Air quality monitoring station

This project consists of three parts:

Air quality monitoring station based of Raspberry Pi Zero W and PMS3003 sensor
Data transfer and storage (AWS MySQL/DynamoDB/S3)
Data visualization (Plotly/Chart.js/R Shiny/Flask hosted on S3/EC2)

https://sylwester.cf/
http://sylwesterf.s3-website.eu-central-1.amazonaws.com/
https://sylwesterf.github.io/

Installation

1. Air quality monitoring station

Raspberry Pi:

# download project files
sudo git clone https://github.com/sylwesterf/pms3003.git
cd pms3003

# install dependencies
sudo pip install -r requirements.txt

Follow RaspberryPi documentation to enable uart: https://www.raspberrypi.org/documentation/configuration/uart.md

Connect PMS3003 to Raspberry Pi as per sensor datasheet:

PMS3003	Rpi
VCC	+5V
GND	GND
RxD	TxD
TxD	RxD

Connect DHT11 (3 PIN) to Raspberry Pi as per sensor datasheet:

PMS3003	Rpi
VCC	+3.3V
OUT	GPIO7 (BCM)
GND	GND

# run a test - output to terminal
sudo python test.py

2. Data transfer and storage

# run aws configure and set AWS Access Key ID and AWS Secret Access Key for DynamoDB/S3 upload
sudo aws configure

# run rpi2dynamodb.py script to load data into DynamoDB and generate a (backup) csv file on Raspberry Pi
# set up the aws (cli) credentials
# verify a project path and DynamoDB table name in rpi2dynamodb.py for csv/DynamoDB output 
sudo python rpi2dynamodb.py

# for automatic archival of csv files into S3 use csv2s3.py (set up variables first)
sudo python csv2s3.py

For mysql refer to pms3003/mysql/ folder.

3. Data visualization

Flask (AWS EC2) - NEW

# just add below commands to EC2 user data when launching an instance or ssh into it and run it afterwards
curl https://raw.githubusercontent.com/sylwesterf/pms3003/master/viz/py-new/prep.sh -o prep.sh
sudo bash prep.sh

/opt/pms3003/
.
├── pms3003.py
├── latest.py
├── all.py
├── fun.py
├── assets/
│   └── favicon.ico
├── file.py
├── wsgi.py
├── prep.sh
├── output.csv
├── cron.txt
└── requirements.txt

Flask (AWS EC2) - OLD

# just add below commands to EC2 user data when launching an instance or ssh into it and run it afterwards
curl https://raw.githubusercontent.com/sylwesterf/pms3003/master/viz/py/prep.sh -o prep.sh
sudo bash prep.sh

R-Shiny:

Deploy the contents of pms3003/viz/R/pms3003/ into your shiny server
For shiny server setup on EC2 follow the pms3003/viz/R/pms3003/ec2_ubuntu_config_R.sh

Chart.js:

Deploy the contents of pms3003/viz/js-chart/ and update the url for json src file in script.js
Make sure to enable CORS for S3 bucket as per: https://stackoverflow.com/questions/49493279/react-js-how-to-get-rid-of-cross-origin-error-in-codesandbox
See https://sylwesterf.github.io/ and corresponding repo https://github.com/sylwesterf/sylwesterf.github.io

plotly.js:

Deploy the contents of pms3003/viz/js-plotly/ and update the url for json src file in script.js
See http://sylwesterf.s3-website.eu-central-1.amazonaws.com

4. Extras

Refer to pms3003/scripts/ for helpful scripts:

dynamodb-update-table.py (add humidity and temperature data to DynamoDB table)
dynamodb2json.py (export data to json format)
index-reset-reload.py (move data from non-indexed to indexed table)
manual-load-from-csv.py (perfrom a manual load from csv to DynamoDB)
power-consumption.sh (reduce RasberryPi W power consumption)

TODO

~~temperature and humidity sensor to viz~~
~~a javascript viz~~
~~add PM2.5=25 limit threshold line~~
~~new layout~~
case for sensors
~~authentication~~
sending data to kafka
contenerize viz
python 3

Acknowledgments

License

This project is licensed under the MIT License - see the LICENSE.md file for details

xkortex/pms3003