Experiencing fire: a phenomenological study of YouTube videos of the 2016 Fort McMurray fire

Natural Hazards - The 2016 Fort McMurray wildfire, which was nicknamed “The Beast,” resulted in the largest evacuation in the history of the province of Alberta, Canada. Experience with...     

Wildfire as an increasingly common natural disaster facing Canada: understanding the 2016 Fort McMurray wildfire

Natural Hazards - On May 3, 2016, a wildfire swept into the city of Fort McMurray, Alberta. This natural disaster was significant in both its scale and impact, as the fire prompted a sudden onset...     

A synthetic method for earthquake prediction by multidisciplinary data

China Metropolitan area around Beijing is one of the earthquake test sites in Continental China. Through more than 20 years of hard work, abundant seismic, geological, geophysical and geochemical data have been obtained, and the variation of seismic, geophysical and geochemical parameters was recorded before several strong earthquakes and some moderate earthquakes in this area. In this paper, we chose 19 high qualified observatory parameters in this area to establish a multidisciplinary system for earthquake forecast, including apparent resistivity, ground water level, ground-level, tilt, radon content in groundwater, volumetric strain, Hg content in groundwater, low frequency electric signal. We calculate the synthetic information by a simple algorithm. The procedure is: firstly, we detect the abnormal intervals of the observatory data by some data analysis methods such as filtering, differencing, etc.; secondly, we endow the value of 1 to the abnormal intervals and 0 to other intervals and produce a new time series of data set of the ith parameter; thirdly, we compose the value of the new time series of 19 observatory parameters and obtain the normalized value as called synthetic information. The result shows that there are high correlations between the high synthetic information and the earthquakes with M ≥ 5.0 in this area. The earthquakes almost occurred several days to several months after the peak value of the synthetic information. This synthetic method might be taken for a short-term prediction method for M ≥ 5.0 earthquakes in this area.     

A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m²/s²), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q _v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.     

Rift induced delamination of mantle lithosphere and crustal uplift: a new mechanism for explaining Rwenzori Mountains’ extreme elevation?

With heights of 4–5 km, the topography of Rwenzori Mountains, a large horst of old crustal rocks located inside a young passive rift system, poses the question “Why are the Rwenzori Mountains so high?”. The Cenozoic Western Rift branch of the East African Rift System is situated within the Late Proterozoic mobile belts between the Archean Tanzania Craton and Congo Craton. The special geological setting of the massif at a rift node encircled by the ends of the northern Western Rift segments of Lake Albert and Lake Edward suggests that the mechanism responsible for the high elevation of the Rwenzoris is related to the rifting process. Our hypothesis is based on the propagation of the rift tips, surrounding the stiff old lithosphere at Rwenzori region, thereby triggering the delamination of the cold and dense mantle lithosphere (ML) root by reducing viscosity and strength of the undermost lower crust. As a result, this unloading induces fast isostatic pop-up of the less dense crustal Rwenzori block. We term this RID—“rift induced delamination of Mantle Lithosphere”. The physical consistency of the RID hypothesis is tested numerically. Viscous flow of 2D models is approximated by a Finite Difference Method with markers in an Eulerian formulation. The equations of conservation of mass, momentum and energy are solved for a multi-component system. Based on laboratory data of appropriate rock samples, a temperature-, pressure- and stress-dependent rheology is assumed. Assuming a simple starting model with a locally heated ML, the ML block between the weakened zones becomes unstable and sinks into the asthenosphere, while the overlying continental crust rises up. Thus, RID seems to be a viable mechanism to explain geodynamically the extreme uplift. Important conditions are a thermal anomaly within the ML, a ductile lower crust with visco-plastic rheology allowing significant strength reduction and lateral density variations. The special situation of a two-sided rifting or offset rift segments to decouple the ML laterally from the surrounding continental lithosphere seems to be most decisive. Further support for the RID mechanism may come from additional crustal thickness and an extensive stress field. Some parameters, such as the excess temperature and yield stress, are very sensitive, small changes determine whether delamination takes place or not.     

Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI _t , SDI _t ? 1, SDI _t ? 2, SDI _t ? 3, and SDI _t ? 4 monthly delays. Two variables including SDI _t and SDI _t ? 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI₃, scenario 1 of SDI₆, and scenarios 2 and 3 of SDI₂₄ represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.     

