Seasonal variations of particulate matter in two size fractions (PM_2.5 and PM₁₀) and their relationship with meteorological parameters in Lahore, Pakistan

Abstract

Air pollution is rising globally in megacities, due to the human population pressure index, expanding automotive industries, factories, and the burning of fossil fuels that have a detrimental influence on the climate and public health. The current research work explores the study of fine and coarse mode particulate matter (PM_2.5 and PM₁₀) and the impact of meteorological parameters such as temperature, pressure, relative humidity, rainfall, and wind speed in Lahore (Pakistan) during 2019. The value of PM_2.5 ranged from 11.55 to 187.77 μg m⁻³ with a mean value of 55.49 ± 32.85 μg m⁻³. Similarly, the value of PM₁₀ varied from 8.57 to 334.26 μg m⁻³, with an average value of 101.49 ± 60.78 μg m⁻³ during the study period. The coefficients of determination between PM_2.5 and PM₁₀ had higher values during autumn (R² = 63.88%), followed by R² = 57.84% (winter), R² = 27.25% (spring), and R² = 22.41% (summer), respectively. Regression of PM_2.5 and PM₁₀ with temperature shows a negative correlation during winter and autumn while a positive correlation was observed during spring and summer seasons. Similarly, PM_2.5 and PM₁₀ are positively correlated with pressure in all four seasons. Throughout all four seasons, relative humidity (RH) has a positive association with PM_2.5 and a negative correlation with PM₁₀. Similarly, in winter, summer, and fall, RF is found to have a negative correlation with both PM_2.5 and PM₁₀. Wind speed shows a negative correlation with both size fractions of PM during all seasons.

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Environmental significance

This research investigates the correlation between PM i.e. PM_2.5 and PM₁₀, and meteorological parameters in urban areas of Lahore during 2019. Air pollution is comparatively more severe in Asian countries, in general, and in South Asian countries, including Pakistan in particular. Our research can enhance the precision of air pollution forecasting in various climatic scenarios and provide a deeper understanding of the mechanisms that mitigate pollution by phasing out high-energy utilization in mobile sources and industries, and by using clean energy sources instead of conventional energy to reduce emissions from local and non-local sources of air pollutants.

Introduction

Air pollution is one of the most significant environmental challenges across the globe. In urban areas, more than 80% of the population is exposed to air quality that does not meet the standards established by the World Health Organization (WHO). Approximately seven million deaths per year around the globe are due to the invisible killer i.e. outdoor and indoor air pollution.^1–3 Atmospheric Particulate Matter (PM) consists of tiny particles (solid or liquid matter) with aerodynamic diameters ranging from nanometers to several micrometers, suspended in the Earth's atmosphere from hours to weeks.³ Based on their size (aerodynamic diameter), they are classified as ultrafine PM₁ (≤1 μm), fine PM_2.5 (≤2.5 μm), and coarse PM₁₀ (≤10 μm).^4–6 PM greatly affects human health, climate change, ecosystems, and economic development.^7,8 Airborne PM can originate from various sources, including geogenic, anthropogenic, and biogenic.^4,9 Air pollution may occur due to stagnant meteorological conditions, open burning incidents, transboundary interference, and emissions from vehicles, factories, etc. in regions.^4,6,9–12

Both PM_2.5 and PM₁₀ pose a significant challenge to the environment, mostly because of their adverse effects on human health. Fine particulate matter, i.e. PM_2.5, which could penetrate deep into the respiratory tract, subsequently increases the mortality risk from respiratory infections and diseases, lung cancer, and cardiovascular disease.^3,7 Particulate matter could become more toxic if it is produced by the formation of certain gases such as sulfur dioxide and nitrogen oxide. Particulate matter could also affect urban and regional air quality, and reduce visibility and it has a significant impact on global climate change.^4,6,9,10

Previous studies have demonstrated that both PM_2.5 and PM₁₀ are influenced by meteorological parameters. Researchers have identified a correlation between PM and meteorological parameters across various geographical locations.^11,13,14 The relationships between air pollutants like ambient PM and meteorological parameters not only vary with geographical locations but also with seasons.^{5,10,11,14–16}

In recent winters, hundreds of kilometers of fog have frequently covered northern India and north-eastern Pakistan. The region—including north-eastern India and adjacent areas of Punjab in Pakistan—experiences a high-pressure system during winter, which leads to dry conditions and low wind speeds. These factors create an environment that promotes the accumulation of atmospheric pollutants.¹⁷

Lahore, the capital of Punjab, Pakistan, is the country's second-largest city and a key hub for culture, history, and economy. Located 217 meters (712 feet) above sea level, it has a hot semi-arid climate with scorching summers, a monsoon season, and dry, mild winters. Many researchers have explored the relationship between ambient PM levels and meteorological factors in Lahore's environment.^6,12,17 However, this study explores the association between PM_2.5 and PM₁₀, and meteorological parameters in Lahore's urban areas throughout the different seasons of 2019. Air pollution is comparatively more severe in Asian countries, in general, and in South Asian countries, including Pakistan in particular. Our research can enhance the precision of air pollution forecasting in various climatic scenarios and provide a deeper understanding of the mechanisms that mitigate pollution by phasing out high-energy utilization of mobile sources and industries, and utilization of clean energy resources instead of conventional energy to reduce emissions from local and non-local sources of air pollutants.

