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2022 Vol. 40, No. 6
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2022, 40(6): 1-11.
doi: 10.13205/j.hjgc.202206001
Abstract:
The construction and application of high-spatial-resolution environmental databases are critical to addressing increasingly prominent environmental issues.High-spatial-resolution data reflects the specific distribution and characteristics of research subjects at a fine scale,which can identify the hotspots of environmental pollution and help formulate environmental control policies tailored to the local conditions.Scholars at home and abroad have built a large number of high-spatial-resolution environmental databases and applied them to solve different environmental problems.Therefore,this paper systematically summarizes the common high-spatial-resolution environmental databases from the aspects of scale and substance type.Moreover,the application of high-spatial-resolution environmental databases has been summarized in three different aspects,including emission source analysis,environmental impact assessment,and economic influencing factors.This article proposes several recommendations to improve the existing high-spatial-resolution environmental databases,including further improving the resolution to ensure data accuracy,improving data quality and broadening the scope of research application.
The construction and application of high-spatial-resolution environmental databases are critical to addressing increasingly prominent environmental issues.High-spatial-resolution data reflects the specific distribution and characteristics of research subjects at a fine scale,which can identify the hotspots of environmental pollution and help formulate environmental control policies tailored to the local conditions.Scholars at home and abroad have built a large number of high-spatial-resolution environmental databases and applied them to solve different environmental problems.Therefore,this paper systematically summarizes the common high-spatial-resolution environmental databases from the aspects of scale and substance type.Moreover,the application of high-spatial-resolution environmental databases has been summarized in three different aspects,including emission source analysis,environmental impact assessment,and economic influencing factors.This article proposes several recommendations to improve the existing high-spatial-resolution environmental databases,including further improving the resolution to ensure data accuracy,improving data quality and broadening the scope of research application.
2022, 40(6): 12-21.
doi: 10.13205/j.hjgc.202206002
Abstract:
Land-ocean coordination management has become a key strategy for beautiful bay construction and sustainable development of coastal zones.Among them,cooperative prevention and control of land and ocean-based pollution are of great importance to improve the coastal eco-environment quality.However,theory and technology support in this regard has fallen short.Here,we review the status quo,challenges,and research fronts of the monitoring technologies applied in multi-media and multi-interface environments of land-ocean-atmosphere as well as the relevant models (e.g.,watershed-river models,estuary-coastal models,atmospheric deposition models).To facilitate the integrated watershed-coast management,an integrated system that incorporates monitoring,modeling,assessment,and decision-making is urgently needed.It is suggested to facilitate the development of monitoring technology on nitrogen and phosphorus flux in multi-interface,the development of the integrated land-ocean-atmosphere model,and the investigation on the sources,transport pathways,and multi-interface process interactions of nitrogen and phosphorus pollution.Future key research tasks are proposed as follows:1) to advance the knowledge of the land-ocean-atmosphere environment at the system level based on the monitoring and modeling techniques;2) to assess eutrophication and ecosystem health from the lens of land-ocean coordination;3) to create the spatially-explicit and category-specific environmental capacity allocation method;4) to study optimal solutions that satisfy multiple objectives for coastal pollution control;5) to facilitate the research on the feedbacks of the society-economy-environment system and collaborative decision-making via multi agents.These applications are expected to modernize our capability for ecological and environmental governance.
Land-ocean coordination management has become a key strategy for beautiful bay construction and sustainable development of coastal zones.Among them,cooperative prevention and control of land and ocean-based pollution are of great importance to improve the coastal eco-environment quality.However,theory and technology support in this regard has fallen short.Here,we review the status quo,challenges,and research fronts of the monitoring technologies applied in multi-media and multi-interface environments of land-ocean-atmosphere as well as the relevant models (e.g.,watershed-river models,estuary-coastal models,atmospheric deposition models).To facilitate the integrated watershed-coast management,an integrated system that incorporates monitoring,modeling,assessment,and decision-making is urgently needed.It is suggested to facilitate the development of monitoring technology on nitrogen and phosphorus flux in multi-interface,the development of the integrated land-ocean-atmosphere model,and the investigation on the sources,transport pathways,and multi-interface process interactions of nitrogen and phosphorus pollution.Future key research tasks are proposed as follows:1) to advance the knowledge of the land-ocean-atmosphere environment at the system level based on the monitoring and modeling techniques;2) to assess eutrophication and ecosystem health from the lens of land-ocean coordination;3) to create the spatially-explicit and category-specific environmental capacity allocation method;4) to study optimal solutions that satisfy multiple objectives for coastal pollution control;5) to facilitate the research on the feedbacks of the society-economy-environment system and collaborative decision-making via multi agents.These applications are expected to modernize our capability for ecological and environmental governance.
2022, 40(6): 22-30.
doi: 10.13205/j.hjgc.202206003
Abstract:
Permeable reactive barrier (PRB) is a green and sustainable groundwater remediation technology.Numerical simulation helps to evaluate the PRB performance under different parameters (such as dimension,installation location and orientation,permeability coefficient,etc.),which is the basis for PRB engineering design.This paper summarizes the application characteristics of typical groundwater models and software in PRB design,compares the application status of domestic and foreign numerical simulation methods in PRB engineering design,longevity evaluation and parameter optimization,and discusses the application prospects and key research directions of numerical simulation in PRB technology,to provide a reference for the promotion and application of PRB technology in China.
Permeable reactive barrier (PRB) is a green and sustainable groundwater remediation technology.Numerical simulation helps to evaluate the PRB performance under different parameters (such as dimension,installation location and orientation,permeability coefficient,etc.),which is the basis for PRB engineering design.This paper summarizes the application characteristics of typical groundwater models and software in PRB design,compares the application status of domestic and foreign numerical simulation methods in PRB engineering design,longevity evaluation and parameter optimization,and discusses the application prospects and key research directions of numerical simulation in PRB technology,to provide a reference for the promotion and application of PRB technology in China.
2022, 40(6): 31-41,47.
doi: 10.13205/j.hjgc.202206004
Abstract:
Based on the self-organizing maps (SOM) and K-means method,this study took a contaminated site to explore the feasibility of applying SOM and K-means method to the zoning control of groundwater pollution.Through descriptive statistical analysis of monitoring data,the groundwater pollution characteristics of the site were obtained.It was found that Cr (Ⅵ),CODMn,SO42-,TDS,NO3-,NH3-N and Mn were the main pollutants in the study area.Derived from SOM,K-means analysis and spatial interpolation,groundwater in the study area was divided into four types of areas.The pollution factors of each cluster that need to be paid attention to were identified.The results showed that NO3- should be paid attention to for cluster Ⅰ;Cr (Ⅵ),CODMn,NO3-,TDS and NH3-N for cluster Ⅱ;SO42- for cluster Ⅲ,and Mn for cluster Ⅳ.This method can be applied to the zoning control of groundwater pollution,which had guiding significance for the prevention and control of groundwater pollution in contamination sites.
Based on the self-organizing maps (SOM) and K-means method,this study took a contaminated site to explore the feasibility of applying SOM and K-means method to the zoning control of groundwater pollution.Through descriptive statistical analysis of monitoring data,the groundwater pollution characteristics of the site were obtained.It was found that Cr (Ⅵ),CODMn,SO42-,TDS,NO3-,NH3-N and Mn were the main pollutants in the study area.Derived from SOM,K-means analysis and spatial interpolation,groundwater in the study area was divided into four types of areas.The pollution factors of each cluster that need to be paid attention to were identified.The results showed that NO3- should be paid attention to for cluster Ⅰ;Cr (Ⅵ),CODMn,NO3-,TDS and NH3-N for cluster Ⅱ;SO42- for cluster Ⅲ,and Mn for cluster Ⅳ.This method can be applied to the zoning control of groundwater pollution,which had guiding significance for the prevention and control of groundwater pollution in contamination sites.
2022, 40(6): 42-47.
doi: 10.13205/j.hjgc.202206005
Abstract:
Given the complex process of salt tide upstream in the estuary area,and it's difficult to control the timing of daily hourly freshwater intake by coastal water plants,a statistical model for hourly water intake evaluation based on the dynamic index of the estuary area was established by using the Logistic method and considering the historical salinity memory effect,to realize the hourly water intake prediction of waterbodies with less than 0.5‰ salinity in the estuary area.Taking the Modaomen estuary of the Pearl River as an example,the accuracy of the prediction results of the model was found higher than 90%,which realized the reliable prediction of hourly water intake the next day,and provided an effective reference basis for the water plants to prepare and stop water intake in time.The model had a certain reference significance for the risk assessment of hourly water intake with low salinity in other estuaries.
