INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
Air Quality Monitoring and Spatial Distribution Mapping of  
Particulate Matter (PM1 and PM2.5) Using Inverse Distance  
Weighting (IDW) At Srinagar City, Kashmir  
Nikhil Savio1* and Farooq Ahmad Lone2  
1Department of Earth and Environmental Sciences, Parul Institute of Applied Sciences, Parul University, Vadodara,  
391760, Gujarat, India  
2Division of Environmental Science, Faculty of Horticulture Sher-e-Kashmir University of Agricultural Sciences and  
Technology of Kashmir, Srinagar, 190025, J&K, India  
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000146  
Abstract: About 27% of the air pollution in India is contributed by vehicles. The continuous increase in the vehicular number, the  
traffic congestion, and adulteration of fuels in vehicles and improper management of traffic is causing increased air pollution due  
to vehicular movement. The present study was carried out at 5 locations in Srinagar city with 4 locations being located in high  
traffic areas of the city and one location situated in the outskirts of the city where traffic movement was less during a period of 1  
year from June 2019 to May 2020. PM1 and PM2.5 particulate matter was monitored at every fortnight on the said locations for a  
period of 1 year using Aerosol Mass Monitor AEROCET-831. The monitoring period coincided with the COVID-19 period too.  
The readings showed significant differences seasonally and also due to changes in traffic flow due to COVID 19 pandemic. The  
observed data for concentration of particulate matter was mapped using IDW mapping to see the changes in concentration of  
pollutants with the change in location.  
Keywords: Particulate Matter, PM1, PM2.5, Inverse Distance Weightage, Air pollution, vehicular pollution, COVID19.  
I. Introduction  
Air pollution has been one of the major concerns of modern world. The Central Pollution Control Board (CPCB) is India's primary  
air pollution monitoring organisation, with 731 monitoring stations spread around the country (CPCB, 2018). The main causes of  
air pollution are vehicle exhaust emissions and flue gases, which are released from industries, refineries, and other sources. The  
diesel exhaust is more cancer-causing. In India, it's thought that diesel exhaust has twice the cancer-causing potential of gasoline  
pollution (Bhandarkar, S., 2013). According to the Global Air Quality report 2022 published by the Swiss company IQAir, 39  
Indian towns were among the top 50 most polluted cities in the world. Data on PM2.5 air quality from 7323 cities in 131 nations,  
regions, and territories are included in this publication. Around 30,000 regulatory air quality monitoring stations and low-cost air  
quality sensors were employed to get the data for this research (IQAir, 2022). To comprehend the emission sources, residence time,  
and dispersion of pollutants in the atmosphere, an analysis of the vertical distribution of such pollutants is required. The majority  
of pollutants are released from ground-based sources, are typically contained in the Planetary Boundary Layer, and vary in height  
during the day depending on atmospheric conditions (Samad, A. et al.2020). The Environmental Performance Index (EPI) of 2020  
showed that India ranked 168 among 180 countries. The researches at Yale and Columbia university say that India’s decarbonization  
agenda needs to accelerate and the country faces a number of serious environmental health risks, including poor air quality. Besides  
the major concern of increasing population, the exponential increase in vehicular number following the population explosion is  
commendable in our country. The ministry of Roadways and Transport have shown the registered number of vehicles in the country  
to have increased from 0.3 million in 1951 to 253 million in 2017. The Compounded Annual Growth Rate of registered vehicles  
was 10.1% in the country for last 10 years outpacing the CAGR of national highways of 5.54% (Anonymous, 2016-2017).  
Srinagar the largest city and the summer capital of the Indian Union Territory of Jammu and Kashmir lies on the banks of Jhelum  
river and famous Dal and Anchar lakes within geographical coordinates of 34˚5’24”N 74˚47’24”E. The annual average summer  
temperature and winter temperature stands at 23.3˚C and 3.2˚C respectively with the annual precipitation of 710mm. The 2011  
census showed an amassed population of 1273312 in the urban area. This increased population is bringing in more anthropogenic  
causes for increasing air pollution like increased vehicular population, increased biomass burning, lack of proper traffic  
management, lack of disposal of old vehicles, etc. The city alone had registered more than 2.91 lakh vehicles as on 31st March,  
2017.  
