Spatial Temporal Analysis of Air Quality and their Relation to Meteorological Parameters, in India

Abstract

Air pollution has become a significant global issue, with India ranking 8th in the world for hazardous air quality based on the latest 2022 report. (World Air Quality Report 2023). The growing urban sprawl and human-induced environmental activities have led to a decline in air quality. The Air Quality Index assesses the level of pollution in the air at a particular region. It is a fundamental right for individuals to be informed about the quality of the air they breathe for their well-being. This study aims to examine air pollution levels and trends across various geographical locations in India using data provided by the Central Pollution Control Board under the Ministry of Environment, Forest, and Climate Change in India. The study focuses on analyzing the annual growth of pollutants such as SO2, NO2, PM10, and PM2.5 from 2016 to 2021, collected from monitoring stations in 28 states across 256 locations. The research evaluates the impact of different particulate matter concentrations and showcases the spatial variation of air pollutants between 2016 and 2021. Results indicate a significant decrease in air pollution levels during lockdown periods compared to pre-lockdown periods. On an average (over all cities), more than 24% decrease has been observed for all the AQI of the pollutants. Additionally, a comparative analysis reveals correlations between meteorological factors and air quality, showing that temperature, relative humidity, and wind speed are negatively correlated with AQI, while surface pressure is positively correlated. Temporal analysis was also conducted to examine the relationship between air pollutant concentrations and meteorological parameters.

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Spatial Temporal Analysis of Air Quality and their Relation to Meteorological
Parameters, in India
Suman Dash1, Aniruddha Jena2
1Assistant Professor, Civil Engineering, Government College of Engineering Keonjhar, Odisha, India
2UG - Civil Engineering, Government College of Engineering Keonjhar, Odisha, India
Email Id: sumandash_fce@gcekjr.ac.in1, jena.aniruddha2002@gmail.com2
Abstract
Air pollution has become a significant global issue, with India ranking 8th in the world for hazardous air
quality based on the latest 2022 report. (World Air Quality Report 2023). The growing urban sprawl and
human-induced environmental activities have led to a decline in air quality. The Air Quality Index assesses
the level of pollution in the air at a particular region. It is a fundamental right for individuals to be informed
about the quality of the air they breathe for their well-being. This study aims to examine air pollution levels
and trends across various geographical locations in India using data provided by the Central Pollution
Control Board under the Ministry of Environment, Forest, and Climate Change in India. The study focuses on
analyzing the annual growth of pollutants such as SO2, NO2, PM10, and PM2.5 from 2016 to 2021, collected
from monitoring stations in 28 states across 256 locations. The research evaluates the impact of different
particulate matter concentrations and showcases the spatial variation of air pollutants between 2016 and
2021. Results indicate a significant decrease in air pollution levels during lockdown periods compared to pre-
lockdown periods. On an average (over all cities), more than 24% decrease has been observed for all the AQI
of the pollutants. Additionally, a comparative analysis reveals correlations between meteorological factors
and air quality, showing that temperature, relative humidity, and wind speed are negatively correlated with
AQI, while surface pressure is positively correlated. Temporal analysis was also conducted to examine the
relationship between air pollutant concentrations and meteorological parameters.
Keywords: Air quality index; Air pollutants; Spatial temporal; Meteorological parameters; Correlation and
regression analysis.
1. Introduction
Air pollution poses a grave concern impacting the
lives of billions worldwide annually. The World
Health Organization (WHO) states that over a
quarter of global deaths may directly result from
pollution. In 2015, Asia recorded its highest
pollution levels, attributing to 35% of deaths
globally due to air pollution. The World Health
Organization's list of the ten most polluted cities
reveals that nine are situated in India, with Delhi
holding the sixth position. The pollution
concentrations are collected from Pollution Control
Board(CPCB) under the Ministry of Environment,
Forest, and Climate [1] Change in India
(https://cpcb.nic.in/), encompassing states like
Andhra Pradesh, Bihar, Chandigarh, Chhattisgarh,
Delhi, Gujarat, Jharkhand, Karnataka, Kerala,
Madhya Pradesh, Maharashtra, Punjab, Rajasthan,
Tamil Nadu, Telangana, Uttar Pradesh, West
Bengal, and Odisha, where their Air Quality Index
(AQI) values are observed. Air pollution stems from
a variety of human activities and natural
occurrences, including industrial emissions, vehicle
exhaust, burning of fossil fuels, agricultural
practices, wildfires, and volcanic eruptions. [2]
These sources release harmful pollutants like
particulate matter, nitrogen oxides, sulfur dioxide,
and volatile organic compounds into the atmosphere,
leading to the deterioration of air quality. The
repercussions of air pollution on human health are
significant, with exposure to pollutants such as
particulate matter (PM10, PM2.5), ozone(O3),
Sulphur dioxide (SO2), and nitrogen oxides (NO2)

