Contrasting fate and binding behavior of Mn and Cu with dissolved organic matter during in situ remediation using multicomponent capping in malodorous black water.

The common use of peroxides in the remediation of malodorous black water may lead to the activation of heavy metals in sediment when eliminating black and odorous substances. The mechanisms of heavy metal interactions with dissolved organic matter (DOM) in response to in situ capping have not been elucidated, but this information could guide the optimization of capping materials. We developed a capping material consisting of hydrothermally carbonized sediment (HCS), hydrated magnesium carbonate (HMC) and sodium percarbonate (SPC) and used microcosm experiments to investigate the dynamics of Mn and Cu at the sediment-water interface in malodorous black water. The results showed that HCS, HMC and SPC contributed multiple functions of mechanical protection, chemical isolation and oxygen provision to the new caps. HMC promoted the conversion of Mn/Cu into carbonate minerals. The optimal mass proportions were 25 % HCS, 60 % HMC and 15 % SPC based on the mixture design. In situ capping altered the fate and transformation of metals in the sediment-overlying water profile in the short term through Mn immobilization and Cu activation. The complexation of Cu(II) ions was significantly stronger than that of Mn(II) ions. In situ capping had a significant effect on the order of complexation of different fluorescent DOM molecules with Mn(II)/Cu(II) ions: microbial byproducts and fulvic acid-like components were preferentially complexed with Cu(II) ions after capping, while phenolic and humic acid-like components preferentially interacted with Mn(II) ions. Humic-like components bound to Cu were affected the most by capping treatment, whereas protein-like components were relatively weakly affected. Our study provides valuable knowledge on the impact of in situ capping on DOM-metal complexes.

An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed.

The use of the Storm Water Management Model (SWMM) to simulate flows in urban river watersheds necessitates the proper calibration of the various parameters involved in the process. Back Propagation Neural Network (BPNN) is often used to establish relationship between two sets of multivariate variables, such as parameters and simulation results of SWMM. The aim of this study is to establish an improved BPNN to calibrate SWMM. It was found that when using gauged flow data obtained from the urban river management system as calibration data, only using BPNN was not sufficient. An improved BPNN framework was proposed with integrating Principal Component Analysis (PCA) and Genetic Algorithm (GA) process, abbreviated as PCA-GA-BPNN. It was proved to be effective for calibration. The BPNN combined with GA process made 90 % of the predicted parameters within reasonable range, which was only 8 % using BPNN alone. The PCA process reduced the training time up to 64 %. Using a hydrograph of 196 h, compared with the nondominated sorting genetic algorithm (NSGA), PCA-GA-BPNN training time can be reduced from 18,142 s down to 4.5 s. Nash efficiency coefficients (NSE) of hydrographs fitting was 0.75. Including more rainfall events data in calibration achieved better fitting than including more gauging station data.

An in-depth investigation of the influence of sample size on PCA-MLR, PMF, and FA-NNC source apportionment results.

The mechanism by which parameters influence the source apportionment results of receptor models is not well understood. Three mature receptor models, namely, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC), were comparatively employed for source apportionment of 16 polycyclic aromatic hydrocarbons in 30 street dust samples. The results indicated that the FA-NNC and PMF models produced results with a higher degree of similarity than the results obtained with the PCA-MLR model. Moreover, when the sample size was gradually decreased, similar source profiles were extracted that were consistent with results obtained from all samples. However, the overall contribution rates were not as stable as the source profiles. The PCA-MLR results remained the most stable in both aspects. FA-NNC and PMF performed better in regards to the stability of contribution rates and source profiles, respectively. Improvements in the goodness of fit of overall and individual pollutants were always accompanied by a decrease in the relevance among the variables, indicating that while the model simulation effect was improved, the credibility of the results decreased. Thus, finding an appropriate number of sample size is more appropriate than simply involving too many samples in source apportionment models.

An improved nonnegative matrix factorization with the imputation method model for pollution source apportionment during rainstorm events.

Data scarcity caused by extreme conditions during storms adds difficulties in performing pollution source apportionment. This study integrated nonnegative matrix factorization with the imputation method (NMF-IM) to fill in missing data (NAs) and conduct source apportionment. A total of 367 river samples and 35 runoff samples were taken from the Banqiao and Nanfei River basins located in Hefei, China, during four rainfall events from June to August 2020. Sixteen indicators were quantified and used for source diagnostics using NMF-IM. The results showed that total phosphorus (TP) had higher concentrations and more violent fluctuations than total nitrogen (TN) in river samples taken from rain. NMF-IM was shown to recover the value distribution of NAs approximately. The source profiles and contribution rates calculated by NMF-IM with NAs were close to the original results calculated by NMF without NAs, with root mean square error of less than 2.3% and differences less than 9.5%. Multiple forms of nitrogen and phosphorus indicators benefit reaching reasonable source diagnostics results. At least four indicators were needed to reach the same contribution rates as 16 indicator diagnostics. The two good indicator combination groups are nitrate (NO3-N), nitrite (NO2-N), ammonia nitrogen (NH3-N), and total suspended solids (TSS) and NO3-N, NO2-N, phosphorus (PO4-P), and TSS. The pollution source contributions changed with the Antecedent dry period (ADPs) of rain events. Treated tailwater and untreated sewage were major sources, contributing more than 80% of the total pollution of the rainstorm events with short ADPs. Dust wash became the dominant contributor after 60 min and contributed 36% of the total pollution of rainstorm events with long ADPs. The average source contribution rates for rainfall events in the Banqiao River were treated tailwater (41%) > untreated sewage (27%) > dust wash (19%) > other sources (16%). The pollution source diagnostics results were verified to be reasonable by simulation using tested run-off data and literature results.

