Tehran Megacity Research Articles

Machine learning models for predicting interactions between air pollutants in Tehran Megacity, Iran

Air pollution has significant detrimental impacts on the environmental compartments and human health. The present study investigates the interactions between selected air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, in Tehran megacity, the capital of Iran, using statistical modeling and simulating approaches. Data on selected air pollutants have been extracted from 16 sensors across Tehran during 2013–2022. Using the development of three machine learning models, including XGBoost (XGB), LightGBM (LGBM) and Random Forest (RF), significant relationships between the air pollutants were observed. The comparison of the models demonstrated that the RF model has the highest level of optimality in forecasting the concentrations of CO, O3, NO2, and SO2 (R2CO = 0.63, R2O3 = 0.6, R2NO2 = 0.54, R2SO2 = 0.55). The LGBM model provided higher optimality for NO2 and PM2.5 (R2NO2 = 0.56, R2PM2.5 = 0.88), while the XGB model exhibited higher accuracy for the PM10 (R2PM10 = 0.88). Regarding the results obtained from the statistical models, it can be inferred that the RF model has superior performance in the forecasting of gaseous pollutants. Moreover, the XGB and LGBM models exhibited comparable performance and therefore may be regarded as appropriate options for PM prediction. The findings from the simulations indicated that a rise in an individual pollutant likely leads to an increase in other pollutants. Consequently, implementing air quality management strategies for a specific pollutant meaningfully, directly influences other pollutants, highlighting the significance of considering the chemical aspect of air pollutants interaction and enforcing air pollution management rules.

First luminescence dating of exhumed fault-zone rocks of the North Tehran Fault, Iran

Here we report luminescence dating results on exhumed fault rocks from the North Tehran Fault (NTF, Iran), including the fault gouges. The NTF is an oblique-dip thrust fault expanding from west to east at the northern fringe of the megacity of Tehran. Although the fault is known to be active and a major threat to the city, only little is known about its earthquake history. We present first data on the timing of past fault motion using dating of fault rocks. Three sites have been selected for this study in which the fault damage zone is associated with Eocene volcaniclastic rocks (Karaj Formation). The fault gouge, transitional and damage zone were sampled for which we observed slip planes in the field. The fault rocks are composed of quartz and alkali feldspar favourable for luminescence dating. However, our study reveals that the quartz optically stimulated luminescence (OSL) signal of the Karaj Formation does not seem to have a measurable fast decaying signal component. Instead, the post-infrared stimulated luminescence at 225 °C (pIRIRSL225) of polymineral fine-grain fraction appears to be the most feasible signal for these samples. The first luminescence-based results presented here comprise an age range from 45 ± 5 ka to 72 ± 13 ka for fault gouges and transitional zone. Since dating the transitional zone close to the fault gouge led to a younger age than the fault gouge at one site, we conclude that the sampling location plays a crucial role in dating exhumed fault rock using luminescence dating. The obtained ages are far older than those of the previous palaeoseismological studies that dated earthquake-related sedimentary features in the region. Therefore, more research is required to shed light on the resetting mechanism and condition of these fault rocks due to the recent activity of the NTF.

Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Monitoring pollution in megacities is challenging due to their vast urban expanses and numerous pollution sources. To address this, cutting-edge deep learning models are proposed for monitoring, managing, and forecasting pollution levels. In this study, data concerning six pollutants (O3, NO2, CO, SO2, PM2.5, PM10) and Air Quality Index (AQI) values were collected over a five-year period from three distinct zones within the Tehran megacity. The gathered data were incorporated into a dataset comprising 37,044 records, encompassing the concentrations of examined pollutants and the related AQI across various study points. The primary aim was to enhance the precision of deep learning models using a regional strategic separation approach. A frequency domain filter method was employed to address data noise. Four models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional Neural Network with LSTM (CNN-LSTM), and Convolutional Neural Network with Bidirectional LSTM (CNN-Bi-LSTM)—were developed to predict AQI values. Evaluation showed disparities in pollution levels among the studied areas. The CNN-Bi-LSTM model exhibited the highest accuracy at 97 %, but challenges in predicting sudden fluctuations reduced accuracy to 95 % in some cases. However, the model consistently captured the underlying pattern of changes. Segregating megacity regions enhances accuracy in forecasting air pollution conditions.

