Concentrated animal production results in significant air pollutant emissions that contribute to environmental pollution and global warming issues. Various air pollutants, such as NH3
, and CO2
, can originate from animal housing, manure storage, and land application [1
]. There is a need to mitigate these pollutants while sustaining animal protein supplies. This mitigation will not only protect the environment but also improve indoor air quality critical for animal health and welfare, as well as the safety and health of farm workers [2
Various mitigation technologies/practices have been researched, including air scrubbers, biofilters, tree barriers, diet manipulation, and improved manure management [4
]. Among them, CO2
fixation by microalgal photosynthesis has recently attracted significant attention due to its eco-friendliness and potential economic benefits. Microalgae have substantially higher cell growth and CO2
fixation rates (about 10–50 times) than terrestrial plants [7
]. They can be further valorised into biofuels, animal feed, nutrition additives, cosmetics, and pharmaceuticals [8
Numerous factors play a crucial role in effectively reducing air pollutants released from animal barns using photobioreactor (PBR) systems. The concentrations of air pollutants vary depending on the livestock breed, animal age, and barn type. Similarly, the growth of microalgae in PBR systems differs in terms of their air pollutant reduction efficiency, biomass yield, and cell growth, which are influenced by the emitted CO2
gas concentrations from the barn. Therefore, to achieve optimal CO2
gas reduction efficiencies, it is essential to determine the gas concentrations at which microalgae exhibit the best cell growth and biomass yield. The existing literature indicates that while the highest cell growth, measured by cell counts, can occur at any CO2
gas concentration, the maximum biomass yield may be obtained at a different gas concentration [9
]. A microalga that demonstrates the highest cell growth at a specific CO2
gas concentration might exhibit lower cell growth at another CO2
concentration due to the presence of NH3
gas in the PBR system, where CO2
gases coexist [10
]. In other words, although cell growth reaches its peak at a given CO2
gas concentration, the maximum biomass yield may not be achieved at the same CO2
gas concentration. Therefore, for the most effective mitigation of air pollutants released from animal barns using PBR systems, it is crucial to collectively evaluate and analyze the biological and environmental parameters that influence microalgae growth. By doing so, maximum efficiency can be obtained from all parameters influencing algal growth, and identifying the air pollutant concentrations at which the highest gas reduction efficiency can be achieved will enhance the system’s effectiveness and economic viability.
Challenges exist to effective CO2
gas mitigation with microalgae due to the complexity of the relevant processes. For example, CO2
fixation efficiencies can be affected by many operating parameters, such as gas loading rates, gas concentrations, pH, light intensity, and temperature. Numerous studies have been conducted to examine the effects of these parameters [8
]. In nearly all these studies, different parameters were examined separately. However, interactions between the parameters should not be neglected. For example, Kang and Wen [14
] reported that the solubility and uptake of CO2
by algae were affected by the presence of NH3
in PBRs. Moreover, algal PBRs for air pollutant mitigation (and other phytoremediation purposes) often involve multiple system input parameters (e.g., CO2
concentrations, pH, and temperature) and multiple output goals (e.g., biomass yield and NH3
removal efficiency), making it challenging to make management decisions concerning algal PBR operation. Previous studies have highlighted the challenge of CO2
concentration when utilizing microalgae for pollutant capture. Traditional statistical methods may not be adequate for addressing the complexities associated with determining suitable CO2
concentrations for algal growth. Therefore, prioritizing the options for mitigating pollutants through CO2
concentration requires a multi-criteria approach. One practical methodology for comparison in this regard is multi-criteria decision-making (MCDM). In this paper, a multi-criteria assessment model is proposed which combines grey relational analysis (GRA) and criterion impact loss (CI-LOS) techniques. This model allows for the ranking of different CO2
interactions based on their environmental and biological performance. By applying the CI-LOS method, objective determination of weights can be achieved for parameters such as cell number, biomass yield, and others that monitor microalgae growth, as well as parameters indicating gas reduction efficiency. Subsequently, using the GRA method and considering these weights, the optimal CO2
gas concentrations can be determined.
