Investigating seasonal air quality variations consequent to the urban vegetation in the metropolis of Faisalabad, Pakistan

Urban atmospheric pollution is global problem and and have become increasingly critical in big cities around the world. Issue of toxic emissions has gained significant attention in the scientific community as the release of pollutants into the atmosphere rising continuously. Although, the Pakistani government has started the Pakistan Clean Air Program to control ambient air quality however, the desired air quality levels are yet to be reached. Since the process of mapping the dispersion of atmospheric pollutants in urban areas is intricate due to its dependence on multiple factors, such as urban vegetation and weather conditions. Therefore, present research focuses on two essential items: (1) the relationship between urban vegetation and atmospheric variables (temperature, relative humidity (RH), sound intensity (SI), CO, CO2, and particulate matter (PM0.5, PM1.0, and PM2.5) and (2) the effect of seasonal change on concentration and magnitude of atmospheric variables. A geographic Information System (GIS) was utilized to map urban atmospheric variables dispersion in the residential areas of Faisalabad, Pakistan. Pearson correlation and principal component analyses were performed to establish the relationship between urban atmospheric pollutants, urban vegetation, and seasonal variation. The results showed a positive correlation between urban vegetation, metrological factors, and most of the atmospheric pollutants. Furthermore, PM concentration showed a significant correlation with temperature and urban vegetation cover. GIS distribution maps for PM0.5, PM1.0, PM2.5, and CO2 pollutants showed the highest concentration of pollutants in poorly to the moderated vegetated areas. Therefore, it can be concluded that urban vegetation requires a rigorous design, planning, and cost–benefit analysis to maximize its positive environmental effects.

its impact on human and environmental well-being 56 .By utilizing GIS, it becomes possible to monitor pollutant emissions and track the effects of harmful airborne pollutants such as smog and dust on plant and human life, as regulated by the Environmental Protection Agency (EPA) 57 .Conservationists can leverage GIS to ensure that no further pollution occurs by monitoring these relationships and identifying the sources of pollutants 58 .GIS technology possesses the advantage of analyzing spatial data effectively and handling large spatial databases, which is particularly valuable for air pollution studies where significant amounts of data are involved, including air pollutants, wind direction, wind speed, traffic flow, solar radiation, and air temperature 59 .
Although much research has been done to evaluate the potential of urban vegetation and seasonal change on the air quality in the developed countries' residential areas but the knowledge about the effect of urban vegetation and seasonal change on the air quality in residential areas of developing countries like Pakistan and its industrial city Faisalabad needs to be more extensive.Therefore, the primary objective of this study was to investigate the relationships between the concentration of atmospheric pollutants PM 0.5 , PM 1.0 , and PM 2.5 , sound intensity, CO, and CO 2 pollutants with urban vegetation and metrological parameter.Moreover, GIS techniques have been employed to map the spatial distribution and dispersion of atmospheric pollutants in the Faisalabad area.These pollutant maps are valuable for establishing the appropriate placement of air pollution measurement stations, ensuring accurate monitoring and assessment of air quality.

Study area
The study focused on urban areas of the Faisalabad province of Punjab, Pakistan.The city is located in the flat plains of northeast Punjab, Pakistan (31°24/N, 73°04/E; Fig. 1A).Owing to the vast textile industry related to weaving, dyeing, printing, and finishing cloth Faisalabad city is rapidly becoming populous and resultantly facing acute problem of atmospheric pollution 60 .The overall climate dominates the subtropical climate with hot and humid summers and cool and dry winters.During the sampling period, average day/night RH was 33.1/75.1% and temperatures was 38.28 ± 4 °C and 22.82 ± 3.6 °C, respectively.

