Urban Non-point Source Pollution Research Articles

Statistical Analysis of Urban Non-Point Source Pollution and Nitrate Contamination in the Groundwater at Thuckalay, Kanyakumari District, South India

Statistical Analysis of Urban Non-Point Source Pollution and Nitrate Contamination in the Groundwater at Thuckalay, Kanyakumari District, South India

Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China

Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH3-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects.

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Pollutant accumulation and microbial community evolution in rain gardens with different drainage types at field scale

Rain gardens play a key role in urban non-point source pollution control. The drainage type affects the infiltration processes of runoff pollutants. The soil properties and microbial community structures were studied to reveal the stability of the ecosystem in rain gardens with different drainage types under long-term operation. The results showed that the soil water content and total organic carbon in the drained rain gardens were always higher than that of the infiltrated ones. With the increase in running time, the contents of heavy metals in rain gardens showed significant accumulation phenomena, especially the contents of Zn and Pb in drained rain gardens were higher than that in infiltrated ones. The accumulation of pollutants resulted in lower microbial diversity in drained rain gardens than in infiltrated rain gardens, but the microbial community structures were the same in all rain gardens. The effects of drainage type on microbial community evolution were not significant, only the accumulation of heavy metals led to changes in the abundance of dominant microorganisms. There were differences in the soil environment of rain gardens with different drainage types. The long-term operation of rain gardens led to fluctuations in the soil ecosystem, while the internal micro-ecosystems of the drained rain gardens were in unstable states.

Fluorescence fingerprint as an indicator to identify urban non-point sources in urban river during rainfall period

Nowadays, the urban non-point source (NPS) pollution gradually evolved as the main contributor to urban water contamination since the point source pollution was effectively controlled. It was imperative to perform urban NPS identification in urban river to meet the requirements of precise source governance. In this study, the real-time detection about water quality parameters and fluorescence fingerprints (FFs) was performed for BX River and its outlets during rainfall period. EEM-PARAFAC and component similarity analyses discovered that the pollution encountered by BX River mainly came from road runoff and untreated municipal wastewater (UMWW) overflow. The C1 (tryptophan-like) and C3 (terrestrial humic-like) components located at Ex/Em = ∼230(280)/340 and ∼275/430 nm were both detected in these two kinds of urban NPS. The C2 components of road runoff and UMWW overflow displayed remarkable differences, which located at Ex/Em = 250/385 and 245/365 nm, respectively, thus could be served as indicators for distinguishing them. During rainfall period, the outflow from rainwater outlets (RWOs) constantly showed similar FF features to road runoff, while the FFs of outflow from combined sewer outlets (CSOs) alternated between those of road runoff and UMWW overflow. The FF features of sections in BX River changed in response to the dynamic variations in FFs of the outlets, which revealed real-time pollution causes of BX River. This work not only realized the identification and differentiation of urban NPS, but also elucidated the dynamic variations of pollution characteristics throughout the entire process of “urban NPS-outlets-urban river”, and demonstrated the feasibility of FF technique in quickly diagnosing the pollution causes of urban river during rainfall period, which provided important guidance for urban NPS governance.

Optimal design of low impact development at a community scale considering urban non-point source pollution management under uncertainty

Urban non-point source (NPS) pollution management is receiving increased attention because of the environmental and ecological consequences of urbanization. We developed a Storm Water Management Model (SWMM)-based interval double-sided chance-constrained programming (SWMM–IDCCP) model for the optimal design of low impact development (LID) during urban NPS pollution management at a community scale. Pollution control efficiency and economic costs related to LID practices were considered in the SWMM–IDCCP model, simultaneously addressing uncertainties in pollutant output and decision-making processes. Specifically, by combining SWMM, Monte Carlo simulation, interval linear programming, and double-sided chance-constrained programming, the SWMM–IDCCP model can evaluate export loads of urban NPS pollutants and pollution control efficiencies of LID practices and analyze the corresponding uncertainties. Concurrently, the proposed model can provide reliable and optimal implementation schemes for LID practices under different scenarios, dealing with uncertainties such as discrete intervals and double-sided random parameters. The proposed model was applied to the Dongguan University of Technology in Dongguan City, South China. The random distribution characteristics of pollution load were identified. The ranges of pollution load for total suspended solids, chemical oxygen demand, total nitrogen, and total phosphorus were 76,430–165,698, 44,366–74,155, 2118–3,830, and 97–177 kg in 2021, respectively. Probability distribution characteristics of pollution control efficiencies of three LID practices (i.e., bio-retention cell, green roof, and permeable pavement) for the four urban NPS pollutants were obtained. Using the SWMM-IDCCP model, LID configuration schemes were obtained under diverse pollution control targets and confidence levels, taking into account variations in the decision preference of different administrators. This research indicates that the SWMM-IDCCP model can be used to manage urban NPS pollution at a community scale under uncertainty.

Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Spatial distribution of non-point source pollution from total nitrogen and total phosphorous in the African city of Mwanza (Tanzania)

Rapid urbanization has caused widespread environmental pollution in Africa, restricting sustainable development programs. Today, non-point source pollution has become a major threat to many African cities. In this study, we have classified urban land use followed by the identification of urban functional areas based on multi-source data. We have then evaluated non-point source pollution based on nitrogen and phosphorus loads at a spatial scale in Mwanza city (Tanzania). We have estimated the spatial distribution of total nitrogen (TN) and total phosphorus (TP) loads at a scale of 1 square kilometer grid. The results show that ① the built-up area of the city was identified as 28.9 km2, accounting for 6.8% of the total area of the city. The area of informal settlements was 56.9 km2, accounting for 13.4% of the total area of the city. About 90.38% of 2.41×105 kg of the total TN load and 87.23% of 4.00×104 kg of the total TP load in the city were derived from the informal settlements alone in 2020. ② The average load intensities of TN and TP in Mwanza city were 6.90 kg·hm-2·yr-1 and 1.14 kg·hm-2·yr-1, respectively. The average TN and TP intensities in Mwanza city were slightly higher than the average TN and TP load intensities in the Lake Victoria basin. However, given the high population density of the Mwanza city area, the average TN and TP intensities were found to be consistent. ③ The TN and TP pollution loads were relatively concentrated in the inner urban area, including the lower reaches of the Mirongo River basin, the eastern side of the Nyamagana Peninsula, and the lakeside area extending north and south from the peninsula, and gradually decreased toward the city’s outskirts. We propose that the identified riparian areas in our study should be prioritized as potentially key construction zones for pollution treatment plant facilities and urban renewal pilot programs for restraining the widespread informal settlements. This study contributes to the development of an innovative urban non-point source pollution control measure, which may be applicable to low-income countries in Africa and elsewhere.

Load quantification and effect evaluation of urban non-point source pollution in the Licun River based on SWAT model.

With the gradual control of point source pollution, the impact of urban nonpoint source pollution on river water quality is becoming more prominent. Regarding the current problem that nonpoint source pollution loads in urban basins are difficult to quantify and the impact on water quality is difficult to analyze, the Licun River basin in Qingdao was selected as the research object. Through the field survey and surface accumulation sampling analysis of the basin, the evaluation model of urban nonpoint source pollution was constructed by revising the land type data of the basin and the urban database of the SWAT model. The results showed that concentration of nitrate in precipitation was most sensitive to the simulation of nitrogen loading; organic P in baseflow was most sensitive to the simulation of phosphorus loading. The Nash-Sutcliffe efficiency coefficient (ENS) and the coefficients of determination (R2) of the SWAT model for runoff, total phosphorus (TP), and total nitrogen (TN) in the simulation validation period meet the model requirements,indicating a good model fit. In addition, the spatial and temporal distribution characteristics of urban nonpoint source pollution of TN and TP in 2021 were analyzed. In July, rainfall-runoff from the Licun River basin was the most polluted.