2022, 40(6): 48-54,62.
doi: 10.13205/j.hjgc.202206006
Abstract:
For improving the low accuracy of the existing models for time series prediction of PM2.5 concentration,a Seq2Seq multi-step PM2.5 concentration prediction model for single-site based on Informer was proposed.With a series of air pollutant data and meteorological data in the past,Informer could make a forecast for PM2.5 concentration in the future.The constructed model extracted the information of the input sequence based on the probsparse self-attention mechanism,which could widely capture the long-range dependency of the input sequence and model the complex nonlinearity between features,to improve the prediction accuracy eventually.The hourly air pollutant data and meteorological data of Beijing from 2015 to 2019 were used for training,validation and testing.Compared with RNN,LSTM and other existing models,the MAE,RMSE and R2 metrics of Informer were the best for the time series prediction of PM2.5 concentration in the next 1 to 6 hours,and then a more accurate prediction was realized.
For improving the low accuracy of the existing models for time series prediction of PM2.5 concentration,a Seq2Seq multi-step PM2.5 concentration prediction model for single-site based on Informer was proposed.With a series of air pollutant data and meteorological data in the past,Informer could make a forecast for PM2.5 concentration in the future.The constructed model extracted the information of the input sequence based on the probsparse self-attention mechanism,which could widely capture the long-range dependency of the input sequence and model the complex nonlinearity between features,to improve the prediction accuracy eventually.The hourly air pollutant data and meteorological data of Beijing from 2015 to 2019 were used for training,validation and testing.Compared with RNN,LSTM and other existing models,the MAE,RMSE and R2 metrics of Informer were the best for the time series prediction of PM2.5 concentration in the next 1 to 6 hours,and then a more accurate prediction was realized.
2022, 40(6): 55-62.
doi: 10.13205/j.hjgc.202206007
Abstract:
It is generally difficult to quantify the phosphorus source of high-intensity and complex human interference rivers,so a pollution source apportionment model based on the relationship between flow and pollutant concentration was constructed to identify the point source and non-point source load and time contribution of river phosphorus.Taking the upstream river,river with reservoir and river with both reservoir and water transfer project in the Lake Dianchi Basin as the objects,the response relationship between river phosphorus concentration and flow was established by LAM model,and the source contribution structure and spatiotemporal distribution of phosphorus in main rivers were analyzed.Results showed that non-point source was the major source of phosphorus in the main rivers entering the Dianchi Lake in 2018,and the non-point source load accounted for 53%~100%,with a negligible difference between flood season and all year round;as for the dominant time of pollution sources,point sources account for the highest time proportion in the Baoxiang River and the Dahuaqiao-Deshengqiao section of the Panlong River,indicating that point source control was still of great significance to improve water quality in low flow seasons.The results can provide reference and guidance for identifying phosphorus pollution sources in many human disturbance rivers in China.
It is generally difficult to quantify the phosphorus source of high-intensity and complex human interference rivers,so a pollution source apportionment model based on the relationship between flow and pollutant concentration was constructed to identify the point source and non-point source load and time contribution of river phosphorus.Taking the upstream river,river with reservoir and river with both reservoir and water transfer project in the Lake Dianchi Basin as the objects,the response relationship between river phosphorus concentration and flow was established by LAM model,and the source contribution structure and spatiotemporal distribution of phosphorus in main rivers were analyzed.Results showed that non-point source was the major source of phosphorus in the main rivers entering the Dianchi Lake in 2018,and the non-point source load accounted for 53%~100%,with a negligible difference between flood season and all year round;as for the dominant time of pollution sources,point sources account for the highest time proportion in the Baoxiang River and the Dahuaqiao-Deshengqiao section of the Panlong River,indicating that point source control was still of great significance to improve water quality in low flow seasons.The results can provide reference and guidance for identifying phosphorus pollution sources in many human disturbance rivers in China.
2022, 40(6): 63-69.
doi: 10.13205/j.hjgc.202206008
Abstract:
On-site investigation of sewage outfalls connected to watercourses is labor-intensive,and real-time tracking is hard to be performed manually.Facing with this challenge,an inverse problem method to trace source location and sewage flow based on hydrodynamic diffusion wave model and the microbial genetic algorithm was developed.The developed method was verified with a hypothetical example of sudden wastewater discharge and a real investigation case of sewage discharges into a river.The study results showed that:1) for the tracking of sudden wastewater discharge in large quantity,the inverse problem model could estimate source parameters including source location,source flow rate,starting and ending time of discharge effectively.After multiple runs of modeling,the median values of inversed source parameters were almost identical to the real ones.2) for the routine investigation of sewage outfalls,the developed method could identify the locations and discharge amounts of potential multiple sewage sources accurately,through the rational layout of hydrologic monitoring stations.3) for either the tracking of sudden wastewater discharge or the routine investigation of sewage discharges,the sewage outfall could be located to a spatial grid resolution of fewer than 200 m using the inverse modeling,on condition that the monitoring stations were set up with a spatial interval of 2000 m.Therefore,the developed inverse model could provide a technical solution for dynamic monitoring of sewage outlets connected to rivers in the future,with the support of online data.
On-site investigation of sewage outfalls connected to watercourses is labor-intensive,and real-time tracking is hard to be performed manually.Facing with this challenge,an inverse problem method to trace source location and sewage flow based on hydrodynamic diffusion wave model and the microbial genetic algorithm was developed.The developed method was verified with a hypothetical example of sudden wastewater discharge and a real investigation case of sewage discharges into a river.The study results showed that:1) for the tracking of sudden wastewater discharge in large quantity,the inverse problem model could estimate source parameters including source location,source flow rate,starting and ending time of discharge effectively.After multiple runs of modeling,the median values of inversed source parameters were almost identical to the real ones.2) for the routine investigation of sewage outfalls,the developed method could identify the locations and discharge amounts of potential multiple sewage sources accurately,through the rational layout of hydrologic monitoring stations.3) for either the tracking of sudden wastewater discharge or the routine investigation of sewage discharges,the sewage outfall could be located to a spatial grid resolution of fewer than 200 m using the inverse modeling,on condition that the monitoring stations were set up with a spatial interval of 2000 m.Therefore,the developed inverse model could provide a technical solution for dynamic monitoring of sewage outlets connected to rivers in the future,with the support of online data.
2022, 40(6): 70-76,115.
doi: 10.13205/j.hjgc.202206009
Abstract:
The prediction model is the premise and foundation of effectively dealing with sudden water pollution accidents.To improve the accuracy of the prediction model,a new parameters identification method was proposed in this paper.This paper first built a prediction model from the perspective of the inverse problem and Bayesian,and then designed a new identification method based on the chaos theory,particle swarm optimization,differential evolution and Metropolis-Hastings sampling method,i.e.IPSO-DE-MH.Finally,the effectiveness and accuracy of the designed method were verified by numerical analysis.The results showed that the new method could better identify the model parameters,and provide a new idea for the construction of an emergency prediction model.
The prediction model is the premise and foundation of effectively dealing with sudden water pollution accidents.To improve the accuracy of the prediction model,a new parameters identification method was proposed in this paper.This paper first built a prediction model from the perspective of the inverse problem and Bayesian,and then designed a new identification method based on the chaos theory,particle swarm optimization,differential evolution and Metropolis-Hastings sampling method,i.e.IPSO-DE-MH.Finally,the effectiveness and accuracy of the designed method were verified by numerical analysis.The results showed that the new method could better identify the model parameters,and provide a new idea for the construction of an emergency prediction model.
2022, 40(6): 77-82,161.
doi: 10.13205/j.hjgc.202206010
Abstract:
Urban drainage and wastewater treatment facilities played a vital role in ensuring public health and sanitation over the past centuries.Recent research suggests that the implementation of integrated wastewater services can contribute to greenhouse gas emissions directly and/or indirectly.Consequently,it is very essential to accurately monitor and assess the carbon emissions during urban drainage and wastewater treatment systems operation and develop decarbonization pathways and carbon neutrality patterns for wastewater management.The deliverables of these investigations could create potential benefits in simultaneously achieving urban water pollution control and the"carbon dioxide emission peaking and carbon neutrality"goal.Such research is also related to the frontier and focus of science and technology in the environmental field.This review is intended to shed light on the current research trends and limitations related to carbon emission monitoring and assessment for urban drainage and wastewater treatment systems.Overall,this review provides useful information to help develop the next generation of carbon emission monitoring and assessment technologies for urban drainage and wastewater treatment systems.
Urban drainage and wastewater treatment facilities played a vital role in ensuring public health and sanitation over the past centuries.Recent research suggests that the implementation of integrated wastewater services can contribute to greenhouse gas emissions directly and/or indirectly.Consequently,it is very essential to accurately monitor and assess the carbon emissions during urban drainage and wastewater treatment systems operation and develop decarbonization pathways and carbon neutrality patterns for wastewater management.The deliverables of these investigations could create potential benefits in simultaneously achieving urban water pollution control and the"carbon dioxide emission peaking and carbon neutrality"goal.Such research is also related to the frontier and focus of science and technology in the environmental field.This review is intended to shed light on the current research trends and limitations related to carbon emission monitoring and assessment for urban drainage and wastewater treatment systems.Overall,this review provides useful information to help develop the next generation of carbon emission monitoring and assessment technologies for urban drainage and wastewater treatment systems.
