NIOSH Mining Program Information Circular IC 9531

Design, Testing, and Modeling 

of Environmental Enclosures for 

Controlling Worker Exposure to 

Airborne Contaminants

Centers for Disease Control

and Prevention

National Institute for Occupational 

Safety and Health
Information Circular 9531

Design, Testing, and Modeling of Environmental Enclosures 

for Controlling Worker Exposure to Airborne Contaminants

John A. Organiscak, Andrew B. Cecala, and Ronald M. Hall 

DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Institute for Occupational Safety and Health 

Office of Mine Safety and Health Research 

Pittsburgh, PA • Spokane, WA

June 2018
This document is in the public domain and may be freely 

copied or reprinted.

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Suggested Citation

NIOSH [2018]. Design, testing, and modeling of environmental enclosures for controlling 

worker exposure to airborne contaminants. By Organiscak JA, Cecala AB, and Hall RM. 

Pittsburgh PA: U.S. Department of Health and Human Services, Centers for Disease Control and 

Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication 

No. 2018–123, IC 9531. https://doi.org/10.26616/NIOSHPUB2018123

DOI: https://doi.org/10.26616/NIOSHPUB2018123

DHHS (NIOSH) Publication No. 2018–123

June 2018

Cover photos by NIOSH.
Contents

Abstract........................................................................................................................... 1

Introduction ..................................................................................................................... 2

Background...........................................................................................................................2

Objective ...............................................................................................................................5

Key Environmental Enclosure Design Considerations .................................................... 5

Using a separate intake pressurizer fan and a high-efficiency respirable dust filter to

supply intake air into the enclosure.......................................................................................6

Ensuring good enclosure integrity to achieve positive pressurization against outside

wind penetration and to minimize outside air leakage around the filtration system ..............9

Providing effective respirable dust filtration on the enclosure’s recirculated airflow ...........13

Applying secondary enclosure design considerations for maintaining an effective

filtration and pressurization system.....................................................................................16

Testing and Monitoring Environmental Enclosures ....................................................... 18

Respirable Dust...................................................................................................................20

Diesel Particulate Matter (DPM)..........................................................................................22

Particle Counting.................................................................................................................23

Gases, Vapors, and Liquid Droplets ...................................................................................29

Airflow and Pressure Measurements ..................................................................................33

Enhanced Video Analysis of Dust Exposures (EVADE) software.......................................34

Data Analysis of Environmental Enclosure Performance .............................................. 36

Graphical Analysis ..............................................................................................................37

Normal Distribution Error Analysis of Sample Data ............................................................37

Propagation of Error Analysis of Protection Factor Ratios..................................................41

Poisson Distribution Error Analysis of Particle Counting Data ............................................44

Mathematical Modeling of Environmental Enclosure Filtrations Systems...................... 47

Conclusions................................................................................................................... 55

References.................................................................................................................... 56
Figures

Figure 1. Relationship between cab pressure and its wind speed resistance to

outside contaminants .................................................................................................... 11

Figure 2. Relationship between cab differential pressure and intake airflow during 

laboratory experiments.................................................................................................. 12

Figure 3. Relationship between intake leakage and differential pressure across the 

intake filter..................................................................................................................... 12

Figure 4. Filter housing using a contiguous series of pre-, HEPA, and charcoal

panel filters (left); outside air leakage around filters on Nelson tractor cab (right)......... 13

Figure 5. Nelson tractor cab test results before and after fixing filtration system

air leakage..................................................................................................................... 13

Figure 6. Final filter system configuration tested with MERV 16 and HEPA filters ........ 15

Figure 7. Long-term field study test results of final filter system using MERV 16

and HEPA filters............................................................................................................ 15

Figure 8. Drill operator opening door to guide drill steel (left); one shift of real-time 

respirable dust levels inside the cab with the door closed and open (right)................... 17

Figure 9. Personal gravimetric sampler (left); passive nephelometer light-scattering 

instrument (right)........................................................................................................... 21

Figure 10. DPM sampler (left); real-time EC/diesel particulate monitor (right) .............. 23

Figure 11. EC concentrations and cab pressure measurements made on an 

underground drill cab..................................................................................................... 24

Figure 12. Particle size-related enclosure PFs measured in the laboratory .................. 25

Figure 13. Particle size-related enclosure PFs measured during field studies .............. 25

Figure 14. Desktop size selectable optical particle counter (left); handheld

fixed-size optical particle counter (right)........................................................................ 26

Figure 15. Laboratory particle counting test replicates for the high-efficiency

intake filter configuration with and without a recirculation filter and wind ...................... 28

Figure 16. Air leakage test measurements made on Clean Air Filter (CAF)

Company’s test stand.................................................................................................... 33

Figure 17. Digital (top) or analog (bottom) enclosure pressure monitoring (left);

hot wire anemometer measurement (right) ................................................................... 34

Figure 18. Helmet-CAM technology used in the enclosed cab of haul truck for 

assessment of truck driver’s respirable dust exposure relevant to the truck

location as viewed by the video..................................................................................... 36

Figure 19. Graphical information of long-term field study of roof bolter

using a three-filter configuration .................................................................................... 39

Figure 20. Two- and single-filter cab systems with and without a recirculation filter ..... 49

Figure 21. Comparison of laboratory measured cab protection factors to the

modeled protection factors ............................................................................................ 49
Figure 22. Three-filter cab system with final filter downstream of intake and

recirculation filters ......................................................................................................... 50

Figure 23. Field-measured protection factors shown with modeled ranges................... 51

Figure 24. Visual observation of multiple air leaks within the air filtration system ......... 52

Figure 25. Three-filter cab system with potential leaks around the filters...................... 52

Figure 26. Measured and modeled PFs of the face drill and roof bolter

during the filter field study ............................................................................................. 55
Tables

Table 1. Respirable dust sampling results of retrofitted cab filtration systems

on mine vehicles ............................................................................................................. 6

Table 2. Averages of laboratory cab performance testing for all filter

combinations tested ........................................................................................................ 7

Table 3. MERV rating efficiency values for three size ranges of dust particles ............... 8

Table 4. Comparison of environmental enclosure performance measures ................... 19

Table 5. CO2 measurements on the John Deere tractor cab......................................... 32

Table 6. Calculation of statistics for MERV 16 and HEPA filter comparisons

on face drill and roof bolter cabs ................................................................................... 40

Table 7. Initial cab filtration system particle counting test results using

the HEPA final filter ....................................................................................................... 43
Acronyms and Abbreviations

ACGIH American Conference of Governmental Industrial Hygienists

ASAE American Society of Agricultural Engineers

ASHRAE American Society of Heating, Refrigerating and

Air-Conditioning Engineers

CAF Clean Air Filter

CRADA Cooperative Research and Development Agreement

DPM diesel particulate matter

EC elemental carbon

EVADE Enhanced Video Analysis of Dust Exposures

HVAC heating, ventilation, and air conditioning

HEPA high-efficiency particulate arrestance

MERV minimum efficiency reporting value

MPPS most penetrating particle size

NIOSH National Institute for Occupational Safety and Health

OEM original equipment manufacturer

PF protection factor

TWA time-weighted average

TC total carbon
Unit of Measure Abbreviations

carbon dioxide

cubic feet

cubic feet per minute

feet

inches

liters

liters per minute

micrometers

milligrams per cubic meter

millimeters

minutes

percent

seconds

CO2

ft3

ft3/min 

ft

in

L 

L/min 

μm

mg/m3

mm

min 

% 

sec 

in wg water gauge
Design, Testing, and Modeling of Environmental Enclosures 

for Controlling Worker Exposure to Airborne Contaminants

John A. Organiscak1, Andrew B. Cecala2, and Ronald M. Hall3

Abstract

Environmental enclosures such as cabs, booths, rooms, etc. are one of the mainstay engineering 

control methods for reducing operators’ exposure to airborne contaminants generated outside the 

enclosure. In order to achieve a cleaner air environment, air filtration is typically incorporated 

into the enclosure’s heating, ventilation, and air conditioning (HVAC) system. The National 

Institute for Occupational Safety and Health (NIOSH) has jointly conducted collaborative 

research efforts with HVAC system manufacturers, cab filtration/pressurization component 

manufacturers, original equipment manufacturers (OEMs) of industrial vehicles, and companies 

using these cabs/environmental enclosures. This report summarizes NIOSH’s laboratory and 

field research results, provides key design guidelines for environmental enclosures, shows 

measurement methods for enclosure performance, and demonstrates mathematical modeling of 

filtration system designs.

Two key elements of an effective environmental enclosure are a good filtration system and an 

enclosure with good integrity (sealed isolation from the outside environment). A good filtration 

system should include filtering out at least 95% or greater of airborne respirable aerosols (dust, 

diesel particulate matter, liquid droplets, etc.) from the intake airflow with an additional 

recirculation filtering component for the inside air. Good enclosure integrity is also needed to 

achieve positive pressure to prevent wind-driven aerosol penetration into the enclosure, as well 

as to minimize air leakage around the filtration system. Test methods and mathematical modeling 

of environmental enclosures are also beneficial for quantifying and optimizing filtration system 

designs, as well as maintaining optimum protection factor (PF) performance for enclosure

occupants. Occupational exposure sampling, particle counting methods, airflow measurements, 

and enclosure pressurization measurements are used to assess the effectiveness of environmental 

enclosures. Node analysis of filtration system designs are beneficial for examining the effects of 

filter placement, filter efficiency, airflow quantities, air leakage, and wind penetration on the 

environmental enclosure’s air cleaning performance.

 

1 Senior Mining Engineer, Dust, Ventilation, and Toxic Substances Branch, NIOSH Pittsburgh Mining 

Research Division (PMRD), Pittsburgh, PA.

2 Principal Supervisory Mining Engineer, Dust, Ventilation, and Toxic Substances Branch, NIOSH PMRD, 

Pittsburgh, PA.

3 Deputy Branch Chief, Engineering and Physical Hazards Branch, NIOSH Division of Applied Research 

and Technology, Cincinnati, OH.
2

Introduction

Enclosed cabs are an engineering control that can provide a safe, comfortable, and healthy work

environment for equipment operators. Most modern day enclosed cabs have heating, ventilation, 

and air conditioning (HVAC) systems for primarily maintaining a comfortable temperature and 

providing a breathable ventilation quantity of air for their occupants. Various levels of filtration 

can be incorporated into the HVAC system to improve the ventilation quality of the air inside the 

cab by removing airborne pollutants such as dusts, diesel particulate matter (DPM), and other 

aerosols. Enclosed operator cabs have been used on agriculture, mining, and construction 

vehicles for several decades and were comprehensively studied by NIOSH for their engineering 

control effectiveness on these respirable aerosols. Several larger stationary enclosures or booths 

were further evaluated to verify respirable filtration system effectiveness within these larger 

enclosure volumes.

Background

Examination of dust levels found in agriculture indicate that ground preparation and harvesting 

operations generate some of the highest dust levels, with enclosed cabs providing a sixtyfold 

reduction on larger size dusts and more than fourfold reduction on respirable sized dusts 

[Nieuwenhuijsen et al. 1998]. Agriculture pesticide sprayers typically generate droplet diameters 

between 50 and 500 m, but some pesticide spraying research shows that the mass median 

aerodynamic diameter of spray aerosols that stay suspended outside a cab can range between 4

and 15 m, with 3% to 23% of the droplet mass concentrations less than 3 m in size [Bémer et 

al. 2007; Hall et al. 2002]. These findings illustrate the need for enclosed cabs to reduce operator

exposure to these respirable aerosols.

In addition to these findings for agriculture, surface mining respirable dust surveys conducted by 

NIOSH show that rotary drills generate some of the highest respirable dust levels at mining 

operations, with dust levels one order of magnitude higher than generated by bulldozers

[Organiscak and Page 1999]. Enclosed cab respirable dust protection factors (PFs) (ratio of 

outside to inside respirable dust concentrations) measured on rotary drills range from 2.6 to 84, 

while those measured on bulldozers range from less than 1 to 45 [Organiscak and Page 1999].

In these earlier studies, few details were revealed about the design of the enclosed cab filtration 

systems. Therefore, NIOSH partnered with HVAC suppliers, filter/pressurizer manufacturers, 

regulatory agencies, and mining companies in conducting field studies to modify older mining 

equipment cab filtration systems, with the goal of identifying the key factors involved in 

achieving effective dust control [Organiscak et al. 2004; Chekan and Colinet 2003; Cecala et al. 

2004; Cecala et al. 2005; Cecala et al. 2009]. These studies highlighted several important key 

factors, such as using high-efficiency respirable dust filters, a separate intake pressurizer fan, and 

a cab with good integrity to achieve positive pressurization [Cecala et al. 2014]. Respirable dust 

concentration measurements made inside and outside of these cabs during mining operations also 

showed a wide range of cab PFs from 2.8 to 89, indicating that highly variable operational 

conditions were present during these cab performance field studies.

The effectiveness of enclosed cab filtration systems can be difficult to quantify in the field under 

diverse operating conditions. Relative measures of enclosed cab performance (such as protection 
3

factor, penetration, and reduction efficiency) from field data can be quite variable or unreliable if 

the outside dust mass concentrations are low or vary significantly between working shifts 

[Organiscak et al. 2004]. Using particle counters to evaluate cabs over shorter periods of time 

can increase the reliability of performance measurements, because of the larger number of 

smaller particles available at lower dust concentrations [Organiscak et al. 2004; Organiscak et al. 

2016]. The American Society of Agricultural Engineers (ASAE) previously devised a consensus 

standard for field testing a cab’s PF performance with particle counters, and specified a 

minimum 50:1 cab PF performance criteria for pesticide applications. This procedure required 

two optical particle counters; one is placed inside and the other outside the cab to examine its PF

on 2-μm to 4-μm particles as the agricultural tractor is driven over unpaved ground surfaces for 

at least four 30-min tests [ASAE 1997]. NIOSH examination of this test procedure indicated that 

inconsistent and low particle count data for the 2-μm to 4-μm particles outside the cab can also 

yield unreliable cab performance results between replicated cab tests [Heitbrink et al. 1998].

Alternative particle counting cab field test procedures have been examined and used by NIOSH 

with submicron particles (0.3-μm to 0.5-μm) to quantify and improve agricultural tractor cab 

protection performance for pesticide spray applications [Heitbrink et al. 2003; Moyer et al.

2005]. One procedure used two optical particle counters alternated between the inside and 

outside locations of the tractor cab during four 30-min tests, similar to the ASAE consensus 

standard test, except that 0.3-μm to 0.4-μm particle sizes were used as the test medium. This 

field study identified and sealed several tractor cab filtration system leaks around a highefficiency particulate air (HEPA) filter, which effectively reduced particle penetration into the 

cab from 11% to less than 1% [Heitbrink et al. 2003]. Another study measured 0.3-μm to 0.5-μm 

particles inside and outside a stationary unoccupied cab parked inside a temporary enclosure 

filled with incense smoke contaminants. In this procedure, a two-channel particle counting 

instrument sampled alternate positions inside and outside an unoccupied cab using equal lengths 

of sample hoses to these locations. This procedure was used to quantify cab PFs of at least 50:1 

(outside to inside particle concentrations) for a pesticide spraying company after refurbishing and 

performing maintenance on 13 tractor cabs [Moyer et al. 2005].

