The Relationship Between Spatial Patterns of Urban Land Uses and Air Pollutants in the Tehran Metropolis, Iran

doi:10.21203/rs.3.rs-1246864/v1

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The Relationship Between Spatial Patterns of Urban Land Uses and Air Pollutants in the Tehran Metropolis, Iran

https://doi.org/10.21203/rs.3.rs-1246864/v1

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Context: Urban expansion has led to land use changes in metropolises, which in turn cause landscape pattern changes and intensive ecological issues in urban areas.

Objective: The main objective of this research is to investigate the relationship between different land use patterns and air pollutants (NO_2, SO_2, CO, O₃) in the metropolis of Tehran.

Method:The Local Climate Zone (LCZ) scheme and Landsat 8 satellite images were used to extract urban land uses in Tehran. Additionally, Sentinel-5P satellite images were used to calculate and evaluate air pollutants in summer (2020) and winter (2021).Then, the relationship between the spatial composition and configuration of urban land uses and air pollutants was computed using Pearson correlation coefficient and multiple linear regression in summer 2020 and winter 2021.

Results: The results indicate that the distribution or concentration of air pollutants is different from the spatial pattern of land use. The spatial composition and configuration of man-made land uses, including the classes of compact mid-rise, compact low-rise, large low-rise, and heavy industry, had a positive correlation with NO₂ and SO₂ (Sig<0/038). In contrast, the pollutant CO had a significant negative correlation with the green spaces of types A (dense trees) (Sig: 0/004, (99%)) and B (scattered trees) (Sig: 0/034, (95%)). Conversely, the spatial composition and configuration of man-made land uses had a negative correlation with O₃(Sig<0/047)while had a positive correlation with green spaces (Sig<0/047).

Conclusion: The obtained results show that quantitative analysis can help to develop more complex strategies and scenarios in future research.

Land Use

Air Pollutants

Landscape Metrics

Local Climate Zone

Tehran Metropolis.

Climate warming is a global issue, which has become a serious threat to human health and the environment (Ou et al., 2013). One of the main reason of climate warming is increasing trend in greenhouse gases emissions especially after industrialization era which has captured the attention of most of the world’s nations(Parry et al., 2007). Urban centers are key players in generating greenhouse gases (Moghbel & Erfanian Salim, 2017). The most common air pollutants in cities include carbon monoxide(CO), sulfur dioxide(SO2), nitrogen oxides(NOx), hydrocarbons(HC), suspended particulate matter (SPM, including PM10, PM2.5), ozone(O3) (Hoek et al., 2002). Urban development and it’s structure, public facilities and traffic patterns are the main factors which can deteriorate of urban air quality. In other word, land use and transportation modes have been changed due to urbanization so that significant increase in energy consumption and the massive emission of air pollutants have exacerbated the current state of air pollution(Fang et al., 2015; Hopke et al., 2008; She et al., 2017).

Rapid urbanization also results in expansion of small urban centers to their original individual boundaries. Merging of small urban centers into each other cause to formation of huge conurbations of urban centers. In this regard, changes in land use along with traffic and industrial emissions, have also been identified as a main reason which can affect urban air quality(Huang et al., 2013; Romero et al., 1999; Weng & Yang, 2006; Xu et al., 2016; Zahari et al., 2016). Urban land use patterns are usually reflected via the dispersion of air pollutants and air quality in urban environments(Huang & Du, 2018). In fact, distribution of pollutants depend on the activities of the land use(Halim et al., 2020). Therefore, deterioration of urban air quality is appeared as an tremendous stress sign due to the rising patterns of land use changes(Du et al., 2010; Hien et al., 2020; Huang & Du, 2018; Tao et al., 2015; Wei & Ye, 2014).

Land use changes affect natural land cover surfaces directly or indirectly in this way it can influence the transport/dispersion of air pollution(Halim et al., 2020). It has been well documented in previous studies that urban building layouts, urban planning patterns, building height, and other factors can affect the air quality in urban environments(Gómez-Baggethun & Barton, 2013; Ng, 2009). Hence, air pollution can concentrated in built-up lands(Wang et al., 2018; Weng & Yang, 2006; Xian, 2007; Zahari et al., 2016). Accordingly,Weng and Yang (2006) examined the impact of land use variables on air pollutants. They showed a positive correlation between spatial trends of air pollutants with the density of built-up land in Guangzhou, China. Also, Ku (2020) have illustrated that the annual averages NO₂ is increased by increase of built-up area in Tiachung city, Taiwan. Whereas inverse relationship was observed between the increase in forest connectivity and forest area and annual average of NO₂. Liu and Shen (2014) have indicated a negative correlation between green spaces and air pollution. Zhu et al. (2019) have represented that concentrations of SO₂, CO and O₃ were all positively correlated with the cultivated land. Similarly, foresty lands lead to decrease of SO₂ and CO concentration. Furthermore, a positive correlation has been found between cultivated land with many air pollutants by Huang and Du (2018), while Nowak et al. (2006) found that there is low concentration of air pollutants in forestry land. Base on a study which conducted by Nowak et al. (2000) local ozone concentrations can reduce by increase of tree covers in urban area by a few parts per billion (ppb) by volume during daytime.

