the benefits of whole-house in-duct air cleaning in reducing exposures to fine particulate matter of outdoor origin: a modeling analysis - indoor air cleaner
Health risks of air pollution of fine particles (PM2. 5)
It is an important public health problem, and it is possible to partially alleviate it through interventions such as air cleaning equipment, which reduce personal exposure to ambient pm2. 5. 5.
Describe features exposed to ambient pm2. 5.
5 as the function of residential air purifier, there are many indoor
The regional indoor air quality model is used to integrate spatial resolution data of house, meteorological and environmental pm2. 5.
5. perform performance tests on residential air purifiers to estimate the short term
Average annual pm2.
Outdoor origins within three metropolitan area residences.
The associated effects of reduced ambient pm2. 5 on public health.
Five exposures were estimated using the standard health impact assessment method.
Estimated indoor level of environmental pm2.
5 varies greatly in ventilation and air cleaning configurations. The median 24-
The average indoor and outdoor ratio of H environment PM2. 5 was 0.
57 households are natural ventilation households, 0.
35 families with central air conditioning (AC)
Filter with regular, 0.
1 for families with efficient central air conditioning
Median modeling 24-
The average indoor concentration of H. pm2.
There are 5 outdoor sources in these three configurations, which are 8. 4, 5. 3, and 1.
5 μg/m3, respectively.
Reduce potential public health benefits of exposure to ambient pm2. 5.
The air cleaning system provides a lot of money.
If the entire single population
Home houses with central air conditioning in the modeling field are converted from traditional filtration to highefficiency in-
The air duct is clean and the environment PM2.
It is estimated that exposure in these metropolitan areas results in a reduction of 700 premature deaths per year, 940 visits to hospitals and emergency rooms, and 130,000 asthma attacks.
In addition to controlling source emissions, high
Indoor air purifiers are expected to reduce particles exposed to outdoor sources and are expected to be an effective means of managing the public health impact of environmental particulate pollution.
The analysis focuses on the counties that make up the metropolitan area of Cincinnati, Cleveland, and Columbus, Ohio, as these regions have detailed information for geographic resolution
Home building inventory, residential heating and refrigeration systems, environmental PM and meteorology.
Together, these data estimate the concentration of outdoor source PM in each county's daily representative residential building inventory in 2005.
Three analyses were conducted for each type of residence and County :(1)
Natural ventilation; (2)
Forced air heating and cooling with traditional
Pipeline filtering and (3)
Forced air heating and coolingefficiency in-
Clean pipe air.
The results of each building type and County were analyzed to determine the determinants of ambient PM in indoor air.
Finally, the potential public health benefits of reducing exposure to environmental PM provided by residential air cleaning were calculated based on the standard method of health impact assessment.
The details of the method and implementation are as follows.
The regional indoor air quality model developed by the National Institute of Standards and Technology is used to estimate the indoor concentration of ambient source PM ().
CONTAM provides internal dynamic simulation
Ribbon airflow, ventilation rate and concentration of gas and aerosol pollutants.
Extensive evaluation of the performance of the model (; ; ; , ).
Especially relevant applications, and found that the average particle level of the model is 30% of the measured value in the study of air purifiers with high performance singlezone test home.
The main inputs of the model include the structure of the residential building, the level of pollutants in the ambient air, meteorology, and the source and sink of indoor pollutants.
Seven residential building templates developed for use in CONTAM are selected to represent a single-
Home building inventory in the modeling field.
These templates are based on the US housing survey of the US Census Bureau ()
And the Energy Consumption Survey of the US Department of Energy ()
Designed to represent a typical inventory of residential buildings in the United States ().
As shown in the figure, the template, including the family roll, structure, and water leakage rate represent the fourth floor of the electronic reverse auction house between 1940 and 1940 between 1969, 1970 and 1989, after 1990
There are four types of independent houses and three types of affiliated houses.
In order to allow natural ventilation and leakage inside and outside the window, the template was modified to include a window of size 11.
5% of the area of each wall ().
The proportion of households assigned to each of the seven templates in each census area is based on age, size, building (
Independent or attached home)
Type of ventilation system (
Forced air or natural Air)
Housing in each county described in the US housing survey. Twenty-
From the us epa air quality system, the four-hour average of 2005 PM in outdoor air distributed at 34 monitoring sites in three metropolitan areas was obtained ()
And serve as the basis for environmental PM exposure estimates.
Based on the concentration of the previous days, the self-regression model was used to estimate the PM level of the number of days missing from the data.
The map of the modeling domain and location of the PM monitor is given.
Combination of general Krieg and inverse distance-
Based on the monitoring data, the weighted method is used to interpolation the ambient PM concentration in each census area ().
These interpolation methods were selected to combine the local details provided by anti-distance weighting and the ability of the general Krieg to describe larger scale trends across the domain.
The average of these two predictions was extracted at the Census Center.
Finally, each of the three metropolitan areas is allocated a population --
A weighted average of the PM concentration for each of its constituent census areas.
Horizontal analysis, we also assessed the potential for ventilation and air cleaning to mitigate short-term levelsterm (hourly)
Assuming the PM level in the home (template DH-72)
For this, we rely on 1-
Average hourly PM level in ambient air (
EPA monitor ID 39-061-0040-88101-1).
Under the four ventilation air cleaning configurations described earlier, we simulated the indoor ambient PM concentration per hour in the home in 2005.
This analysis provides a preliminary assessment of the possibility of modification of indoor air cleaning systems per hour and other short periods of time
Regular risk exposure of PM related to sub-projects
Clinical and intermediate markers of heart and lung effects.
Based on the properties of buildings, meteorology, and windows and door openings, dynamic air exchange rates are calculated in CONTAM through simulation of forced convection and radiation leakage.
The template described above contains the leaking area of each residential building we model.
2005 of hourly wind direction and speed, dry ball and wet ball temperature, relative humidity and cloud coverage data were obtained from national meteorological service stations at major airports in each metropolitan area.
Probability method based on EPA analysis data ()
, The generation of window and door opening schedules, resulting in a total ventilation rate for Central and natural ventilation periods consistent with the corresponding air exchange rates determined by field activities reported elsewhere (; ; ).
During the window opening, assume that 40% of the total window area is open.
Air handling unit (AHU)
The duty schedule and window schedule are linked so that AHU does not run when the window is open.
The front door is set to be open for 15 minutes five times a day.
Assuming the AHU balance of the home with a central forced air heating and cooling system is 0. 18u2009m/min/m (0. 6u2009cfm/ft)
There is air in every room in the house.
Watch schedule during heating and cooling using 1-1 simulation
H resolution based on energy simulation software output of energy Plus ().
Generally speaking, the fraction per hour used to force air heating or cooling is proportional to the difference between ambient temperature and set point 22 °c (72°F).
The hourly duty schedule ranges from 4 minutes per hour during the temperate period to 38 minutes per hour during extreme summer, and 52 minutes per hour during extreme winter.
The duty schedule and window schedule are linked so that AHU does not run when the window is open.
Two air cleaning systems were simulated for families with Central forced air heating and cooling.
The first is a traditional air processor with standard 1-1
Inch media filter.
In this system, the fan operates only during heating or cooling requirements.
The second is the air processor with a variable speed fan and a high speed fan
Efficient electrostatic air purifier with HEPA (high-
Particle suppression efficiency-
For example, the efficiency of Aerosol removal.
In this system, the fan runs at full speed during heating and cooling requirements, and at half
Speed at all other times.
Families without a central mandatory air system are considered to have no indoor air cleaning capacity such as portable air purifiers.
PM removal efficiency is based on measurement results reported by traditional 1-1
Inch media filter (14%)and a high-
Efficiency of electrostatic air purifier (
90%, Trane cleaning effect model CAP591)
Determined in the assessment of the whole
The Clean Air Delivery Rate of the House measured in the fully instrument test home ().
In a subset of the analysis, in addition to traditional filtration, the effect of a portable air purifier with a room size HEPA filter is simulated.
According to the research results of the National Center for Energy Management and Construction Technology, the PM removal efficiency of portable air purifiers is 70% ().
The portable air purifier is located in the center of the residential living room and operates at a flow rate of 5. 6u2009m/min (
200 cubic feet per minute).
In addition to the removal of particles through air cleaning, the rate of PM deposition to the indoor surface is modeled as 0. 5/h ().
It is assumed that the deposition rate is independent of the air exchange rate and the AHU operation. Twenty-
The 4-hour average PM of outdoor sources present in indoor air is estimated to be each of the 7 monomer
As mentioned above, the type of home building represents the housing stock in the selected metropolitan area.
We perform statistical analysis on the output of the simulation to identify the determinants of indoor environment source PM using statistical software (
Cary SAS Institute, North Carolina, USA).
Relationship between indoor PM of environmental source and outdoor PM and AHU-
The operation time and type of ventilation/air purifier are estimated with a generalized linear model, defined as: where ln ()
Natural Log-transformed 24-
H. average concentration of indoor PM in environmental source, outdoor PM is 24-
H. The average outdoor PM concentration, the AHU hour is the number of hours the air treatment unit operates during the day, and the ventilation is the ventilation configuration (
Natural, traditional filter, or high
Efficiency of electrostatic air purifier
Subject measurement error.
The control of the house template does not change the parameter estimation of the explanatory variable, so the variable is not included in the final model.
Public health benefits-
Previously released PM cost assessment, we calculated mortality and selected morbidity benefits associated with reducing exposure to environmental source PM (, ; ; ; ).
