Ted Johnson

Determining the frequency of open windows in residences: a pilot study in Durham, North Carolina during varying temperature conditions

Air pollution exposures in the residential microenvironment can be significantly affected by air exchange rate (AER). A number of studies have shown that AER in residences is significantly affected by the number and location of open windows and doors. A pilot study was conducted in Durham, North Carolina, to determine whether useful data on open windows and doors could be acquired through a visual survey. The study consisted of 72 2-h survey sessions conducted between October 24, 2001 and March 13, 2003. During the first hour of each session, a technician selected a set of corner residences in one of 48 census tracts and completed a survey form and meteorological measurements for each residence. During the second hour, the technician revisited each residence surveyed during the first hour. The resulting database included data on 2200 “residential visits” (1100 residences times two visits per residence). The technician observed one or more open windows during 20.0 percent of the residential visits. One or more open doors were observed during 13.4 percent of the residential visits; 28.2 percent of the residential visits were associated with at least one open window or door. A series of stepwise linear regression analyses were performed on the data to identify factors associated with open windows and doors. Results of these analyses indicated that the likelihood of one or more windows being opened tended to increase under the following conditions: occupancy at time of visit; session during April, May, or June; high population or housing density; window air conditioning (AC) units; absence of AC; large number of doors; and wind speed above 2 mph. The likelihood of open doors tended to increase under the following conditions: occupancy at time of visit; residence within city limits; session during April, May, or June; detached one-story residence; large number of doors; high housing density; school out; and residence within 10 m of road. Transition probabilities (closed to open and open to closed) were determined for windows and doors by time of day.

A pilot study using scripted ventilation conditions to identify key factors affecting indoor pollutant concentration and air exchange rate in a residence

A pilot study was conducted using an occupied, single-family test house in Columbus, OH, to determine whether a script-based protocol could be used to obtain data useful in identifying the key factors affecting air-exchange rate (AER) and the relationship between indoor and outdoor concentrations of selected traffic-related air pollutants. The test script called for hourly changes to elements of the test house considered likely to influence air flow and AER, including the position (open or closed) of each window and door and the operation (on/off) of the furnace, air conditioner, and ceiling fans. The script was implemented over a 3-day period (January 30–February 1, 2002) during which technicians collected hourly-average data for AER, indoor, and outdoor air concentrations for six pollutants (benzene, formaldehyde (HCHO), polycyclic aromatic hydrocarbons (PAH), carbon monoxide (CO), nitric oxide (NO), and nitrogen oxides (NOx)), and selected meteorological variables. Consistent with expectations, AER tended to increase with the number of open exterior windows and doors. The 39 AER values measured during the study when all exterior doors and windows were closed varied from 0.36 to 2.29 h−1 with a geometric mean (GM) of 0.77 h−1 and a geometric standard deviation (GSD) of 1.435. The 27 AER values measured when at least one exterior door or window was opened varied from 0.50 to 15.8 h−1 with a GM of 1.98 h−1 and a GSD of 1.902. AER was also affected by temperature and wind speed, most noticeably when exterior windows and doors were closed. Results of a series of stepwise linear regression analyses suggest that (1) outdoor pollutant concentration and (2) indoor pollutant concentration during the preceding hour were the “variables of choice” for predicting indoor pollutant concentration in the test house under the conditions of this study. Depending on the pollutant and ventilation conditions, one or more of the following variables produced a small, but significant increase in the explained variance (R2-value) of the regression equations: AER, number and location of apertures, wind speed, air-conditioning operation, indoor temperature, outdoor temperature, and relative humidity. The indoor concentrations of CO, PAH, NO, and NOx were highly correlated with the corresponding outdoor concentrations. The indoor benzene concentrations showed only moderate correlation with outdoor benzene levels, possibly due to a weak indoor source. Indoor formaldehyde concentrations always exceeded outdoor levels, and the correlation between indoor and outdoor concentrations was not statistically significant, indicating the presence of a strong indoor source.

Prediction of hourly microenvironmental concentrations of fine particles based on measurements obtained from the Baltimore scripted activity study

