Industrial wastewater treatment using floating wetlands: a review.

Industrial wastewater generated from various production processes is often associated with elevated pollutant concentrations and environmental hazards, necessitating efficient treatment. Floating wetlands (FWs) have emerged as a promising and eco-friendly solution for industrial wastewater treatment, with numerous successful field applications. This article comprehensively reviews the removal mechanisms and treatment performance in the use of FWs for the treatment of diverse industrial wastewaters. Our findings highlight that the performance of FWs relies on proper plant selection, design, aeration, season and temperature, plants harvesting and disposal, and maintenance. Well-designed FWs demonstrate remarkable effectiveness in removing organic matter (COD and BOD), suspended solids, nutrients, and heavy metals from industrial wastewater. This effectiveness is attributed to the intricate physical and metabolic interactions between plants and microbial communities within FWs. A significant portion of the reported applications of FWs revolve around the treatment of textile and oily wastewater. In particular, the application reports of FWs are mainly concentrated in temperate developing countries, where FWs can serve as a feasible and cost-effective industrial wastewater treatment technology, replacing high-cost traditional technologies. Furthermore, our analysis reveals that the treatment efficiency of FWs can be significantly enhanced through strategies like bacterial inoculation, aeration, and co-plantation of specific plant species. These techniques offer promising directions for further research. To advance the field, we recommend future research efforts focus on developing novel floating materials, optimizing the selection and combination of plants and microorganisms, exploring flexible disposal methods for harvested biomass, and designing multi-functional FW systems.

Effect of ultrasonic homogenization on crude oil-water emulsion stability.

This research aims to evaluate the effect of ultrasonic processing parameters (power and sonication time), emulsion characteristics (water salinity and pH) and their interaction on oil-in-water emulsion stability for Cold Lake Blend (CLB) crude oil. Response surface methodology was used to design experimental runs, in which the parameters were investigated at five levels. Emulsion stability was evaluated by measuring creaming index, emulsion turbidity and microscopic image analysis. The effect of crude oil condition (fresh and weathered) on the emulsion stability was also investigated at the optimum sonication parameters and emulsion characteristics. The optimum condition was found at a power level of 76-80 W, sonication time of 16 mins, water salinity of 15 g/L NaCl, and pH of 8.3. Increasing sonication time beyond the optimum value had adverse effect on the emulsion stability. High water salinity (> 20 g/L NaCl) and pH (> 9) decreased the emulsion stability. These adverse effects intensified at higher power levels (> 80-87 W) and longer sonication times (> 16 mins). Interaction of parameters showed that the required energy to generate stable emulsion was within 60 - 70 kJ. Emulsion with fresh crude oil was more stable than those generated with the weathered oil.

Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks.

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 =0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 =0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.

Drinking water management strategies for distribution networks: An integrated performance assessment framework.

Due to rapid population growth, urbanization, water contamination, and climate change, global water resources are under increasing pressure. Water utilities apply drinking water management strategies (DWMS) to ensure that water is safe for drinking. However, in recent years, due to increased inclination towards climate change, environmental emissions, and sustainable development goals; the environmental and economic performance of DWMSs is getting attention. An integrated framework combining life cycle thinking and water quality assessment techniques was developed in this study to evaluate the DWMSs' performance in terms of water quality, environment, and economics. Six DWMSs were assessed using the integrated framework as a case study. The environmental impacts in terms of human health, ecosystem, and resource use ranged from 1.46E-06 to 4.01E-06 DALY, 9.35E-10 to 3.80E-09 species.yr, and 0.0025-0.0071 USD-$, respectively. Pollution water index (PWI) and cost-benefit analysis (CBA) were used as decision-making techniques to assess the overall performance and suitability of DWMSs under given settings. The DWMSs with surface water as a source or ones providing relatively more degree of treatment have a relatively high PWI score (i.e., ≈0.31), reflective of high environmental impacts and water pollution compared to other alternatives. The CBA scores of selected alternatives ranged between 0.22 and 1.0. Furthermore, it was identified that DWMSs applied on relatively bigger water distribution systems can outweigh their costs (i.e., environmental and economic impacts). The proposed framework and approaches are flexible as they can incorporate different criteria in evaluating the performance and applicability of DWMSs.

