Dynamic calibration of process-wide partial-nitritation modeling with airlift granular for nitrogen removal in a full-scale wastewater treatment plant.

A main challenge in rapid nitrogen removal from rejected water in wastewater treatment plants (WWTPs) is growth of biomass by nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB). In this study, partial nitritation (PN) coupled with air-lift granular unit (AGU) technology was applied to enhance nitrogen-removal efficiency in WWTPs. For successful PN process at high-nitrogen-influent conditions, a pH of 7.5-8 for high free-ammonia concentrations and AOB for growth of total bacterial populations are required. The PN process in a sequential batch reactor (SBR) with AGU was modeled as an activated sludge model (ASM), and dynamic calibration using full-scale plant data was performed to enhance aeration in the reactor and improve the nitrite-to-ammonia ratio in the PN effluent. In steady-state and dynamic calibrations, the measured and modeled values of the output were in close agreement. Sensitivity analysis revealed that the kinetic and stoichiometric parameters are associated with growth and decay of heterotrophs, AOB, and NOB microorganisms. Overall, 80% of the calibrated data fit the measured data. Stage 1 of the dynamic calibration showed NO2 and NO3 values close to 240 mg/L and 100 mg/L, respectively. Stage 2 showed NH4 values of 200 mg/L at day 30 with the calibrated effluent NO2 and NO3 value of 250 mg/L. In stage 3, effluent NH4 concentration was 200 mg/L at day 60.

Population response modeling and habitat suitability of Cobitis choii fish species in South Korea for climate change adaptation.

Endangered species ecosystems require appropriate monitoring for assessing population growth related to the emerging pollutants in their habitat conditions. The response of population growth of Cobitis choii, an endangered fish species, under the exposure to emerging pollutants present in the Geum River Basin of South Korea was studied. Toxicity models of concentration addition (CA), independent action (IA), and concentration addition-independent action (CAIA) were implemented utilizing the concentration of a set of 25 chemicals recorded in the study area. Thus, a population-level response analysis was developed based on the abundance of Cobitis choii for period 2011-2015. The results were compared showing that the CA and IA models were the most conservative approaches for the prediction of growth rate. Further, a standard abnormality index (SAI) and habitat suitability (HS) indicators based on the climate, habitat, and abundance data were presented to completely analyze the population growth of the species. Suitability of the species growth was most probable for year 2015 for the variables of air temperature and land surface temperature. A spatial analysis was complementarily presented to visualize the correlation of variables for the best suitability of the species growth. This study presents a methodology for the analysis of the ecosystem's suitability for Cobitis choii growth and its assessment of the chemicals present in Geum River stream.

Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective method for protecting commuters' health, and an important tool for developing early warning systems. Despite the existence of several predicting methods, some tend to fail to forecast long-term dependencies in an effective way. This paper aims to implement a multiple sequences prediction of a comprehensive indoor air quality index (CIAI) traced by indoor PM2.5, utilizing different structures of recurrent neural networks (RNN). A standard RNN (SRNN), long short-term memory (LSTM) and a gated recurrent unit (GRU) structures were implemented due to their capability of managing sequential, and time-dependent data. Hourly indoor PM2.5 concentration data collected in the D-subway station, South Korea, were utilized for the validation of the proposed method. For the selection of the most suitable predictive model (i.e. SRNN, LSTM, GRU), a point-by-point prediction on the PM2.5 was conducted, demonstrating that the GRU structure outperforms the other RNN structures (RMSE = 21.04 µg/m3, MAPE = 32.92%, R2 = 0.65). Then, this model is utilized to sequentially predict the concentration and quantify the health risk (i.e. CIAI) at different time lags. For a 6-h time lag, the proposed model exhibited the best performance metric (RMSE = 29.73 µg/m3, MAPE = 29.52%). Additionally, for the rest of the time lags including 12, 18 and 24 h, achieved an acceptable performance (MAPE = 29-37%).

A systematic approach of removal mechanisms, control and optimization of silver nanoparticle in wastewater treatment plants.

The release of silver nanoparticles (AgNPs) to wastewater caused by over-generation and poor treatment of the remaining nanomaterial has raised the interest of researchers. AgNPs can have a negative impact on watersheds and generate degradation of the effluent quality of wastewater treatment plants (WWTPs). The aim of this research is to design and analyze an integrated model system for the removal of AgNPs with high effluent quality in WWTPs using a systematic approach of removal mechanisms modeling, optimization, and control of the removal of silver nanoparticles. The activated sludge model 1 was modified with the inclusion of AgNPs removal mechanisms, such as adsorption/desorption, dissolution, and inhibition of microbial organisms. Response surface methodology was performed to minimize the AgNPs and total nitrogen concentrations in the effluent by optimizing operating conditions of the system. Then, the optimal operating conditions were utilized for the implementation of control strategies into the system for further analysis of enhancement of AgNPs removal efficiency. Thus, the overall AgNP removal efficiency was found to be slightly higher than 80%, which was an improvement of almost 7% compared to the BSM1 reference value. This study provides a systematic approach to find an optimal solution for enhancing AgNP removal efficiency in WWTPs and thereby to prevent pollution in the environment.