II. Materials and Methods  
The control site included a more tranquil area of the university grounds where the traffic movement is very little compared to the  
city limits and the control site is also at far off limits from the city. The study site for the current research was selected based on a  
greater degree of vehicular movement during the peak hours in the city, traffic congestions, major crossroads or junctions, higher  
populations, etc. Four of the five contaminated areas were inside the city limits and were among the five locations chosen for an  
annual air sample collection from June 2019 to May 2020. The Shalimar campus of SKUAST-K, which is 15 kilometres to the  
north-east of Lalchowk, the main commercial centre of Srinagar, was chosen as the fifth location and used as a control. Figure 1 to  
Figure 5 shows the different locations of monitoring throughout the whole year like Shalimar, Dalgate, Jehangir Chowk, Parimpora  
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and Pantha Chowk respectively. One-minute average values at each location were used to collect the data throughout a 12-month  
period from June 2019 to May 2020 (with three replications for each sampling at each location). The second and fourth weeks of  
every month saw the collection of two samples. The sampling was done three times, from 9:00 to 10:30 in the morning, 1:00 to  
2:30 in the afternoon, and 4:30 to 6:00 in the evening, to better understand how the pollutant load varied during the day at the  
relevant locations. A time frame for sampling was chosen based on the number of vehicles using these locations. The quantity of  
vehicles and their movement varied according to the time of day. The samples were collected, and the particle matter was measured  
using an aerosol mass monitor (AEROCET 831, Met One Inc., Washington, USA). Figure 6 depicts the photograph of the  
instrument AEROCET 831. The AEROCET 831 Aerosol Mass Monitor operates on the theory of light scattering. When a particle  
passes through the detection chamber that only allows single particle sampling, the laser light is scattered by the particle. A photo  
detector detects the scatting light. By analyzing the intensity of the scattering light, instrument can deduce the size of the particle  
(Remer et al., 2005). Also the number of the particle counts can be deduced by counting the number of detecting light on the photo  
detector. The advantage of this approach is that a single analyzer can be used to detect particles with different diameters  
simultaneously. The instrument counts particle sizes in 7 different size ranges then uses a proprietary algorithm to convert count  
data to mass measurements in the unit µg/m3. Fundamentally the AEROCET 831 calculates a volume for each detected particle and  
then assigns a standard density for the conversion. The concentration of the particulate is determined by dividing the mass of the  
SPM by the volume of air sampled (WHO, 1976). The standard density value is augmented by the K-factor setting for each  
measurement range (PM1 and PM2.5). These K-factors can be modified with comet software or with the SK serial port command.  
K-factor values should be empirically derived via comparison with a reference unit. If a reference unit is unavailable, the  
recommended K-factor setting is 3.0.  
Figure 1: Monitoring site at Shalimar  
Figure 2: Monitoring site at Dalgate  
Figure 3: Monitoring site at Jehangir Chowk  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
Figure 4: Monitoring site at Parimpora  
Figure 5: Monitoring site at Pantha Chowk  
Figure 6: Aerosol Mass Monitor (AEROCET-831)  
III. Results and Discussion  
Particulate Matter 1-micron size (PM1)  
The table 1 provided shows PM1 (Particulate Matter 1 micron) pollution levels at different locations over the course of 12 months.  
PM1 refers to fine particulate matter with a diameter of 1 micron or less, which is a serious air pollutant that can have significant  
health impacts, especially for the respiratory system.  