Ground-base/field data measurements
Concentration of atmospheric pollutants were collected at 20 locations within the city during summer and winter seasons, respectively (Fig. 1B) using portable air quality monitor (Series 500, Aeroqual, Auckland, New Zealand).The sound intensity was measured using Sound Level Meter (ACO 6230, Qte Technologies, Vietnam).A portable weather station (RK900-01, RIKA, China) was used to measured temperature and relative humidity.Particulate matter concentration was measured in ug/m 3 , CO and CO 2 concentration was measured in ppm, temperature (C), relative humidity in percentage (%), and sound intensity was measured in dB.Tree density and diversity was measured manually by counting the number of trees.

Statistical analysis
Pearson correlation analysis was performed to describe the pattern of association between urban trees and atmospheric pollutant concentration parameters.Principal component analysis (PCA) based on the correlation matrix.All tests were performed using R Studio software.The maps were developed using ArcMap 10.3 and by using this software we have made the maps.

Plant guidelines
All the experiments were done in compliance with relevant institutional, national, and international guidelines and legislations.

Seasonal changes of particulate matter in an urban area
The PM 0.5 concentration was recorded in different seasons and found to vary from summer to winter season (Fig. 2).In the urban areas, the highest concentration of PM 0.5 was found during the summer season.The humidity in the summer is quite low due to which the particulate matter freely moves in the atmosphere and dry surfaces.Also, vehicular combustion leaves the particle easily transported due to the current of air.During the summer season, concentration of PM 0.5 ranged from 25 to 50 µg m −3 particularly towards the periphery of the during the winter season.The highest concentration of PM 2.5 was recorded in the summer season which was between 130 and 150 µg m −3 .During winter season concentration of PM 2.5 ranged from 120 to 145 µg m −3 .The concentrations of particulate matter varied in both the size from fine to coarse and also declined from summer season to winter seasons for three types of PM 0.5 , PM 1.0 , and PM 2.5 .

Analysis of CO 2 changes to vegetation cover in the city
The CO 2 and CO concentrations were recorded in the summer and winter seasons all over Faisalabad city.The highest CO 2 concentration was found at 390 ppm and a lower was evidenced of 340 ppm.In the summer season, the CO concentration of 9.5 ppm to 12 ppm in the city was lower than the CO 2 concentration.In the winter season, the concentration of 2.0 ppm to 4.5 ppm.The detail is given in Fig. 3.

Temperature mitigation of green space, and humidity in the urban seasons of Faisalabad
In the urban area of Faisalabad city, the ground level temperature varied from maximum to minimum concentration in the winter and summer seasons.During summer, the temperature peaked at 47 °C in the urban areas.Low values were noticed along the water bodies followed by green vegetation and urban green parks (Fig. 4).
Similarly, the winter temperature ranged from 11 to 21 °C at the same locations.In addition, the humidity in the summer season was relatively higher than in winter season.The relative humidity during summer ranged from 37 to 75%, and the moderate humidity in the region of 65% in the southwest of the city has a relatively high vegetation cover.The relative humidity was lower than in summer and ranked 30 < 40 < 55, minimum, moderate, and maximum, respectively in the urban area.

