Let's Build BackpAQ Personal Air Quality Monitor V3

Welcome to the Instructable for the DIY BackpAQ Personal Air Quality Monitor V3! As we did in the Instructable for BackpAQ Version 2, we will build a high-quality instrument capable of measuring and monitoring the most common sources of air pollution, both indoors and outdoor, including particulate matter (PM1, PM2.5, PM10) gases (CO2), along with temperature and relative humidity. New for V3 will be inclusion of an optional sound sensor for identifying and monitoring noise pollution. For better measurement precision ,we'll also add a real time clock to BackpAQ. An upgraded CO2 sensor is also included in the V3 build. Let's take a look!

Supplies

Small Phillips screwdriver, small needle nosed pliers, small diagonal cutters, Hot glue gun, soldering iron if building on your own.

Step 1: The BackpAQ Project, What We'll Build, What We'll Learn

The BackpAQ Project

The BackpAQ project is part of a middle and high school STEM curriculum that promotes learning about and experience with the monitoring of air quality (AQ) particularly in disadvantaged communities, and drives engagement among underrepresented youth in STEM activities. Key to the program is deployment of a suite of community-based mobile air quality monitors that leverage new low-cost sensors. These handheld units can be readily assembled by advanced middle-school and high school students and other STEM-oriented youth who are motivated by interest in obtaining, understanding and sharing hyper-local air quality data.

What we'll build

For this project we are going to build a BackpAQ V3 personal air quality (AQ) monitor. As you can see from the accompanying photos, BackpAQ is designed to be easily carried, clipped to a backpack, or placed in a mobile scenario such as a vehicle or other transport. As BackpAQ utilizes GPS positioning technology, the AQ information we collect will be geo-positioned no matter where the monitor goes. These student-built monitors, encased in a lightweight polycarbonate box, weigh less than a pound and are powered by rechargeable LiPo batteries. They feature carabineers and nylon straps for easy fastening to a backpack or bicycle frame.

What we’ll measure

As designed, the monitors will measure and display criteria pollutants PM1, PM2.5, and PM10 concentrations in ug/m3, as well as display the US EPA Air Quality Index (AQI). Gases such as CO2 are also easily monitored with BackpAQ. Monitoring of additional pollutants, such as CO, O3, NO2 and SO2 are possible future enhancements. V3 add a tiny MEMS-based microphone so that sound can be captured. More about this in an upcoming section.

BackpAQ pairs with a companion smartphone app to provide an interactive user experience and allow customization and personalization of monitored data and how it’s displayed. BackpAQ automatically uploads data to the Thingspeak cloud where it can be visualized using powerful MatLab analytics, and shared with other students or local community officials.

Additionally, data can be analyzed, visualized and shared using the comprehensive AQterra community air quality web application. More about this powerful tool later.

What we'll learn

To begin with, we'll learn design, build and fabrication techniques - along with some pretty powerful electronics, Internet-of-Things (IOT), and sensor technology skills. Perhaps most importantly, we'll learn how to curate and analyze data we capture from the monitors. Learning how to develop and apply critical judgement to the data and subsequent reporting and sharing of findings and implications are key outcomes of this project.

Outcomes

The intended outcome of this project is twofold: one, obtain a richer, deeper understanding of air pollution, where it comes from, how to measure it, how to harness powerful analytics to responsibly report and share findings, and (hopefully) gain some insight that will enable ordinary concerned people to do something about it. And two, build a monitoring device - BackpAQ - to better understand the science and engineering behind sensors, IOT, the Maker Movement, and have hands-on involvement with one of the more critical challenges facing communities today.

Step 2: How This Instructable Is Organized

Introduction

Air Pollution and What We're Measuring

Design Point

Overview of the Project

Materials

The Build

Software and Cloud

Futures

Although we've laid out a sequential learn + build process, feel free to skip around or just jump to a specific Step. For example, you may wish to set up the software first before diving into the BackpAQ device. Your choice...have fun!

Step 3: What We'll Measure

Particulates

Before we get started building, let's take a look at the science behind air quality, sensors and monitoring. So what are "Particulates" (PM), and how do they get into the air?

