Air Quality Standards

Background & objectives: Environmental standards are quantitative criteria for protecting the environment from pollution and degradation. One of the solutions to improve air quality status is to use reasonable limits to prevent excessive release of airborne contaminants from their various sources. On the other hand, these standards are effective in evaluating air pollution reduction strategies.
Methods: In this study, we reviewed different standard setting frameworks using a theoretical analysis. First of all, we studied the ambient air quality guidelines and standards which developed by the World Health Organization (WHO), the United States Environmental Protection Agency, the European Air Quality Management Agency, the Australian National Council for Medical and Health Research and the Canadian federal government. In addition, a framework to set the national air quality standards in Iran was proposed according to international efforts and national-specific conditions.
Results: By investigating the status of standards and guidelines in some parts of the world, it was found that all of them have reasonable and rational methods for standards setting, despite some different in standard setting methods across organizations. All these standards were set on epidemiologic studies and the effects of air pollutants on human health. Except WHO, other organizations had used a quantitative risk assessment methodology to develop their own standards. Also, for developing national standards based on its guideline, WHO has recommended considering the economic, social, and health conditions.
Conclusion: The results of this study indicate that for developing ambient air quality
standards in Iran, a rational and scientific framework is needed.

In this study, investigation of aerosol distribution over southern parts of Nigeria. The study employed the dataset (AIRS CO, OMI / TOMS, NOAA) and analysed on a onestop web-based platform known as GIOVANNI. The results revealed that CH₄ in Oguta, Warri, and Akpabuyo outweigh the value in Port Harcourt value by 30.7%, 24.9%, and 29.2% respectively, while the volume occurrence in Port Harcourt outweighs that of Eket by 3.13%. Findings also indicated elevated values of CH4 observed in Akpabuyo followed by Port Harcourt. Additionally, during the rainy season, there was a reduction in CH4 and CO concentration in all the study locations as well as an increase in NO2 and SO2 loading over the study period in all locations except at Warri where the SO2 loading decreased by 2.09% while NO2 recorded a neutral value. Based on the results, the study recommends formulation of legislation to curtail gas flares around the study area.

In this study, investigation of aerosol distribution over southern parts of Nigeria. The study employed the dataset (AIRS CO, OMI / TOMS, NOAA) and analysed on a onestop web-based platform known as GIOVANNI. The results revealed that CH₄ in Oguta, Warri, and Akpabuyo outweigh the value in Port Harcourt value by 30.7%, 24.9%, and 29.2% respectively, while the volume occurrence in Port Harcourt outweighs that of Eket by 3.13%. Findings also indicated elevated values of CH4 observed in Akpabuyo followed by Port Harcourt. Additionally, during the rainy season, there was a reduction in CH4 and CO concentration in all the study locations as well as an increase in NO2 and SO2 loading over the study period in all locations except at Warri where the SO2 loading decreased by 2.09% while NO2 recorded a neutral value. Based on the results, the study recommends formulation of legislation to curtail gas flares around the study area.

A significant part of the population in Serbia relies on central heating systems during the winter months, with around fifty heating plants in operation across the country. Common fuels used in these plants primarily include fossil fuels such as fuel oil and natural gas. Combustion of some of these fuels leads to significant emissions of air pollutants. This study evaluates the impact on the air quality of the two heating boilers at the Valjevo city (Serbia) heating plant. Air emissions were measured separately for two heating boilers in the facility using standard reference methodology. The AERMOD air dispersion model was used to estimate the dispersion of various pollutants. A combination of topographical and meteorological data was used to set up a receptor grid exposed to air pollution within a 10 km radius around the heating plant. It was found that the resulting distribution and concentration gradient of pollutant gases and particles were less inclined towards the city centre and instead spread eastwards into the surrounding villages. The health risk from the fuel oil boiler was shown to be significantly higher than that caused by the natural gas-fuelled boiler. Nevertheless, the calculated carcinogenic and non-carcinogenic health risks were within acceptable limits. However, further research is required to adequately assess the cumulative health risk generated by other surrounding emitters.

A B S T R A C T This work studies the potential impacts on air quality of an Agro Cargo Airport proposed for Southwestern Nigeria. An emission inventory was carried out for emissions of sulphur dioxide (SO2), oxides of nitrogen (NOX), particulate matter (PM), carbon monoxide (CO) and hydrocarbon (HC) taking into account emission sources characteristics. The resultant concentration on the host area of influence was determined using an ISC-AERMOD View Version 4.8. Combination of all the changes with the present status of air quality in the study area signifies the need for conscientious effort on air quality control in the proposed airport for environmental and health benefits during its operation.