Noise pollution and green belts analysis
The noise pollution recorded in the urban area was found to be different in the winter and summer seasons.In the summer season, the highest value of 60 dBA was in the northeast region of the city, followed by a moderate 55-60 dBA in the central region due to avenue plantation and green space patches in the city.In the city center, the dBA further declined due to the maximum roadside plantation.While in the winter, the dBA was found low due to decreased anthropogenic activities.The maximum and minimum ranges were noise pollution was found between 60 and 35 dBA, respectively.The detail of noise recorded values are illustrated in Fig. 5.   variation in the correlation between atmospheric pollutants, climatic factors, and urban vegetation.Results exhibited a strong positive correlation (0.97) of PM 1.0 with PM 2.5 , while a similarly strong positive correlation was observed between CO, CO 2, and PM 0.5 with temperature.The results exhibit a positive correlation between tree diversity with humidity, PM 0.5 , CO 2 , tree density, and temperature.However, a strong negative correlation (0.95) between temperature and PM 1.0 was observed during the winter (Fig. 6B).
The PCA of different atmospheric pollutants concentration in Faisalabad city in the winter season is presented in Fig. 7A.PCA analysis revealed that eleven principal components (PCs) accounted for 86.63% of the total variation.The temperature, PM 0.5 , CO 2 , tree density, tree diversity, relative humidity, and sound intensity correlated positively, whereas CO, PM 2.5 , and PM 1.0 correlated negatively with PC1 and accounted for 76.95% of the total variation.CO, PM 2.5 and PM 1.0 positively correlated with PC2, whereas temperature, PM 0.5 , CO 2 , tree density, tree diversity, relative humidity, and sound intensity correlated negatively with PC2 and which accounted for 9.68% of the total variation.The different atmospheric pollutants concentrations in the summer season are presented in Fig. 7B.PCA analysis revealed eleven principal components (PCs), which accounted for 87.46% of the total variation.The CO 2 , PM 1.0 , temperature, PM 0.5 , PM 2.5 , CO, and sound intensity correlated positively with PC1, whereas O 2 , tree density and tree diversity correlated negatively with PC1 and accounted for 76.67%  www.nature.com/scientificreports/ of the total variation.O 2 , tree density and diversity correlated positively, whereas CO 2 , PM 1.0 , temperature, PM 0.5 , PM 0.5 , CO, and sound intensity correlated negatively with PC2 and accounted for 10.79% of the total variation.