Investigation of Dioctyl Sodium Sulfosuccinate in Demulsifying Crude Oil-in-Water Emulsions.

This research investigated the performance of dioctyl sodium sulfosuccinate (DSS), a double-chain anionic surfactant, in breaking crude oil-in-water emulsions. The response surface methodology was used to consider the effect of the DSS concentration, oil concentration, and shaking time on demulsification efficiency and obtain optimum demulsification conditions. Further single-factor experiments were conducted to investigate the effects of salinity, crude oil conditions (fresh and weathered), and gravity separation settling time. The results showed that DSS efficiently demulsified stable emulsions under different oil concentrations (500-3000 mg/L) within 15 min shaking time. Increasing DSS concentration to 900 mg/L (critical micelle concentration) increased the demulsification efficiency to 99%. DSS not only improved the demulsification efficiency but also did not impede the demulsifier interfacial adsorption at the oil-water interface due to the presence of the double-chain structure. The low molecular weight enables the homogeneous distribution of DSS molecules in the emulsion, leading to a high demulsification efficiency within 15 min. Analysis of variance results indicated the importance of considering the interaction of oil concentration and shaking time in demulsification. DSS could reduce the total extractable petroleum hydrocarbons in the separated water to <10 mg/L without gravity separation and could achieve promising demulsification performance at high salinity (36 g/L) and various concentrations of fresh and weathered oil. The demulsification mechanism was explained by analyzing the microscopic images and the transmittance of the emulsion. DSS could be an efficient double-chain anionic surfactant in demulsifying stable oil-in-water emulsions.

Selection of green remediation alternatives for chemical industrial sites: An integrated life cycle assessment and fuzzy synthetic evaluation approach.

The concept of green site remediation calls for a model that can consider environmental impacts in the selection of site remediation alternatives. In this study, an integrated life cycle assessment (LCA)-fuzzy synthetic evaluation (FSE) model is developed to help practitioners select the optimal site remediation plan by incorporating life cycle impacts into the comprehensive suitability evaluation. The LCA module quantifies environmental and economic impacts using ReCiPe and Input-Output LCA methods, respectively. The impacts are evaluated along with other suitability considerations, presented in 32 indicators under ten criteria, by practitioners through a questionnaire survey. FSE is used to process the collected subjective judgments and generate a suitability index for informed selection. The integrated model is applied to a case study of an abandoned chemical industrial site contaminated by various organic chemicals and mercury. Four remediation alternatives, designed as the combined uses of ex-situ thermal desorption, in-situ thermal desorption, and in-situ containment, are evaluated. The LCA results show that the alternative with extensive use (treating 93.8 % of the contaminated soil) of in-situ thermal desorption is associated with the highest environmental and economic impacts, followed by the alternative with less extensive use (6.2 %) of in-situ thermal desorption. The FSE results show that the economic, technical, and environmental impact considerations are the top three important criteria. The integrated LCA-FSE results indicate that the alternative with mixed use of ex-situ thermal desorption and in-situ containment could be the optimal plan. Excluding LCA results could alter the suitability ranks of the alternatives.

Evaluation of machine learning techniques to select marine oil spill response methods under small-sized dataset conditions.

Oil spill incidents can significantly impact marine ecosystems in Arctic/subarctic areas. Low biodegradation rate, harsh environments, remoteness, and lack of sufficient response infrastructure make those cold waters more susceptible to the impacts of oil spills. A major challenge in Arctic/subarctic areas is to timely select suitable oil spill response methods (OSRMs), concerning the process complexity and insufficient data for decision analysis. In this study, we used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection. Using a small hypothetical oil spill dataset, the modelling performance was thoroughly compared to find techniques working well under data constraints. The regression-based machine learning models were also compared with integrated and optimized fuzzy decision trees models (OFDTs) previously developed by the authors. OFDTs and GPR outperformed other techniques considering prediction power (> 30 % accuracy enhancement). Also, the use of the Bayesian regularization algorithm enhanced the performance of ANNs by reducing their sensitivity to the size of the training dataset (e.g., 29 % accuracy enhancement compared to an unregularized ANN).