Particle counting measurements of submicron (0.3-μm to 1.0-μm) particulates in ambient air 

were further used by NIOSH during laboratory experiments with multiple instruments to study 

the key design factors of enclosed cab dust filtration systems [Organiscak and Cecala 2008; 

NIOSH 2008b]. These experiments were conducted to methodically study these systems under 

more strictly controlled laboratory operating conditions. During these experiments, two particle 

counting instruments were alternated between test replicates to average out any measurement 

biases between instruments, similar to the ASAE procedure described above. These laboratory 

experiments quantified the effects of intake filter efficiency, intake filter loading, intake air 

leakage around the filter, recirculation filter use, and wind infiltration on cab filtration system 

performance under controlled conditions. Laboratory measurements showed that the largest 

number of particles in the ambient air were submicron (0.3-μm to 1.0-μm) and that 

concentrations remained relatively constant during the testing on a particular day, but varied 

noticeably from day to day [NIOSH 2008b]. Experimental cab test results determined that intake 

filter efficiency and the use of a recirculation filter component were the two most important 

influential factors on cab filtration system performance. The addition of a recirculation filter 

component to the cab’s filtration system significantly reduced its particulate penetration 

performance by an order of magnitude, and reduced the time it took to reach its lowest stable 

concentration after the door was closed by about 60%. Finally, a mathematical model was 
4

developed that describes cab particulate penetration in terms of intake filter efficiency, intake air 

quantity, intake air leakage, recirculation filter efficiency, recirculation filter quantity, and wind 

infiltration [NIOSH 2008b].

NIOSH also developed another quantification method for evaluating the environmental integrity 

of cabs in conjunction with Clean Air Filter (CAF) Company, Defiance, IA, under a Cooperative 

Research and Development Agreement (CRADA). This cab test method was developed as an 

alternative to particle counting test methods for negating any interior cab particle generation 

interferences such as dirty floors, interior surfaces, and abraded blower motor brushes [Cecala et 

al. 2005; Heitbrink and Collingwood 2005]. This method involves replacing the HVAC intake 

filter with a carbon dioxide (CO2) gas filter, running the HVAC system with an unoccupied cab, 

measuring CO2 concentrations inside and outside the cab filtration system, and applying a 

mathematical formula to calculate outside air leakage into the cab enclosure [NIOSH 2012b; 

NIOSH 2012c]. This method was shown to be capable of quantifying a greater than 2% outside 

air leakage into the cab’s filtration system [NIOSH 2012b].

In cooperation with a mining equipment manufacturer, NIOSH examined the performance of a 

new three-filter cab filtration system (intake, recirculation, and final filters) designed for the 

underground industrial minerals industry. Particle count testing and airflow measurements were 

initially conducted at the company’s manufacturing shop to examine the cab filtration system 

performance with several types of final filters and intake/pressurizer filter configurations. The 

performance of this cab filtration system design was further studied in the field on several pieces 

of underground mining equipment. Underground dust and diesel field sampling results during a 

seven-month field study showed inconsistent cab PFs among production shifts because of 

unpredictable equipment operator practices, such as the frequency and duration of opening the 

cab door and the variability in HVAC system operation [Cecala et al. 2012; Noll et al. 2014a]. 

Particle counting and HVAC airflow measurements made on these cabs in ambient air outside 

the mine between production shifts provided a better quantification or benchmarking of their 

optimum long-term PF performance under consistent near steady state cab testing conditions, 

termed “static testing” [Cecala et al. 2012; Organiscak et al. 2013]. Mathematical modeling of 

this three-filter cab filtration system using node analysis techniques was validated by the particle

counting PF field test results, thereby demonstrating the model’s usefulness as a filtration system 

design tool [Organiscak et al. 2014].

NIOSH further examined the cab filtration performance effects of using a HEPA (High 

Efficiency Particulate Arrestance, > 99.97% efficiency on > 0.3-m particles) filter as compared 

to a MERV 16 (Minimum Efficiency Reporting Value, > 95% efficiency on 0.3-m to 1.0-m 

and larger particles) filter as defined by American Society of Heating, Refrigerating and AirConditioning Engineers [ASHRAE 1999]. Filter efficiency comparisons were initially made and 

validated on diesel particulate matter (DPM) in the laboratory [Noll et al. 2011]. DPM 

measurements made on several equipment cabs in an underground limestone mine for multiple 

shifts with HEPA filters showed reduction efficiencies from 41% to 96%, while several cabs 

using MERV 16 filters in another underground limestone mine showed similar reduction 

efficiencies of 46% to 95% [Noll et al. 2011; Noll et al. 2014a]. This wide variation in DPM cab 

reduction efficiencies was a result of operator practices with regard to opening and closing doors 

and windows during the shift [Noll et al. 2011; Noll et al. 2014a].

A long-term HEPA and MERV 16 filter comparative study showed no overall significant 

difference between these filters [Cecala et al. 2016]. This study was conducted on enclosed cabs 
5

on both an underground face drill and a roof bolter using particle counting test methods between 

shifts in order to benchmark protection factor performance under consistent operating 

procedures. Airflow and cab pressure measurements made on these cabs during this long-term 

study, including several other filter combinations tested, showed that multiple leakages around 

the filters in the HVAC system had a noticeable impact on the overall cab’s PF performance, and 

that these additional leakages could be incorporated into mathematical modeling of these 

filtration systems [Cecala et al. 2016].

Finally, several larger control room/booth case studies were conducted to examine the PF

effectiveness of three different types of filtration system installations on much larger enclosures 

than typical mobile equipment cabs. One installation used a ductless heat pump to cool and heat 

the inside control room (1,440 ft3 of volume) at a mineral processing plant, with a separate 

standalone two-filter system for cleaning the intake and recirculated air [Noll et al. 2015]. 

Another installation used a wall-mounted HVAC system on a crusher booth (368 ft3 of volume) 

with two filtered intake pressurizers and recirculation filter units integrated into the HVAC 

system [Organiscak et al. 2016]. A third installation used a ductless heat pump to temper the 

inside of an underground limestone mine crusher booth (458 ft3 of volume), with a separate 

standalone single-filter system for cleaning both the intake and recirculation air [Patts et al. 

2017]. All of these systems provided a significant improvement in PF performance (8 to 216) 

compared to the negligible protection offered to workers in the previously unfiltered enclosures 

using typical window air conditioner units. However, these larger enclosures were more difficult 

to seal and pressurize than smaller cab enclosures on mobile equipment. Only 0.01 to 0.02 inches 

of water gauge (in wg) of positive pressure was achieved with 80 to 118 ft3 of intake air blown 

into the largest control room, which provided PFs from 8 to 25 [Noll et al. 2015]. The smallest

crusher booth achieved 0.04 to 0.12 in wg positive pressure for an intake airflow range of 97 to 

213 ft3, providing PFs of 35 to 127 [Organiscak et al. 2016]. Continuous differential pressure 

monitoring of the third underground limestone crusher booth indicated that its pressure was 

increased from 0.02 in wg to 0.11 in wg because of the 1.8-times increase in filtered airflow due 

to switching from a more restrictive 99% efficient filter to a less restrictive 95% efficient filter, 

which also improved the booth PF by more than four times [Patts et al. 2017]. The booth’s

pressure was further increased by 0.07 in wg by sealing a leaky exterior roof duct plenum [Patts 

et al. 2017].

Objective

The objective of this report is to compile the results of NIOSH’s environmental enclosure 

research, provide key design guidelines for environmental enclosures, describe the measurement 

methods used for quantifying enclosure performance, and demonstrate mathematical modeling of 

filtration system designs.

Key Environmental Enclosure Design Considerations

Environmental enclosures such as cabs, booths, and rooms can be effectively designed for 

removing outside airborne contaminants to reduce the exposure of occupants to respiratory 

hazards. NIOSH field and laboratory research has identified several key factors that affect an 
6

enclosure’s protection factor from outside contaminants [Cecala et al. 2014]. These key 

components for an effective environmental enclosure are detailed in the following sections.

Using a separate intake pressurizer fan and a high-efficiency respirable dust filter to 

supply intake air into the enclosure

Initial NIOSH field testing of retrofitted cab filtration systems on older mining equipment 

included adding separate intake pressurizer units and using intake filters with collection 

efficiencies of at least 95% on respirable-sized dusts [Organiscak et al. 2004; Chekan and 

Colinet 2003; Cecala et al. 2004; Cecala et al. 2005; Cecala et al. 2009]. Table 1 shows the 

respirable dust sampling results from these field studies in ascending order of PFs with their 

associated publication references. The average PFs for the various cabs measured over multiple 

shifts were 2.8 to 89, with other factors likely affecting their performance. As can be seen from 

Table 1, a wide range of PFs were observed for the various shifts of dust sampling with the same 

cab, indicating notable day-to-day operational influences on cab PF measurements.

Table 1. Respirable dust sampling results of

retrofitted cab filtration systems on mine vehicles

Vehicle Cab Tested

(No. of Shifts)

[Reference]

Mining 

Type

Cab 

Pressure, 

in wg

Inside Cab 

Dust Level

Average

(Range),

mg/m

3

Outside Cab 

Dust Level

Average

(Range),

mg/m

3

Protection 

Factor

Average

(Range)

Rotary Drill (4) 

[Organiscak et al. 2004] Surface None detected

0.08

(0.02 to 0.17)

0.22

(0.12 to 0.46)

2.8

(2.0 to 7.9)

Haul Truck (3)

[Chekan and Colinet 

2003]

Underground/

Surface 0.01 0.32(0.23 to 0.43)

1.01

(0.92 to 1.07)

3.2

(2.4 to 4.0)

Front-End Loader (6)

[Organiscak et al. 2004] Surface 0.015 0.03(0.01 to 0.07)

0.30

(0.09 to 0.47)

10

(2.1 to 50)

Rotary Drill (7)

[Cecala et al. 2009] Surface 0.10–0.40 0.16(0.06 to 0.21)

2.85

(0.68 to 4.20)

18

(3.7 to 42)

Rotary Drill (3)

[Cecala et al. 2004]

Surface 0.20–0.40 0.05

(0.03 to 0.07)

2.80

(0.73 to 6.25)

52

(24 to 89)

Rotary Drill (7)

[Cecala et al. 2005]

Surface 0.07–0.12 0.07

(0.02 to 0.13)

6.25

(0.25 to 31.8)

89

(3.6 to 245)

As seen in Table 2, laboratory cab enclosure testing under more consistent steady state 

conditions more clearly shows the direct relationship between the intake filter efficiency on 

enclosure PFs and the direct relationship of an intake pressurizer on intake airflow and cab 

pressure.
7

Table 2. Averages of laboratory cab performance testing for all filter combinations tested

Intake

Filter

(% Eff.)

Intake 

Filter 

Loading

Recir.

Filter

(% Eff.)

Intake

Airflow,

ft3/min

Intake Air

Leakage,

%

Recir.

Airflow,

ft3/min

Cab

Pressure,

in wg

Decay

Time,

min

Protection

Factor,

Co/Ci

Low 

(35%) Unloaded No 48.8 0.8 358 0.24 16 1.5

Low

(32%) Unloaded Yes (72%) 58.7 0.8 318 0.31 7 7.2

Low

(44%) Loaded No 21.5 3.7 378 0.08 18 1.7

Low

(42%) Loaded Yes (72%) 25.2 4.3 337 0.09 9 17.8

High 

(>99%) Unloaded No 22.8 3.4 383 0.09 27 22.9

High 

(>99%) Unloaded Yes (72%) 28.7 3.2 332 0.10 7 223.8

High 

(>99%) Loaded No 14.9 3.7 388 0.06 29 21.4

High 

(>99%) Loaded Yes (72%) 18.8 6.3 344 0.06 9 195.9

Low* 

(29%) Unloaded No 80.1 0.8 342 0.44 22 1.4

Low* 

(29%) Unloaded Yes (72%) 91.8 0.9 310 0.47 8 4.8

Low* 

(39%) Loaded No 30.2 3.8 383 0.10 17 1.6

Low* 

(39%) Loaded Yes (72%) 33.2 3.8 338 0.12 11 13.3

High*

(>99%) Unloaded No 39.2 2.8 370 0.16 25 26.5

High*

(>99%) Unloaded Yes (72%) 44.8 2.7 335 0.20 8 173.8

High*

(>99%) Loaded No 23.1 4.0 387 0.07 20 20.2

High*

(>99%) Loaded Yes (72%) 26.4 4.9 341 0.08 6 176.0

*Cab test stand operated with intake pressurizer fan.

The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 

devised air filter efficiency performance standards that are tested, rated, and reported as minimal 

efficiency reporting values (MERV) [ASHRAE 1999]. Table 3 shows these ASHRAE MERV 

filter ratings with their collection efficiency performance standards [ASHRAE 1999]. Only the 

filters in Group 4 (MERV 14 to MERV 16) have quantified average collection efficiencies for 
8

the smallest respirable particulate range (0.3-m to1.0-m). Also included at the bottom of this 

table is a high-efficiency particulate arrestance (HEPA) filter as specified by the United States 

Department of Energy (DOE), which must be capable of at least 99.97% filtering efficiency on 

all particle sizes > 0.3 m.

4

4 HEPA filter efficiencies are specified for all particles > 0.3 m, whereas the ASHRAE filter specifications 

represent an average filtering efficiency for the smallest 0.3-m to 1.0-m submicron particle range and 

may not be inferred as the same filtering efficiency on the smallest particles of this size range.

Although there is a considerable jump in the filtering efficiency from 

the MERV 16 to HEPA quality filters—increasing from > 95% to > 99.97% collection efficiency

throughout similar respirable particle size ranges, respectively—a long-term particle counting 

field comparison of these two types of filters on two underground limestone equipment cabs 

showed on average an insignificant difference in overall cab PFs [Cecala et al. 2016]. The 

MERV 16 filter started off with a lower cab PF than the HEPA, but reached similar PFs with 

some filter loading. The HEPA intake filter restricted the intake airflow quantity into the cab and 

had to be replaced more often than the MERV 16 intake filter [Cecala et al. 2016].

Table 3. MERV rating efficiency values for three size ranges of dust particles*

Group

MERV 

Rating

Average particle 

size efficiency 

(PSE)

0.3-microns to 

1.0-microns

Average particle 

size efficiency 

(PSE)

1.0-microns to 

3.0-microns

Average particle 

size efficiency 

(PSE)

3.0-microns to 

10.0-microns

1 1 - - <20%

1 2 - - <20%

1 3 - - <20%

1 4 - - <20%

2 5 - - 20–34.9%

2 6 - - 35–49.9%

2 7 - - 50–69.9%

2 8 - - 70–84.9%

3 9 - <50% ≥85%

3 10 - 50–64.9% ≥85%

3 11 - 65–79.9% ≥85%

3 12 - 80–89.9% ≥90%

4 13 <75% ≥90% ≥90%

4 14 75–84.9% ≥90% ≥90%

4 15 85–94.9% ≥90% ≥90%

4 16 ≥95% ≥95% ≥95%

HEPA n/a ≥99.97% ≥99.97% ≥99.97%

*Minimum efficiency reporting values (MERV) according to the American Society of 

Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)

 
9

Many agricultural tractor cabs used in pesticide spray applications use HEPA filters. Mobile 

particle counting field tests of a John Deere 7000 series tractor (John Deere, Moline, IL) and a

retrofitted Nelson spray cab (Nelson Mfg. Co. Inc., Yuba City, CA) on a Massy Ferguson 396 

tractor (Massey Ferguson, Duluth, GA) equipped with a HEPA intake filter exhibited PFs of 43 

and 16, respectively, on particle sizes < 1 m [Hall et al. 2002]. Stationary particle count field 

testing of 13 Massy Ferguson tractors equipped with HEPA filters in Nelson cabs after routine 

maintenance showed PFs of 30 to 445 for 0.3-μm to 0.5-μm particles. Additional repairs increased 

two of the cabs’ PFs below 50 to greater than 50 after cab filtration leaks were identified and 

repaired [Moyer et al. 2005]. This shows that using high-efficiency intake filters on respirable 

sized dusts is important in reducing respiratory hazards in environmental enclosures.

Testing procedures for quantifying the performance of filter elements loaded with dust can be 

found in ASHRAE 1999, ASABE 2013b, and ASABE 2017b. Another particulate filter test 

standard and classification system that can be used to quantify high-efficiency filters (> 95% 

efficiency on the most penetrating particle sizes, MPPS) is ISO 2011. Vapor testing procedures 

for quantifying activated charcoal filter efficiency and assessing filter life for use in agricultural 

pesticide applications can be found in ASABE 2013b and CEN 2009b.

