Ensuring carbon neutrality via algae-based wastewater treatment systems: Progress and future perspectives.

The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients-including carbon, nitrogen, and phosphorus-alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms.

Fate of pesticides in agricultural runoff treatment systems: Occurrence, impacts and technological progress.

The levels of pesticides in air, water, and soil are gradually increasing due to its inappropriate management. In particular, agricultural runoff inflicts the damages on the ecosystem and human health at massive scale. Present study summarizes 70 studies in which investigations on removal or treatment of pesticides/insecticides/herbicides are reported. A bibliometric analysis was also done to understand the recent research trends through the analysis of 2218 publications. The specific objectives of this study are as follows: i) to inventorize the characteristics details of agriculture runoff and analyzing the occurrence and impacts of pesticides, ii) analyzing the role and interaction of pesticides in different environmental segments, iii) investigating the fate of pesticides in agriculture runoff treatment systems, iv) summarizing the experiences and findings of most commonly technology deployed for pesticides remediation in agriculture runoff including target pesticide(s), specifications, configuration of technological intervention. Among the reported technologies for pesticide treatment in agriculture runoff, constructed wetland was at the top followed by algal or photobioreactor. Among various advanced oxidation processes, photo Fenton method is mainly used for pesticides remediation such as triazine, methyl parathion, fenuron and diuron. Algal bioreactors are extensively used for a wide range of pesticides treatment including 2,4-Dichlorophenoxyacetic acid, 2-methyl-4-chlorophenoxyacetic acid, alachlor, diuron, chlorpyrifos, endosulfan, and imidacloprid; especially at lower hydraulic retention time of 2-6 h. This study highlights that hybrid approaches can offers potential opportunities for effective removal of pesticides in a more viable manner.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems.

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Investigations on air quality of a critically polluted industrial city using multivariate statistical methods: Way forward for future sustainability.

Industrially developed cities affect public health, and can directly cause inconvenience to the nearby societies especially due to their associated air pollution. In this context, the present study was conducted in Jharsuguda district of Odisha state (India), which is a well-known worldwide hub of industrial clusters. The study area is having mainly medium to large scale industries which makes it prone to poor air quality. A total of twelve air pollutants, namely, PM10, PM2.5, SO2, NO2, CO, O3, NH3, and heavy metals (Cu, Mn, Ni, Pb, Zn) were monitored during winter season, at the 16 locations of study area. The air quality data was further assessed using multivariate analysis, and the obtained information was presented using histogram plots, box plots, cluster analysis, principal component analysis (PCA), analysis of variance (ANOVA) analysis, and air quality index (AQI). The statistical analysis results revealed that PM10 and PM2.5 levels exceeded the permissible limits of study area, ∼40 and 30% of sampling times, respectively. Contrary, values of other pollution parameters were observed to be well within the permissible limits. The cluster analysis distinguishingly summarized the monitoring data into four clusters types, named as severely polluted, moderately polluted, satisfactory, and fine. The PCA analysis of monitored data resulted in identification of prominent emission sources of analyzed pollutants. These sources were mainly found to be associated with coal burning in power plants, agricultural activities, vehicular emissions, and mining activities. The minimum AQI was observed as 87 at Orient (mine no. 4) and Kinjirma which is under satisfactory category, whereas maximum AQI was observed at Bhedabahal with a value of 132 which is under moderate category. Overall, the results of this study indicated that the air pollution of industrial areas must be evaluated thoroughly on regular basis, considering the sustainability of societies and expanding industries.

Greenhouse gases emission control in WWTS via potential operational strategies: A critical review.

Greenhouse gases (GHGs; particularly, CO2, CH4, and N2O) emission from wastewater treatment systems (WWTS) is one of the inevitable concerns for sustainable development. This indicator is directly linked with the carbon footprint and potential impacts of WWTS on climate change. In this view, various modeling, design, and operational tools have been introduced to mitigate the WWTS associated GHGs, at regional and global scales. In this study, authors have critically reviewed the selected potential operational control strategies for GHGs emission, particularly emitted from the operational stages of biological WWTS. The investigated operational control strategies and/or treatment configurations included intermittent aeration, varying dissolved oxygen, enhanced sludge retention time, coupled aerobic-anoxic nitrous decomposition operation, and microalgae integrated treatment process. Based on this analysis and considering the trade-off between treatment performance of WWTS and GHGs control, an integrated framework is also proposed for existing and upcoming WWTS. The findings of this study and proposed framework will play an instrumental role in mitigating the GHGs at various operational stages of WWTS. Future research works in this direction can lead to a better understanding of investigated operational GHGs emission control strategies in WWTS.

A state-of-the-art review on WWTP associated bioaerosols: Microbial diversity, potential emission stages, dispersion factors, and control strategies.

Wastewater treatment plants (WWTPs) associated bioaerosols have emerged as one of the critical sustainability indicators, ensuring health and well-being of societies and cities. In this context, this review summarizes the various wastewater treatment technologies which have been studied with a focus of bioaerosols emissions, potential emission stages, available sampling strategies, survival and dispersion factors, dominant microbial species in bioaerosols, and possible control approaches. Literature review revealed that most of the studies were devoted to sampling, enumerating and identifying cultivable microbial species of bioaerosols, as well as measuring their concentrations. However, the role of treatment technologies and their operational factors are investigated in limited studies only. Moreover, few studies have been reported to investigate the presence and concentrations of air borne virus and fungi in WWTP, as compared to bacterial species. The common environmental factors, affecting the survival and dispersion of bioaerosols, are observed as relative humidity, temperature, wind speed, and solar illumination. Further, research studies on recent episodes of COVID-19 (SARS-CoV-2 virus) pandemic also revealed that continuous and effective surveillance on WWTPs associated bioaerosols may led to early sign for future pandemics. The evaluation of reported data is bit complicated, due to the variation in sampling approaches, ambient conditions, and site activities of each study. Therefore, such studies need a standardized methodology and improved guidance to help informed future policies, contextual research, and support a robust health-based risk assessment process. Based on this review, an integrated sampling and analysis framework is suggested for future WWTPs to ensure their sustainability at social and/or health associated aspects.