Operational forecasting of daily summer maximum and minimum temperatures in the Valencia Region

Extreme-temperature events have a great impact on human society. Thus, knowledge of summer temperatures can be very useful both for the general public and for organizations whose workers operate in the open. An accurate forecasting of summer maximum and minimum temperatures could help to predict heatwave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The objective of this work is to evaluate the skill of the regional atmospheric and modelling system (RAMS) model in determining daily summer maximum and minimum temperatures in the Valencia Region. For this, we have used the real-time configuration of this model currently running at the Centro de Estudios Ambientales de Mediterráneo Foundation. This operational system is run twice a day, and both runs have a 3-day forecast range. To carry out the verification of the model in this work, the information generated by the system has been broken into individual simulation days for a specific daily run of the model. Moreover, we have analysed the summer forecast period from 1 June to 31 August for 2007, 2008, 2009 and 2010. The results indicate good agreement between observed and simulated maximum temperatures, with RMSE in general near 2 °C both for coastal and inland stations. For this parameter, the model shows a negative bias around ?1.5 °C in the coast, while the opposite trend is observed inland. In addition, RAMS also shows good results in forecasting minimum temperatures for coastal locations, with bias lower than 1 °C and RMSE below 2 °C. However, the model presents some difficulties for this parameter inland, where bias higher than 3 °C and RMSE of about 4 °C have been found. Besides, there is little difference in both temperatures forecasted within the two daily RAMS cycles and that RAMS is very stable in maintaining the forecast performance at least for three forecast days.     

Bias in topographic thresholds for gully heads

Inter-event time series of seismic activities of Sumatra region with magnitude (m _b  ≥ 4.8) and period spanning over 1973–2012 has been analyzed using the techniques of nonlinear dynamics. The earthquake data were collected from the USGS Web catalog. The rank order statistics of inter-event time series reveal mixed response with distinct breaks in slope suggesting that earthquake dynamics in this region are unstable, but “self-organized.” Comparison of return maps of the data with random, stochastic, and chaotic time records shows quasi-deterministic behavior. We assessed the dimensionality of earthquake-generating mechanism using a nonlinear predictor technique on two-dimensional phase portrait constructed by recurrence time series. The nonlinear forecasting analysis suggests that the earthquake processes in the Sumatra region evolve on a non-random low-dimensional chaotic plane. Further, second-order Kolmogorov entropy “K2” analysis revealed a coherent time structure, indicating quasi-deterministic dynamical pattern. Our result is consistent with “self-organized” processes determined by the internal dynamics, where impulsively derived interdependencies cascade through the tectonic stress generated by plate movement. However, we do not preclude the triggering role of other external processes on the coupled underlying system.     

In-situ Rock Spalling Strength near Excavation Boundaries

It is widely accepted that the in-situ strength of massive rocks is approximately 0.4 ± 0.1 UCS, where UCS is the uniaxial compressive strength obtained from unconfined tests using diamond drilling core samples with a diameter around 50 mm. In addition, it has been suggested that the in-situ rock spalling strength, i.e., the strength of the wall of an excavation when spalling initiates, can be set to the crack initiation stress determined from laboratory tests or field microseismic monitoring. These findings were supported by back-analysis of case histories where failure had been carefully documented, using either Kirsch’s solution (with approximated circular tunnel geometry and hence σ _max = 3σ ₁ ?σ ₃) or simplified numerical stress modeling (with a smooth tunnel wall boundary) to approximate the maximum tangential stress σ _max at the excavation boundary. The ratio of σ _max /UCS is related to the observed depth of failure and failure initiation occurs when σ _max is roughly equal to 0.4 ± 0.1 UCS. In this article, it is suggested that these approaches ignore one of the most important factors, the irregularity of the excavation boundary, when interpreting the in-situ rock strength. It is demonstrated that the “actual” in-situ spalling strength of massive rocks is not equal to 0.4 ± 0.1 UCS, but can be as high as 0.8 ± 0.05 UCS when surface irregularities are considered. It is demonstrated using the Mine-by tunnel notch breakout example that when the realistic “as-built” excavation boundary condition is honored, the “actual” in-situ rock strength, given by 0.8 UCS, can be applied to simulate progressive brittle rock failure process satisfactorily. The interpreted, reduced in-situ rock strength of 0.4 ± 0.1 UCS without considering geometry irregularity is therefore only an “apparent” rock strength.     