Ensuring good enclosure integrity to achieve positive pressurization against outside 

wind penetration and to minimize outside air leakage around the filtration system

In addition to high-efficiency intake filters as a key building block for providing clean air into 

environmental enclosures, having environmental enclosure integrity (sealed isolation from the 

outside environment) is another key factor in achieving effective contaminant reductions inside 

the enclosure. Positive pressurization of the enclosure and minimizing any outside air leakage 

around the filtration system are needed to reduce outside contaminant penetration into the 

enclosure.

Low or negligible enclosure pressure provides the potential for wind and contaminants to 

infiltrate open areas into the enclosure. Some evidence of this can be seen from the field studies 

in Table 1. Lower respirable dust PFs of 2.8 to 10 were measured on the drill, truck, and loader 

cabs with pressurization less than or equal to 0.015 in wg. To prevent outside contaminants from 

being blown into the enclosure, its static pressure must be higher than the wind’s velocity 

pressure [Heitbrink et al. 2000; NIOSH 2008b].

Equation 1 shows the relationship developed and tested by Heitbrink using Bernoulli’s Equation, 

when applied to orifice flow at turbulent and uncompressed airflow conditions. He found good 

agreement with outside airflow and contaminant infiltration into an enclosure through the 

circular holes, directly facing into the wind [Heitbrink et al. 2000]. The determination of the 

equivalent wind velocity for a given enclosure pressure that prevents this enclosure infiltration is 

shown in Equation 2 [NIOSH 2008b; NIOSH 2012a]. Figure 1 also graphically illustrates the 

wind speed equivalent relationship with enclosure pressure at standard air temperature and 

pressure. As can be seen from this figure, enclosure pressures of 0.05, 0.1, 0.25, and 0.50 in wg 

would resist wind infiltration of 10.2, 14.4, 22.7, and 32.1 mph, respectively.
10

vo =

Qo

Ao

= Cd√2

(0.5 ρairvw

2 −Δpc)

ρair

; wind penetration when 0.5ρair vw

2 > Δpc

(1)

where:

vo = air velocity through an orifice,

Qo = air quantity through an orifice,

Ao = area of cab orifice openings directly facing into the wind,

Cd = orifice discharge coefficient; 0.61 circular orifice coefficient used by Heitbrink,

air = air density,

vw = wind velocity, and

Δpc = cab static pressure; inside to outside differential pressure.

vw = (4000√Δpc) × 0.011364 at standard air temperature and pressure (2)

where:

vw = wind velocity, mph, and

Δpc = cab static pressure, in wg.

Another factor influencing enclosure pressurization is the quantity of intake airflow. Laboratory 

testing shows that enclosure pressure is directly related to intake airflow quantity delivered into 

the enclosure [Organiscak and Cecala 2008; NIOSH 2008b]. Figure 2 illustrates this enclosure 

differential pressure (Δpc) relationship with intake airflow (QI) measured during laboratory 

testing. As can be seen in this figure, when adding a separate intake air booster fan or pressurizer 

fan to the filtration system, the enclosure intake air quantity and pressure were significantly 

increased. This figure also shows that the wind slightly boosts enclosure pressures at the lower 

intake airflows without the pressurizer, indicating that the enclosure exiting airflow directly 

facing into the wind is restricted/reversed and being forced to exit other openings unopposed by 

the wind [NIOSH 2008b]. However, higher enclosure pressures should not necessarily be 

associated with high PFs. The laboratory results shown in Table 2 clearly demonstrate that the 

lower intake filter efficiency provided the highest intake airflows and enclosure pressures at 

reduced PFs. A minimum specified intake airflow quantity of filtered air into a cab enclosure is 

25 ft3/min per occupant (or 43 m3/hr per occupant) [ASAE 1997; ASABE 2013a]. Another 

source specifies a minimum of 18 ft3/min (or 30 m3/hr) filtered intake air into a cab enclosure

during pesticide spraying [CEN 2009a]. Fifteen ft3/min is the calculated intake ventilation rate 

per person at rest for maintaining exhaled CO2 concentrations at 700 ppm above incoming intake 

air levels into a room or enclosure [ASHRAE 2007]. In order to ensure a breathable filtered air 

supply for enclosed cab occupants, it is recommended that the intake air quantity per person

exceed all of these specified and calculated minimum intake air quantities.
11

Figure 1. Relationship between cab pressure and its wind speed 

resistance to outside contaminants (from Equation 2). 

Finally, good enclosure integrity is important for minimizing outside air leakage around the 

HVAC/filtration system. Air leakage around the intake filter reduces its cleaning efficiency of 

the air being blown into the enclosure. NIOSH laboratory experiments showed that a 0.5-indiameter hole bypassing the intake filter was the third most significant influence on 

environmental enclosure penetration after intake filter efficiency and recirculation filter use 

[NIOSH 2008b]. Figure 3 shows the direct relationship between the percent leakage (l) of intake 

air and differential pressure across the intake filter (Δpf) for the four filtration system 

configurations tested in the laboratory with simulated intake filter loading conditions. Visual 

field evidence of a significant air/dust leak inside the filter housing of the contiguous series of 

pre-, HEPA, and charcoal filters of a Nelson spray cab during a mobile particle counting study 

can be seen in Figure 4 [Heitbrink et al. 2003]. The cab penetration with this leakage was 11% (a 

PF less than 10) for 0.3-m to 0.4-m particles [Heitbrink et al. 2003]. After fixing the bowed 

flanges and filter gaskets in the filter housing, the cab penetration on 0.3-m to 0.4-m particles 

was reduced to less than 1% (a PF greater than 100) [Heitbrink et al. 2003]. Figure 5 shows the 

significant reduction in cab penetration for all the particle sizes measured when the filtration 

system leakage was addressed.
12

Figure 2. Relationship between cab differential pressure and

intake airflow during laboratory experiments.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 10 20 30 40 50 60 70 80 90 100

+Dpc, in wg

QI, ft3/min

O No pressurizer, no wind

Х No pressurizer, wind

∆ Pressurizer, no wind

Figure 3. Relationship between intake leakage and differential

pressure across the intake filter.

0%

5%

10%

15%

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

l, % of QI

-Dpf, in wg

No pressurizer,

no recirculation filter

No pressurizer,

recirculation filter Pressurizer, no

recirculation filter

Pressurizer,

recirculation filter
13

Figure 4. Filter housing using a contiguous series of pre-, HEPA, and charcoal

panel filters (left); outside air leakage around filters on Nelson tractor cab (right).

Leakage

around filters

Photos by NIOSH

Figure 5. Nelson tractor cab test results before and after fixing

filtration system air leakage.

Providing effective respirable dust filtration on the enclosure’s recirculated airflow

Filtering the recirculated inside enclosure air significantly improves the enclosure’s PF and 

reduces the time it takes to achieve the lowest steady state concentrations [NIOSH 2007a; 

Organiscak and Cecala 2008; NIOSH 2008b]. The laboratory test results averages presented in 

Table 2 show that the PF of the enclosure while using the recirculation filter was notably 

improved by 3.4 to 10.5 times more than with the intake filter alone. This notable enclosure PF 

improvement was achieved by using a recirculation filter that was > 70% efficient on submicron 
14

sized particles (0.3-μm to 1.0-μm). Also, this table shows that the average decay time (time to 

reach the lowest steady state inside concentrations after the door was closed) was noticeably 

lower when using the recirculation filter. Measured decay times ranged from 16 to 29 min 

without the recirculation filter and ranged from 6 to 11 min with the recirculation filter. The 

reason for this significant improvement in enclosure performance with the recirculation filter is 

that the enclosure’s interior air is constantly drawn through the filter, thus continually re-filtering 

the dust out of the air [NIOSH 2007a; Organiscak and Cecala 2008; NIOSH 2008b]. 

These laboratory results demonstrate that a recirculation filter efficiency should be at least > 70% 

efficient on submicron particles (0.3-μm to 1.0-μm) in order to realize these significant enclosure 

PF improvements. The ASHRAE filter classification meeting this minimum specification is a 

MERV 14 or higher-rated filter as shown in Table 3. The MERV 14 filter specifications are 

75%–84.9% capture efficiency on 0.3-μm to 1.0-μm particles and > 90% capture efficiency on 

respirable particles > 1.0 μm. These specifications were previously recommended by NIOSH as 

a minimum ASHRAE rating for a recirculation filter [Cecala et al. 2014]. 

Another method of providing very effective filtration of the enclosure’s recirculated airflow is to 

use a final filter configuration within the system. NIOSH worked with an original equipment 

manufacturer (OEM) of mining equipment (J.H. Fletcher & Co., Huntington, WV), an intake 

pressurizer/filter supplier (Sy-klone International, Inc., Jacksonville, FL), and an underground 

limestone mine (Sidwell Materials, Inc., Zanesville, OH) to study the long-term performance of 

this type of filtration system design [Cecala et al. 2012; Cecala and Organiscak 2017]. Figure 6 

shows the layout of the enclosed cab filtration system used on Fletcher’s larger underground face 

drill cab enclosure (160 ft3) as well as their smaller roof bolter cab enclosure (75 ft3). As can be 

seen in this figure, the cab filtration system was configured to employ three filters: an intake, a 

recirculation, and a final filter. 

Initial field testing of this system layout while using the small-panel recirculation filter (3-in 

width by 16-in length by 2-in depth) at the Sidwell underground limestone mine showed that it 

quickly became loaded/restricted and had to be changed much more frequently than the other 

filters. Removal of this filter significantly increased recirculation airflow without a notable 

adverse effect on the cab PF [Cecala et al. 2012]. 

Long-term comparative field testing was further conducted using MERV 16 and HEPA filters at 

both the intake and final filter locations (shown in Figure 6) on these Fletcher equipment cabs 

which were deployed at a new underground limestone mine (Shelly Materials, Zanesville, OH) 

[Cecala et al. 2016; Cecala and Organiscak 2017]. Subsequent particle count testing and airflow 

measurements on these enclosed cabs were conducted over a seven-month period with each type 

of filter on the highest HVAC fan setting, and these results are illustrated in Figure 7. This figure 

shows that the MERV 16 filter started off with a lower cab PF with higher airflows than the 

HEPA, but reached similar PFs with some filter loading. The long-term average PFs for the 

MERV 16 and HEPA filters on the face drill were 3,898 and 3,677, respectively, and were 573 

and 681 on the roof bolter, respectively [Cecala et al. 2016]. There was no significant statistical 

difference between these PFs measured with MERV 16 and HEPA filters on each individual cab 

[Cecala et al. 2016]. The HEPA intake filter restricted the intake airflow quantity into the cab 

and was replaced more often [Cecala et al. 2016]. However, there was a significant statistical 

difference observed between the performance of these equipment enclosures, indicating that 

more outside air and contaminants must have leaked around the filtration system of the smaller 
15

roof bolter cab (75 ft3) [Cecala et al. 2016]. Nevertheless, both of these enclosed cabs with this 

final filter design configuration had some of the highest PFs measured by NIOSH.

Figure 6. Final filter system configuration tested with MERV 16 and HEPA filters. Photo by NIOSH

Figure 7. Long-term field study test results of final filter system

using MERV 16 and HEPA filters.

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

100

110

120

1

1 0

100

1,000

10,000

0 0 133 59 228 108 333 165 489 257 828 418 0 0 100 52 265 171 477 288 477 288 413 982 531 982 1213 6591419 783

Cab Intake

Airflow, cfm

Particle Counting

Protection Factor

Operating Time, hr

Face Drill PF Roof Bolter PF Drill Intake Airflow Bolter Intake Airflow

MERV 16 Filters HEPA Filters Changed Intake Changed Intake

‡ Intake air readings not shown due to measurement anomalies.

‡
16

Applying secondary enclosure design considerations for maintaining an effective 

filtration and pressurization system

Several secondary design considerations are recommended for maintaining the highest 

performance of environmental enclosures [NIOSH 2012a; Cecala et al. 2014]. These secondary 

considerations are described below.

Locating intake air inlet away from highest airborne contaminant generation sources

The air inlet to the cab filtration system should be located away from the highest potential 

sources of contaminant generation. Field dust sampling of several outside locations on 

Caterpillar (CAT) D11N and D10N bulldozers during overburden removal operations showed 

that the respirable dust levels near the cab intake inlet area beneath the cab and fuel tank (6 ft 

above the ground) were between 2 and 3 times higher than those up along the sides of the cab 

(about 11 ft above ground level) [NIOSH 2001a]. These findings suggest that the cab filter 

loading rates can be reduced by these amounts by increasing the air inlet location further away 

from the ground level dust sources. Agricultural cabs used on tractors for pesticide spraying 

applications were observed to have their cab filtration system inlets located on top of the cabs

[Hall et al. 2002]. The John Deere 7000 series tractor had the inlets on the top side of the cab and 

the Nelson spray cab on a Massy Ferguson 396 tractor had them on the top front of the cab, away 

from the typical spraying operations at the back of the tractors.

Avoiding heaters, fans, or HVAC discharge vents at the floor level of the cab

Any air blown across the floor of the environmental enclosure can aerosolize contaminants/dusts 

from the floor. During one field study, a drill cab enclosure had noticeably higher respirable dust 

levels inside the cab when the floor heater was being operated during the winter months. Further 

testing in the maintenance shop during a non-production shift confirmed that respirable dust

concentrations inside the cab were 0.26 mg/m3 with the floor heater operating as compared to 

0.01–0.03 mg/m3 without the floor heater operating [NIOSH 2001b; Cecala et al. 2001; Cecala et 

al. 2005].

Incorporating remote operator control of processes within enclosure to minimize the frequency of 

opening the enclosure to the outside

Doors and windows must be keep closed to maintain pressurization of the cleaned air inside the 

enclosure. A field study of a drill operator frequently opening the cab door to guide the joining of 

additional drill steels showed a 9-fold increase in dust levels inside the cab during the time when 

the door was opened [Cecala et al. 2007; NIOSH 2008a; Cecala et al. 2009]. Average respirable 

dust concentrations measured with a real-time dust monitor were 0.09 mg/m3 with the enclosure 

door closed and 0.81 mg/m3 with the door opened. Figure 8 (left) shows the drill operator 

opening the cab door for the last 30–45 sec of the 3-min process to add drill steels. The operator 

cab was closed for about the first two minutes of remotely maneuvering an additional section of 

drill steel from a rotating turret into place to be threaded. The visible airborne dust from 

operation drilling appeared to be dissipated by the time the operator opened the door to guide the 

jointing process of the additional drill steel; however, Figure 8 (right) shows that there were still 

notably higher respirable dust levels drawn into the cab when the door was opened as compared

to when it was closed. This drill was the 4th vehicle cab field tested in Table 1, and had an 

average PF of 18. The 5th and 6th cab vehicles field tested in Table 1 had higher PFs of 52 and 89, 
17

respectively, with all of their drill operations remotely controlled from inside the cab. Other field

studies showed increases in respirable dust or DPM inside enclosures when pressurization was 

lost from opening doors or windows [Noll et al. 2011; Cecala et al. 2012; Noll et al. 2014a].

Using mechanical filter media

It is recommended to use filters having the mechanical type filter media rather than the 

electrostatic type filter media [NIOSH 2012a; Cecala et al. 2014]. Mechanical filters depend 

primarily on the capture processes of: 1) interception, where particles follow the airstream within 

one particle radius of the fabric fibers and adhere to them; 2) impaction, where particles diverge 

from the airstream and directly embed into one of the fibers; and 3) diffusion, where the smallest 

particles collide with gas molecules, which alters their flow path so they are captured by 

interception or impaction [NIOSH 2012a]. As the mechanical filters load, they tend to acquire a 

dust cake on the media and become more efficient. Continual loading of these filters increases 

their differential pressure or resistance to airflow to a point where they have to be changed. 

