# Use of Remote Sensing Data to Identify Air Pollution Signatures in India

Sivaramakrishnan<EMAIL_ADDRESS>BITS Pilani
Hyderabad Campus, India , Lipika Deka<EMAIL_ADDRESS>Senior
Lecturer, De Montfort University, United Kingdom, IEEE/ACM Professional Member
, Manik Gupta<EMAIL_ADDRESS>Assistant Professor, BITS
Pilani Hyderabad Campus, India, IEEE/ACM Professional Member, APPCAIR member

###### Abstract

Air quality has major impact on a country’s socio-economic position and
identifying major air pollution sources is at the heart of tackling the issue.
Spatially and temporally distributed air quality data acquisition across a
country as varied as India has been a challenge to such analysis. The launch
of the Sentinel-5P satellite has helped in the observation of a wider variety
of air pollutants than measured before at a global scale on a daily basis. In
this chapter, spatio-temporal multi pollutant data retrieved from Sentinel-5P
satellite is used to cluster states as well as districts in India and
associated average monthly pollution signature and trends depicted by each of
the clusters are derived and presented.The clustering signatures can be used
to identify states and districts based on the types of pollutants emitted by
various pollution sources.

##  1 Introduction

Air pollution is one of the major health hazards in a developing country such
as India. Hence, it is necessary to study the composition of the air over the
districts of our country to understand how to tackle pollutants at an
individual level. Zheng et.al [1] has shown that the concentration of
particulate matter less than 2.5 micrometers in diameter ($PM2.5$) has a
direct positive correlation to the number of cases of lung diseases such as
asthma in patients. Other pollutants which directly affect human respiratory
system are nitrogen dioxide, sulphur dioxide, formaldehyde and tropospheric
ozone. High concentrations of carbon monoxide can cause dizziness, confusion,
severe brain damage or even death. Methane is another important gas which
contributes to the greenhouse effect and it is 80 times more harmful than
$CO_{2}$ if it sustains in the air for long periods of time. Hence, there is a
dire need to equip policy makers and policy enforcers with data driven
knowledge of major pollutant sources and their geo-location, as well as
increase the awareness about the ill-effects of these pollutants among the
general public to ensure the well being of the society.

Air-pollutant sources include road-traffic, industries such as brick-kilns,
portable power generators such as diesel power generators etc. [2]. The
information presented in [3] details currently used methods for identifying
pollution sources. Methods can be expensive and may fail to detect all sources
as they may be located in geographical wide areas. Recent efforts to identify
pollution sources involved the use of low-cost sensors [4]. However, such
methods need the additional overhead of sensor installation and maintenance of
each site.

The aim of this chapter is to present clustering methods on air pollution data
derived from larger granularity satellite data and to identify the sources
behind the pollution in each cluster. Grouping of the various states and
districts in India has been done based on the pollution signatures emitted
from different regions. It is cost effective to use satellite data since it
does not involve additional installation overheads and provides a more
holistic wider area picture. Such an approach can also be used to provide an
insight for site selection for low cost sensor installation for more accurate
and localised studies if required.

The outcome will aid the government in reforming policies to help alleviate
the pollution levels effectively. Once it is determined which sources are
responsible for emitting a particular pollutant, government bodies can take
necessary steps to reduce the specific pollutant at the specific geo-location
by placing restrictions targeting only the relevant sources. This will help
the government to efficiently formulate targeted and more effective strategies
to combat individual pollutants levels if there is an alarming spike in the
levels of a specific pollutant.

Rest of the chapter is arranged as follows: section 2 touches upon the most
notable work done in the area till date; section 3 details the method employed
to collect the required data used in this study, with section 4 explaining how
the data was prepared and preprocessed for further analysis; section 5
presents the clustering algorithms employed with the discussion on the results
achieved and finally conclusions in section 6 mentioning briefly now how the
research can be taken ahead.