This study investigated the contribution of anthropogenic emission of benzene from petroleum refineries to the Nigerian's ambient air quality with the view of understanding the impact of petroleum production facilities on host airshed. The Activities of the four existing and 22 proposed petroleum refineries in Nigeria were obtained from the Department of Petroleum Resources and these were combined with emission factors of different units at the refineries to estimate benzene emissions. No-control-measure option (worst case scenario) was assumed because of lack of information on control efficiency of the refineries. The study revealed that two of the existing refineries in one of the oil-producing cities in Nigeria released a total of 10.01 × 10 11 tons/year of benzene into the ambient air of the city, apart from additional 2.35 × 10 13 tons/year anticipated to be released from three of the proposed refineries into the ambient air of the same host community. If operated at full capacity, the estimated benzene emission from the four existing refineries stood at 5.50 × 10 13 tons/year while the 22 proposed refineries have the capacity of releasing additional 16.73 × 10 13 ton/ year. On the overall, the contribution of benzene from refineries on yearly basis to Nigerian airshed was estimated to be 22.24 × 10 13 tons. These concentrations if not checked could lead to devastating environmental issues. Some mitigating measures were suggested to control and subsequently abate benzene emission from these refineries.

This study investigates air pollution from the Warri Refining and Petrochemical Company (WRPC) of Delta Sate, Nigeria with the intent of determining the variations in pollution levels associated with increasing distance from the refinery. The following pollutant gases: Carbon monoxide (CO), Volatile organic compounds (VOC), Hydrogen sulphide (H2S), Nitrogen dioxide (NO2), Sulphur dioxide (SO2) and Particulate Matter (PM2.5 and PM10), were monitored intermittently with the use of digital hand-held probes, at sampling points located between 1,500 meters to 16,000 meters from WRPC. Air sampling was carried out on a weekly basis, for a duration of one (1) year. The average annual concentration of CO, VOC, H2S, NO2, SO2, PM2.5 and PM10 measured were 0.2543 ppm, 4.4922 mg/m3, 0.0004 ppm, 0.0063 ppm, 0.5263 ppm, 36.3825 µg/m3, 91.7346 µg/m3 respectively. The results of the spatial analyses of air pollutants show that concentrations of VOC, NO2, PM2.5, and PM10 shared a significant inverse relationship with distance (p values 0.00 ≤.0.05). The study suggests a minimum of 10,250 meters radial extent of buffers around WRPC, as a long-term strategy in reducing exposure of residents to air pollution. Short-term strategies include enforcement of legislation reducing/banning emissions from industries and bush fires, use of alternative eco-friendly technologies and energy sources, tree planting, revamp of the hydroelectricity power sector and general sanitization of the environment.

The increase in the vehicle population in Port Harcourt associated with increase in economic activities has left a scar in ambient air quality. This study investigated the impact of vehicular traffic in selected junction of Port Harcourt city. Air quality survey was conducted using standard analytical protocols Anemometer, and multipurpose air quality analyzers. The generated results showed that PM2.5 and PM 10 at Garrison, Isaac Boro park, Lagos Bus stop and control were above NAAQS limit of 35.0µg/m 3 and 150.0µg/m 3 respectively. Total suspended particulate matter (TSPM) in Garrison, and Lagos Bus stop were 208.0µg/m 3 , and 398.53µg/m 3 which were above NAAQS limit of 200.0µg/m 3. The other measured parameter including hydrocarbons, CO, N2O, S2O were above NAAQS and FMEnv signifying that the ambient air around study areas were polluted. Results of a 2-way analysis of variance (ANOVA) indicated that there was no significant difference at respective sampling locations; but for individual pollutant characteristics there were significant differences at p<0.05. Cumulative effects under prolong exposure may result in various chronic health challenges. The study recommended that government should establish vehicular integrity and emission testing centers in Port Harcourt City to reduce air pollution.

This paper aims to predict models for controlling and monitoring greenhouse gases efficiently. Besides, find best mathematical forecasting models for the main greenhouse gas CO2. Statistical and simulation approaches are implemented and evaluated to investigate the contribution of atmospheric processes and predicate the level of the CO2 emission level in sultanate of Oman. Three regression models were implemented and compared (Linear, Exponential and Polynomial) in the simulation phase based on the two factors the accuracy and the complexity of model. The Polynomial predicting model is best fitting the desired data 99% based on the value of R2=0.991. Whereas, the Exponential and Linear models are achieved less fit 92.6% and 84.6% correspondingly. The predicting models prove that there is a significant increase in level of greenhouse gases (CO2) in Oman.

IntroductionAmbient air quality standards are not designed to protect people occupationally exposed to outdoor air pollution on a routine basis. This study aimed to assess the effect of exceeding the US ambient air quality standard for carbon monoxide (CO) on motorcycle taxi drivers respiratory health.MethodsA cross-sectional study of 85 current motorcycle taxi drivers with at least 5 years of job tenure in Cotonou (Benin) was conducted. Personal CO was measured with a portable CO data logger for 8 hours per day during working hours. A questionnaire on respiratory symptoms was administered to participants and spirometry was performed. Participants were divided into two groups, those with exposure to CO >9 ppm and ≤9 ppm, according to the US Environmental Protection Agency (EPA) National Ambient Air Quality Standard which is an 8-hour average of 9ppm. 8 and 10 ppm were also used an exposure limit. Analysis was done using these two groups.ResultsSocio-demographic characteristics were well balanced between the two study groups. The drivers with a CO exposure of more than 9ppm had non-significantly more respiratory symptoms (OR=1.67; 95%CI:0.26,10.74), lower FVC and FEV1 compared to the less exposed group but they have a significant lower PEF (-10%, p=0.02). When we used an exposure limit of 8 or 10 ppm the results were not statistically different.ConclusionDrivers with a CO exposure >9 ppm tend to have more respiratory problems. More research is needed to reinforce this result in order to improve air quality standards to protect workers occupationally exposed to outdoor air pollution.