Electrostatic filters use fabric media that is able to sustain an electrostatic charge for primarily 

attracting the particles to adhere to the fabric fibers [NIOSH 2012a]. These filters tend to provide 

a lower resistance to airflow or pressure drop as compared to mechanical filters. However, 

electrostatic filter efficiency can be reduced with filter loading or when exposed to substances 

that can reduce the electrostatic charge on the filter fibers [Martin and Moyer 2000; Raynor and 

Chae 2004].

Figure 8. Drill operator opening door to guide drill steel (left); one shift of

real-time respirable dust levels inside the cab with the door closed and open (right).

0.0

0.5

1.0

1.5

2.0

 07:28:33 08:58:33 10:28:33 11:58:33 13:28:33 14:58:33

Dust 

concentration, 

mg/m3

Time, hh:mm:ss

Tuesday 3/13/07

Inside cab, door closed Inside cab, door open

Photo by NIOSH

Monitoring cab system performance with a cab pressure instrument

Continually measuring enclosure pressurization shows when its status has notably changed. A 

measurable loss in pressure over time can indicate several possible developments to the system

which include the reduction in intake airflow into the enclosure and/or a loss of enclosure 

integrity. The most likely cause of intake airflow loss into the enclosure is filter loading as 

discussed above [Cecala et al. 2016]. Other causes for intake airflow loss could be a missing 

recirculation filter, pressurizer fan failure, or HVAC fan failure. Another source of pressure loss 
18

is deterioration in enclosure seals to the outside (doors, windows, etc.). Therefore, continually 

measuring cab pressure can provide important feedback as to when the system needs to be 

inspected and requires maintenance—so as not to diminish the air cleaning performance of the 

environmental enclosure as previously discussed above.

Size and locate filter element(s) into the system to facilitate easy replacement and maintenance

Filter elements should be sized so that they can remove their intended contaminants over a 

reasonable time period of operation and should be located for easy access and replacement.

Particulate or dust filter(s) should have enough filter media area to capture and retain ample 

amounts of dust while operating within a suitable filter pressure range to maintain adequate 

HVAC system airflow. Initial field testing of the Fletcher filtration systems (shown in Figure 6)

showed that an inadequately sized small-panel recirculation filter became more quickly 

loaded/restricted and had to be changed approximately three times more often than the other 

filters in the system [Cecala et al. 2012]. These filters should also be located for easy 

accessibility to facilitate maintenance of their replacement and inspection. The easier a filter is to 

change, the less contamination should occur to the worker performing the task and to the work 

area. Securing filter elements and compression-sealing their gaskets are usually accomplished 

with screw-in fixtures, wingnuts, spring clips, etc. Figure 4 illustrates an example of easy access 

to the filter elements in the back of a pesticide sprayer tractor cab, secured with a screw-in 

pressure application bar. This figure further illustrates visible leakage caused by a bowed flange 

in the filter housing. After the bowed flange and filter gasket in the filter housing were fixed, cab 

penetration was reduced from 11% (a PF greater than 100) to less than 1% (a PF greater than 

100) [Heitbrink et al. 2003]. Finally, incorporating a protective screen into the design of a filter 

element can prevent media damage during its replacement, thereby increasing the cab 

enclosure’s protection factor performance [Organiscak et al. 2013].

Use impermeable materials on interior enclosure surfaces for lower dust retention and easier 

housecleaning

When workers enter enclosures with muddy shoes and dusty clothes, this material can become an 

internal source of dust exposure. Using interior materials that retain less dust and are more easily 

cleaned can reduce dust generation inside the enclosure. For example, the use of vinyl seating 

and rubber floor mats as compared to fabric seats and carpeted floors can reduce dust generation. 

Limited field research has shown that applying sweeping compound for the working shift on the 

floor had a greater effect in reducing inside dust concentrations in cabs with rubber floor matting

when compared to carpeted floors [NIOSH 2001c]. The sweeping compound and dirt on the 

floor is then swept out at the end of the working shift.

Testing and Monitoring Environmental Enclosures

Sampling of airborne contaminants (dusts, DPM, droplets, vapors) inside and outside of 

environmental enclosures is essential in determining the exposure of the occupants to these 

substances and the relative effectiveness of the enclosure on controlling these airborne 

contaminants. Control of outside contaminants is typically expressed as protection factor (PF), 

reduction efficiency (η), or penetration (Pen) as shown below, using inside and outside enclosure 

concentrations (Equation 3 through Equation 5). Equation 6 shows how these measures are 
19

mathematically related and Table 4 compares these measures. As shown in the table, an 

enclosure with a PF of 25 has a reduction efficiency of 96% or a penetration of 4%.

PF = c

x

(3)

η = 1 x

c

(4)

Pen = x

c

(5)

PF = 1

1−η

=

1

Pen

(6)

where:

PF = protection factor, ratio,

η = reduction efficiency, fractional or expressed in percent,

Pen = penetration, fractional or expressed in percent,

x = contaminant concentrations inside environmental enclosure, and

c = contaminant concentrations outside environmental enclosure.

Table 4. Comparison of environmental enclosure performance measures

Protection 

Factor

(Ratio)

Reduction Efficiency x 100

(Percent)

Penetration x 100

(Percent)

5 80 20

10 90 10

25 96 4

50 98 2

100 99 1

1000 99.9 0.1

Also, measuring environmental enclosure airflows and pressures is helpful in maintaining the 

system’s optimum performance. Notable airflow and pressure changes to the environmental 

enclosure usually indicate filter loading and/or cab integrity maintenance issues that need to be 

addressed.
20

Respirable Dust

Respirable dust is the one of most common aerosols sampled at mining and construction sites for 

industrial hygiene risk assessment of the work environment and worker exposures. Permissible 

exposure levels (PELs) for respirable dust and other airborne contaminants mandated by the U.S. 

government in mining and construction can be found in the Code of Federal Regulations (CFR) 

Titles 30 and 29, respectively [CFR 2016]. Other occupational exposure limits for airborne 

contaminants are threshold limit values (TLVs) published by the American Conference of 

Governmental Industrial Hygienists (ACGIH) and recommended exposure levels (RELs) 

published by NIOSH [ACGIH 2016; NIOSH 2007b].

Gravimetric Respirable Dust Sampler

The most common type of respirable dust sampler is gravimetric based. This sampler is usually 

comprised of a constant-flow personal sampling pump, a size-selective cyclone, and a filter 

cartridge. Figure 9 (left) shows the personal gravimetric sampler (Zefon International, Ocala, FL) 

approved for use in the metal/nonmetal mining industry. As part of the sampler depicted in 

Figure 9 (left), a 10-mm Dorr-Oliver cyclone separates the respirable fraction (dust with an 

aerodynamic diameter less than 10 m in size) from the oversize dust which is deposited into the 

grit pot at the bottom of the cyclone. The respirable fraction flows out of the top of the cyclone 

and is deposited on a pre-weighed filter cassette. The personal sampler pump flow rate specified 

for measuring respirable dust at metal/nonmetal mining operations with this approved sampler is 

1.7 L/min. Some duty of care must be taken during sampling and handling of this personal 

sampler to keep the cyclone assembly in the upright position so that no oversized particles in the 

grit pot are deposited into the filter cassette. Other gravimetric sampler devices used for 

sampling airborne agents in different industries are specified by MSHA, OSHA, ACGIH, and 

NIOSH [CFR 2016; ACGIH 2016; NIOSH 2007b].

Once the filter cassette is removed from the cyclone assembly, it is post-weighed in a laboratory 

at nearly identical temperature and humidity conditions as the pre-weight measurement to 

determine the net weight gain of the respirable dust sample. The time-weighted average (TWA) 

dust concentration of the sample (typically for an 8-hr working shift) is determined by Equation 

7 below. The deposited dust on the filter media can be further analyzed for its particular mineral 

content such as crystalline quartz by either NIOSH Method 7500 (X-ray diffraction or XRD) or 

NIOSH Method 7603 (infrared or IR absorption analysis) [NIOSH 2016].

Dust Concentration (mg⁄m3) = Net Dust Wt.(mg)

Sampling Time(min)×Pump Flow Rate(L

min ⁄ )

×

1000(L)

1(m3)

(7)

Table 1 shows the TWA gravimetric respirable dust concentrations measured for cab field 

studies at mining operations and their associated PFs. As observed from this table, the shift-toshift TWA dust concentrations and protection factors can vary notably for a particular cab 

because of operational and work practice variations. The lowest PF shown for each cab may also 

be a result of the outside concentrations not being high enough during some shifts to accurately 

ascertain the effectiveness of the cab. Outside dust concentrations must be one to two orders of 

magnitude higher than the inside concentrations to validate PFs from 10 to 100 [Organiscak et al. 

2004; Organiscak et al. 2016].
21

Nephelometer

Given that the gravimetric respirable dust sampler only provides a TWA concentration during an 

operational sampling period, another type of instrument is needed to indicate the operational dust 

variations within a sampling period. A nephelometer is an electronic light-scattering instrument 

that detects real-time respirable concentrations. The nephelometer can be adjusted to a measured 

dust mass concentration to improve its accuracy. Figure 9 (right) shows a ThermoScientific 

Model 1000AN personal DataRam (pDR) (Thermo Fisher Scientific, Waltham, MA) lightscattering instrument commonly used by NIOSH in conjunction with personal gravimetric 

samplers during respirable dust studies. Used together, these instruments measure and record 

time-stamped dust levels for download to a personal computer. Adjacent sampling with both 

types of instruments allows the nephelometer to be specifically calibrated to the gravimetric 

concentration of the particular type of dust sampled. The gravimetric TWA dust concentration 

divided by the nephelometer TWA dust level is the multiplying factor used to calibrate the 

nephelometer dust level data to gravimetric concentrations [Listak et al. 2007]. This real-time 

dust data was used to examine the dust concentrations measured inside a drill cab with the door 

opened and closed as previously described and shown in Figure 8. Such real-time dust 

information allows for analysis of operational variables related to high respirable dust 

concentrations.

Figure 9. Personal gravimetric sampler (left); passive nephelometer

light-scattering instrument (right).

Photos by NIOSH
22

Diesel Particulate Matter (DPM)

Worker DPM exposure is regulated by the U.S. government for operating diesel vehicles in 

underground metal/nonmetal mines as described in CFR Title 30 [CFR 2016]. As shown in 

Figure 10 (left), a DPM sampler is comprised of a constant-flow sampling pump, a size-selective 

cyclone, and a DPM filter cartridge. This sampler is similar to the respirable gravimetric dust 

sampler in that it also uses a 10-mm SKC conductive cyclone (SKC Inc., Eighty Four, PA) 

operating at an airflow rate of 1.7 L/min to separate the non-respirable from the respirable 

aerosols. The respirable-sized aerosols flow to a special SKC DPM cassette consisting of an 

internal 0.8-m cut-point impactor to remove the respirable dust before reaching the two tandem 

quartz fiber filters [Cauda et al. 2014]. The DPM aerosol is deposited on the first fiber filter with 

the second downstream filter used as a dynamic blank. These samples are analyzed for elemental 

carbon (EC) and total carbon (TC) by NIOSH Method 5040 [NIOSH 2016]. DPM concentrations 

measured by this method are TWA concentrations measured for the sampling period.

In order to measure changing DPM concentrations during the shift, NIOSH developed a realtime EC monitor using laser extinction [Noll et al. 2013; Noll and Janisko 2013; Noll et al. 

2014b]. EC was used as the analyte because it makes up most of the DPM, it is not prone to 

interferences, and it is a proportional to laser extinction. This instrument was commercially 

developed into a portable personal real-time sampler unit for DPM sampling. Figure 10 (right)

shows the Airtec diesel particulate monitor that is commercially available (FLIR Instruments, 

Boston, MA). This instrument uses a size-selective cyclone and a 0.8-m cut impactor cassette 

(without the quartz filters) to separate the dust from the DPM, which is drawn into the EC 

monitor. The Airtec measures and time-stamps short-term EC/DPM concentrations throughout 

the shift (5- , 10- , and 15-min moving averages recorded every minute) which can be 

downloaded to a computer for operational analysis.

Underground comparisons of the FLIR DPM sampler with the standard method of DPM 

exposures (NIOSH 5040 analytical method) showed no significant differences between the 

measurement methods, and the FLIR DPM instrument was found not to be affected by dust and 

humidity [Noll and Janisko 2013]. This instrument was used to document the effects of opening 

and closing a cab’s door or window on EC (or DPM) concentrations measured on a face drill at 

an underground limestone mine (see Figure 11) [Noll et al. 2014a]. This figure illustrates that the 

inside cab EC concentrations similarly tracked the outside concentrations when the cab pressure 

was zero from an open window or door. Inside EC concentrations deviated from this trend and 

remained notably lower than outside concentrations only when positive cab pressures were 

achieved from blowing filtered intake air into the cab with the windows or doors closed. Opening 

a door or window clearly reduces the enclosed cab’s pressure to zero and has a negative impact 

on controlling DPM inside the cab.
23

Figure 10. DPM sampler (left); real-time EC/diesel particulate monitor (right).

Photos by NIOSH

Particle Counting

Particle counting is the measurement method used to quantify filtration performance with respect 

to the number and size of airborne particles removed. This measurement method is used by 

ASHRAE to classify air filter efficiency ratings in Table 3 [ASHRAE 1999]. ASAE also 

previously devised a consensus standard for field testing a cab’s protection factor performance 

with particle counters, and specified a minimum 50:1 cab PF performance criteria on 3-mdiameter particles for pesticide applications. This procedure required two optical particle 

counters, one placed inside and the other outside the cab, to examine its protection factor on 2-

μm to 4-μm particles on an end-use tractor as it drives along [ASAE 1997]. NIOSH examination 

of this test procedure indicated that inconsistent and low particle count data for the 2-μm to 4-μm 

particles outside the cab provided unreliable cab performance results. NIOSH recommended 

using smaller submicron particles as test media for cab filtration system performance, given the 

larger number of submicron particles present in the air [Heitbrink et al. 1998]. The advantage of 

particle counting as compared to gravimetric sampling for measuring filtration system 

performance is that a large number of smaller particles (submicron particles) are available at 

lower respirable dust mass concentrations, providing a better test medium for comparative

performance testing of environmental enclosures [Organiscak et al. 2004; Organiscak et al. 

2016].
24

Figure 11. EC concentrations and cab pressure measurements

made on an underground drill cab.

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

-50

0

50

100

150

200

250

300

350

6:00 8:24 10:48 13:12 15:36 18:00

EC Concentration, 

μg/m3

Time

outside drill

inside drill

pressure

Pressure, in wg

NIOSH laboratory and field testing of cab enclosures shows that the largest number of particles 

in the atmosphere are submicron, with the greatest number of particles detected in the 

instrument’s smallest 0.3-μm to 0.5-m size range [NIOSH 2008b; Organiscak et al. 2013]. High 

correlation (r > 0.98) was observed between the enclosures’ PFs determined from the 0.3-μm to 

0.5-m particle size range and the accumulation of all the submicron particles size ranges (0.3-

μm to 1.0-m). Figures 12 and 13 illustrate these high correlations along the unity line for the 

laboratory and field tests, respectively. These figures also show that lower correlations (r < 0.68) 

exist between the PFs measured with submicron particles (0.3-μm to 1.0-m) and the larger 

particles (> 1.0-m) because of fewer particles available at these larger size ranges in the 

ambient air for reliable enclosure performance testing. Therefore, NIOSH recommends using the 

submicron particle ranges such as 0.3-μm to 0.5-m or 0.3-μm to 1.0-m for enclosure 

performance testing, because these size ranges are where the largest portion of particles are 

available in the ambient air and are the most challenging to be removed by the enclosure’s 

filtration system.
25

Figure 12. Particle size-related enclosure PFs measured in the laboratory.