Materials and methods

Description of the study area: Lahore (31.32°N; 74.22°E) is the historical, cultural, administrative, and capital city of Punjab (Pakistan), with a population of about 12 million (Fig. 1). It is located near the Indian border along the River Ravi and experiences a variable climate during the whole year. It remains excessively hot and humid in the summers and dry-cold during winter. Lahore has diverse sources of pollution like clay bricks, and manufacturing industries like steel, chemicals, motor vehicles, engineering products, pharmaceuticals, and construction materials. Due to intense pollution, the urban environment of Lahore was chosen as the study area in the present research work. Sampling was conducted on the rooftop of the Center of Excellence in Solid-State Physics, Punjab University.

Fig. 1 Map of the study area.

Research area meteorological conditions

Throughout the study period, the temperature ranged from a minimum value of 10 °C (Dec) to a maximum value of 38 °C (June) with an average value of 24.80 ± 7.55 °C (Fig. 2(a–e)). Likewise, the maximum and minimum pressure during the study periods was 1022.7 hPa (Jan) and 991 hPa (July) with an average value of 1007.22 ± 8.36 hPa. Relative humidity has a minimum value of 16% (May) and a maximum value of 100% (Dec), with a mean value of 51.66 ± 18.65%. Rainfall (RF) varied from 0 mm (Nov) to 74.80 mm (July) with an average value of 2.48 ± 9.10 mm during the study period. Similarly, the maximum recorded wind speed was found to be 22 knots during December, and a minimum of 0 knots during March with an average value of 2.54 ± 2.90 knots.

Fig. 2 Representation of meteorological parameters during the study.

Sample collection

A low-volume sampler (LVS) (Leckel, Germany) was used for the collection of particulate matter in two-size fractions of fine PM_2.5 and coarse PM₁₀. Sampling was carried out at a constant flow rate of 16.00 liters per minute daily from 1 January to 31 December (2019).

Particulate matter was collected using 47 mm diameter quartz fiber filter paper (Tissuquartz, Pall Life Sciences). Pre-weighed and conditioned quartz fiber filters were inserted into the sampler's filter holder and tightly fastened.

The loaded filters were kept in a refrigerator at 4 °C to prevent contamination and moisture absorption. From the difference between loaded and unloaded filter masses, we found the ambient gravimetric mass concentrations of PM. A single-pan top-loading digital balance (Denver, Model TB-2150) with a precision of ±10 μg was used to weigh each filter paper before and after sampling.^4,6,11

Analysing the interaction between airborne particulate matter and meteorological variables using regression

The particulate matter in two size fractions (fine, and coarse) and meteorological parameters data for one year, from January 1 to December 31, 2019, were arranged using an Excel worksheet in Microsoft (2018). The data were organized into four seasons: winter (December, January, and February), spring (March, April, and May), summer (Jun, July, and August), and autumn (September, October, and November).

Descriptive statistics were used to analyze the data, and Graph Pad version 8 was used to display the results shown in Fig. 2. Regression analysis was used to determine the association between particulate matter and meteorological parameters, with PM as the dependent variable on the Y-axis, and the meteorological variables are independent variables along the X-axis (Fig. 3–7). R-software (version 4.01) and R-Studio were utilized to conduct the regression analysis and other plots.

Results and discussion

Mass concentration of PM_2.5 and PM₁₀

In the current study, the annual average concentrations of both PM_2.5 and PM₁₀ in Lahore city were found to be 55.24 ± 32.85 μg m⁻³ and 101.09 ± 60.78 μg m⁻³, respectively (Table 1). Likewise, the mean concentrations of fine particulate matter (PM_2.5) were found to be 79.29 ± 42.37, 42.26 ± 16.06, 58.09 ± 21.80, and 54.84 ± 34.96 μg m⁻³ during winter, summer, spring, and autumn, respectively. While coarse mode PM₁₀ has average concentrations of 104.94 ± 53.75, 135.83 ± 63.86, 105.95 ± 57.67, and 87.80 ± 57.46 μg m⁻³ during winter, summer, spring, and autumn, respectively. The mass concentrations of both PM_2.5 and PM₁₀ were found to be higher than the average threshold values set by the World Health Organization (WHO)¹ i.e. PM_2.5 = 25 μg m⁻³ and PM₁₀ = 50 μg m⁻³ during all seasons.² Seasonal average PM_2.5 concentrations peaked in winter, followed by spring, autumn, and summer. Similarly, PM₁₀ concentrations were found to be highest in the summer, followed by spring, winter, and autumn, respectively. The highest investigated values in winter for PM_2.5 exceeded the NEQSAA level by 2.2 times and the WHO level by 5.28 times, while PM₁₀ exceeded the WHO threshold limit by 2.12 times in summer.¹

Table 1 Mass concentrations of fine (PM_2.5) and coarse (PM₁₀) particulate matter in Lahore during the study period

