Arc Re-Entry:

Guide to Measuring PM2.5
By Kristina Koh, Chris Pyke, and Dagny Carlsson

ArcSkoru.com • November 2021

ContentsIntroduction.................................................................................................................... 1Instructions..................................................................................................................... 1
Placement of PM2.5 Sensors in a School........................................................................................ 2
Placement of PM2.5 Sensors within a Classroom.......................................................................... 3

Measuring PM2.5............................................................................................................. 3
PM2.5 Comprehensiveness Score................................................................................................... 3
Tips for Entering Floor Area Coverage............................................................................................ 4
Tips for Entering Occupied Time Data Coverage.......................................................................... 5About Arc....................................................................................................................... 7

Guide to Measuring PM2.5Introduction
As students spend an increasing amount of time in classrooms, the indoor air quality (IAQ) ofclassrooms plays a larger role in students’ health. Perhaps one of the most notable measures of IAQ,
particulate matter (PM) is a mixture of aerosols and solids that vary in composition and size. Particles
are categorized by size such that PM10 is a particle that has a diameter of 10 microns or less and PM2.5 isthat of 2.5 microns or less; it is important to note that, due to this categorization, particles of PM2.5 would
be included when discussing PM10. It is helpful to categorize them in this way to better identify possible
emission sources.
The majority of the PM2.5 sources originate from gasoline and diesel-powered vehicles (Maloney andSicherer 2005). Indoor sources often include fibers from clothing or calcium-rich particles from chalk orbuilding deterioration (Amato et al. 2014).
Due to their small size, these particles can have drastic effects on human health. Children canbe exposed to heavy metals and products of combustion through inhalation of PM, increasing
carcinogenic risks and asthma symptoms (Sah et al. 2019). In a study surveying 6,590 children attending
French schools where students were exposed to varying levels of PM2.5 it was found that the higher
the exposure, the greater the likelihood of the child experiencing asthma-related symptoms, having
rhinitis, or developing asthma (Annesi-Maesano et al. 2012 and Yang et al. 2018). Correspondingly, highconcentrations of PM2.5 have shown to cause respiratory and cardiovascular damage and chronic
obstructive pulmonary disease (Hu et al. 2018). In an eight-year study examining 1,759 children exposed
to PM2.5 in Southern California schools, lung development was reduced in those who were exposed
to higher levels of air pollution (Gauderman et al. 2004). There exists a wide range of possible PMconcentration and composition due to variation between school location, building materials, and othervariables in academic environments.
Along with the negative health effects and multiple sources of PM, another reason to measure is its
unpredictability. When investigating mitigation measures for other indoor air quality factors such ascarbon dioxide, ventilation management is often a solution. However, with regard to PM2.5, naturally
ventilated schools in Palestine were studied, and there was no significant correlation between
ventilation rate and PM2.5 nor wind speed and PM2.5 (Elbayoumi et al. 2013). In another study measuring
IAQ in schools in Portugal, it was concluded that floor area variation such as outdoor location andcleaning practices impacted IAQ measurements more than any classroom factor, emphasizing theneed to measure more schools (Madureira et al. 2015).
With the risk associated with high amounts of particulate matter in the air, particularly for children,and the lack of correlation between PM and building or location characteristics, PM levels need tobe measured in schools accurately and efficiently to support the health, comfort, and productivity ofstudents and faculty. Ideally, every classroom, office, gym, and library would be measured across all
hours to best illustrate a school’s IAQ. However, due to possible financial and time constraints, this may
not be achievable. This guide provides instructions and thresholds to help gauge a school’s air quality
without requiring air monitoring in every room so as to reduce equipment costs and time, ensuring that
schools of various means can still monitor air quality for their students. It also includes our approachto defining whether a school has “good”, “better”, or “best” occupied time coverage, floor area
coverage, and measured PM2.5 levels.

Instructions
Schools have varying HVAC systems, classrooms, and schedules that force each school to measure itsair quality in different ways. To better streamline this process, the following are suggestions for where in aschool and where within a classroom to place PM sensors.