Study on temporal and spatial distribution characteristics of urban non-point source pollution in Hohhot

The non-point source pollution is not easy to be controlled, and it is one of the important environmental problems facing the world. Taking Hohhot, a typical city in the arid semi-arid region in China, as an example, four monitoring sites in the city were selected, water samples from six rainfall events were collected, and pollutants such as heavy metals, ammonia, nitrate nitrogen, phosphorus and suspended matter were examined, and the water quality of the rain runoff was comprehensively evaluated by the principal component analysis and the correlation analysis. The results showed that the total phosphorus contents of monitoring points in the street were the largest and those of monitoring points in gas stations were the smallest in rainfall runoff. Arsenic, cobalt, and chromium were less than the Class I standard for environmental quality standards for surface water according to Chinese standard (GB 3838-2002). Concentrations of cadmium, copper, and iron were greatest in street monitoring sites, while nickel, lead, and zinc were greatest in roof monitoring sites, and manganese was greatest in car park monitoring sites. There was minimal contamination at gas station monitoring sites and the greatest contamination at street monitoring sites. Heavy metals and phosphorus were the main contributing factors to pollution at street monitoring sites, while the main contributing factors at gas station monitoring sites were total suspended substances.

The global progress on the non-point source pollution research from 2012 to 2021: a bibliometric analysis

BackgroundWith effective control of point source pollution, non-point source (NPS) pollution has been widely concerned as the primary reason for the improvement of global water environmental quality. Some bibliometric analysis related to NPS pollution has been carried out before the mid-2010s. Analyzing the research status and hot issues of NPS pollution in the past decade is important for guiding the control and management of NPS pollution in the future.ResultsA bibliometric analysis was conducted based on 3407 publications retrieved from the Web of Science during 2012–2021. China, USA and UK were the most productive countries. Sci. Total Environ, Environ. Sci. Pollut. Res, and Water were the most productive journals. The NPS pollutant, pollution types, driving forces, technology and the research object were retrieved from the keywords analysis. The common NPS pollutants of nitrogen, phosphorus, and heavy metals grabbed the highest attention, while the emerging contaminants have attracted increased attention. The migration and transformation of agricultural NPS pollution and urban NPS pollution driven by climate change and land use change were hot issues related to NPS pollution studies. Technologies related to the combination of 3S technology (RS, GIS, and GPS) and NPS pollution models, the sustainable control technologies, the technology of accurate traceability and automatic monitoring, and the comprehensive management plan were the important research areas related to NPS pollution. Although the research locations were mostly concentrated in the surface water and groundwater, the ocean and drinking water have great potential for future research.ConclusionsThis study illustrates the global focuses related to NPS pollution during 2012–2021 according to analyzing the publication outputs, source journals, source country, author, institution and the high-frequency keywords. Results demonstrated that the migration and transformation mechanism and ecological risk assessment for heavy metals and emerging pollutants, accurate traceability techniques, sustainable ecological restoration control techniques, and marine pollution have attracted rising attention. Additionally, developing countries will have a higher interest in NPS pollution in the future, because developed countries have already made great progress in controlling NPS pollution.

Nitrogen transport and sources in urban stormwater with different rainfall characteristics.

Nitrogen pollution in urban stormwater has led to serious quality issues in urban water. Nitrogen pollution mitigation requires fully understanding the transport process and major nitrogen sources in urban stormwater. In this study, the concentrations and flux of various nitrogen forms during urban stormwater transport were analyzed. It was found that the concentration and flux of NO3--N and NH3-N decreased in the order of roof runoff, road runoff, and drainage runoff, while the concentration and flux of dissolved organic nitrogen (DON) and particulate nitrogen (PN) were found to be the highest in road runoff. Source quantification found that roof runoff (34%) and rainwater (34%) were the main contributors to nitrate pollution during light and moderate rains (<25 mm), while road runoff was identified as the major source (49%) of nitrate pollution during heavy rains (>25 mm) due to the large road runoff volume. Regarding particulate organic nitrogen (PON), the road runoff in commercial areas contributed most (23%) to PON pollution during light and moderate rains, while the runoff generated from pervious surfaces and drainage sediments were the primary two sources (22%) of PON during heavy rains. Moreover, the influence of rainfall characteristics on nitrate and PON source contributions was identified. The results show that antecedent dry periods were an important factor influencing nitrogen source contributions during light and moderate rains, while rainfall amount and intensity were critical factors impacting the nitrogen source contributions during heavy rains. Taking various transport processes, source contributions, and rainfall characteristics into consideration, several recommendations were given for the mitigation of nitrogen pollution in urban stormwater. This study can provide a useful perspective to understand the transport and sources of nitrogen, thus developing constructive strategies to control urban nonpoint source pollution management.