Seasons	PM2.5 (μg m^−3)					PM10 (μg m^−3)
	Winter	Summer	Spring	Autumn	Yearly	Winter	Summer	Spring	Autumn	Yearly
a Different superscripts show significant differences based on t-tests (p-value < 0.01).
Mean	79.29^a	42.26^c	58.09^b	54.84^b	55.24^b	104.94^b	135.83^a	105.95^b	87.8^c	101.09^b
±SD	42.37	16.06	21.8	34.94	32.85	53.75	63.86	55.67	57.46	60.78
Min	17.72	11.55	15.28	14.69	11.55	20.59	223.47	8.57	20.69	8.58
Max	187.77	114.29	123.08	177.35	187.77	299.01	334.26	277.38	299.01	334.26
Range	170.05	99.59	107.8	162.66	176.22	278.42	310.79	268.81	278.42	325.72
Median	79.22	39.48	56.57	38.43	77.45	106.35	131.21	110	65.02	80.01
^aWHO (24 h per annual)	5.28	2.82	3.87	3.65	11.04	2.09	2.71	2.12	1.76	5.05
^a(15/05) for PM2.5		^a(50/20) for PM10

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Numerous variables like dense population, the usage of three- and four-wheeled vehicles, transboundary interference, and metrological characteristics are the causes of high PM concentration at the sampling site. Temperature inversions also play an important role during winter causing particulate matter to disperse and scatter less, raising the ambient air mass concentrations.¹¹

The ambient air quality in Lahore and other locations across the world was established by earlier research. According to a study conducted between 2007 and 2011, the average summertime PM_2.5 concentrations at Town Hall and Township (Lahore) were 99 and 115 μg m⁻³, respectively.¹⁷ According to Alam et al.¹² the average PM₁₀ concentration in the Lahore metropolitan region during the spring was 406 μg m⁻³ (254 to 555 μg m⁻³).

The exceedance factor analysis revealed that the health effects of PM exposure are particularly alarming, especially for populations in urban areas. PM₂.₅ has received significant attention due to its ability to penetrate deep into the respiratory system and enter the bloodstream. Prolonged exposure to PM₂.₅ has been linked to various respiratory and cardiovascular diseases, including asthma, chronic obstructive pulmonary disease (COPD), lung cancer, heart attacks, and strokes. Similarly, PM₁₀ has been extensively studied for its detrimental effects on human health. Research has established strong correlations between airborne PM₁₀ and increased mortality rates, hospital admissions, and respiratory complications, particularly in industrialized and highly polluted urban areas.

Table 2 provides a comparative view of fine PM_2.5 and coarse PM₁₀ mass concentrations in urban environments across different study sites in Pakistan, as well as other countries including India, China, Iran, Korea, Bangladesh, and Nigeria. The PM concentrations are higher in Lahore as compared to other locations like Swat, Pune, Shanghai, Ulsan, and Port Harcourt. On the other hand, the present site mass concentration was lower as compared to Peshawar, Beijing, and Wuhan.

Table 2 Comparison of PM_2.5 and PM₁₀ mass concentrations with different locations in Pakistan and abroad

Country	City	Characteristics	PM2.5 (μg m^−3)	PM10 (μg m^−3)	Reference
Pakistan	Lahore	Urban (winter)	79.29 ± 42.37	104.94 ± 53.75	Present study
	Peshawar	Monthly (2016)	286 ± 00	638 ± 00	4
	Swat	Max (2019)	56.00 ± 00	78.00	11
	Lahore	Mean 24 h (2018)	170 ± 54.90		18
	Lahore	November (2019–21)	271.80 ± 00		19 and 20
	Faisalabad		297.20 ± 00
	Gujranwala		201.60 ± 00
India	Pune	Yearly (2012)	73.60	121.40	21
	Delhi	Yearly (2013–14)	108.0 ± 86.5	233.0 ± 124.6	22
	Delhi	Yearly (2012)	135.16 ± 41.34	—	13
	Delhi	Yearly (2016–18)	107.32 ± 71.06	210.61 ± 95.90	22
	Varanasi	Yearly (2019–20)	111.34 ± 00	180.70 ± 00	23 and 24
Iran	Tehran	Yearly (2016)	104.30	39.50	25
China	Beijing	Yearly (2013–14)	87.00	109.40	14 and 26
	Shanghai	-do-	56.10	79.90
	Wuhan	Yearly (2013–16)	80.00	118.00
Korea	Ulsan	Yearly (2011–12)	20.90	38.50	27
Nigeria	Port Harcourt	Yearly (2019)	58.80	164.50	28
Bangladesh	Khulna	Mean 24 h (2018–20)	302 ± 109.89	415 ± 184.01	29
	Dhaka	Yearly (2013–15)	76.34 ± 34.12	136.25 ± 68.94	30

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Statistical analysis of PM_2.5 and PM₁₀: regression and frequency distribution with meteorological parameters

First, the temperature in 2019 was divided into six intervals, 10–15, 15–20, 20–25, 25–30, 30–35, and 35–40, as shown in Fig. 3(a). The high-temperature interval (35–40 °C) has the lowest frequency of 6% and the 30–35 °C interval has the highest frequency of 29%. In the range of 20 and 35 °C, the plot illustrates a declining trend in fine PM concentration.

Fig. 3 Frequency/4 distributions (%) of five metrological parameters: (a) temperature, (b) pressure, (c) relative humidity, (d) rainfall and (e) wind speed.