Evaluating the support vector machine for suspended sediment load forecasting based on gamma test

Due to the various influencing factors on river suspended sediment transportation, determining an appropriate input combination for developing the suspended sediment load forecasting model is very important for water resources management. The influence of pre-processing of input variables by Gamma Test (GT) was investigated on performance of Support Vector Machine (SVM) with two kernels; Radial Basis Function (RBF) and polynomial in order to forecast daily suspended sediment amount in the period between 1983 and 2014 at Korkorsar basin, northern Iran. The best input combination was identified using GT and correlation coefficient analysis. Then, the SVM model was developed and the suspended sediment amount was forecasted with RBF and polynomial kernels. The obtained results in testing phase showed that GT-SVM (RBF kernel) model can estimate suspended sediment more accurately with the lowest RMSE (14.045 ton/day), highest correlation coefficient (0.88) and highest NSEC coefficient (0.88) than SVM (RBF kernel) model (RMSE?=?18.36ton/day, \( {R}^2=0.79, \) \( NSEC=0.73 \)) and had a better performance than the other models. The results indicated that in forecasting the first nine maximum values of suspended sediment load, GT-SVM (RBF) had a higher capability than the other models and could provide a more accurate estimation from the maximum rate of suspended sediment. The results of this study showed the capability of identifying the priority of the input parameters can change GT to a useful and technical test for input variables pre-processing to forecast the amount of suspended sediments.     

Assessment of heavy metal contamination status in sediments and identification of pollution source in Daye Lake,Central China

This research presents a detailed study which was performed to infer the quantity of metal (Cd, Cr, Pb, Zn, Cu and Fe) contents in sediments of Daye Lake, Central China. The geo-accumulation (I _geo) and potential ecological risk (PER) of these metals were assessed. The results reveal that: (1) the mean value of I _geo ranked an order of Fe (class 6) > Cd (class 5) > Pb (class 3) > Zn (class 2) > Cr (Class 1) > Cu (Class 0); (2) Potential ecological risk (PER) values calculated for all these metals at different sampled points in Daye Lake exceeded the value of very high risk. Multivariate statistical analyses were carried out to determine the relationship between these six metals and to identify the possible pollution sources, with the results suggesting that the metal content in the sediments has three patterns: the first pattern includes Pb, Cd and Cr which were mainly present due to discharged water by smelting industries; second pattern contains Zn and Cu which mainly originated from the waste residue of the copper mining industry; the third pattern is Fe which is mainly related to mine tailing leaches. This study indicates very high metal content levels in the sediments, which may have adverse risks (average PER = 7,771.62) for the lake’s ecosystem and human beings associated with Daye Lake.     

New 40Ar/39Ar constraints for the “Grande Nappe”: The largest rhyolitic eruption from the Mont-Dore Massif (French Massif Central)

Since the 1960s, an early explosive activity in the Mont-Dore Massif is associated with a major pyroclastic rhyolitic eruption (5–7 km³) known as the “Grande Nappe” (GN). This event, linked to the formation of a 6-km-diameter cryptic caldera named “Haute Dordogne”, was before our investigation dated by ⁴⁰Ar/³⁹Ar at 3.07 ± 0.04 Ma. Our new single-crystal laser fusion ⁴⁰Ar/³⁹Ar dates obtained on two outcrops of the GN (Rochefort-Montagne and Ludières) questioned several hypotheses made concerning this “landmark” event of the Mont-Dore Massif history. We demonstrate that: (1) the GN rhyolitic eruption has occurred much later than previously estimated (i.e. 2.77 ± 0.02–0.07 Ma full external uncertainties); (2) the correlation made between the Vendeix rhyolitic complexes (intra-caldera position) dated back to 2.74 ± 0.04 Ma and the GN is proposed; (3) xenocryst contamination could be very high (i.e. 70% for the Rochefort-Montagne GN outcrop) and explains the noticeable older age obtained previously; (4) a link between the GN eruption and the formation of a caldera is questionable; the hypothesis of a northward-oriented blast channeled eastward toward the paleo-Allier River is thus proposed.     