2022, 40(6): 83-91,187.
doi: 10.13205/j.hjgc.202206011
Abstract:
Resource scarcity and climate change have become grand challenges affecting human prosperity.How to reuse valuable resources from wastewater is important to achieve circular economy,and low-carbon transformation of wastewater treatment.This review is intended to provide an overview of the current state-of-the-art in research on both direct and indirect carbon emissions of different wastewater resource recovery technologies and processes,with the aim of producing clean water,energy,microbial protein,struvite,and other high value-added products.We found that wastewater resource recovery and valorization could create substantial benefits in reducing unwanted carbon emissions,but the advantages would vary with different end-use practices,which reflects the necessity of the inclusion of contextual scenarios in system analysis.Moreover,we highlighted the importance of data source,system boundary,and functional unit,since they would greatly influence the results of a carbon emission assessment.Last but not the least,we also claimed that many future efforts are still needed to analyze the life cycle carbon reduction effects attributed to wastewater resource recovery and valorization,create novel approaches enabling zero-emission or even negative emissions of greenhouse gases through wastewater resource valorization,and eventually evolve wastewater treatment and resource recovery schemes in a truly carbon-free manner.
Resource scarcity and climate change have become grand challenges affecting human prosperity.How to reuse valuable resources from wastewater is important to achieve circular economy,and low-carbon transformation of wastewater treatment.This review is intended to provide an overview of the current state-of-the-art in research on both direct and indirect carbon emissions of different wastewater resource recovery technologies and processes,with the aim of producing clean water,energy,microbial protein,struvite,and other high value-added products.We found that wastewater resource recovery and valorization could create substantial benefits in reducing unwanted carbon emissions,but the advantages would vary with different end-use practices,which reflects the necessity of the inclusion of contextual scenarios in system analysis.Moreover,we highlighted the importance of data source,system boundary,and functional unit,since they would greatly influence the results of a carbon emission assessment.Last but not the least,we also claimed that many future efforts are still needed to analyze the life cycle carbon reduction effects attributed to wastewater resource recovery and valorization,create novel approaches enabling zero-emission or even negative emissions of greenhouse gases through wastewater resource valorization,and eventually evolve wastewater treatment and resource recovery schemes in a truly carbon-free manner.
2022, 40(6): 92-96,279.
doi: 10.13205/j.hjgc.202206012
Abstract:
Wastewater system is an important emission source of nitrous oxide (N2O,a potent greenhouse gas).In recent years,relevant research work has emerged one after another.Using the bibliometric method,this paper systematically analyzed the research progress of N2O in the sewage system in 2000-2020.It was found that the number of articles related to N2O in sewage system was increasing year by year,with major contributions from researchers in China.In the early stage,the research mainly focused on the production process of N2O,system and influencing factors.Recent studies had paid more attention to the production and emission of N2O in composting,the application of biochar in mitigating N2O emission,and the analysis of related microbial communities.Subsequent research could further focus on the related processes of recovering N2O as energy material,and attempt to combine the big data of actual wastewater system with modelling tools,which might provide guidance for the operation and optimization of the actual wastewater treatment systems.
Wastewater system is an important emission source of nitrous oxide (N2O,a potent greenhouse gas).In recent years,relevant research work has emerged one after another.Using the bibliometric method,this paper systematically analyzed the research progress of N2O in the sewage system in 2000-2020.It was found that the number of articles related to N2O in sewage system was increasing year by year,with major contributions from researchers in China.In the early stage,the research mainly focused on the production process of N2O,system and influencing factors.Recent studies had paid more attention to the production and emission of N2O in composting,the application of biochar in mitigating N2O emission,and the analysis of related microbial communities.Subsequent research could further focus on the related processes of recovering N2O as energy material,and attempt to combine the big data of actual wastewater system with modelling tools,which might provide guidance for the operation and optimization of the actual wastewater treatment systems.
2022, 40(6): 97-106,122.
doi: 10.13205/j.hjgc.202206013
Abstract:
Nitrous oxide (N2O) is a greenhouse gas with an approximately 265-fold stronger warming effect than carbon dioxide.N2O can be produced and directly emitted during biological nitrogen removal from wastewater.The carbon footprint from wastewater treatment plants may be significantly increased if N2O production and emissions are not controlled.Mathematical modeling of N2O emissions is of great importance for the in-depth clarification of N2O production mechanisms,the quantification of N2O emissions,the optimization of biological nitrogen removal,and the development of mitigation strategies.Combing with the state of the art,the production mechanisms of N2O were described.N2O mathematical models based on different mechanisms were concluded,including the ones predicting N2O production by ammonia-oxidizing bacteria (AOB) through the hydroxylamine oxidation pathway and the AOB denitrification pathway,by heterotrophic denitrifiers through the denitrification pathway and by both groups of microbes through the integration of these pathways.The models of N2O emissions in advanced nitrogen removal processes,practical engineering application of N2O models,and the existing problems in model calibration were summarized in detail,and the future research directions of N2O modeling were prospected.
Nitrous oxide (N2O) is a greenhouse gas with an approximately 265-fold stronger warming effect than carbon dioxide.N2O can be produced and directly emitted during biological nitrogen removal from wastewater.The carbon footprint from wastewater treatment plants may be significantly increased if N2O production and emissions are not controlled.Mathematical modeling of N2O emissions is of great importance for the in-depth clarification of N2O production mechanisms,the quantification of N2O emissions,the optimization of biological nitrogen removal,and the development of mitigation strategies.Combing with the state of the art,the production mechanisms of N2O were described.N2O mathematical models based on different mechanisms were concluded,including the ones predicting N2O production by ammonia-oxidizing bacteria (AOB) through the hydroxylamine oxidation pathway and the AOB denitrification pathway,by heterotrophic denitrifiers through the denitrification pathway and by both groups of microbes through the integration of these pathways.The models of N2O emissions in advanced nitrogen removal processes,practical engineering application of N2O models,and the existing problems in model calibration were summarized in detail,and the future research directions of N2O modeling were prospected.
2022, 40(6): 107-115.
doi: 10.13205/j.hjgc.202206014
Abstract:
Nitrous oxide (N2O) is a greenhouse gas and a strong oxidizing substance with the potential of energy recovery.This paper critically reviewed the novel nitrogen removal bioprocesses and methods for increasing N2O production from wastewater.The operating conditions and N2O conversion efficiencies in these bioprocesses were compared.Then,the shortages of these methods were pointed out.Moreover,this paper comprehensively reviewed the research progress on data-driven modeling of N2O emission in wastewater treatment processes from two perspectives:1) identifying key factors related to N2O,and 2) predicting N2O production.Currently,the main methods for recovering N2O include coupled aerobic-anoxic nitrous decomposition operation (CANDO),single reactor process,applications of recombinant strains or semiconductor modification strains.Big data of wastewater treatment plants could be utilized to establish the data-driven models for N2O emissions,whereas the existing N2O models mainly focus on reducing N2O emission.The future trends of N2O recovery include:1) developing new methods for recovering N2O;2) optimizing functional microbes in N2O recovering processes;3) establishing the relationships between data-driven modeling of N2O and N2O recovering processes.
Nitrous oxide (N2O) is a greenhouse gas and a strong oxidizing substance with the potential of energy recovery.This paper critically reviewed the novel nitrogen removal bioprocesses and methods for increasing N2O production from wastewater.The operating conditions and N2O conversion efficiencies in these bioprocesses were compared.Then,the shortages of these methods were pointed out.Moreover,this paper comprehensively reviewed the research progress on data-driven modeling of N2O emission in wastewater treatment processes from two perspectives:1) identifying key factors related to N2O,and 2) predicting N2O production.Currently,the main methods for recovering N2O include coupled aerobic-anoxic nitrous decomposition operation (CANDO),single reactor process,applications of recombinant strains or semiconductor modification strains.Big data of wastewater treatment plants could be utilized to establish the data-driven models for N2O emissions,whereas the existing N2O models mainly focus on reducing N2O emission.The future trends of N2O recovery include:1) developing new methods for recovering N2O;2) optimizing functional microbes in N2O recovering processes;3) establishing the relationships between data-driven modeling of N2O and N2O recovering processes.
2022, 40(6): 116-122.
doi: 10.13205/j.hjgc.202206015
Abstract:
The issue of greenhouse gases (GHGs) production is one of the problems to be solved for the wastewater treatment industry under the background of "carbon neutrality" .An accurate grasp of the production characteristics and variations of China's major urban areas is the prerequisite of formulation reduction policies on GHGs emissions.The emission factor method based on the treated sewage amount was used to establish the emission inventory of GHGs of carbon dioxide (CO2),methane (CH4),and nitrous oxide (N2O) from municipal wastewater treatment plants (MWWTPs) of five major urban groups in China during 2015-2019 in this article,and the temporal and spatial distribution and influencing factors of GHGs were analyzed.The results showed that the GHGs emission amount from MWWTPs of the five major urban groups increased during the five years.The highest emission amount was observed in the Yangtze River Delta urban group with the value of 2042.78 Gg CO2-eq in 2019,and the lowest was observed in Fenwei Plain urban group.The Pearl River Delta urban agglomeration had the highest per capita GHGs emission amounts,with a value of 20.36 kg/person in 2019.Correlation analysis showed that GHGs emission from MWWTPs were significantly positively correlated with the factors of population,GDP,wastewater treatment capacity,and wastewater treatment rate.