This study investigated the contribution of anthropogenic emission of benzene from petroleum refineries to the Nigerian's ambient air quality with the view of understanding the impact of petroleum production facilities on host airshed. The Activities of the four existing and 22 proposed petroleum refineries in Nigeria were obtained from the Department of Petroleum Resources and these were combined with emission factors of different units at the refineries to estimate benzene emissions. No-control-measure option (worst case scenario) was assumed because of lack of information on control efficiency of the refineries. The study revealed that two of the existing refineries in one of the oil-producing cities in Nigeria released a total of 10.01 × 10 11 tons/year of benzene into the ambient air of the city, apart from additional 2.35 × 10 13 tons/year anticipated to be released from three of the proposed refineries into the ambient air of the same host community. If operated at full capacity, the estimated benzene emission from the four existing refineries stood at 5.50 × 10 13 tons/year while the 22 proposed refineries have the capacity of releasing additional 16.73 × 10 13 ton/ year. On the overall, the contribution of benzene from refineries on yearly basis to Nigerian airshed was estimated to be 22.24 × 10 13 tons. These concentrations if not checked could lead to devastating environmental issues. Some mitigating measures were suggested to control and subsequently abate benzene emission from these refineries.

To simulate ozone (O 3) air quality in future decades over the eastern United States, a modeling system consisting of the NASA Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model, the Pennsylvania State University/National Center for Atmospheric Research mesoscale regional climate model (MM5), and the Community Multiscale Air Quality model has been applied. Estimates of future emissions of greenhouse gases and ozone precursors are based on the A2 scenario developed by the Intergovernmental Panel on Climate Change (IPCC), one of the scenarios with the highest growth of CO 2 among all IPCC scenarios. Simulation results for five summers in the 2020s, 2050s, and 2080s indicate that summertime average daily maximum 8-hour O 3 concentrations increase by 2.7, 4.2, and 5.0 ppb, respectively, as a result of regional climate change alone with respect to five summers in the 1990s. Through additional sensitivity simulations for the five summers in the 2050s the relative impact of changes in regional climate, anthropogenic emissions within the modeling domain, and changed boundary conditions approximating possible changes of global atmospheric composition was investigated. Changed boundary conditions are found to be the largest contributor to changes in predicted summertime average daily maximum 8-hour O 3 concentrations (5.0 ppb), followed by the effects of regional climate change (4.2 ppb) and the effects of increased anthropogenic emissions (1.3 ppb). However, when changes in the fourth highest summertime 8-hour O 3 concentration are considered, changes in regional climate are the most important contributor to simulated concentration changes (7.6 ppb), followed by the effect of increased anthropogenic emissions (3.9 ppb) and increased boundary conditions (2.8 ppb). Thus, while previous studies have pointed out the potentially important contribution of growing global emissions and intercontinental transport to O 3 air quality in the United States for future decades, the results presented here imply that it may be equally important to consider the effects of a changing climate when planning for the future attainment of regional-scale air quality standards such as the U.S. national ambient air quality standard that is based on the fourth highest annual daily maximum 8-hour O 3 concentration.

Research highlights  Emissions and concentrations in three Brazilian metropolitan areas are reviewed  In 2014, vehicular emissions contributed 34% of the total Brazilian PM2.5 emissions  Non-exhaust sources could represent 90% of the Brazilian PM emissions from 2020 onwards  Brazilian cities have relatively relaxed guidelines of PM concentration compared with WHO  Studied metropolitan areas showed up to 3-fold high concentrations above WHO guidelines

To simulate ozone (O 3) air quality in future decades over the eastern United States, a modeling system consisting of the NASA Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model, the Pennsylvania State University/National Center for Atmospheric Research mesoscale regional climate model (MM5), and the Community Multiscale Air Quality model has been applied. Estimates of future emissions of greenhouse gases and ozone precursors are based on the A2 scenario developed by the Intergovernmental Panel on Climate Change (IPCC), one of the scenarios with the highest growth of CO 2 among all IPCC scenarios. Simulation results for five summers in the 2020s, 2050s, and 2080s indicate that summertime average daily maximum 8-hour O 3 concentrations increase by 2.7, 4.2, and 5.0 ppb, respectively, as a result of regional climate change alone with respect to five summers in the 1990s. Through additional sensitivity simulations for the five summers in the 2050s the relative impact of changes in regional climate, anthropogenic emissions within the modeling domain, and changed boundary conditions approximating possible changes of global atmospheric composition was investigated. Changed boundary conditions are found to be the largest contributor to changes in predicted summertime average daily maximum 8-hour O 3 concentrations (5.0 ppb), followed by the effects of regional climate change (4.2 ppb) and the effects of increased anthropogenic emissions (1.3 ppb). However, when changes in the fourth highest summertime 8-hour O 3 concentration are considered, changes in regional climate are the most important contributor to simulated concentration changes (7.6 ppb), followed by the effect of increased anthropogenic emissions (3.9 ppb) and increased boundary conditions (2.8 ppb). Thus, while previous studies have pointed out the potentially important contribution of growing global emissions and intercontinental transport to O 3 air quality in the United States for future decades, the results presented here imply that it may be equally important to consider the effects of a changing climate when planning for the future attainment of regional-scale air quality standards such as the U.S. national ambient air quality standard that is based on the fourth highest annual daily maximum 8-hour O 3 concentration.

