Seasonal occurrence, source apportionment, and cancer risk assessment of PAHs in the second largest international holy metropolitan: Mashhad, Iran.

Street dust resuspension is one of the main sources of particulate matter with impacts on air quality, health, and climate. This research was aimed to determine the concentration, source, and health risk of polycyclic aromatic hydrocarbons (PAHs) in street dust of Mashhad city. To this end, USEPA-regulated 16 PAHs were measured in 84 dust samples using gas chromatography coupled to mass spectrometry (GC-MS). The source of Σ16PAHs was identified using diagnostic ratios (DRs), positive matrix factorization (PMF), and principal component analysis (PCA). The measured Σ16PAHs demonstrated different spatial concentrations (from 1,005 to 9,138.96 µg kg-1) and showed higher levels in summer (1,206.21-9,138.96 µg kg-1), although 4-ring PAHs exhibited maximum levels in both summer and winter. The findings revealed that the dust-deposited PAHs are predominantly emitted through combustion of fossil fuels (such as diesel and gasoline) and natural gas. The total incremental lifetime cancer risk (ILCR) was assessed by considering three possible exposure routes separately for children and adults and calculated carcinogenic risk values of 2.24E-06 and 2.14E-06, respectively. ILCR is above the baseline value (1.0E-06) for children and adults in both seasons.

The effectiveness of urban trees in reducing airborne particulate matter by dry deposition in Tehran, Iran.

Deposition of atmospheric pollution as particulate matter (PM) has become a serious issue in many urban areas. This study measured and estimated the amount of atmospheric PM deposition onto oriental plane (Platanus orientalis L.) trees located in Tehran Megapolis, Iran. PM deposited on the leaves of urban trees during spring and summer was estimated using leaf wash measurements. In addition to direct measurements, the dry deposition velocity and the yearly whole-tree PM deposition were estimated using both field measurements and a theoretical model of deposition flux. We estimated air quality improvement as a result of the trees at respiratory height (1.5 m), tree height (10 m), and boundary layer height (1719 m). Foliar PM deposition during spring and summer was estimated to average 0.05 g/leaf and 41.39 g/tree using direct measurements. The annual PM deposited on the leaves, trunk, and branches of an average urban tree was calculated to be 78.60 g/tree. Trees were estimated to improve air quality at 1.5 m, 10 m, and 1719 m from ground level by 25.8%, 5.8%, and 0.1%, respectively. Hence, oriental plane trees substantially reduce PM at respiratory height.

Using a hybrid approach to apportion potential source locations contributing to excess cancer risk of PM2.5-bound PAHs during heating and non-heating periods in a megacity in the Middle East.

Polycyclic aromatic hydrocarbons (PAHs) represent one of the major toxic pollutants associated with PM2.5 with significant human health and climate effects. Because of local and long-range transport of atmospheric PAHs to receptor sites, higher global attentions have been focused to improve PAHs pollution emission management. In this study, PM2.5 samples were collected at three urban sites located in the capital of Iran, Tehran, during the heating and non-heating periods (H-period and NH-period). The US EPA 16 priority PAHs were analyzed and the data were processed to the following detailed aims: (i) investigate the H-period and NH-period variations of PM2.5 and PM2.5-bound PAHs concentrations; (ii) identify the PAHs sources and the source locations during the two periods; (iii) carry out a source-specific excess cancer risk (ECR) assessment highlighting the potential source locations contributing to the ECR using a hybrid approach. Total PAHs (TPAHs) showed significantly higher concentrations (1.56-1.89 times) during the H-period. Among the identified PAHs compounds, statistically significant periodical differences (p-value < 0.05) were observed only between eight PAHs species (Nap, BaA, Chr, BbF, BkF, BaP, IcdP, and DahA) at all three sampling sites which can be due to the significant differences of PAHs emission sources during H and NH-periods. High molecular weight (HMW) PAHs accounted for 52.7% and 46.8% on average of TPAHs during the H-period and NH-period, respectively. Positive matrix factorization (PMF) led to identifying four main PAHs sources including industrial emissions, petrogenic emissions, biomass burning and natural gas emissions, and vehicle exhaust emissions. Industrial and petrogenic emissions exhibited the highest contribution (19.8%, 27.2%, respectively) during the NH-period, while vehicle exhaust and biomass burning-natural gas emissions showed the largest contribution (40.7%, 29.6%, respectively) during the H-period. Concentration weighted trajectory (CWT) on factor contributions was used for tracking the potential locations of the identified sources. In addition to local sources, long-range transport contributed to a significant fraction of TPHAs in Tehran both during the H- and NH-periods. Source-specific carcinogenic risks assessment apportioned vehicle exhaust (44.2%, 2.52 × 10-4) and biomass burning-natural gas emissions (33.9%, 8.31 × 10-5) as the main cancer risk contributors during the H-period and NH-period, respectively. CWT maps pointed out the different distribution patterns associated with the cancer risk from the identified sources. This will allow better risk management through the identification of priority PAHs sources.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air.