Researchers have developed a variety of computer-based models to estimate population exposure to air pollution. These models typically estimate exposures by simulating the movement of specific population groups through defined microenvironments. During the summer of 1998 and winter of 1999, researchers with the Harvard School of Public Health (HSPH) conducted a field study in Baltimore, MD, to acquire data for improving microenvironmental models. Using a special roll-around instrument system, a technician measured 1- and 12-h pollutant concentrations while engaging in scripted sequences of activities typical of retirees. Each scripted activity assigned the technician to a geographic location and to a microenvironment. The technician recorded special conditions associated with each activity (e.g., open windows, environmental tobacco smoke) in a real-time diary. Data on ambient pollutant levels, temperature, and other potential explanatory factors were also collected. Eleven pollutants were measured by the roll-around instrument system, including particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), ozone, carbon monoxide, and benzene. This article presents the results of statistical analyses performed solely on the 1-h PM2.5 data measured by a DustTrak monitor, which ranged from 1.5 to 444.8 µg/m3 with a median value of 14.6 µg/m3. Results of stepwise linear regression (SLR) suggest that PM2.5 exposure is significantly increased by passive smoking, high ambient PM2.5 concentrations reported by fixed-site monitors, food preparation, charcoal grills, car travel, outdoor roadside locations, and high humidity. Analysts should explicitly represent the effects of these parameters within any model developed to estimate population exposure to PM2.5. In a related study, a panel of volunteer retirees each carried a personal PM2.5 monitor and a real-time diary for nominal 24-h sampling periods as they engaged in normal daily activities. A regression equation derived from SLR analysis of the scripted activity database was applied to eight subject-days of diary data provided by the volunteer seniors to produce estimates of PM2.5 exposure for each event documented in each diary. The event-specific exposure estimates were then averaged over all events in each sampling period to produce nominal 24-h average exposure estimates. The absolute difference between the estimate obtained from the regression equation and the corresponding personal monitor measurement averaged 13%. The fixed-site monitors generally provided poorer estimates of exposure; the absolute differences for the Old Town and Clifton Park monitors averaged 26.7% and 19.5%, respectively, of the personal monitor values.

Concentrations of mobile source air pollutants in urban microenvironments

Human exposures to criteria and hazardous air pollutants (HAPs) in urban areas vary greatly due to temporal-spatial variations in emissions, changing meteorology, varying proximity to sources, as well as due to building, vehicle, and other environmental characteristics that influence the amounts of ambient pollutants that penetrate or infiltrate into these microenvironments. Consequently, the exposure estimates derived from central-site ambient measurements are uncertain and tend to underestimate actual exposures. The Exposure Classification Project (ECP) was conducted to measure pollutant concentrations for common urban microenvironments (MEs) for use in evaluating the results of regulatory human exposure models. Nearly 500 sets of measurements were made in three Los Angeles County communities during fall 2008, winter 2009, and summer 2009. MEs included in-vehicle, near-road, outdoor, and indoor locations accessible to the general public. Contemporaneous 1- to 15-min average personal breathing zone concentrations of carbon monoxide (CO), carbon dioxide (CO2), volatile organic compounds (VOCs), nitric oxide (NO), nitrogen oxides (NOx), particulate matter (<2.5 μm diameter; PM2.5) mass, ultrafine particle (UFP; <100 nm diameter) number, black carbon (BC), speciated HAPs (e.g., benzene, toluene, ethylbenzene, xylenes [BTEX], 1,3-butadiene), and ozone (O3) were measured continuously. In-vehicle and inside/outside measurements were made in various passenger vehicle types and in public buildings to estimate penetration or infiltration factors. A large fraction of the observed pollutant concentrations for on-road MEs, especially near diesel trucks, was unrelated to ambient measurements at nearby monitors. Comparisons of ME concentrations estimated using the median ME/ambient ratio versus regression slopes and intercepts indicate that the regression approach may be more accurate for on-road MEs. Ranges in the ME/ambient ratios among ME categories were generally greater than differences among the three communities for the same ME category, suggesting that the ME proximity factors may be more broadly applicable to urban MEs. Implications: Estimates of population exposure to air pollutants extrapolated from ambient measurements at ambient fixed site monitors or exposure surrogates are prone to uncertainty. This study measured concentrations of mobile source air toxics (MSAT) and related criteria pollutants within in-vehicle, outdoor near-road, and indoor urban MEs to provide multipollutant ME measurements that can be used to calibrate regulatory exposure models.

Measurement of microenvironmental ozone concentrations in Durham, North Carolina, using a 2B Technologies 205 Federal Equivalent Method monitor and an interference-free 2B Technologies 211 monitor

During August and September of 2012, researchers conducted a microenvironmental (ME) monitoring study in Durham, North Carolina, using two 2B Technologies O3 monitors: a dual-beam model 205 Federal Equivalent Method (FEM) 254 nm photometer and a newly developed model 211 interference-free dual-beam photometer. The two monitors were mounted in a wheeled, fan-cooled suitcase together with a battery, a disposable N2O cartridge for the model 211 monitor, and filtered sample lines. A scripted technician made paired O3 measurements in a variety of MEs within 2 miles of a fixed-site FEM O3 photometer at the Durham National Guard Armory. The ratio of the 211 to Armory O3 concentrations tended to be lowest (<0.3) for 45 indoor MEs and highest (>0.8) for 104 outdoor MEs. The mean values of the ratio for in-vehicle MEs tended to fall between 0.2 and 0.7—the mean for all 27 in-car tests was 0.3. The ratio values for indoor MEs tended to be higher when the enclosure was well ventilated. The outdoor ratios tended to be lower when the measurement was made downwind of nearby roadways, likely due to exhaust NO. The in-vehicle ratios tended to be larger with windows open than closed; the smallest occurred with closed windows, active air conditioning, and vent recirculation. The 205 − 211 measurement differences were generally small, with 94% of the 176 sample differences below 5 ppb. Five differences were above 10 ppb with the largest values (173.9 and 63.6 ppb) occurring inside a violin repair shop. Roadway proximity tended to increase the differences for outdoor locations. The largest in-vehicle difference (6 ppb) occurred at a convenience store service station. As addressed in regulatory models, such differences may reduce estimated population O3 exposure by 30–50% in indoor and in-vehicle MEs where individuals spend more than 80% of their time. Implications: Computer models used to estimate exposures of human populations—such as the Air Pollution Exposure Model (APEX) developed by the U.S. Environmental Protection Agency—can be improved by use of direct microenvironmental (ME) measurement comparisons to nearby fixed-site monitors used for determining regulatory compliance. Simultaneous measurements made by model 211 and model 205 ozone monitors in a variety of MEs indicated that Federal Equivalent Method photometers similar to the model 205 may read high in the presence of various interferences associated with indoor sources and motor vehicles, increasing modeled exposures in such environments by 20–100%.

