Effectiveness of Germicidal UV Radiation for Reducing Fungal Contamination within Air-Handling Units

Levels of fungi growing on insulation within air-handling units (AHUs) in an office building and levels of airborne fungi within AHUs were measured before the use of germicidal UV light and again after 4 months of operation. The fungal levels following UV operation were significantly lower than the levels in control AHUs.
Fungal contamination of air-handling units (AHUs) is a widespread phenomenon in buildings with central heating, ventilation, and air-conditioning (HVAC) systems and is a potential source of contamination for occupied spaces (1, 8, 16, 20). Fungi have been found growing on air filters, insulation, and cooling coils, as well as in ducts. This contamination often contributes to building-related diseases, including both infectious diseases and hypersensitivity diseases, such as allergic rhinitis, asthma, and hypersensitivity pneumonitis (4, 11, 13). In addition, acute toxicosis and cancer have been attributed to respiratory exposure to mycotoxins (5).
Control of fungi in indoor environments has traditionally focused on source control, ventilation, and air cleaning. Source control emphasizes the reduction or elimination of moisture to limit fungal growth. Although this can be effective in many areas, it is not achievable in HVAC systems during cooling. By design, air-conditioning systems cause moisture to condense from air. As a result, other methods are needed to reduce fungal contamination. Ventilation relies on using filtered outdoor and recirculated indoor air. Ventilation is ineffective, however, when unfiltered outdoor air introduces outdoor bioaerosols or when the HVAC system itself is contaminated. Air cleaning has focused on using properly maintained high-quality filters within HVAC systems as well as portable air-cleaning devices. Recently, there has been renewed interest in the use of germicidal UV irradiation to disinfect indoor environments for control of infectious diseases in hospitals, other health care facilities, and public shelters (14, 15, 18, 19).
Although it has been known for many years that UV light has various effects on fungi (3, 9, 10), only a few studies have specifically focused on the effects of germicidal UV light (2, 7, 12, 17, 22, 23). Currently, various manufacturers are marketing germicidal UV lamps for controlling contamination, including fungal contamination in indoor environments, as well as AHUs and ducts. Studies have shown that these measures may be effective for controlling the spread of bacterial diseases (14, 15, 18, 19); however, little is known about the effectiveness of UV-C radiation for controlling fungal contamination. The present investigation was undertaken to determine the effectiveness of germicidal UV radiation for reducing fungal contamination within AHUs.
This investigation was conducted in a 286,000 square-foot office building in Tulsa, Okla. The building was originally constructed in the 1920s and was completely remodeled in 1976. Each floor of this four-story building is equipped with four primary AHUs and two perimeter units; these units were installed when the building was remodeled. Beginning in 1996, the air handlers were retrofitted with germicidal UV lamps. During the fall of 1996 all the AHUs in the building were inspected. At this time UV lamps were installed in AHUs on one floor, and work was progressing to install them on a second floor. Acoustical insulation within many of the AHUs exhibited abundant mold growth, as did drain pans. Preliminary air samples and insulation samples were collected to develop the sampling protocols used in this study.
AHUs on two floors were selected for further investigation; no UV lamps had been installed in these AHUs. The floors were designated the study floor and the control floor. Only the four main AHUs on each of these floors were used for the remainder of the investigation. In May 1997, air samples and insulation samples were collected from the eight AHUs. UV lamps were installed on both floors, but they were activated only in the AHUs on the study floor. Each AHU was retrofitted with 10 lamps, which were installed downstream of the coils. The output of each lamp was 158 μW/cm2 at 1 m or 10 μW/cm2 for every 2.54 cm of tube length at 1 m (21). The lamps were operated 24 h a day throughout the summer and early fall in the AHUs on the study floor. On the control floor, no UV lights were operated. Throughout the building, air conditioning was in use during this period. In late September, samples were collected from all eight AHUs.
Preliminary data showed that air sampling in the AHUs conducted while the AHUs were running resulted in collection of few or no fungal spores because the high airflow rate produced nonisokinetic conditions. For this reason the supply fan in each AHU was shut off prior to sampling. Although this action caused some mechanical disturbance, it provided a method for estimating the potential load of fungal propagules available for dispersal.