Size comparisons for PM particles

PM stands for particulate matter (also called particle pollution): the term for a mixture of solid particles and liquid droplets found in the air. Some particles, such as dust, dirt, soot, or smoke, are large or dark enough to be seen with the naked eye. Others are so small they can only be detected using an electron microscope. Particle pollution includes: PM10 : inhalable particles, with diameters that are generally 10 micrometers and smaller; and PM2.5 : fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller. How small is 2.5 micrometers? Think about a single hair from your head. The average human hair is about 70 micrometers in diameter – making it 30 times larger than the largest fine particle.

Sources of PM

These particles come in many sizes and shapes and can be made up of hundreds of different chemicals. Some are emitted directly from a source, such as construction sites, unpaved roads, fields, smokestacks or fires. Most particles form in the atmosphere as a result of complex reactions of chemicals such as sulfur dioxide and nitrogen oxides, which are pollutants emitted from power plants, industries and automobiles.

What are the Harmful Effects of PM?

Particulate matter contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. Some particles less than 10 micrometers in diameter can get deep into your lungs and some may even get into your bloodstream. Of these, particles less than 2.5 micrometers in diameter, also known as fine particles or PM2.5, pose the greatest risk to health. Fine particles are also the main cause of reduced visibility (haze) in parts of the United States, including many of our treasured national parks and wilderness areas.

The PM Sensor for BackpAQ

The particulate sensor we have chosen for this project is the Plantower PMS7003/PMSA003i. It is able to measure the concentration of fine particles of less than 1μm (PM1); less than 2.5μm (PM2.5) and less than 10μm (PM10). The operating principle of the PMS7003 sensor is as follows: a laser illuminates airborne particles. An optical sensor captures the laser light and generates an electrical signal proportional to the rate and size of the particles in the air. This block diagram shows what's going on inside the sensor. Note that there is a microprocessor built-in that does some computation and digitization of the signal so that we can read the data in our own hardware.

Measuring CO2

Carbon dioxide is a gas heavier than air. In small quantities of up to 5000ppm (0.5% ) can cause headaches, lethargy, slowing of intellectual ability, irritability, sleep disturbance. In larger quantities can cause dizziness, loss of sight, hearing or knowledge. Typically, fresh air contains between 360ppm and 410 ppm of CO2. What are the benefits of monitoring CO2 levels? When a group of people are indoors, the concentration of CO2 is expected to increase, as humans naturally exhale CO2. However, at high concentrations, humans can experience negative effects, including reduced concentration and a compromised well-being. CO2 sensors like the SCD4x serve to measure and control elevated CO2 concentrations to counteract these negative symptoms. Studies have shown positive effects on productivity and health when CO2 concentrations in an environment are below 1000 ppm. Comparatively, the normal CO2 level for outdoor environments is at around 400 ppm. CO2 sensors can be used to maintain optimal CO2 levels indoors, as their measurements can be used to monitor levels and act accordingly by bringing in fresh air via mechanical ventilation (Demand Controlled Ventilation (DVC), air handling units, etc.) or natural ventilation (open doors or windows).

Indoor levels of CO2 concentration and its effect on well-being

Additionally, studies have shown that high indoor CO2 concentrations have an impact on cognitive and work performance (see figure below). In a specific test case, individuals that were taking an exam in a room with high CO2 levels of around 2500 ppm generally performed worse and scored lower than when exposed to a CO2 environment at 1000 ppm (Source: NY Times). Moreover, in tighter office spaces like conference rooms, high CO2 levels are often heightened, resulting in a negative impact on human productivity and decision making.

Measuring Sound and Noise Pollution

Sound travels as pressure fluctuations in the air, in the form of waves of measurable frequency and amplitude. Noise is often defined as unwanted or unpleasant sound.

Exposure to noise at home, at work, or during sleep has been associated with many adverse health and emotional outcomes. These include distraction, annoyance, cardiovascular disease, tinnitus and hearing loss. The World Health Organization states that traffic noise alone is harmful to the health of almost every third person in Europe, and that one in five Europeans is regularly exposed to sound levels at night that could significantly damage health.

The MEMS chip we use has a high-performance digital microphone to detect frequencies between 50 Hz and 8000 Hz, covering the dominant range of human hearing.

Sound Pressure Level (A-weighted)

The Sound Pressure Level (SPL) is a popular measurement system for noise. Sound amplitudes measured by a microphone are averaged over all frequencies to produce a single SPL number, expressed on a logarithmic scale in decibel units. SPL measurements are best for ongoing constant noise, while peak amplitude measurements are best for brief, sudden sounds.

