Sabah A. Abdul-Wahab

Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

Abstract This work deals specifically with the use of a neural network for ozone modelling in the lower atmosphere. The development of a neural network model is presented to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The development of the model was based on the realization that the prediction of ozone from a theoretical basis (i.e. detailed atmospheric diffusion model) is difficult. In contrast, neural networks are useful for modelling because of their ability to be trained using historical data and because of their capability for modelling highly non-linear relationships. The network was trained using summer meteorological and air quality data when the ozone concentrations are the highest. The data were collected from an urban atmosphere. The site was selected to represent a typical residential area with high traffic influences. Three neural network models were developed. The main emphasis of the first model has been placed on studying the factors that control the ozone concentrations during a 24-hour period (daylight and night hours were included). The second model was developed to study the factors that regulate the ozone concentrations during daylight hours at which higher concentrations of ozone were recorded. The third model was developed to predict daily maximum ozone levels. The predictions of the models were found to be consistent with observations. A partitioning method of the connection weights of the network was used to study the relative percent contribution of each of the input variables. The contribution of meteorology on the ozone concentration variation was found to fall within the range 33.15–40.64%. It was also found that nitrogen oxide, sulfur dioxide, relative humidity, non-methane hydrocarbon and nitrogen dioxide have the most effect on the predicted ozone concentrations. In addition, temperature played an important role while solar radiation had a lower effect than expected. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modelling.

Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach

Abstract This paper presents an artificial neural network model that is able to predict ozone concentrations as a function of meteorological conditions and precursor concentrations. The network was trained using data collected during a period of 60 days near an industrial area in Kuwait. A mobile monitoring station was used for data collection. The data were collected at the same site as the ozone measurements. The data fed to the neural network were divided into two sets: a training set and a testing set. Various architectures were tried during the training process. A network of one hidden layer of 25 neurons was found to give good predictions for both the training and testing data set. In addition, the predictions of the network were compared to measurements taken during other times of the year. The inputs to the neural network were meteorological conditions (wind speed and direction, relative humidity, temperature, and solar intensity) and the concentration of primary pollutants (methane, carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, non-methane hydrocarbons, and dust). A backpropagation algorithm with momentum was used to prepare the neural network. A partitioning method of the connection weights of the network was used to study the relative % contribution of each of the input variables. It was found that the precursors carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, and sulfur dioxide had the most effect on the predicted ozone concentration. In addition, temperature played an important role. The performance of the neural network model was compared against linear and non-linear regression models that were prepared based on the present collected data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modeling appears to be a promising technique for the prediction of pollutant concentrations.

The need for inclusion of environmental education in undergraduate engineering curricula

Environmental degradation is a global concern and an increasing one. Increasing population pressures, escalating consumption patterns and rapid industrial development are key contributors to this degradation. There is a growing recognition that sustainable development policies, plans and actions have a better chance of being implemented when they are supported by an educated, informed public. The objective of this paper is to highlight the need for the inclusion of environmental education into the curricula of engineering studies in order to raise environmental awareness at an early stage in their careers. The main aim of such environmental education is to provide engineers with the background to environmental issues such that they develop solutions that take into account the needs of the natural environment and which seek to minimise any negative impact.

The environmental impact of gold mines: pollution by heavy metals

The gold mining plant of Oman was studied to assess the contribution of gold mining on the degree of heavy metals into different environmental media. Samples were collected from the gold mining plant area in tailings, stream waters, soils and crop plants. The collected samples were analyzed for 13 heavy metals including vanadium (V), chromium (Cr), manganese (Mn), nickel (Ni), copper (Cu), cadmium (Cd), cobalt (Co), lead (Pb), zinc (Zn), aluminium (Al), strontium (Sr), iron (Fe) and barium (Ba). The water in the acid evaporation pond showed a high concentration of Fe as well as residual quantities of Zn, V, and Al, whereas water from the citizens well showed concentrations of Al above those of Omani and WHO standards. The desert plant species growing closed to the gold pit indicated high concentrations of heavy metals (Mn, Al, Ni, Fe, Cr, and V), while the similar plant species used as a control indicated lesser concentrations of all heavy metals. The surface water (blue) indicated very high concentrations of copper and significant concentrations of Mn, Ni, Al, Fe, Zn, lead, Co and Cd. The results revealed that some of the toxic metals absorbed by plants indicated significant metal immobilization.

Patterns of SO2 emissions: a refinery case study

Abstract Air quality modelling is an essential tool for most air pollution studies and the introduction of SO 2 standards creates a need for modelling the dispersion of SO 2 . This work deals specifically with the use of the Industrial Source Complex Short Term (ISCST) model at a refinery. The study is performed over a period of 21 days. The first objective of this study was to measure the atmospheric levels of SO 2 and then to compare their values with the international standard limits. The second objective was to evaluate the ISCST model by comparing the calculated and measured concentrations. The third objective was to demonstrate the effect of wind regimes on the dispersion of SO 2 and to determine the spatial distribution of SO 2 over the modelled area. The results showed that the levels of SO 2 were well below the ambient air quality standard. Based on isopleths for SO 2 distribution in the study area (as output from the ISCST model), it can be stated that no health risk is present in areas adjacent to the refinery.