Watershed health assessment using the coupled integrated multistatistic analyses and PSIR framework.

Quantitatively assessing watershed health under anthropogenic activities and management responses is important for the scientific planning and management of watersheds. The current research on watershed health assessments insufficiently reflects watershed scale information from different dimensions, which leads to the incomplete understanding of watersheds and thus the lack of systematic management. This study investigated the health status in the Chaohu Lake watershed (CLW) based on monthly sampling data at 46 river sites in 2018. Watershed health assessment comprehensively considered four dimensions including socioeconomic and natural pressures, nonpoint pollution export, river water quality and management responses with the pressure-state-impact-response (PSIR) framework. Canonical correlation analysis (CCA) and variance partitioning analysis (VPA) were integrated to further quantify the inter-relationships among the variables of each PSIR index. An obstacle degree model was applied to examine the factors of mainly affecting the status of watershed health. The results showed that phosphorus, nitrogen and sediment exports of CLW increased more and river water quality in CLW worsened due to socioeconomic and natural pressures. Water quality improvement effectively responds to increasing woodland and grassland. Compared with natural factors, phosphorus, nitrogen and sediment exports had closer relationships with the pressures from socioeconomic activities. Moreover, socioeconomic pressures explained more changes in phosphorus and nitrogen exports, while natural factors explained relatively more changes in sediment exports. Phosphorus, nitrogen and sediment exports and woodland and grassland coverage explained <35 % of the variation in river water quality. Additionally, the obstacle degrees of pressures and phosphorus, nitrogen and sediment exports were lower, and the obstacle degrees of river water quality and woodland and grassland coverage were higher in urban sub-watersheds, which was the opposite in agricultural sub-watersheds. This research provides a new evaluation framework of watershed health and its obstacle factors, which is crucial to improve watershed health.

Refining the diagnostics of non-point source metals pollution to urban lakes based on interaction normalized PMF coupled with Bayesian network.

Spatiotemporal variability complicates source apportionment of metals in urban lakes, especially when rainfall drives urban non-point source pollution. As, Cd, Cr, Pb, Hg, Ag, Co, Cu, Fe, Mn, Ni, Sb, Sr and Zn concentrations in 648 water samples collected before and after rain in 6 urban lakes of Beijing, China were determined during 2013-2015. The response of metals concentrations after rain to the interaction between rainfall and antecedent dry days was significant. Metals concentrations were normalized pursuant to the interaction effect as the input of positive matrix factorization (PMF) to develop the interaction normalized-PMF (IN-PMF). Four primary pollution sources were diagnosed. Sediment release was considered to be the main source of Fe, Co and Ni independent of rainfall. Hg, As and some Cr associated with pesticides and fertilizers were likely to come from soil erosion and runoff from green space. It is probable that road runoff was the dominant source for heavy metals related to traffic emissions, including Pb, Cd, Cu, Sb, Mn and Zn. Cr, Sr and some Cu and Zn as key elements of rooftops can be regarded as from roof runoff. The IN-PMF lowered roof and road runoff contributions and raised the contribution of soil erosion from green space, with Pb, Sb, Cu, Zn, Cd and Mn increasing by 15.9%, 10.7%, 13.1%, 12.2%, 13.3% and 16.8%. The results shed more light on the stormwater runoff pollution mitigation on impervious surfaces and metals enrichment problems in infiltration soil on green space in the low impact development (LID) setting. The Bayesian network revealed the spatial variability of transport and fate of metal elements from land surfaces to urban lakes, supplementing the secondary pollution sources from different land use. This study will provide new insights for source apportionment of non-point source pollution under the background of sponge city construction.

The influence of rainfall and catchment characteristics on runoff generation in urban catchments-a case study in Hebi City of China.

An in-depth understanding of the rainfall-runoff process is essential for effective stormwater management. However, the understanding of the hierarchy of rainfall characteristics in terms of their importance in influencing runoff generation is limited. This paper investigates the influence of rainfall characteristics and catchment characteristics on runoff generation in urban catchments. The outcomes showed that there are 4 dominant factors affecting runoff generation: total precipitation TP and maximum 60-min rainfall intensity MAX60 are the two top-ranked factors while average rainfall intensity RI and maximum 5-min rainfall intensity MAX5 are ranked second. Additionally, compared to the moderate rainfall regime (MR), the heavy rainfall regime (HR) tends to produce higher peak flow rates, higher total inflow per unit area, and lower runoff control effect. Note that the antecedent precipitation has a more significant effect on runoff generation and is even the dominant factor when rainstorm events with daily rainfall larger than 50 mm are not considered. The results of analyzing the influence of catchment characteristics suggest that only under HR regime conditions do the catchment characteristics have an impact on runoff generation and behave as smaller catchment areas, and higher proportions of green landscapes always lead lower peak flow rates, lower total inflows per unit area, and higher runoff control effects.