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Guide to Measuring PM2.5

Placement of PM2.5 Sensors in a School
With a finite number of monitors, schools have limited options for monitor placements. Below are somepotential goals a school may want to achieve and suggestions for sensor placement to achieve thesegoals based on their ventilation mode.
1. Mechanical Ventilation
Mechanically ventilated schools measuring PM2.5 often have goals of determining air handler filterefficiency, human contribution or internal generation of PM2.5 within a room, or source contribution of
PM2.5 usually from an outside combustion source.
To determine air handler filter efficiency and test how well the ventilation system protects the schoolfrom its ambient surroundings, a sensor can be deployed outside 5 meters away from the inlet of the air
handler (RESET Standard). The next sensor should be placed in a room with low traffic and occupancy
like a library such that the PM2.5 levels can be attributed to the filtered air as closely as possible. This room
is ideally far away from potential PM2.5 sources like kitchen stoves or vehicular traffic. If a third monitor
is allowed, two monitors should be placed in this low occupancy/traffic room to ensure precision. The
difference between the monitor deployed outside near the inlet and the monitor(s) placed inside canhelp determine HVAC filter efficiency.
If the goal is to determine occupant contribution or international generation of PM2.5 within a classroom
or space, monitors should first be placed in the same manner as the air handler filter efficiency strategy
above to determine a baseline for what PM2.5 levels are in low traffic and low occupancy rooms awayfrom sources. Subsequent monitors can be deployed in a classroom far away from PM2.5 sources and ina high occupancy room such that the difference in PM2.5 levels between the baseline classroom andhigh occupancy classroom can be attributed to human contribution and internal PM2.5 generation.
Schools may also want to find the source contribution of PM2.5 such as the PM concentration due toincreased traffic in the mornings. Exploring how much ambient sources breach the classroom can bebeneficial in determining the level of exposure students are exposed to when near vehicular traffic.
To achieve this, the baseline air handler efficiency setup is needed before comparing PM2.5 levels toclassrooms near sources of PM2.5. After air handler efficiency is established, monitors can be placed in low
occupant rooms nearest to the PM2.5 source. This may be an outward facing perimeter classroom rightnext to the drop-off zone. The low occupancy will allow the PM levels to be mostly attributed to the source.
2. Natural Ventilation
Schools that operate solely on natural ventilation may have some similar goals such as determining
source contribution, internal PM generation, or building efficiency. Below is a chart of where schools
could measure based on their goal of testing envelope and window infiltration factor against ambient
sources or internal PM generation which can be from indoor combustion or human generation.

Far from PM2.5 Source

Near PM2.5 Source

Low Occupant Density

Envelope and Windows

Envelope and Windows

High Occupant Density

Internal PM Generation

Internal PM Generation

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Guide to Measuring PM2.5

Sensor Placement–Best Practices
The sensor placement strategies above are considered best practices for those with enoughmonitors to place multiple in one classroom. However, measurement of any space and at least
one monitor within a school is enough to gain recognition for monitoring PM2.5.
Some of these strategies suggest placing a monitor 5 meters away from the inlet of an air handler
unit. If there are not enough monitors to achieve this, one can also use online resources such as
BreezeoMeter or PurpleAir to find the school’s general ambient PM2.5 levels.

Placement of PM2.5 Sensors within a Classroom
For measurements that have the goal of finding internal generation or human contribution, sensors
should be deployed in the breathing zone (ie. the height of a sitting student) of around 1 meter (3.3 feet)
as illustrated in Razali et al. 2015, Jan et al. 2017, and Faria et al. 2020. Because classroom activities andstudent tampering can compromise a sensor’s accuracy, there is less agreement on where within aclassroom sensors should be placed. Some options have been beside a teacher’s desk (Deng and Lau2019), the middle of the classroom (Dorizas et al. 2015), and in the back wall away from the door (Frommeet al. 2007). In general, it is best that the sensor is in a position where it can be protected from tampering.

