As global water environmental governance moves toward precision, organizations like the United Nations, the World Health Organization (WHO), and the U.S. Environmental Protection Agency (EPA) have enacted stringent policies, driving the sewage treatment industry from "passively meeting standards" to "actively exceeding standards." The United Nations' Sustainable Development Goal (SDG6) calls for global sewage treatment rates to exceed 80% by 2030, with effluent water quality consistently exceeding regional standards. WHO's "Guidelines for Drinking-water Quality" impose strict controls on disinfection by-products (DBPs) and the reuse of reclaimed water. In April 2024, the EPA will release its first national drinking water standards for per- and polyfluoroalkyl substances (PFAS), setting maximum concentration limits for six types of PFAS and strengthening the regulatory oversight of instantaneous and daily average emission values. Against this backdrop, deep learning, with its exceptional capabilities in time-series and multivariate analysis, has become a key technical support to address water quality fluctuations and achieve continuous optimal discharge standards.

Water quality fluctuations are a core challenge in global sewage treatment. A report from the United Nations Environment Programme (UNEP) points out that industrial wastewater can fluctuate by 200%-400%, with some chemical zones exceeding 500%, making traditional manual control methods ineffective. EPA monitoring shows that around 6%-10% of public drinking water systems in the U.S. will need to adopt PFAS reduction measures to meet the new standards, with remediation costs expected to exceed $10 billion. Additionally, under the Stockholm Convention, while PFAS is restricted in regions like Japan and the EU, elevated heavy metals have still been detected in Japan's Gunma Prefecture several times higher than the standard, and priority pollutants in certain sections of the Rhine River in the EU have shown fluctuation ranges above 350%, underscoring the urgency of managing such fluctuations.

"Effluent indicators always superior to discharge standards" is a set of quantitative requirements: key indicators' daily averages must be controlled below 75% of the corresponding standard limit, with instantaneous values not exceeding 1.3 times the daily average and never exceeding the standard, ensuring a 100% compliance rate annually, and a coefficient of variation (CV) ≤ 8%. For example, if the industrial wastewater COD standard is 60 mg/L, the daily average must be ≤ 45 mg/L; for PFAS, with an EPA standard of 4.0 ng/L for PFOS, the effluent must remain consistently below 3.0 ng/L. Achieving such stringent targets is difficult with traditional methods, driving the widespread adoption of intelligent predictive technologies.

Deep learning has made significant strides in global research by overcoming traditional prediction limitations. The TransformerBloomformer-2 model has achieved Nash efficiency coefficients of 0.82-0.99 in global algae biomass prediction, far outperforming traditional LSTM models. The GRU model has shown outstanding performance in predicting COD, heavy metals, and PFAS, improving R² by 0.91%-5.23%, and providing early warnings of anomalies 4-6 hours in advance. The EU developed a CNN-LSTM hybrid architecture, successfully applied in several river basins, including the Rhine and Danube, reducing mean squared errors by 0.28 and the average absolute percentage error by 6.3%.

The core advantage of this water quality prediction system lies in its global adaptability and authoritative standards. Data integration includes WHO indicators, UNEP process parameters, World Meteorological Organization environmental data, and time characteristics, employing preprocessing methods based on European and American standards to effectively handle the widespread issue of missing data globally (missing rates range from 6%-17% in developed countries to over 20% in developing countries). The model incorporates WHO guideline physical and compliance constraints, ensuring the predictions are scientifically feasible. Time-wise, the system constructs a three-level prediction framework for short-term (1-24 hours), medium-term (1-7 days), and long-term (1-30 days) forecasts, with short-term predictions achieving R² ≥ 0.96 and errors within ±4.5 mg/L, fully meeting real-time control requirements.

The system has been successfully scaled and recognized in several countries. In a chemical sewage treatment plant in California, USA, PFAS removal rates increased by 18%, and effluent concentrations remained consistently below EPA standards, saving approximately $1.2 million annually. In a municipal plant along the Rhine River in Germany, COD decreased from 42 mg/L to 32 mg/L, ammonia nitrogen dropped from 3.2 mg/L to 2.4 mg/L, and 12 instances of heavy rain impact were successfully predicted. In Gunma Prefecture, Japan, a plant used the system to accurately predict heavy metal fluctuations, reducing reductant usage by 30% while ensuring effluent compliance and supporting safe sludge fertilizer reuse.

Looking to the future, as global regulations on emerging pollutants such as PFAS tighten, and UNEP advances the "urban wastewater resource utilization" goal, the application of deep learning water quality prediction technology will become inevitable. By integrating edge intelligence and federated learning, global collaboration can be achieved while safeguarding data privacy, helping especially developing countries improve treatment levels and amplifying the comprehensive benefits of every $1 invested in wastewater treatment to over $3, ultimately providing solid support for achieving SDG6 and sustainable water environmental governance worldwide.