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

A one-year monitoring of spatiotemporal variations of PM2.5-bound PAHs in Tehran, Iran: Source apportionment, local and regional sources origins and source-specific cancer risk assessment.

PM2.5-bound PAHs were analyzed in a total of 135 daily samples collected during four seasons from 2018 to 2019, at three urban sites in Tehran, Iran. This study aims to investigate spatio-temporal variations, source apportionment, potential local and regional sources contributions and lung cancer risks associated with the 16 US EPA priority PAHs. PM2.5 concentrations ranged from 43.8 to 80.3 µg m-3 with the highest concentrations observed in summer. Total PAHs (TPAHs) concentrations ranged between 24.6 and 38.9 ng m-3. Autumn period exhibited the highest average concentration (48.3 ng m-3) followed by winter (29.5 ng m-3), spring (25.9 ng m-3) and summer (16.1 ng m-3). Five PAHs sources were identified by positive matrix factorization (PMF) analysis: diesel exhaust, unburned petroleum-petrogenic, industrial, gasoline exhaust and coal/biomass combustion-natural gas emissions, accounting for 22.3%, 15.6%, 7.5%, 30.9%, and 23.6% of TPAHs, respectively. Site-specific bivariate polar (BP) and conditional bivariate probability function (CBPF) plots were computed to assess PM2.5 and TPAHs local source locations. CBPF pointed out that TPHAs sources are likely of local origin, showing the highest probability close to the sampling sites associated with low wind speed (<5 m s-1). The potential source contribution function (PSCF) and the concentration weighted trajectory (CWT) models were applied to investigate the long-range transport of PM2.5 and TPAHs. In addition to local sources contributions, Eastern areas were highly related to long-distance transport of PM2.5 and the Western areas showed the highest contribution of the total, medium molecular weight (MMW) (4 rings) and high molecular weight (HMW) (5-6 rings) PAHs. The upper bound of incremental lifetime cancer risk (ILCR) via inhalation exposure to PM2.5-bound PAHs was at a moderate risk level (3.14 × 10-4 to 6.17 × 10-4).

Spatio-temporal variation and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in surface dust of Qom metropolis, Iran.