The issue of greenhouse gases (GHGs) production is one of the problems to be solved for the wastewater treatment industry under the background of "carbon neutrality" .An accurate grasp of the production characteristics and variations of China's major urban areas is the prerequisite of formulation reduction policies on GHGs emissions.The emission factor method based on the treated sewage amount was used to establish the emission inventory of GHGs of carbon dioxide (CO2),methane (CH4),and nitrous oxide (N2O) from municipal wastewater treatment plants (MWWTPs) of five major urban groups in China during 2015-2019 in this article,and the temporal and spatial distribution and influencing factors of GHGs were analyzed.The results showed that the GHGs emission amount from MWWTPs of the five major urban groups increased during the five years.The highest emission amount was observed in the Yangtze River Delta urban group with the value of 2042.78 Gg CO2-eq in 2019,and the lowest was observed in Fenwei Plain urban group.The Pearl River Delta urban agglomeration had the highest per capita GHGs emission amounts,with a value of 20.36 kg/person in 2019.Correlation analysis showed that GHGs emission from MWWTPs were significantly positively correlated with the factors of population,GDP,wastewater treatment capacity,and wastewater treatment rate.
2022, 40(6): 123-129,193.
doi: 10.13205/j.hjgc.202206016
Abstract:
Methane (CH4) generated from municipal sewer systems is one of the major sources of urban carbon emissions.Understanding of emission characteristics and mechanisms of CH4 is essential to reducing carbon emission and improving wastewater treatment systems.Therefore,this paper reviews the current research on the CH4 emission in municipal sewer systems,analyzes the main factors and emission mechanisms related to the CH4 emission.The mathematical modeling methods for evaluating CH4 emission are discussed as well.The results show that internal environment of sewers,water quality and hydrodynamic conditions greatly affect the CH4 emission.The biochemical reactions and mass transfer on the multiphase interface of biofilm-pipe sediment-wastewater cause differences in the emission characteristics of CH4.The mathematical models for the CH4 emission from sewer systems should be established by coupling pipeline hydrodynamics,particular sedimentation,and biochemical reaction kinetics.The evaluation and control of CH4 emission from sewer systems could be supported by these mathematical models in the future.
Methane (CH4) generated from municipal sewer systems is one of the major sources of urban carbon emissions.Understanding of emission characteristics and mechanisms of CH4 is essential to reducing carbon emission and improving wastewater treatment systems.Therefore,this paper reviews the current research on the CH4 emission in municipal sewer systems,analyzes the main factors and emission mechanisms related to the CH4 emission.The mathematical modeling methods for evaluating CH4 emission are discussed as well.The results show that internal environment of sewers,water quality and hydrodynamic conditions greatly affect the CH4 emission.The biochemical reactions and mass transfer on the multiphase interface of biofilm-pipe sediment-wastewater cause differences in the emission characteristics of CH4.The mathematical models for the CH4 emission from sewer systems should be established by coupling pipeline hydrodynamics,particular sedimentation,and biochemical reaction kinetics.The evaluation and control of CH4 emission from sewer systems could be supported by these mathematical models in the future.
2022, 40(6): 130-137.
doi: 10.13205/j.hjgc.202206017
Abstract:
Under the background of China's carbon peak and carbon neutralization goals,there is a lack of quantitative comprehensive impact assessment on process design in carbon-neutral planning of the wastewater treatment industry.Therefore,based on the LCA framework,a comprehensive impact assessment model of carbon footprint,environment,and economy of the whole life cycle (LCA-CEE) was established.And the model was used to analyse the effects of two different sludge treatment processes in sewage treatment plants (A plant:sludge landfill;B plant:sludge-kitchen waste co-digestion).The comprehensive impact assessment and comparative analysis were conducted on energy consumption,material consumption and pollution discharge in the construction,operation and demolition stages within 30 years.The results showed that the power generation of cogeneration system in plant B reached 38.9 MW·h,realizing energy self-sufficiency and with a carbon neutralization rate up to 133%.Compared with plant A,the economic benefit was 1.6 times higher and the environmental impact was significantly reduced.The LCA-CEE model developed in this study evaluated the energy-saving and emission reduction path from the whole process,providing theoretical support for carbon neutrality planning of the sewage treatment industry.
Under the background of China's carbon peak and carbon neutralization goals,there is a lack of quantitative comprehensive impact assessment on process design in carbon-neutral planning of the wastewater treatment industry.Therefore,based on the LCA framework,a comprehensive impact assessment model of carbon footprint,environment,and economy of the whole life cycle (LCA-CEE) was established.And the model was used to analyse the effects of two different sludge treatment processes in sewage treatment plants (A plant:sludge landfill;B plant:sludge-kitchen waste co-digestion).The comprehensive impact assessment and comparative analysis were conducted on energy consumption,material consumption and pollution discharge in the construction,operation and demolition stages within 30 years.The results showed that the power generation of cogeneration system in plant B reached 38.9 MW·h,realizing energy self-sufficiency and with a carbon neutralization rate up to 133%.Compared with plant A,the economic benefit was 1.6 times higher and the environmental impact was significantly reduced.The LCA-CEE model developed in this study evaluated the energy-saving and emission reduction path from the whole process,providing theoretical support for carbon neutrality planning of the sewage treatment industry.
2022, 40(6): 138-144.
doi: 10.13205/j.hjgc.202206018
Abstract:
Intelligent control of wastewater treatment is the leading edge in the water pollution control field.The rapid development of artificial intelligence technology injects fresh vitality into the development of wastewater treatment intelligent control.It is strongly desirable to explore a scientific route of combining mechanism and data-driven models to reconstruct the logical mode of wastewater treatment intelligent control system and hence promote its technical development level.This paper proposed a tentative plan of dual-loop logical structure based on the certainty-randomness features of wastewater treatment processes,which is likely to provide a new technical route of wastewater treatment intelligent control through future practice and exploration.First,this paper reviewed the essential factors of wastewater treatment intelligent control and dissected the control role of the mechanism model in the certainty scale,as well as the role of the data-driven model in the randomness scale.Then,a dual-loop logical structure and its control principle combining mechanism and data-driven models were proposed,and the topology in the application of complex wastewater treatment processes was clarified.Finally,a brief perspective centering on the future development of wastewater treatment intelligent control technologies was presented.
Intelligent control of wastewater treatment is the leading edge in the water pollution control field.The rapid development of artificial intelligence technology injects fresh vitality into the development of wastewater treatment intelligent control.It is strongly desirable to explore a scientific route of combining mechanism and data-driven models to reconstruct the logical mode of wastewater treatment intelligent control system and hence promote its technical development level.This paper proposed a tentative plan of dual-loop logical structure based on the certainty-randomness features of wastewater treatment processes,which is likely to provide a new technical route of wastewater treatment intelligent control through future practice and exploration.First,this paper reviewed the essential factors of wastewater treatment intelligent control and dissected the control role of the mechanism model in the certainty scale,as well as the role of the data-driven model in the randomness scale.Then,a dual-loop logical structure and its control principle combining mechanism and data-driven models were proposed,and the topology in the application of complex wastewater treatment processes was clarified.Finally,a brief perspective centering on the future development of wastewater treatment intelligent control technologies was presented.
2022, 40(6): 145-153.
doi: 10.13205/j.hjgc.202206019
Abstract:
Wastewater treatment systems are usually very complicated and may be affected by many external factors.Therefore,control and management of these systems are always one of the great challenges in environmental engineering.Traditional controlling and managing approaches could not meet the needs of the increasingly complex wastewater treatment facilities.While the recently-developed machine learning methods provide a series of effective solutions for such problems.This article introduces the characteristics of machine learning methods,including artificial neural networks,support vector machines,random forests,etc.,and explains the application of machine learning methods in the field of wastewater treatment systems from three aspects,i.e.water quality prediction and early warning,wastewater treatment system fault diagnosis and intelligent control.The advantages of machine learning methods and the challenges of their applications in wastewater treatment systems are also presented.In addition,the future development trends of machine learning methods in the field of wastewater treatment are outlined.
Wastewater treatment systems are usually very complicated and may be affected by many external factors.Therefore,control and management of these systems are always one of the great challenges in environmental engineering.Traditional controlling and managing approaches could not meet the needs of the increasingly complex wastewater treatment facilities.While the recently-developed machine learning methods provide a series of effective solutions for such problems.This article introduces the characteristics of machine learning methods,including artificial neural networks,support vector machines,random forests,etc.,and explains the application of machine learning methods in the field of wastewater treatment systems from three aspects,i.e.water quality prediction and early warning,wastewater treatment system fault diagnosis and intelligent control.The advantages of machine learning methods and the challenges of their applications in wastewater treatment systems are also presented.In addition,the future development trends of machine learning methods in the field of wastewater treatment are outlined.