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resulting in respiratory issues, exacerbation of
conditions like asthma, and heightened risks of lung
diseases. Prolonged exposure is associated with
cardiovascular ailments, diminished lung function,
and premature mortality. Furthermore, air pollution
can contribute to the formation of smog, causing
irritation to the eyes and respiratory system
Figure 1 Arcgis Map of Study Areas
Above figure 1 showing the different states that have
taken for study. coastal regions are the regions which
contains mostly water body like sea, river etc.
2. Materials and Methods
2.1 Study Area
The study area encompasses the map of India,
spanning numerous states and locations. India,
currently one of the fastest-developing countries in
Asia, stretches between 8°4′ North and 37°6′ North
latitudes, and 68°7′ East and 97°25′ East longitudes.
It spans 3214 km from north to south and 2933 km
from east to west. [3] With a population of
approximately 141.72 crores as of 2022, India holds
the top rank globally. Figure 1 delineates the various
states focused on in the study. Situated in South
Asia, India is the seventh-largest country globally by
land area and the second-most populous. Renowned
for its rich history, diverse culture, and vibrant
traditions, India is divided into 28 states and 8 Union
Territories, each with its unique identity. The 28
states in India include Andhra Pradesh, Arunachal
Pradesh, Assam, Bihar, Chhattisgarh, Goa, Gujarat,
Haryana, Himachal Pradesh, Jharkhand, Karnataka,
Kerala, Madhya Pradesh, Maharashtra, Manipur,
Meghalaya, Mizoram, Nagaland, Odisha, Punjab,
Rajasthan, Sikkim, Tamil Nadu, Telangana, Tripura,
Uttar Pradesh, Uttara hand, and West Bengal.
Additionally, there are 8 union territories, namely
Andaman and Nicobar Islands, Chandigarh, Dadra
and Nagar Haveli and Daman and Diu,
Lakshadweep, Delhi, Puducherry, Jammu and
Kashmir, and Ladakh. Each state is characterized by