Also as shown in the plot the greatest average concentration of fine PM_2.5 is 145 μg m⁻³ in the 20–25 °C interval, and the lowest value of PM_2.5 is 73 μg m⁻³ at a temperatures of 15 °C. Additionally, the lowest PM₁₀ concentration of 35 μg m⁻³ occurs at 30 °C, while in the temperature range of 15–20 °C, a maximum mass concentration of 87 μg m⁻³ was observed. In cities like Lahore, Delhi, and Beijing, winter pollution spikes due to frequent temperature inversions, trapping pollutants. In the winter, when temperatures are between 10 and 15 degrees Celsius, inversions would prevent vertical mixing, causing PM_2.5 to accumulate, whereas in the summer, warmer temperatures would prevent inversions, allowing pollutants to disperse and wet depositions.^30–32

Likewise, Li et al.³² also noted that fine mode (PM_2.5) concentration decreases from 42 to 10 μg m⁻³ due to strong air convection in the region, our results also agreed with Usman et al.¹¹ and Elminir et al.³³

The pressure ranges between 995 and 1020 hPa, as seen in Fig. 3(b). As pressure increases, PM_2.5 and PM₁₀ concentrations often do as well. The center generates an upward wind that helps disperse pollution because surrounding air masses with high pressure gravitate toward it when pressure is low. Conversely, when high pressure predominates near the surface, downward airflow occurs in the middle zone. This condition prevents the dilution and dispersion of pollutants, increasing the ambient PM mass concentrations.³³

The frequency distribution with respect to RH is shown in Fig. 3(c). The largest frequency, 26%, occurs in the 50–60 interval, while the lowest frequency, 2%, occurs in the 90–100 range. Examining the findings, it can be shown that as relative humidity rises, the concentrations of PM_2.5 and PM₁₀ fall.^11,32,34,35

The rainfall frequency distribution is shown in Fig. 3(d), with 87% of rainfall occurring at intervals less than 1 mm and 2% occurring at intervals more than 45 mm. Increased rainfall often results in lower mass concentrations of both fine and coarse mod PM. PM_2.5 and PM₁₀ concentrations significantly decreased from 60 to 39 μg m⁻³, particularly when rainfall varied from less than 1 to 1–15. Rainfall can effectively remove particulate matter from the atmosphere.^{11,32,34–36}

Similarly, Fig. 3(e) illustrates that 84% of wind speed occurs in the intervals between 0 and 5 knots. While PM₁₀ concentrations rise and subsequently fall in the final interval, PM_2.5 concentrations fall as wind speed increases. The transport of PM from nearby contaminated regions shows no dilution effect, leading to increased mass concentrations of coarse PM.^15,16

Seasonal regression analysis of PM_2.5 and PM₁₀ with meteorological parameters

During winter, as shown in Fig. 4(a and g), fine (PM_2.5) and coarse (PM₁₀) particulate matter exhibit a significant negative association with temperature having coefficients of determination of R² = 41.44%, and R² = 17.46% for fine PM_2.5, and coarse PM₁₀ particulate matter, respectively. High temperatures promote strong convection currents, which transport PM swiftly and effectively through the atmosphere, causing PM to disperse quickly.^11,28,32,35

Fig. 4 Linear regression of PM_2.5 and PM₁₀ with meteorological parameters in winter, i.e., (a) PM_2.5 vs. temperature, (b) PM_2.5 vs. pressure, (c) PM_2.5 vs. relative humidity, (d) PM_2.5 vs. rain fall, (e) PM_2.5 vs. wind speed, (f) PM_2.5 vs. PM₁₀, (g) PM₁₀ vs. temperature, (h) PM₁₀ vs. pressure, (i) PM₁₀ vs. relative humidity, (j) PM₁₀ vs. rain fall, (k) PM₁₀ vs. wind speed, and (l) PM₁₀ vs. PM_2.5.

Because of the high ambient temperature in winter, Nguyen et al.²⁷ found that nitrate and ammonium concentrations were much lower than in summer. As a result, a significant decrease in the ambient PM occurs as the temperature rises throughout the winter. Similar findings were reported by Li et al.³² for Hong Kong's wintertime temperature and PM_2.5, which showed a correlation coefficient of 0.044.

Likewise, Fig. 4(b and h) reveal that fine mode PM_2.5 has a coefficient of determination of R² = 31.05% with pressure, and coarse PM₁₀ has a moderate value of R² = 12.25% respectively. Particulate matter (PM) at the sampling location falls because of a converging updraft at low pressure that promotes the dispersion of PM from the ground into the air. On the other hand, a downdraft that happens at high pressure slows the upward flow of PM, which causes an accumulation of particles in the atmosphere.^32,37

Additionally, Fig. 4(c and i) show that PM_2.5 has non–significant relationships with RH having a coefficient of determination R² = 0.45%, while PM₁₀ also has different value of R² = 1.20%. Akyüz et al.³⁸ found that relative humidity and PM in two size fractions have weak and negative correlations throughout winter and summer in Zonguldak, Turkey, from January to December 2007, having non-significant r-values of −0.1108 for PM_2.5 and −0.230 for PM₁₀ in the winter season.

