Carbon based sensors for air quality monitoring networks; middle east perspective.

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Estimation of Anthropogenic VOCs Emission Based on Volatile Chemical Products: A Canadian Perspective.

Volatile organic compounds (VOCs) in urban areas are of great interest due to their significant role in forming ground-level ozone and adverse public health effects. Emission inventories usually compile the outdoor VOCs emission sources (e.g., traffic and industrial emissions). However, considering emissions from volatile chemical products (e.g., solvents, printing ink, personal care products) is challenging because of scattered data and the lack of an effective method to estimate the VOCs emission rate from these chemical products. This paper aims to systematically analyse potential sources of VOCs emission in Canada's built environment, including volatile chemical products. Also, spatial variation of VOCs level in the ambient atmosphere is examined to understand the VOC relationship with ozone and secondary organic aerosol formation. The study shows that VOCs level may vary among everyday microenvironments (e.g., residential areas, offices, and retail stores) depending on the frequency of product consumption, building age, ventilation condition, and background ambient concentration in the atmosphere. However, it is very difficult to establish VOC speciation and apportionment to different volatile chemical products that contribute most significantly to exposure and target subpopulations with elevated levels. Thus, tracer compounds can be used to identify inventory sources at the consumer end. A critical overview highlights the limitations of existing VOC estimation methods and possible approaches to control VOC emissions. The findings provide crucial information to establish an emission inventory framework for volatile chemical products at a national scale and enable policymakers to limit VOCs emission from various volatile chemical products.

Update on air pollution control strategies for coal-fired power plants.

Abstract: Coal is expected to remain a significant power supply source worldwide and shifting to carbon-neutral fuels will be challenging because of growing electricity demand and booming industrialization. At the same time, coal consumption results in severe air pollution and health concerns. Improvement in emission control technologies is a key to improving air quality in coal power plants. Many scientists reported removing air pollutants individually via conventional control methods. However, controlling multiple pollutants combinedly using the latest techniques is rarely examined. Therefore, this paper overviews the current and advanced physical technologies to control multi-air pollutants synergistically, including carbon control technologies. Also, the paper aims to examine how potential air pollutants (e.g., PM2.5, SO2, NOx, CO2), including mercury from the coal-fired power plants, cause environmental impacts. The data synthesis shows that coal quality is the most significant factor for increasing air emissions, regardless of power plant capacity. It is found that selecting techniques is critical for new and retrofitted plants depending on the aging of a power plant and other socio-economic factors. Considering the future perspective, this paper discusses possible pathways to transform from linear to a circular economy in a coal power plant sector, such as utilizing energy losses through energy-efficient processes and reuse of syngas. The article provides an in-depth analysis of advanced cost-effective techniques that would help to control the air pollution level. Additionally, a life cycle assessment-based decision-making framework is proposed that would assist the stakeholders in achieving net-zero emissions and offset the financial burden for air pollution control in coal-fired power plants. Supplementary Information: The online version contains supplementary material available at 10.1007/s10098-022-02328-8.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review.

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

A regional numerical environmental multimedia modeling approach to assess spatial Eco-Environmental exposure risk of perfluorooctane sulfonate (PFOS) in the Pearl river basin.

This paper presents a novel numerical environmental multimedia modeling system (RNEMM) for assessing the environmental fate of emerging organic contaminants and their relative health risk at a regional scale. The RNEMM is developed based on an integrated numerical algorithm that comprises four sub-models: a river network simulation module, a gaseous phase simulation module, a mass balance based simulation module for soil compartment, and a food web analysis module. This RNEMM has been applied to simulate the spatial distribution of PFOS and assess the consequent health risks for a central water basin region of the Pearl River in China. The study region includes the urban areas of Guangzhou, Foshan, and Dongguan Cities with emission sources of PFOS, which was detected in local water, sediments, and air environment. The spatial concentration distributions of PFOS in water, sediment, air, soil, and various fish species are examined based on RNEMM and compared with the measured data. With a focus on water environment, it shows that the simulated results essentially agree well with measured concentrations. Comparing the simulated results and the measured data collected in 2013, the relative errors are mostly less than 40 % in the surface water and sediment zones for this regional scale field study. Whereas the relative error in the atmosphere zone is less than 5%. In addition, the health risk assessment for children and adults is conducted based on the RNEMM approach. The hazard quotient (HQ) values for the 95th percentile in most subareas of the study region are higher than 0.1, showing a low-risk level for the study period. The results indicate that the RNEMM is a useful modeling tool to manage the environmental and health risks associated with emerging contaminants on regional air, water, soil, and ecosystem at an adequate spatial-temporal resolution.

Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis.

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%-7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.

Air quality modeling for effective environmental management in the mining region.

Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet's boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM10, PM2.5, TSP, NO2, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM2.5, from 0.71 to 0.82 for PM10, and from 0.71 to 0.89 for NO2. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM2.5, PM10, TSP, and NO2) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory. IMPLICATIONS: The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.

Examining Screening-Level Multimedia Models Through a Comparison Framework for Landfill Management.

Two models for evaluating transport and fate of benzene were studied and compared in this paper. A fugacity model and an analytical environmental multimedia model (AEMM) were used to reconcile fate and mass transfer of benzene observed in a landfill site. The comparison of two models were based on average concentrations and partition behavior of benzene among three different phases i.e., air, soil, and groundwater. In the study of fugacity method about 99.6 % of the total benzene flux was distributed into air from landfill source. According to AEMM the diffusion gas flux was also predominant mechanism for benzene released from landfill and advection of gas and liquid was second dominant transport mechanism at steady-state conditions. Overall study of fugacity modeling (Level I and II) confirms the fate and transport mechanism of benzene released from landfill by comparing it with AEMM. However, the values of predicted concentrations, advection, and diffusion flux of benzene using fugacity model were different from AEMM results due to variation in input parameters. In comparison with experimental observations, fugacity model showed more error difference as compared to AEMM as fugacity model is treated as a single unit box model. This study confirms that fugacity model is a screening level tool to be used in conjunction with detailed remediation followed by AEMM that can be evolved as strategic decision-making stage.

Environmental management in North American mining sector.

This paper reviews the environmental issues and management practices in the mining sector in the North America. The sustainable measures on waste management are recognized as one of the most serious environmental concerns in the mining industry. For mining activities, it will be no surprise that the metal recovery reagents and acid effluents are a threat to the ecosystem as well as hazards to human health. In addition, poor air quality and ventilation in underground mines can lead to occupational illness and death of workers. Electricity usage and fuel consumption are major factors that contribute to greenhouse gases. On the other hand, many sustainability challenges are faced in the management of tailings and disposal of waste rock. This paper aims to highlight the problems that arise due to poor air quality and acid mine drainage. The paper also addresses some of the advantages and limitations of tailing and waste rock management that still have to be studied in context of the mining sector. This paper suggests that implementation of suitable environmental management tools like life cycle assessment (LCA), cleaner production technologies (CPTs), and multicriteria decision analysis (MCD) are important as it ultimately lead to improve environmental performance and enabling a mine to focus on the next stage of sustainability.