Staged energy and water quality optimization for large water distribution systems.

Simultaneous optimization of energy and water quality in real-time large-sized water distribution systems is a daunting task for water suppliers. The complexity of energy optimization increases with a large number of pipes, scheduling of several pumps, and adjustments of tanks' water levels. Most of the simultaneous energy and water quality optimization approaches evaluate small (or hypothetical) networks or compromise water quality. In the proposed staged approach, Stage 1 uses a risk-based approach to optimally locate the chlorine boosters in a large distribution system based on residual chlorine failures and the associated consequences in different land uses of the service area. Integrating EPANET and CPLEX software, Stage 2 uses mixed integer goal programming for optimizing the day-ahead pump scheduling. The objective function minimizes the pumping energy cost as well as the undesirable deviations from goal constraints, such as expected water demand. Stage 3 evaluates the combined hydraulics and water quality performances at the network level. The implementation of the proposed approach on a real-time large-sized network of Al-Khobar City in Saudi Arabia, with 44 groundwater wells, 12 reservoirs, 2 storage tanks, 191 mains, 141 junctions, and 17 pumps, illustrated the practicality of the framework. Simulating the network with an optimal pumping schedule and chlorine boosters' locations shows a 40% improvement in water quality performance, desired hydraulics performance with optimal pump scheduling, and an average 20% energy cost reduction compared to the normal (unoptimized) base case scenario.

Evaluation of offshore oil spill response waste management strategies: A lifecycle assessment-based framework.

This study presents a novel life cycle assessment-based framework for low-impact offshore oil spill response waste (OSRW) management. The framework consists of design of experiment, life cycle assessment (LCA), multi-criteria decision analysis (MCDA), operational cost analysis, and generation of regression models for impact prediction. The framework is applied to four OSRW management strategies as different combinations of solid and liquid oily waste collection, segregation, transportation, and treatment/disposal technologies. Hypothetical scenarios based on oily waste compositions are developed, and the associated environmental impacts and operational costs are evaluated. The LCA results show that oily waste composition accounts for < 5% of the total environmental impacts. Chemical demulsification has the highest total impacts due to high marine ecotoxicity and human toxicity, followed by incineration and transportation. The cost analysis reveals that the strategy comprised of centrifugation and landfilling is most preferable while the combination of chemical demulsification and incineration is least favorable. The strategy of combined use of centrifugation and landfilling is ranked as the most suitable in the MCDA. Regression models are developed to predict environmental impacts based on important factors. The framework can help waste management practitioners select low-impact strategies for handling offshore OSRW.

Integrated probabilistic-fuzzy synthetic evaluation of drinking water quality in rural and remote communities.

An integrated probabilistic-fuzzy synthetic evaluation (PFSE) approach was developed for assessing drinking water quality in rural and remote communities (RRCs) through the lens of health risks and aesthetic impacts. The probabilistic health risk assessment can handle aleatory uncertainty raised by the variation of contaminant concentrations, and fuzzy synthetic evaluation (FSE) can address vagueness and ambiguity in human perception of risks and aesthetic impacts. The PFSE approach was applied to five RRCs in British Columbia, Canada where different drinking water quality issues, including high metal(loids) concentrations, the presence of coliforms, and poor aesthetics were reported. Cancer, non-cancer, and microbial risks assessed, as well as both quantitative and qualitative aesthetic impact assessment outcomes, were aggregated into synthetic water quality indices for water quality ranking. The probabilistic health risk assessment results revealed significant health risks for a community with relatively high arsenic concentrations (mean value = 7.0 µg/L) in the water supply. The microbial risks were also found significant (disability-adjusted life years >1 × 10-6) for all communities because of the presence of coliforms in the water. The FSE results indicated that the drinking water quality of five RRCs was associated with high aggregated impacts, which concurred with the "poor" water quality ratings according to the Canadian Water Quality Index. The water quality of the five RRCs was ranked based on the synthetic water quality evaluation indices. The PFSE approach can help decision-makers prioritize RRCs in effective resource allocation for addressing drinking water quality issues.