Kliengchuay et al.³⁹ reported that there was a negative correlation between PM₁₀ and relative humidity in Thailand (2009–2017). Increased atmospheric humidity promotes aqueous processing to produce bigger particles and causes hygroscopic growth to produce larger particles. Relative humidity may also remove air particles, according to similar findings by other studies.^14,15

Furthermore, Fig. 4(d–j) illustrate that PM and rainfall have a moderately negative coefficient of determination R² = 2.88% for fine PM_2.5, and R² = 3.82% for coarse PM₁₀. Similar results were reported by Nguyen et al.²⁷ in Ulsan, Korea, indicating that throughout the winter (2011–12), a negative correlation was observed between PM and RH, with values of −0.56 for PM_2.5 and −0.55 for PM₁₀ . Liu et al.¹⁵ similarly reported comparable non-significant results for PM and RF in Beijing: r = −0.03 for PM₁₀ and r = −0.01 for PM_2.5 during the winter season in their nine-year study.

Rainfall has two main effects on the ambient PM in the atmosphere. First, moist deposition of PM results from interactions between raindrops and PM through microphysical processes such as impact and adsorption. Second, the quantity of PM suspended in the air is greatly decreased by rainfall.^11,31

The coefficient of determination is moderately negative for fine PM_2.5 having the value of R² = 4.48% with wind speed; on the other hand, R² = 4.10% for PM₁₀, as shown in Fig. 4(e and k). Usman et al.¹¹ also documented in a similar study conducted in Swat (Pakistan) that a negative regression exists between the fine and coarse mod PM with wind speed in the winter season. PM disperses due to wind speed in the sampling site and as a result, the decrease in PM occurs in both fine and coarse modes. The findings of Zhou et al.⁴⁰ also show that the correlation between PM_2.5 and PM₁₀, and WS during winter in Beijing and Nanjing (China) are −0.42 and −030 for PM_2.5 and −0.43 and −0.32 for PM₁₀.

Likewise, the regression analysis between both PM fractions (PM_2.5 and PM₁₀) showed a coefficient of determination of R² = 57.84% during winter (Fig. 4(f and l)). Yadav et al.²¹ in Pune, India, reported the coefficient of determination R² = 45% between the two size fractions of PM during winter (2011–12).

Li et al.³¹ also noted a strong positive regression between PM₁₀ and PM_2.5 in Chengdu during 2009–11, similar to our findings. Similarly, Trivedi et al.⁴¹ noted, in Delhi, a strong positive correlation exists between two size fractions of PM in winter seasons, having a value of 0.95. Similarly, the findings of Zhou et al.¹⁶ show a strong positive correlation between PM₁₀ and PM_2.5 having a coefficient of determination R² = 92%, in Northern China and for Southern China R² = 87%, during winter (2012).

As seen in Fig. 5(a and g), the coefficient of determination between PM and temperature throughout the summer season reveals non-significant values of R² = 0.25% for fine PM_2.5, and R² = 2.83% for coarse PM₁₀, respectively. The results of Zhang et al.¹⁴ for three Chinese megacities during 2013–14 correlations of −0.26 for PM_2.5 and −0.06 for PM₁₀ in Beijing, 0.10 for PM_2.5 and 0.39 for PM₁₀ in Shanghai, and 0.37 for PM_2.5, and 0.48 for PM_10, in Guangzhou which are consistent with our findings for the same season. In summer, higher temperatures significantly raise sulfate concentrations while lowering nitrate concentrations, according to Nguyen et al.;²⁷ consequently increasing the secondary PM concentration.

Fig. 5 Linear regression of PM_2.5 and PM₁₀ with meteorological parameters in summer, i.e., (a) PM_2.5 vs. temperature, (b) PM_2.5 vs. pressure, (c) PM_2.5 vs. relative humidity, (d) PM_2.5 vs. rain fall, (e) PM_2.5 vs. wind speed, (f) PM_2.5 vs. PM₁₀, (g) PM₁₀ vs. temperature, (h) PM₁₀ vs. pressure, (i) PM₁₀ vs. relative humidity, (j) PM₁₀ vs. rain fall, (k) PM₁₀ vs. wind speed, and (l) PM₁₀ vs. PM_2.5.

Likewise, as depicted in Fig. 5(b and h), in the summer season, R² = 1.25% for fine PM_2.5 and R² = 0.005% for coarse PM₁₀ respectively, with respect to pressure. The findings of Chithra et al.⁴² also show a poor association between pressure and PM (R² = 0.16–0.21) in Chennai, India. Large pressure fluctuations usually result in convection, which spreads contaminants globally instead of affecting their microscopic movements. Liu et al.¹⁵ also documented similar findings in Beijing in their nine-year study (2004–12) having a non-significant correlation of r = −0.05 for PM₁₀ and r = −0.04 for PM_2.5 during the summer season.

Furthermore, as shown in Fig. 5(c and i), the regressions between PM and relative humidity are again non-significant and distinct having R² values of 1.52% for PM_2.5, while for PM₁₀ R² = 1.44%, respectively. These results agree with the findings of Yang et al.¹⁰ having correlation values of 0.30 and 0.06, respectively in north and central China.