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There is a clear seasonal variation in PM1 pollution levels, with the highest levels occurring in late 2019 (October to December),  
followed by a sharp decline in early 2020 (particularly in April). This suggests that pollution might be influenced by seasonal  
factors, such as heating during colder months or increased vehicular traffic. Besides COVID 19 lockdown restrictions reduced the  
pollution level due to low vehicular movements. While all locations show similar peaks in December 2019, the absolute values  
vary slightly from one location to another, possibly indicating differences in local factors like traffic congestion, industrial activities,  
or geography (proximity to natural pollution sources). PM1 levels in Shalimar start at 33.37 µg/m3 in June 2019, decrease slightly  
in the following months, then peak significantly at 108.90 µg/m3 in December 2019, before dropping to 19.07 µg/m3 in May 2020.  
The sharp rise in December and the subsequent fall suggests that local conditions (possibly related to weather, local sources of  
pollution, or heating activities) contributed to higher pollution during the winter months along with the vehicular movements.  
Dalgate shows a higher range of pollution, starting at 37.57 µg/m3 in June 2019, peaking at 90.83 µg/m3 in December 2019, and  
dropping to 32.60 µg/m3 in May 2020.  
The consistent rise in pollution through the latter half of 2019, peaking in December, could be related to increased vehicular  
emissions or industrial activity. The drop in April-May 2020 could indicate a reduction in human activity (e.g., COVID-19  
lockdowns or reduced traffic). Jehangir Chowk had PM1 relatively high, starting at 36.00 µg/m3 in June 2019 and peaking at 92.20  
µg/m3 in December 2019, followed by a sharp drop to 34.90 µg/m3 in May 2020. Similar to Dalgate, there seems to be an increase  
in pollution towards the end of 2019, with a possible environmental or anthropogenic cause. The sharp drop could be tied to external  
factors like less vehicular movement or industrial shutdowns. In Parimpora, PM1 levels begin at 25.73 µg/m3in June 2019, rise  
steadily to 95.33 µg/m3 in December 2019, and then decrease to 26.33 µg/m3 by May 2020. As with other locations, Parimpora  
shows a significant spike in the winter months, particularly in December. This could indicate local heating sources or the build-up  
of dust due to lower temperatures, which are common contributors to PM1 levels in many urban areas. In case of Pantha Chowk,  
PM1 levels here range from 34.03 µg/m3 in June 2019 to 87.80 µg/m3 in December 2019, with a decline to 30.50 µg/m3 in May  
2020. The trend is similar to the other locations, showing an increase in pollution levels during the second half of 2019, followed  
by a decrease in early 2020.  
December 2019 consistently shows the highest PM1 levels across all locations, which is likely due to increased heating (burning of  
wood, coal, or other fuels), vehicle emissions, and possibly fog or temperature inversions, all of which can trap particulate matter  
in the air during colder months. April 2020 sees a notable drop in pollution levels across all locations. This decline could be tied to  
reduced human activity as a result of the COVID-19 lockdowns, which led to lower traffic, industrial activity, and overall air  
pollution. Alternatively, it could be related to changing weather conditions or even the transition to spring, when dust and particulate  
matter may disperse more easily due to higher temperatures or rainfall. The sharp increase in pollution towards the end of 2019  
could be due to a combination of factors such as: Higher vehicular traffic (due to the holiday season), increased use of solid fuels  
for heating during colder months, stagnant atmospheric conditions (e.g., temperature inversions) that trap pollutants near the surface.  
Table 1: Concentration of PM1 (µg/m3) at different locations in Srinagar city on monthly basis  
Jun-  
19  
Aug-  
19  
Sep-  
19  
Oct-  
19  
Nov-  
19  
Dec-  
19  
Jan-  
20  
Feb-  
20  
Mar-  
20  
Apr-  
20  
Location  
Jul-19  
May-20  
Shalimar  
Dalgate  
33.37  
37.57  
28.40  
40.50  
28.43  
41.77  
46.00  
50.30  
54.17  
73.03  
94.73 108.90 51.23  
48.80  
52.60  
29.83  
46.33  
15.10  
26.30  
19.07  
32.60  
87.87  
85.60  
82.77  
83.97  
90.83  
92.20  
95.33  
87.80  
65.13  
67.47  
64.23  
62.60  
Jehangir  
Chowk  
36.00  
34.80  
33.37  
32.47  
40.13  
35.67  
37.63  
50.60  
41.43  
45.23  
72.60  
63.90  
59.70  
56.87  
53.13  
55.70  
44.43  
42.97  
41.07  
28.40  
21.23  
24.47  
34.90  
26.33  
30.50  
Parimpora 25.73  
Pantha  
34.03  
Chowk  
C.D.  