Discussion
Pollutant particulate matters, which remain suspended due to buoyancy, are in the sub-micron range, i.e., 10-6 m in diameter 61 .An improved understanding between the associations of particulate morbidity suggests the importance of sub-micron particles (PM 0.5 , PM 1.0 & PM 2.5 ) to which motor vehicles are significant contributors 62 .The average concentration of airborne PM at all the sampling locations is represented in the results.The spatial variations of all measured PM across all the sampling locations were found to be significantly different during the measuring seasons.In this study, the general trend of PM 0.5 , PM 1.0 & PM 2.5 , CO 2 , CO, and O 2 were in the order of Winter < Summer (Figs. 4, 5).Overall, the average concentrations of PMs, CO and CO 2 were found higher in commercial and non-vegetative areas compared to locations with less vegetation.However, the PM concentrations exceeded the permissible limits even in areas with reasonable vegetation as the PM retention capability is different for different leaf surfaces.Furthermore, PM deposition blocks the intensity of sunlight and suppresses the photosynthesis and growth of plants.It also reduces visibility through absorption and scattering by solid and liquid droplets 61 .
The sampling sites with ample vegetative cover also had high traffic densities including heavy-duty diesel vehicles like trucks, buses, vans, etc.Consequently, use of coal and wood for the combustion process and diesel fuel for running electric generators can be the main contributor to PM pollution in the study area 63 .Similar results have been reported previously where high PM concentrations were found in small industrial and commercial centers due to heavy traffic loads, inefficient diesel engines, and poor-quality fuel 64,65 .Apart from this, the suspended road dust and particulate emissions from public and commercial transportation, especially auto-rickshaws and bikes, are also considered as the main contributors to PMs 64,66 .Similarly, higher PM levels at non-vegetative sites were mainly due to the emissions from brick kilns located proximal distance to these sampling points.Some sites like Jinnah Park and Gatwala that were having high vegetation showed low PM due to relatively fewer vehicular and industrial activities.
The overall atmospheric quality of Faisalabad city was found to be very poor, ranging from poor (unhealthy) to very hazardous.The findings of this study indicated that the maximum deteriorated atmospheric quality was recorded around small industries and industrial complexes 67 .Generally, the average atmospheric quality of locations with no or less vegetation was poor than those with high vegetation.The urban areas with higher air quality index indicated that air pollution significantly impacts humans 61 .Similar findings have been reported by Joshi and Swami 68 , where industrial areas are found heavily polluted compared to residential sampling sites in Haridwar, India.
It was noticed that all the studied locations of Faisalabad city were highly polluted.The concentration of PM 0.5 , PM 1.0 & PM 2.5 , CO 2 , and CO was higher during the summer, while the lowest concentration was measured during winter across all the locations.As all the selected locations share similar environmental conditions, but sources of pollutants like fuel combustion, heavy traffic, and brick kilns had a significant effect on all the airborne particulate concentrations 66 .The concentration of PM 0.5 , PM 1.0 & PM 2.5 , CO 2 , and CO were found to be higher in commercial and residential areas as compared to green spaces like Jinnah garden site during all three seasons, with maximum values in summer and minimum in winter while the concentration O 2 , relative humidity was higher during winter compared to summer.These findings are consistent with a previous study which showed that the areas with a reasonable number of trees have a particular impact on their ability to reduce air particles 69 .The more significant number of trees and vegetation in the urban areas showed a more remarkable ability to reduce airborne particulate matter 55 .Moreover, proper management, like the pruning of plants in green spaces and residential areas, removes particles from their surface.
This study shows that the monitoring season significantly affected the PM and other airborne particles.The reason behind this could be credited to the interface between the pollution sources of the surroundings and changes in meteorological factors during the particular period 70,71 .Furthermore, the concentration of these particles increased with the wind velocity and relative humidity, thus indicating the effect of weather conditions on the accumulation of these particles in selected sites of Faisalabad city.Similar observations were documented 72,73 , who found a more significant accumulation of particles, including CO 2 and CO, in urban areas during high relative humidity weather conditions.The result of the present study is also in line with those reported by Zheng et al. 74 and Zhao et al. 75 , who found higher levels of PM along with CO 2 and CO in open spaces as compared to green spaces as sometimes the particles absorbed by leaves of different trees prone to bounce back and suspended in the air, thus increasing the concentration of air particles.CO concertation in the current study does not exceed the standard values across all locations during three seasons 76 .These findings are similar to those explained by Rahman et al. 77 , who found less concentration of CO in two cities in China compared to standard values.
The United States EPA has described the acceptable level of noise for road traffic noise as 70 dB.The results of this study indicated that the noise level of all the selected sites of Faisalabad city had exceeded the standard limits.The higher level of noise pollution across all these selected sites is chiefly connected to more significant motor vehicular traffic, mainly the use of vehicle horns, poor maintenance of urban vehicles, etc 78 .These higher levels of noise pollution, as compared to the standard limit, are considerable and can damage the health of exposed individuals in the studied areas 79 .The lowest noise pollution was found in locations with vegetation having less commercial activities and vehicle disturbance.The findings of this study showed that a higher concentration of all measured pollutants and particles was found in industrial areas, followed by residential and vegetative locations.Apart from this, the higher noise levels were also computed in industrial and commercial areas in Faisalabad city compared to vegetative locations.The vegetative areas away from commercial and industrial locations have the

Figure 1 .
Figure 1.(A) Map of Pakistan and Faisalabad city and (B) Faisalabad city map with their twenty working sites.

Figure 2 .
Figure 2. Effect of urban vegetation on particulate matter in Faisalabad city throughout winter and summer season.

Figure 3 .
Figure 3.Effect of urban vegetation on carbon dioxide and carbon monoxide concentration in Faisalabad city throughout winter and summer season.

Figure 4 .
Figure 4. Effect of urban vegetation on temperature and relative humidity in Faisalabad city throughout winter and summer season.

Figure 5 .
Figure 5.Effect of urban vegetation on sound intensity in Faisalabad city throughout winter and summer season.

Figure 6 .
Figure 6.(A, B) Correlation between atmospheric pollutants in summer and winter season.

Figure 7 .
Figure 7. Principal Component Analysis of various atmospheric pollutants measured during the study.