Akyüz et al.³⁸ relative humidity and PM in two size fractions have weak and negative correlations in summer, having non-significant r-values of −0.053 for PM_2.5 and −0.104 for PM₁₀ in the summer season. Kliengchuay et al.³⁹ noted in Thailand from 2009 to 2019, a negative correlation between RH and PM₁₀ having r = −0.26. The findings of Liu et al.¹⁵ also revealed similar results in summer, reporting a correlation of 0.11 for PM₁₀ and 0.26 for PM_2.5 with RH in Beijing (China).

Besides, in the hot summer season the relationship between PM and rainfall is non-significant, with determination coefficients of R² = 2.91% for fine PM_2.5, and R² = 3.80% for coarse PM₁₀ as shown in Fig. 5(d and j). Li et al.³¹ pointed out that rainfall at the sample location significantly affects the elimination of contaminants from the atmosphere. According to Mkoma et al.⁴³ when daily rainfall was within the 5 mm range, the mass concentration of PM₁₀ was continuously low during rainstorm events and did not exceed 20 μg m⁻³.

In addition, fine and coarse PM versus wind speed in the summer season has an R² value of 4.50%, for fine PM_2.5, and while an R² value of 4.10% for PM₁₀ respectively, as shown in Fig. 5(e and k).

Zhang et al.¹⁴ documented that in Shanghai China a negative correlation exists between PM and WS having values of R² = −0.68 for fine PM_2.5, while R² = −0.58 for PM₁₀. Similarly, the findings of Usman et al.¹¹ also documented a negative correlation having a correlation coefficient of −0.34 for PM_2.5 and −0.39 for PM₁₀ during a study conducted in Swat Pakistan (2019), and with the increase in WS, particulate matter disperses in the atmosphere and hence reduces its concentration causing negative correlation.

Likewise, in Fig. 5(f and l) the regression between PM_2.5 and PM₁₀ is significantly positive having a coefficient of determination of R² = 22.41%, While for PM₁₀ and PM_2.5 is 22.41% during summer. Li et al.³¹ also documented r = 0.92 between two size fractions of PM in Chengdu (China) and the findings of Yadav et al.²¹ in Pune (India) show a strong positive regression having values of R² = 69% between PM fractions during summer (2011–12). Our findings are consistent with previous results of Trivedi et al.⁴¹ Additionally, Zhou et al.¹⁶ noted a strong positive association between PM₁₀ and PM_2.5 R² = 68%, Northern China and Southern China R² = 97%, during summer (2012) analogous to our results.

Throughout the spring season, as depicted in Fig. 6(a and g) a non-significant negative value of R² = 1.30% was observed for fine PM_2.5 and a strong positive value of R² = 22.31% was observed for coarse PM₁₀. Our results are in line with the findings of Zhang et al.²⁶ in Beijing, China, during the spring, PM_2.5 has a negative correlation (r = −0.03) and PM₁₀ has a positive correlation (r = 0.21) with temperature.

Fig. 6 Linear regression of PM_2.5 and PM₁₀ with meteorological parameters in spring, i.e., (a) PM_2.5 vs. temperature, (b) PM_2.5 vs. pressure, (c) PM_2.5 vs. relative humidity, (d) PM_2.5 vs. rain fall, (e) PM_2.5 vs. wind speed, (f) PM_2.5 vs. PM₁₀, (g) PM₁₀ vs. temperature, (h) PM₁₀ vs. pressure, (i) PM₁₀ vs. relative humidity, (j) PM₁₀ vs. rain fall, (k) PM₁₀ vs. wind speed, and (l) PM₁₀ vs. PM_2.5.

Likewise, PM and pressure revealed a non-significant positive value of R² = 0.07% for PM_2.5 while a significant negative value of R² = 15.00% for PM₁₀ as clear from Fig. 6(b–h). The findings of Chithra et al.⁴² also revealed a weak association (R² = 0.0237) between PM and pressure. Liu et al.¹⁵ also documented similar findings in Beijing (2004–12), in their nine-year research work that the association of PM and pressure is significant during spring having correlation coefficients for PM_2.5 (r = −035) and PM₁₀ (r = −0.31), respectively.

Furthermore, in Fig. 6(c–i) again the regression of PM versus relative humidity has different values of R² = 0.85% for fine PM_2.5 while, R² = 9.58% for PM_10. Similar findings were observed by Zhang et al.¹⁴ in Shanghai (−0.33 for PM10) and Beijing (0.65 for PM_2.5).

As shown in Fig. 6(d and j), the non-significant values for PM_2.5 are R² = 0.49% and for PM₁₀ is R² = 0.33% with RF in the spring season. In the same way, Li et al.³¹ found that while PM_2.5 concentrations decreased less than PM₁₀, PM₁₀ concentrations dropped sharply during periods of intense rain. While there was an insignificant positive association between PM_2.5 and rainfall having values of r = 0.06 and p = 0.733.

