Bell, 2009; Hajat et al., 2007; McMichael et al., 2008).

Studies relating weather condition to health unremarkably employ a single population-level indicator – an outdoor fundamental site monitor - as an indicator of personal exposure. This approach may lead to misclassification of exposure that is likely more variable at the home or personal level (White-Newsome et al., 2012) due to individual differences in time-action patterns and sources of exposure (due east.g., local indoor, person-generated, and outdoor sources in homes and workplaces) (Rhomberg et al., 2011). Since people in industrialized countries by and large spend more than xc% of their time indoors (Hoppe and Martinac, 1998), indoor atmospheric condition may exist a improve mensurate of personal exposure than outdoor measures. Still, if indoor conditions correlate strongly with ambience outdoor conditions, using conditions service observations of outdoor atmospheric condition would be a sufficient, practical indicator of personal exposure.

Normally used weather measures in health studies are outdoor hateful daily temperature, minimum and maximum temperature, and indices that combine air temperature and humidity, such as apparent temperature and the humidity alphabetize (Conlon et al., 2011; Ye et al., 2012). No single temperature measure has been reported to be the preferred measure for relating weather to human wellness (Barnett and Astrom, 2012). Farther, few studies take examined how indoor temperature and humidity are related to outdoor, ambient levels. Characterization of this relationship would help in understanding sources of measurement error in epidemiological studies.

The purpose of this study was to examine the relationship betwixt indoor conditions to outdoor weather observations in Greater Boston, Massachusetts, United states of america. We focused on four atmospheric condition measures – temperature, apparent temperature, relative humidity (RH), and absolute humidity (AH).

MATERIALS AND METHODS

Study population

The target population for this report was homes in Greater Boston, Massachusetts. In Nov 2010, occupants of potential participant homes were approached without prior knowledge of housing characteristics or indoor climate command. Participation was solicited from faculty and staff known to the study authors at the Harvard Schoolhouse of Public Health, and homes were eligible if the current occupants planned to remain in the same residence for at least 12 months and were able to substitution samplers for periodic data downloads. Of 25 persons contacted, 4 declined and 4 homes were ineligible because the occupants planned to movement within the next year. Seventeen homes were enrolled. One habitation was excluded when the occupant left the Harvard School of Public Health in May 2011. Two homes were withdrawn from the study early because the occupants inverse residence (one in October, one in December), merely we retained their data. Occupants completed a brief questionnaire nigh their residence, including residence type (apartment or single family house), type of habitation heating system (baseboard, forced hot air, or radiator), cooling system (key air conditioning, window ac or none), and use of a humidifier or de-humidifier.

Indoor measurements

Temperature (°C) and RH (%) was measured continuously from December 1, 2010 to April thirty, 2012. Occupants were given one HOBO U8 Data Logger (H08-004-02 or H08-007-02, Onset Corporation; Bourne, Massachusetts) in late November 2010. These loggers measure out temperature from -20°C to 70°C with accurateness of ± 0.7°C at 21°C and RH from 25% to 95% with accuracy ± v%. The occupants were asked to place the information loggers in their living rooms abroad from sources of heat, cold, moisture and dryness. The loggers were brought to Harvard School of Public Health approximately every two months for data downloading. Afterwards initial examination of the data, the measurement range of the U8 loggers was found to be insufficient to capture low wintertime indoor RH (i.e., the U8 loggers could not measure RH beneath ~ 25%). Data recorded betwixt December 2010 and Apr 2011 were discarded and the end of the study was extended to April xxx, 2012. In September and October 2011, the U8 loggers were replaced with HOBO U12-011 Temperature and RH Data Loggers (Onset Corporation). Occupants were instructed to place the U12 logger in the same location where the U8 logger had been. These loggers measure temperature from -20°C to seventy°C with accuracy of ± 0.35°C from 0°C to l°C and RH from 5% to 95% with accurateness of ± 2.5% from x% to xc%. The U12 loggers were calibrated in one case using National Institute of Standards and Technology instrumentation, EDGETECH Model DS2 Dew Bespeak Hygrometer in a calibration environment of approximately 25°C and 50% RH. 10 loggers were calibrated in July 2011; the remaining 6 were calibrated in October 2011.