2022, 40(6): 154-161.
doi: 10.13205/j.hjgc.202206020
Abstract:
In this paper,the LBM-IBM coupling method was used to simulate the flow field in porous media,and the cellular automata model was used to simulate the growth and decay process of microorganisms on the surface of porous media,to reveal the dynamic development process of biological blockage and the essence of the permeability change caused by the blockage in porous media at the mesoscopic level.The study found that when the inlet concentration of nutrients increased by 100%,the relative permeability coefficient attenuation increased by 6.25%~45.5% within 30 hours.There was a critical moment of blockage in biological plugging,and the critical moment of blockage in porous media biofilm was advanced with the increase of nutrient inlet concentration.Under different conditions,the permeability of porous media showed a decreasing trend in different degrees,and the spatial distribution of biological blockage in porous media showed obvious heterogeneity.The growth of organisms was concentration-oriented,and the growth and decay behavior of microorganisms in local pores determined the plugging degree of porous media,but the decline of some organisms didn't change the declining trend of the overall permeability of porous media.When the temperature rose by 25%,the relative permeability coefficient of porous media attenuation could increase by more than 5 times.The sensitivity of biological plugs to temperature was different in different locations.
In this paper,the LBM-IBM coupling method was used to simulate the flow field in porous media,and the cellular automata model was used to simulate the growth and decay process of microorganisms on the surface of porous media,to reveal the dynamic development process of biological blockage and the essence of the permeability change caused by the blockage in porous media at the mesoscopic level.The study found that when the inlet concentration of nutrients increased by 100%,the relative permeability coefficient attenuation increased by 6.25%~45.5% within 30 hours.There was a critical moment of blockage in biological plugging,and the critical moment of blockage in porous media biofilm was advanced with the increase of nutrient inlet concentration.Under different conditions,the permeability of porous media showed a decreasing trend in different degrees,and the spatial distribution of biological blockage in porous media showed obvious heterogeneity.The growth of organisms was concentration-oriented,and the growth and decay behavior of microorganisms in local pores determined the plugging degree of porous media,but the decline of some organisms didn't change the declining trend of the overall permeability of porous media.When the temperature rose by 25%,the relative permeability coefficient of porous media attenuation could increase by more than 5 times.The sensitivity of biological plugs to temperature was different in different locations.
2022, 40(6): 162-170,181.
doi: 10.13205/j.hjgc.202206021
Abstract:
A comprehensive evaluation system of the aerobic sludge granulation process was established based on the traditional fuzzy comprehensive evaluation method.Based on the data of the laboratory scale reactor that successfully cultivated aerobic granular sludge,the membership function and membership matrix were constructed by taking the seven parameters including SVI5/SVI30,specific gravity,roundness,length-diameter ratio,shape factor,fractal dimension,and compactness as the evaluation indexes.The weight matrix was constructed by combining analytic hierarchy process and entropy weight method.The process of aerobic sludge granulation was divided into four stages:floc stage,initial granulation stage,rapid granulation stage,and complete granulation stage.The model verification group was established,and the sludge granulation stage at different time in the system was predicted based on the established evaluation system.This comprehensive evaluation system can make an accurate and objective judgment for the real-time granulation status of sludge in aerobic granular sludge cultivation system,and can timely find the abnormal situation of the system,which is of great significance to maintaining the stability of the system.
A comprehensive evaluation system of the aerobic sludge granulation process was established based on the traditional fuzzy comprehensive evaluation method.Based on the data of the laboratory scale reactor that successfully cultivated aerobic granular sludge,the membership function and membership matrix were constructed by taking the seven parameters including SVI5/SVI30,specific gravity,roundness,length-diameter ratio,shape factor,fractal dimension,and compactness as the evaluation indexes.The weight matrix was constructed by combining analytic hierarchy process and entropy weight method.The process of aerobic sludge granulation was divided into four stages:floc stage,initial granulation stage,rapid granulation stage,and complete granulation stage.The model verification group was established,and the sludge granulation stage at different time in the system was predicted based on the established evaluation system.This comprehensive evaluation system can make an accurate and objective judgment for the real-time granulation status of sludge in aerobic granular sludge cultivation system,and can timely find the abnormal situation of the system,which is of great significance to maintaining the stability of the system.
2022, 40(6): 171-181.
doi: 10.13205/j.hjgc.202206022
Abstract:
Nanomaterials (NM),as a new class of chemicals,recognizing and controlling their adverse environmental health risks are important for their applications.In recent years,machine learning (ML),as a data-driven approach,has attracted extensive attention in various fields,such as environment,chemistry,and materials.The review introduces the applications of ML from the aspects of NM-induced cytotoxicity,individual effects,protein corona and ecological corona predictions,and analyzes the problems and solutions during the application process from the aspects of data sets,descriptors,machine learning models,and model interpretability.The review also points out that the innovation and development of data extraction and mining methods,new descriptors,new models,and model interpretation methods will promote the application of ML in the biological effects of NM.With the development of ML,new types of NM and complex effects are expected to be predicted and their principles can be revealed.This review focuses on the discussion on the important scientific issues faced in the prediction of NM biological effects using ML,which will help the following researchers clarify the ideas,solve the problems in this field,and promote the healthy and sustainable development of the nano-industry.
Nanomaterials (NM),as a new class of chemicals,recognizing and controlling their adverse environmental health risks are important for their applications.In recent years,machine learning (ML),as a data-driven approach,has attracted extensive attention in various fields,such as environment,chemistry,and materials.The review introduces the applications of ML from the aspects of NM-induced cytotoxicity,individual effects,protein corona and ecological corona predictions,and analyzes the problems and solutions during the application process from the aspects of data sets,descriptors,machine learning models,and model interpretability.The review also points out that the innovation and development of data extraction and mining methods,new descriptors,new models,and model interpretation methods will promote the application of ML in the biological effects of NM.With the development of ML,new types of NM and complex effects are expected to be predicted and their principles can be revealed.This review focuses on the discussion on the important scientific issues faced in the prediction of NM biological effects using ML,which will help the following researchers clarify the ideas,solve the problems in this field,and promote the healthy and sustainable development of the nano-industry.
2022, 40(6): 182-187.
doi: 10.13205/j.hjgc.202206023
Abstract:
With the rapid increase in the capacity and complexity of data generated in the field of environmental functional materials,the high cost and long cycle time of traditional experimental methods can no longer meet the current trend of functional materials.The rapid development of machine learning in recent years can dig deeper and analyze the data,which provides an effective solution.Machine learning has the advantages of high efficiency and accuracy,which effectively compensates for the shortcomings of the traditional "trial and error" strategy.This paper outlines the basic working principles and algorithms of machine learning,summarizes the recent advances in machine learning in the field of carbon-based environmental functional materials (e.g.,predicting physicochemical properties,assisting structural characterization as well as guiding the synthesis of advanced functional materials),and presents the existing problems and challenges of machine learning in this field.Future perspectives of machine learning in environmental functional materials is analyzed as well.
With the rapid increase in the capacity and complexity of data generated in the field of environmental functional materials,the high cost and long cycle time of traditional experimental methods can no longer meet the current trend of functional materials.The rapid development of machine learning in recent years can dig deeper and analyze the data,which provides an effective solution.Machine learning has the advantages of high efficiency and accuracy,which effectively compensates for the shortcomings of the traditional "trial and error" strategy.This paper outlines the basic working principles and algorithms of machine learning,summarizes the recent advances in machine learning in the field of carbon-based environmental functional materials (e.g.,predicting physicochemical properties,assisting structural characterization as well as guiding the synthesis of advanced functional materials),and presents the existing problems and challenges of machine learning in this field.Future perspectives of machine learning in environmental functional materials is analyzed as well.
2022, 40(6): 188-193.
doi: 10.13205/j.hjgc.202206024
Abstract:
Under the context of carbon neutrality,there is an urgent need to develop high-efficiency and clean energy to reduce the dependence on petrochemical energy.As a material that can directly convert solar energy or other light energy into electrical energy,organic photovoltaic materials have become an increasingly promising low-carbon energy material with great application prospects.In the process of exploring high-performance organic photovoltaic materials,although machine learning can improve the efficiency of material design,its predictive ability is still greatly restricted by the development and selection of descriptors.In the present study,the authors applied recurrent neural network,convolutional neural network,and graph neural network to build end-to-end deep learning models to predict the photoelectric conversion efficiency,and the constructed models could directly extract chemical features from SMILES symbols,molecular images,and molecular graph networks without the need of descriptors calculation and selection.The resulted models could not only accurately predict the photoelectric conversion efficiency of organic photovoltaic materials (the optimal model obtained R2>0.73 for both 5-fold cross validation and external validation),but also identify the key structural features that affect the conversion efficiency.The results could provide theoretical guidance for the design of new environmental functional materials.