##  2 Literature Review

Air pollution monitoring has been a major challenge faced by the Government of
India, especially considering the fact that 26 of the 50 most polluted cities
in the world are located in India as of 2019[5]. $PM2.5$, particulate matter
of aerodynamic diameter less than 2.5 $\mu$m, was found to cause damage to the
air passages, cardiovascular impairments, increase the likelihood of Diabetes
Mellitus in humans and cause adverse effects in infancy [6]. Thus, with the
advent of low cost sensors [7], it has become possible to evaluate the air
quality at a given location by only taking into consideration the $PM2.5$
levels. But there are other pollutants as well that can cause adverse health
effects, such as $NO_{2}$, $SO_{2}$ and $CO$, which can be measured by a high
quality air pollution monitoring system, but which requires a higher
installation cost and maintenance.

As of 2020, there are only 804 pollution monitoring stations placed across 344
cities in India, as part of the National Air Quality Monitoring Program
(NAMP)[8]. This means than on an average there is only 1 pollution monitoring
station available per 1.6 million people in the country, the density varying
from state to state with the North Eastern states having the least number of
stations. In contrast there are about 700 Air Quality Monitoring Areas in the
UK, which translates to about 1 site per 100,000 individuals [9]. Even though
the NAMP has been running since 1984, the data from the sensors is publicly
available only from 2016. The number of stations covering rural areas are very
sparse and even the data collected through the monitoring stations is patchy
and prone to errors as some of them are manually collected and uploaded [10].

There have been attempts to perform spatial interpolation of ground-based
sensors to fill the gaps in the areas where pollution monitoring sites are not
present. Various techniques have been employed to create these pollution maps
such as the Kriging algorithm, Inverse Distance Weighting[11] and more
recently Artificial Neural Networks (ANNs)[12] and Long Short-Term Memory
(LSTM) neural networks[13] have been used to increase the accuracy of these
interpolations. But there are various drawbacks of these models, Inverse
Distance Weighting cannot estimate values which fall outside the range of the
training data, neural networks do not consider the spatio-temporal
associations and the LSTM requires a large set of tagged historical data and
plenty of time for the training. Thus the lack of finer resolution data points
makes it hard to use spatial interpolation techniques to monitor air quality
effectively in India.

The alternative approach is to use remote sensing data to monitor the air
quality over a region. The satellites measure the particulate matter present
in the atmosphere by using spectroscopic retrieval methods. A significant
positive correlation of 0.96 was observed between the Aerosol Optical
Thickness (AOT) measured by the MODIS satellite and the $PM2.5$ measurements
from the ground[14]. It was possible to get a wider coverage of pollution
measure as compared to ground based sensors but this came at a lower spatial
resolution, which meant that the data was not adequate for pinpointing the
source of the pollution. Spatial scaling techniques were employed to enhance
the resolution of the AOT product from 10km to 1km [15] which helped gain a
finer insight. The temporal AOT data from Moderate Resolution Imaging
Spectroradiometer (MODIS) has been used to perform back trajectory analysis by
which the transportation of particulate matter across borders can be traced
and the source of the pollutant can be determined [16]. The Global Ozone
Monitoring Experiment (GOME)[17] was launched in 1995 and it was able to
measure tropospheric Ozone and $NO_{2}$ on a global scale for the first time,
but it had a very poor spatial resolution of 80 x 40 km2 per pixel. Apart from
this, MODIS has a 1-2 day temporal resolution and GOME has a monthly temporal
which meant that data is not available as frequently as the 4hr or 8hr
intervals as provided by ground based sensors.

The launch of Sentinel-5P by the European Space Agency (ESA) in October 2017
brought with it an increase in the spectral radius as well as spatial
resolution. The on-board TROPOMI sensor can measure
$O_{3}$,$NO_{2}$,$SO_{2}$,$CH_{4}$,$CO$,$HCHO$ and $AER_{A}I$ at a spatial
resolution of ${[7x7km^{2}]}$, which is about 6 times better as compared to
GOME and also improve the sensitivity by an order of magnitude[18].
Sentinel-5P also has a temporal resolution of 1, which meant that it would
cover the entire surface area of the earth once every day. Thus, there is now
a wider spectrum of pollutants being measured at a very good spatial and
temporal resolution, though not as fine granularity as a ground based sensor.
Since Sentinel-5P has a wider spectral range and a finer spatial resolution as
compared to previous satellites, it helps study the air quality over an area
in much finer details and is the preferred choice of data source for
experiments carried out in this paper.