DIURNAL VARIATIONS OF AIR POLLUTION FROM MOTOR VEHICLES IN RESIDENTIAL AREA

This paper employs the air quality data assembled by the Kuwait University mobile air pollution monitoring laboratory (Chemical Engineering Department). The experimental work has been conducted in the urban atmosphere of Khaldiya residential area in Kuwait University during 1997. The site was selected to represent a typical residential area which is mainly under high traffic influences. The data collected consist mainly of measurements of carbon monoxide (CO), nitrogen oxides (NOx), non‐methane hydrocarbon (NMHC) and Ozone (O3). It is important to determine the behavior of these pollutants relative to meteorological parameters. Wind speed and direction were monitored simultaneously. The first objective of this paper is to measure the atmospheric levels of these pollutants and then compare their values with the international standard limits for an urban area. The second objective is to understand the diurnal and monthly variations of these pollutants. The third objective is to study the distribution levels of these pollutants with respect to meteorological parameters such as wind speed and direction. The results showed that the levels of NMHC and NOx exceeded the proposed ambient air quality standard for residential areas in Kuwait. The monthly mean distributions of NMHC, CO and NOx showed distinct patterns with the lower concentrations during the summer period (July and August). The distribution of O3 was different from the other gases. The maximum was seen in July and August. The hourly mean distribution of pollutants reported two types of concentration variations. The hourly mean distribution of NMHC, CO and NOx were generally characterized by three peaks which were associated with the traffic loads on the main streets. On the other hand, the variation corresponding to O3 revealed the occurrence of two daily maxima. The main distribution of the various pollutants according to wind speed indicated a marked drop with stronger winds and this was common to NMHC, CO and NOx. The mean O3 level with the wind speed showed an opposite picture.

SIMULATION OF THE CONDENSER OF THE SEAWATER GREENHOUSE Part I: Theoretical development

A theoretical model is formulated in this Part 1 of the paper for simulating the physical process of condensation of the humid air in the condenser of seawater greenhouse that is located in Muscat, Oman. Analyses to the equations, in addition to the theoretical developments of the proposed model are discussed.

Levels of heavy metals in outdoor and indoor dusts in Muscat, Oman

The concentrations of lead, copper, nickel, zinc and chromium in outdoor and indoor dusts collected from different sites in Muscat, Oman, were determined by flame atomic absorption. Results showed a wide range of concentrations, the means in the outdoor dust being, 65 ± 50, 124 ± 316, 47 ± 45, 930 ± 666 and 64 ± 26 mg kg− 1 for lead, zinc, copper, nickel and chromium, respectively. The 2001 Omani phasing out of leaded fuel resulted in low levels of lead in outdoor dust compared to those reported in the literature. Outstanding was the high nickel concentration in outdoor dust when compared to that in the literature, the reason being natural soil pollution due to the local geology of the northern parts of Oman. The concentrations of chromium, copper and zinc are lower than or comparable to these in other cities around the world. The results also showed that the industrial activities in Muscat do not contribute significantly to metal pollution in street dusts. On the other hand, the mean concentrations of lead, zinc, copper, nickel and chromium in indoor dust were 108 ± 65, 753 ± 1162, 108 ± 91, 130 ± 125 and 34 ± 14 mg kg− 1, respectively. In general, zinc and nickel levels are higher than those reported in the literature while lead, copper and chromium levels are lower or comparable. When outdoor and indoor dusts were correlated, the ratios between indoor–outdoor mean concentrations revealed that lead, zinc, and copper were generated internally, while nickel and chromium were from external sources.

Impacts on ambient air quality due to flaring activities in one of Oman's oilfields.

ABSTRACT This work was conducted to assess the impacts on workplace and ambient air quality due to release of sulfur dioxide (SO2) into the atmosphere at Al-Noor production station, located in southern desert of Sultanate of Oman. The SO2 is released because of oxidation of H2S to SO2 on flaring of H2S rich off gas at the Al-Noor. In the first phase of the study, CALPUFF modeling system was used to predict the ground level concentrations of SO2 emissions from the flare stacks. The evaluation of the modeling system was carried out by comparing the predicted results with that of the measured. In the second stage of the study, the estimated results were compared with the air quality standards/guidelines set by Omani regulatory authorities as well as by World Health Organization (WHO). It was concluded on the basis of current study that the sensitive individuals in the workplace of the Al-Noor could experience adverse health effects due to short-term exposure of SO2.

Total fog and rainwater collection in the Dhofar region of the Sultanate of Oman during the monsoon season

Data on fog and rainwater collection were collected by building large residential-type fog collectors at a selected house located in the mountainous region of the Dhofar area, in the southern part of the Sultanate of Oman, during the monsoon season between 4 July 2006 and 3 September 2006. Three different types of screen material were tested: aluminium shade mesh, green plastic shade mesh and aluminium solid plate. The aluminium shade mesh collected by far the most water, followed by the green plastic shade mesh. The total collection of the aluminium solid plate was relatively insignificant.