Air quality in Yanbu,Saudi Arabia

Yanbu, on the Red Sea, is an affluent Saudi Arabian industrial city of modest size. Substantial effort has been spent to balance environmental quality, especially air pollution, and industrial development. We have analyzed six years of observations of criteria pollutants O₃, SO₂, particles (PM_2.5 and PM₁₀) and the known ozone precursors—volatile organic compounds (VOCs) and nitrogen oxides (NO_x). The results suggest frequent VOC-limited conditions in which ozone concentrations increase with decreasing NO_x and with increasing VOCs when NO_x is plentiful. For the remaining circumstances ozone has a complex non-linear relationship with the VOCs. The interactions between these factors at Yanbu cause measurable impacts on air pollution including the weekend effect in which ozone concentrations stay the same or even increase despite significantly lower emissions of the precursors on the weekends. Air pollution was lower during the Eids (al-Fitr and al-Adha), Ramadan and the Hajj periods. During Ramadan, there were substantial night time emissions as the cycle everyday living is almost reversed between night and day. The exceedances of air pollution standards were evaluated using criteria from the U.S. Environmental Protection Agency (EPA), World Health Organization (WHO), the Saudi Presidency of Meteorology and Environment (PME) and the Royal Commission Environmental Regulations (RCER). The latter are stricter standards set just for Yanbu and Jubail. For the fine particles (PM_2.5), an analysis of the winds showed a major impact from desert dust. This effect had to be taken into account but still left many occasions when standards were exceeded. Fewer exceedances were found for SO₂, and fewer still for ozone. The paper presents a comprehensive view of air quality at this isolated desert urban environment.

Implications: Frequent VOC-limited conditions are found at Yanbu in Saudi Arabia that increase ozone pollution if NO_x is are reduced. In this desert environment, increased nightlife produces the highest levels of VOCs and NO_x at night rather than the day. The effects increase during Ramadan. Fine particles peak twice a day—the morning peak is caused by traffic and increases with decreasing wind, potentially representing health concerns, but the larger afternoon peak is caused by the wind, and it increases with increasing wind speeds. These features suggest that exposure to pollutants must be redefined for such an environment.     

Environmental public health tracking: piloting methods for surveillance of environmentally related diseases in England and Wales

An effective environmental public health tracking system integrates data and intelligence on environmental hazards, exposures, and health outcomes to focus interventions on reducing the impact of environmental contamination on public health. Most work in this area in the UK has focused on assessing data on hazards that are relatively easy to obtain. However, most hazards will present no actual risk and information on exposure is required to make an effective risk assessment. Obtaining exposure data is technically challenging, expensive, and potentially raises ethical concerns. Consequently, the Health Protection Agency is exploring methods for targeting geographical zones for efficient detailed environmental assessment (including exposure assessment). This paper describes and assesses three methods (indirect standardization, statistical process control (SPC) and kernel density contouring) for the surveillance of potentially environmentally related diseases for this purpose. While the evaluation demonstrates the utility of the three methods, particularly SPC, the comparison was limited due to ethical approval issues.     

Correction to: Dye decomposition and air de‑pollution performance of TiO2/SiO2 and N‑TiO2/SiO2 photocatalysts coated on Portland cement mortar substates

Environmental Science and Pollution Research -     

Antitumor effect of copper nanoparticles on human breast and colon malignancies

Environmental Science and Pollution Research - Breast and colon carcinomas are two types of common cancers which lead to cancer-related deaths. Due to their cytotoxic potential against cancer...     

Per- and polyfluoroalkyl substances (PFASs) in contaminated coastal marine waters of the Saudi Arabian Red Sea: a baseline study

Environmental Science and Pollution Research - Per- and polyfluoroalkyl substances (PFASs) are today considered important constituents of the continuously growing substance group of persistent...     

Mechanistic insights into acetaminophen removal on cashew nut shell biomass-derived activated carbons

Environmental Science and Pollution Research - Activated carbons prepared from cashew nut shells by chemical activation with phosphoric acid were tested for the removal of acetaminophen. It was...     

Improving the decolorization of glycerol by adsorption using activated carbon derived from oil palm biomass

Environmental Science and Pollution Research - This study provides insight into the decolorization strategy for crude glycerol obtained from biodiesel production using waste cooking oil as raw...     

Enhancing the performance of data-driven models for monthly reservoir evaporation prediction

Environmental Science and Pollution Research - The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional...     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.     

Detection of hazardous pollutants in chrome-tanned leather using locally developed laser-induced breakdown spectrometer

K?z?l?rmak River has been used as Ankara??s drinking water source for approximately 1.5?years. Therefore, this region??s water, sediment, and fish samples are measured for detecting the heavy metals. This is important for the current situation as well as the future in terms of potential impact. The amount of heavy metals in drinking water should be within the limited values; otherwise, the accumulation of heavy metals will cause many problems to living organisms. Especially high levels of arsenic, cadmium, nickel, mercury, etc. are very dangerous to freshwater ecosystems as for human if the water is being use as drinking water. In this study, water, sediment samples, muscle, and gills of three fish species (Capoeta tinca, Capoeta capoeta, Leuciscus cephalus) were analyzed for the presence of heavy metals such as (Al, Fe, As, Cd, Ni, Mn, Se, Si) to determine present accumulation levels and possible toxic effect. The accumulation pattern of heavy metals in the water, sediment, and fish tissue follows the sequence: Si > Fe > Al > Mn > As > Ni > Se > Cd, Fe > Al > Mn > Ni > As > Se > Cd, and Fe > Al > Mn > As > Ni > Si > Cd. In addition, the detected concentrations of heavy metals in the K?z?l?rmak and Delice Rivers are compared with other heavy metal studies in the other main rivers and lakes in Turkey.