1.37  
0.41  
2.19  
0.66  
1.25  
0.38  
1.12  
0.34  
1.61  
0.49  
0.65  
0.20  
1.49  
0.45  
0.65  
0.20  
2.56  
0.77  
0.79  
0.24  
2.23  
0.67  
2.68  
0.81  
SE(m)  
As per table 2, the PM1 levels in Shalimar during summer (30.03 µg/m3) are the lowest across all locations and seasons. Dalgate,  
Jehangir Chowk, Parimpora and Pantha Chowk all show moderate levels of PM1, with Dalgate having the highest summer pollution  
(39.93 µg/m3). Summer shows generally lower PM1 levels compared to other seasons, likely due to higher temperatures and greater  
air circulation, which help disperse pollutants. The presence of rain in some regions may also aid in the removal of particles from  
the air. PM1 levels increase significantly in autumn, with Dalgate having the highest levels (70.40 µg/m3), followed by Jehangir  
Chowk (69.60 µg/m3), Shalimar (64.97 µg/m3), Parimpora (62.70 µg/m3), and Pantha Chowk (62.97 µg/m3). The increase in autumn  
pollution could be due to dry conditions, reduced rainfall, and the onset of falling leaves, which can contribute to dust and particulate  
matter. Additionally, the transition to cooler weather might trap pollutants near the ground (due to temperature inversions), leading  
to higher concentrations of PM1.  
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Table 2:Concentration of PM1 (µg/m3) at different locations in Srinagar city on seasonal basis  
Location Season  
Summer  
Autumn  
Winter  
Shalimar  
30.03  
Dalgate  
39.93  
70.40  
69.50  
35.07  
53.77  
Jehangir Chowk  
36.97  
Parimpora  
31.60  
Pantha Chowk  
34.70  
Mean  
34.65  
66.13  
70.17  
30.91  
64.97  
69.60  
62.70  
62.97  
69.63  
72.17  
70.90  
68.67  
Spring  
21.33  
35.93  
30.20  
32.03  
Mean  
46.50  
53.63  
48.87  
49.60  
Factor  
C.D  
SE(d)  
0.47  
0.53  
1.05  
0.23  
0.26  
0.52  
Season(S)  
Location(L)  
S×L  
Winter sees high PM1 levels across all locations, with Jehangir Chowk (72.17 µg/m3) recording the highest levels. PM1 levels in  
winter are higher than in autumn at most locations, indicating that pollution is particularly elevated during this season. This could  
be due to heating activities (e.g., burning of wood, coal, and biomass), increased vehicular emissions due to colder temperatures,  
and temperature inversions, which trap pollutants close to the ground. PM1 levels during spring are the lowest after summer, with  
Shalimar (21.33 µg/m3) showing the lowest PM1 levels overall. While still higher than summer, spring shows moderate PM1 levels,  
with the lowest values observed in Shalimar and Parimpora (31.60 and 30.20 µg/m3, respectively). The highest spring levels are in  
Jehangir Chowk (35.93 µg/m3) and Dalgate (35.07 µg/m3). Spring generally sees moderate PM1 levels due to improved air  
circulation and occasional rainfall, which helps reduce airborne particulate matter. The drop in pollution could also be linked to a  
reduction in heating and other combustion-based activities compared to winter. Dalgate and Jehangir Chowk consistently show the  
highest annual PM1 levels (53.77 and 53.63 µg/m3, respectively), which may indicate more significant local pollution sources such  
as traffic congestion, industrial activities, or construction.  