The U8 series of loggers recorded measurements at 24-minute intervals and the U12 loggers recorded measurements at 30-minute intervals. Daily averages for each habitation were computed simply when all measurements on a given 24-hour interval were available. The daily indoor average was computed using measurements from at least ten homes. Otherwise, the daily indoor average was considered missing. Due to the schedule of data downloading, 10 indoor daily averages are missing. Apparent temperature and AH were derived from the measured temperature and RH. Credible temperature is a measure of perceived temperature that takes into account the event of humidity (Steadman, 1979). Apparent temperature was calculated using the post-obit formula (Zanobetti and Schwartz, 2005): apparent temperature = -2.653 + (0.994 × T_c) + (0.0153 × T_d ²), where T_c is temperature in °C and T_d is dew point temperature in °C. RH is the proportion of water vapor in the air relative to the maximum h2o vapor that can exist held in air at a given temperature, and thus a temperature-dependent measure. AH is a measure of the water vapor content in air expressed equally a density (1000/m^three). This metric is not functionally dependent on temperature (Mendell and Mirer, 2009).

Outdoor measurements

The daily outdoor, ambient temperature (°C), dew point temperature (°C), and RH (%) were obtained from the National Weather condition Service Station at Boston Logan Airport, Eastward Boston, MA. Apparent temperature and AH values were computed from the temperature, RH, and dew point.

Statistical analysis

Nosotros calculated Pearson correlation coefficients (r) and 95% confidence intervals (CI) between the indoor and outdoor daily averages. Nosotros examined the shape of the relationships using linear, piecewise linear, and loess regression models. We tested for deviation from linearity using an approximate F-test comparison the linear model to the non-parametric loess regression model (Keele, 2008). Piecewise linear regression was fit using the "significant zero crossings" method, a non-parametric smoothing method that identifies the existence of a threshold based on where the office's derivatives alter significantly (Sonderegger et al., 2009). We constructed boxplots comparing the magnitude and variability of home-specific daily correlations in the heating season (November - April) vs. the non-heating flavor (June - September), allowing for ane transition calendar month between seasons (May and Oct). We examined the spatial variation of the home-specific daily correlations using the Moran's I and Local Moran's I, measures of global and local spatial autocorrelation, respectively. For both spatial tests, Z-scores signal the probability of autocorrelation. We establish evidence of spatial variation for AH. We then assessed if distance to the Boston airport and the ocean were significant predictors of the residential AH correlations. Because air conditioning was significantly associated with distance to the airport and coast, we adjusted for air conditioning (central, window, or none) using residuals from a logistic model. In sensitivity analyses, we examined the relationship between indoor and outdoor maximum and minimum daily average temperatures.

SAS version 9.ii (SAS Institute; Cary, Northward Carolina) was used to construct data sets and calculate descriptive statistics and correlation coefficients. R version 2.14.0 (R Foundation for Statistical Computing; Vienna, Austria) was used for regression modeling. The Analyzing Patterns and Mapping Clusters toolsets in ArcGIS v10.01 (ESRI; Redlands, California) were used for spatial analyses.

RESULTS

The 16 participating homes were located an average of 12.6 miles (20.iii km) from Boston Logan airport (range: 4.ii, 26.iv miles, Figure 1 ). Twelve (75%) were single family homes, 11 (69%) had either central or window air conditioning, humidifiers were used in 4 (25%) homes and de-humidifiers were used in but two (12.five%) homes ( Table i ).

Map of participant residences by air conditioning (Air conditioning) blazon and Boston Logan Airdrome, Massachusetts.

Table one

Indoor climate command characteristics of 16 homes in Greater Boston, Massachusetts.

Characteristic	North (%)
Residence Type
Apartment	4 (25.0)
Single family business firm	12 (75.0)
Heating System
Baseboard	5 (31.three)
Forced hot air	6 (37.v)
Radiator	5 (31.3)
Cooling System
Central ac	7 (43.8)
Window air conditioning	4 (25.0)
None	v (31.3)
Apply of a humidifier
Yes	4 (25.0)
No	12 (75.0)
Employ of a de-humidifier 1
Yes	2 (12.five)
No	14 (87.5)

Temperature, apparent temperature, and AH display a seasonal pattern both indoors and outdoors, with highs during the summer months. In contrast, indoor RH follows a like seasonal design, only outdoor RH fluctuates with no consistent pattern ( Figure 2 ). The average daily weather conditions from May 2011 – April 2012 are presented in Tabular array 2. Correlation coefficients betwixt the indoor and outdoor levels are provided in Table 3 .