An episode of exceptionally high PM 10 and PM 2.5 levels was observed during the night of the 2-3 March 2000 throughout England and Wales. The weather was characterised by strong westerly winds and widespread rainfall associated with a low pressure system to the north of Scotland, conditions usually associated with relatively clean, unpolluted air. Possible sources included volcanic ash from an eruption on 26 February 2000 in Iceland, or dust from large sandstorms over the Sahara. A combination of atmospheric transport modelling using the Lagrangian dispersion model NAME, an analyses of satellite imagery and observational data from Mace Head has shown that the most likely origin of the episode was long range transport of dust from the Sahara region of North Africa. Further modelling studies have revealed a number of previously unidentified dust episodes, and indicate that transport of dust from the Sahara can occur several times a year. Dust episodes are of interest for a number of reasons, particulate levels can be elevated over a wide area and in some instances can significantly exceeded current air quality standards. If a natural source is identified over which there can be no control, there are implications for the setting of air quality standards.

The United States and Canada currently use exposure-based metrics to protect vegetation from O 3. Using 5 years (1999e2003) of comeasured O 3 , meteorology and growth response, we have developed exposure-based regression models that predict Populus tremuloides growth change within the North American ambient air quality context. The models comprised growing season fourth-highest daily maximum 8-h average O 3 concentration, growing degree days, and wind speed. They had high statistical significance, high goodness of fit, include 95% confidence intervals for tree growth change, and are simple to use. Averaged across a wide range of clonal sensitivity, historical 2001e2003 growth change over most of the 26 M ha P. tremuloides distribution was estimated to have ranged from no impact (0%) to strong negative impacts (e31%). With four aspen clones responding negatively (one responded positively) to O 3 , the growing season fourth-highest daily maximum 8-h average O 3 concentration performed much better than growing season SUM06, AOT40 or maximum 1 h average O 3 concentration metrics as a single indicator of aspen stem cross-sectional area growth.

Air pollution is a recent public health issue. In 2006, the World Health Organization (WHO) published updated air quality guidelines for a number of air pollutants (including PM10 and PM2.5), which recommended for particulate matter annual average concentration levels at half or less the limit values set by European legislation. In the European Union, around 80% of the European urban population is exposed to air pollution above the levels recommended by the WHO guidelines. Only in 2015 the WHO addressed for the first time the topic of the health impacts of air pollution in its general assembly, which adopted a resolution clearly defining air pollution as the world’s largest single environmental health risk factor. Nowadays, the WHO considers air pollution as a major public health threat, causing a 7% increase in overall mortality for each increase of 10 μg/m3 in annual average of PM2.5. This result has been achieved thanks to the outstanding efforts of the director of the WHO’s Envi...

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical re-leases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four exist-ing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concen-tration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nige-ria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450- 1875 µg/m3 for 24-h averaging pe-riod was obtained at Tonwei Oil Ref...

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical releases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four existing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concentration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nigeria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450-1875 µg/m 3 for 24-h averaging period was obtained at Tonwei Oil Refinery, Ekeremor Local Government, Bayelsa State, while the lowest ground level concentration predicted to be 0.0099-0.1 µg/m 3 for 24-h averaging period was obtained at Amakpe International Refinery, Eket Local Government, Akwa Ibom State. Percentage set limits of criteria air pollutants ranging from 0.02% to 94.5% are within the set standard limits and no health risk is associated with areas around the plant's locations while percentage set limits of criteria pollutants ranging from 1.1 to 55.6 folds of the standards exceed the maximum permitted limits, hence affecting areas around the plants. The air quality standards guiding petroleum refinery emissions must be strictly considered, in order to ensure that the ground level concentrations do not exceed the required standard limits and prevent the adverse effects of air pollution in the Nigeria airshed.

this paper aims to design and implement an environmental monitoring and forecasting system based on neural computing approach. The output information is used for feeding the alarming systems. The data are collected in real-time through pollution monitoring sensors at Sohar region. Air pollution is a serious problem and coming from different sources that can lead to a catastrophic, which is needed to be monitored and controlled. The proposed work is monitored and managed the alerts on the emissions of Hydrogen sulfide (H2S) in Oman. It is forecasting the Level of H2S in the Sohar region based Neural Computation. The SOFM is used to compute and predict the ratio of H2S and then issue an alarm to take the proper decision which helps to implement the necessary precautions. The experiments are giving evidence that the predicted values are closely to true values that gained from real sensors with accuracy of 78% and less MSE of 0.03865.