Additionally, the non-significant value of the coefficient of determination for fine PM_2.5 is R² = 0.79%, and for coarse PM₁₀ R² = 1.22% demonstrating a negligible regression between PM and WS during the spring season as shown in Fig. 6(e and k). However, there was a considerable correlation (p < 0.05) between wind speed and PM₁₀. Even when the city's ambient air is clean, the wind from the surrounding areas may bring pollutants to the sample location, which is the exception cause of the positive correlation between PM₁₀ and WS.¹⁰ Similarly, Liu et al.¹⁵ also documented that the correlation is not significant for PM₁₀ having r = −0.07, while significant for PM_2.5 having r = −0.38 during spring.

Likewise, both PM_2.5 and PM₁₀ show a significant positive coefficient of determination (R² = 27.25%, for PM₁₀ and 27.25% for PM_2.5) during spring as shown in Fig. 6(f and l). Li et al.³¹ noted that the correlation between two size fractions of PM each other during the research period in Chengdu (China) had a correlation coefficient (r = 0.92). Yadav et al.²¹ also documented that the coefficient of determination was strongly positive between PM₁₀ and PM_2.5 having R² = 61%, in Pune (India), similar to our results. Trivedi et al.⁴¹ also documented the significant correlation between two size fractions of PM, revealing that the sources of these PM were the same and originated from the same regions in the case of different sources. Similarly, Zhou et al.¹⁶ investigated a strong positive correlation between PM₁₀ and PM_2.5 having an R² value of 54%, Northern China and Southern China R² = 88%, during spring (2012) similar to our results.

In a similar way in the autumn season a negative coefficient of determination is observed between PM and temperature, with R² = 42.00% for PM_2.5, and R² = 43.00% for PM₁₀, as shown in Fig. 7(a and g). The research work of Usman et al.¹¹ Mukta et al.³⁶ and Onuorah et al.²⁸ revealed that due to a rise in temperature convection plays its role because of rapid dispersion of ambient PM and hence causes a reduction in the mass concentration. Our findings agree with the results of Zhang et al.¹⁴ in Guangzhou (China) having correlation values between PM and temperature of r = −0.38 for fine PM_2.5 and r = −0.33 for coarse PM₁₀ respectively, during the autumn season.

Fig. 7 Linear regression of PM_2.5 and PM₁₀ with meteorological parameters in autumn, i.e., (a) PM_2.5 vs. temperature, (b) PM_2.5 vs. pressure, (c) PM_2.5 vs. relative humidity, (d) PM_2.5 vs. rain fall, (e) PM_2.5 vs. wind speed, (f) PM_2.5 vs. PM₁₀, (g) PM₁₀ vs. temperature, (h) PM₁₀ vs. pressure, (i) PM₁₀ vs. relative humidity, (j) PM₁₀ vs. rain fall, (k) PM₁₀ vs. wind speed, and (l) PM₁₀ vs. PM_2.5.

Likewise, the R² value of fine PM_2.5 is 34.42%, while the R² value of 29.49% for PM₁₀ between PM and pressure as shown in Fig. 7(b and h). The results of Li et al.³¹ show that during autumn the correlation between PM_2.5 and pressure is positive having r = 0.42, in Hong Kong. Similarly in Beijing, Liu et al.¹⁵ also noted in their nine-year research the correlation coefficients are r = −0.18 for PM₁₀ and r = −0.14 for PM_2.5 during the autumn season.

Besides, PM in both fine and coarse modes shows a non-significant negative correlation with relative humidity having R² values of 0.06% for PM_2.5, and R² = 9.60% for PM₁₀, as shown in Fig. 7(c and i). The findings of Onuorah et al.²⁸ in Nigeria showed that the correlation values of relative humidity are r = −0.01 for PM_2.5 and r = 0.04 for PM₁₀, which are non-significant values. This suggests that as relative humidity increases, PM_2.5 concentrations experience a slight reduction. This minor decrease occurs because particles absorb moisture, increasing their mass, which subsequently promotes dry deposition.

Similarly, a negative correlation having values of r = −0.43 for PM_2.5 and r = −0.53 for PM₁₀, during autumn was reported in Guangzhou.^14,40 Likewise, the findings of Liu et al.¹⁵ also revealed r = 0.40 for PM₁₀ and r = 0.47 for PM_2.5 in Beijing China during autumn.

As shown in Fig. 7(d and j), a non-significant negative correlation is observed having values of R² = 2.27% for fine PM_2.5, and R² = 0.33% for coarse PM₁₀ with rainfall during the autumn season. Similarly, the findings of Li et al.³¹ recognized that PM₁₀ concentrations intensely drop during heavy rain, although fine PM_2.5 concentrations declined to a lesser extent as compared to coarse PM₁₀. The washout process during rainfall is responsible for the reduction of PM concentration in the atmosphere.

Similarly, the coefficient of determination between fine and coarse mod PM and wind speed revealed non-significant negative and non-significant positive values of 0.005%, and 1.20% respectively, as shown in Fig. 7(e and k). The ambient air at the study site is clean, but the wind from the surrounding areas might transport contaminants to the sampling location, due to this a positive association between PM₁₀ and WS exists.¹⁰

Likewise, during autumn both fine PM_2.5 and coarse PM₁₀ show strong positive regression having a coefficient of determination value of R² = 64.00%, while R² = 27.00% between PM₁₀ and PM_2.5, as revealed in Fig. 7(f and l). In Pune, India, the findings of Yadav et al.²¹ showed strong positive regressions between fine and coarse PM fractions having an R² value of 61%, for the whole study period. Our results are consistent with the previous results of Zhou et al.¹⁶ that showed a strong positive correlation between PM₁₀ and PM_2.5 with an R² = 85%, and R² = 98% in Northern China and Southern China, during autumn (2012).

