Environmental implications of the quality of charcoal briquettes and lump charcoal used for grilling

Evaluating the properties that affect the quality of the charcoal product, determining the limits of toxic emissions during combustion, and studying their impact on human health

This research focuses on examining the potential impact of charcoal briquettes and lumps on human health due to the emissions they release, and verifying their quality standards. Quality assessment was conducted using a device capable of measuring toxic gases to identify contaminants from various sources such as biomass, synthetic resins, coal, metals, and mineral matter. Toxicity assessments were carried out on five types of briquettes and two varieties of lump charcoal. All charcoal samples were subjected to elemental analysis (SEM/EDAX), including the examination of Ca, Al, Cr, V, Cu, Fe, S, Sr, Si, Ba, Pb, P, Mn, Rb, K, Ti, and Zn. The results showed that burning lump charcoal had toxicity indexes ranging from 2.5 to 5, primarily due to NOx emissions. Briquettes, on the other hand, exhibited higher toxicity indices between 3.5 and 6.0, with CO2 being the main contributor to toxicity. The average 24-h CO content of all charcoal samples exceeded the World Health Organization's 24-h Air Quality Guideline of 6.34 ppm, with a measurement of 37 ppm. The data indicates that most of the products tested did not meet the prevailing quality standard (EN 1860-2:2005 (E) in Appliances, solid fuels and firelighters for barbecuing-Part 2: Barbecue charcoal and barbecue charcoal briquettes-Requirements and test method, 2005), which specifies a maximum of 1% contaminants, with some products containing as much as 21% impurities. The SEM analysis revealed irregularly shaped grains with an uneven distribution of particles, and the average particle size distribution is quite broad at 5 μm. Malaysia Charcoal had the highest calorific value at 32.80 MJ/Kg, with the value being influenced by the fixed carbon content-higher carbon content resulting in a higher calorific value.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

A Deep Learning Approach to Intrusion Detection and Segmentation in Pellet Fuels Using Microscopic Images

Pellet fuels are nowadays commonly used as a heat source for food preparation. Unfortunately, they may contain intrusions which might be harmful for humans and the environment. The intrusions can be identified precisely using immersed microscopy analysis. The aim of this study is to investigate the possibility of autonomous identification of selected classes of intrusions using relatively simple deep learning models. The semantic segmentation was chosen as a method for impurity identification in the microscopic image. Three architectures of deep networks based on UNet architecture were examined. The networks contained the same depth as UNet but with a successively limited number of filters. The input image influence on the segmentation results was also examined. The efficiency of the network was assessed using the intersection over union index. The results showed an easily observable impact of the filter used on segmentation efficiency. The influence of the input image resolution is not so clear, and even the lowest (256 × 256 pixels) resolution used gave satisfactory results. The biggest (but still smaller than originally proposed UNet) network yielded segmentation quality good enough for practical applications. The simpler one was also applicable, although the quality of the segmentation decreased considerably. The simplest network gave poor results and is not suitable in applications. The two proposed networks can be used as a support for domain experts in practical applications.

Investigating Patterns of Air Pollution in Metropolises Using Remote Sensing and Neural Networks During the COVID-19 Pandemic

The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH₄), Carbon Monoxide (CO), Carbon Dioxide (CO₂), Nitrogen Dioxide (NO₂), Ozone (O₃), Sulfur Dioxide (SO₂), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R²=0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R²=0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.

Inhalation risk to PAHs and BTEX during barbecuing: The role of fuel/food type and route of exposure

The manuscript presents an innovative and holistic approach to quantifying PAHs and BTEX emissions from the grilling process and indicates a novel driven-toxicity-based solution to recognize health effects related to BBQ emissions. The exposure scenario includes the type of grilling device, food type, and individual attitudes, but also a keen understanding of the broad health implications related to the gaseous/particulate PAHs emission, or age-related effects. The calculated incremental lifetime cancer risk (ILCR) associated with the exposure to PAH congeners and BTEX indicates an unacceptable level in the case of charcoal and briquette grilling with the highest values for professional cooks. The sum of 15 PAH concentrations in grilled foods was highest for meat grilling over charcoal briquettes - 382,020.39 ng/m³ and lowest for meat grilling on a gas grill - 1442.16 ng/m³. The emissions of BTEX from lump charcoal grilling were 130 times higher compared to the gaseous grill. In all considered scenarios lump-charcoal and charcoal briquettes grilling derive the ILCR above the 10^-4, indicating negative effects of traditional grills on human health. The paper completes knowledge of wide-ranging health implications associated with BBQs, a topic that is almost completely unaddressed among the scientific community and policymakers.

Charcoal Production in Portugal: Operating Conditions and Performance of a Traditional Brick Kiln

Charcoal is produced in large quantities in the Portuguese region of Alentejo mainly using traditional brick kilns. Information about this type of carbonization technology is scarce, which makes it urgent to characterize the process as a starting point for performance improvements. In this context, this study aims to characterize the operation of a cylindrical brick kiln (≈80 m3) during regular wood carbonization cycles. Relevant process parameters were monitored along with the yields and/or composition of the main products (carbonization gas, charcoal, and charcoal fines) to evaluate the mass balance of the process. The results show that the bulk of the kiln operates at temperatures below 300 °C, which greatly limits the quality of the charcoal. For instance, the fixed carbon content of charcoal can easily be as low as 60 wt.%. The yield of charcoal is also low, with values below 25 wt.% of dry wood feed. This means that significant quantities of by-products are generated in the process with little or no commercial value. Modifications in the carbonization process are needed to improve efficiency, charcoal quality, and environmental acceptance to sustain this activity in regions where it still represents vital income related to wood-waste management.