1

1 0

100

1000

1 1 0 100 1000

0.3 - 0.5 m

or > 1.0 PF

0.3 - 1.0 m PF

0.3 - 0.5 um > 1.0 um

r = 0.996 r = 0.678

Unity line

Figure 13. Particle size-related enclosure PFs measured during field studies.

1

1 0

100

1,000

10,000

1 1 0 100 1,000 10,000

0.3 - 0.5 m

or > 1.0 m PF

0.3 - 1.0 m PF

0.3 - 0.5 um > 1.0 um

Unity line

r = 0.989 r = 0.359
26

Optical particle counters are available with different performance specifications and userselectable configurations. They operate by detecting the physical relationship between the 

forward scatter of a laser beam and particle diameter in a sample volume of air. Many of the 

instruments available can measure down to submicron particle sizes of 0.3-μm to 0.5-m. The 

more costly instruments provide a user-selectable and larger number of particle size ranges, 

while the lower cost instruments usually have a lower number of defined particle size ranges. 

Most instruments have data logging capabilities with user-selected sampling periods or sample 

volumes.

Figure 14 (left) shows TSI optical particle sizer model 3330 desktop instrument (TSI 

Incorporated, Shoreview, MN) used by NIOSH with up to 16 user-selectable size ranges. This 

instrument is capable of operating in aerosol concentrations up to 3,000,000 particles/L with 5% 

coincidence error. Coincidence error is primarily the undercounting of particles hidden behind 

other particles as particle concentrations increase. Figure 14 (right) shows a handheld Met One 

model HHPC-6 instrument (Met One Instruments, Grants Pass, OR) used by NIOSH with six

fixed particle size ranges of 0.3-μm to 0.5-μm; 0.5-μm to 0.7-μm; 0.7-μm to 1.0-μm; 1.0-μm to 

3.0-μm; 3.0-μm to 5.0-μm, and > 5.0-μm. It is capable of counting up to 70,670 particles/L with 

5% coincidence error. The higher grade TSI instrument is about 4 times the cost of the handheld 

Met One and could be used in higher dust/particle concentrations during production operations. 

The lower cost Met One instrument is more suitable for static testing cab enclosures with the 

particles in the ambient air under nonproduction settings.

Figure 14. Desktop size selectable optical particle counter (left);

handheld fixed-size optical particle counter (right).

Photos by NIOSH

Particle counting performance testing of enclosures can use either a one- or two-instrument 

sampling methodology. Ideally this type of testing should be performed at or near steady state 

concentration conditions. A steady state enclosure condition is the point in time where the 

interior contaminant concentrations reach their optimum lowest achievable concentrations 
27

provided by the filtration system. A two-instrument sampling methodology simultaneously 

measures upstream and downstream of the filtration system and introduces individual instrument 

biases into the experimental error, so it is recommended that the instruments be rotated between 

the sampling locations an equal number of times to average out these biases. The two-instrument 

methodology was specified for mobile testing of a cab by ASAE and was primarily used by 

NIOSH in laboratory and field studies [ASAE 1997; Heitbrink et al. 1998; Hall et al. 2002; 

Heitbrink et al. 2003; NIOSH 2008b; Organiscak and Cecala 2008; Cecala et al. 2012; 

Organiscak et al. 2013; Cecala et al. 2016; Organiscak et al. 2016]. To evaluate optimum 

performance, enclosure filtration effectiveness should be determined from the particle count data 

when the inside enclosure concentrations are at their lowest steady state conditions.

The sampling time period needed to collect sufficient stable interior data may be shorter or 

longer, depending on the enclosure volume and airflow quantity of the filtered air. The time to 

reach near steady state conditions after the enclosure is closed off from the outside contaminants

can be roughly estimated by using the ventilation dilution equation for the enclosure volume 

(Equation 8, assuming no interior generation) [ACGIH 2010]. The time estimate to achieve a 

95% enclosure interior concentration reduction near steady state conditions is calculated by using 

0.05 for x/xo ratio in the dilution equation, yielding a time period of 3 times the ratio of enclosure 

volume to airflow quantity as show in Equation 9. A time estimate to achieve a 99% enclosure 

interior concentration reduction near steady state conditions is calculated by using 0.01 for x/xo

ratio in the dilution equation, yielding a time period of 4.6 times the ratio of enclosure volume to 

airflow quantity as shown in Equation 10. As can be seen from these equations, the time is 

directly related to enclosure volume and indirectly related to airflow quantity. The airflow 

quantity used in these equations should be the airflow quantity that is filtered. Intake airflow rate 

should be used if it is the only air filtered, or total airflow rate should be used if both intake and 

recirculated air are filtered.

Δt = V

Q

l (

x⁄xo

) (8)

Δt = 3V

Q

(9)

Δt = 4.6 V

Q

(10)

where:

Δt = time to reach enclosure interior concentration,

V = volume of enclosure,

Q = airflow quantity of filtered air into enclosure,

xo = starting concentration inside environmental enclosure,

x = concentration reached inside the enclosure after dilution with

100% contaminant-free air, and

x/xo= proportional concentration ratio inside the enclosure from contaminant-free

ventilation; use 0.05 for 95% concentration dilution to steady state

conditions or 0.01 for 99% concentration dilution to steady state conditions.
28

When performing this type of enclosure testing, particle concentrations should be continuously 

measured to detect when they reach their lowest and most stable interior levels for comparison 

with the outside concentrations for the same time period. In NIOSH laboratory and field testing, 

researchers typically conducted enclosure testing for 45 minutes when using only an intake filter 

and for 30 minutes when using both an intake and recirculation filter [NIOSH 2008b; Organiscak 

and Cecala 2008; Cecala et al. 2012; Organiscak et al. 2013; Cecala et al. 2016; Organiscak et al. 

2016]. The most stable and last 15 minutes of data for these test periods were used for the data 

analysis.

Laboratory enclosure results have shown that identical filter test configurations were repeatable 

on different days of testing with different ambient submicron (0.3-m to 1.0-m) particle count 

concentrations outside the enclosure. Figure 15 illustrates some of the laboratory test results 

when using a high-efficiency intake filter with and without recirculation filter and wind. PFs 

were found to be relatively consistent when outside particle count concentrations were above 

20,000 counts per liter and also above the instrument’s 5% coincidence error level of 70,670 

particles/L. Although the enclosure’s PF performance may have been slightly reduced in some of 

the tests when coincidence levels of the outside particle counter instrument were exceeded, these 

laboratory results indicate that enclosure performance was still adequately quantified under less 

than ideal test conditions [NIOSH 2008b; Organiscak et al. 2013]. A significant improvement in 

enclosure PF was attained by using a recirculation filter, and the wind was found to primarily 

have an adverse impact on particulate penetration into the enclosure when no recirculation filter 

was used. Finally, this particle counting method was also used in field studies for examining the 

performance of a three-filter system and a long-term comparison of MERV 16 and HEPA filters 

on cab filtration system performance [Organiscak et al. 2013; Cecala et al. 2016].

Figure 15. Laboratory particle counting test replicates for the high-efficiency intake filter

configuration with and without a recirculation filter and wind.

1

1 0

100

1,000

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

Outside Cab Particle Count Concentration, 0.3 - 1.0 m Counts/L

< 20,000 counts/L

Intake filter

Intake filterwith wind

Intake and recirculation filter

Intake and recirculation filter with wind

Cab Protection Factor
29

A single-instrument sampling methodology removes instrument bias from the testing, but it does 

not continuously measure both sampling locations for a more thorough examination of testing 

variability or experimental error. To perform this type of sampling, equal lengths of sample 

tubing are routed to the inside of the cab and outside of the cab near the intake airflow inlet. The 

particle counter should be initially used to monitor the inside of the cab until it reaches the 

lowest steady state conditions, and then be alternated for several minutes between the inside and 

outside sampling locations. When alternating the instrument between sampling locations, the 

sample lines should be purged for at least the first minute and the data collected after the line was 

purged should be used for the data analysis. Sampling inside and outside the enclosure should be 

replicated at least twice and may require additional replicates if the outside concentrations are 

highly variable during testing. This procedure was used to quantify cab PFs of at least 50:1 

(outside to inside particle concentrations) for a pesticide spraying company after refurbishing and 

performing maintenance on 13 tractor cabs [Moyer et al. 2005]. ASABE specifies a minimum of 

four 10-minute sampling replicates on 0.3-m to 0.5-m size particles outside and inside the cab 

enclosure using the single-instrument sampling method [ASABE 2017a].

Gases, Vapors, and Liquid Droplets

Many gases can be measured continuously with direct reading electronic instruments that use 

electrochemical, infrared, photoacoustic, etc. principles of detection. Vapors and gases can also 

be sampled by being collected in sorbent tubes using an air sampling pump for subsequent 

laboratory analysis. These sampling methods can be used to monitor environmental enclosures 

when suspected gas or vapor contaminants could be present from the outside environment.

NIOSH conducted some pesticide droplet sampling to help address concerns over the size of 

pesticides aerosols generated during air blast spay operations. Pesticide size distributions were 

measured at a site on an orchard in California’s central valley near Visalia. During this study, the 

air blast sprayer directed a pesticide solution containing chlorpyrifos at two instruments used to 

evaluate the particle size distribution. One of the instruments was an aerodynamic particle sizer 

(APS Model 33b, TSI, St. Paul, MN), which sizes particles based on the transit time as they are 

accelerated between two laser beams within the instrument. As particles are passed through the 

two laser beams, scattered light is detected by two photomultiplier tubes and the time difference 

between these two events is measured—thus the particles are sized based upon transit time.

The other instrument used in this study was the micro-orifice uniform deposit impactor 

(MOUDI, MSP Corporation, St. Paul, MN). The MOUDI is an eight-stage cascade impactor that 

was operated at a flow rate of 30 L/min. This impactor is unique in that each stage rotates, which 

allows the collected material to uniformly deposit on the filters. In this study, the pesticide spray 

mist (liquid droplets) was collected on aluminum substrates that were coated in PAM vegetable 

oil (Conagra Brands, Chicago, IL). The 50 percent cut diameters for the MOUDI are: 18-μm, 10-

μm, 6.2-μm, 3.2-μm, 1.8-μm, 1.0-μm, 0.32-μm, and 0.18-μm. The last stage of the impactor was 

not used in this study, and the stages were backed up with a 0.8-μm mixed cellulose ester 

membrane filter. The impactor stages and back-up filter were then analyzed for pesticide 

chlorpyrifos by a modification of NIOSH Manual of Analytical Methods 5600 [Hall et al. 2002].

During this study, the particle size measurements were not made at the same time with the APS 

and the MOUDI. The instruments both sampled air through a custom sampling train that had an 

omnidirectional inlet from a PM10 sampler (Anderson-Grasby). The design flow for the PM10
30

5 The dried droplet size is proportional to the cube root of the mass fraction of the nonvolatiles (spherical 

diameter) left over after all the water is evaporated from the pesticide droplet solution.

sampler is 17 L/min and the design flow for the MOUDI is 30 L/min. Therefore, the sampling 

train had to be designed so that 17 L/min could be pulled through the PM10 sampler, and an 

additional 13 L/min of sheath air was added after the sample inlet to supply a total of 30 L/min to 

the MOUDI. In addition, the MOUDI was enclosed in a positive pressure container to eliminate 

the possibility of small aerosols (generated from the blast air sprayer) leaking between the 

sample stages. The PM10 sampler was also used in the sampling train with the APS. The APS 

operated at a design flow rate of 5 L/min. Therefore, to pull air at the PM10 sampler designed 

flow rate (17 L/min), an additional pump had to be used for the APS sample train. The APS and 

the additional pump pulled air through the PM10 sampling inlet, and the sampling train was 

designed so that the APS would collect isokinetic samples [Hall et al. 2002]. 

The measured distributions from this study indicated that there is a noticeable mass of aerosols in 

sizes less than 3 μm. This may be surprising, because most of the literature on this subject 

indicates that pesticide spray is typically larger than 100 μm. However, for this study the spray 

concentration was 1.5 gallons of concentrate in 500 gallons of water. The concentrate was 50 

percent petroleum distillate. When the water evaporates from this spray droplet solution, a 100-

μm spray droplet will become an 11-μm particle.5 This indicates that pesticide spray that does 

not deposit on the crop or on the ground could become a much smaller particle due to 

evaporation, thus explaining the generation of particles smaller than 3 μm [Hall et al. 2002]. The 

results of this study indicated that a range of 3 percent to 23 percent of the chlorpyrifos aerosol 

mass was smaller than 3 μm. However, the mass concentration for these particles was less than 

the ACGIH TLV for chlorpyrifos at the time of the study [Hall et al. 2002]. 

Also, NIOSH has examined some direct reading carbon dioxide (CO2) measurements to 

demonstrate that the quantity of intake airflow delivered into an environmental enclosure was 

more than adequate for human respiration for multiple people. A minimum recommended intake 

airflow quantity per person inside a cab enclosure is between 15 and 25 ft3/min [ASAE 1997; 

ASABE 2013a; ASHRAE 2007; CEN 2009a]. Eighty to 118 ft3/min delivered into a larger 

control room (1,440 ft3 of volume) typically had less than 1,100 ppm of CO2 concentrations, 

which was more than adequate for human respiration by multiple people inside the enclosure 

[Noll et al. 2015]. 

NIOSH furthermore conducted research on using CO2 gas sampling at comparative locations 

inside and outside an unoccupied cab enclosure using a special CO2 intake filter to quantify air 

leakage around the enclosure’s filtration system. This research was conducted under a NIOSH 

CRADA with Clean Air Filter Company (CAF) to develop an expedient, simple, quantitative, 

and reliable field test method for measuring air leakage into enclosed cab filtration systems. This 

method was developed as an alternate to particle counting environmental enclosures, and 

removes enclosure measurement interferences from internal particulate generation such as dirty 

floors, interior surfaces, and abraded blower motor brushes [Cecala et al. 2005; Heitbrink and 

Collingwood 2005]. 

Several application approaches were investigated for the air leakage measurement method. A 

time decay model of inside enclosure concentration was developed (see Equation 11 below) for a 

timed concentration decay test, with a single instrument for determining if the enclosure is 
31

meeting a particular air leakage target [Organiscak and Schmitz 2006]. This test is relatively easy 

to perform in that the monitor can be placed outside the enclosure for several minutes to measure 

ambient CO2 concentrations, and then put inside the enclosure to start the decay test with a 

stopwatch. However, the time calculation for comparison with the observed time to reach a 

particular concentration requires accurate knowledge of intake air quantity (Q), enclosure 

volume (V), and filter efficiency (ηf).

This test approach was demonstrated in the laboratory because the enclosure parameters and 

filter efficiency could be accurately measured, but would be quite challenging to measure 

accurately in the field [Organiscak and Schmitz 2006]. Steady state testing (represented by 

Equation 12 below) with two CO2 instruments similar to the particle counting testing (previously 

described above) was also examined in the laboratory, and researchers found that this testing 

reasonably quantified the leakage, but that instrument biases and reduced CO2 filter efficiency 

over time could diminish the accuracy of this approach [Organiscak and Schmitz 2006].

Δt = V

Q

1n cQ(1−ηf+lηf)−xQ

cQ(1−ηf+lηf)−xoQ

 (11)

Pen = x

c

= 1 ηf + lηf (12)

l = x−i

c−i

 (13)

where:

Δt = time to reach enclosure interior concentration,

V = volume of enclosure,

Q = intake airflow quantity into enclosure, volume per time,

ηf = intake filter efficiency, fractional, (1‒ i/c),

l = proportion of cab intake air leakage, fractional,

xo = starting concentration inside environmental enclosure,

x = concentration reached inside the enclosure,

c = concentration outside environmental enclosure,

i = immediate concentration after the filter, and

Pen = enclosure penetration, ratio, x/c.