The relative contributions of traffic and non-traffic sources in ultrafine particle formations in Tehran mega city

Emissions of ultrafine particles (UFPs; diameter < 100 nm) are strongly associated with traffic-related emissions and are a growing global concern in urban environments. The aim of this study was to investigate the variations of particle number concentration (PNC) with a diameter > 10 nm at nine stations and understand the major sources of UFPs (primary vs. secondary) in Tehran megacity. The study was carried out in Tehran in 2020. NOx and PNC were reported from a total of nine urban site locations in Tehran and BC concentrations were examined at two monitoring stations. Data from all stations showed diurnal changes with peak morning and evening rush hours. The hourly PNC was correlated with NOx. PNCs in Tehran were higher compared to those of many cities reported in the literature. The highest concentrations were at District 19 station (traffic) and the lowest was at Punak station (residential) such that the average PNC varied from 8.4 × 103 to 5.7 × 104 cm−3. In Ray and Sharif stations, the average contributions of primary and secondary sources of PNC were 67 and 33%, respectively. Overall, we conclude that a decrease in primary emission leads to a decrease in the total concentration of aerosols, despite an increase in the formation of new particles by photo nucleation.

Trend analysis of measured surface ozone at the megacity of Tehran for the summertime 2007–2021

High concentrations of surface ozone in urban and industrial regions worldwide have been a major air quality issue. Tehran, as the capital and the most populous city of Iran, has experienced intense ambient air pollution over the last three decades. This study aims to assess the long-term trends of ozone over this city in recent decades. Data series of maximum 8-h daily average (MDA8) ozone were obtained for the period between 2007 and 2021. To determine the MDA8 ozone trend attributed to the long-term changes in ozone precursor emissions, we isolated the effects of meteorology and short-term ozone variability. The results show a downward trend from 2007 to 2014 (P1) and an upward trend from 2014 onward (P2). The long-term precursor emissions changes contribute to a 29.1% negative trend (=−0.7 ppb year−1) over P1 and an 8.95% upward trend (=0.1 ppb year−1) over P2. To find the causes of the trend transition, we investigated the relationship between the adjusted MDA8 and several ozone precursors, namely NOx and CO. It is found that, over P1, the dominant chemistry regime is mixed, in which the decline of CO and NOx (attributed to industrial sources) is associated with the downward trend of ozone. On the other hand, the upward trend of ozone over P2 is associated with an increase in NOx mostly attributed to road transport (passenger cars) and residential sectors.

Health and economic impact assessment of active travel modes in Tehran megacity

Health and economic benefits of active travel modes were investigated compared to air pollution and crash risk in Tehran, the capital of Iran using Health Economic Assessment Tool (HEAT) developed by World Health Organization (WHO). By increasing walking time duration to the range of 10–30 min from 5 and population proportion to 16.2% from 8.5%, the mortality rate would decrease to ∼112–108 from 113 and ∼735–654 from 764 in the age groups of 20–44 and 45–74 respectively. Similarly, in the cycling mode, increasing the time duration to 15–40 min from 10 and population proportion to 1.2% from 0.9%, would lead to increasing prevented premature deaths. Moreover, results of increasing PM2.5 concentration within the time range of present study showed that the benefits of physical activity outweighed the risks caused by air pollution and crash fatality. So promoting active transportation modes has significant health and economic benefits, especially for older adults.