Parameter optimization of waste coal briquetting and particulate matter emissions test during combustion: A case study

The abundant coal powder generated as a waste by-product during the lignite upgrading process is harmful to the environment. Lignite briquetting offers a practical solution for lignite usage. Altering the process parameters of briquetting can significantly improve briquette quality. In this paper, the characteristics of lignite briquettes, including drop strength and compressive strength were investigated. A combination of quadratic orthogonal rotation combination designs and regression equations established the best process parameters to be 40% weight of #2 upgraded coal, 20% weight of briquetting moisture, 25 MPa of briquetting pressure, and 12 h of drying time. The low error variance of the drop strength and compressive strength, at 0.01% and 1.83% respectively, verified the feasibility of the model. The analysis by scanning electron microscope (SEM) showed that the surface morphology of briquette was denser than that of raw coal. Finally, the combustion test of briquettes revealed that the particulate matter emission (PM_2.5) of briquette was 16.7% lower than that of raw coal. In summary, these data provide a theoretical reference for realizing the reasonable utilization potential of waste products derived from industrial processes.

Model Supporting Development Decisions by Considering Qualitative–Environmental Aspects

Dynamic changes in customers’ expectations and unfavorable climate changes have generated the need to consider such aspects in the process of creating new products and the modernization of existing products. Simultaneously including customers’ expectations and environmental impact is a key element of the sustainable development of products. Enterprises attempt, within their awareness and possibilities, to apply the idea of sustainability; they do this more or less methodically. As such, an instrument to support decision-making in the area of product development is still needed because it would both be desirable for customers and have less of a negative effect on the natural environment. The purpose of this study was to develop a model that supports decision-making in the development of products while considering sustainability. The model determines the key criteria of the product, criteria states (current and future), and their positive correlations (e.g., achieving high levels of product quality and no (or a reduction in) destructive impact on the environment). To reduce the fuzzy decision-making environment, multiplicative decision methods with the fuzzy Saaty scale were implemented. These methods were the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The model is able to support qualitative–environment decisions in the development of any product.

Pyrolysis Characteristics of Undervalued Wood Varieties in the Portuguese Charcoal Sector

Charcoal production in Portugal is mostly based on the valorization of woody residues from cork oak and holm oak, the latter being considered a reference feedstock in the market. Nevertheless, since wildfire prevention became a priority in Portugal, after the recent dramatic wildfires, urgent actions are being conducted to reduce the fuel load in the forests, which is increasing the amount of biomass that is available for valorization. Additionally, biomass residues from agriculture, forest management, control of invasive species, partially burnt wood from post-fire recovery actions, and waste wood from storm devastated forests need also to be considered within the national biomass valorization policies. This has motivated the present work on whether the carbonization process can be used to valorize alternative woody biomasses not currently used on a large scale. For this purpose, slow pyrolysis experiments were carried out with ten types of wood, using a fixed bed reactor allowing the controlled heating of large fuel particles at 0.1 to 5 °C/min and final temperatures within 300–450 °C. Apart from an evaluation of the mass balance of the process, emphasis was given to the properties of the resulting charcoals considering its major market in Portugal—barbecue charcoal for both recreational and professional purposes.

Densification of agro-residues for sustainable energy generation: an overview

The global demand for sustainable energy is increasing due to urbanization, industrialization, population, and developmental growth. Transforming the large quantities of biomass resources such as agro-residues/wastes could raise the energy supply and promote energy mix. Residues of biomass instituted in the rural and industrial centers are enormous, and poor management of these residues results in several indescribable environmental threats. The energy potential of these residues can provide job opportunities and income for nations. The generation and utilization of dissimilar biomass as feedstock for energy production via densification could advance the diversity of energy crops. An increase in renewable and clean energy demand will likely increase the request for biomass residues for renewable energy generation via densification. This will reduce the environmental challenges associated with burning and dumping of these residues in an open field. Densification is the process of compacting particles together through the application of pressure to form solid fuels. Marketable densification is usually carried out using conventional pressure-driven processes such as extrusion, screw press, piston type, hydraulic piston press, roller press, and pallet press (ring and flat die). Based on compaction, densification methods can be categorized into high-pressure, medium-pressure, and low-pressure compactions. The common densification processes are briquetting, pelletizing, bailing, and cubing. They manufacture solid fuel with desirable fuel characteristics-physical, mechanical, chemical, thermal, and combustion characteristics. Fuel briquettes and pellets have numerous advantages and applications both in domestic and industrial settings. However, for biomass to be rationally and efficiently utilized as solid fuel, it must be characterized to determine its fuel properties. Herein, an overview of the densification of biomass residues as a source of sustainable energy is presented.