The objective of this research was to determine seasonal variation, distribution, potential health risk, and source identification of 16 polycyclic aromatic hydrocarbons (PAHs) in the surface dust of eight urban areas of Qom. The total levels of 16 PAHs ranged from 364.83 to 739.26 ng g-1, with an average of 478.27 ng g-1. Sites 1 and 8 showed the highest (491.33 ng g-1) and lowest (465.08 ng g-1) concentrations of PAHs, respectively. The PAHs demonstrated the highest and the lowest levels in autumn (553.41 ng g-1) and summer (402.30 ng g-1), respectively. Naphthalene (Nap) showed the highest amounts in all of the areas (75.57 ng g-1). Source apportionment indicated that vehicular emissions and combustion of fossil fuels (liquid fossil fuel, crude oil, and gas) are the main sources of the PAHs. Toxic equivalency quantities (TEQs) index exhibited a mean concentration of 47.41 ng g-1, and benzo[a]pyrene (BaP) and dibenzo[a,h]anthracene (DBA) together contributed more than 80% of TEQ, indicating high risk potential of these compounds. Total incremental lifetime cancer risk (ILCR) presented higher value (2.62 × 10-7) for children than for adults (2.53 × 10-7), one-fold lower than the threshold (10-6). The spatial ILCR for the study areas and seasons showed the highest cancer risk in site 2 and winter. Taken together, the carcinogenic risk of PAHs to children and adults, respectively, through direct ingestion and dermal contact pathways illustrated values close to the baseline, suggesting that more attention should be paid to the issue in the study area.

Scenario-based planning for reduction of emitted CO2 from the Pars Special Economic Energy Zone (PSEEZ) of Iran.

The Pars Special Economic Energy Zone (PSEEZ) is one of the largest gas reserves in the world. To reduce the CO2 emission from the area in line with the international climate commitments, two groups of scenario have been developed: the first is using the ecosystem services of mangrove forests, and the second is technology development for oil and gas industries. For the first scenario, the carbon sequestrated by the trees was calculated using the allometric equation, and the carbon of sediments was determined by using the Walkley-Black method. For the second scenario, CO2 emitted from the PSEEZ was calculated by using the Iranian Petroleum Ministry Guidelines. CO2 emission from the PSEEZ was 0.030 Gt year-1. The maximum CO2 sequestered by mangrove scenarios was 0.12% of the PSEEZ emissions, while the flaring elimination technology reduces 36% of the PSEEZ emissions. Generally, the scenario of using oil and gas industry technologies is more effective.

Source identification and pollution degree of deposited dust on green space in Tehran.

The information about concentration, sources, and pollution degree of heavy metals belong to deposited dust (DS) can be used for decision-making in air quality control, removal role of green space, and urban forest management. The objectives of the study were to identify and evaluate DS pollution degree with the introduction of a new index. DS samples were collected from five tree species. The concentration of heavy metals and pollution degree of DS were evaluated by enrichment factors (EF), geoaccumulation index (Igeo), modified degrees of contamination (mCd), and a new comprehensive index as weighted degree of contamination factor (wCd). The values of EF showed that all samples were significant to extremely high enrichment and DS samples were emitted from anthropogenic activities. Igeo values indicated that DS samples were polluted by Cd (80-97%) and Pb (100%). In addition, mCd results showed more than 67% of samples were unpolluted. There was a clear fact that Igeo results showed high pollution levels for Pb, Cd, and low for Ni, and the mCd results were incompatible with them. When all heavy metals were used without their importance to mCd, calculation can cause bias from reality. For this reason, the new index was suggested as wCd for all heavy metals that its results showed high to very high degree of pollution and that it was compatible with other indices results.

Applying landscape metrics and structural equation modeling to predict the effect of urban green space on air pollution and respiratory mortality in Tehran.

Green space and its spatial formation are important elements of public welfare in urban environments and green ecosystems in big cities largely contribute to the mental and physical health of citizens. Tehran is Iran's biggest and most polluted city and air pollution in this city causes loss of human lives due to respiratory diseases. The effect of green area has been less studied in former researches in Tehran, and the reducing effects of green landscape on the mortality of respiratory diseases have not yet been evaluated. To measure the effects of green area landscape patterns (fragmentation, area-edge, shape, and aggregation) on public health, the current study evaluated the pathways and effects of green space on air pollution and the mortality of respiratory diseases using structural equation modeling approach and the partial least squares method. The results of the study indicated green space has a significant mitigating effect on air pollution and mortality of respiratory diseases and also air pollution has a meaningful increasing effect on mortality due to respiratory diseases in Tehran. The most important latent variable in green space is class area that indicates more area of green space is correlated with less mortality of respiratory diseases. The most important indicator of air pollution was the PM2.5 that needs to be considered and controlled by urban policymakers. Accordingly, maximizing the green area and its cohesion and minimizing fragmentation and green patch edge can contribute to a reduction in air pollution and consequently lower mortality of citizens.

