Complying with strict PM₁₀ and PM_2.5 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 PM_2.5 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 PM_2.5 contributing up to 98% of total PM₁₀. The distribution of PM₁₀ and PM_2.5 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.

Environmental pollution and air quality are increasingly important topics within the areas of Smart City and state regulations. The aim of the article is to present the progress in the design of the emission monitoring system intended for use in non-industry heating appliances in households. The article analyses potential opportunities in aims of improving the design of a monitoring system that was experimentally tested on small heat source. Geometry modifications, sensor placements and orientation were evaluated using Computational Fluid Dynamics in Ansys Fluent software 2022 R2 including both air and CO/CO₂ dispersion models. The final geometry introduced a streamlined funnel along with a separation element, which distributed the air flow evenly between the sensors. The results showed more uniform air flow as the standard deviation of downstream velocity improved from ± 0,693 m/s to ± 0,297 m/s. In conclusion, these outcomes assist in the reduction of air pollution.

This article presents an intelligent design approach for frequency-controlled small-scale heating systems, incorporating low-cost sensors to enhance efficiency and reduce emissions. It begins with a literature review focusing on the use of modern sensors and frequency control technologies for regulating fan and fuel speeds. As part of the review, a basic outline of neural networks applied in biomass combustion systems is also provided, highlighting their potential for optimizing combustion and emission control through adaptive, data-driven strategies. The proposed smart control system is built around dual Raspberry Pi units, supported by Arduino Uno R3 and Arduino Nano microcontrollers, chosen for their compatibility with available sensor libraries. The frequency control consists of two IVT 0,75kW converters. The system integrates a range of low-cost environmental sensors from DFRobot, including the SCD41 CO₂ sensor, an electrochemical oxygen sensor, an ozone sensor, the ENS160+BME280 environmental sensor (measuring air quality, temperature, humidity, and pressure), a separate temperature and humidity sensor, and a laser-based particulate matter (PM) sensor for total suspended particles (TSP).The fan and pellet feeder are driven by frequency inverters connected via an FT232RL USB to RS485 adapter, allowing precise real- time control based on sensor feedback. The article presents detailed hardware and software requirements, followed by a step-by-step methodology for constructing and testing the system. Special emphasis is placed on the integration of these components into an experimental boiler setup, demonstrating how intelligent regulation can be practically implemented in low-cost biomass heating applications to improve combustion efficiency and reduce harmful emission.

In this paper, different and innovative ideas of monitoring and analysis of emissions produces by small heat sources are presented and described. This paper follows up research made in master’s thesis from 2022 where two wireless prototypes meant for analyzing emission production were designed, constructed and successfully tested. First prototype made from Polymethymethacrylate (PMMA) successfully analyzed parameters such as concentration of CO, CO2, VOC or dust particles PM in air of indoor temperature during various types of tests. Second prototype made from stainless steel was used to analyze flue gases directly connected to the chimney from a household using small stove and different kinds of fuel. Evaluation of these experiments in IoT platform named ThingSpeak showed significant increase of concentration of emissions produced during burning unsuitable fuel (such as wet grass) compared to more suitable fuel such as dry wood.