Residential woodsmoke is an under-regulated source of fine particulate matter (PM2.5), often surpassing mobile and industrial emissions in rural communities in North America and elsewhere. In the province of British Columbia (BC), Canada, many municipalities are hesitant to adopt stricter regulations for residential wood burning without empirical evidence that smoke is affecting local air quality. The objective of this study was to develop a retrospective algorithm that uses 1-h PM2.5 concentrations and daily temperature data to identify smoky days in order to prioritise communities by smoke impacts. Levoglucosan measurements from one of the smokiest communities were used to establish the most informative values for three algorithmic parameters: the daily standard deviation of 1-h PM2.5 measurements; the daily mean temperature; and the daytime-to-nighttime ratio of PM2.5 concentrations. Alternate parameterizations were tested in 45 sensitivity analyses. Using the most informative parameter values on the most recent two years of data for each community, the number of smoky days ranged from 5 to 277. Heat maps visualizing seasonal and diurnal variation in PM2.5 concentrations showed clear differences between the higher- and lower-ranked communities. Some communities were sensitive to one or more of the parameters, but the overall rankings were consistent across the 45 analyses. This information will allow stakeholder agencies to work with local governments on implementing appropriate intervention strategies for the most smoke-impacted communities.

Receptor models were typically used in air pollution studies and few publications are available for Positive Matrix Factorization (PMF) that consider the details of parameters and procedures in evaluating the trace organic pollutants in sediments. In this study, environmental fate and source composition of Lake Eymir sediments contaminated by polychlorinated biphenyls (PCBs) were explored by applying two PMF models, Paatero's PMF2 and United States Environmental Protection Agency's (US EPA) EPA-PMF3. PMF2 and EPA-PMF3 rely on different algorithms; Paatero's algorithm and multilinear engine algorithm, respectively. Here, the approaches of two PMF models were compared for the identification of PCB patterns taking into consideration the effects of various uncertainty matrices, residual matrices and goodness-of fit parameters. As a result of the study, it was understood that both models resolved five factors and indicated Clophen A60 as the source of PCBs. These results were consistent with the results resolved by Chemical Mass Balance model applied to the same data set in a previous study. However, source contributions identified by two models differed in quantity, but with similar patterns. This study indicates a way in understanding behavior, fate and global source of persistent organic pollutants in sediment by applying and comparing with a special data including high percentage of below detected value (38.2%) to understand the dynamics of PMF model parameters.

The aim of this study is to investigate the variability of aerosol over The Kingdom of Saudi Arabia. For this analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Aerosol Optical Depth (AOD) product from Terra and Aqua satellites for the years 2000–2013 is used. The product is validated using AERONET data from ground stations, which are situated at Solar Village Riyadh and King Abdullah University of Science and Technology (KAUST) Jeddah. The results show that both Terra and Aqua satellites exhibit a tendency to show the spatial variation of AOD with Aqua being better than Terra to represent the ground based AOD measurements over the study region. The results also show that the eastern, central, and southern regions of the country have a high concentration of AOD during the study period. The validation results show the highest correlation coefficient between Aqua and KAUST data with a value of 0.79, whilst the Aqua and Solar Village based AOD indicates the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values which are, 0.17 and 0.12 respectively. Furthermore, the Relative Mean Bias (RMB) based analysis show that the DB algorithm overestimates the AOD when using Terra and Solar Village data, while it underestimates the AOD when using Aqua with Solar Village and KAUST data. The RMB value for Aqua and Solar Village data indicates that the DB algorithm is close to normal in the study region.

This paper presents a novel operational oil spill modelling system based on HF radar currents, implemented in a northwest European shelf sea. The system integrates Open Modal Analysis (OMA), Short Term Prediction algorithms (STPS) and an oil spill model to simulate oil spill trajectories. A set of 18 buoys was used to assess the accuracy of the system for trajectory forecast and to evaluate the benefits of HF radar data compared to the use of currents from a hydrodynamic model (HDM). The results showed that simulated trajectories using OMA currents were more accurate than those obtained using a HDM. After 48h the mean error was reduced by 40%. The forecast skill of the STPS method was valid up to 6h ahead. The analysis performed shows the benefits of HF radar data for operational oil spill modelling, which could be easily implemented in other regions with HF radar coverage.

A surface oil entrainment model and droplet size model have been developed to estimate the flux of oil under surface breaking waves. Both equations are expressed in dimensionless Weber number (We) and Ohnesorge number (Oh, which explicitly accounts for the oil viscosity, density, and oil-water interfacial tension). Data from controlled lab studies, large-scale wave tank tests, and field observations have been used to calibrate the constants of the two independent equations. Predictions using the new algorithm compared well with the observed amount of oil removed from the surface and the sizes of the oil droplets entrained in the water column. Simulations with the new algorithm, implemented in a comprehensive spill model, show that entrainment rates increase more rapidly with wind speed than previously predicted based on the existing Delvigne and Sweeney's (1988) model, and a quasi-stable droplet size distribution (d<~50μm) is developed in the near surface water.

Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the key indicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomass fires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid in health advisory during haze episodes at the four administrative districts of the country. A framework based on random forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computational intelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybrid combination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets of largely available meteorological, persistency of high pollution levels, short and long term variations of emissions rates parameters. The inputs selection procedure does not depend on the back propagation training algorithm. The numerical results presented in this paper show that the proposed model not only produced satisfactory forecasts but also consistently performed better via several statistical performance indicators when compared with the standard BPNN and GA optimisation based on back propagation training algorithm. The model also showed satisfactory threshold exceedances forecasts achieving for instance best true predicted rate of 0.800, false positive rate of 0.014, false alarm rate of 0.333 and success index of 0.786 at Brunei-Muara district monitoring station. Overall, the current study has profound implications on future studies to develop a real-time air quality forecasting system to support haze management.

We introduce a methodology for representation of a surface oil slick using a Voronoi diagram updated at each time step. The Voronoi cells scale the Gaussian random walk procedure representing the spreading process by individual particle stepping. The step length of stochastically moving particles is based on a theoretical model of the spreading process, establishing a relationship between the step length of diffusive spreading and the thickness of the slick at the particle locations. The Voronoi tessellation provides the areal extent of the slick particles and in turn the thicknesses of the slick and the diffusive-type spreading length for all particles. The algorithm successfully simulates the spreading process and results show very good agreement with the analytical solution. Moreover, the results are robust for a wide range of values for computational time step and total number of particles.

In this paper three well known Gaussian dispersion models have been evaluated for a case study of a steel plant using complete and incomplete upper air meteorological data.In developing countries, the availability of surface and upper air meteorological data is limited. AMS/EPA Regulatory Model (AERMOD), Advanced Dispersion Modeling System (ADMS) and Industrial Source Complex Model (ISC3) have been evaluated for both real and estimated upper meteorological data and the results have been compared with field measurements both in the horizontal and vertical directions.The results show significant differences in predicted concentrations when modeling with real (actual) and estimated upper meteorological data. The differences ranged from 100% to 450%. Comparison of model performance suggests that AERMOD and ADMS with real meteorological data produce consistent results in the horizontal direction while ISC3 output over-predicts in general. In AERMOD and ISC3 the predicted concentrations have a similar trend of variation in the vertical direction but in ADMS the concentration variation in the vertical direction exhibited different trends. In general, the ADMS predicted concentrations under-estimated field observations.The paper suggests that upper data must be used for modeling and the default values must be used with care. In absence of upper meteorological data, users could estimate upper meteorological data by different available algorithm rather than only default option of models.

Manizales is a city in Colombia that presents high rates of thyroid cancer. It has a medium industrial development and there are concerns of the impact of their emissions on health, particularly on thyroid cancer. In this paper we characterize the geographical pattern of industrial atmospheric emissions of suspected thyroid carcinogens.We systematized data of industries in two groups. First, those with reports of atmospheric emissions of suspected thyroid carcinogens (reporting facilities – RFs), and then, industries not required to report or facilities with no-available emissions data but belonging to the same SIC-codes than RFs (nonreporting facilities – non-RFs). For non-RFs, annual average atmospheric emissions were estimated using a per-employee algorithm. The spatial pattern of sources emitting carcinogens was represented by plotting facilities by size and amounts of specific pollutants released.We found 11 RFs and 25 non-RFs in urban Manizales. RFs belong to the metalworking industries, plastics & rubber, manufacture of electrical and electronic devices, waste incineration, cremation, and meat production. Most of them were concentrated in the southeast of the city. Several RFs reported atmospheric emissions of carcinogens exceeding maximum permitted emission limits set in Colombian law. Most of non-RFs were micro and small industries, and were clustered in the southeast of the city and along the main road axis.We found clusters of pollution sources near densely populated areas. Thyroid cancer incidence might be greater in areas closer to industries than in furthest areas. We will submit a paper that studies this hypothesis soon.

Biochars, derived from rambutan (Nepheliumlappaceum) peel through slow pyrolysis, were characterised and investigated as potential adsorbent for the removal of copper ion, Cu(II) from aqueous solution. Characteristics of five biochars of rambutan peel with different pyrolytic temperatures ranging from 300 to 700 °C (B300, B400, B500, B600, B700) were studied, and adsorption abilities of respective biochars were evaluated. Adsorption experiments were carried out by varying adsorbent dosage (0.2, 0.4, 0.8, 1.0, 2.0, and 4.0 g/L) and initial copper ion, Cu(II) concentrations (50 and 100 mg/L) to determine the optimum pyrolytic temperature of biochar with high adsorption affinity. The adsorption kinetics were best described by the pseudo-second order model for all the tested biochars, while the adsorption equilibrium best fitted by Langmuir isotherm. The overall results showed that biochar derived at 600 °C can be used as an effective adsorbent for removal of Cu(II) from aqueous solutions. Furthermore, feedforward artificial neural network (FFBP) modelling was performed to compare the simulated results with experimental output data of Thermogravimetric analysis (TGA) and atomic absorption spectroscopy (AAS) analysis which were trained using Levenberg-Marquardt (LM) backpropagation algorithm. The FFBP structure for pyrolysis process comprised of TGA temperature as input and biomass final weight as output. The adsorption modelling was simulated using adsorption time, temperature, biochar dosage and initial Cu(II) concentration as input data, while final Cu(II) concentration was used as output data to the network. Finally, modelling structure of 1-9-1 and 4-8-1 gave best performance with regression, R ² value of 0.9999 and 0.9547 for TGA and AAS analysis, respectively.