Studies have been conducted to analyse the $NO_{2}$ pollutant, in particular
the one conducted by Kaplan et al.[19] which correlates it with statistical
indicators such as population density. In this study, multiple pollutants
gathered from remote sensing data have been taken into consideration as well
and used to perform a clustering of Indian states and districts based on their
pollution signatures.

##  3 Data Collection

This section will provide a brief description of how the remote sensing data
was obtained. The images were retrieved using Google Earth Engine’s Level 3
products for Sentinel-5P. The method by which the Level 2 products, released
by the ESA[20], are processed by Earth Engine are described in the next
section.

A yearly average of Level 3 $NO_{2},SO_{2}$, $CO$, $AER\\_AI$, $O_{3}$ and
$HCHO$ products was taken over the latitudinal and longitudinal extents of
India from January 2019 to December 2019. and was then processed for further
analysis. The following subsections describe the various Level-2 products that
are released by the ESA and explain some of their retrieval methods and their
significance in terms of their contribution to air pollution.

###  3.1 Nitrogen Dioxide ($NO_{2}$)

Sentinel-5P have two sub products which are measured for $NO_{2}$, namely the
tropospheric and the total column. In this study the tropospheric $NO_{2}$
column, which is the $NO_{2}$ between the surface of the earth and the
troposphere, is used as it plays a major role in determining the level of
photochemical Ozone [21]. It must be noted that the TROPOMI $NO_{2}$
underestimates the $NO_{2}$ level as compared to ground based sensors but the
correlation coefficient was found to be 0.84 and appropriately calibrated,
which makes the product accurate enough to be used for analysis. The data
measures trace gas concentrations in $mol/m^{2}$[22].

###  3.2 Sulphur Dioxide ($SO_{2}$)

The sources of Sulphur Dioxide pollution in the atmosphere are both natural
and man-made. The majority of pollution (70%) arise from coal power plants,
smelting industries and mines. Apart from incurring both long term and short
term effects on climate, it affects vegetation and water quality when it
washes down as acid rain[23] [24].

###  3.3 Aerosol UV Index ($AER\\_AI$)

This product is calculated based on the spectral contrast in the ultraviolet
spectral range for the $354\text{\,}\mathrm{mm}$ and $388\text{\,}\mathrm{mm}$
wavelengths. It is a long established air quality index monitor and is useful
in tracking the plumes released from dust, biomass burning and volcanic ash
[25].

###  3.4 Carbon Monoxide ($CO$)

Carbon monoxide is an important atmospheric trace gas for the understanding of
tropospheric chemistry and in certain urban areas, it is a major atmospheric
pollutant. In the $2.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ spectral range
of the shortwave infrared (SWIR) part of the solar spectrum, TROPOMI clear sky
observations provide $CO$ total columns with sensitivity to the tropospheric
boundary layer. This data has been validated against TCCON and NDACC ground-
based network and the MOPITT satellite with a resulting bias of less than 10%
[26].

###  3.5 Formaldehyde ($HCHO$)

Formaldehyde is an intermediate gas in almost all oxidation chains of non-
methane volatile organic compounds (NMVOC), leading eventually to $CO_{2}$.
$HCHO$ satellite observations are used in combination with tropospheric
chemistry transport models to constrain NMVOC emission inventories in so-
called top-down inversion approaches. This data has a mean bias of 50% with
ground based sensors and other satellites such as GOME-2 and OMI and is
measured in $mol/cm^{2}$ [27].