The distribution and source of organic matter in reservoir sediments

The bottom sediments of two reservoirs, one with significant river sediment input and one without, were analyzed for organic matter content. Lake Texoma sediments average 1.0% organic carbon, of which 0.26% organic carbon is deposited by the river sediments of the Red and Washita River deltas. In Fort Gibson reservoir, where there is minimal river sediment input, the organic carbon averages 1.2% and is deposited with a strong correlation to water depth (+0.9). There is a significant difference between the C/N ratio of Lake Texoma sediments (11.5) and Fort Gibson sediments (9.6). The higher C/N ratio is suggested to be a result of the larger input of terrestrial plant debris (with a high original C/N ratio) by the rivers draining into Lake Texoma and the relatively high resistance of the lignin material in the plant debris to decomposition in the reservoir sediments.     

Meteotsunami in the Great Lakes and on the Atlantic coast of the United States generated by the “derecho” of June 29–30, 2012

Tsunami-like intense sea-level oscillations, associated with atmospheric activity (meteorological tsunamis), are common in the Great Lakes and on the East Coast of the United States. They are generated by various types of atmospheric disturbances including hurricanes, frontal passages, tornados, trains of atmospheric gravity waves, and derechos. “Derecho” is a rapidly moving line of convectively induced intense thunder storm fronts producing widespread damaging winds and squalls. The derecho of June 29–30, 2012 devastatingly propagated from western Iowa to the Atlantic coast, passing more than 1,000 km and producing wind gusts up to 35 m/s. This derecho induced pronounced seiche oscillations in Lake Michigan, Chesapeake Bay, and along the US Atlantic coast. Sea-level records from the updated National Oceanic and Atmospheric Administration (NOAA) tide gauge network, together with the NOAA and automated surface-observing system air pressure and wind records, enabled us to examine physical properties and temporal/spatial variations of the generated waves. Our findings indicate that the generation mechanisms of extreme seiches in the basins under study are significantly different: energetic winds play the main role in seiche formation in Chesapeake Bay; atmospheric pressure disturbances are most important for the Atlantic coast; and the combined effect of pressure oscillations and wind is responsible for pronounced events in the Great Lakes. The “generation coefficient,” which is the ratio of the maximum observed sea-level height and the height of air pressure disturbance, was used to map the sea-level response and to identify “hot spots” for this particular event, i.e., harbors and bays with amplified seiche oscillations. The Froude number, Fr = U/c, where U is the speed of the atmospheric disturbance and c is the long-wave speed, is the key parameter influencing the water response to specific atmospheric disturbances; the maximum response was found for those regions and disturbance parameters for which Fr ~1.0.     

Heavy metal contamination in surface sediments of Gökçekaya Dam Lake,Eskişehir,Turkey

The present study to find seasonal (September 2010–June 2011) heavy metal (Cd, Pb, Cr, Co, Ni, Zn, Cu, Fe, As) contamination and the origins thereof in surface sediments of Gökçekaya Dam Lake, as constructed on Sakarya River, the third-longest river in Turkey and the largest river of the Northwestern Anatolia. Upon analyses for the purpose thereof, heavy metal contamination in annual average concentrations in the lake sediment varied, respectively, as Fe > Zn > Cr > Ni > Cu > Pb > Co > As > Cd. Statistical assessments performed in order to see whether the average values of the heavy metal contamination as measured at stations placed in the lake changed by seasonal periods. There found statistically significant differences especially in Cd, Zn, and Pb between seasonal periods. In accordance with the Sediment Quality Index, Gökçekaya Dam Lake sediment was classified as “highly polluted” in terms of the amount of anthropogenic contaminants of As, Cr, Cu, Ni, Pb, and Zn. Enrichment factor and geoaccumulation index values (I _geo) were calculated in order to geochemically interpret the source of contamination due to heavy metal concentration in the lake sediment and the level of pollution. The As, Co, Cr, Cu, Ni Pb, and Zn values demonstrated that the sediment was rich for anthropogenic contaminants. The lake was found especially rich for arsenic (14.97–34.70 mg/kg) and lead (68.75–98.65 mg/kg) in accordance with annual average values. In general the lake was geochemically characterized as “moderately contaminated” in terms of As, Co, Cr, Cu, Ni, Pb, and Zn content.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015

In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km² in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil.     