An integrated geospatial correlation analysis and human health risk assessment approach for investigating abandoned industrial sites.

An integrated geospatial correlation analysis (GCA)-human health risk assessment (HHRA) approach was developed to investigate abandoned industrial sites featured by heterogeneous contamination data. Critical areas of high health risk concerns can be prioritized for remediation using the integrated approach. An abandoned chemical complex site in Hubei, China was investigated as a case study. GCA and HHRA were performed using soil and groundwater sampling data collected in 2016 and 2019. Benzene, chlorobenzene, dichlorobenzenes, 2-nitrochlorobenzene, and α-hexachlorocyclohexane were determined to be critical contaminants in soil. The 2019 sampling data revealed new contaminated locations that were not found in the 2016 sampling campaign. High concentrations (89.81-386.55 mg/L) of vinyl chloride were also found in groundwater samples. Several critical location clusters of high concentrations of dichlorobenzenes, chlorobenzene, and α-hexachlorocyclohexane were found within the site according to the GCA outcomes. These contaminants could pose significant cancer and non-cancer risks to onsite workers. The critical areas were ranked according to cancer and non-cancer risks estimated by HHRA, respectively, for informed remediation planning. Among the critical contaminants, α-hexachlorocyclohexane, 2-nitrochlorobenzene, and 1,4-dichlorobenzene in soil, as well as vinyl chloride in groundwater, contributed a predominant part to the total health risk. The integrated approach can be used to assess the contamination of other similar abandoned industrial complex sites.

Intelligent computational techniques in marine oil spill management: A critical review.

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic impacts of oil spills. MOSM is a complex system affected by various factors, such as characteristics of spilled oil and environmental conditions. Oil spill detection, characterization, and monitoring; risk evaluation; response selection and process optimization; and waste management are the key components of MOSM demanding timely decision-making. Applying robust computational techniques based on real-time data (e.g., satellite and aerial observations) and historical records of oil spill incidents may considerably facilitate decision-making processes. Various soft-computing and artificial intelligence-based models and mathematical techniques have been used for the implementation of MOSM's components. This study presents a review of literature published since 2010 on the application of computational techniques in MOSM. A statistical evaluation is performed concerning the temporal distribution of papers, publishers' engagement, research subfields, countries of studies, and selected case studies. Key findings reported in the literature are summarized for two main practices in MOSM: spill detection, characterization, and monitoring; and spill management and response optimization. Potential gaps in applying computational techniques in MOSM have been identified, and a holistic computational-based framework has been suggested for effective MOSM.

Performance indicators for aquatic centres in Canada: Identification and selection using fuzzy based methods.

Aquatic centres (ACs) are becoming exceedingly popular in the urban agglomerations of cold climate countries like Canada but functioning without assessing the state of their sustainability performance. Previous studies examined health and safety, water and indoor air quality, and energy consumption aspects without aiming at the holistic sustainability performance assessment. The present research is the first systematic effort for benchmarking of ACs. A hierarchical-based framework arranged 81 performance indicators to appraise the key components, including water management, indoor environment, personnel, service quality, energy, social, and operations. Fuzzy AHP and fuzzy mean clustering methods evaluated the identified PIs based on the opinion of experts (from Canadian aquatic centres) on their importance, measurability, and understandability. Finally, the selection process ranked a set of 63 most suitable PIs under 14 sub-criteria. Fuzzy-based methods efficiently handled the subjective scoring process and the difference of opinion among the experts. The criteria performance indices inform the top-level management while the sub-indices stipulate the operations management for honing in the lacking indicators. Using the selected PIs, the AC's management can allocate the available resources for both the short-term (e.g., efficient response to complaints) and long-term (e.g., replacing failed manually operated fixtures with the sensor-operated ones) improvement actions. The selected PIs will enhance the sustainability of ACs in Canada and other cold regions around the globe through a structured benchmarking process.