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its industrial or non-industrial nature, coastal or non-
coastal location, and tier classification. India's cities
serve as vibrant hubs of commerce, culture, and
opportunity. To comprehend and navigate this
diverse urban landscape, the Indian government has
classified cities into four tiers: Tier I, II, III, and IV.
These classifications serve as valuable indicators for
factors such as population size, infrastructure
development, economic growth, and quality of life.
The tier classifications serve multiple purposes.
Firstly, they aid in administrative efficiency by
enabling governments to manage and govern cities
more effectively, facilitating resource allocation,
policy implementation, and decision-making.
Secondly, they assist in economic assessment by
evaluating a city's economic strength and potential,
aiding businesses and investors in identifying
lucrative markets and growth opportunities. Thirdly,
they contribute to urban planning and infrastructure
development by prioritizing resource allocation and
ensuring appropriate attention to cities in need of
development
2.2 Data Collection
This paper refers the data were collected from the
Central Pollution Control Board (CPCB) and State
Pollution Control Boards (SPCB) of each state on a
yearly and monthly basis. Meteorological data was
obtained online from NASA's website. Based on
previous research findings, four key air pollutant
components (PM2.5, PM10, SO2, NO2) and four
important meteorological factors were selected for
analysis of air quality such as temperature(T),
relative humidity (RH), average wind speed (WS),
and surface pressure(PS). The ambient air quality
across the country was assessed through two main
method [4] manual monitoring as part of the
National Ambient Air Quality Monitoring
Programme (NAMP) and real-time monitoring via
the Continuous Ambient Air Quality Monitoring
System (CAAQMS). The National Ambient Air
Quality Monitoring Programme (NAMP)
commenced in 1984 with the establishment of
monitoring stations located in Agra and Anpara.
Under the National Ambient Air Quality Monitoring
Programme (NAMP), the State Pollution Control
Boards (SPCBs), Pollution Control Committees
(PCCs), National Environmental Engineering
Research Institute (NEERI) in ambient air quality
monitoring at various stations. NAMP focuses on
monitoring several criteria pollutants, including
Particulate Matter (PM10), Sulphur Dioxide (SO2),
Nitrogen Dioxide (NO2), Carbon Monoxide (CO),
Ammonia (NH3), Ozone (O3), PM2.5,
Benzo(a)pyrene (B(a)P), Lead (Pub), and Nickel
(Ni) at selected locations. The objectives of the
National Ambient Air Quality Monitoring
Programme (NAMP) include: Evaluating the status
and trends of ambient air quality, assessing
compliance with prescribed ambient air quality
standards, identifying cities where national
standards for air quality are not met, Gathering the
knowledge and insights required for formulating
preventive and corrective measures. Figure 2 shows
the Adopted Methodology. The Continuous
Ambient Air Quality Monitoring System
(CAAQMS) is a specialized setup housed within a
temperature-controlled container or room, equipped
with various analyzers for monitoring ambient air
pollutants in real-time. These CAAQMS stations
play a crucial role in generating the daily National
Air Quality Index (NAQI) of cities. Currently, the
network of Continuous Ambient Air Quality
Monitoring Stations (CAAQMS) is expanding
across India. Presently, there are 296 CAAQMS
stations covering 148 cities in 22 States and 4 Union
Territories. Under the Continuous Ambient Air
Quality Monitoring System (CAAQMS), various
pollutants including Particulate Matter (PM10 &
PM2.5), Sulphur Dioxide (SO2), Nitrogen Dioxide
(NO2), Ammonia (NH3), Carbon Monoxide (CO),
Ozone (O3), and Benzene (C6 H6) are monitored at
all locations. Additionally, CAAQM stations are
equipped with sensors to measure meteorological
parameters such as Wind Speed, Wind Direction,
Ambient Temperature, Relative Humidity, Solar
Radiation, and Rainfall. But the meteorological
datas we have collected from the NASA website as
per availability.
2.3 Calculation of Air Quality Index
Various methods are utilized worldwide to calculate
air quality values. The Air Quality Index (AQI) is
computed for specific locations to assess pollution

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levels, with classification into six categories: good
(0-50), satisfactory (51-100), moderate (101-200),
poor (201-300), very poor (301-400), and severe
(>400) as delineated in Table 1. Table 2 provides the
breakpoint concentrations for the four primary
pollutants. Daily air quality data is aggregated
monthly and annually. Sub-indices for each pollutant
concentration are determined, with the final AQI for
a location derived from the maximum sub-index
calculated. The AQI for each concentration is
computed using Equation (1), while the final AQI is
determined using Equation (2)
AQIp=𝐼𝐻−𝐼𝐿
𝐵𝑃𝐻−𝐵𝑃𝐿(CP - BPL)+IL Eq (1)
AQI = Max (S02, NO2, PM2.5, PM10 (2)
Table 1 Applied Tools and Utilization
Tool
Version
Utilised for
ArcGIS
10.7.1
For mapping
Q-GIS
3.36.0
For interpolated spatial
mapping
Tableau
2024.1
For temporal analysis
Mendeley
1.19.5
For references
Data collection
Data arrangements
Mapping and analysis
Figure 2 Adopted Methodology for This Study
Table 2 Showing The Ranges Used for Calculations of AQI by Using Concentrations Concentrations
AQI
RANGE OF CONCENTRATION (µg/m3)
SO2
NO2
PM 2.5
PM 10
401-500
1600+
400+
250+
250+
301-400
801-1600
281-400
121-250
121-250
201-300
381-800
181-280
91-120
91-120
101-200
81-380
81-180
61-90
61-90
51-100
41-80
41-80
31-60
31-60
0-50
0-40
0-40
0-30
0-30