When calculating SPL, some frequencies can be emphasized relative to others – this is known as the weighting. The most common method is “A-weighting”, an internationally recognized standard which accounts for the variation in how the human ear hears different sound frequencies. For example, people’s perception of loudness tends to peak at around 3 kHz and drops at low and high frequencies. Noise around 3 kHz is therefore given a greater weighting when calculating the SPL.

The A-weighted Sound Pressure Level is a useful and very commonly used measure of environment noise and sound “loudness”. Table 4 and Table 5 give some example sound sources and typical SPL values. Note that the sound level (perceived or measured) generally depends on:

What is creating the sound
Distance from the source to the meter or ear
Direction, angle or alignment
Other factors such as nearby objects, air conditions, etc.

Ideas for further investigation of sound

Road/aircraft traffic noise – what levels are reached and which times of the day are worst?
Use of sound interrupts for control of appliances e.g. turn on light with specific sequence of hand claps.
Is your home music system set at an appropriate volume?
Are areas for work or study maintained at a low enough sound level?
Test the effectiveness of sound-proofing methods for reducing noise in the home from external sources.
Use the frequency band SPL values to display a frequency spectrum for live visualization of music.

An Important Caveat

The data that comes from the low-cost sensors BackpAQ provide an estimation of particulate mass concentration that relies on several assumptions for shape, diameter and density. The quality of your data will depend on those assumptions as well as environmental considerations such as humidity, light and temperature.Because of the fact that optical counters rely on these assumptions, the data produced by them are not FRM or FEM certified.

Step 4: Why Mobile Monitoring?

So why a use a mobile monitor?

At this point you might be asking yourself (or your teacher) why are we going to the trouble of building an inexpensive, battery-powered mobile monitor when perfectly good fixed monitors can be had for about the same outlay? Well, here's a bit about why:

Mobile monitoring can thought of as a preliminary step of any air pollution field study design because it enables preliminary exploration of fine-scale spatial variability within a neighborhood, providing confidence in placement of stationary air monitors. Several characteristics of mobile monitoring facilitate its utility as a tool for understanding complex conditions, and, if carefully designed, for disentangling some aspects of temporal and spatial variation.

First, mobile monitoring is cost-effective; the route can be customized to focus on particular areas of concern, such as high traffic roads or neighborhood fixed sources.

Second, concentrations are typically measured at short intervals using continuous instruments which, with good quality-control efforts, can provide information about short-term peak exposures associated with adverse acute health effects. Therefore, through carefully repeating time- and location-specific measures, this technique can provide some stability in determining PM concentrations.

Third, mobile monitoring can also be used to validate conceptual dispersion models by capturing data at multiple points downwind of the source, under varying wind speed and direction conditions.

Finally, leveraging the repeated measures and integrating meteorology and land use characteristics, mobile monitoring data can be used to more richly characterize spatial variability throughout the region, by more knowledgeably tailoring the spatial and temporal characteristics of a fixed-site monitoring network. So that's why.

Beyond this, mobile monitoring enables hyper-local sampling of air quality wherever you go, throughout your neighborhood, community, school, or workplace.

Step 5: Design Characteristics

BackpAQ Design Point

Personal, portable air quality monitoring
Inexpensive to build, operate
Access and control through comprehensive smartphone app
Easily manage data, save & share measurements on the cloud
Measure most common pollutants, including PM2.5, CO2 and [OPTIONAL] sound
User-managed data privacy controls
Realtime clock for time/date accuracy
Access to powerful Cloud-based data management and analytics
Battery powered, rechargeable, solar power option
Over-the-air (OTA) software update capability

BackpAQ V3 Characteristics

Size: 5" x 2" x 4"
Weight: 10 OZ
Case Material: Polycarbonate
Measured particles: PM1, PM2.5 and PM10
CO2 sensor for indoor/outdoor air quality measurement
[OPTIONAL] MEMS microphone for sound level measurement
Advanced Feather Huzzah ESP32 MCU processor, 240Mhz, with built-in OLED display and LiPO charging
Battery Life: around 8 hours
Lithium-ion battery: 3.7V - 2200-3400 mAh
USB micro connector for charging
Realtime clock
SD Card Storage (optional)
Sampling period: adjustable 5 - 60 minutes
On-device OLED interface for local data display and control
Smartphone Interface (Android, iOS) via Wifi and Blynk
Powerful analytics back-end with ThingSpeak/MatLab
Regular OTA software updates

Step 6: Technical Overview

Technical Overview

The BackpAQ project is constructed from a powerful combination of hardware, software and cloud. In building BackpAQ, the student will utilize a full stack, with Arduino-based IDE (C++), a handful of powerful APIs and data abstractions, two advanced environmental sensors, and a powerful cloud capability.