The increase in price of the available refined petroleum products for local consumption in Nigeria had led to the emergence of indigenous technology for petroleum refining in some parts of the Niger Delta region. This study, therefore characterized and quantified artisanal refineries' gaseous emissions for possible air pollutants based on various unit operations involved and evaluated their impacts. It measured the emissions directly from source using E8500 Portable Combustion Analyzer. It also categorized oven sizes/processing capacity of the refineries into various ranges in order to estimate emissions according to processing capacity. The result revealed that; pollutants emission varied significantly between the unit operations and increased with increase in processing capacity. When the emissions were compared with daily limits set by the Environmental Guidelines and Standard for Petroleum Industry in Nigeria (EGASPIN) 2002, the emissions (CO, NO x , and SO 2 ) breached the available set limits. While with the Federal Environmental Protection Agency (FEPA), 1991 set limits for emissions from stationary source; HC and CO breached their limits. SO 2 and H 2 S breached their lower limits but were below the upper limit, while NOx emissions were found within its set limit. The study concluded that, Nigeria Artisanal Petroleum Refineries are sources of air pollution, as they impact the host environment.

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical re-leases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four exist-ing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concen-tration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nige-ria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450- 1875 µg/m3 for 24-h averaging pe-riod was obtained at Tonwei Oil Ref...

In the last decade, government policies promoting foreign investments in the industrial sector particularly for small and medium scale enterprises in Nigeria have led to increased establishment of scrap-iron recycling factories in many states of the federation. Albeit the economic benefits in terms of waste material sourcing and job creation, these scrap-iron recycling factories have attracted significant public criticism due to the characteristic uncontrolled pollution (toxic gases) plume released from their operations into the atmospheric environment of host communities and thus aggravating existing and unresolved rural and urban air pollution problems in Nigeria. This study therefore provides model-based estimates of atmospheric dispersion for gaseous pollutants (SO 2 and NO x ) released from a scrap-iron recycling factory located in Ile-Ife, southwest Nigeria as a case study for investigating emission signature from such sources. Meteorological parameters measured at the factory location in 2012 and 2013 were used to execute U.S recommended short range (<50 km) Gaussian-based regulatory air pollution dispersion model (AERMOD: American Meteorological Society/Environmental Protection Agency Regulatory Model, v12060). Isopleths of maximum concentration values for 1 h averaging

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical releases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four existing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concentration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nigeria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450-1875 µg/m 3 for 24-h averaging period was obtained at Tonwei Oil Refinery, Ekeremor Local Government, Bayelsa State, while the lowest ground level concentration predicted to be 0.0099-0.1 µg/m 3 for 24-h averaging period was obtained at Amakpe International Refinery, Eket Local Government, Akwa Ibom State. Percentage set limits of criteria air pollutants ranging from 0.02% to 94.5% are within the set standard limits and no health risk is associated with areas around the plant's locations while percentage set limits of criteria pollutants ranging from 1.1 to 55.6 folds of the standards exceed the maximum permitted limits, hence affecting areas around the plants. The air quality standards guiding petroleum refinery emissions must be strictly considered, in order to ensure that the ground level concentrations do not exceed the required standard limits and prevent the adverse effects of air pollution in the Nigeria airshed.

The recorded reports of the World Health Organization (WHO) show that a total of 4.2 million death cases is due to exposure to PM2.5 particulate matter. This paper aims to analyze and examine the impact of particulates (PM 2.5 and PM10) on human health in Oman. Also, it proposed neural and mathematical prediction models for predicting predict the future levels of particulate matter (PM2.5 and PM10) and its influence on human health. The paper performs a critical comparative study of proposed models, which is evident that the proposed models were fast, cheap, and accurate. The first model is based on Linear regression that obtained results of the coefficient of determination R2 =0.7604, mean square error (MSE=0.0673), and root mean square error (RMSE=0.2595). The second model is based on non-linear regression polynomial that achieved excellent results of (R2) value of 0.9394 and (MSE) value of 0.0209 and (RMSE) value of 0.1447. The Neural model is more accurate in predicting the expe...

There is no doubt that the keeping the air quality clean and protect it from various sources of emissions represents a major global issues and concern for any government . Today, the people are suffering from overpopulation and urban extension and industrial problems on resources and environmental circles account. This paper aims to design and implement a framework based cloud management system for air quality in Arabic region. The proposed framework model is integrating and managing different types of modules and services, which is a customized version of the ISO 14001 EMS standard. This system can used effectively to monitor and predict the level of air quality and issue suitable alert messages depending on the levels of dangerous. This system help to collect and examine the data cleaning through the environmental stations that are capable of recording the relevant environment information such the level of gases such as O3, CO, HCT, CH4, NCH4, SO2, H2S, NO, NOx and NO2 . Besides, determine the dust level, wind direction and speed, humidity and temperature. Data mining and soft computing based neural and genetic optimization systems could use for analyzing and visualizing the air quality level.