Advancements in decision-making sciences have made MCDM methods increasingly accessible and accepted recently. MCDM refers to decision-making in the presence of multiple criteria that are inconsistent or even contradictory [15
]. It is particularly useful for systems with multiple criteria and alternatives. The development of MCDM methods is motivated by not only various real-life problems requiring the consideration of multiple criteria but also by advances in mathematical optimization, scientific computing, and computer technology [16
]. A fundamental task of MCDM is to evaluate a set of alternatives with a set of criteria. This involves the determination of the criteria that can be organised according to the expectation of a decision to be made for possibly solving a problem and other alternatives [17
MCDM has been used in solving various problems in social sciences such as supplier selection [18
], financial performance [21
], and cyber security [23
], as well as in engineering and science problems such as geographic information systems [25
], construction equipment evaluation [27
], and machine tool selection [29
]. This method has been applied successfully in environmental research areas, from optimising waste management systems [31
] to selecting gas mitigation methods [32
]. Only a few reports are available concerning its applications for algal research, with the majority focusing on harvesting technologies [34
]. Others include the identification of the best microalgae strain for biodiesel production [36
], the best areas for microalgal cultivation [37
], and the best algal wastewater treatment systems [38
]. To our knowledge, no application of MCDM for algal PBR operation has been reported.
This study presents the first such attempt to identify optimal NH3
concentrations for algal PBR operation. The primary aim of using the multi-criteria analysis is to find the relative importance of the factors and the criteria that affect cultivation of S. dimorphus
with gas concentrations typical of pig house exhaust air. As a restriction, the specified concentrations must be within the typical concentration range of swine barn exhaust. The optimization was conducted to maximize the (1) biological, (2) environmental, and (3) overall performance of algal PBR systems. For each of the system output goals (scenarios), multiple performance indicators were considered. Specifically, the biological performance (i.e., algal growth) was measured by algal cell concentration, dry algal biomass, maximum specific growth rate, and cell weight; the environmental performance (i.e., pollutant mitigation) was measured by CO2
fixation rate, NH3
fixation efficiency, CO2
removal efficiency, and NH3
removal efficiency; and the overall performance was measured by all the indicators stated above. Sixteen experimental data sets were analyzed with two MCDM methods (criterion impact loss (CILOS) and grey relational analysis (GRA)) following the multi-step procedure outlined in Figure 1
. Such optimization cannot be performed with regular statistical tools.
This study demonstrated the use of a multi-criteria analysis methodology combined with different CO2 and NH3 levels to improve the CO2 and NH3 fixation ability of S. dimorphus. The MCDM results show that for both the biological and environmental performance, the optimal combination of CO2 and NH3 concentrations is 12 ppm NH3 and 3500 ppm CO2. However, the optimal CO2 and NH3 concentrations for the overall performance were 25 ppm NH3 and 2350 ppm CO2. The results of this study allow producers to determine the maximum mitigation of CO2 and NH3 concentrations and optimise the operating parameters of PBRs for various housing types, such as dairy, poultry, and pig barns.
For example, in the poultry sector, air pollutants released from poultry houses vary depending on the breeding period of the chickens. While the air pollutant concentrations released from hen houses are low in the first days of the production period, they increase towards the end of production. Meanwhile, engineering options are available to adjust the CO2 and NH3 concentrations in the exhaust air from animal barns and the PRB’s algal growth. For the exhaust air, the CO2 and NH3 concentrations that feed into a PBR can be regulated by changing ventilation/heating configuration or settings, installing pre-scrubbers, and so on. For the algal broth, the concentrations can be adjusted by amending the liquid with carbonate or ammonium salts that are readily available on most farms. Therefore, knowing the gas concentrations at which the most effective CO2 and NH3 reductions can be achieved can facilitate the setup and operation of the PBR system. This study indicates that such information can be generated from MCDM analyses. Although still technically intimidating to most producers, the selected MCDM methods are far simpler and require less computing power than artificial intelligence methods (e.g., machine learning). Therefore, they can be translated into Web calculators or MS Excel-based tools that are easily adopted by producers, or implemented in animal barn controllers to automate the facility operation.
Overall, this study presents clear results that can guide decision-makers in choosing the best PBR operating parameters. This MCDM method will aid future algal phytoremediation research for enhancing CO2 and NH3 mitigation and minimizing the environmental impacts of animal feeding operations.
In light of the above conclusions, the following tasks are recommended for future research:
Additional MCDM methods should be explored to broaden the scope of the multi-criteria decision-making process in air pollutant mitigation using PBR systems.
The significance of the carbohydrate, protein, and fat values of microalgae as criteria should be further investigated. The analysis would facilitate a multi-criteria assessment not only regarding reducing air pollutants emitted from barns but also in evaluating the potential utilization of the obtained biomass in sectors such as animal feed, biodiesel, and others.
It is advisable to develop an evaluation tool utilizing the MCDM methods examined in this study. Such a tool would simplify the air pollutant mitigation process and facilitate the comparison of the applicability of microalgae in various sectors.