Shalimar has the lowest overall PM1 levels (46.50 µg/m3), indicating that this area may have fewer pollution sources or better air  
quality due to factors such as green spaces, better traffic management, or more favourable atmospheric conditions. Parimpora and  
Pantha Chowk show moderate PM1 levels throughout the year, with their highest readings occurring in winter and autumn.  
The critical difference (C.D) for season is 0.47, and the standard error is 0.23. Since the seasonal differences in PM1 levels exceed  
this value, it suggests that seasonal variation in PM1 pollution is statistically significant. The mean PM1 values vary considerably  
between seasons, confirming that the time of year has a clear impact on pollution levels.  
The C.D for location is 0.53, and the standard error is 0.26. This suggests that the differences in PM1 levels between locations are  
also statistically significant. Locations like Dalgate and Jehangir Chowk tend to have higher pollution levels, indicating that local  
sources of pollution (e.g., traffic, industrial emissions) contribute significantly to variations in air quality. The C.D for the interaction  
between season and location is 1.05, and the standard error is 0.52. This indicates that the seasonal patterns of PM1 pollution differ  
across locations, with some locations seeing larger seasonal variations than others. For example, Shalimar shows a significant  
reduction in pollution during spring, whereas Jehangir Chowk and Dalgate experience sustained high levels of PM1 across all  
seasons, particularly in winter.  
Table 3:Annual average day time concentration of Particulate matter PM1 (µg/m3) based on time period in Srinagar city  
PM1  
Locations  
Morning  
Afternoon  
Evening  
Mean  
Annual Readings  
46.50  
Shalimar  
52.33  
59.40  
56.90  
54.37  
54.00  
0.92  
40.43  
49.03  
50.47  
44.53  
44.73  
0.98  
46.67  
52.73  
53.60  
47.67  
50.07  
1.38  
Dalgate  
53.77  
53.63  
48.87  
49.60  
Jehangir Chowk  
Parimpora  
Pantha Chowk  
C.D.  
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SE(m)  
0.28  
0.30  
0.42  
Higher morning levels of PM1 (table 3) can be attributed to rush hour traffic and commuting patterns, which significantly contribute  
to elevated emissions from vehicles and other sources. In the early morning, lower temperatures and limited atmospheric mixing  
can also trap pollutants near the surface, leading to higher concentrations of particulate matter. In the afternoon, PM1 levels tend to  
decrease, likely due to higher temperatures and increased air dispersion, which help dilute and spread out pollutants. The reduction  
in traffic intensity during the day (after the morning rush) and increased atmospheric mixing also contribute to lower pollutant  
concentrations. Evening levels are higher than in the afternoon but lower than in the morning. This is likely due to increased  
vehicular emissions as people return home from work or activities, which could raise PM1 levels. The cooler temperatures at night  
may also lead to some atmospheric trapping of pollutants again, although less so than in the morning. This time of day often sees a  
resurgence in traffic-related emissions (Tong et al. 2020).  
Locations like Dalgate and Jehangir Chowk consistently show the highest average PM1 levels throughout the day, indicating that  
these areas may have higher levels of traffic density and potentially other sources of pollution (e.g., industrial emissions or  
construction activities). Shalimar shows the lowest mean PM1 levels, suggesting it mayhave relatively lower local pollution sources.  
The IDW maps are given to pictorially depict the change in concentration of pollutants at different locations. Below Figure 7  
pictorially depicts the pollutants concentration maps for all the months of monitoring. 12 maps representing 12 months are given  
below.  
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Figure 7: IDW maps showing variation in concentrations of pollutants (PM1) at different locations during each month from June  
2019 to May 2020.  