In order to negate instrument bias, frequent instrument calibration in the field, and changing filter 

efficiency over time, another simpler single instrument assessment was examined to determine 

air leakage. This method measures relative CO2 concentration differences inside and outside the 

cab filtration system using the same instrument at or near steady state cab conditions. Equation 

13 shown above was derived from Equation 12 to quantify air leakage around the CO2 intake test 
32

filter by measuring CO2 concentrations inside the cab, immediately after the intake filter, and 

outside the cab by using the same instrument.6

6 The instrument sample airline to each of these locations should be purged for a minute or two after it is 

switched to the new location before measuring CO2 concentrations. Air leakage is defined as the 

fractional amount (or percentage) intake air that bypasses the intake CO2 filter and is unfiltered.

Figure 16 shows good agreement between the test results for a Sable CA-10a CO2 instrument 

(Sable Systems International, Las Vegas, NV) and two Vaisala GM70 CO2 instruments 

(Helsinki, Finland) simultaneously rotated between these three sampling locations as compared 

to a mass flowmeter measured air leakage conducted on CAF’s laboratory cab test stand. Table 5

shows the data collected on a John Deere tractor cab while simultaneously using a Sable and 

Vaisala 1 instrument, and illustrates the consistent measurement biases between the two 

instruments at the sampling locations. However, each instrument measured comparable air 

leakages because their relative CO2 concentration differences in the cab filtration system were 

used to calculate leakage. This “Method for Leak Testing an Environmental Enclosure” has been 

patented in Australia, Canada, the United States, and the European Union [Organiscak and 

Schmitz 2009; Organiscak and Schmitz 2010a; Organiscak and Schmitz 2010b; Organiscak and 

Schmitz 2016].

Table 5. CO2 measurements on the John Deere tractor cab

Instrument

Cab

Concentration, 

ppm

Filter

Concentration, 

ppm

Outside

Concentration, 

ppm

Cab Leakage, 

percent

Sable 19 14 420 1.2

Sable 18 13 405 1.2

Sable 17 13 429 1.0

Vaisala 1 47 43 441 0.8

1 50 46 432 1.1

Vais1 52 42 438 2.5

 
33

Figure 16. Air leakage test measurements made on

Clean Air Filter (CAF) Company’s test stand.

-2%

0%

2%

4%

6%

8%

10%

12%

14%

0% 2% 4% 6% 8% 10% 12% 14%

Airflow Leakage, %

Sable Vaisala 1 Vaisala 2

Unity line

CO2 Measured Leakage

Airflow and Pressure Measurements

In addition to the methods outlined above, taking airflow and pressure measurements within 

environmental enclosures are other means of monitoring and maintaining desired filtration 

system performance. Figure 17 (left) shows digital pressure instrumentation that can be 

electronically data logged (top) or monitored via an analog gauge (bottom) that can be visually 

observed by the enclosure occupant. Continuous pressure monitoring of an environmental 

enclosure showed pressure decreases for filter dust loading and pressure increases from filter

replacement and enclosure integrity repairs, thereby enhancing timely maintenance actions [Patts 

et al. 2017]. Figure 17 (right) shows a hotwire measurement of intake airflow being taken 

through a straight, smooth section of pipe attached to the cab enclosure. This measurement

directly indicates when intake airflow decreases below a desired minimum and the filter needs to 

be replaced [Cecala et al. 2016, Patts et al. 2017].

Recirculation airflow in many cases has to be measured with a vane anemometer traversing the 

recirculation inlet area. Sometimes the total airflow blown back into the enclosure at the 

discharge outlet has to be measured to determine either the intake or recirculation airflow by 

subtraction. Although some of these airflow measurements may not be made under ideal 

conditions as recommended in ACGIH’s industrial ventilation practices, consistency in 

measurement procedures and locations is important for examining changes to a particular 

HVAC/filtration system [ACGIH 2010].
34

Intake and recirculation airflows were measured during field studies to benchmark filters as they 

were loaded with dust and to determine when they needed to be replaced. Recirculation filters 

were replaced when their airflow dropped below the intake airflow into the enclosure [Cecala et 

al. 2012; Organiscak et al. 2013]. Intake HEPA filters were replaced when intake air quantity 

dropped below 25 ft3/min, which corresponded to cab pressure reductions below 0.05 in wg 

[Cecala et al. 2016]. Another source for pressure loss is deterioration in enclosure seals to the 

outside (doors, windows, etc.) as was observed in the case with one cab having an increase in 

intake airflow with a corresponding loss of cab pressure [Organiscak et al. 2013].

Figure 17. Digital (top) or analog (bottom) enclosure pressure monitoring (left);

hot wire anemometer measurement (right).

Photos by NIOSH

Enhanced Video Analysis of Dust Exposures (EVADE) software

Video exposure monitoring of an employee’s work environment is a valuable tool for examining 

their dustiest job tasks and control methods to reduce their exposures while performing these 

tasks. “Helmet-CAM” is a system developed by NIOSH and Unimin Corporation to record 

worker activities and contaminant exposure levels corresponding to those activities. This system

can be used in enclosed cabs, operator compartments, and control rooms to provide a simple and 

relatively inexpensive method to assess worker respirable dust exposures in these areas [Cecala 

et al. 2013, Cecala et al, 2015]. This system consists of a lightweight video camera and an 

instantaneous dust monitor that are worn by a worker as normal work duties are conducted. Once 

the video and dust data are obtained, the information is downloaded to a computer and the
35

NIOSH-developed EVADE software merges the video and dust data to assess the areas, tasks, 

and functions that impacted the individual’s respirable dust exposure7[NIOSH 2014].

7 Since its original creation as a software to help monitor dust exposure, EVADE has been enhanced 

significantly in its features, including its ability to help monitor exposure of multiple contaminants 

simultaneously. The EVADE version 2.0 software is available at NIOSH’s website at 

http://www.cdc.gov/niosh/mining/Works/coversheet1867.html.

There are many different types of commercially available video cameras that can be used to 

provide the video component for this assessment technology. The primary requirements for the 

camera are that it can record video for one to two hours, and that it outputs the video file in avi, 

mp4, or wmv formats. The video unit most frequently used by NIOSH since the initial 

development of this technology was the POV (point-of-view) camera by V.I.O. design (Extreme 

Technologies, LLC, Minneapolis, MN). This system employs a video lens that was originally 

attached to the worker’s hardhat using a commercially available flashlight clip or other methods, 

with duct tape being the most common technique. This POV camera uses a thin cable 

approximately 18 inches long to connect the video lens to the digital video recording portion of 

the device, and creates two digital video files—with an “avi” and a “thm” extension—which are 

then processed by the EVADE software. 

The second component of the Helmet-CAM system for dust monitoring is the instantaneous 

respirable dust monitor. For its testing, NIOSH used the Thermo Fisher Scientific pDR-1500 

Aerosol Monitor (personal DataRAM) (Thermo Fisher Scientific, Waltham, MA). This 

instrument was chosen because it is an active sampler using an internal pump to collect the dust 

sample [Reed et al. 2012]. The unit was slightly modified so that a 3-ft section of conductive 

tubing could be connected to the 10-mm Dorr-Oliver cyclone, which is the normal pre-classifier 

device used for respirable dust sampling in the metal/nonmetal mining industry. The pDR-1500 

was also set to a 1.7-liter/min flow rate, which is the required flow rate used for compliance 

respirable dust sampling by MSHA for the metal/nonmetal mining industry [MSHA 1990].

When the Helmet-CAM assessment analysis was performed, a 10-mm Dorr-Oliver cyclone was 

placed on the miner’s lapel and within the individual’s breathing zone, identical to the method 

used for a compliance-type dust sample. The 3-ft length of conductive tubing was used to connect 

the cyclone to the main body of the pDR-1500 monitor, and the instrument was configured to 

integrate samples over a two-second period for this testing. The worker would typically wear the 

dust monitor and video recording unit in a backpack so as not to interfere with work tasks. The 

worker was then asked to perform his or her normal job duties for an approximate pre-determined 

time period—normally between 90 minutes and 2 hours. Afterwards, the video and respirable dust 

exposure data files were downloaded using the instrument’s output functions to perform the 

respirable dust assessment using the EVADE software.

In addition to monitoring a worker’s personal dust exposure, the Helmet-CAM system has 

proven to be effective for use in enclosed cabs of mobile equipment or in operator booths and 

control rooms [Cecala and O’Brien 2014]. The one major difference in these types of 

applications is instead of the worker wearing the system, it is statically positioned in the cab, 

booth, or room. In these applications, the backpack is normally attached to the back of the 

operator’s seat or chair so as to not impact the worker’s ability to comfortably sit and perform 

normal duties. In enclosed cabs, the video camera lens is normally duct taped somewhere within 

 
36

the cab or on the dashboard to provide the video footage of the vehicle’s location and task, as 

shown in Figure 18. This particular video segment illustrates that the truck cab dust 

concentrations noticeably increased when it drove into the loading area of the mining pit. For 

operator booths or control rooms, the video camera should be positioned to provide footage of 

ingress or egress to the area as well as individuals within the enclosure and their activities.

Figure 18. Helmet-CAM technology used in the enclosed cab of haul

truck for assessment of truck driver’s respirable dust exposure

relevant to the truck location as viewed by the video.

Data Analysis of Environmental Enclosure Performance

Data from testing environmental enclosures needs to be analyzed into useful information for 

optimizing long-term performance. This can be primarily accomplished through graphical 

representation of the performance measurements made on the environmental enclosure. Timerelated graphical information can provide visual observations of enclosure performance trends 

for determining inspection and maintenance intervals. Data averaging and error analysis is also 

useful for examining the level of statistical uncertainty for the evaluation of significant enclosure 

performance factors. A description of these analytical methods is discussed below.
37

Graphical Analysis 

Graphical representation of data is one of the most useful analytical methods for examining 

environmental enclosure performance. Graphing time-related data of multiple performance 

measurements is very helpful for observing corresponding changes to the environmental 

enclosure. This type of observational analysis has already been discussed above and is 

particularly illustrated by Figure 7. This figure shows the time history of the long-term MERV 

16 and HEPA filter benchmarking comparisons on a Fletcher face drill and roof bolter machine 

at the Shelly limestone mine. This figure illustrates cab enclosure protection factors (PFs) and 

intake airflow changes observed during the study. It shows that the cab intake airflows were 

higher for the MERV 16 filters and that the HEPA intake filter had to be replaced more often 

without any observed long-term benefit to cab PF [Cecala et al. 2016]. This figure also shows the 

95% confidence intervals determined for the measured PFs and that the face drill PFs were

significantly higher than the roof bolter PFs during this study. Confidence intervals can be 

determined by several statistical methods and are discussed below.

Figure 19 is an additional time-history performance graph of the Fletcher roof bolting machine at 

the Shelly mine while initially using the small recirculation filter in the three-filter cab filtration 

system design [Organiscak et al. 2013]. This figure indicates that the cab PF increased with filter 

loading over time and that the recirculation filter airflow dropped quickly and was restored to its 

original airflow when it was replaced twice at 106 and 337 hours. After the recirculation filter 

was removed at 526 hours, the recirculation airflow was increased to levels slightly higher than 

with a new recirculation filter (by 11 ft3/min) with a corresponding drop-off in intake airflow (by 

22 ft3/min). This change initially reduced the cab enclosure PF from 745 to 247, then steadily 

increased to a PF of 2,451 after an additional 263 hours of operation. Recirculation airflow, 

intake airflow, and cab enclosure PF simultaneously decreased after 789 hours of operation, with 

the testing concluded at 842 hours of operation with new filters installed.

This graph illustrates that the recirculation filter capacity was undersized in this three-filter 

system, and removing it did not diminish its PF performance over the long term [Cecala et al. 

2012]. This figure additionally shows the 95% confidence intervals determined for the measured 

PFs, and indicates that the filtration system changed significantly with filter loading over the 

time history of this study. This roof bolting machine was later used during the long-term MERV 

16 and HEPA filter comparisons.

Normal Distribution Error Analysis of Sample Data 

Performance variables measured during environmental enclosure testing can be examined for 

measurement errors or uncertainty involved with the testing. Descriptive statistical measures 

typically associated with sampling a variable include a mean, standard deviation, relative standard 

deviation, sample size, standard error, and confidence intervals of these measurements. These 

descriptive statistics are shown below in Equation 14 through Equation 20 [Johnson 2005].

x =

∑ xi

n

i=1

n

̅ (14)

s = √

∑ (xi−x)

n 2

i=1

n−1

̅

(15)
38

RSD = s

x̅

(16)

SE =

s

√n

(17)

Etwo−tailed = tn−1,α/2 ∙ SE (18)

Eone−tailed = tn−1,α ∙ SE (19)

CI = x ± E ̅ two−tailed = x +̅ or Eone−tailed (20)

where:

x = sample mean of variable,

xi = i

th sample of the variable,

n = number of samples collected; n would be the number of test replicates

performed when the variable is PF or penetration ratios from particle

counting tests; n could represent the number of paired test replicates

conducted when the instruments are switched (to average out and

eliminate their systematic biases from testing), with the variable xi

becoming the average of the paired tests,

s = standard deviation of sample data,

RSD = relative standard deviation,

SE = standard error,

E = error estimate for sample mean or average,

tn-1,α/2 = two-tailed student’s t statistic with n-1 degrees of freedom at the

significance level α,

tn-1,α = one-tailed student’s t statistic with n-1 degrees of freedom at the

significance level α,

α = significance level or probability that the population average exceeds

the confidence interval about the sample average;1-α is the confidence level

or the certainty that the population average is within the confidence interval;

for example a 95% confidence level has a 5% probability that the population

average exceeds the confidence interval about the sample average,

CI = confidence interval about the sample mean or average; the confidence

interval expresses a range where the population average is expected to be

for a specified confidence level of 1-α; a two-tailed confidence interval will

assign one-half the significance level or probability of α/2 to the error

estimates of the lower and upper range limits around the average, a singletailed confidence interval will assign all of the significance level or

probability of α to the error estimate of either a lower (‒) or upper (+) limit

about the mean for greater than or less than comparisons with the sample

mean.
39

Figure 19. Graphical information of long-term field study of roof bolter

using a three-filter configuration.

0

5 0

100

150

200

250

300

1 0

100

1,000

10,000

0

1 63 37 4

106106138232337337445518526622707789842

Cab Airflow, cfm

Roof Bolter Operating Time, hrs.

Protection Factor Intake Airflow Recirculation Airflow

Cab Protection Factor

These statistics were used to calculate the confidence intervals for comparison of the average cab 

PFs for the MERV 16 and HEPA filters during the long-term field study. Table 6 shows the PF 

data collected (illustrated in Figure 7) and statistics calculated for similar face drill and roof 

bolter cab filtration systems with the MERV 16 and HEPA filters. As can be seen from this table,

the two-tailed CI values overlap between the MERV 16 and HEPA filters test comparisons on 

each cab. However, the two-tailed CI values do not overlap when comparing the face drill and 

roof bolter cab PFs, indicating that the performances of the cab filtration systems are 

significantly different although their design appears to be the same. These differences are likely 

due to the change/variation in the air leakage around the filters between these two seemingly 

identical filtration systems for the face drill and roof bolter [Cecala et al. 2016]. Furthermore, the 

one-tailed lower CI values for the face drill show that its PF sample means are significantly

greater than 1,000 (at the 95% confidence level), while the roof bolter one-tailed higher CI

values show that its PF sample means are significantly less than 1,000 (at the 95% confidence 

level). These normal distribution sampling statistics allow examination of measurement 

uncertainty from environmental enclosure performance testing, assuming that the sampling is 

random, independent, and approximates a normal distribution.
40

Table 6. Calculation of statistics for MERV 16 and HEPA filter

comparisons on face drill and roof bolter cabs

i

th Sample Measured 

and Statistics 

(italicized)

Face Drill

Cab PF*for 

MERV 16 

Filters

Face Drill

Cab PF*for 

HEPA Filters

Roof Bolter

Cab PF*for 

MERV 16

Filters

Roof Bolter

Cab PF*

for HEPA

Filters

1 612 8,036 300 400

2 4,106 8,133 790 1,425

3 6,337 4,853 1,021 1,116

4 2,301 2,028 783 923

5 6,227 3,116 465 1,129

6 3,807 1,178 77 274

7 NA†2,509 NA 182

8 NA 2,550 NA 390

9 NA 685 NA 290

x

3,898 3,667 573 681

s 2,226 2,763 353 465

n 6 9 6 9

RSE 0.571 0.753 0.616 0.683

SE 909 921 144 155

Significance Level α 0.05 0.05 0.05 0.05

Confidence Level 1-α 0.95 or 95% 0.95 or 95% 0.95 or 95% 0.95 or 95%

Two-tailed tn-1, 0.025 2.571 2.306 2.571 2.306

Etwo-tailed 2,336 2,124 371 357

CI 1,562 to 6,234 1,543 to 5,791 202 to 944 324 to 1038

One-tailed tn-1, 0.05 1.943 1.860 1.943 1.860

Eone-tailed 1,766 1,713 280 288

Lower CI 2,132 1,954 293 393

Upper CI 5,664 5,380 853 969

*Cab PFs are the averages of alternated instrument paired test replicates

†NA-not available
41

Propagation of Error Analysis of Protection Factor Ratios

A propagation of error analysis approach can be used when the ratios of variables measured are 

not random, independent, and may not approximate the normal distribution. These conditions 

can occur when environmental enclosure testing is not conducted at steady state conditions and 

the inside concentrations are dependent on the changing outside concentrations. Furthermore, a 

small sample size of 2 to 4 tests adds to the level of uncertainty to the testing that is not quite at 

steady state conditions, and typically occurs during field testing of the environmental enclosure 

[Organiscak et al. 2013].