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Table 3 Showing The Ranges Used for Determination of Pollution Zone
AQI RANGE WITH CATEGORIZATION AND HEALTH AND IMPACT
AQI VALUE
CATEGORY
IMPACT ON HUMAN HEALTH
0-50
GOOD
Minimal Impact
51-100
SATISFACTORY
Minor Breathing Discomfort To Sensitive People
101-200
MODERATE
Breathing Discomfort To The People With Lung, Heart Disease
201-300
POOR
Breathing Discomfort To People On Prolonged Exposure
301-400
VERY POOR
Respiratory Illness To The People On Prolonged Exposure
>400
SEVERE
Respiratory Effects Even On Healthy People
In the Above Table 3 showing the breakpoint
concentration values for each concentration which is
used to calculate the individual AQI of
concentrations. The concentration value for SO2 is
measured as 34 (ug/m3) which is ranging between 0
and 40 (ug/m3) where 0 and 40 (ug/m3) represents
the lower and upper breakpoint concentration values
respectively. Table 3 shows the ranges of AQI value
and their significant impacts on human health by
category wise where 0-50 (Good), 51-
100(Satisfactory), 101-200(Moderate), 201-
300(Poor), 301-400(Very Poor) and >400(Severe).
Table 4 Showing The 5 Year MLR Analysis Between AQI and Air Concentrations
States
Cities
Intercept
Coeff of
No2
Coeff of
So2
Coeff of
Pm10
Coeff of
Pm2.5
Significant
Rajasthan
Kota
23.043
-0.060
0.739
-0.067
-
PM10
Jaipur
17.53
-0.067
0.745
0.7875
-
PM10
Gujarat
Gandhi
Nagar
-162.106
-0.669
0.687
0.501
3.062
Pm2.5
Punjab
Ludhiana
2.94E-14
-1.0E-16
1.9E-16
1.0
-2.7E-16
PM10
Maharashtra
Pune
14.545
0.630
0.09
0.432
-
PM10 , NO2
Mumbai
17.655
0.406
0.06
0.60
-
NO2 , PM2.5
Chhattisgarh
Durg
10.947
0.441
0.558
0.488
0.214
PM10,NO2,PM2.5,
SO2
Bilaspur
1.42E-14
-5.33E-17
2.733E-17
1
-
PM10
Andhra
Pradesh
Gunter
-1.40E-14
-3.30E-17
1.01E-17
1
-5.30E-17
PM10
Sikkim
Namchi
0.1983
0.023
-0.042
0.99
-
PM10,SO2
Odisha
Angul
0.7430
0.2583
-0.471
0.856
0.242
PM10,PM2.5
Bhubanes
war
3.5183
0.3709
0
0.7860
0.2391
PM10,PM2.5
2.4 Multi Linear Regression Model (MLR)
Multiple linear regression is utilized to establish a
mathematical connection among multiple variables.
In essence, MLR investigates how numerous
independent variables relate to a single dependent
variable. Our study employed MLR to assess the
significance Table 4 of data from 2017 to 2022, as
well as to analyze the relationship between air
quality and meteorological factors. [5] The multiple
linear regression model can be described as shown
in Equation 3. In the linear equation (Eq.3), where Y
represents the AQI value for observation i and x