Application of Functional Principal Component Analysis in the Spatiotemporal Land-Use Regression Modeling of PM2.5

Functional data are generally curves indexed over a time domain, and land-use regression (LUR) is a promising spatial technique for generating high-resolution spatial estimation of retrospective long-term air pollutants. We developed a methodology for the novel functional land-use regression (FLUR) model, which provides high-resolution spatial and temporal estimations of retrospective pollutants. Long-term fine particulate matter (PM2.5) in the megacity of Tehran, Iran, was used as the practical example. The hourly measured PM2.5 concentrations were averaged for each hour and in each air monitoring station. Penalized smoothing was employed to construct the smooth PM2.5 diurnal curve using averaged hourly data in each of the 30 stations. Functional principal component analysis (FPCA) was used to extract FPCA scores from pollutant curves, and LUR models were fitted on FPCA scores. The mean of all PM2.5 diurnal curves had a maximum of 39.58 µg/m3 at 00:26 a.m. and a minimum of 29.27 µg/m3 at 3:57 p.m. The FPCA explained about 99.5% of variations in the observed diurnal curves across the city using just three components. The evaluation of spatially predicted long-term PM2.5 diurnal curves every 15 min provided a series of 96 high-resolution exposure maps. The presented methodology and results could benefit future environmental epidemiological studies.

A novel Energy Resources Allocation Management model for air pollution reduction.

Although air pollution has been reduced in various industrial and crowded cities during the COVID-19 pandemic, curbing the high concentration of the crisis of air pollution in the megacity of Tehran is still a challenging issue. Thus, identifying the major factors that play significant roles in increasing contaminant concentration is vital. This study aimed to propose a mathematical model to reduce air pollution in a way that does not require citizen participation, limitation on energy usage, alternative energies, any policies on fuel-burn style, extra cost, or time to ensure that consumers have access to energy adequately. In this study, we proposed a novel framework, denoted as the Energy Resources Allocation Management (ERAM) model, to reduce air pollution. The ERAM is designed to optimize the allocation of various energies to the recipients. To do so, the ERAM model is simulated based on the magnitude of fuel demand consumption, the rate of air pollution emission generated by each energy per unit per consumer, and the air pollution contribution produced by each user. To evaluate the reflectiveness and illustrate the feasibility of the model, a real-world case study, i.e., Tehran, was employed. The air pollution emission factors in Tehran territory were identified by considering both mobile sources, e.g., motorcycles, cars, and heavy-duty vehicles, and stationary sources, e.g., energy conversion stations, industries, and household and commercial sectors, which are the main contributors to particulate matter and nitrogen dioxide. An elaborate view of the results indicates that the ERAM model on fuel distribution could remarkably reduce Tehran's air pollution concentration by up to 14%.

Projection of passenger cars’ air pollutants and greenhouse gas emissions and fuel consumption in Tehran under alternative policy scenarios

Passenger cars (PCs) are a major contributor to urban air pollution, greenhouse gas emissions, and energy security challenges in megacities in developing countries because of their high growth potential. Since the PC fleet changes through time, the projection of air pollutant emissions from PCs is essential for effective emission reduction policymaking. However, the extensive amount of data and proper models are required for this purpose that are not available in many developing countries. Hence, this study develops multiple econometric and emission models and integrate them into a framework to simulate the future stock and activity of PCs and projects PCs’ air pollutants and CO2 emissions and fuel consumption in the megacity of Tehran by 2035. Various data treatments and estimations are used in response to poor transport-related data availability at the city-level. Eight alternative scenarios are considered to assess the effectiveness of different fuel consumption and emission reduction policies. Projections show that the PC stock will grow 70% by 2035 and reach 5.4 million while the activity level stays stable. Annual gasoline consumption of PCs passes 5.8 billion liters and CO2 and PM2.5 emissions elevate by 62% and 76%, respectively, under the business-as-usual scenario. Based on the results, implementation of stricter emission standards cuts 65% of gaseous emissions while promotion of compressed natural gas bi-fuel cars has no benefit in reducing air pollutants emissions. Cutting fuel subsidies is the most effective policy to limit PM2.5 and CO2 emissions, and fuel consumption growth by controlling the activity level of the fleet.