Under the context of carbon neutrality,there is an urgent need to develop high-efficiency and clean energy to reduce the dependence on petrochemical energy.As a material that can directly convert solar energy or other light energy into electrical energy,organic photovoltaic materials have become an increasingly promising low-carbon energy material with great application prospects.In the process of exploring high-performance organic photovoltaic materials,although machine learning can improve the efficiency of material design,its predictive ability is still greatly restricted by the development and selection of descriptors.In the present study,the authors applied recurrent neural network,convolutional neural network,and graph neural network to build end-to-end deep learning models to predict the photoelectric conversion efficiency,and the constructed models could directly extract chemical features from SMILES symbols,molecular images,and molecular graph networks without the need of descriptors calculation and selection.The resulted models could not only accurately predict the photoelectric conversion efficiency of organic photovoltaic materials (the optimal model obtained R2>0.73 for both 5-fold cross validation and external validation),but also identify the key structural features that affect the conversion efficiency.The results could provide theoretical guidance for the design of new environmental functional materials.
2022, 40(6): 194-203.
doi: 10.13205/j.hjgc.202206025
Abstract:
The rapidly developing technologies in data science have provided powerful tools for the data analytical process in wastewater treatment plants (WWTPs).Successfully applying data analytics in WWTPs needs the systematical approach to overcome the gaps in data,algorithm,and computing power.In this paper,firstly we summarized the advances in data analytics for WWTPs,and discussed the challenges that remained in the data quality control and mathematical models.Secondly,we summarized four typical four scenarios of data analytics in WWTPs and introduced twelve cases of integral application of data analytics in the wastewater treatment systems.Thirdly,the technical maturity of data analytics in WWTPs was estimated using the classic tools of hyper curves and technical readiness levels.Finally,the demands of water sectors on data analytics were analyzed to clarify the trends of the technical evolution of data analytics for WWTPs.This review was expected to help the operators and managers in WWTPs understand the advances in data analytics and utilize the developed tools to solve the process problems.
The rapidly developing technologies in data science have provided powerful tools for the data analytical process in wastewater treatment plants (WWTPs).Successfully applying data analytics in WWTPs needs the systematical approach to overcome the gaps in data,algorithm,and computing power.In this paper,firstly we summarized the advances in data analytics for WWTPs,and discussed the challenges that remained in the data quality control and mathematical models.Secondly,we summarized four typical four scenarios of data analytics in WWTPs and introduced twelve cases of integral application of data analytics in the wastewater treatment systems.Thirdly,the technical maturity of data analytics in WWTPs was estimated using the classic tools of hyper curves and technical readiness levels.Finally,the demands of water sectors on data analytics were analyzed to clarify the trends of the technical evolution of data analytics for WWTPs.This review was expected to help the operators and managers in WWTPs understand the advances in data analytics and utilize the developed tools to solve the process problems.
2022, 40(6): 204-211,225.
doi: 10.13205/j.hjgc.202206026
Abstract:
A hybrid control modelling method was proposed for optimal control of urban drainage systems,which adopted both data-driven and mechanism-driven simplification strategies.The method divided urban drainage systems into different regions,according to connected degree to the control target.These regions were modeled by LSTM model and Saint Venant equation separately.The method was verified within the service area of a wastewater treatment plant in City A in China.The proposed method established a surrogate control model based on a detailed hydraulic model.Compared with tank model,simulation accuracy in two CSO outfalls was improved by 3.85% and 22.86%,and improved by 5.66% and 3.57% compared to the LSTM model (measured in an average root mean value).The simulation time could be reduced by 98.7% in comparison to the detailed hydraulic model.Due to the advantage in accuracy and efficiency,the modelling strategy can provide references for the implementation of real-time control in urban drainage systems.
A hybrid control modelling method was proposed for optimal control of urban drainage systems,which adopted both data-driven and mechanism-driven simplification strategies.The method divided urban drainage systems into different regions,according to connected degree to the control target.These regions were modeled by LSTM model and Saint Venant equation separately.The method was verified within the service area of a wastewater treatment plant in City A in China.The proposed method established a surrogate control model based on a detailed hydraulic model.Compared with tank model,simulation accuracy in two CSO outfalls was improved by 3.85% and 22.86%,and improved by 5.66% and 3.57% compared to the LSTM model (measured in an average root mean value).The simulation time could be reduced by 98.7% in comparison to the detailed hydraulic model.Due to the advantage in accuracy and efficiency,the modelling strategy can provide references for the implementation of real-time control in urban drainage systems.
2022, 40(6): 212-218,271.
doi: 10.13205/j.hjgc.202206027
Abstract:
Sodium acetate and methanol are widely used as an external carbon source to enhance denitrification for nitrogen removal in wastewater treatment plants.However,the carbon dosage hardly matches the carbon requirement for denitrification properly in practice.To satisfy the dynamic requirements of carbon for denitrification in wastewater biological treatment processes,a real-time online surveillance based intelligent carbon dosing control algorithm was proposed and an intelligent carbon dosing control system was therefore developed to solve the problems caused by manual control of carbon dosing,e.g.over-dosing,time delay,extra carbon dioxide emission,and process stability.Full-scale tests were carried out at a wastewater treatment plant for four months,and the results indicated that the effluent achieved the discharging proposal constantly,meanwhile the sodium acetate consumption was reduced by 21.2% and the chemical cost for acetate acid was reduced by RMB 47200 per month.This work showed an approach to control carbon dosing accurately as well as stabilize the process,which provided technical and equipment support for WWTP automation and carbon emission reduction.
Sodium acetate and methanol are widely used as an external carbon source to enhance denitrification for nitrogen removal in wastewater treatment plants.However,the carbon dosage hardly matches the carbon requirement for denitrification properly in practice.To satisfy the dynamic requirements of carbon for denitrification in wastewater biological treatment processes,a real-time online surveillance based intelligent carbon dosing control algorithm was proposed and an intelligent carbon dosing control system was therefore developed to solve the problems caused by manual control of carbon dosing,e.g.over-dosing,time delay,extra carbon dioxide emission,and process stability.Full-scale tests were carried out at a wastewater treatment plant for four months,and the results indicated that the effluent achieved the discharging proposal constantly,meanwhile the sodium acetate consumption was reduced by 21.2% and the chemical cost for acetate acid was reduced by RMB 47200 per month.This work showed an approach to control carbon dosing accurately as well as stabilize the process,which provided technical and equipment support for WWTP automation and carbon emission reduction.
2022, 40(6): 219-225.
doi: 10.13205/j.hjgc.202206028
Abstract:
The whole plant models were set up for Phase Ⅰ and Phase Ⅱ processes at Yongchuan wastewater treatment plant (WWTP) to evaluate the operation conditions and to determine the potential optimization strategies.The historical data for 1.5 years and supplementary experimental data of Chongqing Yongchuan sewage treatment plant were collected,analyzed and used for the model calibration.And its influent water quality characteristics,operation mode and biochemical reaction state were determined.The digital simulation based on the mechanism model was completed for its annual average state and duration fluctuation state.The historical and current operation conditions of the WWTP was assessed for the potential problems and risks.The optimization strategies including operation improvement and external carbon reduction were determined using the calibrated models.Through the adjustment of operation parameters,the risk of overload of secondary sedimentation tank and uneven distribution of sludge in the system could be effectively avoided with operating at a reasonable sludge age and using inlet water quality characteristics,it could reduce the addition of additional carbon source and reagent on the premise that the effluent quality was stable and up to standard.The quantitative evaluation of the external carbon addition was carried out under average,extreme,and dynamic conditions,respectively.As the result of dynamic simulation analysis:with the improvement on operation parameters such as recycle flow and sludge wastage,the annual average methanol addition can be reduced to 80% for Phase Ⅰ and 60% for Phase Ⅱ,respectively,as compared to the actual dosage at WWTP.
The whole plant models were set up for Phase Ⅰ and Phase Ⅱ processes at Yongchuan wastewater treatment plant (WWTP) to evaluate the operation conditions and to determine the potential optimization strategies.The historical data for 1.5 years and supplementary experimental data of Chongqing Yongchuan sewage treatment plant were collected,analyzed and used for the model calibration.And its influent water quality characteristics,operation mode and biochemical reaction state were determined.The digital simulation based on the mechanism model was completed for its annual average state and duration fluctuation state.The historical and current operation conditions of the WWTP was assessed for the potential problems and risks.The optimization strategies including operation improvement and external carbon reduction were determined using the calibrated models.Through the adjustment of operation parameters,the risk of overload of secondary sedimentation tank and uneven distribution of sludge in the system could be effectively avoided with operating at a reasonable sludge age and using inlet water quality characteristics,it could reduce the addition of additional carbon source and reagent on the premise that the effluent quality was stable and up to standard.The quantitative evaluation of the external carbon addition was carried out under average,extreme,and dynamic conditions,respectively.As the result of dynamic simulation analysis:with the improvement on operation parameters such as recycle flow and sludge wastage,the annual average methanol addition can be reduced to 80% for Phase Ⅰ and 60% for Phase Ⅱ,respectively,as compared to the actual dosage at WWTP.