Here's a quick rundown of the basic components.

Processor: 240Mhz Feather Huzzah ESP32 microcontroller (MCU) with attached .96" OLED display
PM Sensor: Plantower PMSA003 laser particle
CO2 Sensor: Sensirion SCD-4X( photo-acoustic
Temperature/Humidity Sensor: built in to CO2 sensor
Realtime clock and SDCard storage
[OPTIONAL:] MEMS Microphone: Adafruit SPH-0645 or similar
Development Platform: Arduino IDE with simplified C++ programming
Data Storage/Cloud: Thingspeak (Matlab) via well-documented APIs
IOS or Android Smartphone App via Blynk

The heart of BackpAQ V3 is the Adafruit Huzzah32 ESP32 MCU. The WiFi-based ESP32 chip communicates with the PMSA003 sensor via the I2C bus. A small microcontroller inside the sensor transmits the particle concentration values and the number of particles. For CO2, same thing happens for the SCD4X sensor. The BackpAQ firmware continuously reads the data stream, and sends the calculated values to the local OLED display, and to both the connected smartphone and the ThingSpeak cloud. BackpAQ also calculates PM-based AQI, the Air Quality Index. AQI is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you.

The software also continuously measures the battery voltage. If it is below its limit voltage (about 3.5V), the device goes into standby. The battery is 100% when the voltage is 4.2V and is visually indicated on the companion smartphone app. The companion smartphone app is really the control center of BackpAQ. Through the app, you can monitor all functions and display virtually any data collected or computed by the device. There are also mapping and reporting capabilities that will help you get the most out of your portable AQ monitoring device. And to get the most out of BackpAQ you'll need a companion smartphone. Currently BackpAQ (and underlying Blynk app) require IOS 14.1 or higher or Android 5.0 or higher.

Step 7: Design and Component Choices

Before we dive into the construction of our BackpAQ, let's review what key decisions and choices we must make in order to fulfill the design characteristics we just talked about.

Choice of PM Sensor

In the past few years a new generation of low-cost sensors has become available. Several of these have been the subject of some prominent evaluations, most recently from AQMD. As noted there and elsewhere, these sensors are generally more suitable for deployment in large numbers in terms of their cost but their precision and accuracy may not be sufficient for regulatory use. Based on our own evaluation, though, one of these would suffice for the usage scenarios we envision for BackpAQ. In this recent study four PM sensors were compared: Plantower PMS5003, Plantower PMS7003, Honeywell HPMA115S040 and Alphasense OPC-N241. The table above lists their main characteristics. These sensors were incorporated into an evaluation breadboard for testing; and they are sufficiently small to be deployed in an enclosure small enough for mobile or wearable applications. They all report PM2.5 and PM10 concentrations in μg/m3. The Plantower PMS5003, the Plantower PMS7003 and the Alphasense OPC-N2 also report PM1 and particle count for different bin sizes - the Plantowers report size distribution for 0.3, 0.5, 1.0, 2.5, 5.0, 10 μm bins and the Alphasense OPC-N2 reports 16 bins ranging from 0.38 μm and17 μm. The Plantower sensors claim a counting efficiency of 98% for particles of diameter 0.5 μm and 50% for diameter 0.3 μm38. All use a sampling interval <10 s. According to the manufacturers, their accuracy is between ±10–15 μg/m3. In reviewing these studies and others, we observed that the PMS5003/7003 received strong ratings in terms of accuracy, precision, and data recovery while also being price-appropriate for this type of instrument (see PurpleAir Evaluation Summary for details.) The Alphasense, at $450, was not appropriate for this project. The Honeywell, though low-cost, did not do particle counts. So that pretty much left the two Plantowers. At BackpAQ Labs we also looked briefly at the Shinyei PPD42NS, Samyoung DSM501A and Sharp GP2Y1010AU0F sensors. Although these were much lower cost, their lack of digital output (in most modes) was a disqualifying factor for our design (electronics must sample the analog output and convert o digital form for the MCU.) Ultimately we chose the PMSA003 (successor to the PMS-7003) for performance, form factor, low cost and support among developers.