Satellite remote sensing of particulate matter in the atmosphere of megacities: A case study of Tehran, Iran

Here, we investigate the annual variations of particulate matter (PM) levels for Tehran megacity during 2018, using ground-based measurements and remotely sensed data, taken from CALIOP space-borne lidar and MODIS-Aqua sensor. Furthermore, the aerosol types of Tehran are determined against three other megacities, namely Delhi, Helsinki and Calgary. Satellite data attest the dust/polluted dust as the dominant aerosol types in Tehran during warm/cold seasons, respectively. The values of particulate depolarization ratio (PDR), particulate color ratio (PCR) and aerosol optical depth (AOD) are calculated along the CALIPSO satellite night-time trajectories over Tehran. This demonstrates that the maximums of PDR and PCR occur during July. The seasonal analyses of vertically averaged PDR and PCR attest that the maximum values take place during summer time, while minimum records belong to the fall season. The correlation analysis emphasizes a sensible conformity between column AOD data (retrieved from MODIS-Aqua sensor) and the PM2.5 concentrations (acquired from ground-based meteorological stations). Besides, PDR is inversely correlated to relative humidity. In this study, the origins of pollutants are envisaged based on PSCF and frequency analysis of back-trajectories for February and July 2018, indicating the main origin of dust arises from Turkmenistan deserts in summer and Iraq/western Iran arid areas during winter.

Seasonal and spatial variations of atmospheric depositions-bound elements over Tehran megacity, Iran: Pollution levels, PMF-based source apportionment and risks assessment

A total of 32 atmospheric depositions (ADs) samples were collected from eight sampling sites from April 2018 to March 2019 in the Tehran megacity. Elemental concentration, pollution characteristics, ecological and human health risks, and source apportionment of ADs in the < 63 μm fraction were investigated. Descriptive statistical analysis revealed that the average concentration of Zn, Cd, Pb, and Cu had the highest values over the upper continental crust (UCC). The influence of anthropogenic activities on the level of elements pollution was determined using the Geo-accumulation index (Igeo), Enrichment factor (EF), and Pollution index (PI). The findings revealed the highest Igeo values for Cd at the “extremely contaminated” level due to great influence from Cd emission sources, especially in spring. The extremely high EF values for Zn in 18.8% of samples could be attributed to the low distance of these sampling sites from the nearby industrial areas and adjacent road traffic density. PI values in the range of “considerable to extremely high” pollution were in the order of Zn > Cd > Pb > Cu > Ni > Cr. Eventually, The pollution load index (PLI) indicated the higher than unity of PLI values for all sampling sites reflecting considerable polluted sites across the study area. The ecological risk index (RI) indicated a considerable ecological risk, with the highest monomial risk values for Cd and Pb. The cumulative hazard index (HI) results showed that children encounter higher non-carcinogenic (NCR) risks than adults. However, the cumulative carcinogenic risk (CCR) of adults was 2.25 times higher than for children. The positive matrix factorization (PMF) model extracted four factors, including traffic-related sources (29.7%), local soils and crustal dust resuspension sources (24.8%), demolition and construction activities (23.5%), and industry-related emissions (22%). Among the identified sources, traffic-related sources showed the largest contribution to elements and ecological risks.

Long-term exposure to air pollution and risk of preterm birth and low birth weight in Tehran, Iran