Plot of indoor and outdoor daily (a) temperature, (b) credible temperature, (c) relative humidity, and (d) absolute humidity from May 2011 – April 2012, Greater Boston, Massachusetts.

Tabular array 2

Distribution of daily average outdoor and indoor temperature and humidity in Greater Boston, Massachusetts, May 2011 – Apr 2012

Measure	Northward	Mean (SD)	Min	25%	Median	75%	Max
Temperature (°C)
Outdoor	366	13.1 (8.6)	-10.half-dozen	6.ane	13.three	20.6	33.3
Indoor	356	21.0 (2.4)	18.2	19.2	nineteen.8	23.4	27.2
Apparent Temperature (°C)
Outdoor	366	12.iv (ten.1)	-7.iv	3.5	xi.6	21.8	37.1
Indoor	356	19.9 (3.ix)	fifteen.5	16.7	18.0	23.5	28.3
Relative Humidity (%)
Outdoor	366	64.8 (fourteen.eight)	21.0	54.0	65.0	76.0	97.0
Indoor	356	46.4 (12.2)	23.3	35.0	47.5	55.v	71.0
Absolute Humidity (thou/m3)
Outdoor	366	viii.2 (four.vi)	0.9	iv.ii	7.three	12.iii	xviii.5
Indoor	356	8.ix (3.two)	iii.8	5.ix	8.2	11.six	16.iii

Table 3

Pearson correlation coefficients ¹ for daily average outdoor and indoor temperature and humidity in Greater Boston, Massachusetts, May 2011 – April 2012

	Indoor
Outdoor	Temperature	Apparent Temperature	Relative Humidity	Absolute Humidity
Temperature (°C)	0.87	0.89	0.fourscore	0.90
Credible Temperature (°C)	0.92	0.94	0.81	0.92
Relative Humidity (%)	0.20	0.thirty	0.55	0.45
Absolute Humidity (chiliad/m3)	0.88	0.93	0.88	0.96

We found significant deviation from linearity for the relationship betwixt indoor and outdoor temperature, apparent temperature, and AH (all p-values < 0.05), but not for RH. However, test of the scatterplot for AH indicated that linear regression provided an acceptable and parsimonious model fit ( Figure iii ). Piecewise linear regression identified a threshold of 12.7°C (54.9°F) for temperature and a threshold of 9.8°C (49.vi°F) for apparent temperature. When outdoor temperatures are ≥ 12.7°C, at that place is a strong linear correlation with the average indoor temperature (r = 0.91, 95% CI: 0.89, 0.93, β = 0.41, standard error, se(β) = 0.02). The relationship is considerably weaker below this threshold (r = 0.xl, 95% CI: 0.27, 0.52, β = 0.04, se(β) = 0.01). When outdoor apparent temperature is ≥ nine.viii°C, in that location is a strong correlation with indoor apparent temperature (r = 0.94, 95% CI: 0.93, 0.96, β = 0.42, se(β) = 0.02). The correlation weakens beneath this threshold (r = 0.66, 95% CI: 0.57, 0.74, β = 0.09, se(β) = 0.01). The correlation for RH was pocket-size (r = 0.55, 95% CI: 0.47, 0.62, β = 0.45, se(β) = 0.04). AH exhibited the strongest indoor-to-outdoor correlation (r = 0.96, 95% CI: 0.95, 0.97, β = 0.69, se(β) = 0.01).

Scatterplot and regression results relating indoor to outdoor (a) temperature, (b) apparent temperature, (c) relative humidity, and (d) absolute humidity from May 2011 – Apr 2012, Greater Boston, Massachusetts. Red line, piecewise linear regression for (a) and (b) and linear regression for (c) and (d); black solid line, reference line placed at knot value; black dashed line, y=10 (45 degree) reference line. SE, standard fault; r, correlation.

In sensitivity analyses examining maximum and minimum temperature, results were similar to those for boilerplate temperature. Both atmospheric condition metrics exhibited a piecewise linear relationship, with the threshold shifting to 15.6°C for maximum temperature and nine.8°C for minimum temperature. Similarly, the correlations were weak at lower outdoor temperatures (r = 0.16, 95% CI: 0.002, 0.31, β = 0.02, se(β) = 0.01 for maximum temperature; r = 0.64, 95% CI: 0.55, 0.72, β = 0.08, se(β) = 0.01 for minimum temperature), and stronger at warmer outdoor temperatures (r = 0.87, 95% CI: 0.84, 0.xc, β = 0.40, se(β) = 0.02 for maximum temperature; r = 0.92, 95% CI: 0.89, 0.94, β = 0.49, se(β) = 0.02 for minimum temperature).