The studies mentioned above show that there is is considerable relationship between urban land use and changes in air pollutants. However, most previous studies have examined this relationship using the maps of current urban land use conditions and data from air quality monitoring stations. Air pollution has become one of the most tangible environmental problems in cities due to harmful problems caused by it. The city of Tehran is no exception to this rule and due to the increase in such pollutants of sulfur dioxide and carbon monoxide, the number of respiratory patients has increased by 60%. Thus, to achieve a deeper understanding of the effectiveness of urban air quality improvement strategies (reducing air pollution), it is nessesary to use integrated research methods such as the quantification of the spatial structure of urban patterns (composition and configuration) and the analysis of the correlation between air pollutants and land (Ku, 2020). Accordingly, a spatial-temporal analysis based on the spatial structure of urban patterns (composition and configuration) was conducted in this study. It is included the analysis of the relationship between the spatial composition and configuration of urban land uses and air pollutants using Pearson correlation coefficient and multiple linear regression, respectively, in summer and winter. The purpose of this study was to investigate the relationship between different land use patterns and air pollutants and answer the question of what is the relationship between the spatial patterns of urban land uses and air pollutants in summer and winter?

In the present study, the Local Climate Zone (LCZ) scheme and Landsat 8 satellite images were used to extract urban land uses in Tehran. Then, FRAGSTATS software (version 4.2.1) was used to calculate land use metrics based on spatial patterns (composition and configuration) of each land use. Additionally, Sentinel-5P satellite images were used to calculate and evaluate air pollutants in summer (2020) and winter (2021). Finally, considering 22 districts of Tehran, Pearson correlation coefficient and multiple linear regression were used to investigate the relationship between the spatial composition and configuration of urban land uses and air pollutants, respectively (Fig. 1). It should be noted that land use maps were generated at the same scale used to generate the images for air pollutants, namely 1 km.

2. 1. The Case Of Study

Tehran as the most populous metropolis of Iran is located in the geographical position of 35°36'46'' N and 51°17'23" E. The area of the city is equal to 730 square kilometers and its population is about 9 million people, so it has the highest population density in Iran with a population density of 12,910 people per square kilometer. Tehran now has 22 regions (Fig. 2) and is the 25th most populous city and the 27th largest city in the world (www.tehran.ir). Due to the rapid population growth and increasing urbanization, the metropolis of Tehran includes various types of urban land uses. Additionally, the special geographical location of the city, which is surrounded by Shemiran heights in the north, Damavand in the east, and Karaj in the west and is open only from the south, has led to the increased concentration of pollutants in this metropolis (Zebardast & Riazi, 2015). It is ranked 27th in 2019 among the most polluted cities in the world (https://www.iqair.com/iran/tehran). Thus, to investigate the relationship between urban land uses and air pollution, the metropolis of Tehran was considered as the study case in the present research.

2. 2. Methods

The local climate zone (LCZ) scheme was used to extract urban land uses. The LCZ scheme is currently considered as a standard for linking landscape to air pollutants at an urban scale. Generally, this classification scheme can be applied in three areas: 1. study of urban heat islands (Alexander & Mills, 2014; Emmanuel & Krüger, 2012; Leconte et al., 2015; Lehnert et al., 2015), 2. Modeling (Alexander et al., 2015; Bokwa et al., 2015; Geletič et al., 2016), and 3. extraction of land cover maps based on different geometric features (Bechtel & Daneke, 2012; Danylo et al., 2016; Lelovics et al., 2014). One of the advantages of LCZ classification is the complete description of land-use types in an urban environment because it meets the standards for measuring physical properties and urban morphology (Stewart & Oke, 2010). This classification divides urban land uses into 10 types of man-made and built spaces (1-10) and 7 types of natural land cover (A-G) (Fig. 3)(Stewart & Oke, 2012). Each class can be identified using structural features and land cover that influence air temperature at a height of 1-2 meters above the ground (Das & Das, 2020). One of the methods for generating a map based on the LCZ classification is the remote sensing imagery-based method (Bechtel et al., 2016; Lin & Xu, 2016). Using this method, we followed three basic steps of pre-processing Landsat-8 satellite images (TM & OLI / TIR) (Summer, 2020; Winter, 2021) by SagaGIS software(Conrad et al., 2015), digitization of each class of land uses by Google Earth, and the classification of land uses based on the LCZ schema by SAGA GIS software.