Classifications of mental illness such as schizophrenia are very broad; therefore, the proposed approach attains at practical and task-relevant diagnostic categories by use of clustering techniques. A Self-Organizing Feature Map (SOFM) approach was design and implemented for classifying transcribed speech samples and determines mental disorders. An unsupervised Artificial Neural Network was implemented using the NeuroSolution. The proposed classification system is used to determine whether a text or speech sample is generated by a person has mental illness or not. The proposed approach shows clearly that all the categories are identified and classified appropriately, with the proposed SOFM achieving a high accuracy of (97) in the classification phase for predicting the desired output.

This study investigated the air quality index patterns for PM 2.5 and PM 10 in the airshed of a haulage vehicle park located around a major highway connecting Lagos, the commercial centre of Nigeria to its other parts. Measurements of PM 2.5 and PM 10 were done at five different sub-parks using the aerosol mass monitor GT-331 by Met one instrument. The measured concentrations ranged between 16.07-29.95 μg m −3 and 125.95-433.08 μg m −3 for PM 2.5 and PM 10 , respectively. The air quality index (AQI) within the park with respect to PM 2.5 could be described as moderate but unhealthy within at least two sub parks when PM 10 is considered; hence, the health of vulnerable people could be at risk. This study establishes that vehicular activities at the park could have significant impact on the park's ambient air quality, thus calls for appropriate regulatory measure to protect commuters plying the major highway around the park.

Nigeria is Africa's largest producer of crude oil and the sixth largest exporter of oil within the OPEC bloc. However, virtually all chemical and petrochemical products used in the Nigerian oil and gas industry are currently imported. There is therefore the need for the Nigerian petrochemical industry to reposition itself to take advantage of these increasing market opportunities. This paper examines the strategic fit of the petrochemical industry within the value chain of the oil and gas industry, using critical petrochemical-based products as examples.

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical re-leases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four exist-ing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concen-tration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nige-ria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450- 1875 µg/m3 for 24-h averaging pe-riod was obtained at Tonwei Oil Ref...

Results and Discussion : The concentrations for the gaseous emission from the combustion of PMS were 69.85 mg/m HC, 117.33 mg/m CO, 334 mg/m NOx for 10 ml, 58.93 mg/m 3 HC, 130.33 mg/m CO, 784.33 mg/m NOx for 20 ml, 50.20 mg/m 3 HC, 84.00 mg/m CO, 798.67 mg/m NOx for 30 ml, 65.48 mg/m 3 HC, 160.33 mg/m CO, 850.33 mg/m NOx for 40 ml, 87.31 mg/m 3 HC, 212.67 mg/m CO, 801.33 mg/m NOx, 3.67 mg/m 3 SO2 for 50 ml. This study shows that CO and HC exceeded the permissible limit for stationary sources while NOx and SO2 were below the permissible limit.

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R 2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R 2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

Nigeria is Africa's largest producer of crude oil and the sixth largest exporter of oil within the OPEC bloc. However, virtually all chemical and petrochemical products used in the Nigerian oil and gas industry are currently imported. There is therefore the need for the Nigerian petrochemical industry to reposition itself to take advantage of these increasing market opportunities. This paper examines the strategic fit of the petrochemical industry within the value chain of the oil and gas industry, using critical petrochemical-based products as examples.

Nigeria is Africa's largest producer of crude oil and the sixth largest exporter of oil within the OPEC bloc. However, virtually all chemical and petrochemical products used in the Nigerian oil and gas industry are currently imported. There is therefore the need for the Nigerian petrochemical industry to reposition itself to take advantage of these increasing market opportunities. This paper examines the strategic fit of the petrochemical industry within the value chain of the oil and gas industry, using critical petrochemical-based products as examples.

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R 2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R 2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R 2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R 2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

Artificial intelligent techniques are very much needed to design the environmental monitoring systems. These systems must be smart enough so that all the decisions taken by the system must be accurate. Soft Computing (SC) it is a set of computational methods that attempt to determine satisfactory approximate solutions to find a model for real-world problems. It based on various techniques such as Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms. The aim of this paper is to implement a soft computing technique which is artificial neural network based on Self-Organizing Feature Map (SOFM). SOFM model for monitoring and collecting of the data are real-time and static datasets acquired through pollution monitoring sensors and stations. In the environmental monitoring systems the ultimate requirement is to establish controls for the sensor based data acquisition systems and need interactive and dynamic reporting services. SOFM techniques are used for data analysis and processing. The processed data is used for control system which even feeds to the alarming systems.

There is no systematic attempt to evaluate the air quality of any settlement in the Niger Delta region over a long period. Records of air quality data for this study were generated through secondary sources from impact assessment of facilities aimed at implementing air quality regulations on the environment. Suspended particulate matter in the region's atmosphere ranged from 40 µg/m 3 in Brass to 98 µg/m 3 in Port Harcourt. Carbon monoxide concentrations were highest in Mbiama (191 µg/m 3 ). Nitrogen dioxide concentration was highest in Bonny (187 µg/m 3 ), and sulphur dioxide concentrations ranged from 19 µg/m 3 in Ukwugba to 90 µg/m 3 in Port Harcourt. Total hydrocarbon ranged from 78 µg/m 3 in Odukpani to 192 µg/m 3 in Nchia. Carbon dioxide ranged from 400 ppm in Buguma to 450 ppm in Port Harcourt. The most abundant of the VOCs is benzene and toluene. Ethylene was detected only in one station at concentration of 0.1 µg/m 3 which was negligible. The most abundant of the metals was zinc, which was present at above 2 µg/m 3 in most of the study settlements. In remote settlements like Buguma and Emuoha, some of the metals were not detected at all. In some instances, short-term limits for the pollutants exceeded WHO standards. The need for stakeholders in the region to articulate initiatives that support quality environmental practices was emphasized as laws pertaining to air quality regulations which are weak and less enforceable.