Low-temperature thermal desorption and secure landfill for oil-based drill cuttings management: Pollution control, human health risk, and probabilistic cost assessment.

Oil-based drill cuttings (OBDCs) were managed in two scenarios including low-temperature thermal desorption (LTTD) and secure landfill through a case study. The removal of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in OBDCs by LTTD under different conditions was investigated. Probabilistic human health risk assessment was performed to quantify the health risk posed to waste management workers under the two scenarios, while the associated costs were also analyzed. The results show that LTTD at 300 °C for >20 min could remove 96.27% of PAHs in OBDCs but its removal effect on heavy metals was not significant. It was found that cancer risks posed by PAHs in both securely landfilled and LTTD-treated OBDCs were not significant (<1e-06); however, significant cancer risks (7.95e-05-9.45e-05) were identified for exposure to toxic heavy metals. Increased health risk was observed as a result of exposure to LTTD treatment residues compared to securely landfilled OBDCs. Inhalation of chromium(VI) and oral ingestion of arsenic in OBDCs were critical exposure routes. Both cancer and non-cancer risks in the secure landfill scenario were negligible. The cost analysis results suggest that LTTD combined with stabilization/solidification could be more economically attractive than secure landfill for the handling of OBDCs.

Analyzing present and future availability of critical high-tech minerals in waste cellphones: A case study of India.

Critical high-tech minerals (CHTMs) are raw materials that are essential for a future clean-energy transition and the manufacture of high-end products. Cellphones, one of the fastest growing electronic products, contain various CHTMs. Since 2019, India has surpassed the United States to become the second largest smartphone market in the world. An increasing and alarming number of excessive waste cellphones will be generated in India in the near future. In this study, the dynamic material flow analysis approach and the Weibull distribution are adopted to analyze the volumes of accumulated waste cellphones and the contained CHTMs based on the differentiation between smartphones and feature phones in India. Moreover, a market supply model is adopted to predict the future trends of CHTMs in waste cellphones. The results show a general upward tendency of waste cellphone volume in India, which indicates that various CHTMs contained in cellphone waste can be properly reused or recycled. Future implications based on the analysis results are provided for efficient cellphone management in India.

Life cycle assessment of low-temperature thermal desorption-based technologies for drill cuttings treatment.

The life cycle impacts of treatment of typical oil-based drill cuttings (OBDCs) using three low-temperature thermal desorption (LTTD)-based systems, including thermomechanical cuttings cleaner (TCC), screw-type dryer (STD), and rotary drum dryer (RDD), were explored with a case study in British Columbia, Canada. Two energy supply scenarios, including diesel generator-based onsite (scenario i) and hydropower-based offsite (scenario ii) treatments, were considered in the assessment. The results show that RDD generated the lowest life cycle impacts in terms of damages to human health, ecosystems, and resources in scenario i. TCC-scenario ii generated the lowest impacts among all assessed cases, suggesting that using renewable energy can greatly reduce the impacts of LTTD-based OBDCs treatment. Also, net environmental benefits could be achieved considering the reuse of recovered oil, and the highest net environmental benefits were obtained in TCC-scenario ii. The process contribution analysis found that thermal desorption process accounted for 80-95 % of impacts in almost all impact categories. Energy consumption contours and linear regression models were also developed to help drilling waste managers estimate the life cycle impacts of using hydropower-driven TCC to treat OBDCs with different water and oil contents.

Selection of oil spill response method in Arctic offshore waters: A fuzzy decision tree based framework.

A fuzzy decision tree (FDT) based framework was developed to facilitate the selection of suitable oil spill response methods in the Arctic. Hypothetical oil spill cases were developed based on six identified attributes, while the suitability of three spill response methods (mechanical containment and recovery, use of chemical dispersants, and in-situ burning) for each spill case was obtained based on expert judgments. Fuzzy sets were used to address the associated uncertainties, and FDTs were then developed through generating: i) one decision tree for all three response methods (FDT-AP1) and ii) one decision tree for each response method and the development of linear regression models at terminal nodes (FDT-LR). The FDT-LR approach exhibited higher prediction accuracy than the FDT-AP1 approach. A maximum of 100% accurate predictions could be achieved for testing cases using it. On average, 75% of suitable oil spill response methods out of 10,000 performed iterations were predicted correctly.