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denotes the meteorological variables, a stand for the
intercept term, bm represents the regression coefficient for meteorological factor m, and ε is the
error term.
Table 5 Showing the MLR analysis of AQI with meteorological parameters of combine years
States
Location
Intercepts
Temp
coefficient
Surface
press.
coefficient
Specific
humidity
coefficient
Wind speed
coefficient
Andhra Pradesh
Gunter
-1101.67
-0.52
11.62
0.62
1.90
Punjab
Ludhiana
8522.88
-00.97
82.21
-13.46
-73.41
Chhattisgarh
Durg
-2185.39
0.09
22.84
-0.36
15.16
Sikkim
Namchi
-373.87
0.54
4.64
-0.40
-2.66
Maharashtra
Pune
96.63
-2.14
1.26
-5.56
-0.92
3. Results and Discussion
In 2016 CPCB changes the formula which is mostly
used currently not only in India but all over the
world. The formula may deviate some extends in
different countries but the measured pollution level
is nearly same with less error.
1.1 Data Analysis
The data we have collected are analyzed in two ways
as spatial and temporal analysis to show the
variations.
1.2 Spatial Analysis
Spatial analysis is defined as the process of studying
entities by assessing, evaluating and modelling
spatial data features such as locations, attributes and
their relationships that reveal the geography. We
have done the spatial analysis of concentrations from
2016 to 2021 by using the QGIS software (QGIS
3.36.0). From the analysis of concentrations, we
analyzed that the North(N) and N-E areas are more
polluted than other areas. Figure 3 In the year 2019
fig (5) it shows higher values compared to other
years. It also shows that the concentrations decreased
in the year 2020 as compared to other previous and
post years. Air quality in India’s northeast state is
worsening and while still much better than pollution
hotspots in other parts of the country. So far air
pollution is largely seen as a crisis of the Indo
Gangetic plains (North Indian plains), [7]
particularly in winter when Delhi and several cities
in Uttar Pradesh and Haryana find themselves in the
lists of the worlds most polluted cities. Air in the
northeast States, in the popular imagination, is less
befouled due to the region's topography that is less
conducive to fossil-fuel led industrialization and
geographical isolation. According to the latest report
2021-22 (CPCB) North and East India remained the
most polluted regions, with North India experiencing
a significant deterioration in air quality compared to
the previous winter, while East India showed signs
of improvement.
1.3 Temporal Analysis
In our temporal analysis, we examine the correlation
between AQI and meteorological factors. We
selected four sites, including coastal areas like
Bhubaneswar and Pune, and non-coastal regions like
Uttar Pradesh and Rajasthan and as a result we
concluded that from temporal analysis, [6] it is
observed that there is a decrease in AQI as wind
speed, temperature, and specific humidity increase.
Given that temperature, specific humidity, and wind
speed are higher in coastal areas, the AQI tends to be
higher in noncoastal regions and lower in coastal
regions.
1.4 MLR Analysis of AQI with
Meteorological Parameters
Here we have done the MLR analysis of AQI vs
Meteorological parameters of month wise average
data for combined years from 2016 to 2020,
accordingly we got the intercept and coefficient
values of corresponding parameters.
Y= a+b1X1+b2X2+b3X3+b4x4+Ƹ Eq(3)

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1.5 Correlation Analysis
The correlational method involves looking for
relationships between variables. Correlation
coefficients are used to assess the strength and
direction of the linear relationships between pairs of
variables. Here we have correlated AQI with
meteorological parameters and we got the value of r
(coefficient of correlation/Pearson coefficient).
Table 6 Pearson Coefficient(R) Values of Different States That Has Taken (Month Wise)
States
Years
r(temp)
r(sp)
r(rh)
r(ws)
Gujarat
2018
-0.35
0.43
-0.5
-0.31
2019
0.01
0.05
-0.41
-0.11
2020
NA
NA
NA
NA
2021
-0.48
0.68
-0.58
-0.51
Punjab
2018
-0.02
0.16
-0.61
-0.53
2019
-0.47
0.53
-0.3
-0.43
2020
-0.45
0.52
-0.64
-0.32
2021
-0.6
0.63
-0.63
-0.01
Andhrapradesh
2018
-0.39
0.36
-0.3
-0.46
2019
-0.17
0.4
-0.26
-0.33
2020
NA
NA
NA
NA
2021
-0.42
-0.65
0.69
-0.06
Chattisgarh
2018
0.26
0.07
-0.3
-0.16
2019
-0.015
-0.01
0.07
0.19
2020
-0.19
-0.05
0.11
0.14
2021
-0.43
0.33
-0.18
-0.23
Rajasthan
2018
-0.38
0.63
-0.83
-0.62
2019
-0.28
0.7
-0.95
-0.3
2020
-0.44
0.68
-0.72
-0.51
2021
-0.44
0.68
-0.72
-0.51
Above table showing the values of Pearson
coefficient(r) for different states as given in the
table 6. From here we got that the
temperature(temp), relative humidity(rh) and wind
speed(ws) has a negative correlated(r=-ve) impact
on AQI where surface pressure has a positive
correlated impact on AQI (r=+ve). Here the data
analyzed are between AQI with meteorological
parameters in month wise.
Figure 4 Showing the ArcGIS Mapping of AQI in Year Wise