Emplacement ages and sources of kimberlites and related rocks in southern Africa: U–Pb ages and Sr–Nd isotopes of groundmass perovskite

Groundmass perovskite has been dated by LA-ICPMS in 135 kimberlites and related rocks from 110 localities across southern Africa. Sr and/or Nd isotopes have been analysed by LA-MC-ICPMS in a subset of these and integrated with published data. The age distribution shows peaks at 1,600–1,800, 1,000–1,200, 500–800 and 50–130 Ma. The major “bloom” of Group I kimberlites at ca 90 ± 10 Ma was preceded by a slow build-up in magmatic activity from ca 180 Ma. The main pulse of Group II kimberlites at 120–130 Ma was a distinct episode within this build-up. Comparison of the isotopic data with seismic tomography images suggests that metasomatized subcontinental lithospheric mantle (SCLM) with very low ε _Nd and high ⁸⁷Sr/⁸⁶Sr, (the isotopic signature of Group II kimberlites) was focused in low-Vs zones along translithospheric structures. Such metasomatized zones existed as early as 1,800 Ma, but were only sporadically tapped until the magmatic build-up began at ca 180 Ma, and contributed little to the kimberlitic magmas after ca 110 Ma. We suggest that these metasomatized volumes resided in the deep SCLM and that their low-melting point components were “burned off” by rising temperatures, presumably during an asthenospheric upwelling that led to SCLM thinning and a rise in the ambient geotherm between 120 and 90 Ma. The younger Group I kimberlites therefore rarely interacted with such SCLM, but had improved access to shallower volumes of differently metasomatized, ancient SCLM with low ⁸⁷Sr/⁸⁶Sr and intermediate ε _Nd (0–5). The kimberlite compositions therefore reflect the evolution of the SCLM of southern Africa, with metasomatic-enrichment events from as early as 1.8 Ga, through a major thermal and compositional change at ca 110 Ma, and the major kimberlite “bloom” around 90 Ma.     

Groundwater fluxes into a submerged sinkhole area,Central Italy,using radon and water chemistry

The groundwater contribution into Green Lake and Black Lake (Vescovo Lakes Group), two cover collapse sinkholes in Pontina Plain (Central Italy), was estimated using water chemistry and a ²²²Rn budget. These data can constrain the interactions between sinkholes and deep seated fluid circulation, with a special focus on the possibility of the bedrock karst aquifer feeding the lake. The Rn budget accounted for all quantifiable surface and subsurface input and output fluxes including the flux across the sediment–water interface. The total value of groundwater discharge into Green Lake and Black Lake (∼540 ± 160 L s⁻¹) obtained from the Rn budget is lower than, but comparable with historical data on the springs group discharge estimated in the same period of the year (800 ± 90 L s⁻¹). Besides being an indirect test for the reliability of the Rn-budget “tool”, it confirms that both Green and Black Lake are effectively springs and not simply “water filled” sinkholes. New data on the water chemistry and the groundwater fluxes into the sinkhole area of Vescovo Lakes allows the assessment of the mechanism responsible for sinkhole formation in Pontina Plain and suggests the necessity of monitoring the changes of physical and chemical parameters of groundwater below the plain in order to mitigate the associated risk.     

Contamination and ecological risk assessment of heavy metals in street dust of Tehran,Iran

Heavy metals are introduced in human tissue through breathing air, food chain and human skin. They can cause damage to the nervous system and internal organs. In the present study, sixty street dust samples were collected from the central area of Tehran and were digested in the laboratory to determine the content of Zn, Ni, Cd, Cr, Cu and Pb, using inductively coupled plasma optical emission spectrometry (ICP-OES). The level of contamination with the analyzed metals was determined according to the following indices: geo-accumulation index (I _geo), enrichment factor (EF), pollution index (PI), integrated pollution index (IPI) and potential ecological risk index (RI). The average concentration of heavy metals found was in the order of Zn > Cu > Pb > Ni > Cr > Cd. The average I _geo values for Cd, Cr, Cu, Ni, Pb and Zn were 1.53, ?1.88, 2.68, ?0.67, 1.62 and 2.70, respectively. Among the investigated heavy metals, zinc and copper had the maximum average EF values and were placed into the “very severe enrichment” class. Potential ecological risk factor (E _r) also indicated that Cd had the highest risk, and it was classified as of considerable potential ecological risk. Therefore, it is necessary to pay more attention to the appearance of Cd in the human environment. The calculated potential ecological risk index values also illustrated that the street dust samples presented a “moderate ecological risk.” The calculated IPI values showed that the pollution levels of the street dust samples ranged from high to extremely high.