Contamination, ecological risk and source identification of metals by multivariate analysis in surface sediments of the khouran Straits, the Persian Gulf.

Sediments are important hosts for metals and such should be incorporated in aquatic environment monitoring programs. In the present study, the concentrations of metals, multiple geochemical indices and guidelines along with multivariate statistical analysis were applied to identify the accumulation, sources and pollution levels of metals in the surface sediment. The mean metal concentrations in the sediments were ranked as follows: Fe > Cr > Ni > V > Zn > Cu > Pb > Cd. Based on the geo-accumulation index and multiple pollution indices the overall pattern of environmental quality status of the khouran Straits were uncontaminated to slightly polluted. Pearson correlation matrix combined with multivariate principal component analysis and cluster analysis suggest that Cd and Pb originated from anthropogenic sources, whereas Fe, Cu, Zn, Ni, V and Cr mostly originated both from natural processes and human activities with predominant anthropogenic influence in the investigated ecosystem.

Using GIS, geostatistics and Fuzzy logic to study spatial structure of sedimentary total PAHs and potential eco-risks; An Eastern Persian Gulf case study.

GIS, geo-statistics and autocorrelation analysis were employed to reveal spatial structure of sedimentary ∑16PAHs. Global Moran's I index outlined significant ∑16PAHs clusters for the entire region (Moran's I index =0.62, Z-score = 25.6). Anselin Moran's I index specified locations of the significant low/high spatial clusters. The levels of random and structural variance of ∑16PAHs were about 0.083 and 0.154, respectively. Nugget to sill ratio confirmed that ∑16PAHs has a moderate level of spatial structure and the major part of PAHs variability is not random. Prediction and standard error maps of ∑16PAHs, produced by ordinary kriging, highlighted that more samples should be taken from high cluster region for next studies. Fuzzy logic functions (OR and AND) were used to develop eco-risk maps. It revealed that the potential hazards of PAHs are considerable at the vicinity of petrochemical facilities.

Sand Dune Encroachment and Desertification Processes of the Rigboland Sand Sea, Central Iran.

Early studies on sand dune movement and desertification in Iran have not always been convincingly demonstrated because of problems with the field-based measurements. In some areas where various land uses have been engulfed by aeolian sand dunes, desertification is clear, but in other less settled areas, it may not be so obvious. The objective of this study is to demonstrate encroachments of the Rigboland sand sea, central Iran, in its different directions and variable magnitude rates. Determining the rate and direction of the sand sea movements is critical for specifying which lands should be prioritized and quickly protected. The study has trialed a change detection technique which uses a Cross-Tabulation module to compare two available LandsatTM images over the Rigboland sand sea. This indicates that within a ten-year span (from 1988 to 1998) more than 200 ha/yr were added to the Rigboland sand sea, from the alluvial fan landforms in the eastern upstream, outer margins of the Rigboland sand sea. Coupled with GIS techniques, this type of analysis of the remote sensing (RS) images provides an effective tool for the monitoring and prognostication of sand dune movement and sand sea change.

Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.

Source apportionment of PAHs and n-alkanes in respirable particles in Tehran, Iran by wind sector and vertical profile.

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM4) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m(3), respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: 'diesel' (56.3% of total PAHs on average), 'gasoline' (15.5%), 'wood combustion, and incineration' (13%), 'industry' (9.2%), and 'road soil particle' (6.0%). The four n-alkane source factors identified were: 'petrogenic' (65% of total n-alkanes on average), 'mixture of petrogenic and biomass burning' (15%), 'mixture of biogenic and fossil fuel' (11.5%), and 'biogenic' (8.5%). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4% of total PAHs and 5.0% of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.

Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj).

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron's I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data. As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP. The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions.