.. _delhi_tutorial:

Evaluating Delhi's AQ Using OpenAQ

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.


In [1]:
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

Choosing Locations

First, let's figure out which locations we should use for our analysis. Let's grab all locations from Delhi for all parametrs:


In [2]:
api = openaq.OpenAQ()

locations = api.locations(city='Delhi', df=True)

locations.location


Out[2]:
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.


In [3]:
locations = locations.query("count > 100").query("lastUpdated >= '2017-03-01'")

locations.location


Out[3]:
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!


In [4]:
params = []

for i, r in locations.iterrows():
    [params.append(x) for x in r.parameters if x not in params]
    
params


Out[4]:
['pm10', 'so2', 'no2', 'pm25', 'o3', 'co']

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!)...