Correlations for all atmospheric condition measures were lowest during the heating season (November – April). However, for AH, the magnitude remained strong and was similar to the variability observed in the non-heating flavor (June – September, Effigy four ). Homes with a high (or low) correlation with one weather measure did non tend to accept a loftier (or low) correlation with other weather measures. For instance, the dwelling with the lowest year-circular absolute humidity correlation (r = 0.82) had the 5^th highest year-circular correlation for temperature (r = 0.86).

Boxplots of the variability in residential indoor-to-outdoor correlations by heating season among xvi homes for (a) temperature, (b) credible temperature, (c) relative humidity, and (d) absolute humidity from May 2011 – April 2012, Greater Boston, Massachusetts. Red dashed line placed for reference at correlation coefficient = 0.5. Meridian whisker, maximum; lesser whisker, minimum.

In that location was evidence of a spatial pattern in the dwelling-specific daily AH correlations; correlations were significantly lower for homes located further west (Global Moran's Alphabetize = 0.24, Z-score = iii.65, p < 0.0005). Central air workout was significantly associated with distance to the airport (p < 0.005) and the coast (p < 0.001). Adjusting for air conditioning, the home-specific correlations significantly decreased with increasing distance from the Boston airport (p = 0.02, Figure 5 ) and from the ocean (β = - 0.003, p = 0.03). There was no significant pattern in the home-specific daily correlations for temperature (p = 0.sixteen), apparent temperature (p = 0.91), or RH (p = 0.74).

Scatterplot and linear regression of dwelling house-specific indoor absolute humidity correlations to Boston Logan Airport in relation to distance to the airdrome from May 2011 – April 2012, Greater Boston, Massachusetts. Results are adjusted for ac type.

Discussion

Exposure measurement error is a common limitation of studies of the environment and health (Zeger et al., 2000). When measurements from a central site monitor, such as an airport conditions station, are used to guess personal exposures, the true exposure variability is underestimated (Rhomberg et al., 2011) and results in measurement fault. Measurement error affects estimation of the exposure-response relationship by changing the apparent shape of the human relationship, masks population-level thresholds, reduces statistical power (Rhomberg et al., 2011), and makes information technology hard to interpret associations (Zeger et al., 2000). A meliorate understanding of how indoor weather condition vary with outdoor conditions would help in estimating the likelihood and magnitude of exposure measurement error.

In a sample of homes in eastern Massachusetts, nosotros found that outdoor, airport AH is strongly correlated to indoor AH, which suggests that outdoor AH may be a skillful indicator of personal exposure to AH. Outdoor temperature and apparent temperature were strongly correlated to indoor temperatures merely when outdoor temperatures were warmer. If our results can be extended to other areas, our findings suggest that studies that take reported associations of outdoor warm temperature with health are unlikely to exist strongly afflicted past measurement error, and the results are reasonably interpretable as a temperature result. In contrast, the weak correlation indoors-to-outdoors for temperature on libation days suggests that outdoor temperature is a poor indicator of personal temperature exposure on absurd days. Nevertheless, studies have reported associations between cold weather and increased morbidity and mortality. Given the high degree of measurement error, the reason for these significant effects needs farther exploration. The high correlation between outdoor temperature and indoor AH we observed (r = 0.xc) may provide an explanation. Cold effects reported in the literature might, at least in part, be reflecting an association with AH, since outdoor cold temperature is a better surrogate for lower indoor AH than for lower indoor temperature. Further studies need to examine this question in a wider range of climatic conditions.

The correlation between indoor and outdoor RH was weak and suggests that outdoor RH is not a good indicator of indoor RH exposure.

We measured temperature and humidity in the living room of 16 homes, but there could be room-to-room and flooring-to-floor variations in temperature and humidity (Wallace et al., 2002; Collins, 1986). Private measurements are as well affected past the proximity to sources of heat and wet, such as laundry, showers and cooking (Wallace et al., 2002). We instructed occupants to place the information loggers in a location away from such sources. Differences in personal behavior in indoor climate regulation may vary by season. For example, windows are more probable to be opened for extended periods of time when the outside temperature is moderate (Wallace et al., 2002). Systematic variation in the relationship between personal exposures and ambience levels at different times and in different locations can result in a classical-type error that biases exposure-response relationships (Rhomberg et al., 2011). Our results propose that systematic variation is of concern for temperature, apparent temperature, and RH. Indoor AH, however, exhibited a strong correlation with outdoor levels year-round, in both the heating and non-heating seasons. This suggests that systematic variation is unlikely to be a significant source of measurement mistake when using AH as an exposure measure out.