We focused on the analysis of premature mortality and the following incidence outcomes: hospital stay for cardiovascular and respiratory diseases, emergency room visits for asthma deterioration, and asthma deterioration.
The inputs required for this application include the size of the affected population, changes in indoor concentration of ambient source PM, mean time at home, and PM concentration-response function and baseline incidence of selected health outcomes.
The metropolitan area of these three cities has more than 5,000,000 residents, distributed in 19 counties, including 20,877 kilometers of cities and suburbs.
The age distribution of the population is similar to that of the United States. 7% of the population is less than 5 years old, 58% of the population is over 30 years old, and 12% of the population is over 65 years old.
The scale of the affected population in the modeling field is from single-
Each county has a family home with a central mandatory air system ranging from 39% to 63% (, , ).
Estimated number of houses per template per county from county-
The specific age of family information reported by the US census in 2000 ()
Housing Survey of the United States (, , ).
A key issue in this analysis is that the PM concentration-response function comes from environmental monitoring data, but we are estimating changes in individual exposure.
Therefore, the direct application of the concentration-response function results in a system deviation of information mismatch and output.
While simulating the distribution of personal exposure before and after the intervention we assume, and explaining that the relationship between these distributions and the potential individual exposure profile of the epidemiology study is beyond the scope of this analysis, we do some first
In order for us to estimate the health benefits reasonably, we make some assumptions.
We assume that the epidemiology evidence described below relates to a population with a typical time.
Activity mode (; )
Indoor/outdoor ratio and environment PM ().
Using this data, an average of 69% of the time spent at home, 13% of the time spent outdoors or on vehicles, 13% of the time spent in other indoor environments or bars/restaurants, and 5% of the time spent
We assume that the reported median indoor/outdoor ratio of sulfur is applicable to all residential and other indoor environments, where exposure in the outdoor or vehicle corresponds to the environmental level.
This means that the 1g/m change in the ambient PM will correspond to the approximate 0.
Average individual PM change 6g/m.
So when we simulate the change of the average personal PM, we need to multiply the function given below by about 1.
Before applying them to our modeling output.
This is obviously a simple approximation, but it captures a general view that 1 μg/m variation in ambient PM corresponds to a smaller variation in individual PM.
We determined the reasonable central estimate of the ambient PM concentration-response function according to the previously published method ().
The estimation of concentration-response function is usually based on inverse
Variance weighted element
Analysis of the most relevant literature.
For premature mortality, the central estimates in the relevant literature range from 0. 6% to 1.
The average PM level in ambient air increased by 7% per g/m (; ; ; ).
Formal inspiration for 12 expert opinions on the concentration of long-time-response relationships
Long-term PM exposure and risk of death produce a median estimate range starting from 0. 4% to 2.
0%, the average median is 1%, the median is 1. 05% ().
Taking into account the relevant literature and expert inspiration, we use the value of an increase of 1% per mu g/m mortality per year of average PM as a reasonable central estimate of the current knowledge base.
For inpatient treatment of cardiovascular diseases, we rely on the recent meta-analysis ()
In combination with 51 published studies, the estimated increase in cardiovascular hospital admissions was determined to be 0.
PM increases by 9% per 10 μg/m.
This rate is converted to an estimated 0.
According to the monitoring data reported by EPA, with the increase of PM/PM ratio, the number of inpatient in cardiovascular hospitals increased by 16% per mu g/m ().
For admission to the respiratory hospital, we derive a concentration-response function of 0.
For each additional 1g/m respiratory hospital inpatient rate of meta-1-based PM increased by 2%
Analysis of the most relevant literature (; , ; ; ; ; ; ; ; ; ).
Similar to admission to the respiratory hospital, we derive a concentration-response function of 0.
8% increase in asthma
Meta-based PM increased related ER visits per g/m
Analysis of the most relevant literature (; ; ; ; ).
For asthma deterioration, we derive a concentration-response function that increases asthma deterioration by 2% (
Only in patients with asthma)
PM per g/m growth based on meta-model
A study analysis that measures asthma deterioration based on overall symptoms, rather than specifically looking at the results of cough, breathing, or other definitions (, ; ; ; ).
The baseline incidence of health outcomes is developed from various sources.
We calculated the death rate in the county.
No mortality according to incidence.
Accidental death (
International Classification of Diseases (ICD).
Revision on the 10 th and group code A00-R99
Population Data ().
Admission Results and baseline incidence of asthma
The relevant emergency room visits are based on the data of the National Hospital patient discharge survey in 2000 (NHDS)
And the national hospital outpatient medical survey in 2000.
In NHDS, hospital records for all respiratory systems (
Revision 9 ICD, 460-519)
All the cardiovascular
Related enrollment (
Revision 9 ICD, 390-429)
We used a baseline rate of 20 for asthma deterioration.
Each asthma patient is increased by 08 times a year ()
The estimated prevalence of asthma patients in the United States was 3. 86% ().