Air samples were collected in duplicate by using paired single-stage Andersen (N-6) samplers with malt extract agar plates for viable fungi and paired Burkard personal samplers for total spores. Two-minute Andersen samples and 5-min Burkard samples were collected approximately 40 cm downstream of the cooling coils 30 s after the supply air fan in each AHU was turned off. All samples were started simultaneously, but the Andersen samplers were switched off after 2 min. Samples were obtained from each AHU at least twice in both the spring and the fall.
Plates from the Andersen samplers were incubated at room temperature for 5 to 7 days. Colonies were counted, fungi were identified, and concentrations were expressed in CFU per cubic meter of air. Burkard slides were made permanent by using a lactophenol-polyvinyl alcohol mounting medium, and the slides were examined microscopically at a magnification of ×1,000. Spores were identified and counted. Counts were converted into atmospheric concentrations and expressed in numbers of spores per cubic meter of air. Data from all samples for each AHU were averaged for each time period.
For each AHU, pieces of fiberglass insulation (approximately 60 cm2) were cut from the insulation directly opposite the cooling coils, approximately 1 m from the base, 2 m from the end wall, and less than 30 cm from the UV lights. The insulation samples were individually sealed in sterile plastic bags for transport to the laboratory. In the laboratory, a smaller square of each insulation sample (6.5 cm2) was cut from the center of the larger piece. The small square was soaked in 10 ml of sterile distilled water for 20 min. The suspension was vortexed for 30 s and then dilution plated in triplicate on malt extract agar plates. The plates were incubated at room temperature for 5 to 7 days. Colonies were counted, fungi were identified, and concentrations were expressed in CFU per square centimeter. Data from replicate samples were averaged for each AHU.
For each type of sample collected (viable spores, total spores, and insulation) the concentrations obtained for each AHU were averaged to determine means for the study floor and means for the control floor. Mann-Whitney U tests were used to compare the means in May and in September by using Statistica 5.0 software.
The dominant fungi found within the AHUs for both the air samples and the insulation samples included Penicillium corylophyllum, Aspergillus versicolor, and a strain of an unidentified Cladosporium species which was somewhat similar to Cladosporium sphaerospermum (6) and may be a strain of this species. These three taxa accounted for more than 90% of all viable fungi isolated. Other fungi identified included Acremonium spp., Cladosporium cladosporioides, Cladosporium sphaerospermum, Cladosporium elatum, and Hyalodendron sp. Occasionally other Aspergillus and Penicillium species also occurred in the samples.
In May before the UV lights were turned on, the mean concentrations of the total fungi isolated from the insulation samples on the two floors were similar (Table (Table1),1), and there was no significant difference (P > 0.05). In the fall the mean concentration on the study floor had decreased, while on the control floor the concentrations had increased and were significantly greater than the concentrations on the study floor (P < 0.05). In September the mean concentrations of both A. versicolor and the unknown Cladosporium species were significantly lower in the AHUs on the study floor (P < 0.05). Similar results were obtained with the air samples (Table (Table2).2). In the spring before the UV lights were turned on, the mean concentrations of total viable airborne fungi in the AHUs on the two floors were not significantly different (P > 0.05). In the fall, the mean concentration of viable fungi in the AHUs on study floor was an order of magnitude lower, while on the control floor the concentration of viable fungi in the AHUs had increased. The total concentrations of viable fungi in the AHUs on the study floor and the control floor in the fall were significantly different (P < 0.05). Because many of the AHUs contained high concentrations of viable fungi, there were frequently multiple impactions and multiple colonies at each impaction point on a culture plate. As a result, it was not always possible to identify each colony to the species level. Therefore, the concentration data in Table Table22 are only genus-level data. The concentrations of Penicillium, Aspergillus, and Cladosporium were significantly lower in the AHUs on the study floor than in the AHUs on the control floor after the use of UV lights (P < 0.05). The total spore levels obtained with the Burkard samplers were far greater than the viable spore levels (Table (Table3).3). Prior to the use of UV lights, there was not a significant difference (P > 0.05) between the mean levels of total spores in the AHUs on the two floors. In September, the total concentrations on the study floor were significantly lower than the total concentrations on the control floor (P < 0.05). The fungal taxa identified were consistent with the data obtained with the Andersen sampler and also with the insulation data. However, because it is not possible to differentiate Penicillium and Aspergillus conidia without conidiophores, the two genera are combined as Penicillium-Aspergillus in Table Table3.3. The concentrations of Cladosporium and Penicillium-Aspergillus on the two floors were significantly different in September (P < 0.05).