2022, 40(6): 226-232,153.
doi: 10.13205/j.hjgc.202206029
Abstract:
The optimal scheduling model for urban water supply networks often requires repeated scheduling of the hydraulics model to calculate the objective function and constraints,which leads to high computational and time cost.In the optimization iteration process,searching is usually performed at the boundaries of the constraints in the search space to obtain better optimization results,which leads to a large number of samples that do not satisfy the constraints and optimization efficiency.To solve this problem,an efficient constraint-based dynamic pruning method was proposed,which used the data accumulated during the optimization calculation to determine whether the samples met the constraints before running the hydraulics model,to eliminate the samples that do not satisfy the constraints and improve the optimization efficiency.The model was tested on a case network and a real network,and the results showed that the use of Naïve Bayes,decision trees,and support vector machines as dynamic pruning models reduced the number of computation by 56.4%,58.5%,and 56.8%,respectively,while obtaining the similar results as the original optimization model.
The optimal scheduling model for urban water supply networks often requires repeated scheduling of the hydraulics model to calculate the objective function and constraints,which leads to high computational and time cost.In the optimization iteration process,searching is usually performed at the boundaries of the constraints in the search space to obtain better optimization results,which leads to a large number of samples that do not satisfy the constraints and optimization efficiency.To solve this problem,an efficient constraint-based dynamic pruning method was proposed,which used the data accumulated during the optimization calculation to determine whether the samples met the constraints before running the hydraulics model,to eliminate the samples that do not satisfy the constraints and improve the optimization efficiency.The model was tested on a case network and a real network,and the results showed that the use of Naïve Bayes,decision trees,and support vector machines as dynamic pruning models reduced the number of computation by 56.4%,58.5%,and 56.8%,respectively,while obtaining the similar results as the original optimization model.
2022, 40(6): 233-240.
doi: 10.13205/j.hjgc.202206030
Abstract:
Leakage in water distribution network (WDN) is a common and worldwide problem.How to effectively reduce the leakage rate and locate the leakage is an important problem which perplexing China's water supply industry.The leakage location methods based on hydraulic model and online monitoring data can quickly identify the leakage events in WDN and achieve preliminary location.However,such methods depend heavily on model accuracy,and also lack sufficient actual leakage case data in WDN for testing.To address this,the laboratory pipe network model was built in this study,and the SCADA system was arranged to monitor the pressure and flow data,so as to realize the simulation of WDN,which can be used to generate the simulation data of various leakage in WDN and test the effect of leakage location method.Moreover,by changing the leakage location and flow,model accuracy,as well as optimal layout of monitoring points,this study identified the influencing factors of the leakage location.
Leakage in water distribution network (WDN) is a common and worldwide problem.How to effectively reduce the leakage rate and locate the leakage is an important problem which perplexing China's water supply industry.The leakage location methods based on hydraulic model and online monitoring data can quickly identify the leakage events in WDN and achieve preliminary location.However,such methods depend heavily on model accuracy,and also lack sufficient actual leakage case data in WDN for testing.To address this,the laboratory pipe network model was built in this study,and the SCADA system was arranged to monitor the pressure and flow data,so as to realize the simulation of WDN,which can be used to generate the simulation data of various leakage in WDN and test the effect of leakage location method.Moreover,by changing the leakage location and flow,model accuracy,as well as optimal layout of monitoring points,this study identified the influencing factors of the leakage location.
2022, 40(6): 241-252.
doi: 10.13205/j.hjgc.202206031
Abstract:
Aquatic ecosystem simulation is the basis of the diagnosis of water ecological degradation,risk early-warning,and restoration.Under the superposition of various environmental pressures,the water ecosystem integrity state often shows the response characteristics of accumulation,complexity and lag,which is difficult to be characterized and predicted by conventional static experiments and water quality models.In this paper,the latest progress of water ecological simulation was reviewed,and the basic principles,applicability and application cases of different models were clarified according to five types of models including aquatic biology,ecosystem dynamics,habitat suitability,statistical experience and watershed water system coupling.Results showed that China's environmental management urgently needs the support of water ecosystem integrity simulation tools.We should carry out major basic scientific research in this field,break through the key technologies of river and lake aquatic biological simulation prediction and multi-scale coupling,accelerate the construction of water ecosystem integrity model database and parameters'database with independent intellectual property rights,promote the construction of large scientific devices of river basin water ecosystem simulation in China,and support the construction of comprehensive water ecosystem management system of key rivers such as the Yellow River and the Yangtze River.
Aquatic ecosystem simulation is the basis of the diagnosis of water ecological degradation,risk early-warning,and restoration.Under the superposition of various environmental pressures,the water ecosystem integrity state often shows the response characteristics of accumulation,complexity and lag,which is difficult to be characterized and predicted by conventional static experiments and water quality models.In this paper,the latest progress of water ecological simulation was reviewed,and the basic principles,applicability and application cases of different models were clarified according to five types of models including aquatic biology,ecosystem dynamics,habitat suitability,statistical experience and watershed water system coupling.Results showed that China's environmental management urgently needs the support of water ecosystem integrity simulation tools.We should carry out major basic scientific research in this field,break through the key technologies of river and lake aquatic biological simulation prediction and multi-scale coupling,accelerate the construction of water ecosystem integrity model database and parameters'database with independent intellectual property rights,promote the construction of large scientific devices of river basin water ecosystem simulation in China,and support the construction of comprehensive water ecosystem management system of key rivers such as the Yellow River and the Yangtze River.
2022, 40(6): 253-260,294.
doi: 10.13205/j.hjgc.202206032
Abstract:
With the effective control of point source pollution,non-point source pollution has gradually become the main focus of water environment management in China.However,the source and transport of non-point source pollution are hard to monitor,and models are usually necessary.Based on the comparison of the statistical model and the mechanism model for non-point source pollution simulation,SPARROW (SPAtially referenced regressions on watershed attributes) was proved to be a more practical model between the statistical model and the mechanism model,which is a hybrid (statistical and mechanistic) watershed model,and widely used in many countries and regions.By summarizing the different aspects of the model,such as nutrient transport,scenario analysis,combining with other methods,and the improvement of the model,the following conclusions can be drawn:1) SPARROW model can meet the demand of watershed management in China since it doesn't need huge data and its establishment is not very difficult;2) SPARROW model is spatially explicit,and it could estimate the delivery of pollutants from subbasins towards the outlet,therefore,it can provide sufficient support for the simulation of non-point source pollution;3) SPARROW model can be more widely used by improving uncertainty analysis,temporal resolution,and spatial difference.
With the effective control of point source pollution,non-point source pollution has gradually become the main focus of water environment management in China.However,the source and transport of non-point source pollution are hard to monitor,and models are usually necessary.Based on the comparison of the statistical model and the mechanism model for non-point source pollution simulation,SPARROW (SPAtially referenced regressions on watershed attributes) was proved to be a more practical model between the statistical model and the mechanism model,which is a hybrid (statistical and mechanistic) watershed model,and widely used in many countries and regions.By summarizing the different aspects of the model,such as nutrient transport,scenario analysis,combining with other methods,and the improvement of the model,the following conclusions can be drawn:1) SPARROW model can meet the demand of watershed management in China since it doesn't need huge data and its establishment is not very difficult;2) SPARROW model is spatially explicit,and it could estimate the delivery of pollutants from subbasins towards the outlet,therefore,it can provide sufficient support for the simulation of non-point source pollution;3) SPARROW model can be more widely used by improving uncertainty analysis,temporal resolution,and spatial difference.
2022, 40(6): 261-271.
doi: 10.13205/j.hjgc.202206033
Abstract:
In order to realize the water environmental carrying capacity evaluation and early warning,a monitoring and early warning index system of water environmental carrying capacity in Baiyangdian Basin was constructed by coupling DPSR model and time difference analysis method.In addition,T-S fuzzy neural network model was built by combining neural network and fuzzy mathematics,and the threshold value of monitoring and early warning index was determined by the control graph method,which solved the randomness and fuzziness of the water environment systems.Finally,the effective evaluation and early warning of water environmental carrying capacity in Baiyangdian Basin were realized.The results showed that:1) the water environmental carrying capacity of Baiyangdian Basin was in a weak carrying state from 2012 to 2015,and in a medium carrying state in 2016 and 2017.The status evaluation level changed from level Ⅳ(orange warning light) to Level Ⅲ(yellow warning light);2) under the current development trend,the overall water environmental carrying capacity of Baiyangdian Basin will increase first and then decrease from 2018 to 2035.The overall water environment of Baiyangdian Basin will deteriorate after 2026,the water environmental carrying capacity will gradually change from a medium carrying state (yellow warning light),to a weak carrying state (orange warning light) and even weaker carrying state (red warning light);3) the growth of the regional population and rapid development of Xiong'an New Area in the future will bring huge pressure to the water environment of Baiyangdian Basin.Therefore,the protection of the regional water environment should be strengthened,the refined environmental control schemes based on spatial units should be implemented to promote the green transformation of the regional economy,the overall improvement and healthy development of regional water environment quality,as well improve the level of regional sustainable development.