Introduction: Adverse effects of air pollution on human health has been identified as a major concern worldwide. We conducted this study to investigate the association between prenatal exposure to ambient air pollution and Low Birth Weight (LBW) and preterm among newborns in the megacity of Tehran. Materials and methods: This cross-sectional study was carried out on 1605 newborns. Data on the residential locations of mothers were collected from birth certificates. To estimate the prenatal exposure to air pollutants, data of 21 air quality monitoring stations in Tehran were used. The Man-Whitney test was used to estimate the association between exposure to ambient air pollution and LBW and preterm. Results: The mean birth weight in our analyses was 3117 g. A significant association was found between maternal exposure to particulate matter less than 2.5 µm (PM2.5) in the first trimester and incidence of preterm (p=0.011) and LBW (p=0.003) in newborns. Also, a close association was observed between exposure to Carbon Monoxide (CO) in the first and second trimesters and LBW (p=0.002, p=0.015). There were no statistically significant associations in LBW in the case of Particulate Matter less than 10 µm (PM10), Sulfur Dioxide (SO2), and Ozone (O3). Conclusion: The results revealed that the higher risk for LBW was related to ambient PM2.5 and CO. Since many factors may affect LBW, and the pathogenic mechanisms of the effect of air pollution on LBW have not been completely elucidated, the findings should be interpreted with caution and further studies need to be conducted on this issue considering the large sample size.

Air pollution and hospital admissions and deaths due to respiratory infections in megacity of Tehran: A time series analysis

Introduction: Air pollution is one of the main causes for the significant increase of respiratory infections in Tehran. In the present study, we investigated the associations between short-term exposure to ambient air pollutants with the hospital admissions and deaths. Materials and methods: Health data from 39915 hospital admissions and 2459 registered deaths associated with these hospital admissions for respiratory infections were obtained from the Ministry of Health and Medical Education during 2014-2017. We used the distributed lag non-linear model (DLNM) for the analyses. Results: There was a statistically positive association between PM2.5 and AURI in the age group of 16 years and younger at lags 6 (RR 1.31; 1.05-1.64) and 7 (RR 1.50; 1.09-2.06). AURI admissions was associated with O3 in the age group of 16 and 65 years at lag 7 with RR 1.13 (1.00-1.27). ALRI admissions was associated with CO in the age group of 65 years and older at lag 0 with RR 1.12 (1.02-1.23). PM10 was associated with ALRI daily hospital admissions at lag 0 for males. ALRI admissions were associated with NO2 for females at lag 0. There was a positive association between ALRI deaths and SO2 in the age group of 65 years and older at lags 4 and 5 with RR 1.04 (1.00-1.09) and 1.03 (1.00-1.07), respectively. Conclusion: Exposure to outdoor air pollutants including PM10, PM2.5, SO2, NO2, O3, and CO was associated with hospital admissions for AURI and ALRI at different lags. Moreover, exposure to SO2 was associated with deaths for ALRI.

Dilution of PAHs loadings of particulate matter in air, dust and rivers in urban areas: A comparative study (Tehran megacity, Iran and city of Tübingen, SW-Germany)

PAHs (polycyclic aromatic hydrocarbons) in urban areas are usually bound to particles. Concentrations are different in different compartments (airborne particles, street dust, suspended sediments in rivers and channels). This study follows concentrations of PAHs from particles in air to street dust and finally suspended sediments in the city of Tehran, Iran compared to Tübingen, Germany. Data sets are based on own investigations (PAHs on suspended sediments), or taken from literature studies (PAHs in street dust and airborne particles). Based on a cross-comparison of concentrations of PAHs on particles, and their congener distribution patterns, the occurrence, interrelation (exchange and mixing processes), as well as possible dilution processes among PAHs in the different particle classes are disentangled. Results show that for Tehran and Tübingen PAHs in airborne particles are very high (in the range of 500 mg kg−1). However, in street dust and suspended sediments PAHs concentrations on particles are around 100 times lower. Surprisingly concentrations in street dust and suspended sediments are 5 to 10 times lower in Tehran (average 0.5 mg kg−1) than in Tübingen (average 5 mg kg−1). Since it is unlikely that PAHs emissions are lower in the Tehran megacity, an effective dilution of the atmospheric signal by uncontaminated (background) particles is hypothesized. Uncontaminated particles may stem from wind erosion of bare surfaces, construction and sand mining sites or even dust from the desert areas, which are frequent in arid climate in Tehran.

Temporal variations, regional contribution, and cluster analyses of ozone and NOx in a middle eastern megacity during summertime over 2017-2019.

