We have assessed variation in brittle star distribution patterns along a contamination gradient identified by fecal steroids and aliphatic hydrocarbons in Paranaguá Bay, southern Brazil. A hierarchical design using multiple spatial scales (centimeters-kilometers) was applied. Generalized linear mixed models (GLMMs) were used to investigate the spatial and temporal variability of brittle stars. Main principal components from the contamination and environmental matrices were used to investigate the best explanatory dataset. The abundance of brittle stars was significantly lower in sites with high concentrations of fecal steroids and aliphatic hydrocarbons. The best model fitting always included components from the contamination gradients, which precludes a purely environmental driving of brittle star abundance. Variability in spatial scales lower than kilometers was probably driven by sediment characteristics. We highlighted the importance of a robust multi-scale sampling design for a better biological indication of coastal contamination.

Potential toxicity of sedimentary Cu, Zn and Pb were evaluated based on their fractionation at the inner part of Jurujuba, a small sound at the eastern margin of Guanabara Bay, Rio de Janeiro. Biogeochemical composition revealed an environment extremely enriched with anthropic organic matter and characterized as a detrital heterotrophic system. The fractionation analysis showed that Pb exhibited more affinity with the residual fraction followed by the amorphous Fe/Mn fraction. Cu and Zn were more expressive in the amorphous Fe/Mn fraction followed by the organic one and crystalline Fe/Mn fraction, respectively. According to Igeo index, sediments proved to be highly polluted by Zn and Cu and moderated polluted by Pb. Despite the actual contamination of Jurujuba sediments, the mobility of these elements seems to be limited since the most excessive concentrations were found in the less available fractions, depending on extreme physico-chemical variations to be released.

The present study aims to optimize the slow release biostimulant ball (BSB) for bioremediation of contaminated coastal sediment using response surface methodology (RSM). Different bacterial communities were evaluated using a pyrosequencing-based approach in contaminated coastal sediments. The effects of BSB size (1–5cm), distance (1–10cm) and time (1–4months) on changes in chemical oxygen demand (COD) and volatile solid (VS) reduction were determined. Maximum reductions of COD and VS, 89.7% and 78.8%, respectively, were observed at a 3cm ball size, 5.5cm distance and 4months; these values are the optimum conditions for effective treatment of contaminated coastal sediment. Most of the variance in COD and VS (0.9291 and 0.9369, respectively) was explained in our chosen models. BSB is a promising method for COD and VS reduction and enhancement of SRB diversity.

The complexity of analyzing and predicting smoke plumes that originate from forest fire events and impact populated regions of southern Ontario motivates the innovative application of analytical techniques including trajectory–based receptor modeling for spatial source apportionment of the observed near–real–time particulate matter (PM) impacts. PM2.5 was selected as an indicator of a pollutant emitted by fires that could be transported over long distances (when entrained into the transport layer above the planetary boundary layer (PBL), and subject to sink and transformation processes) and be monitored using the existing air quality monitoring network. The source term modeling technique of simplified Quantitative Transport Bias Analysis (sQTBA) was applied to several summertime forest fire events to identify the locations of sources affecting air quality in Ontario during these events. Complementary techniques that helped to understand the movement of smoke plumes included satellite remote sensing of carbon monoxide and aerosols. All of these techniques, along with meteorological analysis, jointly provide a means of identifying the forest fire events that resulted in noticeably higher pollutant levels in Ontario. Specifically, three forest fire events in July of 2011, 2012 and 2013 were analyzed, and source regions of near–real–time PM2.5 concentrations were revealed to be both within Ontario and across northern Canada from Quebec to Yukon. The sQTBA was found to successfully identify the relative importance of various source regions contributing plumes from forest fires and non–wildfire related sources that caused higher pollutant levels that were measured in Ontario. The use of near–real–time PM2.5 data in this study facilitates the identification of the exact periods with high pollution impacts across multiple receptor sites, thus improving the overall quality of the analyses. This work shows how trajectory–based receptor models can be integrated with meteorological analyses for thorough source apportionment of wildfire–related pollution events.

The understanding of aerosol deposition in the indoor environment is relevant for assessing the exposure of occupants. This study investigates the effects of microclimatic parameters on the deposition rates of aerosols emanating from the use of household spray products in indoor environment. A three–factor factorial design was used to study the effects of interactions of air temperature, relative humidity and Air Exchange Rate (AER) on the deposition rate of particulate matter (PM). The highest deposition rate of 0.3μm particles (PM0.3) was 627.8h–1 when the relative humidity, temperature and AER were 40%, 40°C, and 12 h–1, respectively while the highest deposition rate of 5.0μm particles (PM5.0) was 709.20h–1 when the relative humidity, temperature and AER were 70%, 25 °C, and 12h–1, respectively. Regression analysis showed that air temperature and air exchange rate had significant effects on the deposition of PM0.3, while relative humidity and air exchange rate had significant effects on the deposition of PM5.0 at p<0.05. The experimental values were very close to the predicted values and were not statistically different at 95% confidence level.