Particulate Matter 2.5 micron size (PM2.5)  
The data provided (Table 4) shows PM2.5 levels (in µg/m³) for various locations from June 2019 to May 2020. There is a significant  
spike in PM2.5 levels during the winter months (November 2019 to January 2020), likely due to factors like increased heating,  
reduced wind, and atmospheric inversion. PM2.5 levels generally start to decrease from February 2020 and continue lowering  
through May 2020, possibly due to improved weather conditions (e.g., more wind or rain that disperses pollutants). Besides this  
COVID-19 lockdown was a major reason for the decrease of pollutant level after March 2020. Shalimar shows moderately high  
levels from June to October 2019 (ranging between 45–87 µg/m³), with a large increase during the winter months (183.70 in  
November, peaking at 233.67 in December). In Dalgate, PM2.5 levels increase steadily throughout 2019, peaking sharply at 577.50  
µg/m³ in December 2019. This location has consistently high levels compared to other locations, indicating serious pollution issues,  
particularly in winter. Jehangir Chowk follows a similar trend to Dalgate, with a sharp increase in PM2.5 levels during the winter,  
reaching 569.77µg/m³ in December. Parimpora starts relatively low in June but sees a dramatic rise in the winter, particularly in  
December 2019, reaching 497 µg/m³. Pantha Chowk follows the same trend of rising PM2.5 during winter, peaking at 571.43 µg/m³  
in December 2019.  
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Table 4: Concentration of PM2.5 (µg/m3) at different locations in Srinagar city on monthly basis  
Jun-  
19  
Aug-  
19  
Sep-  
19  
Oct-  
19  
Nov-  
19  
Dec-  
19  
Jan-  
20  
Feb-  
20  
Mar-  
20  
Apr-  
20  
Location  
Jul-19  
May-20  
Shalimar  
Dalgate  
50.23  
62.30  
45.00  
68.07  
58.50  
82.27  
87.43 183.70 233.67 77.87  
76.80  
44.20  
81.53  
28.70  
50.13  
37.53  
65.10  
80.67 100.97 137.67 183.37 577.50 123.73 97.60  
Jehangir  
Chowk  
68.87  
70.67  
83.50  
92.40  
76.53  
65.77  
67.93  
99.40 132.30 188.60 569.77 160.17 119.40  
80.63 126.33 177.57 497.00 128.67 103.90  
90.00 117.77 166.17 571.43 126.47 121.00  
77.30  
75.10  
72.33  
51.83  
39.90  
45.43  
67.37  
52.03  
59.10  
Parimpora 50.67  
Pantha  
72.00  
Chowk  
C.D.  
10.38  
3.14  
10.56  
3.19  
1.33  
0.40  
4.36  
1.32  
5.78  
1.75  
7.64  
2.31  
40.10  
12.11  
7.60  
2.30  
8.01  
2.42  
3.06  
0.93  
3.41  
1.03  
4.34  
1.31  
SE(m)  
Table 5: Concentration of PM2.5 (µg/m3) at different locations in Srinagar city on seasonal basis  
Location Season  
Summer  
Autumn  
Winter  
Shalimar  
51.27  
Dalgate  
70.33  
Jehangir Chowk  
72.03  
Parimpora  
66.63  
Pantha Chowk  
77.43  
Mean  
67.54  
130.27  
238.97  
56.50  
117.83  
129.43  
36.80  
140.63  
266.23  
65.57  
140.07  
128.20  
243.13  
55.70  
124.60  
283.10  
272.97  
Spring  
65.50  
58.93  
Mean  
83.83  
135.69  
140.18  
123.42  
133.48  
Factor  
C.D  
SE(d)  
3.12  
3.49  
6.98  
1.54  
1.72  
3.43  
Season(S)  
Location(L)  
S×L  
All locations show a dramatic spike in PM2.5 levels during December 2019, exceeding 500 µg/m³ in some places. This is far above  
the WHO's guideline for annual PM2.5 exposure, which is 5 µg/m³, and even above the 24-hour guideline limit of 15 µg/m³. Dalgate  
and Jehangir Chowk areas experience the highest pollution, particularly during winter, posing severe health risks to the population,  
especially those with respiratory conditions. The data reflects severe seasonal pollution in most locations, particularly in winter.  
Dalgate, Jehangir Chowk, and Pantha Chowk are notably affected, with dangerous PM2.5 concentrations in December 2019. Efforts  
to mitigate pollution, especially during winter, would be crucial to improve air quality and public health in these regions (Li et al.,  
2015).  