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Above Figure 4 showing the representation of some
AQI value of some different locations for the year
2019,2020 and 2021for the November month. All
the data used are averaged to year wise then by using
ArcGIS software we mapped the locations according
to CPCB standards.
1.6 Spatial Distribution of SO2(Ug/M3)
Figure 5 Showing spatial analysis of SO2concentrations in year wise
The spatial distributions of SO2 concentrations (in
µg/m³) from 2016 to 2021, as depicted in the Figure
5, reveal varying levels of pollution. The color
scheme categorizes concentrations into different
ranges: 0-5, 5-10, 10-15, 15-25, and >25, with red
indicating the highest concentration in a given
location. Analysis of the Figure 6 suggests that the
north-western (N-W) and north-eastern (N-E)
regions exhibit higher pollution levels compared to
other areas. In 2019, notably high values are
observed across the monitored locations. However,
in 2020, there was a significant decrease in
concentrations, which correlates with the global
impact of the COVID-19 pandemic. The restrictions
imposed during the pandemic likely contributed to
the reduction in pollution levels. Subsequently, in
2021, concentrations rebounded, indicating a return
to pre-pandemic levels or possibly even higher. The
same analysis has done for NO2 concentrations.
These observations indicate a potential fluctuation in
NO2 concentrations over the years, with specific
attention drawn to the higher levels witnessed in
2016 and 2017. The subsequent decline in 2020
followed by an increase in 2021 suggests a dynamic
interplay of various factors influencing NO2
emissions and dispersion. It's crucial to delve deeper
into the underlying causes behind these trends,
considering factors such as industrial activities,
vehicular emissions, and meteorological conditions.
Additionally, [8] policy interventions and mitigation
strategies may need to be revisited and reinforced,
particularly in regions with consistently elevated
NO2 levels like Delhi, to address air quality
concerns effectively. Further analysis incorporating
additional datasets, alongside an exploration of
contributing factors, could provide valuable insights
for informed decision-making aimed at improving
air quality and environmental health. Concentration
ranges were segmented into <12, 12-24, 24-36, 36-
48, 48-60, and >60 µg/m³, each represented by
distinct colors in the Figure 7. The same spatial
distributions have done for PM10 concentrations
(µg/m³) from 2016 to 2021, segmented into distinct
concentration ranges. Regions situated to the north
exhibit comparatively higher levels of PM10
pollution when compared to other areas. This
suggests a spatial concentration pattern, possibly
influenced by various local factors such as industrial

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activities, traffic density, and geographical features.
There appears to be a decreasing trend in PM10
concentrations over the years under study. This
reduction may indicate the effectiveness of pollution
control measures or other environmental
interventions implemented during this period. PM10
concentrations were categorized into distinct ranges
(<50, 50-100, 100-150, 150-200, 200-250, and >250
µg/m³), with each range represented by a specific
color in the Figure 8. This segmentation aids in
visualizing the severity of pollution levels across
different areas. These observations underscore the
importance of ongoing efforts to monitor and
mitigate air pollution, particularly in regions
experiencing elevated PM10 concentrations.
Implementing targeted interventions, such as
emissions regulations, urban planning strategies, and
public awareness campaigns, can contribute to
sustained improvements in air quality and public
health. Further analysis, incorporating additional
data sources and exploring potential drivers of PM10
pollution, can provide deeper insights into the
observed trends and inform evidence-based
policymaking aimed at protecting the environment
and human well-being.
1.7 Temporal Analysis
Temporal analysis is the examination of data and
events across various time intervals, such as hours,
days, weeks, months, or years. This analytical
approach enables the study of patterns, trends, and
relationships over time, shedding light on event
frequency, consistency, and temporal dependencies.
Figure 8 Temporal Variation of AQI and Meteorological Parameters for The Year 2019 And 2020
In our temporal analysis, we investigated the
relationship between Air Quality Index (AQI) and
meteorological factors across four distinct sites,
encompassing both coastal and non-coastal regions.
The selected sites include Bhubaneswar and Pune
representing coastal areas, and Uttar Pradesh and
Rajasthan representing non-coastal regions.
Utilizing Tableau Software, we analyzed variations
in AQI and meteorological parameters over the years
2019 and 2020. Temperature Impact: We observed a
negative correlation between temperature and AQI
levels. As temperature increased, AQI tended to
decrease. This suggests that higher temperatures
may contribute to lower AQI levels. Wind Speed
Influence: Similarly, we found a negative correlation
between wind speed and AQI. Higher wind speeds
were associated with lower AQI levels, indicating
that increased wind dispersal might lead to reduced
air pollution concentrations. Specific Humidity
Dynamics [9] Our analysis also demonstrated a
negative correlation between specific humidity and
AQI. Higher specific humidity levels correlated with
lower AQI values, suggesting that increased
humidity may aid in pollutant removal from the
atmosphere. Regional Disparities: We observed that
coastal regions such as Bhubaneswar and Pune