Measuring PM2.5
PM2.5 will be assessed based on the area measured (square feet), occupied time measured (hours), andconcentration level (ug/m3). In turn, these data are interpreted to estimate the fraction of occupied
hours when conditions are in the good, better, or best ranges.

PM2.5 Comprehensiveness Score
Arc will generate a PM2.5 Comprehensiveness Score (“PM2.5 Comp Score”) by adding together 1-100
scores for floor area coverage (floor area, rooms, or HVAC zones), occupied time coverage (operatinghours), and performance (time in “good”, “better”, or “best” range). Because the maximum value foreach subscore is 100, the maximum possible Comprehensiveness Score is 300.
A few details about how each subscore is computed:
Floor area coverage indicates the fraction of the project covered by each type of sensor. Dependingon the school’s measuring goal, users can divide their school into zones in accordance with thePNNL education building prototypes’ occupied zones such that each floor area coverage can becalculated as a fraction of the number of zones measured compared to the total number of zones.
They can also determine floor area coverage as a fraction of classrooms measured, HVAC zones, orother functional units.
PNNL Building Prototypes considers the following as an occupied zone:• Classroom• Computer Lab• Office space• Gym, stadium (play area)• Kitchen• Cafeteria, dining• Library
Arc assumes that coverage continues from the last reported value through the present unless this value
is changed due to reorganisation of space or sensor layouts. Arc combines values as an average ofdaily values to provide an estimate for a given time period.

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Figure 2. Draft thresholds used to categorize floor area coverage: good, better, best. These thresholds
are subject to further review and adjustment based on sensor uncertainty.

Floor Area Coverage %Good0Better25Best75

100

Tips for Entering Floor Area Coverage
Some users may not be able to provide a quantitative estimate of floor area data coverage. We
know that there are many potentially confounding factors and unknowns (e.g., the area coveredby any given sensor). It is important to remember that the purpose is to provide a rough estimateof the fraction occupied space associated with each measurement (i.e., are you covering asmall fraction of the area, most of the space, or all of the space).
If a quantitative estimate is not possible, users may estimate coverage and enter the following values:• Low Coverage: <25% of occupied space is associated with measurements• Enter 25% for the applicable date range.
• Medium Coverage: 25%-75% of occupied space is associated with measurements• Enter 50% for the applicable date range.
• High Coverage: >75% of occupied space is associated with measurements• Enter 100% for the applicable date range.
Describe your estimate with a piece of document. This can be a simple note explaining your rationale.

Occupied time coverage indicates the fraction of time covered by PM2.5 measurements during the last
90 days. For Arc, a time period is “covered” if at least one measurement is taken during the occupied
period in a project.
For the purpose of measuring schools, we are assuming 8 occupied hours a day of operations and 1
hour as a fundamental unit of occupied time coverage (a.k.a., freshness period for measurements).
Consequently, occupied time coverage is defined as the percentage of hours within a period thathave one or more readings for a given parameter. Additional measurements during a given period do
not increase coverage (i.e., occupied time coverage has a maximum value of 100% which is satisfied
by at least one reading each hour). This means that 1 day has a maximum of 8 readings that will counttowards occupied time coverage.
A high measurement rate, e.g., those recommended by standards such as WELL and RESET, are betterfor a richer, more representative characterisation of PM2.5 measurement. To keep this metric simple, Arcrequires a report only every hour (i.e., the reporting interval).
Arc assumes that coverage continues from the last reported value through the present unless this value
is changed with new value. Arc combines values as an average of daily values to provide an estimate
for a given time period.

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Guide to Measuring PM2.5
Figure 2. Draft thresholds used to categorize occupied time measured: good, better, best. Thesethresholds are subject to further review and adjustment based on sensor uncertainty.
Performance is divided by thresholds into three categories: good, better, and best. Each period isassigned to one of the three categories.

