The Impact of Solid Fuel Residential Boilers Exchange on Particulate Matter Air Pollution

Koval, Silvie; Vytisk, Jiri; Růžičková, Jana; Raclavská, Helena; Hellebrandová, Lucie

doi:10.3390/app11125400

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensor devices would autonomously monitor the flue gas temperature, CO and particulate matter concentrations. Koval et al [25] estimated if residential heating affects the quality of air by modeling three provided conditions of a solid fuel boiler altered at chosen places and compared the outcomes with measured data. Akarsu et al [26] aimed to comparatively estimate the outcome of a hydrothermal carbonization (HTC) condition on the produce and fuel properties of hydrochar attained in food waste (FW) and its digestate (FD).…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

View full text Add to dashboard Cite

Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.

show abstract

Section: Related Workmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Impact of Seasonal Heating on PM10 and PM2.5 Concentrations in Sučany, Slovakia: A Temporal and Spatial Analysis

Jandacka,

Durcanska,

Nicolanska

et al. 2024

Fire

View full text Add to dashboard Cite

Complying with strict PM10 and PM2.5 limit values poses challenges in many European regions, influenced by diverse factors such as natural, regional, and local anthropogenic sources. Urban air pollution, exacerbated by road transport, local industry, and dust resuspension, contrasts with rural areas affected by solid fuel-based local heating and increasing wood burning. This study focuses on village of Sučany, located in Slovakia, analysing PM concentrations during non-heating and heating seasons. The method of analysis relies on the use of the MP101M air quality analyser that utilises beta radiation absorption method. One set of measurements was conducted at five distinct locations during the heating season (18/01/2019 to 28/02/2019) and non-heating season (14/08/2018 to 1/10/2018). Significant differences emerged during the non-heating season with corresponding PM10 averages of 23.0 µg/m3 and PM2.5 at 19.3 µg/m3. In contrast, the PM10 averaged 53.9 µg/m3 and 52.8 µg/m3 during the heating season. The heating season shows PM2.5 contributing up to 98% of total PM10. The distribution of PM10 and PM2.5 pollution and the location of the potential source obtained using polar plots differed during the heating and non-heating seasons. This research underscores the impact of local heating on air quality in a typical Slovak village. The key recommendation for targeted interventions is supporting up-to-date air quality data, education, and financial incentives for citizens in order to implement cleaner and modern heating solutions.

show abstract

The Impact of Solid Fuel Residential Boilers Exchange on Particulate Matter Air Pollution

Cited by 2 publications

References 24 publications

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Impact of Seasonal Heating on PM10 and PM2.5 Concentrations in Sučany, Slovakia: A Temporal and Spatial Analysis

Contact Info

Product

Resources

About