Homes with a high correlation for one weather measure did not tend to have a high correlation for other atmospheric condition measures. Differences in thermal preferences, levels of temperature control, socioeconomic status, and the health states of the homes' occupants likely all contribute to this variability. In Boston, the toll of oestrus is generally included in the hire for apartments, and apartment renters may or may not be able to control the thermostat. In homes with accessible thermostats, thermal command and air workout apply might increase with college household wealth. Humidifiers are expected to be used more often in homes with occupants suffering from respiratory and/or peel ailments.

There was spatial variability in the correlation between indoor and outdoor AH. This finding was not surprising, since in the northeast Usa, excursions of warm, moist air from the Gulf of Mexico provide the major source of moisture and pb to local spatial variability in AH (Robinson, 1998). Correlations weakened with increasing distance from the Boston airdrome. Nonetheless, the pass up with altitude is small; a thirty-mile increase in distance lowers the expected correlation by less than 10%. The observed design cannot be explained by dehumidifier use, which reduces indoor AH levels independent of temperature (Bernstein et al., 2005), because just i occupant residing farther inland reported using a de-humidifier. Spatial variability in exposure is considered to exist a Berkson-type fault that is unlikely to bias measures of association in epidemiologic studies (Zeger et al., 2000).

Temperature and humidity touch on the thermal balance of the man torso through furnishings on the skin and respiratory organs (Reinikainen and Jaakkola, 2003). Changes in air temperature trigger a sympathethic reflex via the skin that strengthens with lower air temperature (Schneider et al., 2008). Exposure to common cold increases plasma concentrations of norepinephrine and induces peripheral vasoconstriction via stimulation of alpha-adrenergic receptors. Vasoconstriction limits heat loss past redistributing blood to the core and causes an increase in cardiac output that supports higher metabolic production (Castellani et al., 2002). Cold extremities and the lowering of core trunk temperature can induce short-term increases in heart rate and claret pressure level and promote increased blood viscosity (Collins, 1986), hemoconcentration and arterial thrombosis that could lead to triggering of acute cardiac events. Overexertion in a common cold environment (e.g. shoveling snow) could trigger increases in blood pressure level that pb to coronary plaque rupture and subsequent coronary thrombosis (Medina-Ramon and Schwartz, 2007). Redistribution of blood flow, reduced plasma book, increased cardiac output, and activation of the sympathetic nervous system all bear upon components of the allowed system. Leukocyte, granulocyte (Castellani et al., 2002) and macrophage (Larsson et al., 1998) counts increment later exposure to cold air, but whether common cold exposure really depresses immune function is still unclear (Castellani et al., 2002). Loftier temperatures and humidity require the human torso to respond by increasing heat loss through the peel surface via claret circulation (Hoppe and Martinac, 1998). Loss of common salt and water in sweat results in hemoconcentration (Keatinge, 2002). This places strain on the cardiovascular and respiratory systems, and combined with increased claret viscosity and cholesterol levels may increase the run a risk of cardiovascular and respiratory deaths (Medina-Ramon and Schwartz, 2007).

AH is considered to be the most of import measure of humidity for the interpretation of the effect of humidity on the human body (Fielder, 1989). Separating the effects of cool air from dry air on the airways is difficult because cold air is necessarily dry. Isolating the effect of dry air past breathing warm, dry air is too bereft considering evaporative water loss causes cooling of the lungs (Giesbrecht, 1995). Inspired air is warmed to body temperature, saturated with h2o in the airways, and results in substantial rut and water loss in the larger airways (Larsson et al., 1998). In fact, increasing the water vapor content in inspired gas has the same biophysical consequences as warming of the airway mucosa (Fontanari et al., 1996). The degree of airway cooling and drying increases and moves to more primal airways as the temperature and/or water content of the inspired air decreases (Giesbrecht, 1995). Cooling of the upper airways activates cold receptors or osmoreceptors in the nasal mucosa (Fontanari et al., 1996) that induce reflex-mediated bronchoconstriction (Koskela, 2007). Cold, dry air can promote respiratory infection by drying the mucosal surface (Reinikainen and Jaakkola, 2003) and decreasing the action of cilia that help to remove airway contaminants before they tin exist absorbed in the respiratory mucosa (Castellani et al., 2002; Collins, 1986).