The table 5 presents a seasonal summary of PM2.5 concentrations across five locations (Shalimar, Dalgate, Jehangir Chowk,  
Parimpora, Pantha Chowk), alongside their seasonal means and interactions. Below is an interpretation of the seasonal changes and  
the statistical factors. The mean PM2.5 level across locations is 67.54µg/m³, indicating moderate air quality. Pantha Chowk records  
the highest summer average at 77.43 µg/m³, while Shalimar is the lowest at 51.27 µg/m³. Summer has the lowest pollution levels,  
likely due to better air dispersion and less heating-related pollution. During Autumn (September–November), the average PM2.5  
rises sharply to 130.27 µg/m³, signalling a decline in air quality as colder months approach. Dalgate and Jehangir Chowk see the  
highest autumn averages (140.63 and 140.07 µg/m³, respectively), while Shalimar records the lowest (117.83 µg/m³). The rise in  
autumn pollution could be due to increased heating, crop burning and other seasonal activities. Winter (December to February) has  
the highest PM2.5 levels, with a mean of 238.97µg/m³, representing a significant increase in pollution. Jehangir Chowk records the  
highest average PM2.5 (283.10 µg/m³), followed by Pantha Chowk (272.97 µg/m³). These values reflect a critical level of pollution,  
driven by factors such as heating fuel use, atmospheric inversion, and increased combustion activities. The mean PM2.5 level during  
Spring (March-May) drops to 56.50 µg/m³, showing a significant improvement in air quality compared to winter (Mukta et al.,  
2020). The highest average PM2.5 in spring is recorded at Dalgate (65.57 µg/m³), while Shalimar has the lowest at 36.80 µg/m³.  
Spring offers improved conditions for dispersing pollutants, reducing PM2.5 levels. Besides the COVID 19 lockdown during the  
spring season added to decreasing pollution.  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
Location-Specific Patterns show that Dalgate and Jehangir Chowk consistently have the highest PM2.5 levels across all seasons,  
with winter being the worst. These locations have higher traffic or industrial activity contributing to sustained poor air quality.  
Shalimar tends to have the lowest pollution levels in most seasons, particularly in spring and summer, indicating a relatively cleaner  
environment compared to other locations. Pantha Chowk and Parimpora experience elevated PM2.5 levels, particularly in winter,  
but show improvement in the spring. The Factor C.D. value for season is 3.12 with a standard error (SE(d)) of 1.54, which suggests  
a significant seasonal effect on PM2.5 levels. Pollution levels clearly fluctuate based on the time of year. The Factor C.D. value for  
location is 3.49 with SE(d) of 1.72, indicating that different locations experience distinct levels of pollution. Some locations are  
consistently more polluted than others. The interaction effect (season and location combined) is 6.98 with an SE(d) of 3.43. This  
value suggests that certain seasons affect locations differently. For instance, while Dalgate and Jehangir Chowk experience  
particularly high spikes in winter, Shalimar and Parimpora may not see as drastic increases.  
The overall mean PM2.5 levels range from 83.83 µg/m³ in Shalimar to 140.18 µg/m³ in Jehangir Chowk. The mean across all  
locations is quite high, at 122.92 µg/m³, far exceeding the WHO's guideline of annual average for PM2.5 exposure, highlighting  
serious air quality issues. Dalgate, Jehangir Chowk, and Pantha Chowk are the most polluted areas overall. The coloured IDW maps  
below show the readings as discussed based on the concentrations of pollutants. Based on the colour patterns the pollutants are  
shown to be high and low. Table 6 verifies the observations where the readings during different time of a day are given on an  
average basis. The findings are similar to that of PM1. Figure 8 shows the IDW maps for different months of PM2.5 pollutant  
concentration.  
