Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R² = 0.77), while RF demonstrated superior performance for dry biomass estimation (R² = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation.

Recently, the demand for new alternative feedstuffs that do not contain chemical residue and are not genetically modified has been increased for sustainability in poultry production. In this respect, the usage of algae as animal feed is very promising as an alternative feed ingredient that reduces pollutant gases from animal production facilities. The aim of the current study is to investigate the usage possibility of algae, through determining nutritional value and production cost, as a feed ingredient in poultry nutrition. Three microalgae species, including Scenedesmus sp., Ankistrodesmus sp., and Synechococcaceae, were produced with batch and continuous cultivation to determine the difference in the lipid, protein, carbohydrate, fatty acid, and amino acid profiles, as well as the color characteristics and production cost. The highest lipid content of 72.5% was observed in algae biomass produced from Synechococcaceae with batch cultivation, whereas the highest protein level was found in algae biomass produced by Synechococcaceae under continuous cultivation practice (25.6%). The highest content of PUFA was observed in Scenedesmus sp. harvested from both batch and continuous cultivation (35.6 and 36.2%), whereas the lowest content of PUFA was found in Synechococcaceae harvested with continuous cultivation (0.4%). Continuously cultivated of Scenedesmus sp. had higher carbohydrate content than batch-cultivated Scenedesmus sp. (57.2% vs. 50.1%). The algae biomass produced from Synechococcaceae was found to have a higher content of essential amino acids, except lysine and histidine, compared to Scenedesmus sp. and Ankistrodesmus sp. Cultivation practices also affected the amino acid level in each algae species. The continuous cultivation practice resulted in a higher level of essential amino acids, except glycine. Synechococcaceae had richer essential amino acid content except for proline and ornithine, whereas continuous cultivation caused an incremental increase in non-essential amino acids. The lightness value was found to be the lowest (13.9) in Scenedesmus sp. that was continuously cultivated. The current study indicated that Scenedesmus sp. could be offered for its high PUFA and lysine content, whereas Synechococcaceae could have potential due to its high content of methionine and threonine, among the investigated microalgae and Cyanobacteria.

Artificial rain, a technology primarily used for drought relief, has recently been used for combating regional air pollution. However, there are limited available measurement data to confirm the effectiveness of this control practice. In this study, we summarize control theories and indirect but relevant observations/findings, including air pollutant reduction after natural rain events and roadside sprinkling. A brief review of artificial rain basics is also provided. Our work shows that artificial rain appears to be a promising management strategy for air pollution control. However, field measurements are needed to further assess the cost-effectiveness of the practice, as well as the other benefits or challenges it may create.

Algae provide a rich source of proteins, lipids, vitamins, and minerals, making them valuable feed ingredients in animal nutrition. Beyond their nutritional benefits, algae have been recognized for their potential to mitigate the negative environmental impacts of poultry production. Poultry production is crucial for the global food supply but contributes to environmental concerns, particularly in terms of ammonia and carbon dioxide gas emissions. This study emphasizes the importance of reducing greenhouse gas and ammonia production in poultry operations by utilizing algae species suitable for animal consumption, highlighting the need for sustainable feed sources. This study investigated the effects of poultry exhaust air and culture conditions on the amino acid profiles of three microalgae species, namely, Scenedesmus sp. (AQUAMEB-60), Ankistrodesmus sp. (AQUAMEB-33), and Synechococcaceae (AQUAMEB 32). The experiments were conducted in a commercial broiler farm in Bursa, Turkey, focusing on reducing pollutant gas emissions and utilizing poultry exhaust air in algae cultivation. The highest protein content of 50.4% was observed in the biomass of Synechococcaceae with BBM and DI water. Scenedesmus sp. had the highest carbohydrate content of 33.4% cultivated with DI water. The algae biomass produced from Synechococcaceae growth with DI water was found to have the highest content of essential and nonessential amino acids, except for glutamic acid and glycine. The arsenic, cadmium, and mercury content showed variations within the following respective ranges: 1.076–3.500 mg/kg, 0.0127–0.1210 mg/kg, and 0.1330–0.0124 mg/kg. The overall operating costs for producing 1.0 g L⁻¹ d⁻¹ of dry algal biomass with the existing PBR system were $0.12–0.35 L⁻¹ d⁻¹, $0.10–0.26 L⁻¹ d⁻¹, and $0.11–0.24 L⁻¹ d⁻¹ for Scenedesmus sp., Ankistrodesmus sp., and Synechococcaceae, respectively. The operating cost of producing 1.0 g L⁻¹ d⁻¹ of protein was in the range of $0.25–0.88 L⁻¹ d⁻¹ for the three algae species. The results provide insights into the potential of algae as a sustainable feed ingredient in animal diets, emphasizing both environmental and economic considerations. The results demonstrated a considerable reduction in the production costs of dry biomass and protein when utilizing poultry house exhaust air, highlighting the economic viability and nutritional benefits of this cultivation method.