After producing the classification maps based on the LCZ schema, we matched the detected data with the Google Earth map related to the time of extraction by ENVI5.3 software. Thus, we checked the classification accuracy using the kappa coefficient and overall accuracy. If the Kappa coefficient is above 85% and the overall accuracy is above 90% at this stage, the accuracy of the generated map can be considered acceptable (Kerle et al., 2004).

2. 3. Selected Metrics

After generating land use maps, the characteristics of the land uses were quantified using landscape metrics and the calculations were performed by FRAGSTATS 4.2.1 software. As Table(1) shows, 6 metrics with the following characteristics were selected: 1. significant role in theory and practice (Li & Wu, 2004; Peng et al., 2010; Zhou et al., 2011), 2. Ease of calculation and high interpretive power (Li et al., 2012; Zhou et al., 2011), and 3. The least exaggeration in the data (Li & Wu, 2004; Riitters et al., 1995; Zhou et al., 2011).

A wide range of studies have evaluated air pollution using satellite images at various scales (Alvarez-Mendoza et al., 2019; Basu et al., 2019; Fernández-Pacheco et al., 2018; Meng et al., 2016; Zhang et al., 2019). One of the satellites used for this purpose is Sentinel-5P (Table 2). It is an earth observation satellite that was launched on 13 October 2017 by the European Space Agency for the global monitoring of the environment and the air pollutants such as carbon dioxide, nitrogen dioxide, ozone, and sulfur dioxide with a resolution of 1 km*1 km and a very high speed (Sannigrahi et al., 2020; Veefkind et al., 2012). In this study, the spatial and temporal variation of air pollutants was investigated using the Sentile-5P satellite and its TROPOMI instruments on the Google Earth Engine (GEE) platform. GEE is a geographic data processor launched by Google in 2010 that allows users to access GEE through an Internet-based Application Programming Interface (API) and an interactive web-based development environment (Amani et al., 2020).

Table 2

Sentile-5P satellite information about air pollutants
Satellite	Name	Description	Min*	Max*	Unit
Sentinel-5p	CO	Vertically integrated CO column density	-279	4.64	mol/m^2
	NO₂	Total vertical column of NO₂ (ratio of the slant column density of NO₂ and the total air mass factor).	-0.0006	0.0096
	SO₂	SO₂ vertical column density at ground level, calculated using the DOAS technique.	-48	0.24
	O3	Total atmospheric column of O3 between the surface and the top of atmosphere, calculated with the [DOAS algorithm]	0.0047	0.272
* = Values are estimated, Earth Engine Code Editor (google.com)

3.1. Extraction of land uses and air pollutants in Tehran metropolis

Using Landsat 8 satellite, land use maps were generated with the help of LCZ schema for the summer 2020 and winter 2021. Land uses were classified into 8 types, including compact buildings of 3-9 stories and more (2), compact buildings of 1-3 stories (3), large industrial warehouses (8), industrial structures (10), dense coniferous/deciduous trees with green ground cover (A), scattered coniferous/deciduous trees with green ground cover (B), scattered bush and scrub with green ground cover (C), low plants without/ with scattered bushes and green ground cover (mostly agricultural lands) (D). Kappa coefficient and overall accuracy were obtained respectively to be 0.8603 and 87.23% for the map of summer 2020 and 0.8706 and 88.17%, for the map of winter 2021, indicating the accuracy of the maps generated. On the other hand, according to the measurement of air pollutants and Figure(4) which shows the trend of changes in air pollutants, the trend of changes in CO and O₃ was declining in summer so that the maximum and minimum density of these pollutants were (0.0350), (0.1262) and (0.03295), (0.1238) mol/m², respectively. In contrast, changes in NO₂ showed an increasing trend in summer, with a maximum density of (0.00046) and a minimum density of (0.00035) mol/ m². In winter, changes in the three pollutants CO, SO₂, and NO₂, in contrast to O₃, showed a sharply declining trend with the maximum density of (0.0033), (0.0008), and (0.058) mol/ m² and the minimum density of (0.0001), (0.0004), and (0.0355) mol/ m², respectively.