Table 6:Annual average day time concentration of Particulate matter PM2.5 (µg/m3) based on time period in Srinagar city  
PM2.5  
Locations  
Mean  
Annual Readings  
83.82  
Morning  
Afternoon  
Evening  
Shalimar  
98.07  
173.77  
161.63  
153.37  
163.03  
12.79  
71.67  
111.07  
119.07  
102.43  
111.23  
4.61  
81.73  
122.30  
139.83  
114.40  
126.17  
2.78  
Dalgate  
135.71  
Jehangir Chowk  
Parimpora  
Pantha Chowk  
CD.  
140.17  
123.40  
133.47  
SE(m)  
3.86  
1.39  
0.84  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
Figure 8: IDW maps showing variation in concentrations of pollutants (PM2.5) at different locations during each month from June  
2019 to May 2020.  
IV. Conclusion  
The major observations from the readings suggest that the pollutant levels in the locations of city limits were high. Baring the  
unusual spike in Shalimar during the December 2019 month which may be due to the localised burning of crops in fields, this  
location was seen to be the least polluted. Shalimar location being in the outskirts of city near to a village saw less pollution. The  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
city areas like Dalgate and Jehangir Chowk showed incredibly high pollutants level. The increased vehicular movement with less  
managed traffic showed stagnation of vehicles in thes places giving higher pollutant concentrations. Even during the COVID19  
lockdown, the locations had higher concentration of pollutants in the city as compared to the Shalimar location.  
References  
1. Anonymous, 2016-2017. Road Transport year book 2016-2017. Ministry of Road Transport & Highway Transport. Govt.  
of India.  
2. Bhandarkar, S. (2013). Vehicular pollution, their effect on human health and mitigation measures. Veh. Eng, 1(2), 33-40.  
3. CPCB (2018). Guidelines for continuous emission monitoring systems (Revision 1). New Delhi, India: Central Pollution  
Control Board, Government of India.  
4. IQAir (2022). World Air Quality Report 2022. https://www.iqair.com/world-air-quality-report.  
5. Li, R., Li, Z., Gao, W., Ding, W., Xu, Q., & Song, X. (2015). Diurnal, seasonal, and spatial variation of PM2. 5 in  
Beijing. Science Bulletin, 60(3), 387-395.  
6. Mukta, T. A., Hoque, M. M. M., Sarker, M. E., Hossain, M. N., & Biswas, G. K. (2020). Seasonal variations of gaseous  
air pollutants (SO2, NO2, O3, CO) and particulates (PM2. 5, PM10) in Gazipur: an industrial city in Bangladesh. Advances  
in Environmental Technology, 6(4), 195-209.  
7. Remer, L. A., Kaufman, Y. J., Tanre, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C.,  
Kleidman, R. G., Eck, T. F., Vermote, E. and Holben, B. N. 2005. The MODIS aerosol algorithm, products and validation.  
Journal of the Atmospheric Sciences 62: 947-973.  
8. Samad, A. et al. (2020) “Vertical Distribution of particulate matter, Black Carbon and ultra-fine particles in Stuttgart,  
Germany,”  
Atmospheric  
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11(8),  
pp.  
1441–1450.  
Available  
at:  
https://doi.org/10.1016/j.apr.2020.05.017.  
9. Tong, R., Liu, J., Wang, W., & Fang, Y. (2020). Health effects of PM2. 5 emissions from on-road vehicles during weekdays  
and weekends in Beijing, China. Atmospheric Environment, 223, 117258.  
10. WHO, 1976. Elected Methods of Measuring Air Pollutants. WHO offset publication No. 24E, World Health Organization,  
Geneva.  
Statements & Declarations  
The authors declare that no funds, grants or other support were received during the preparations of this manuscript.  
Competing Interests  
The authors have no relevant financial or non financial interests to disclose.  
Author Contributions  
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by  
Nikhil Savio. The first draft of the manuscript was written by Nikhil Savio and Farooq Ahmed Lone and all authors commented  
on previous versions of the manuscript. All authors read and approved the final manuscript.  
Ethical Approval  
Not Applicable  
Consent to Participate  
Not Applicable  
Consent to Publish  
Not Applicable  
Funding  
Not Applicable  
Data Availability Statement  
Further data will be available on request if provided the need.  
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