The atmospheric deposition of some heavy metals was investigated using lichen (Parmelia caperata) and moss (Polytrichum juniperinum, Calymperes erosum and Racopilum africanum) samples collected from two oil-producing host communities viz Eket and Ibeno Local Government Areas of Akwa Ibom State, Nigeria. Lichen and moss samples were analysed using atomic absorption spectrometry equipped with flame and graphite furnace after decomposition using acid digestion technique. The concentrations of heavy metals in lichen and moss samples ranged from 0.001 -0.092 μg g −1 for cadmium (Cd); 0.004 -8.793 μg g −1 for chromium (Cr); 0.989 -1.950 μg g −1 for cobalt (Co); 2.350 -110.760 μg g −1 for copper (Cu); 10.530 -153.320 μg g −1 for manganese (Mn); 1.425 -21.730 μg g −1 for nickel (Ni); 0.001 -17.380 μg g −1 for lead (Pb), and 23.530 -130.600 μg g −1 for zinc (Zn). The statistical significance of correlations between Cu-Pb, Cu-Zn, Pb-Ni and Mn-Zn concentrations confirmed anthropogenic sources mainly due to emissions from vehicular traffic, fossil fuel combustion, solid waste disposal and other local anthropogenic activities. In a direct comparison, some of the target heavy metals such as Cd, Cr, Mn, Ni and Zn were accumulated at higher concentrations in moss samples compared to lichen from the same sampling site. The results obtained reveal important contributions towards understanding of heavy metal deposition patterns and provide baseline data that can be used for potential identification of areas at risk from atmospheric heavy metals contamination in the region. The use of epiphytic lichens and mosses provides a cost-effective approach for monitoring regional atmospheric heavy metal contamination and may be effectively used in large scale air pollution monitoring programme.

The assessment of changes in air quality was carried out to assess variation in the air quality status in the wet season in the study area by using the portable air quality equipment and this was done in-situ. The wet season results obtained around Eleme area showed that the mean concentrationof SO 2 was above permissible limits in the wet season; the mean concentration of NO 2 also exceeded the Federal Ministry of Environment (FMEnv) and National Ambient Air Quality Standards (NAAQS) limits.The mean value recorded for H 2 S was 0.37±0.48ppm in the wet season; similarly, the mean value of VOCs was 3.45±2.84ppmin the wet season.The mean concentration of CO is within stipulated limits in the wet season. The mean value observed forammonia was 2.6±1.92ppm; the mean value of methane was 1.27±1.82ppm. Furthermore, the wet season mean concentration of TSP fall within both FMEnv and NAAQS limits. Also, mean concentrations of PM 10 fallbelowNAAQS limit. Similarly, the wet season mean value of PM 2.5 is within the limit. These results revealed that some sampling points and air pollutants pose hazard while some other sampling point and air pollutants pose no risk to the residents in the wet season.

This study assessed the impac
ambient air quality of their host communities. The three
includes emission inventory, determination of the ground level concentrations of the
identified air pollutants in th
view atmospheric dispersion model and comparing the ground level concentrations
with the emission standards of World Health Organization (WHO), World Bank and
Nigeria’s Ambient Air Quality standard.
For all the existing and proposed cement plants results showed that the predicted
ground level concentrations for 24
CO were in the range of0.0005
0.012 – 46 μg/m3 respectively.
was observed that the ground level concentration of
0.001 % – 122.66% of the set limit