Go to:
ACKNOWLEDGMENTS
Partial support for this project was provided by a grant from Steril-Aire, Inc., Cerritos, Calif.
We thank Melinda Sterling Sullivan, Jodi Keller, and Mary Pettyjohn for assisting with sampling and/or culturing activities. We also acknowledge the unending support and accommodations provided by Tom McKain, Building Supervisor, and Argel Johnson, Maintenance Director, throughout this study.
Go to:
REFERENCES
1. Ahearn D G, Crow S A, Simmons R B, Price D L, Mishra S K, Pierson D L. Fungal colonization of air filters and insulation in a multi-story office building: production of volatile organics. Curr Microbiol. 1997;35:305–308. [PubMed]
2. Asthana A, Tuveson R W. Effects of UV and phototoxins on selected fungal pathogens of citrus. Int J Plant Sci. 1992;153:442–452.
3. Atlas R M, Bartha R. Microbial ecology: fundamentals and applications. 4th ed. Menlo Park, Calif: Benjamin/Cummings Science Publishing; 1998.
4. Burge H A. Bioaerosols: prevalence and health effects in the indoor environment. J Allergy Clin Immunol. 1990;86:687–701. [PubMed]
5. Croft W A, Jarvis B B, Yatawara C S. Airborne outbreak of trichothecene toxicosis. Atmos Environ. 1986;20:549–552.
6. Ellis M B. Dematiaceous hyphomycetes. Oxon, United Kingdom: CAB International; 1971.
7. Ensminger P A. Control of development in plants and fungi by far-UV radiation. Physiol Plant. 1993;88:501–508.
8. Ezeonu I M, Noble J A, Simmons R B, Price D L, Crow S A, Ahearn D G. Effect of relative humidity on fungal colonization of fiberglass insulation. Appl Environ Microbiol. 1994;60:2149–2151. [PMC free article] [PubMed]
9. Gregory P H. The microbiology of the atmosphere. 2nd ed. New York, N.Y: Halstead Press; 1973.
10. Henson J M, Butler M J, Day A W. The dark side of the mycelium: melanins of phytopathogenic fungi. Annu Rev Phytopathol. 1999;37:447–471. [PubMed]
11. Lacey J. Aerobiology and health: the role of airborne fungal spores in respiratory disease. In: Hawksworth D L, editor. Frontiers in mycology. Oxon, United Kingdom: C.A.B. International; 1991. pp. 157–185.
12. Lennox J E, Tuveson R W. The isolation of ultraviolet sensitive mutants from Aspergillus rugulosus. Radiat Res. 1967;31:382–388. [PubMed]
13. Levetin E. Fungi. In: Burge H, editor. Bioaerosols. Boca Raton, Fla: Lewis Publishers, CRC Press; 1995. pp. 87–120.
14. Macher J M. The use of germicidal lamps to control tuberculosis in healthcare facilities. Infect Control Hosp Epidemiol. 1993;14:723–729. [PubMed]
15. Macher J M, Alevantis L E, Chang Y-L, Liu K-S. Effect of ultraviolet germicidal lamps on airborne microorganisms in an outpatient waiting room. Appl Occup Environ Hyg. 1992;7:505–513.
16. Mahoney D H, Steuber C P, Starling K A, Barrett F F, Goldberg J, Fernbach D J. An outbreak of aspergillosis in children with acute leukemia. J Pediatr. 1979;95:70–72. [PubMed]
17. Menzies D, Pasztor J, Rand T, Bourbeau J. Germicidal ultraviolet irradiation in air conditioning systems: effect on office worker health and wellbeing: a pilot study. Occup Environ Med. 1999;56:397–402. [PMC free article] [PubMed]
18. Miller S L, Macher J M. Evaluation of a methodology for quantifying the effect of room air ultraviolet germicidal irradiation on airborne bacteria. Aerosol Sci Technol. 2000;33:274–295.
19. Nardell E A. Environmental control of tuberculosis. Med Clin N Am. 1993;77:1315–1334. [PubMed]
20. Samson R A. Occurrence of moulds in modern living and working environments. Eur J Epidemiol. 1985;1:54–61. [PubMed]
21. Scheir R, Fencl F B. Using UVC technology to enhance IAQ. Heating Piping Air Conditioning. 1996;68:109–117.
22. Sommer R, Haider T, Cabaj A, Heidenreich E, Kundi M. Increased inactivation of Saccharomyces cerevisiae by protraction of UV irradiation. Appl Environ Microbiol. 1996;62:1977–1983. [PMC free article] [PubMed]
23. Wang Y, Casadevall A. Decreased susceptibility of melanized Cryptococcus neoformans to UV light. Appl Environ Microbiol. 1994;60:3864–3866. [PMC free article] [PubMed]

What is Air quality?