3.2. The relationship between the spatial composition of urban land uses and air pollutants

Pearson correlation coefficient was used to investigate the relationship between the spatial composition of urban land uses and air pollutants. Table (3) shows the correlation coefficients between the CA metric of the eight types of land uses defined for Tehran and the pollutants NO₂, SO₂, CO, and O₃. The results showed that CO and NO₂ had a significant positive correlation with built land uses (compact midrise (2), compact low-rise (3), large low-rise (8), and heavy industry (10)) at 95% and 99% confidence levels (Sig<0/038). In contrast, while having a significant negative correlation with green spaces of types A (dense trees) (Sig: 0/004) and B (scattered trees) (Sig: 0/034) in both seasons, the pollutant CO had a significant negative correlation with the green spaces of type D (low plants) (Sig: 0/048) in summer. It should be noted that in addition to the negative correlations mentioned for CO, the pollutant NO₂ had also a significant negative correlation with the green spaces of type C (scattered bush and scrub) (Sig: 0/042) in summer.

The pollutant SO₂ had a significant positive correlation with compact buildings of 3-9 stories and more, large industrial warehouses, and industrial structures in both seasons (0/001< Sig < 0/02), and it had a significant positive correlation with compact buildings of 1-3 stories (Sig: 0/043) only in winter. This pollutant had a significant negative correlation with green spaces including dense and scattered trees in both seasons (0/003<Sig<0/032) and the lands including bushes, grasslands and agricultural lands in summer at 95% and 99% confidence levels. The correlations obtained in relation to the pollutant O₃ were in contrast to the correlations of other pollutants. This pollutant had a significant negative correlation with compact buildings of 3-9 stories, large industrial warehouses, and industrial structures (Sig<0/047), and a significant positive correlation with green spaces including scattered trees and green ground cover in both seasons (0/043<Sig<0/047) and the green spaces including dense trees, grasslands, and agricultural lands in summer (0/006<Sig<0/041). Among the pollutants, the average concentration of the pollutant O₃ (-0.612) in summer and the average concentration of the pollutant CO (0.621) in winter had the highest correlation with the composition of land uses.

Considering the land uses, the CA metric of lands with natural cover (dense trees (A), scattered trees (B), bush and scrub (C), low plants (D)) had a negative correlation with the concentration of air pollutants except O₃. In other words, the larger the area of these lands, the lower the concentration of air pollutants. In contrast, the CA metric of built and man-made land uses (compact midrise (2), compact low-rise (3), large low-rise (8), and heavy industry (10)) had a positive correlation with the concentration of all pollutants except O₃ so that the concentration of pollutants increased with the increase in the area of these land uses. It should be noted that the CA metric of compact midrise (summer) and heavy industry (winter) had the highest correlation with the concentration of air pollutants.

Table 3 correlation coefficients between the CA metric and air pollutants

land use	CA of Summer 2019				CA of Winter 2020
land use	CO	SO₂	NO₂	O₃	CO	SO₂	NO₂	O₃
2	0.487^**	0.363^*	0.549^**	-0.612^**	0.541^**	0.424^*	0.591^**	-0.443^*
Sig	0.006	0.048	0.002	0.00	0.002	0.002	0.001	0.014
3	0.407^*	0.299	0.476^**	-0.437^*	0.435^*	0.372^*	0.513^**	-0.425^*
Sig	0.026	0.108	0.008	0.016	0.016	0.043	0.004	0.019
8	0.381^*	0.482^**	0.411^*	-0.266	0.533^**	0.438^**	0.524^**	-0.239
Sig	0.038	0.007	0.024	0.156	0.002	0.015	0.003	0.196
10	0/482^**	0.566^**	0.524^**	-0.380^*	0.621^**	0.516^**	0.619^**	-0.353^*
Sig	0.007	0.001	0.003	0.038	0.00	0.004	0.00	0.047
A	-0.515^**	-0/520^**	-0.519^**	0.379^*	-0.482^**	0.487^**	-0.525^**	0.459^**
Sig	0.004	0.003	0.003	0.039	0.007	0.006	0.003	0.008
B	-0.395^*	-0.496^**	0.478^**	0.343^*	-0.371^*	-0.392^*	-0.446^*	0.360^*
Sig	0.034	0.005	0.008	0.047	0.044	0.032	0.013	0.043
C	-0.282	-0.376^*	-0.376^*	0.352^*	-0.262	-0.256	-0.322	0.324^*
Sig	0.138	0.041	0.042	0.041	0.162	0.173	0.083	0.044
D	-0.352^*	-0.532^**	0.368^*	0.493^**	-0.174	-0.299	-0.272	0.187
Sig	0.048	0.002	0.045	0.006	0.358	0.109	0.0147	0.305