###  3.6 Ozone ($O_{3}$)

Ozone in the tropical troposphere plays various important roles. The intense
UV radiation and high humidity in the tropics stimulate the formation of the
hydroxyl radical ($OH$) by the photolysis of $O_{3}$. The $O_{3}$ Tropospheric
Column gives the measurement of tropospheric ozone between the surface and the
270 hPa pressure level. It is based on the convective cloud differential (ccd)
[28].

##  4 Data Preprocessing

The Earth Engine uses the Level 2 product, from the ESA, and filters the
pixels based on minimum pixel quality level corresponding to each scene. These
images are then broken into tiles according to the orbit number to make it
easier for ingestion and retrieval.

###  4.1 Quality Assurance Filtering

The quality of the individual observations depends on many factors, including
cloud cover, surface albedo, presence of snow-ice, saturation, geometry etc.
These observations are filtered in order to avoid misinterpretation of the
data quality and to avoid the effects of sun glint. Each of the satellite
products comes with a qa_value (quality assurance) band which can be used to
filter out less accurate values. Different thresholds of qa_value are chosen
for different products as defined in the Sentinel-5P Product User Manual.

For making the Level-3 products, Google Earth Engine filters pixels associated
with a qa_value below 0.8 for the $AER\\_AI$ product, 0.75 for Tropospheric
$NO_{2}$ and 0.5 for all other products, except $O_{3}$ for which quality
filtering is not done. This takes care of erroneous scenes and problematic
retrievals.

Fig.1(a) shows a Level-2 scene of Aerosol Index for a single day released by
the ESA. In Fig.1(b) the missing pixels can be observed after filtering, which
correspond to those that had a qa_value of less than 0.8.

(a) Before QA Filtering

(b) After QA Filtering

Figure 1: Level-2 $AER\\_AI$ Index

A yearly average of these Level-3 products was taken in which these missing
pixels were filled in with values from those scenes which had data over that
region.

###  4.2 Regional Masking

The district level and state level administrative boundaries for India were
used to mask the Level-3 products. The $CH_{4}$ column contained a lot of
missing data as the retrieval of this pollutant was of low quality, therefore
this pollutant was dropped in the subsequent analysis. The average pixel
values of $NO_{2}$, $SO_{2}$, $CO$, $AER\\_AI$, $O_{3}$ and $HCHO$ was
calculated for each of the masked district and was stored in a tabular format.
A total of 594 districts with six pollutant values was used to frame the
monthly pollution data set. This was further cleansed and the rows containing
null values were removed.

Figure 2: Single Level-3 scene masked with Telangana district boundaries

Similarly, the Sentinel-5P data was also masked using state border shape files
to extract the state-wise average value of pollutants and converted into a
tabular data set. The state-wise data had 33 rows and 6 features.

District | $NO_{2}$ | $SO_{2}$ | CO | AER_AI | $O_{3}$ | HCHO
---|---|---|---|---|---|---
Andaman Islands | 4.28E-05 | -4.73E-08 | 0.037036 | -1.27958 | 0.117128 | 9.89E-05
Nicobar Islands | 3.89E-05 | -9.24E-06 | 0.033289 | -1.23807 | 0.117677 | 8.95E-05
Anantapur | 5.97E-05 | 4.64E-05 | 0.035942 | -1.11059 | 0.116844 | 0.000145
Chittoor | 5.81E-05 | 4.95E-05 | 0.035912 | -1.17883 | 0.117122 | 0.000152
Cuddapah | 6.20E-05 | 6.00E-05 | 0.037118 | -1.14565 | 0.117214 | 0.000153
East Godavari | 6.15E-05 | 8.09E-05 | 0.039594 | -1.17801 | 0.11709 | 0.000174
Guntur | 6.56E-05 | 8.16E-05 | 0.039045 | -1.11898 | 0.117253 | 0.000156
Table 1: A Snippet of the District-wise Pollution Data

###  4.3 Standardization

The dataset was then re-scaled by first removing the mean of each pollutant
and scaling each column by the standard deviation as shown in Eqn.1. This
ensures that the features are of comparable denominations before running the
clustering algorithms on them.