Particulate matter is usually regarded as the dominant pollutant in Tehran megacity in Iran. However, the number of ozone exceedance days significantly increased in recent years. This study analyzes simultaneous measurements of O3 and NOx (NO+NO2) concentrations to improve our understanding of ozone evolution during the summers of 2017 to 2019. The k-means clustering technique was used to select five representative air quality monitoring sites in Tehran to capture O3 and NOx concentrations' variability. The findings show that all of the investigated sites failed to meet the ozone non-attainment criterion. The ozone weekend effect is seen in the study of weekday/weekend differences in 2017 and 2018, but not in 2019, which can be due to the shift in the ozone production regime. The summer mean variation analysis can also be used to deduce this regime change. In 2017, the O3 and NO2 summer mean variations suggest a holdback in the NO2 upward trend and a reversal in the O3 downward trend that had been in place since 2012. Air mass back trajectory clustering reveals that east and north-east air mass clusters have the most significant impact on Tehran's O3 pollution and the highest regional contribution to OX. The study of OX against NOx shows that the regional contribution to OX increased from 2017 to 2018 and then decreased in 2019; however, the local contribution is the opposite. The diurnal analysis of the regional and local contributions to OX indicated that OX in Tehran might be primarily affected by pollutants from a short distance. The findings reveal critical changes in the behavior of O3 in recent years, indicating that decision-makers in Tehran should reconsider air pollution control measures.

The Categorized Orienteering Problem with Count-Dependent Profits

This article addresses the Categorized Orienteering Problem with Count-Dependent Profits (COPCDP), in which nodes are clustered into different categories, and each category is assigned an interest rate. The goal is to find a tour among all nodes from various categories, which maximizes the total collected profit. However, different from the basic Orienteering Problem (OP), the profit of each node is not fixed, but decreases based on the number of nodes selected from the same category. This way, it is encouraged to visit fewer nodes from the same category. The COPCDP may have different exciting applications, but in this research, it is focused on its application in personal tourist trip planning. Two meta-heuristic algorithms based on the combination of a Genetic Algorithm with a Variable Neighborhood Descent structure (GA-VND), as well as a Simulated Annealing algorithm combined with a Variable Neighborhood Search (SA-VNS), are proposed. Computational experiments over a large set of instances show the efficiency of both algorithms. However, the GA-VND is proved to perform better in terms of solution quality. Additionally, a real-size problem instance based on the real data from the megacity of Tehran is generated and solved by using the proposed GA-VND to prove the usability of the method in practice.

Temporal trends in the spatial-scale contributions to black carbon in a Middle Eastern megacity

The analysis of high-resolution changes in black carbon (BC) concentrations was examined to distinguish and quantify various spatial-scale contributions to BC concentrations from nearby sources within 1 km distance to ranges of emission sources distributed over a larger city scale spanning tens of kilometers. Our analysis illustrated that BC emissions on the neighborhood scale only contribute a minor fraction (~15%) to total BC concentrations in the megacity of Tehran. Approximately 62% of the total black carbon is part of the city emissions, and around 23% is transported into the city from local nearby surroundings. Our analysis in highly polluted areas, including industrial and traffic hotspots in Tehran, demonstrated that the contributions of the urban mixture were relatively smaller than the average (~56%) in highly polluted areas; however, larger local-scale (~30%) contributions were observed in these areas. Our analysis in traffic hotspot areas also demonstrated significantly smaller contributions of BC from neighborhood surroundings (~9%). These results imply that the city-scale BC emissions in Tehran are a major contributor to BC exposures even in locations with local high-emitting sources. Polar annulus analysis of BC from city-scales in Tehran showed a mixture of hotspot locations ranging from north to easterly directions implying that city-scale emissions contribute to wider pollution plume expansions and larger-scale transport and vertical mixing corresponding to mixtures of emitters located further away.