3.3. The relationship between the spatial configuration of urban land uses and air pollutants

The relationship between the spatial configuration of land uses and the air pollutants in Tehran was studied using 5 metrics of AREA_MN, PD, ED, LPI, and GYRATE_AM and multiple linear regression in which the dependent variable Y indicated the average change of air pollutants in summer and winter and the independent variable X indicated the metrics selected for different land uses. After examining the regression models that had reached a significant level, it was found that CO had a significant positive correlation with the AREA_MN, PD, and ED metrics of compact buildings of 1-3 stories, the AREA_MN, PD, and LPI metrics of industrial warehouses, and the PD, ED, LPI, and GYRATE_AM metrics of compact buildings of 3-9 stories and industrial structures in summer. This correlation was also seen for this pollutant in winter so that it had a significant positive correlation with the AREA_MN, PD, ED, and LPI metrics of compact buildings of 3-9 stories, the AREA_MN and PD metrics of compact buildings of 1-3 stories, and the AREA_MN, PD, and LPI metrics of industrial structures. Regarding the relationship between this pollutant and natural land uses, it was recognized that CO had a negative correlation with AREA_MN, PD, and ED metrics of green spaces including dense and scattered trees in summer and winter. It had also the same relationship with the AREA_MN and GYRATE_AM metrics of agricultural lands in summer. The pollutant SO₂ had a significant positive correlation with AREA_MN, PD, ED, and LPI metrics of compact buildings of 3-9 stories and industrial structures in both summer and winter. This pollutant had the same relationship with compact buildings of 1-3 stories so that it had a positive correlation with the AREA_MN and PD metrics in summer and the LPI metric in winter. It should be noted that SO₂ had also a positive correlation with the LPI metric of large industrial warehouses in summer and the LPI and PD metrics of them in winter. AREA_MN and LPI metrics of scattered agricultural lands and grasslands had a negative correlation with SO₂ in summer, which was also the case for the AREA_MN metric of scattered grasslands in winter. In addition to the above metrics, the PD and ED metrics of green spaces including trees (dense and scattered) had a negative correlation with SO2 in both seasons. The relationship between NO₂ and the metrics defined for the configuration of land uses followed the same trends observed in relation to previous pollutants. However, the only difference was that AREA_MN, PD, and ED metrics of compact buildings of 3-9 stories and 1-3 stories and industrial structures had a positive correlation with this pollutant in summer. This correlation was more considerable in winter and the significant positive correlation could also be seen with regard to the LPI metric. Industrial warehouses also had a positive significant correlation with this pollutant in AREA_MN and PD metrics.

The investigation of the relationship between this pollutant and green spaces demonstrated that the AREA_MN, PD, and GYRATE_AM metrics of green spaces including trees (dense and scattered) and the ED and LPI metrics of scattered agricultural lands and grasslands had a negative correlation with this pollutant in both seasons. The relationship between the metrics determined for the configuration of land uses and the pollutants were such that the man-made land uses had a negative correlation with pollutants and the natural land uses had a positive correlation with them. However, the correlations recognized for O₃ were similar to what was observed regarding the spatial composition of land uses. Built land uses (ED, LPI, and GYRATE_AM metrics) had a negative correlation with this pollutant while the LPI and AREA_MN metrics of green spaces including trees (dense and scattered) and the AREA_MN, LPI, and GYRATE_AM metrics of agricultural lands had a positive correlation with O₃.

