Analyzing air pollutants is of key importance for the environmental protection goals. High concentrations of particulate matter (PM) have a particularly negative impact on human life and health. The use of an autonomous multirotor flying robot (drone) for the purposes of locating PM sources requires the design of a dedicated measurement system from scratch. The aim of this study was to make the most important design decision, which is the correct localization of the inlet of the measurement system, taking into account disturbances in the flow field caused by the rotors. To achieve this, a computational model was built with the use of a finite-volume method in Ansys Fluent software. Based on its results, a novel criterion was proposed and applied. In addition to the trivial position outside the rotors on the extended arm, it gave the second location in the space limited by the rotors below the robot. Finally, a robot prototype was built, and a series of verification experiments were carried out, first indoors and then outdoors, at different levels of ambient PM concentrations with and without a pollution source. The field results were compiled as histograms and scatter plots and clearly demonstrated the validity of the adopted criterion. The determination coefficient between measured concentrations showed a stronger relationship when the rotors were operating. Furthermore, in cases with a present pollution source, higher concentrations were observed around the internal sensor, making it more suitable for the studied application.

The study addresses the impact of selected environmental factors on the energy production of photovoltaic systems under real outdoor conditions, with particular emphasis on the application of evolutionary computation techniques. The experiment was carried out on a dedicated test stand, where measurements were made under natural environmental conditions. Parameters such as solar irradiance, wind speed, temperature, air pollution, and obtained PV power were continuously recorded. Initial correlation analysis using Pearson and Spearman coefficients confirmed associations between environmental factors and power output, especially solar irradiance. In order to advance the analysis beyond conventional methods, a linear regression model was developed in which the model weights were optimized using evolutionary algorithms, allowing for a more robust assessment of the contribution of each parameter. The results showed that solar irradiance accounted for 97.79% of the variance in photovoltaic power, while temperature (0.95%), air pollution (0.72%), and wind speed (0.54%) had significantly lower impacts. The implementation of evolutionary algorithms represents a novel approach in this context and has proven to be effective in quantifying environmental influence under complex real-world conditions. Furthermore, the findings highlight the indirect role of air pollution in attenuating irradiance and reducing system efficiency. These insights provide a foundation for the development of adaptive control strategies and predictive models to optimize the performance of the photovoltaic system in dynamic environmental settings.

The aim of this paper is to present methods of digitally synthesising the sound generated by vibroacoustic systems with distributed parameters. A general algorithm was developed to synthesise the sounds of selected musical instruments with an axisymmetrical shape and impact excitation, i.e., Tibetan bowls and bells. A coupled mechanical-acoustic field described by partial differential equations was discretized by using the Finite Element Method (FEM) implemented in the ANSYS package. The presented synthesis method is original due to the fact that the determination of the system response in the time domain to the pulse (impact) excitation is based on the numerical calculation of the convolution of the forcing function and impulse response of the system. This was calculated as an inverse Fourier transform of the system’s spectral transfer function. The synthesiser allows for obtaining a sound signal with the assumed, expected parameters by tuning the resonance frequencies which exist in the spectrum of the generated sound. This is accomplished, basing on the Design of Experiment (DOE) theory, by creating a meta-model which contains information on its response surfaces regarding the influence of the design parameters. The synthesis resulted in a sound pressure signal in selected points in space surrounding the instrument which is consistent with the signal generated by the actual instruments, and the results obtained can improve them.