An air quality index (AQI) is a number used by government agencies  to communicate to the public how polluted the air currently is or how polluted it is forecast to become. As the AQI increases, an increasingly large percentage of the population is likely to experience increasingly severe adverse health effects. Different countries have their own air quality indices, corresponding to different national air quality standards. Some of these are the Air Quality Health Index (Canada), the Air Pollution Index (Malaysia), and the Pollutant Standards Index (Singapore). Definition and usage
An air quality measurement station in Edinburgh, Scotland
Computation of the AQI requires an air pollutant concentration over a specified averaging period, obtained from an air monitor or model. Taken together, concentration and time represent the dose of the air pollutant. Health effects corresponding to a given dose are established by epidemiological research.[4] Air pollutants vary in potency, and the function used to convert from air pollutant concentration to AQI varies by pollutant. Its air quality index values are typically grouped into ranges. Each range is assigned a descriptor, a color code, and a standardized public health advisory.
The AQI can increase due to an increase of air emissions (for example, during rush hour traffic or when there is an upwind forest fire) or from a lack of dilution of air pollutants. Stagnant air, often caused by an anticyclone, temperature inversion, or low wind speeds lets air pollution remain in a local area, leading to high concentrations of pollutants, chemical reactions between air contaminants and hazy conditions.
Signboard in Gulfton, Houston indicating an ozone watch
On a day when the AQI is predicted to be elevated due to fine particle pollution, an agency or public health organization might:
advise sensitive groups, such as the elderly, children, and those with respiratory or cardiovascular problems to avoid outdoor exertion.
declare an “action day” to encourage voluntary measures to reduce air emissions, such as using public transportation.
recommend the use of masks to keep fine particles from entering the lungs
During a period of very poor air quality, such as an air pollution episode, when the AQI indicates that acute exposure may cause significant harm to the public health, agencies may invoke emergency plans that allow them to order major emitters (such as coal burning industries) to curtail emissions until the hazardous conditions abate.
Most air contaminants do not have an associated AQI. Many countries monitor ground-level ozone, particulates, sulfur dioxide, carbon monoxide, and nitrogen dioxide, and calculate air quality indices for these pollutants.
The definition of the AQI in a particular nation reflects the discourse surrounding the development of national air quality standards in that nation. A website allowing government agencies anywhere in the world to submit their real-time air monitoring data for display using a common definition of the air quality index has recently become available.
Indices by location
Canada
Main article: Air Quality Health Index (Canada)
Air quality in Canada has been reported for many years with provincial Air Quality Indices (AQIs). Significantly, AQI values reflect air quality management objectives, which are based on the lowest achievable emissions rate, and not exclusively concern for human health. The Air Quality Health Index or (AQHI) is a scale designed to help understand the impact of air quality on health. It is a health protection tool used to make decisions to reduce short-term exposure to air pollution by adjusting activity levels during increased levels of air pollution. The Air Quality Health Index also provides advice on how to improve air quality by proposing behavioral change to reduce the environmental footprint. This index pays particular attention to people who are sensitive to air pollution. It provides them with advice on how to protect their health during air quality levels associated with low, moderate, high and very high health risks.
The Air Quality Health Index provides a number from 1 to 10+ to indicate the level of health risk associated with local air quality. On occasion, when the amount of air pollution is abnormally high, the number may exceed 10. The AQHI provides a local air quality current value as well as a local air quality maximums forecast for today, tonight, and tomorrow, and provides associated health advice.

12345678910+
Risk:Low (1–3)Moderate (4–6)High (7–10)Very high (above 10)
Health RiskAir Quality Health IndexHealth Messages
At Risk population*General Population
Low1–3Enjoy your usual outdoor activities.Ideal air quality for outdoor activities
Moderate4–6Consider reducing or rescheduling strenuous activities outdoors if you are experiencing symptoms.No need to modify your usual outdoor activities unless you experience symptoms such as coughing and throat irritation.