The results of this study showed that the spatial pattern of built and man-made land uses, including compact mid-rise (2), compact low-rise (3), large low-rise (8), and heavy industry (10) had a positive correlation with NO₂, SO₂, and CO so that the larger the area of compact buildings with more stories, the higher the correlation between spatial pattern and pollutants. This finding is in line with the results of the study by Wang et al. (2017), indicating that compactness, height, and other spatial features of buildings affect significantly the direction of wind and its reduction. As a result, these spatial features can increase also the concentration of pollutants. In addition, one of the main reasons for the positive correlation between built and man-made land uses and the pollutant CO is the dispersion of land uses and the increase in travel demand, leading to increased concentration of this pollutant (Ou et al., 2013). On the other hand, in relation to industrial land uses, the presence of factories and heating systems in addition to vehicles has a great impact on the increased concertation of NO₂. This positive correlation has also been shown in previous studies (Cárdenas Rodríguez et al., 2016; King et al., 2014; McCarty & Kaza, 2015). Generally, due to the phenomenon of air inversion and the heavy cold weather that causes the air to not rise in winter, the concentration of pollutants increases and the correlation between pollutants and man-made land uses is intensified. In contrast, the correlation between these land uses with O₃ was negative. As this pollutant is a secondary element and is created by the reactions between volatile organic compounds, NO₂, and sunlight, it has a stronger correlation with land uses in summer when the angle of sunlight is more vertical.

A negative correlation was found between spatial patterns of green spaces, including dense trees (A), scattered trees (B), bush and scrub (C), and low plants (D), and the pollutants NO₂, SO₂, and CO. In line with the findings of the study by Ku (2020), the investigation of the spatial configuration of such land uses showed that the increased PD of trees led to a reduced concentration of pollutants. This can be due to increased evapotranspiration and more photosynthesis in trees than other plants (Cao et al., 2010; Lin & Xu, 2016; Zhang et al., 2009). In general, green spaces including more trees had a stronger inverse relationship with NO₂, SO₂, and CO in summer. However, as in the case of man-made land uses, the relationship between green spaces and O₃ was different. It should be noted that this pollutant had a negative correlation with agricultural land uses in summer due to the presence of various plants and such a correlation could not be seen in winter due to lack of plants.

Using landscape metrics, including class area (CA), mean patch area (AREA_MN), patch density (PD), edge density (ED), largest patch index (LPI), and area-weighted radius of gyration (GYRATE_AM), the present study aimed to investigate the relationship between spatial patterns of various urban land uses and the pollutants NO₂, SO₂, CO, and O₃ in summer 2020 and winter 2021 in Tehran. To generate land use maps, LCZ scheme was used and 8 types of land uses, including compact mid-rise (2), compact low-rise (3), large low-rise (8), heavy industry (10), dense trees (A), scattered trees (B), bush and scrub (C), and low plants (D), were determined. The Sentinel-5P satellite images were employed to extract data related to the considered pollutants.

The findings of this study are in line with the results of the studies by Halim et al. (2020), Xu et al. (2016), and Zhu et al. (2019) in the field of man-made land uses and the results of the research by Civerolo et al. (2000), Huang and Du (2018), and Irga et al. (2015) in the field of green spaces. The results showed that the spatial composition and configuration of man-made land uses, including the classes of compact mid-rise (2), compact low-rise (3), large low-rise (8), and heavy industry (10), had a positive correlation with NO₂, SO₂, and CO. They had a negative correlation with O₃. The positive correlations were stronger in winter due to air inversion, especially in the land uses of compact mid-rise and heavy industry. Regarding green spaces, which included the classes of dense trees (A), scattered trees (B), bush and scrub (C), and low plants (D), the relationships were in contrast to those observed in the case of man-made land uses. In other words, these lands had a negative correlation with NO₂, SO₂, and CO and a positive correlation with O₃. These correlations were much stronger in green spaces with dense trees and in summer due to the increased density of the plants and more photosynthesis.

Based on the models used, the relationship between spatial patterns of urban land uses and air pollutants was determined. The results obtained show that quantitative analyses can help to develop more complex strategies and scenarios in future research. Thus, in addition to the factors studied, such factors as traffic volume, population density, confinement ratio (street height-to-width ratio), and access to public transportation and climatic factors such as changes in temperature, rainfall, humidity, and wind may affect air quality and should be considered.

Funding

The authors declare that no funds, grants, or other support were received during the preparation 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 Soheil Gheshlaghpoor, Seyedeh Sanaz Abedi and Masoumeh Moghbel. The first draft of the manuscript was written by Soheil Gheshlaghpoor and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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The Relationship Between Spatial Patterns of Urban Land Uses and Air Pollutants in the Tehran Metropolis, Iran

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Abstract

Figures

1. Introduction

2. Methodology

2. 1. The Case Of Study

2. 2. Methods

2. 3. Selected Metrics

3. Results

3.1. Extraction of land uses and air pollutants in Tehran metropolis

3.2. The relationship between the spatial composition of urban land uses and air pollutants

3.3. The relationship between the spatial configuration of urban land uses and air pollutants

4. Discussion

5. Conclusions

Declarations

References

Supplementary Files

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