Choice of Microcontroller

This is obviously at the core of BackpAQ and is perhaps the most important design choice. For V3 we looked at several of the latest options here, including Arduino-compatible MCUs such as Adafruit Feather, Heltec, ESP32, NodeMCU, and WeMos D1. The ESP32 base was chosen for the second generation device based mainly on processor power, tight WiFi/Bluetooth integration and sufficiently low-power consumption.

The ESP32 is a tiny, relatively inexpensive module with a dual-core 32-bit CPU Controller. The ESP32 is the ESP8266 successor loaded with lots of new features. The version we chose for V3 - Adafruit Feather ESP32 - is a development board that combines Wi-Fi and Bluetooth wireless capabilities, and it’s dual-core. It supports a wide variety of peripherals such as capacitive touch, ADC, DAC, I2C, SPI, UART, I2S, PWM, and much more. It is one of the best solutions for DIY Internet of Things Projects and Smart Home Projects.

Performance was virtually identical with all of these units and choice ultimately came down to footprint and ease of build and interconnection (QWIIC) with sensors and other components. Ultimately, we chose a development board based around the ESP32, an Adafruit MCU we have used in previous projects and know to be dependable and quite inexpensive (as low as $18 from several Amazon suppliers). As the design evolves we will revisit this choice and re-evaluate best fit.

Choice of CO2 Sensor

We are using the Sensirion SCD4x Miniaturized CO2 Sensors which sense CO2 and RH/T and fit in a space of just one cubic centimeter. These CO2 sensors offer a 2.4V to 5.5V supply voltage range, fully calibrated digital I2C output, and ±(30ppm + 3%MV) accuracy rating. Compared to the SCD30, the SCD4x footprint has been miniaturized by a factor of 5, resulting in dimensions of just 10.1mm x 10.1mm x 6.5mm. With the use of photoacoustic sensing principle, the dimensions of the optical cavity are greatly reduced without compromising on sensor performance. The SCD4x series features a quality humidity and temperature sensor that delivers two additional sensor outputs. Sensirion SCD4x Miniaturized CO2 Sensors are ideal for sensing markets such as IoT, automotive, HVAC, appliances, and consumer goods.

Communications Mode

To fulfill the design requirement to be 100% mobile and be able to interact with a smartphone and the Internet, we evaluated several comms scenarios. At the beginning of the project, we looked at (1) Cellular; (2) Bluetooth; and (3) WiFi. As was expected, and based on our previous experience with sensor projects, each mode of communications brings advantages and tradeoffs. So we built prototypes to evaluate all three. Cellular, which was arguably the most portable and scalable, cost the most and was the most power hungry. Bluetooth, in particular Bluetooth Low Energy (BLE), was the most flexible and least power intensive. WiFi, with its wide availability, ease of use, and relatively low power needs, seemed to be the way to go. In the end we chose WiFi, based on its low power draw, excellent integration with our choice of microcontroller (ESP32), and near ubiquity across the range of usage scenarios we envision. We may revisit the use of BLE in a future iteration.

Smartphone as Communications Hub

As we've just discussed, WiFi seems the best technology choice for communicating the measurements from the sensors to be stored and obtaining the GPS position of the BackpAQ device. Since the ESP8266 contains networking capabilities in the form of WiFi, it could theoretically perform all tasks of the system but with the prerequisite that an active network connection is available at all times. This would require a local area network or the addition of a GSM modem connecting to a mobile network connection. For location services, another module would be needed to provide GPS support. So..... we need to thoughtfully consider how to inexpensively provide these capabilities. Let's look closer at what is missing. What we need is an active network connection and navigational services for determining our current location. The solution may be right in our pocket! A piece of technology already including both of these features is a regular smartphone which could be used as a gateway between the BackpAQ and network, receiving sensor data and forwarding it. Using a smartphone, much computational work can be moved from the BackpAQ to the phone, also providing the network connection and location services. This means that the ESP32-powered BackpAQ could be solely responsible for reading sensor data, receiving and acting upon commands and broadcasting data to the phone. The connection between BackpAQ and smartphone can be achieved using WiFi or Bluetooth since both protocols are supported by both devices. Using the network provider of the SIM-card, a connection to online services can be established. In addition to containing the basic features of network capabilities and WiFi/Bluetooth, the phone will also provide GPS in order to give us precise geo-location of the sensors. We'll need this later when we're doing our data analysis, matching AQ to locations.

