Spatial and temporal variability of metals in inter-tidal beach sediment of Mumbai, India

Nine metals were monitored in the beach sediment in Mumbai from May 2011 to March 2012 to evaluate the spatial and temporal distributions. The average heavy metal concentrations exhibited the following order: Fe > Mn > Cr > Co > Ni > Pb > Zn > Cu > Cd for the four sampling sites. The mean concentrations (± SD) of Fe, Mn, Cr, Co, Ni, Pb, Zn, Cu and Cd were estimated to be 31.15?±?10.02 g kg^?1, 535.04?±?76.42, 151.98?±?97.90, 92.76?±?14.18, 67.52?±?11.32, 59.57?±?15.19, 54.65?±?15.01, 32.24?±?8.07 and 18.75?±?1.76 mg kg^?1, respectively. The results indicated that the sediments were polluted with Cd, Cr, Co and Pb due to high anthropogenic influences. Spatial variation of metals revealed that most of the metals were high in Dadar beach and low in Aksa beach. Cd was the highest contaminant metal studied with a mean contamination factor of 93.75. The pollution load indices of the studied beaches ranged from 1.63 (Aksa) to 1.91 (Dadar) and indicated that the beach sediments were polluted with heavy metals. The heavy metal contents increased in relation to monsoon, and most of the heavy metals showed significantly high concentrations in November during the post-monsoon. The statistical analysis revealed significant effect of study site on all the metals studied. Further, there was a significant difference on metal accumulation on bimonthly basis in relation to weather pattern in Mumbai beaches.     

Vegetation response to hydrologic and geomorphic factors in an arid region of the Baja California Peninsula

Remotely sensed imageries were used to analyze the response of desert vegetation to physiographic factors and accumulated precipitation in drier and wetter years within a region of >16,500 km² sampled with 5,000 random pixels of 30 m. Vegetation development was indexed by the annual maximum values for greenness (SAVI) and canopy water content (NDII). Precipitation was interpolated from the 0.25° grid of the Tropical Rainfall Measurement Mission satellite-based estimates, showing a regional average of ～55 mm in the wetter year. The vegetation indices were only weakly related to total precipitation, often in a negative sense. Terrain factors that most often affected the vegetation indices, in multiple regression models, were Topographic Wetness Index, elevation, and slope gradient; these often had different signs for SAVI and for NDII. Models for NDII on intrusive igneous rocks gave better results than on extrusive igneous rocks. The strongest patterns in vegetation development were the contrast among Pacific coast, Cordillera, and Gulf coast subregions and the generally stronger results for NDII than SAVI.     

Polycyclic aromatic hydrocarbons (PAHs) in surface sediments from the Bizerte Lagoon,Tunisia: levels,sources, and toxicological significance

To assess the status of polycyclic aromatic hydrocarbon (PAH) contamination in sediments from the Bizerte Lagoon (northern Tunisia), 18 surface sediment samples were collected in March 2011 and analyzed for 14 US Environmental Protection Agency priority PAHs by high-performance liquid chromatography. The total concentrations of the 14 PAHs (ΣPAHs) ranged from 16.9 to 394.1 ng g^?1 dry weight (dw) with a mean concentration of 85.5 ng g^?1 dw. Compared with other lagoons, coasts, and bays in the world, the concentrations of PAHs in surface sediments of the Bizerte Lagoon are low to moderate. The PAHs’ composition pattern was dominated by the presence of four-ring PAHs (45.8 %) followed by five-ring (26.8 %) and three-ring PAHs (12.7 %). The PAH source analysis suggested that the main origin of PAHs in the sediments of the lagoon was mainly from pyrolytic sources. According to the numerical effect-based sediment quality guidelines of the USA, the levels of PAHs in the Bizerte Lagoon should not exert adverse biological effects. The total benzo[a]pyrene toxicity equivalent values calculated for the samples varied from 3.1 to 53.7 ng g^?1 dw with an average of 10.6 ng g^?1 dw.     

Impact of rapid urbanisation and industrialisation on river sediment metal contamination

This study aimed at evidencing contaminant inputs from a rapidly growing population and the accompanying anthropogenic activities to river sediments. The Fez metropolitan area and its impacts on the Sebou's sediments (the main Moroccan river) were chosen as a case study. The Fez agglomeration is surrounded by the river Fez, receiving the wastewaters of this developing city and then flowing into the Sebou. The sediment cores from the Fez and Sebou Rivers were extracted and analysed for major elements, butyltins and toxic metals. Normalised enrichment factors and geoaccumulation index were calculated. Toxicity risk was assessed by two sets of sediment quality guideline (SQG) indices. A moderate level of contamination by butyltins was observed, with monobutyltin being the dominant species across all sites and depths. The lowest level of metal pollution was identified in the Sebou's sediments in upstream of Fez city, whilst the Fez' sediments were heavily polluted and exhibited bottom-up accumulation trends, which is a clear signature of recent inputs from the untreated wastewaters of Fez city. Consequently, the sediments of Fez and Sebou at the downstream of the confluence were found to be potentially toxic, according to the SQG levels. This finding is concerned with aquatic organisms, as well as to the riverside population, which is certainly exposed to these pollutants through the daily use of water. This study suggests that although Morocco has adopted environmental regulations aiming at restricting pollutant discharges into the natural ecosystems, such regulations are neither well respected by the main polluters nor efficiently enforced by the authorities.     

Tolerance values of benthic macroinvertebrates for stream biomonitoring: assessment of assumptions underlying scoring systems worldwide

Tolerance values (TVs) based on benthic macroinvertebrates are one of the most widely used tools for monitoring the biological impacts of water pollution, particularly in streams and rivers. We compiled TVs of benthic macroinvertebrates from 29 regions around the world to test 11 basic assumptions about pollution tolerance, that: (1) Arthropoda are < tolerant than non-Arthropoda; (2) Insecta < non-Insecta; (3) non-Oligochaeta < Oligochaeta; (4) other macroinvertebrates < Oligochaeta + Chironomidae; (5) other macroinvertebrate taxa < Isopoda + Gastropoda + Hirudinea; (6) Ephemeroptera + Plecoptera + Trichoptera (EPT) < Odonata + Coleoptera + Heteroptera (OCH); (7) EPT < non-EPT insects; (8) Diptera < Insecta; (9) Bivalvia < Gastropoda; (10) Baetidae < other Ephemeroptera; and (11) Hydropsychidae < other Trichoptera. We found that the first eight of these 11 assumptions were supported despite regional variability. In addition, we examined the effect of Best Professional Judgment (BPJ) and non-independence of TVs among countries by performing all analyses using subsets of the original dataset. These subsets included a group based on those systems using TVs that were derived from techniques other than BPJ, and groups based on methods used for TV assignment. The results obtained from these subsets and the entire dataset are similar. We also made seven a priori hypotheses about the regional similarity of TVs based on geography. Only one of these was supported. Development of TVs and the reporting of how they are assigned need to be more rigorous and be better described.     

Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.