The elderly are specially vulnerable to cold, dry out air (Anderson and Bong, 2009; Hajat et al., 2007). Hemoconcentration resulting from peripheral vasoconstriction in response to common cold weather condition makes claret more than prone to clotting. Amidst the elderly, who oftentimes have roughened arteries by atheroma, this process increases the likelihood of thrombus formation. The loss of salt and water in sweat in response to hot temperatures may also increase the gamble of clotting (Keatinge, 2002). Older persons have lower metabolic heat production and some develop disorders of thermoregulatory function. Debilitation and immunological senescence amongst older persons increases susceptibility to infectious diseases (Collins, 1986).

In general, people living in areas with higher mean temperatures are more than vulnerable to common cold days (Bhaskaran et al., 2009; The Eurowinter Group, 1997), though other studies have reported increased risk of mortality from colder weather in milder climates (Healy, 2003) and increased vulnerability to hotter temperatures in cities with milder warm seasons (Braga et al., 2002; Medina-Ramon and Schwartz, 2007). 1 caption for this variability in health effects is differences in housing standards. For example, the Nordic countries of Sweden, Kingdom of norway and Finland have very high habitation energy efficiency standards that aid in adapting to a comparatively cold climate, while homes in southern and western Europe have lower thermal efficiency (eastward.k., less insulation, fewer double-glazed windows) (Healy, 2003). Another possibility is that air workout use reduces the event of temperature on health. Ostro et al. (2010) estimated a 0.76% absolute reduction in backlog take chances of cardiovascular illness for each 10% increment in air conditioner ownership for persons aged 65 years or older.

In that location are several limitations to this work. This study was limited by a small sample size and was bars to a small geographical area. Harvard faculty and staff may be different from the general population with respect to socioeconomic status, pedagogy, and knowledge on temperature and humidity as risk factors for adverse health outcomes, all of which may touch on the level of temperature and humidity control of the dwelling house. The relationships found in this report may not be applicable to other regions due to community differences in the methods of climate adaptation (e.g., quality of housing, the prevalence of air conditioning, type and employ of protective clothing) and the type of surface area (i.eastward., urban, suburban, or rural). Urban areas tend to be warmer than suburban and rural areas, due to increased oestrus retention in more heavily built-up and population-dense areas (Hajat et al., 2007). Finally, the relationships presented here cannot be directly linked to personal exposures due to differences in time-activity patterns within the home, movement between work, home, and the outdoors, and individual clothing preferences (Kim et al., 2011). These results employ to residential indoor exposure, and may non apply to indoor exposure experienced in settings such equally work environments, office buildings and nursing homes.

Of four outdoor weather measures, we found that AH had the strongest correlation to indoor conditions. Examination of these relationships in other geographical locations could help explain why weather-related health risks have variable thresholds at which health effects occur at different geographical locations. Several studies have linked low AH levels to influenza-like illness, influenza epidemics and influenza-related mortality (Shaman et al., 2010; Shoji et al., 2011; van Noort et al., 2011), just few studies accept examined AH exposure in relation to other health outcomes. Future epidemiologic studies of the association of weather with morbidity and bloodshed should include AH every bit a maybe better measure of exposure in terms of both measurement error and biological relevance.

​

Practical Implications

This report examined the relationship between indoor and outdoor ambience weather conditions using 4 different measures. The results suggest that when only outdoor information are bachelor for human weather condition exposure, absolute humidity is the outdoor measure least prone to measurement error. The results also prove that outdoor temperature is a poor indicator of indoor temperature exposure during colder months in the New England region, USA. Studies relating outdoor weather to human health should take into consideration how well outdoor weather condition serve as indicators of indoor or personal weather exposure in the studied geographical area.

ACKNOWLEDGEMENTS

We would like to thank all written report volunteers for their participation. This report was supported in office by National Found of Ecology Health Sciences (NIEHS) grant ES000002. J.Fifty.Due north. received support from the Benjamin Greely Ferris, Jr. Fellowship Fund and NIEHS grants T32ES007069 and R21ES020194. JS received support from NIEHS R21ES020695 and NIA R21AG040027.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791146/

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