Battery Monitoring

With any battery-powered device it's critical to be able to monitor power usage, or at least battery voltage. The Adafruit Feather MCU we are using already has an internal voltage divider that connects a digital pin to an internal voltage divider is made up of 220k (R1) and 100k (R2) resistors. So, by polling this pin we can directly measure the voltage of the battery against the max and display current voltage, capacity left, etc.

A word about the software

Due to its vast ubiquity and relative ease-of-use, the Arduino IDE development environment was chosen as the development platform for the BackpAQ device software or more properly firmware as the code runs directly on and is stored within the microcontroller. As discussed above, the BackpAQ firmware continuously reads the sensor data stream, and sends the calculated values to the local OLED display, and to both the WiFi-connected smartphone and the ThingSpeak time-series database in the cloud.

The full source for the latest BackpAQ is available on Github.

Early prototypes and MVP candidate

In the photos above you can see some of the early design and functional prototypes we did. These were extremely valuable in evaluating basic functionality (are we meeting our design criteria), user experience (can the intended user actually use this thing?), and understanding the limits of our design -- what it can and can't do. After 5 or 6 iterations with electronics, software, packaging, and overall user experience, we settled on our current design and generated our minimally viable product or MVP. Things we left out...for now. One obvious thing we have left out, for now, is the ability to measure other pollutants, such as NOx, SOx, O3, CO, and other criteria gases. One reason is cost. To purchase reasonably accurate and calibratable sensors is cost-prohibitive for this low-cost project. Sure, there are dozens of low cost gaseous monitors available, but most carry huge disclaimers stating "not for quantitative measurement" or "use only in indoor environments". Others tell you that the sensors themselves will only last for a short time before needing replacement of their electrochemical elements.

And, Above All...

"A good scientist is a person with original ideas. A good engineer is a person who makes a design that works with as few original ideas as possible. There are no prima donnas in engineering" - Freeman Dyson

Step 8: Use Cases

There are several use cases envisaged for BackpAQ:

Mobile with Smartphone, Data Transmission via WiFi
Mobile with Smartphone, Data Transmission via mobile hotspot or cellphone
Mobile with local SD card storage (not selected)
Stationary with Data Transmission via WiFi
Stationary with Data Transmission via hotspot

To keep costs low, it is assumed that BackpAQ will utilize the smartphone's GPS location service. If this service is not available and the BackpAQ is in a fixed location, the user can enter a lat and lon location at config time so that the data is still geolocated.

Step 9: Bill of Materials

KITS

In the near future the BackpAQ Kit can be ordered from BackpAQ Labs. As of 11/1/23, kits remain special only. For this project we'll utilize kits made available through BackpAQ Labs, a research unit of Sustainable Silicon Valley. The kit includes all the necessary components needed to build your own BackpAQ. Some benefits of getting the kit: overall lower cost, little soldering is required and the holes on the enclosure are pre-drilled. The option is also available to purchase the individual components which are listed below)

PARTS

Here's an annotated list of what you'll currently need to build a BackpAQ V3:

Adafruit Feather Huzzah32 w/avail snap-on .96" Feather OLED Adafruit
Adafruit PMSA003I Air Quality Breakout w/ STEMMA QT AdaFruit
Sensirion SCD-4X CO2 Sensor Adafruit
[OPTIONAL] Adafruit SPH0645 MEMS Microphone board Adafruit
Sparkfun GPS SAM-M8Q (Qwiic) Mouser
3.7V 2200 mAh LiPO Battery AdaFruit
Realtime clock board AdaFruit
Openlog SDCard logger board SparkFun
5V LiPO charger board AdaFruit
NeoPixel 5mm diffused LED AdaFruit
Pelican Model 1010 Clear Poly Case B&H B&H Photo
Sub-miniature toggle or push switch Anchor Electronics
Sub-miniature momentary switch
USB Micro power jack Anchor Electronics
Misc parts kits (screws, standoffs, wire, etc.)