$z=\frac{x-\overline{x}}{\sigma}$ (1)

##  5 Clustering

Clustering is a method of grouping based on patterns in the data [29]. This
technique is primarily used to find clusters of data points with inherent
similarity in unlabelled datasets. In this case, the unlabelled dataset
consists of different pollutants emitted ($NO_{2}$, $SO_{2}$, $CO$,
$AER\\_AI$, $O_{3}$ and HCHO) for each state or district in India. The aim is
to find clusters comprising of states or districts that emit a similar
pollution signature which will help isolate pollution sources more easily.

###  5.1 Clustering Methods

Unsupervised clustering was performed on the pollution datasets using three
different algorithms, namely - K-Means clustering, Agglomerative clustering
and DBSCAN [30]. The distance measure which was used in all three methods was
the L-2 norm, Euclidean Distance.

####  5.1.1 K-Means Clustering

Partitioning based unsupervised clustering method reallocate data points by
moving them from one cluster to another, starting from an initial
partitioning. This algorithm works by initializing K points as cluster
centers. In each iteration, every point in the dataset is assigned to the
cluster it is closest to (using the L2 norm distance). The cluster center is
then reinitialized to the mean of the cluster set and the clustering iteration
until convergence is achieved.

####  5.1.2 Ward Agglomerative Clustering

Hierarchical clustering constructs the clusters by recursively splitting or
combining the data points. In agglomerative clustering (a method within the
broader class of hierarchical clustering methods)[31], the clusters are built
by iteratively merging smaller clusters starting from individual data point up
until the required number of clusters are reached. The Ward’s distance
minimizes the overall inter-cluster sum of squared distances within all
clusters.

$\Delta\big{(}A,B\big{)}=\sum_{i\in{A\cup B}}\|\vec{x}_{i}-\vec{m}_{A\cup
B}\|^{2}-\sum_{i\in A}\|\vec{x}_{i}-\vec{m}_{A}\|^{2}-\sum_{i\in
B}\|\vec{x}_{i}-\vec{m}_{B}\|^{2}$ (2)

Here, $\vec{x}_{i}$ represents the center of the $i^{th}$ cluster and
$\Delta\big{(}A,B\big{)}$ represents the cost of merging clusters A and B.

####  5.1.3 DBSCAN Clustering

Density based clustering tries to associate each point to a set of probability
distributions. This algorithm does not take the number of clusters as an input
and uses two parameters $min\\_pts$ and $epsilon$ to form clusters. $Epsilon$
determines the maximum distance between two points upto which they can be
grouped within the same cluster and the $min\\_pts$ determines the minimum
number of points that must fall within a cluster for it to not be designated
as a noise point. It is useful in detecting noise and outliers in the data.

###  5.2 Optimal Number of Clusters

The elbow method [32] is a technique used to determine the optimal number of
clusters to be chosen based on heuristics such as inter cluster similarity and
intra-cluster similarity. In the method presented here, the number of clusters
is iteratively increased from 2 till 15 and the point at which the graph of
the cost function has the highest curvature is taken as the elbow, or optimal
number of clusters.

The elbow method was performed using the distortion score as the cost function
to find the optimal number of clusters for K-means clustering and the
silhouette score was used to ensure that the intra-cluster similarity was
optimal. Both scores are explained below. The same number of clusters as
derived from the elbow method was used to slice the hierarchical clustering to
compare the results of the two algorithms.

####  5.2.1 Distortion Score

This metric give information about the overall cluster dissimilarity. It is
calculated as the mean sum of squared distances to centers.

$S=\sum_{i=1}^{n}(x_{i}-\overline{x})$ (3)

In Eqn.3, xi represents the ith row of the dataset and $\bar{x}$ represents
the mean of the cluster it belongs to. the lower the value of S, lower the
dissimilarity, the more optimal is the solution.