The propagation of error analysis approach was used to estimate the measurement uncertainty of 

cab PFs (PF = outside/inside, particles counted) measured during shop and field testing of 

underground face drills, a scaling machine, and a roof bolter [Organiscak et al. 2013]. Since 

multiple one-minute particle count samples were collected inside and outside the cab during a 

test, their sample averages were used to calculate the cab PF (ratio) for the test. The cab’s 

protection factor was determined from the average of the test replicates, and its measurement 

uncertainty would typically be estimated from the standard deviation of the test replicates. 

Conducting a two-replicate cab test procedure in the field would generally be considered too 

small a sample size for determining the measurement uncertainty. An alternative approach for 

estimating the cab PF measurement uncertainty is to determine the standard error for each test 

replicate by using the propagation of error analysis of the sample data for a two-variable ratio 

[Bevington 1969]. The standard errors of the individual test replicates can be further combined 

(or pooled) by using Satterthwaite’s standard error approximation, which assumes unequal 

variance between the test replicates [Satterthwaite 1946]. This approach uses the variation from 

the one-minute samples collected during each test replicate to estimate the measurement 

uncertainty of the average cab PF for the test.

Equation 21 shows how to determine the standard error of estimate for a PF ratio of a particle 

counting test [Bevington 1969]. This equation assumes that there are an equal number of outside 

and inside samples (n) collected during the test [Bevington 1969]. Covariance can be ignored in 

the equation if the variables are independent or there are insufficient pairs of data for a good 

estimate. Since outside and inside cab sample data are not independent outside of steady state 

conditions, covariance was used in the standard error of estimate calculations for 15 pairs of 

concurrent sampling data analyzed per test replicate at near steady state sampling conditions. 

This PF (ratio) propagation of error analysis, shown in Equation 21, could also be used for the 

penetration (ratio) into the environmental enclosure.

sPF

√n

=

PF

√n

√(

so

o

)

2

+ (si

i

)

2

 2 soi

2

oi

=

PF

√n

√RSDo

2 + RSDi

2 2roi

sosi

oi

 (21)

where:

o = mean of outside particle counts or concentrations,

so = standard deviation of outside particle counts or concentrations,

RSDo= relative standard deviation of outside particle counts or concentrations, so/o,

i = mean of inside particle counts or concentrations,

si = standard deviation of inside particle counts or concentrations,

RSDi = relative standard deviation of inside particle counts or concentrations, si/i,
42

s

2

oi = covariance between outside and inside particle counts or concentrations,

PF = protection factor, o/i,

sPF = standard deviation of protection factor,

roi = correlation coefficient between outside and inside particle count data, and

n = equal number of particle count samples taken inside and outside the cab.

Equation 22 illustrates the method for Satterthwaite’s standard error approximation for multiple 

test replicates, assuming unequal variances between test replicates [Satterthwaite 1946]. 

Equation 23 shows how to determine the degrees of freedom for Satterthwaite’s approximation 

[Satterthwaite 1946].

sep = √

s1

2

n1

+

s2

2

n2

+ ⋯ +

sk

2

nk

(22)

df =

(

s1

2

n1

 + 

s2

2

n2

 + ⋯ + 

sk

2

 nk

)

2

(n1−1)−1 (s1

2

n1

)

2

+ (n2−1)−1 (s2

2

n2

)

2

+ ⋯ +(nk−1)−1 (sk

2

nk

)

2 (23)

where:

sep = Satterthwaite’s standard error approximation,

sk = standard deviation of the kth test replicate,

nk = number of samples in the kth test replicate,

k = number of test replicates, and

df = degrees of freedom.

The confidence intervals for the PF of similar test replicates can be determined by multiplying 

the Satterthwaite’s standard error approximation (Equation 22) by the student’s t-statistic for the 

calculated degrees of freedom (Equation 23). This uncertainty analysis uses the particle count 

sample data during each test replicate to estimate the confidence level of the average PF for the 

replicates. Table 7 illustrates the particle count information and standard error calculations for 

four test replicates conducted with two particle counting instruments on the initial underground 

drill cab filtration system design with a HEPA final filter at the manufacturing shop facility

[Organiscak et al. 2013]. The test information in Table 7 shows the particle count concentrations 

measured for the 0.3-μm to 1.0-μm size range.
43

Table 7. Initial cab filtration system particle counting test

results using the HEPA final filter

Test Information Test Replicate 1 Test Replicate 2 Test Replicate 3 Test Replicate 4

Outside Average*99,266 128,649 91,065 94,022

Outside Std. Dev*19,192 17,478 6314 10,129

Inside Average*1,301 1,668 1,114 1,074

Inside Std. Dev*230 211 93 83

Covariance 4,142,231 3,162,488 498,549 758,239

No. of Samples 15 15 15 15

Protection Factor†76.3 77.1 81.7 87.5

Standard Error‡1.32 1.41 0.93 1.14

*0.3-μm to 1.0-μm sized particle count per liter

†Outside average  inside average

‡Standard error determined from Equation 21

The Protection Factor (PF) for test replicate 1 would be calculated as follows:

PF =

99,266

1,301 = 76.3

The PF standard error of estimate for test replicate 1 would be calculated as follows:

sPF

√n

=

76.3

√1 

× √(

19,192

99,266)

2

+ (

230

1,301)

2

 2 × (

4,142,231

99,266x1,301) = 1.32

The PFs and standard error of estimates for the other test replicates would be similarly 

calculated. The Satterthwaite’s standard error approximation for all the test replicates would be 

the square root of the summation of the standard errors squared by using Equation 22, as shown 

below for the four test replicates in Table 7.

sep = √1.322 + 1.412 + 0.932 + 1.142 = 2.43
44

The degrees of freedom for the Satterthwaite’s standard error approximation would be calculated 

by Equation 23, and is shown below for the four test replicates. To be conservative, the degree of 

freedom calculation was rounded down to the nearest whole number, which would be 51.

df =

(1.322 + 1.412 + 0.932 + 1.142)

2

1.324

(1 -1)

+

1.414

(1 -1)

+

0.934

(1 -1)

+

1.144

(1 -1)

= 1.6

The average cab PF would be calculated by taking the sample mean of the test replicates 

(Equation 14). The two-tailed error for this mean PF (at the 95% confidence level) is determined 

by multiplying the Satterthwaite’s standard error approximation by the student’s t-statistic for the 

calculated 51 degrees of freedom. The two-tailed confidence interval (at the 95% confidence 

level) from this analysis would be 80.6 + 4.8 (two-tailed E = 1.96 x 2.43). 

By comparison, the calculation of the standard deviation and standard error from the PFs of the 

four test replicates using Equation 15 and Equation 17 would yield the following results of 5.15 

and 2.58, respectively. Although the 2.58 standard error is similar to the Satterthwaite’s standard 

error approximation of 2.43, its two-tailed confidence interval (at the 95% confidence level) would 

be larger at 80.6 + 8.2 (two-tailed E = 3.182 x 2.58) because of the greater uncertainty in the 

Student’s t-statistic for three degrees of freedom. This greater uncertainty also includes all 

experimental errors within the testing, whereas the propagation of error analysis has the covariance 

element of the testing errors removed (subtracted out) from the standard error estimate.

Given the additional measurement uncertainty expected with conducting only two cab test 

replicates during the field studies, the alternate propagation of error analysis and Satterthwaite’s 

error approximation procedures were used in determining the PFs confidence intervals for the 

field study results shown in Figures 7 and 19. This analysis uses all the available sampling data 

collected during the two test replicates. The confidence levels of the cab PF test results shown in 

these figures illustrate that the cab enclosures’ performance significantly changed during these 

long-term studies because of filter loading and airflow changes in the filtration system 

[Organiscak et al. 2013].

Poisson Distribution Error Analysis of Particle Counting Data

Error analysis of microscopic particle classification on filter media (sizing and counting 

particles) was developed using the Poisson distribution to ensure that enough particles were 

counted to evaluate the certainty or uncertainty of the test results [Leith and First 1976]. This 

method can also be applied to optical particle counters, and was referred to as an alternate 

method for particle counting error analysis of cabs in the previous ASAE Standard S525-1.1 

[ASAE 1997; Heitbrink et al. 1998]. Based on the Poisson distribution, the relative standard 

deviation associated for the average particle count concentration is equal to the square root of the 

reciprocal of the total number of particles counted as shown in Equation 24 below [Leith and 

First 1976]. The average particle count concentration is equal to the mean number of particles 

counted in representative samples within a volume of air. Confidence intervals for these average 

particle concentrations are also shown below in Equation 25 for a two-tailed t-statistic and in 

Equation 26 for a one-tailed t-statistic. The t-statistic approximates the normal distribution for

sample sizes greater than 30 [Johnson 2005].
45

When computing ratios from two concentrations such as PF of an environmental enclosure, the 

confidence intervals for the ratios incorporate the propagation of error from both sampling 

locations and are shown below in Equation 27 and Equation 28 for a two-tailed t-statistic and a 

one-tailed t-statistic, respectively [Leith and First 1976]. These PF error equations can also be 

applied to filter penetration or environmental enclosure penetration ratios. For the normal 

distribution to serve as a reasonable approximation of the Poisson distribution for these cab 

performance ratio comparisons, it is suggested that a total of 50 particle counts should be 

measured inside the enclosure [ASAE 1997; Heitbrink et al. 1998]. 

σp

P

= √

1

N

(24)

CI = P ± tα

2

⁄

∙ P√

1

N

 (25)

CI = P + or tα ∙ P√

1

N

(26)

CI = PF ± tα

2

⁄

∙ PF√

1

No

+

1

Ni

 (27)

CI = PF + or tα ∙ PF√

1

No

+

1

Ni

(28)

where:

P = average particle count concentration,

σp = standard deviation of particle count concentration samples,

N = total number of particles counted to determine the concentration,

No = total number of particles counted outside the environmental enclosure,

Ni = total number of particles counted inside the environmental enclosure,

PF = protection factor of environmental enclosure; the ratio of the average

outside to inside concentrations, and if these concentrations are determined

from the same volume of air the total outside and inside counts can also

be used to determine the PF,

tα/2 = two-tailed student’s t statistic with infinite degrees of freedom at the

significance level α

tα = one-tailed student’s t statistic with infinite degrees of freedom at the

significance level α,

α = significance level or probability that the population average exceeds

the confidence interval about the sample average; 1-α is the confidence level is

or the certainty that the population average is within the confidence interval:

for example a 95% confidence level has a 5% probability that the population

average exceeds the confidence interval about the sample average,
46

CI = confidence interval about the sample mean or average; the confidence

interval expresses a range where the population average is expected to be

for a specified confidence level of 1-α; a two-tailed confidence interval will

assign one-half the significance level or probability of α/2 to the error

estimates of the lower and upper range limits around the average, a singletailed confidence interval will assign all of the significance level or probability

of α to the error estimate of either a lower (‒) or upper (+) limit about

the mean for a greater than or less than comparisons with the sample mean.

An example on how to apply this error analysis can be conducted with the particle count data 

previously presented in Table 7. Shown below is the PF calculation based on the average of the 

outside and inside concentrations. The two-tailed confidence interval for a 95% confidence level 

is calculated by using the total number of particles counted outside and inside. The total number 

of particles outside and inside are determined by multiplying their average concentrations 

(particles/liter) by the sampling airflow rate (2.83 liters/min) times the total sampling time for the 

testing (15 min × 4 = 60 min). The slight difference in this average PF as compared to the 

previous average calculated above is due to rounding errors in averaging the individual PFs of 

each replicate. However, the confidence intervals for the Poisson distribution analysis are 

noticeably smaller than the previous ones calculated above, because this analysis only accounts 

for the errors in particle counting and not the experimental errors included during these particular 

test replicates (which deviated from steady state conditions), which are included in the normal 

and propagation of error analysis methods performed above. The Poisson distribution analysis is 

more applicable for error analysis of lower particle counts and/or lower sample volumes when 

there is less data to compute errors from the other analysis.

PF =

99,266+128,649+91,06 +94,022

4

1,301+1,668+1,114+1,074

4

∙=

103,2 0. 

1,289.2 = 80.1

CI = 80.1±1.96×80.1√

1

103,2 0. ×2.83×60 +

1

1,289.2 ×2.83×60 = 80.1±0.3

This Poisson distribution error analysis approach is more suitable for particle count data 

collected over short time periods (several minutes of sampling) and at lower concentrations, 

given that the same sample volumes are measured outside and inside the enclosure. The error 

analysis can be performed with either the one-particle or two-particle counter sampling methods,

alternated between inside and outside locations with the enclosure at near steady state conditions. 

As an example, an 80 PF can be quantified with average particle count concentrations of 100 

particles/L inside and 8,000 particles/L outside the environmental enclosure for two-minute 

sampling intervals replicated twice at each location with a 2.83-L/min sampling rate. The 

calculation of two-tailed confidence interval at the 95% confidence level for this example is 

shown below.
47

CI = 80±1.96× 80√

1

8,000×2.83×4 +

1

100×2.83×4 = 80±4.7

As can be seen from this calculation, the PF confidence interval increased dramatically from 80.1 

+ 0.3 to 80 + 4.7 when a smaller sample size, air volume, or particle count was used. If a lower 

(‒) or higher (+) one-tailed confidence interval about the average is to be calculated, then a tvalue of 1.645 instead of 1.96 is used.

Mathematical Modeling of Environmental Enclosure 

Filtrations Systems

Mathematical modeling of environmental enclosure filtration systems was advanced by NIOSH 

to examine the key design parameters influencing enclosure protection factor (PF) performance. 

The first mathematical modeling exercise was conducted in conjunction with laboratory testing 

to validate its usefulness in examining filtration system design parameters on cab enclosure 

penetration or PF. The key filtration system design parameters tested in the laboratory were 

intake filter efficiency, air leakage around the intake filter, intake filter loading, recirculation 

filter usage, and wind infiltration [NIOSH 2008b]. This first mathematical model formulation 

was based on a time-dependent mass balance model of airborne substances within a control 

volume [NIOSH 2008b]. This model was a differential equation that was converged to a steady 

state solution for ease of use [NIOSH 2008b; Organiscak and Cecala 2009]. To simplify 

mathematical derivation of other filtration system models, a node analysis technique was utilized 

to illustratively assist with the algebraic formulation of these models at steady state conditions 

[Organiscak et al. 2014].