Note: The parts in your kit may vary a bit from those in the photos or parts list. Some kits come with major parts pre-installed which eliminates soldering. Don't worry...we've tested them and they'll work fine.

SOFTWARE

The software that runs BackpAQ comes pre-flashed on the MCU, but can also be downloaded from Github here Be sure to grab the latest version and the associated config file. You'll also need to utilize, download or update the following open source software packages (current as of 8/1/23):

Blynk IOS app V 3.6.4 or Blynk Android V 1.10.3
ThingSpeak (Free Web-based Cloud application)
Chrome, Edge or Safari Web browser for access to AQterra
Optional: Arduino IDE V1.8.10 (to compile BackpAQ software)
Optional: [Needed only if doing firmware recompile] Blynk Library v1.2.0 (or the newest Blynk IOT version)

If the need arises to modify or extend the software you can do this via the Arduino IDE platform. The open source code is available here.

SMARTPHONE

To get the most out of BackpAQ you'll need a companion smartphone. Currently BackpAQ (and underlying Blynk app) requires IOS 14.1 or higher or Android 5.0 or higher.

TOOLS REQUIRED

Although the kit is mostly plug-and-play, the following tools are recommended for building the unit: Phillips-head screwdriver, needle nose pliers, small diagonal cutters, hot glue gun and glue stick. A digital multimeter is helpful to verify polarity and check continuity.

Step 10: The Build

Let's get started!

Ok, we're ready to get started building! First, let's lay out all of the parts on a flat, clean work surface. Pro tip: use an egg carton or old-school ice cube tray to organize the very small parts like fasteners, nuts, etc. Next, scan through the included Parts List to make sure you have all of the parts you'll need to build the BackpAQ.

Assembly Instructions

The kit comes with a pre-drilled Pelican 1010 clear polycarbonate case and all parts necessary to complete the build. If you choose to order the parts and build yourself, you'll have to do some very basic mechanical and electronics (like soldering a few wires and drilling a few holes) to complete the build. And believe it or not, all of the parts you see in front of you will indeed fit in here! But it's a good idea to follow the suggested assembly sequence in order to fit the lower-level parts first before the one's that go on top. When finished, you'll also want to carefully fold down some of the wires in the top part of the case so that all fit neatly. Small cable ties are also provided to help keep wiring tidy.

Some kits come with pre-installed (and pre-soldered) components. If this is the case with your kit, just skip the sections that call for installing these parts.

As you examine the components you'll notice that we've eliminated the need to solder most of the connections. Instead, you'll make most connections with either STEMMA cables (4 colored wires), jumper wires (each end has a tiny black sleeve that's terminated with a rectangular connector), or other special wire, plug or socket.

Let's Build It!

Here's the recommended assembly sequence for the BackpAQ unit. See above photos for placement, and consult schematic for wiring details, and take your time to do it right. Should take an hour or two to complete these steps, depending on where you are starting from. One more thing: always verify polarity of power connections before activating your device for the first time . Ok, let's get started!