Application of Ionic Liquids for Chemical Demulsification: A Review.

In recent years, ionic liquids have received increasing interests as an effective demulsifier due to their characteristics of non-flammability, thermal stability, recyclability, and low vapor pressure. In this study, emulsion formation and types, chemical demulsification system, the application of ionic liquids as a chemical demulsifier, and key factors affecting their performance were comprehensively reviewed. Future challenges and opportunities of ionic liquids application for chemical demulsification were also discussed. The review indicted that the demulsification performance was affected by the type, molecular weight, and concentration of ionic liquids. Moreover, other factors, including the salinity of aqueous phase, temperature, and oil types, could affect the demulsification process. It can be concluded that ionic liquids can be used as a suitable substitute for commercial demulsifiers, but future efforts should be required to develop non-toxic and less expensive ionic liquids with low viscosity, and the demulsification efficiency could be improved through the application of ionic liquids with other methods such as organic solvents.

Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach.

Heavy metal(loids) in drinking water have long been a critical water quality concern. Chronic exposure to toxic heavy metals and metalloids (TMMs) through water ingestion can result in significant health risks to the public, while elevated concentrations of less toxic heavy metals (LTMs) can compromise the aesthetic value of water. An integrated probabilistic-fuzzy approach was developed to help water utilities assess water quality regarding heavy metal(loids) (WQHM). In probabilistic assessments, the probabilities of exceedance of health risk guidelines due to chronic exposure to TMMs and exceedance of aesthetic objectives due to elevated LTMs concentrations were quantified through Monte Carlo simulations. The probabilistic assessments can address the aleatory uncertainties due to random variations of health risk parameters. A fuzzy inference system, composed of fuzzy membership functions, operators, and rules, was used to facilitate interpreting WQHM based on the probabilities of guideline exceedance. Epistemic uncertainties due to vagueness and imprecision in linguistic variables used for describing health risks and aesthetic impacts can be reduced by fuzzy inferencing. The developed approach was applied to four water quality scenarios characterized by different combinations of TMMs and LTMs concentrations. Reasonable decisions were recommended for WQHM management under the four scenarios. The developed approach offers a useful tool for systematically assessing WQHM from a health risk mitigation perspective by addressing different types of uncertainties.

Investigating the effects of design and management factors on DBPs levels in indoor aquatic centres.

Disinfection by-products (DBPs) in indoor swimming pool water and air have long been a critical human health risk concern. This study investigated the effects of several indoor swimming pool design and management factors (e.g. ventilation, water treatment, pool operations, pool type) on the concentrations of DBPs, such as trihalomethanes (THMs) and chloramines, in pool water and air. Two sampling campaigns, A and B, were carried out to measure the concentrations of DBPs under different conditions. In both campaigns, 46 pool water samples, seven tap water samples, and 28 ambient air samples were collected and analyzed. Regression models were also developed and validated for investigating the combined effects of design and management factors on total trihalomethanes (TTHM) and trichloramine. The model results show that pool water characteristics (e.g., total organic content, temperature, conductivity, pH and alkalinity) and management factors (e.g., the number of bathers and sprayers) have direct effects on DBP concentrations. Pool water characteristics such as UV absorbance, hardness, and oxidation-reduction potential and a management factor UV intensity have inverse effects on DBPs levels. Based on the correlation analysis, other factors such as fan speed, fresh air, pool age, and basin area were found to be correlated with the concentrations of individual THMs and trichloramine in both water and air. It was also observed that the concentration of THMs varies with pool type. It is note worthy that the effects of the number of sprayers was quantified for the first time. This study comprehensively assessed pool design and management factors and identified their effects on DBPs, providing indoor swimming pool facilities with useful information to control DBPs in the indoor swimming environment.