The statistical regression and specific computational intelligence based models are presented in this paper for the forecasting of hourly NO2 concentrations at a historical monument Taj Mahal, Agra. The model was developed for the purpose of public health oriented air quality forecasting. Last ten–year air pollution data analysis reveals that the concentration of air pollutants increased significantly. It is also observed that the pollution levels are always higher during the months of November at around Taj Mahal, Agra. Therefore, the hourly observed data during November were used in the development of air quality forecasting models for Agra, India. Firstly, multiple linear regression (MLR) was used for building an air quality–forecasting model to forecast the NO2 concentrations at Agra. Further, a novel approach, based on regression models, principal component analysis (PCA) was analyzed to find the correlations of different predictor variables between meteorology and air pollutants. Then, the significant variables were taken as the input parameters to propose the reliable physical artificial neural network (ANN)-multi layer perceptron model for forecasting of air pollution in Agra. MLR and PCA–ANN models were evaluated through statistical analysis. The correlation coefficients (R) were 0.89 and 0.91 respectively, for PCA–ANN and were 0.69 and 0.89 respectively for MLR in the training and validation periods. Similarly, the values of normalized mean square error (NMSE), index of agreement (IOA) and fractional bias (FB) were in good agreement with the observed values. It was concluded that PCA–ANN model performs better and can be used for forecasting air pollution at Taj Mahal, Agra.

Intensive fire ignition and cracker work activities takes place during the festival of light called Diwali in India, celebrated for a period of few days in the month of October or November every year. The firecracker releases several pollutants [such as particulate matter (PM), black carbon (BC), organics, trace gases] near the surface. The effect of firecrackers on the atmospheric constituents is evaluated over Dibrugarh by monitoring the concentrations of PM, PM10 (particle radius ≤10 µm), PM2.5 (particle radius ≤2.5 µm) and BC during the Diwali and post-Diwali days (5 days after the Diwali Festival) in the years 2009 and 2010. Monthly average concentrations of each species except for the Diwali and post Diwali days is considered as the background concentrations. The concentration levels of the pollutants as recorded on the Diwali days are found to be a number of times higher (5.33 and 2.50 times for PM10, 5.74 and 2.65 times for PM2.5, 1.21 and 1.66 times for BC for the year 2009 and 2010, respectively) than the background levels at the peak hours of the fire work activity. To delineate the contribution of fireworks to the high concentrations of the species we performed air mass back trajectory analysis using the NOAA–HYSPLIT model in order to examine the existence of the transported aerosols. The ten day accumulated MODIS fire maps are also analyzed to mark out the contribution of aerosols from biomass burning. These analyses reveal that the higher concentrations of near surface aerosols including BC during the festival is due to the local effect of firework activities, neither because of long–range transport nor due to biomass burning activities. However, the higher concentration of pollutants for short periods has not degraded air quality substantially to cause health risks to people exposed to the festival in this environment.

Glyphosate, the active ingredient in Monsanto’s broad-spectrum herbicide Roundup, consists of one of the most used pesticides worldwide, but its effects on the marine flora are still not well understood. Were examined Roundup toxic effects on Ruppia maritima specimens collected from Jansen Lagoon (São Luís, MA, Brazil) and acclimatized under laboratory conditions. The numbers of new and dead leaves, the root and leaf length, the chlorophyll a content, and the weight of R. maritima branches were determined before and after exposure to different Roundup concentrations for seven days. High concentrations caused a significant lethal effect. In addition, significant changes were observed in the wet and dry weights, the number and length of the leaves, and the chlorophyll a content. Leaf elongation was observed in the branches exposed to low concentrations, and this change was likely activated as a compensatory mechanism. The results indicate that high concentrations of this herbicide may compromise estuarine flora.

A comprehensive basin wide hydrodynamic evaluation has been carried out to assess the long term impacts of climate change and coastal effluents on the salinity and seawater temperature of the Arabian Gulf (AG) using Delft3D-Flow model. The long term impacts of climate change scenarios A2 and B1 of the IPCC-AR4 on the AG hydrodynamics were evaluated. Using the current capacity and production rates of coastal desalination, power, and refinery plants, two projection scenarios until the year 2080 with 30year intervals were developed namely the realistic and the optimistic discharge scenarios. Simulations of the individual climate change scenarios ascertained overall increase of the AG salinity and temperature and decrease of precipitation. The changes varied spatially with different scenarios as per the depth, proximity to exchange with ocean water, flushing, vertical mixing, and flow restriction. The individual tested scenarios of coastal projected discharges showed significant effects but within 10–20km from the outfalls.

Advanced oxidation processes (AOPs) were first proposed in the 1980s for drinking water treatment and later were widely studied for treatment of different wastewaters. During the AOP treatment of wastewater, hydroxyl radicals (OH·) or sulfate radicals (SO₄ ·⁻) are generated in sufficient quantity to remove refractory organic matters, traceable organic contaminants, or certain inorganic pollutants, or to increase wastewater biodegradability as a pre-treatment prior to an ensuing biological treatment. In this paper, we review the fundamental mechanisms of radical generation in different AOPs and select landfill leachate and biologically treated municipal wastewater as model wastewaters to discuss wastewater treatment with different AOPs. Generally, the treatment efficiencies rely heavily upon the selected AOP type, physical and chemical properties of target pollutants, and operating conditions. It would be noted that other mechanisms, besides hydroxyl radical or sulfate radical-based oxidation, may occur during the AOP treatment and contribute to the reduction of target pollutants. Particularly, we summarize recent advances in the AOP treatment of landfill leachate, as well as advanced oxidation of effluent organic matters (EfOM) in biologically treated secondary effluent (BTSE) for water reuse.