Systematic evaluation of management measure effects on the water environment based on the DPSIR-Tapio decoupling model: A case study in the Chaohu Lake watershed, China.

Watershed management measures have been widely implemented worldwide to reduce the water quality deterioration in rivers and lakes, which continue to face increasing stresses from human activities. Due to the complexity of influential factors within watersheds, systematic and reliable approaches are urgently needed to evaluate the effects of watershed managerial practices on scientific applications. In this study, the driving force-pressure-state-impact-response (DPSIR) model integrated by Tapio decoupling analysis was established using 30 quantitative indicators to systematically evaluate their effects on overall watershed water environmental health of Chaohu Lake watershed, China, which was under intensive management practices during 2000-2019. The DPSIR model outcomes revealed that the driving force subsystem with 7 indictors accounted for 34.2% of the watershed water environmental health, in which gross domestic product (GDP), gross industrial output value, crop planting and urbanization contributed a larger proportion. Management measure implementation positively improved the watershed water environmental health, with the second largest proportion being 23.4%. During the study period, a trend of simultaneous improvement in the water quality of the rivers and lakes existed. The Tapio decoupling analysis indicated that watershed water quality was weakly decoupled with socioeconomic development and related pressures, and management responses. The response strategy is the main force in alleviating the pressure from socioeconomic development on the watershed water quality. Overall, the method proposed in this study would improve the understanding of watershed management practice effects and provide guidance for future management measure applications.

Differential analysis of FA-NNC, PCA-MLR, and PMF methods applied in source apportionment of PAHs in street dust.

Many source apportionment models have been applied to identify pollution sources, and differences often exist in the diagnostic results. The reasons causing these differences have not been fully elucidated. In this study, three receptor models, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and factor analysis-nonnegative constraints (FA-NNC), were compared and applied for the analysis of 16 EPA priority polycyclic aromatic hydrocarbons (PAHs) adsorbed in street dust samples from Harbin City (China). The differences in the results were caused by different calculation approaches, including matrix decomposition, variable grouping extraction, and nonnegative constraints, especially between PCA-MLR and the other two models. PCA-MLR has no nonnegative constraints, making PCA-MLR less similar to the real world than the other two. Both PMF and FA-NNC have a nonnegative constraint process, which may be the main reason why their results were much more similar to each other than to those of PCA-MLR. PCA-MLR distinguishes variables into several groups that have the greatest variances from each other, whereas the other two methods find similarities among variables and extract them. In the case study of Harbin City, the contributions of mobile and industrial sources ranged from 47 to 69%, and the contributions of coal and other sources ranged from 30 to 52%. The recognized types of pollution sources were generally equivalent, but the proportional contributions were different. PCA-MLR performed best in calculating contributions, whereas PMF and FA-NNC were better in terms of source diagnosis.

[Comparison of Characteristics and Source Apportionment of Polycyclic Aromatic Hydrocarbons in Different Environmental Media in the Urban Area of Daqing City].

In order to investigate the various pollution characteristics and sources of polycyclic aromatic hydrocarbons (PAHs) in different environmental media, 23 street dust samples and four soil samples were collected in October 2012 in Daqing City. After extraction by Dionex ASE300 and purification, the content of the US EPA priority pollutants[16 individual PAH and total PAHs (ΣPAHs)] was determined by using gas chromatography-mass spectrometry (GC/MS). The results showed that the range of ΣPAHs content in street dust was 579.5-4656.7 ng·g-1, and the average content was 1839.7 ng·g-1. The mass percentage of PAHs in the street dust in different functional areas in Daqing showed a similar mass ratio range, with the average mass percentage of low ring (2-3 rings) PAHs of 37.9%, medium ring (4 rings) PAHs of 37.3%, and high ring (5-6 rings) PAHs of 24.8%. However, low ring PAHs, with mass ratios of 69.3%-99.97%, overwhelmingly dominated the Daqing soil, Daqing lake sediment, Daqing lakes, and Daqing ponds (data from literature). The distribution of ΣPAHs content was not significant among different functional areas and was closely related to the type of the plants around the sampling sites. The isomer ratio method confirmed that the sources of PAHs in the street dust in the study area were mixed sources, including oil spills, fuel oil combustion, and coal/biomass burning. Positive matrix factorization (PMF) results showed that the PAHs in the street dust in the center of Daqing originated from coal combustion, oil spill sources, industrial sources, and traffic sources, with contribution rates of 30.1%, 26.9%, 23.6%, and 19.3%, respectively, which were not exactly the same trend as that in other media.