In order to realize the water environmental carrying capacity evaluation and early warning,a monitoring and early warning index system of water environmental carrying capacity in Baiyangdian Basin was constructed by coupling DPSR model and time difference analysis method.In addition,T-S fuzzy neural network model was built by combining neural network and fuzzy mathematics,and the threshold value of monitoring and early warning index was determined by the control graph method,which solved the randomness and fuzziness of the water environment systems.Finally,the effective evaluation and early warning of water environmental carrying capacity in Baiyangdian Basin were realized.The results showed that:1) the water environmental carrying capacity of Baiyangdian Basin was in a weak carrying state from 2012 to 2015,and in a medium carrying state in 2016 and 2017.The status evaluation level changed from level Ⅳ(orange warning light) to Level Ⅲ(yellow warning light);2) under the current development trend,the overall water environmental carrying capacity of Baiyangdian Basin will increase first and then decrease from 2018 to 2035.The overall water environment of Baiyangdian Basin will deteriorate after 2026,the water environmental carrying capacity will gradually change from a medium carrying state (yellow warning light),to a weak carrying state (orange warning light) and even weaker carrying state (red warning light);3) the growth of the regional population and rapid development of Xiong'an New Area in the future will bring huge pressure to the water environment of Baiyangdian Basin.Therefore,the protection of the regional water environment should be strengthened,the refined environmental control schemes based on spatial units should be implemented to promote the green transformation of the regional economy,the overall improvement and healthy development of regional water environment quality,as well improve the level of regional sustainable development.
2022, 40(6): 272-279.
doi: 10.13205/j.hjgc.202206034
Abstract:
The subsidence of the coal mining area not only affects the surface structure,but also significantly changes the hydrological cycle and the regional water supply of the river basin.In this paper,the Huainan mining area in the Xifei River Basin was selected,and the SWAT-FLUS integrated model was used to simulate the process and future scenarios of the hydrological cycle.The result showed:1) the integrated model could accurately simulate the hydrological process due to land-use changes and be used for scenario prediction.2) for scenarios under different collapse rates in the future,evapotranspiration showed an increasing trend.The'non-restoration'mode increased water surface evapotranspiration,and the other two modes increased the land surface evapotranspiration;besides,the'non-restoration'mode increased the infiltration volume of the watershed,while reduced the others.3) from the perspective of the distribution of runoff in typical hydrological years,the'non-restoration'mode significantly affected the distribution and the peak value of runoff during the year,while the regular-and ecological-restoration modes didn't have such impacts.4) from the perspective of inter-annual influence,if there were no restoration measures,the hydrological pattern within the basin would undergo a dramatic change between 2020 and 2022;by 2030,the surface runoff would be reduced by 27.1% and 2.5% under non-and regular-restoration mode,respectively,but ecological restoration could increase the surface runoff by 4.4%.
The subsidence of the coal mining area not only affects the surface structure,but also significantly changes the hydrological cycle and the regional water supply of the river basin.In this paper,the Huainan mining area in the Xifei River Basin was selected,and the SWAT-FLUS integrated model was used to simulate the process and future scenarios of the hydrological cycle.The result showed:1) the integrated model could accurately simulate the hydrological process due to land-use changes and be used for scenario prediction.2) for scenarios under different collapse rates in the future,evapotranspiration showed an increasing trend.The'non-restoration'mode increased water surface evapotranspiration,and the other two modes increased the land surface evapotranspiration;besides,the'non-restoration'mode increased the infiltration volume of the watershed,while reduced the others.3) from the perspective of the distribution of runoff in typical hydrological years,the'non-restoration'mode significantly affected the distribution and the peak value of runoff during the year,while the regular-and ecological-restoration modes didn't have such impacts.4) from the perspective of inter-annual influence,if there were no restoration measures,the hydrological pattern within the basin would undergo a dramatic change between 2020 and 2022;by 2030,the surface runoff would be reduced by 27.1% and 2.5% under non-and regular-restoration mode,respectively,but ecological restoration could increase the surface runoff by 4.4%.
2022, 40(6): 280-285.
doi: 10.13205/j.hjgc.202206035
Abstract:
Manning coefficient is the most widely used water flow resistance coefficient in water flow calculation and practical engineering,and it is an important direction in the research of river and lake resistance.Based on the indoor circulating flume,the variation characteristics of the Manning coefficient under different submerged plant densities and water flow rates were studied with a three-dimensional acoustic Doppler velocimeter.According to the similarity principle of friction resistance,the conversion formula of the actual equivalent and the experimental Manning coefficient values,the Manning coefficient of the Taihu Lake under different conditions were calculated.Through regression fitting,the optimal fitting equation of the Manning coefficients of the Taihu Lake with plant density,plant height and water velocity were obtained.The results showed that when the plants'condition at the bottom of the lake was constant,the Manning coefficient of the Taihu Lake and the water velocity presented an obvious monotonic decreasing power function.When the plant density was no more than 200 plants/m2,water velocity and plant height were constant,the Manning coefficient of the Taihu Lake increased slightly with the increase of plant density.In addition,when the water velocity and plant density were constant,the Manning coefficient of the Taihu Lake was monotonically increasing with plant height.The actual Manning coefficient of the Taihu Lake obtained through experiments was of great significance to the study of the migration and transformation of nitrogen and phosphorus nutrients at the sediment-water interface,the improvement of eutrophication,and the long-term management of the lake.
Manning coefficient is the most widely used water flow resistance coefficient in water flow calculation and practical engineering,and it is an important direction in the research of river and lake resistance.Based on the indoor circulating flume,the variation characteristics of the Manning coefficient under different submerged plant densities and water flow rates were studied with a three-dimensional acoustic Doppler velocimeter.According to the similarity principle of friction resistance,the conversion formula of the actual equivalent and the experimental Manning coefficient values,the Manning coefficient of the Taihu Lake under different conditions were calculated.Through regression fitting,the optimal fitting equation of the Manning coefficients of the Taihu Lake with plant density,plant height and water velocity were obtained.The results showed that when the plants'condition at the bottom of the lake was constant,the Manning coefficient of the Taihu Lake and the water velocity presented an obvious monotonic decreasing power function.When the plant density was no more than 200 plants/m2,water velocity and plant height were constant,the Manning coefficient of the Taihu Lake increased slightly with the increase of plant density.In addition,when the water velocity and plant density were constant,the Manning coefficient of the Taihu Lake was monotonically increasing with plant height.The actual Manning coefficient of the Taihu Lake obtained through experiments was of great significance to the study of the migration and transformation of nitrogen and phosphorus nutrients at the sediment-water interface,the improvement of eutrophication,and the long-term management of the lake.
2022, 40(6): 286-294.
doi: 10.13205/j.hjgc.202206036
Abstract:
The Tarim River Basin plays a major part in the development of Xinjiang and the Silk Road Economic Belt.Clarifying the utilization of its water and soil resources and its ecological carrying status is of great significance to the high-quality development of the Tarim River Basin.This paper proposed the use of an improved three-dimensional ecological footprint model to calculate the ecological footprint of the Tarim River Basin.In addition,based on the three-dimensional ecological footprint theory,the water pressure index and land were also introduced.Comprehensive pressure index and ecological tension index were used to investigate whether the current land and water resources were overloaded and assess the ecological carrying status of the Tarim River Basin.The results showed that:1) there has been a noticeable,rapid and continued increase in the per capita ecological footprint of the Tarim River Basin in 2000-2019,2.091 ha/person to 5.864 ha/person,with an average annual growth rate of 5.58%.The ecological deficit problem was severe,increased by 2.98 times during the last 20 years;2) the depth of the regional ecological footprint per capita continued to grow in 2000-2019,from 9.283 to 23.905.The breadth of the per capita ecological footprint was increased by 8.89%;3) the water resources pressure index,comprehensive land pressure index,and ecological pressure index were all greater than 1.Production and social living have caused a huge burden on productive land,and the ecological carrying status of the Tarim River basin was in a severe level.
The Tarim River Basin plays a major part in the development of Xinjiang and the Silk Road Economic Belt.Clarifying the utilization of its water and soil resources and its ecological carrying status is of great significance to the high-quality development of the Tarim River Basin.This paper proposed the use of an improved three-dimensional ecological footprint model to calculate the ecological footprint of the Tarim River Basin.In addition,based on the three-dimensional ecological footprint theory,the water pressure index and land were also introduced.Comprehensive pressure index and ecological tension index were used to investigate whether the current land and water resources were overloaded and assess the ecological carrying status of the Tarim River Basin.The results showed that:1) there has been a noticeable,rapid and continued increase in the per capita ecological footprint of the Tarim River Basin in 2000-2019,2.091 ha/person to 5.864 ha/person,with an average annual growth rate of 5.58%.The ecological deficit problem was severe,increased by 2.98 times during the last 20 years;2) the depth of the regional ecological footprint per capita continued to grow in 2000-2019,from 9.283 to 23.905.The breadth of the per capita ecological footprint was increased by 8.89%;3) the water resources pressure index,comprehensive land pressure index,and ecological pressure index were all greater than 1.Production and social living have caused a huge burden on productive land,and the ecological carrying status of the Tarim River basin was in a severe level.