ARTICLE INFO ABSTRACT Crude oil is a natural endowment to the people of Nigerian. Hence, refineries were positioned to provide finished petroleum products to both domestic use and for export. Till date, Nigeria still depends largely on the importation of petroleum products for both domestic and industrial applications. The aim of this research work therefore, is to find out the possible causes of this downward trend in the productivity pattern of our Refineries using WRPC as a case study. A survey approach was adopted in this work using questionnaires. Experts in the oil and gas industries were briefed and debriefed on the purpose of the work. A data matrix of 35 by 50 was generated from the questionnaires administered. This data matrix formed was used as the input data for the principal component analysis (PCA) using statisti-XL soft ware. The PCA used facilitated the formation of the factor matrix, scree plot and the transformation matrix. The result of the study showed that regional insecurity, chronic vandalism, managerial constrain, government policies, epileptic power supply and dearth of infrastructural facilities among others were the major factors influencing to a greater extent the productivity pattern of the WRPC among others. Copyright©2016, Ashiedu and Izelu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The atmospheric deposition of some heavy metals was investigated using lichen (Parmelia caperata) and moss (Polytrichum juniperinum, Calymperes erosum and Racopilum africanum) samples collected from two oil-producing host communities viz Eket and Ibeno Local Government Areas of Akwa Ibom State, Nigeria. Lichen and moss samples were analysed using atomic absorption spectrometry equipped with flame and graphite furnace after decomposition using acid digestion technique. The concentrations of heavy metals in lichen and moss samples ranged from 0.001 -0.092 μg g −1 for cadmium (Cd); 0.004 -8.793 μg g −1 for chromium (Cr); 0.989 -1.950 μg g −1 for cobalt (Co); 2.350 -110.760 μg g −1 for copper (Cu); 10.530 -153.320 μg g −1 for manganese (Mn); 1.425 -21.730 μg g −1 for nickel (Ni); 0.001 -17.380 μg g −1 for lead (Pb), and 23.530 -130.600 μg g −1 for zinc (Zn). The statistical significance of correlations between Cu-Pb, Cu-Zn, Pb-Ni and Mn-Zn concentrations confirmed anthropogenic sources mainly due to emissions from vehicular traffic, fossil fuel combustion, solid waste disposal and other local anthropogenic activities. In a direct comparison, some of the target heavy metals such as Cd, Cr, Mn, Ni and Zn were accumulated at higher concentrations in moss samples compared to lichen from the same sampling site. The results obtained reveal important contributions towards understanding of heavy metal deposition patterns and provide baseline data that can be used for potential identification of areas at risk from atmospheric heavy metals contamination in the region. The use of epiphytic lichens and mosses provides a cost-effective approach for monitoring regional atmospheric heavy metal contamination and may be effectively used in large scale air pollution monitoring programmer.

 Abstract—this paper aims to design and implement an environmental monitoring and forecasting system based on neural computing approach. The output information is used for feeding the alarming systems. The data are collected in real-time through pollution monitoring sensors at Sohar region. Air pollution is a serious problem and coming from different sources that can lead to a catastrophic, which is needed to be monitored and controlled. The proposed work is monitored and managed the alerts on the emissions of Hydrogen sulfide (H2S) in Oman. It is forecasting the Level of H2S in the Sohar region based Neural Computation. The SOFM is used to compute and predict the ratio of H2S and then issue an alarm to take the proper decision which helps to implement the necessary precautions. The experiments are giving evidence that the predicted values are closely to true values that gained from real sensors with accuracy of 78% and less MSE of 0.03865.

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical releases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four existing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concentration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nigeria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450 -1875 µg/m 3 for 24-h averaging period was obtained at Tonwei Oil Refinery, Ekeremor Local Government, Bayelsa State, while the lowest ground level concentration predicted to be 0.0099 -0.1 µg/m 3 for 24-h averaging period was obtained at Amakpe International Refinery, Eket Local Government, Akwa Ibom State. Percentage set limits of criteria air pollutants ranging from 0.02% to 94.5% are within the set standard limits and no health risk is associated with areas around the plant's locations while percentage set limits of criteria pollutants ranging from 1.1 to 55.6 folds of the standards exceed the maximum permitted limits, hence affecting areas around the plants. The air quality standards guiding petroleum refinery emissions must be strictly considered, in order to ensure that the ground level concentrations do not exceed the required standard limits and prevent the adverse effects of air pollution in the Nigeria airshed.

The emission factor approach was employed to predict the emission potential of volatile organic compounds (VOCs) from both operating and proposed petroleum refineries in Nigeria. The emission sources from the refineries (4 operating and 14 newly licensed) were classified as point and area with anticipated VOC release into the environment estimated. The overall contribution was also estimated using human population and land mass distribution. The no-control-measure option gave estimated VOCs from the existing 4 petroleum refineries located in the states of Delta, Kaduna, and Rivers as 147,212 ton/annum (if operated at full capacity), while the successful construction and commissioning of the approved 14 refineries located in the states of Akwa Ibom, Anambra, Cross River, Delta, Lagos, Ogun, Ondo, and Rivers can raise this by about 240%. Rivers State with the highest number of refineries has the highest emission distribution per head while Lagos State has the least; the southern part of the country was noted to be at great risk of VOC emission from petroleum refineries. Both technology and policy options are suggested to control the potential emissions.

In the process of crude oil production, sulfur dioxide (SO 2 ) gas is produced as a result of flaring and booming of crude oil associated gases. This paper presents the study carried out on the monitoring of SO 2 emission from some oilfields in the crude oil producing areas of Niger-Delta region of Nigeria. Six locations were studied while monitoring involved morning and evening times for four months using a Testo 350 Flue Gas Analyzer. Comparison of the mean measurements with Nigerian FEPA tolerance limits indicate that SO 2 values from 60 m away from the point sources for all the fields were comparatively higher than their maximum tolerance limits of 50 g/m 3 for long term exposure and SO 2 values were also comparatively higher than the emission limit range (30-300 g/m 3 ) for SO 2 from stationary sources. It is concluded that this breach of tolerance level may result in acid rain formation, which can be detrimental to the environment while the long-term effect can alter the ecology of the areas. It is recommended that the environmental matrix quality for soil, water, and air be carried out for these areas periodically.