Figure 20 shows the node analysis diagram for one- (intake filter) and two-filter (with a 

recirculation filter) filtration systems. The model formulations are based on the mass balance of 

incoming and outgoing contaminants (dust, particles, etc.) at the interior cab node. Equation 29 

and Equation 30 were developed for the one- and two-filter systems, respectively. These models 

are dimensionless and show that the enclosure PF or penetration (Pen) are related to the intake 

filter efficiency, intake airflow, intake air leakage, recirculation filter efficiency, recirculation 

airflow, and wind quantity infiltration. If adequate environmental enclosure positive 

pressurization is achieved to resist wind infiltration (as previously discussed), the wind airflow 

quantity (Qw) would be zero in the model. Figure 21 shows the results of the model predictions 

compared to the laboratory testing results (previously presented in Table 2). As illustrated in this 

graph, there is good positive correlation (correlation coefficient, r = 0.995, at significance level p

< 0.02) along the unity line between the measured and modeled cab PFs. A notable difference in 

PFs was also observed between the two intake filters tested (filter efficiencies, ηI < 40% and ηI > 

99%) and when the recirculation filter (ηR = 72% efficiency) was added to the filtration system. 

Therefore, mathematical models can be used for examination of environmental enclosure 

filtration system design parameters on PF or Pen performance.
48

PF = c

x

=

QI+QW

QI(1−ηI+lηI)+QW

=

1

Pen

(29)

PF = c

x

=

QI+QRηR+QW

QI(1−ηI+lηI)+QW

=

1

Pen

(30)

PF = c

x

=

QI+QR(ηR+ηF−ηRηF)+QW

QI(1−ηI+lηI)(1−ηF)+QW

=

1

Pen

(31)

where:

c = outside contaminant concentration penetrating the filtration system,

x = inside cab contaminant concentration (interior cab node),

η = filter reduction efficiency, fractional,

1-η = filter penetration, fractional,

Q = airflow quantity,

l = intake air leakage, fractional,

with the following filter efficiency and air quantity subscripts:

I - intake,

F - final,

R - recirculation, and

W - wind.

A mathematical model for a three-filter system design studied on several underground limestone 

equipment cabs was also formulated. This model was developed to examine the cab system’s 

operational parameters with respect to field study performance measurements [Organiscak et al. 

2014]. Figure 6 shows the intake, recirculation, and final filter locations within the HVAC 

system. Figure 22 shows the node layout of this system and Equation 31, above, represents the 

model formulated for this system [Organiscak et al. 2014]. Note that if zeros are used for the 

final filter efficiency, Equation 31 reduces down to Equation 30, which is the two-filter system.

Similarly, putting in zero for the recirculation filter efficiency of equation 30 reduces it down to 

equation 29, which is the single-filter system.

The initial three-filter systems tested in the field were on cabs of an underground face drill and 

roof bolter machine. A MERV 16 final filter was used on both cabs with a > 95% filter 

efficiency on respirable-sized particulates. The face drill used unrated intake and recirculation 

filters and did not use a separate intake pressurizer. The roof bolter used a MERV 16 intake

filter/pressurizer unit with an unrated recirculation filter similar to the face drill. Particle 

counting, airflows, and cab pressures were measured on these cabs during a non-production time 

period. New and used unrated intake and recirculation filters were taken back to the Pittsburgh 

Mining Research Division’s (PMRD) laboratory and particle count tested for their filter 

efficiency. The measured respirable-sized particle efficiency (on 0.3-m to 1.0-m particles) of 

the unrated new and used intake filters were 22.8% and 98.4%, respectively. Recirculation filter 

efficiencies (on 0.3-m to 1.0-m particles) of the unrated new and used recirculation filters 

were 11.9% and 76.1%, respectively.
49

Figure 20. Two- and single-filter cab systems with and without a recirculation

filter, respectively (Q’s denote air quantities, x’s & c’s denote contaminant

concentrations, and η’s denote filter efficiencies).

Figure 21. Comparison of laboratory measured cab protection factors

to the modeled protection factors.

1

10

100

1000

1 10 100 1000

Measured Protection Factor

ηI <40% ηI >99% ηI <40%, ηR=72% ηI >99%, ηR=72%

Unity line

r = 0.995, p < 0.02

Modeled Protection Factor
50

Figure 23 shows the cab PFs measured during the field studies (data points) as compared to the 

expected lower (blue solid) and upper (green solid) operating ranges modeled with the new and 

used filters, respectively. Twenty-nine out of 33 PF measurements were found to be within the 

modeled operating ranges (curves) expected from these cab filtration systems. The cab-measured 

points were spread out between the lower and upper model curves because the filter efficiencies 

and airflows were changing due to dust loading over the seven-month sampling period [Cecala et 

al., 2012; Organiscak et al. 2014]. The outlying points can be accounted for simply by slightly 

increasing or decreasing the model’s filter collection efficiencies or intake leakage values by 1%

to 2%, which may be caused by filter product variations, filter airflow efficiency variations, and 

diverse dust loading conditions experienced between cabs during mining.

Operating the roof bolter filtration system without the recirculation filter (open points in Figure 

23) increased the recirculation-to-intake airflow ratio while maintaining comparable PFs with the 

three-filter system. This particular two-filter system arrangement performed well because the 

high-efficiency final filter cleans both the filtered intake air and unfiltered recirculation air 

before it is discharged into the cab. Finally, modeling one- and two-MERV 16 filter systems 

(intake and recirculation filter systems) illustrated in Figure 23 for the same recirculation to

intake airflow ratios shows that their performance would have been inferior as compared to the 

final filter systems modeled and tested [Organiscak et al. 2014].

Figure 22. Three-filter cab system with final filter downstream of intake

and recirculation filters (Q’s denote air quantities, x’s & c’s denote

contaminant concentrations, and η’s denote filter efficiencies).

Additional modeling and testing was conducted on these two cabs during a long-term field study 

of MERV 16 versus HEPA filters. New intake pressurization filtration units were first added to 

the cabs and the MERV 16 and HEPA filters were successively installed at the intake and final 

filter locations (see Figure 6) over comparative seven-month periods. These field study results 

were previously discussed and are shown in Figure 7 and Table 6. Although no significant longterm PF difference was observed between the MERV 16 filters and the HEPA filters used in each 

cab, a significant difference was observed between the roof bolter and the face drill when using

the identical filters. This difference is speculated to be the result of sealing or integrity deviations 

between the mechanical structures of the two identical HVAC/filtration systems. Additional 
51

evidence to this effect was observed early in the testing of the MERV 16 filters on the roof 

bolter, when particle count measurements were taken with several extra filter combinations to 

examine the mathematical modeling of these filtration system changes. The filter combinations 

tested included adding a used recirculation filter to the system and removing the final filter from 

the system. Applying the final filter model (Equation 31) to represent these filtration system 

changes indicated that a greater than 65% intake air leakage was needed to conform the modeled 

results to the measured cab test results [Cecala et al. 2016]. Examination of the HVAC systems 

during filter changes indicated that dust deposits were not only forming downstream of the intake 

filter but downstream of the final filters (see Figure 24), indicating multiple leaks in the HVAC 

system around the filters. Air leakage around other filters in the system can be modeled by 

multiplying the portion of airflow passing through the filter (Q(1 - l)) by the filter penetration (1 

- η), plus the portion of unfiltered airflow leaking around the filter (Ql). A more refined cab 

filtration system model with these additional leaks was formulated by node analysis and is shown 

in Figure 25 [Cecala et al., 2016].

Figure 23. Field-measured protection factors shown with modeled ranges.

 New filters and MERV16 final MERV16 intake 

MERV 16 intake and recirculation Ӿ Jumbo face drill 

Loaded filters and MERV 16 final • Roof bolter 

O Roof bolter without recirculation 

Cab Protection Factor 

1

10

100

1000

10000

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Recirculation to Intake Airflow Ratio
52

Figure 24. Visual observation of multiple air leaks within the air filtration system.

Red circles represent some areas with evidence of leakage.

Leakage around filters

Potential HVAC Leakage 

Areas

Photos by NIOSH

Figure 25. Three-filter cab system with potential leaks around the filters 

(Q’s denote air quantities, x’s & c’s denote contaminant concentrations, 

and η’s denote filter efficiencies).
53

Figure 25 illustrates this node analysis of the three-filter system with the addition leaks around 

the filters. As shown in this diagram, the final filter is downstream of the intake and recirculation 

filters. Outside contaminants enter into the filtration system circuit through the intake filter, 

leakage around the intake filter, and direct penetration into the cab enclosure openings when 

wind velocity pressure exceeds cab pressure. Other air leaks in the system can occur around the 

recirculation and final filters. Some of the filtered interior cab air is pushed outside by the intake 

airflow and any outside wind penetration, while the remaining portion of the cab air is 

recirculated through the recirculation and final filters. The filtration system model is formulated 

as an equality of the incoming contamination mass to the exiting contamination mass at the 

interior cab node while assuming steady state conditions, as shown by Equation 32:

Massin = Massout (32)

The incoming mass is what penetrates the cab filtration system and the outgoing mass is what 

leaves the cab interior and is recirculated through the filtration system, as shown in Equation 33 

using the same variable designations and subscripts as defined above:

[c I

(1 lI)(1 ηI) + c IlI + x R(1 lR)(1 ηR) + x RlR

] ×

[(1 lF)(1 ηF) + lF] + c W = x( I + W) + x R (33)

The bracketed intake, recirculation, and final filter terms are multiplied and rearranged:

[c I

(1 ηI + lIηI)(1 ηF + lFηF)] + [x R(1 ηR + lRηR)(1 ηF + lFηF)] + c W =

x( I + W) + x R (34)

The outside and inside concentration terms are rearranged to opposing sides in Equation 35:

[c I

(1 ηI + lIηI)(1 ηF + lFηF)] + c W =

x( I + R + W) [x R((1 ηR + lRηR

)(1 ηF + lFηF)] (35)

Next, we solve for protection factor (PF) ratio or penetration (Pen = 1/PF). Equation 36 below 

was solved for protection factor and can be easily inverted to determine penetration:

PF = c

x

=

QI+QR−[QR(1−ηR+lRηR)(1−ηF+lFηF)]+QW

[QI

(1−ηI+lηI)(1−ηF+lFηF)]+QW

=

1

Pen

(36)

This equation provides a more sensible proportioning of air leakages observed throughout the 

filtration system. Additional two- and three-filter system combinations—intake filter with final 

and/or recirculation filter; MERV 16 and HEPA filters; new and used—were also tested on both 

cabs throughout this long-term study and were modeled using their measured airflow quantities 

with assumed proportional air leakages. Face drill air leakages modeled bypassing the new 
54

intake, recirculation, and final filters were 4%, 2%, and 2%, respectively, and were doubled 

when modeling used filters at these locations. Roof bolter air leakages used in the model were 

further doubled over those used for the face drill, given its significantly lower cab PF field 

measurements. For the two-filter systems, zero efficiency was used in the equation for the 

missing filter, thereby removing its air filtering effect from the model.

Shown below is an example of the filtration system model PF calculation for the roof bolter with 

new MERV 16 filters (95% efficiency or 0.95) installed at the intake and final filter locations,

and with measured intake and recirculation airflows of 83 and 246 ft3/min, respectively. Since 

the cab was adequately pressurized, we can assume 0 ft3/min of wind infiltration for the model. 

The calculated cab PF with these cab parameters is shown below to be 334 as compared to the 

measured PF of 300 (particle counting at near steady state conditions). Other filter combinations 

tested in the field were similarly measured and modeled.

PF =

83 + 246 [246 × (1 0 + (0 × 0)) × (1 0.9 + (0.04 × 0.9 ))] + 0

[83 × (1 0.9 + (0.08 × 0.9 )) × (1 0.9 + (0.04 × 0.9 ))] + 0

= 334

Figure 26 shows the comparison of the measured cab PFs and the modeled cab PFs, with 

reasonable agreement along a unity line. The spread in the data is presumed to be primarily a 

result of the actual unknown field leakage deviations from the assumed modeled leakages 

specified above. This figure also shows that the lowest PFs were measured and modeled when no 

final filter was used. Additionally, the opening points in the figure show there was no observable 

cab PF benefit to adding the recirculation filter into the system when using the final filter. 

Adding the recirculation filter into the system significantly reduced the recirculation airflow and 

cab PF [Cecala et al. 2016]. A negative aspect of not having the recirculation filter in the system 

is that dirt and dust from inside the cab would get drawn into and deposited in the HVAC 

system, thereby increasing maintenance issues. An alternative solution to improving this cab 

filtration system would be to increase the size of the recirculation filter to increase its airflow 

capabilities. Finally, multiple leakages in the HVAC/filtration system can have a significant 

impact on cab PFs, as shown when comparing the measured and modeled PFs of the two vehicle 

cabs. Therefore, the cab HVAC/filtration system needs to be well-sealed to extract the benefits of 

using high-efficiency dust filters.
55

Figure 26. Measured and modeled PFs of the face drill and

roof bolter during the filter field study.

1

1 0

100

1,000

10,000

1 1 0 100 1,000 10,000

Modeled PF

Measured PF

Face Drill Roof Bolter

Unity line

No final filter used

Added recirculation filter –open points 

Conclusions

Two key elements of an effective environmental enclosure are a good filtration system and an 

enclosure with good integrity (sealed isolation from the outside environment). A good filtration 

system should include filtering out at least 95% or greater of airborne respirable aerosols (dust, 

diesel particulate matter, liquid droplets, etc.) from the outside intake air with an additional 

recirculation filtering component for the inside air. A separate intake air pressurizing fan (booster 

fan) is recommended as good practice to ensure adequate intake airflow into the enclosure 

irrespective of the HVAC fan speed operation. Good enclosure integrity is needed to achieve 

positive pressure to prevent wind penetration into the enclosure and to minimize air leakage 

around the filtration system. Air leakage around high-efficiency filters was found to have a 

detrimental effect on environmental enclosure protection factor (PF) performance, and air 

leakage around the filters increases with filter loading. Therefore, the proper seals around the 

filters and maintenance/change-out schedules for the filters are needed. Wind penetration into the 

enclosure was only a noticeable factor if the environmental enclosure’s positive interior static 

pressure was less than the outside wind velocity pressure exerted on the enclosure, and if the 

filtration system did not have a recirculation filtering component to continuously re-filter the 

enclosure’s interior air. Finally, adding a final filtering element downstream of both the intake 

and recirculation filtering circuits significantly increased the enclosure PF performance, because 

of the additional level of filtration added onto the total airflow of the environmental enclosure’s 

filtration system. Both laboratory and field testing confirmed these design guidelines.
56

Testing, monitoring, and mathematical modeling of environmental enclosures are also beneficial 

to quantifying and optimizing filtration system designs as well as maintaining optimum PF

performance for enclosure occupants. Particle counting the outside and inside air quality of 

environmental enclosures was found to be one of the best test methods for determining relative 

effectiveness in removing airborne contaminants. Occupational exposure sampling (gravimetric,

dust, DPM, etc.), enclosure airflow measurements, and enclosure pressurization measurements 

are also useful in assessing the operational effectiveness of environmental enclosures. Long-term 

monitoring programs or benchmarking is very valuable for historically examining environmental 

enclosure maintenance needs and for optimizing performance such as filter change intervals. 

Analytical techniques can be used on measurement data to determine the certainty of 

environmental enclosure changes with respect to operating parameters.

Node analysis techniques were utilized to develop mathematical models of environmental 

enclosure filtration systems. These models were advanced to examine the filtration system 

design effects of filter placement, filter efficiency, airflow quantities, air leakage, and wind 

penetration on the environmental enclosure’s air cleaning performance. Mathematical modeling 

comparisons with laboratory and field testing results illustrate their usefulness for optimizing 

enclosure filtration system performance parameters.

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