Long term exposure to ambient air particulate matter and mortality effects in Megacity of Tehran, Iran: 2012–2017

Ambient particulate matter (PM) is the major environmental concern in Tehran, Iran. Exposure to such particles is a serious global health concern and substantial evidence indicates that exposure to particulate matter contributes to cardiovascular and respiratory disease. Here, we provide a detailed health impact of ambient air pollution in Tehran by linking PM pollution with mortality caused by cardiovascular and respiratory disease. Air quality data of PM obtained from Tehran Air Quality Monitoring Stations (TAQMSs) during 2012–2017. Data mining methods conducted by Spatio-temporal outlier detection and “Air Pollution and Health: a European Approach (APHEA) project” was applied to impute missing values. Time-series data were analyzed to estimate the associations between PM pollution and cause-specific mortality at different lags in Tehran. Based on descriptive analysis, PM2.5 is expected to be a major influencing factor in the increased cardiovascular-related death (CVD) and respiratory dysfunction related death (RD) deaths. The cross-correlation analysis showed the same source for PM2.5 and PM10 in the ambient air of Tehran. Also, the results indicated delays of about 7–15 days from the increased concentrations of PMs and the observed rising in CVD and RD death. Trend analysis showed a more decreasing trend for PM2.5 pollutions than PM10 pollution and a slightly increasing trend for cause-specific deaths during 2012–2017. Long term exposure to high PM levels increases the CVD and RD deaths and control strategies along with control regulations should be considered to control PM.

Impact of suspending traffic management plan in Tehran on air quality and COVID-19 mortality and morbidity

Introduction: The novel coronavirus disease 2019, namely COVID-19, has been known as a global pandemic by the World Health Organization (WHO). To prevent of COVID-19 spread, most countries including Iran have implemented many preventive measures. This study aimed to evaluate the effect of implementation (Phase-01)/non- implementation (Phase-02) of the traffic plan on confirmed cases, suspected cases, and mortality cases by COVID-19 as well as on air quality in Tehran. Materials and methods: Daily data of confirmed COVID-19 cases, suspected COVID-19 cases, mortality COVID-19 cases, air pollutants concentration and meteorological variables were obtained from 26 February, 2020 to 5August, 2020 in Tehran megacity and data were compared during the period’s implementation /non-implementation of traffic plan in Tehran. Results: Our results showed that the non-implementation of traffic plan has been effective in reducing the number of daily confirmed cases, suspected cases and mortality by COVID-19. Also, the average concentration of PM10, PM2.5, NO2 , O3 , SO2 and CO have reduced significantly during nonimplementation traffic plan phase. Conclusion: Our findings indicate that there is a significant association between non-implementation traffic plan and reduce risk of COVID-19 infection.

Revitalization of bio infrastructure network, an approach to urban regeneration: A case study of the qanats of Tehran

Over the past decades, replacement of natural ecosystems with built-up areas has put cities under increasing pressures, such as degradation in water and air quality. The main natural resource diminished significantly is water. In arid regions of Iran, settlements have traditionally been supported by qanat, which is the oldest and most efficient method of extracting water. Qanats- These sustainable systems- play a vital role in meeting local water needs and have served Iran as the main water supply throughout history. Due to the technological and socioeconomic changes, many of these systems have been replaced and abandoned during the past decades. It is necessary to create suitable solutions to increase the efficiency of qanats in order to achieve sustainable development especially in populated cities. One of the aims of this study is to suggest ways to revive the use of qanat routs in District 7, Tehran. For this purpose, the qualitative method was carried out due to determine the capabilities of urban environment and specify the green patches and open spaces. First of all, field surveys and data collection were conducted in order to detect the exact locations of the qanats’ routes, Identify environmental potentials and opportunities and finally explicate the research participant’ attitudes through map and interview analysis. Another objective of the study is to investigate the power of these systems in planning public open spaces and investigating how revitalization of this ancestral infrastructure can be served to fertilize open spaces, restore the fragmented orchards and bring oxygen to the polluted megacity of Tehran. Finally, an ambience was designed in order to create lively public open spaces and improve the social life in this residential district.