Fig. 8 reveals wind rose plots for winter, summer, spring, and autumn seasons during 2019. Most air masses reach the facility from a westerly or south-easterly direction. The wind rose for winter indicates that winds predominantly originate from the west (W) direction. The most frequent wind speed range observed was 6–10 knots, with occasional gusts reaching 10 knots. Periods of calm winds accounting for 7.30% contribute to the accumulation of pollutants; a slow speed of 40% (2–3.9 knots) also localized pollutants accumulation, and a high wind speed (4–10 knots), having a frequency of 52.70% of the total time during winter, promotes the movement of air masses across regions and facilitates the long-range transport of pollutants. Moreover, in summer the winds originate from the south-easterly and west directions. Periods of calm winds were 0%, slow winds occurred 38.33% (2–4 knots) of the time, and high speed (4–10 knots) accounted for 61.66% of the total duration during summer. Similarly in spring, periods of calm winds were 20.77%, slow speed was 19.48% (2–4 knots), and high speed (4–10 knots) had a frequency of 59.74%, and winds predominantly originate from the west (W) direction. Furthermore, the wind rose for autumn indicates that winds predominantly originate from the north-westerly and south-westerly directions. Periods of calms were 14.30%, slow speed 42.85% (2–4 knots), and high speed (4–10 knots) having a frequency of 42.86% of total time during the autumn season. The findings of Pawar et al.⁴⁴ also agree with our results.

Fig. 8 Wind rose plot for (a) winter, (b) summer, (c) spring and (d) autumn seasons (2019) at 5.00 PM (PST).

There are also some limitations and uncertainties in our study. First, the relationships between PM concentration and meteorological factors are very complex, and we cannot figure out the specific interaction process only through regression analysis. More multivariate analyses are needed. In addition, physical models such as the Chemical Transport Model (CTM) can also simulate and describe the complex relationship better. In the future study, we will pay more attention to multivariate analysis and CTM; the combination of these two different methods would also be very interesting and worth further research.

Conclusions

The present study investigated the relationship between meteorological parameters and ambient PM (PM_2.5 and PM₁₀) concentrations during 2019 in Lahore, Pakistan. The average mass concentration of fine PM_2.5 was highest in winter, followed by spring, autumn, and summer, whereas PM₁₀ concentration was highest in summer, followed by spring, winter, and autumn. The reported concentrations were above the threshold levels of WHO, i.e., the yearly average of PM_2.5 is 11 times and PM₁₀ is 5 times the interim targets of WHO. Regression between PM_2.5 and PM₁₀ revealed that the autumn season had the highest coefficient of determination (R² = 63.88%), followed by the winter season (R² = 57.84%), spring (R² = 27.25%), and summer (R² = 22.41%).

Temperature, pressure, relative humidity, rainfall, and wind speed were among the meteorological parameters that affected the mass concentration of particulate matter PM_2.5 and PM₁₀. In winter and summer, the regressions between PM_2.5 and PM₁₀, and climatic factors including temperature and rainfall were negative; however, it was weak and negative during spring and autumn. Both fine and coarse PM have positive regressions with pressure, RH, and WS in winter but have a weak positive correlation during spring. Similarly, a negative correlation of PM_2.5 and PM₁₀ was found to exist with RH and WS during autumn.

Therefore, we concluded that both meteorological parameters and local anthropogenic activities have an impact on the PM mass concentrations at the research site. These findings can also be used to develop plans to reduce PM pollution in the area for the protection of human health and the environment. Integrated management involving citizens and organizations (governmental and non-governmental) must play its role in controlling aerial pollution to improve the overall health of the densely populated area of Lahore, Pakistan. There is a need for the proper air quality management of vehicular and industrial sources as well as emission factors should be calculated for the proper control and abatement of air pollution sources. Moreover, legislation and political dialogues between the nations (Pakistan and India) are key to controlling the transboundary interference of air pollution along the border as most of the pollutants are transported through atmospheric air movement.

Data availability

Data will be made available on reasonable request from the corresponding author.

Author contributions

Shafiq Ahmad: writing – original draft, visualization, validation, software, resources, methodology, investigation, formal analysis, data curation, conceptualization. Bahadar Zeb: writing – review & editing, visualization, validation, supervision, software, resources, project administration, methodology, funding acquisition, formal analysis. Farooq Usman: writing – review & editing, visualization, validation, software, resources, project administration, methodology, formal analysis, conceptualization. Khan Alam: writing – review & editing, visualization, validation, software, resources, project administration, methodology, formal analysis, conceptualization. Iftikhar Ahmad: writing – review & editing, visualization, validation, software, resources, project administration, methodology, formal analysis, conceptualization. Allah Ditta: writing – review & editing, visualization, validation, software, resources, project administration, methodology, formal analysis, conceptualization. All authors read and approved the final manuscript.

Conflicts of interest

There are no conflicts to declare.

Notes and references

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