A Pilot Study to Identify Factors Affecting UV-B Radiation Exposure in Selected Microenvironments

A pilot study was conducted to test a protocol for collecting data useful for identifying local factors affecting exposure to ultraviolet-B radiation (UV-B). A trained technician followed a prepared script through a series of microenvironments representing varying conditions of overhead shielding and ground cover while collecting UV-B irradiance and related data on days selected to represent varying conditions of solar radiation, cloud cover, groundlevel ozone concentration, and fine particulate matter (PM 2.5 ) concentration. The resulting data were combined with air pollution, UV-B, and meteorological data obtained from local fixed-site monitoring stations and analyzed to identify the principal factors affecting (1) UVI (UV Index) measured by the Safe Sun monitor and (2) fixed-site UV-B. Results of these analyses indicated that the best predictors of UVI were degree of shielding, solar angle, fixed-site UV-B, cloud cover, wind speed, time of day, and season. Although cloud cover was an important predictor of UVI, the clearest days did not correspond to the highest UVI values. Variations in cloud parameters can cause intraday UVI variations not reflected in a daily forecast. Residential outdoor microenvironments tended to have lower mean UVI values than outdoor recreation microenvironments (e.g., athletic field, pool), which may have implications for estimating total personal exposure to UV-B.

Frequency of open windows in motor vehicles under varying temperature conditions: a videotape survey in Central North Carolina during 2001

Air pollution exposures in the motor vehicle cabin are significantly affected by air exchange rate, a function of vehicle speed, window position, vent status, fan speed, and air conditioning use. A pilot study conducted in Houston, Texas, during September 2000 demonstrated that useful information concerning the position of windows, sunroofs, and convertible tops as a function of temperature and vehicle speed could be obtained through the use of video recorders. To obtain similar data representing a wide range of temperature and traffic conditions, a follow-up study was conducted in and around Chapel Hill, North Carolina at five sites representing a central business district, an arterial road, a low-income commercial district, an interstate highway, and a rural road. Each site permitted an elevated view of vehicles as they proceeded through a turn, thereby exposing all windows to the stationary camcorder. A total of 32 videotaping sessions were conducted between February and October 2001, in which temperature varied from 41°F to 93°F and average vehicle speed varied from 21 to 77 mph. The resulting video tapes were processed to create a vehicle-specific database that included site location, date, time, vehicle type, vehicle color, vehicle age, window configuration, number of windows in each of three position categories (fully open, partially open, and closed), meteorological factors, and vehicle speed. Of the 4715 vehicles included in the database, 1905 (40.4%) were labeled as “open,” indicating a window, sunroof, or convertible top was fully or partially open. Stepwise linear regression analyses indicated that “open” window status was affected by wind speed, relative humidity, vehicle speed, cloud cover, apparent temperature, day of week, time of day, vehicle type, vehicle age, vehicle color, number of windows, sunroofs, location, and air quality season. Open windows tended to occur less frequently when relative humidity was high, apparent temperature (a parameter incorporating wind chill and heat index) was below 50°F, or the vehicle was relatively new. Although the effects of the identified parameters were relatively weak, they are statistically significant and should be considered by researchers attempting to model vehicle air exchange rates.

A multipollutant evaluation of APEX using microenvironmental ozone, carbon monoxide, and particulate matter (PM2.5) concentrations measured in Los Angeles by the exposure classification project

Abstract This paper describes an operational evaluation of the US Environmental Protection Agency’s (EPA) Air Pollution Exposure Model (APEX). APEX simulations for a multipollutant ambient air mixture, i.e. ozone (O3), carbon monoxide (CO), and particulate matter 2.5 microns in diameter or less (PM2.5), were performed for two seasons in three study areas in central Los Angeles. APEX predicted microenvironmental concentrations were compared with concentrations of these three pollutants monitored in the Exposure Classification Project (ECP) study during the same periods. The ECP was designed expressly for evaluating exposure models and measured concentrations inside and outside 40 microenvironments. This evaluation study identifies important uncertainties in APEX inputs and model predictions useful for guiding further exposure model input data and algorithm development efforts. This paper also presents summaries of the concentrations in the different microenvironments.