Countries' classification by environmental resilience

Moghim, Sanaz; Garna, Roja Kaveh

doi:10.1016/j.jenvman.2018.09.090

Cited by 36 publications

(20 citation statements)

References 12 publications

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“…Population growth, industrialization, climate change, and the ever-growing fact of urbanization are further accelerating the production of emissions and the severe impacts on air quality [4][5][6]. Air pollution is known as a profound contributor to human mortality and as a potential danger to the environment and ecological systems [2,7,8]. Thus, intelligent monitoring of air pollutants is of the utmost importance to maintain acceptable levels of air quality for well-being [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…It is the most dangerous hazard for infants and young adults as it influences the central nervous system, causing severe illnesses [22]. Previous studies (e.g., [7][8][9][10][11]) report that a substantial amount of mercury outflows into the environment come from coal-fired power plants. In 2010, roughly 1960 tonne/year of mercury flowed into the air from various industries worldwide [23], including the burning of coal, which had a relatively high share of 24% [24].…”

Section: Introductionmentioning

confidence: 99%

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Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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“…Over the last decade, there has been a substantial amount of research on, and investment in, disaster risk reduction and developing the concept of urban resilience (Hernantes et al 2019;Kabir, Sato, et al 2018). A focus has been on mitigating the ''shocks'' of natural hazards and disasters (Birkmann and Peduzzi 2007;Cai et al 2018;Zobel and Baghersad 2018), and on understanding how to ensure that our socio-technical systems respond, adapt, and recover from natural and anthropogenic hazards (Moghim and Garna 2019). However, in order to build climate-resilient societies, the human capacity of vulnerable populations to cope with long-term stressors arising from environmental hazards must also be enhanced (Akter and Mallick 2013).…”

Section: Introductionmentioning

confidence: 99%

The Influence of Underlying Stresses from Environmental Hazards on Resilience in Bangladesh: A System View

Lake

Fenner

2019

Int J Disaster Risk Sci

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Bangladesh is one of the countries most vulnerable to climate change, while its people also suffer from a range of environmental hazards linked to the growing prevalence of non-communicable diseases. These diseases are responsible for increasing morbidity and mortality and lead to other stresses within the population. Such stresses create continuous impacts on the health and well-being of the population, compounding their vulnerability and inhibiting their capacity to cope with frequent event-related shocks, such as floods and drought. A systems approach is taken to examine four important environmental hazards in Bangladesh-arsenic contamination of drinking water, arsenic transmission through the food chain, indoor air quality, and air pollution. A review of these hazards is presented in a conceptual framework that links human well-being with the key system components of infrastructure, institutions, knowledge, and behavior. This reveals key underlying factors between the hazards and uncovers system structures that can lead to more effective hazard mitigation, and the establishment of strategic intervention points. The article concludes that elimination of these continuous stresses will only come about through the culmination of multiple interventions over time, undertaken in an iterative manner that builds on the continual advancement of hazard understanding. The role of individual behaviors, together with factors such as risk awareness and perception of the hazards, has been identified as crucial for achieving successful mitigation solutions. Improved knowledge of the hazards, public awareness, and government accountability are focus points to reduce population exposure and enhance response capacity.

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“…The air pollution is known as a profound contributor to human mortality and potential danger to the environment and ecological systems. [2,7,8]. Thus, intelligent monitoring of the air pollutants is of utmost importance to maintain acceptable air quality for well-being [9][10][11][12].…”

mentioning

confidence: 99%

“…Previous studies, e.g. [7][8][9][10][11], report that a substantial amount of mercury outflows to the earth comes from coal-fired power plants. In , roughly 1960 tone/year mercury outflowed to the air from various sections worldwide [23].…”

mentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

show abstract

Countries' classification by environmental resilience

Cited by 36 publications

References 12 publications

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

The Influence of Underlying Stresses from Environmental Hazards on Resilience in Bangladesh: A System View

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

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