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exhibited lower AQI levels compared to non-coastal
regions like Uttar Pradesh and Rajasthan. This
finding aligns with the notion that coastal areas tend
to benefit from higher temperatures, wind speeds,
and specific humidity levels, which contribute to
improved air quality. Overall, our findings suggest
that meteorological factors play a significant role in
shaping AQI levels, with coastal regions
experiencing comparatively lower pollution levels
due to favorable meteorological conditions. These
insights underscore the importance of considering
both geographical and meteorological factors in air
quality management strategies tailored to specific
regions. By leveraging temporal analysis techniques
and visualizations, we can enhance our
understanding of the complex interactions between
air quality and meteorology, ultimately informing
more effective decision-making and intervention
strategies to mitigate air pollution.
4. Impact of Air Pollution in Human Health
Exposure to air pollution, whether short- or long-
term, poses significant health risks, encompassing a
spectrum of diseases such as stroke, chronic
obstructive pulmonary disease, and various cancers
including those affecting the trachea, bronchi, and
lungs. The International Agency for Research on
Cancer has identified PM2.5, a common component
of air pollution, as a primary contributor to cancer.
Recent global assessments have revealed that
prolonged exposure to air pollution can impact
virtually every organ in the body, exacerbating
existing health conditions and complicating overall
well-being. Children and adolescents are especially
susceptible to the adverse effects of air pollution due
to their ongoing physical development and maturing
immune systems. Unfortunately, their limited ability
to influence air quality policies leaves them
particularly vulnerable. Among the pollutants, sulfur
dioxide (SO2), largely emitted from the combustion
of fossil fuels and sulfur-containing materials, poses
a significant threat. Its presence can harm trees,
impede plant growth, and damage delicate
ecosystems and water bodies. Moreover, it
contributes to respiratory ailments and can worsen
pre-existing heart and lung conditions.
Conclusion
In our study, we conducted an extensive analysis
examining the intricate relationship between air
pollutant concentrations and various meteorological
factors such as wind speed, temperature, and
humidity. Through multiple regression analysis
Table 5, we observed significant findings regarding
PM10 values across many areas. Notably, in coastal
regions, both PM10 and PM2.5 exhibited
significances, with p-values below 0.05 (p < 0.05).
Conversely, in non-coastal areas, NO2 demonstrated
significance alongside PM10 and PM2.5. Globally,
coastal regions are frequently affected by sea-land
breeze phenomena, influencing air quality
significantly. This phenomenon involves alternating
land and sea breezes induced by temperature
disparities between land and water surfaces. Our
analysis underscores the substantial impact of
particulate matters on Air Quality Index (AQI),
emphasizing their pivotal role in air pollution
dynamics. highlights the persistent air pollution in
northern regions, particularly evident in Delhi
throughout the year. In 2023, Delhi earned the
dubious distinction of being the world's most
polluted capital city, according to a report by a
Swiss-based air-quality monitoring group. India,
including Delhi, ranked as the world's third-most
polluted country after Bangladesh and Pakistan, as
reported by IQ Air. Air pollution in India stems from
various factors, including rapid industrialization and
lax enforcement of environmental regulations. Poor
industrial practices and inadequate pollution-control
measures exacerbate the problem. Additionally,
unchecked urban development and construction
activities contribute to escalating pollution levels.
Delhi's air quality deteriorates significantly during
winter due to multiple factors, including the burning
of crop residues by farmers in neighboring states,
industrial and vehicular emissions, stagnant wind
conditions, and firecracker usage during festivals.
Consequently, the government has implemented
measures such as temporary school closures to
mitigate health risks associated with toxic air.
Furthermore, Beguserai in northern India and
Guwahati in the northeast emerged as the world's
most polluted cities, underscoring the widespread

International Research Journal on Advanced Engineering
and Management
https://goldncloudpublications.com
https://doi.org/10.47392/IRJAEM.2024.0200
e ISSN: 2584-2854
Volume: 02
Issue: 05 May 2024
Page No: 1480-1490
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nature of India's air pollution crisis. Also we got the
relation trend between AQI and meteorological
parameters from where we got that temperature,
humidity and wind speed are negatively correlated
withAQI where surface pressure is positively
correlated with AQI.
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