Figure 3: Distortion Score Elbow for K-Means

Fig.3 represents the plot for the distortion scores as a result of fitting the
K-Means model to the dataset, while varying the value of K from 2-15. The
elbow method analysis results in the elbow line being drawn at K=5 as this is
the point of maximum curvature in the curve.

####  5.2.2 Silhouette Score

The silhouette score metric represents the intra-cluster similarity. It is
calculated as the mean ratio of intra-cluster and next nearest-cluster
distance.

$S=\frac{b-a}{max(a,b)}$ (4)

In Eqn.4, ${a}$ is the mean distance between a sample and all other points in
the same class and $b$ is the mean distance between a sample and all other
points in the next nearest cluster. The score is higher when clusters are
dense and well separated.

Figure 4: Silhouette score Elbow for K-Means

Fig.4 represents the plot for silhouette scores as a result of fitting the
K-Means model to the dataset while varying the value of K from 2-15. The elbow
method analysis results in the elbow line being drawn at K=6.

The elbow method using distortion score resulted in 5 being the optimal number
of clusters. From the silhoutte score plot, it can be inferred that the intra-
cluster similarity of K=5 was 0.290 which was not too far away from the
optimal silhouette score of 0.295. Hence, K=5 was chosen as the optimal number
of clusters to prevent over-fitting on the data and get well rounded clusters.

###  5.3 Cluster Validation

The silhouette method was used to determine the intra-cluster similarity of
the clusters formed by the three algorithms mentioned in the previous section.
It gives an idea of how closely related a state or district is to the cluster
it has been assigned to by the respective method. Values close to 1 indicate a
high affinity to that cluster and negative values imply that the data point
might have been wrongly assigned to that cluster.

Figure 5: Silhouette Score for K-Means

Each color in Fig.5 represents the distribution of the silhouette score of
each state that falls within the cluster. The wider the graph for a cluster,
the more the number of states that fall into it. The average silhouette score
for K-Means was 0.290 and for Ward Agglomerative was 0.280.

The lower polluting states and the higher polluting states had silhouette
coefficients of almost 0.5, which indicates a higher intra-cluster similarity.
Both K-Means and Ward Agglomerative clustering resulted in a similar
distribution of states across clusters. Only the state of Telangana was placed
in cluster 1 in K-Means and cluster 0 in Ward Agglomerative. In the case of
DBSCAN, it does not need to specify the number of clusters and instead the
values of the parameter $min\\_pts$ was fixed to 3 and that of $epsilon$ was
1.7. DBSCAN Clustering resulted in a few states being classified as outliers
since their pollution signatures did not match with any other state. But the
remaining clusters that were formed were in correlation with K-Means and Ward
Agglomerative clustering. The results from K-Means clustering have been
presented and used for analysis in the following section.

###  5.4 Analysis of Pollution Signatures and Clustering Results across Indian
States and District

In this section, the various results from state as well as district wise
clustering and the corresponding pollution signatures obtained for different
clusters have been presented and analysed in depth.

The bar plots shown below in Fig.6 represent the average pollution signatures
for every cluster obtained as a clustering of pollution data across different
states and districts. These pollution signatures serve as a unique
representation for each cluster. Each pollution signature shows the average
pollutant magnitude for a given cluster. States and Districts which are part
of Cluster 0, on average, have the lowest pollution profile and those which
fall into Cluster 4 emit the highest level of pollutants as can be seen in
Fig.6. The varying trends for each pollutant across each cluster for both
state-wise and district-wise clustering have also been shown in Fig.7.
Fig.8(a) shows the state-wise cluster map and Fig.8(b) shows the district-wise
cluster map across India as obtained from K-means clustering algorithm.

