Among the considerable factors in poultry include climatic conditions, where the breeding takes place. This work focuses on the content of solid particles PM10 and PM2,5 occurring in this environment. These particles can negatively affect the health status of breeding and staff, ensuring the necessary care in poultry farming. Dust particles settle on the skin, where they have an irritant effect or are inhaled into the respiratory tract bodies of birds. These particles are transferred microorganisms causing a variety of diseases. It is therefore desirable to eliminate the amount of such particles to the maximum extent possible.

Hazards associated with air pollution motivate the search for technologies capable of monitoring individual exposure to gaseous pollutants and particulate matter (PM). A Low-cost Optical Particle Counter (OPC), costing less than 50 USD, is an example of such technologies. Currently, OPCs are widely used to measure the concentration of particle matter in ambient air. While these low-cost air quality sensors are widely available, the accuracy and precision of these devices is highly uncertain. Consequently, the purpose of this thesis is to present an analytical model of two generic, low-cost OPCs based on the Laws of Conservation of Mass, Momentum, and Energy. These models utilize Mie scattering theory and Computational Fluid Dynamics models to quantify uncertainty and accuracy in low-cost OPCs based first principles. Modeling results indicate that the measurement of forward-scattered light may dramatically increase the accuracy of low-cost OPCs. These results also indicate that careful attention must be placed on the design of sensor flow passages so as to most efficiently transport particles to the scattering volume where they may be detected. A combination of careful attention to photodetector placement in the forward scattering regime as well as efficient transport to the scattering volume may increase low-cost OPC accuracy by magnitudes of order.