Occupied Time Coverage %Good0Better

25Best75100Tips for Entering Occupied Time Data Coverage
Some users may not be able to provide a quantitative estimate of occupied time datacoverage. We know that there are many potentially confounding factors and unknowns.
It is important to remember that the purpose is to provide a rough estimate of the fraction
occupied hours associated with each measurement (i.e., are you covering a small fraction,
most hours, or all of them).
If a quantitative estimate is not possible, users may estimate coverage and enter the following values:• Low Coverage: <25% of occupied hours are associated with measurements• Enter 25% for the applicable date range.
• Medium Coverage: 25%-75% of occupied hours are associated with measurements• Enter 50% for the applicable date range.
• High Coverage: >75% of occupied hours are associated with measurements• Enter 100% for the applicable date range.
Describe your estimate with a piece of document. This can be a simple note explaining your rationale.

Performance is typically integrated over some period of time, most often hours or days. These periods
may contain any number of sensor readings. The intent is to communicate the percentage of occupied
time in each category. For example:• If hourly (for sub-hourly) occupied data are available, Performance would be calculated as theaverage of hourly values. Missing values would be excluded from Performance, because they are
already accounted for in the preceding metrics.
• If daily data during occupied hours are available, Performance would be calculated as theaverage of daily values. Again, missing values would be excluded.
Figure 3. Draft thresholds used to assign time in each condition: good, better, best. These thresholds are
subject to further review and adjustment based on sensor uncertainty. The upper limit of 35 ug/m3 isdefined by the EPA as their standard 24-hour mean limit. Source: Parag Rastogi, arbnco.

PM2.5 Indoor Measurement Performance (ug/m3)
Good

0Better

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Guide to Measuring PM2.5
The result would be the average fraction of periods in each of the three categories over the last 90
days. Data older than 90 days do not contribute to the PM2.5 Comprehensiveness Comp Score.
The PM2.5 Comprehensive Score combines the three equal elements:• Floor Area Coverage• Occupied time Coverage• Performance (as the fraction of time in the “good”, “better”, or “best” condition)

Note on Data Quality
Objective measures of data quality are not currently part of the PM2.5 Comp Score. However, the qualityof PM2.5 measurements is a significant management issue, and it varies significantly based on a varietyof factors, including sampling design, sensor placement, sensor capabilities, sensor maintenance, dataprocessing, and more (RESET Standard). An extensive peer-review literature is emerging in this areawith publications such as Sun et al. 2019 and Chojer et al. 2020, and the U.S. Environmental Protection
Agency Air Sensor Toolbox. Arc Re-Entry users may consider the benefits of third-party accredited
hardware as one element of a comprehensive strategy to promote data quality.

PM2.5 Comp Score Example
Floor area coverage• PM2.5 is measured in 2 out of 20 classrooms or enclosed spaces.
• The floor area coverage is estimated at 10%
Occupied time coverage• PM2.5 measurements occurred over 2 hours per school day for 90 days• The school operates 40 hours a week
The occupied time coverage is estimated at 25% for the week
Measurement Performance• PM2.5 measurements ranged from 2 ug/m3 to 52 ug/m3 in an equal distribution of hours.
• The performance value is 20% “best” (≤12 ug/m3), 6% in “better” (≤15 ug/m3), and 40% in “good”(≤35 ug/m3).
• The total percentage of measurements in “good”, “better”, or “best” are summed (ie. 20+6+40=66)
Total PM2.5 Comp Score = 10/100 + 25/100 + 66/100 = 101/300

Performance Reports
Projects can input their floor area coverage, temporal coverage, and PM2.5 performance data into Arc
Re-Entry. Arc Re-Entry provides tools to help facility managers use indoor air quality measurements toinform operations. Every Arc user can collect, manage, and score data through the Re-Entry section
under Meters & Surveys. Arc Essentials users can create, download, and share customized reports forprojects and portfolios.

Contact
Contact Chris Pyke (cpyke@arcskoru.com) to provide feedback or get more information.

About Arc
Arc™ is a global technology platform that allows teams overseeing the sustainability of buildings andplaces to collect data, manage and benchmark progress, measure impact and improve performance.
Created and operated by Green Business Certification Inc. (GBCI), Arc empowers its users tounderstand and enhance their sustainability performance, promote human health and wellbeing andcontribute to a higher quality of life.
Learn more at www.arcskoru.com.
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