High7–10Reduce or reschedule strenuous activities outdoors. Children and the elderly should also take it easy.Consider reducing or rescheduling strenuous activities outdoors if you experience symptoms such as coughing and throat irritation.
Very highAbove 10Avoid strenuous activities outdoors. Children and the elderly should also avoid outdoor physical exertion.Reduce or reschedule strenuous activities outdoors, especially if you experience symptoms such as coughing and throat irritation.

The AQI is based on the five “criteria” pollutants regulated under the Clean Air Act: ground-level ozone, particulate matter, carbon monoxide, sulfur dioxide, and nitrogen dioxide. The EPA has established National Ambient Air Quality Standards (NAAQS) for each of these pollutants in order to protect public health. An AQI value of 100 generally corresponds to the level of the NAAQS for the pollutant.[10] The Clean Air Act (USA) (1990) requires EPA to review its National Ambient Air Quality Standards every five years to reflect evolving health effects information. The Air Quality Index is adjusted periodically to reflect these changes.
Computing the AQI
The air quality index is a piecewise linear function of the pollutant concentration. At the boundary between AQI categories, there is a discontinuous jump of one AQI unit. To convert from concentration to AQI this equation is used:[35]
If multiple pollutants are measured at a monitoring site, then the largest or “dominant” AQI value is reported for the location. The ozone AQI between 100 and 300 is computed by selecting the larger of the AQI calculated with a 1-hour ozone value and the AQI computed with the 8-hour ozone value.
8-hour ozone averages do not define AQI values greater than 300; AQI values of 301 or greater are calculated with 1-hour ozone concentrations. 1-hour SO2 values do not define higher AQI values greater than 200. AQI values of 201 or greater are calculated with 24-hour SO2 concentrations.
Real time monitoring data from continuous monitors are typically available as 1-hour averages. However, computation of the AQI for some pollutants requires averaging over multiple hours of data. (For example, calculation of the ozone AQI requires computation of an 8-hour average and computation of the PM2.5 or PM10 AQI requires a 24-hour average.) To accurately reflect the current air quality, the multi-hour average used for the AQI computation should be centered on the current time, but as concentrations of future hours are unknown and are difficult to estimate accurately, EPA uses surrogate concentrations to estimate these multi-hour averages. For reporting the PM2.5, PM10 and ozone air quality indices, this surrogate concentration is called the NowCast. The Nowcast is a particular type of weighted average that provides more weight to the most recent air quality data when air pollution levels are changing.[40][41] There is a free email subscription service for New York inhabitants – AirNYC.[42] Subscribers get notification about AQI values changes for selected location (eg home address), based on air quality conditions.
Public Availability of the AQI
Real time monitoring data and forecasts of air quality that are color-coded in terms of the air quality index are available from EPA’s AirNow web site.[43] Historical air monitoring data including AQI charts and maps are available at EPA’s AirData website.[44] Detailed map about current AQI level and its two day forecast is available from Aerostate web site.[45]
History of the AQI
The AQI made its debut in 1968, when the National Air Pollution Control Administration undertook an initiative to develop an air quality index and to apply the methodology to Metropolitan Statistical Areas. The impetus was to draw public attention to the issue of air pollution and indirectly push responsible local public officials to take action to control sources of pollution and enhance air quality within their jurisdictions.
Jack Fensterstock, the head of the National Inventory of Air Pollution Emissions and Control Branch, was tasked to lead the development of the methodology and to compile the air quality and emissions data necessary to test and calibrate resultant indices.[46]
The initial iteration of the air quality index used standardized ambient pollutant concentrations to yield individual pollutant indices. These indices were then weighted and summed to form a single total air quality index. The overall methodology could use concentrations that are taken from ambient monitoring data or are predicted by means of a diffusion model. The concentrations were then converted into a standard statistical distribution with a preset mean and standard deviation. The resultant individual pollutant indices are assumed to be equally weighted, although values other than unity can be used. Likewise, the index can incorporate any number of pollutants although it was only used to combine SOx, CO, and TSP because of a lack of available data for other pollutants.
While the methodology was designed to be robust, the practical application for all metropolitan areas proved to be inconsistent due to the paucity of ambient air quality monitoring data, lack of agreement on weighting factors, and non-uniformity of air quality standards across geographical and political boundaries. Despite these issues, the publication of lists ranking metropolitan areas achieved the public policy objectives and led to the future development of improved indices and their routine application.