(a) State-wise Pollution Signatures

(b) District-wise Pollution Signatures

Figure 6: Pollution Signatures for K-Means Clustering

(a) State-wise Pollution Trend

(b) District-wise Pollution Trend

Figure 7: Varying Pollution Trends across K-Means Clusters

Each of the cluster behaviour can be explained in terms of its corresponding
pollution signature and trends as follows:

Cluster 0 - As can be seen in Fig.6, cluster 0 has the least concentration of
pollutants. States such as Jammu & Kashmir, Uttarkhand, Himachal Pradesh and
Sikkim fall under this cluster. These states have a very low population
density and consequently low vehicular traffic as they mainly comprise of
mountainous terrains. These states also have significantly lower industrial
activity and hence do not contribute much to the air pollution.

Cluster 1 - On an average, the regions which fall under this cluster have a
higher level of emission of all pollutants when compared to cluster 0, except
for $SO_{2}$ as can be seen in Fig.7. This cluster comprises of the southern
states and a majority of the north eastern states.

Cluster 2 -The western and central states such as Telangana, Odisha, Gujarat,
Madhya Pradesh and Maharashtra fall under this cluster. It can be noted that
in the district-wise clustering in Fig.8b, the city of Chennai falls under
this cluster 2, whereas it came under cluster 1 along with the state of Tamil
Nadu in Fig.8(a).

Cluster 3 - This is the second highest polluting cluster and most of the
states which fall under this have a lot of industrial presence which is
attributed with high pollutant and in particular high $NO_{2}$ emissions.
States such as Uttar Pradesh, Haryana, Rajasthan and Punjab fall under this in
the state-wise clustering.

Cluster 4 - This is the cluster which has the higher average percentage of
pollutants $SO_{2}$ and $NO_{2}$ as can be seen in Fig.7. These states have
some of the highest population densities in India and contain some of the
worst polluting cities in the world. It is worth noting here that the results
are consistent with the report from TERI - The Energy and Resources Institute
[33] , where the states belonging to the cluster 3 and cluster 4 are those
which have the highest $PM10$ emissions from brick kilns as well as from coal
and iron ore mining.

The biggest difference in terms of state vs. district level clustering can be
noted from the regions which fall under this cluster . In Fig.8(a) it can be
seen that only the state Delhi falls under this cluster 4, but in the
district-wise clustering as shown in Fig.8(b), it can be seen that a majority
of the districts from Uttar Pradesh, Bihar, Haryana, Punjab and Rajasthan as
well now come under cluster 4, representing the highest overall pollution
signature.

(a) State-wise Clusters

(b) District-wise Clusters

Figure 8: Visualization of K-Means Clusters

Further comparing the two maps in Fig.8(a) it can be seen that the mining
intensive states of Jharkhand, West Bengal, parts of Chhattisgarh and Odisha
fall under cluster 2, whereas in Fig.8(b) the districts from these states fall
under cluster 3, which indicates that they belong to a lower polluting
category. Upon comparing the state-wise and district-wise clustering, we can
see that cities like Mumbai, Chennai and Hyderabad fall under a higher
polluting cluster as compared to other districts within the same state. This
is in line with expectations since these urban cities tend to emit higher
levels of pollution. Thus based on this analysis, it is observed that a finer
resolution regional pollution classification can be seen in district-wise
clustering as compared to the state-wise clustering.

##  6 Conclusions and Future Work

Acknowledging the importance of managing pollution levels so that it does not
affect the socio-economic and health status of the general population, it is
important to understand the precise pollutant signature over a certain area
and the possible sources of the pollutants. The presented study explored three
clustering algorithms on data retrieved from ESA’s Sentinel-5P satellite to
address this issue. The clustering algorithms were used to assign unique
pollutant signatures to states and districts across India. The results have
shown to be promising. To take this work further, it is planned to improve on
the clustering algorithms to understand, if a similar or higher accuracy can
be attained at even finer granularity than a district level. In addition,
studies need to be conducted that will help correlating pollution levels with
socio-economic factors of the region. Furthermore, there is a need to study
the affect of other variables such as wind, atmospheric pressure etc. to
understand the transport of pollutants from its source [34]. This will help
further identify pollution sources with higher accuracy.

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