Locate the clear Pelican case and orient so that the opening tab is at the bottom, facing you. The groups of holes used for air access and exit for the PM sensor will be on your left. All of the steps that follow assume the case is oriented this way. This is an important first step...check again to make sure you've got it right.
If you received a BackpAQ kit your case will have pre-drilled holes. If you need to drill the holes yourself, see the diagram above for reference in hole location and suggested drill size. Note that these are approximate sizes and may need to be adjusted based on part availability, especially with the switch and power jack. It is highly recommended that you drill all of the holes first before proceeding to the assembly steps.
First, locate and place the SCD4X CO2 sensor board at the top of the case in the left-most hole. Using 2X M2 hardware (nylon standoffs and screws), fasten the sensor to the case bottom so that it lines up with the inlet holes in the case. See photos for positioning and alignment. Don't worry if it slants up or down slightly...the case has some built-in curvature.
Locate and install the micro-USB charging jack in the same-sized hole to the right of the SCD4X. Fasten through the holes with M2 screws. Make sure the two pin connectors are facing "up" (see photos)
Locate and install the mini charging board to the bottom of the case just behind the micro-USB jack, using 4X M2 hardware. Align the black battery connector down toward the bottom of the case.
Moving on to the bottom part of the case, line up the blue PMSA003 sensor (it has a double-sided adhesive) to align with the air intake and outlet holes on the left-hand side of the case. Go ahead and plug in long black-coated STEMMA cableBEFORE ATTACHING the PM sensor to the case, as it will be difficult to fit when other parts are installed. Don't peel the adhesive just yet. The sensor inlet and outlet holes may not exactly line up with the holes in the case. Just fit to center on the holes as best you can, and place as close as possible to the left edge of the case. Ensure that the thin black gasket is attached to the side of the sensor that faces the holes and is not blocking the holes! Now peel the adhesive and stick the sensor firmly to bottom of the case.
Next install the power switch in the provided hole in the upper right-hand side of the case. Use pliers or small wrench to tighten the lock washer and nut onto the shaft. The long 12" red wire should be pre-soldered and will be connected later.Connect the short red wire to the BATTpin on the mini charger board.
The Adafruit Feather Huzzah32 is actually two pieces: the processor itself and the "feather" OLED display. They are installed in the top of the case, just below the white "BackpAQ" label. Locate the Feather OLED and four sets of M2 hardware (spacers and screws.) [When we say "hardware" we mean either screws or screws and nuts or threaded spacers. Sometimes the spacers will already be attached to the part.] Attach the OLED to the top of the case as shown in the photos above. Go ahead and attach to the 4 screws at the top of the case.
Locate the momentary (push) switch and insert into its hole next to the power switch. Note: in some kits this switch ay be located in the top part of the case, to the right of the OLED display. Locate and connect one of the 3" white wires to Huzzah32 pin 33. Do not connect the other end yet.
Now, on to the LED. Locate the clear LED and press into its hole in the top of the case, to the right of the Huzzah32. Align the LED so that the shorter wires are toward the case center, and the clipped wire is to the right.
Let's now connect the LED. Locate the 3 wires we are going to use: 5" blue or yellow, and 5" red. Connect the end of the blue/yellow wire to the leftmost LED pin marked data in, and the end to pin 27 on the Huzzah32.Next, connect one end of the red wire to the pin marked +5V to Huzzah32 pin 3V. Finally, push the third pin (ground) over toward the bottom pin of the push switch. Locate the black wire from Step 9. Attach the free end to the junction of the push switch pin and LED pin. Solder securely (obviously skip if pre-soldered)
Locate the battery connector with the long red and blue wires from Step 7. Plug the connector end into the mini battery connector on the Huzzah32 (and not on the mini-charger board!)
Next, locate the red GPS board. Attach to the top of the PMSA003 using the 2-way adhesive, with the STEMMA connectors facing to the top and bottom of the case, aligning the left side with the left edge of the case.
Locate a 2" STEMMA cable and connect one end to the GPS STEMMA connector and the other end to the right-hand side STEMMA connector on the PMSA003.
Locate the red Realtime Clock (RTC) board. Connect the Stemma cable from the RTC board to the bottom Stemma connector on the GPS board. Connect the other Stemma cable to the right-side Stemma connector on the PM sensor board (see assembly diagram above.)
Locate the second 2" STEMMA cable. Connect one end to the upper GPS board connector. Connect the other to the connector on the right hand side of the SCD4X.
Connect the long STEMMA cable from the PMSA003 to the STEMMA connector on the Huzzah32.
Locate the battery clip (may be white or black.) Slide in the 3.7V LiPO battery and attach both to the case bottom with the long 4-40 screw, lockwasher and nut with wires pointing down. Leave the connector unplugged for now.
Attach the 4 rubber bumpers to the underside of the case, one in each corner.
Now, take a breath, and review these 20 steps and make sure all connections have been securely made to the correct pins and components.
Finally, connect the LiPO battery cable to the black input connector on the mini charging board.

Once again, verify all connections and that the LiPO battery is charged. Turn power on with the toggle switch...if all is well the OLED display should light up with BackpAQ logo and then "BackpAQ V3". Green LEDs on the GPS, SCD4X, and PMSA003 should all light up as well.

Now that we've verified that our BackpAQ is up and running correctly, we're ready to provision and configure the unit.

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Step 11: Provisioning and Configuration

Congratulations on building your BackpAQ Air Quality Monitor! Please consult the Quick Start Guide for how to operate and get the most out of this quality instrument.

Introduction: Let's Build BackpAQ Personal Air Quality Monitor V3