Most of my own atmospheric chemistry research as a PhD student at MIT is based in Delhi. Thus, for this tutorial, we will take a deeper look at the air quality data made available to us through OpenAQ. We will begin by figuring out exactly what data is available to us, and then further examine the most relevant and up-to-date sources. We will take a look at longer trends for some pollutants where possible.
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import openaq
import warnings
warnings.simplefilter('ignore')
%matplotlib inline
# Set major seaborn asthetics
sns.set("notebook", style='ticks', font_scale=1.0)
# Increase the quality of inline plots
mpl.rcParams['figure.dpi']= 500
First, let’s figure out which locations we should use for our analysis.
Let’s grab all locations from Delhi for all parametrs:
api = openaq.OpenAQ()
locations = api.locations(city='Delhi', df=True)
locations.location
0 Anand Vihar
1 Anand Vihar, Delhi - DPCC
2 Aya Nagar, Delhi - IMD
3 Burari Crossing, Delhi - IMD
4 CRRI Mathura Road, Delhi - IMD
5 Civil Lines
6 Delhi College Of Engineering
7 Delhi Technological University
8 Delhi Technological University, Delhi - CPCB
9 East Arjun Nagar
10 East Arjun Nagar-Delhi CPCB
11 IGI Airport
12 IGI Airport Terminal-3, Delhi - IMD
13 IHBAS
14 IHBAS, Delhi - CPCB
15 Income Tax Office
16 Income Tax Office, Delhi - CPCB
17 Lodhi Road, Delhi - IMD
18 Mandir Marg
19 Mandir Marg, Delhi - DPCC
20 NSIT Dwarka
21 NSIT Dwarka, Delhi - CPCB
22 North Campus, Delhi - IMD
23 Punjabi Bagh
24 Punjabi Bagh, Delhi - DPCC
25 Pusa, Delhi - IMD
26 Pusa2 IMD
27 R K Puram
28 R K Puram, Delhi - DPCC
29 RK Puram
30 Shadipur
31 Shadipur, Delhi - CPCB
32 Siri Fort
33 Sirifort, Delhi - CPCB
34 US Diplomatic Post: New Delhi
Name: location, dtype: object
Let’s go ahead and filter our results to only grab locations that have been updated in 2017 and have at least 100 data points.
locations = locations.query("count > 100").query("lastUpdated >= '2017-03-01'")
locations.location
0 Anand Vihar
1 Anand Vihar, Delhi - DPCC
2 Aya Nagar, Delhi - IMD
3 Burari Crossing, Delhi - IMD
4 CRRI Mathura Road, Delhi - IMD
7 Delhi Technological University
8 Delhi Technological University, Delhi - CPCB
10 East Arjun Nagar-Delhi CPCB
12 IGI Airport Terminal-3, Delhi - IMD
13 IHBAS
14 IHBAS, Delhi - CPCB
15 Income Tax Office
16 Income Tax Office, Delhi - CPCB
17 Lodhi Road, Delhi - IMD
18 Mandir Marg
19 Mandir Marg, Delhi - DPCC
20 NSIT Dwarka
21 NSIT Dwarka, Delhi - CPCB
22 North Campus, Delhi - IMD
23 Punjabi Bagh
24 Punjabi Bagh, Delhi - DPCC
25 Pusa, Delhi - IMD
27 R K Puram
28 R K Puram, Delhi - DPCC
30 Shadipur
31 Shadipur, Delhi - CPCB
32 Siri Fort
33 Sirifort, Delhi - CPCB
34 US Diplomatic Post: New Delhi
Name: location, dtype: object
Now that we have several up-to-date locations in Delhi we can use, let’s see what parameters we have to play with!
params = []
for i, r in locations.iterrows():
[params.append(x) for x in r.parameters if x not in params]
params
['pm10', 'pm25', 'so2', 'o3', 'co', 'no2']
Great. Now we have a list of parameters that we can evaluate.
The rest of this tutorial will be finished in the future when I have away from writing manuscripts (unless someone wants to take a stab at it and send a pull request!)…