Identifying spatial influence of urban elements on road-deposited sediment and the associated phosphorus by coupling Geodetector and Bayesian Networks

Elevated particles and phosphorus washed from road-deposited sediment (RDS) are noteworthy causes of eutrophication in urban water bodies. Identifying how urban elements (e.g., dwellings, roads) spatially influence RDS and the associated phosphorus can help pinpoint the primary management areas for RDS pollution and therefore effectively mitigate this problem. This study investigated spatial influence of urban elements on RDS build-up load and phosphorus load in Hanyang district of Wuhan city in central China. Bayesian Networks (BNs), combined with geographical detector (Geodetector) and correlation analysis, were applied to quantify spatial association between kernel density of urban elements, RDS build-up load and phosphorus load in RDS. Results showed that (1) areas with higher density of factories related elements usually had elevated level of RDS build-up load, aluminum-bound phosphorus (Al-P), occluded phosphorus (Oc-P), organophosphorus (Or-P). Higher load of RDS associated iron-bound phosphorus (Fe-P) and apatite phosphorus (Ca-P) usually occurred where dwellings, catering, and entertainment related elements were concentrated. (2) Urban elements mainly showed positive correlation with RDS build-up load, Fe-P, Ca-P, De-P (detrital apatite phosphorus), while they chiefly showed negative correlation with Ex-P (exchangeable phosphorus), Al-P, Oc-P, and Or-P. Bus stations, dwellings, and factories related elements had relatively strong determinant power over spatial stratified heterogeneity of RDS and RDS-associated phosphorus. (3) Geodetector and correlation analysis could boost factors filtering and construction of network structures in the process of developing BNs models. The developed BNs resulted in sound prediction of <150 μm RDS build-up load and phosphorus load, given that the prediction accuracy of models ranged from 0.532 to 0.657. These findings demonstrate that urban elements are useful spatial predictors of RDS pollution, and coupling Geodetector and BNs is promising in RDS pollution prediction and supporting urban nonpoint source pollution management.

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.

Coupling with high-resolution remote sensing data to evaluate urban non-point source pollution in Tongzhou, China

Urban non-point source (NPS) pollution has gradually become one of the important factors affecting the urban water environment. The quantitative evaluation of urban NPS pollution is the priority to identify key control area of urban NPS pollution. Current model applied in China is mainly focused on small-scale area, large-scale spatial continuous simulation is lacking. In this study A spatial continuous evaluation method coupled with high-resolution remote sensing data has been established and the method was applied to Tongzhou, China. With the spatial distribution of land-use type and built-up area which were been obtained by remote sensing technology, the accumulative and wash-off load of urban NPS nitrogen and phosphorus were estimated for the prominent problems of nitrogen and phosphorus nutrient pollution in the rivers in the study area. The main sources of urban NPS Nitrogen and phosphorus pollution are roof and road rainfall runoff respectively. Compared to other urban NPS pollution models, the method developed in this study can quickly realize spatial visualization assessment of urban NPS pollution and provide a means to estimate urban NPS loads in entire city or urban agglomeration, it is applicable for common urban NPS pollutants and also has advantages in areas without data.

Assessing effects of non-point source pollution emission control schemes on Beijing's sub-center with a water environment model

A high-quality water environment is a strategic demand for constructing Beijing's Sub-center. Controlling non-point source (NPS) pollution is the key to water environment management in Beijing's Sub-center. We developed a water environment model of Beijing's Sub-center using MIKE 11. Ammonia-Nitrogen and Total Phosphorus concentrations and Chemical Oxygen Demand were used as pollution indicators. Water quality response to the control of agricultural and urban NPS pollution under different scenarios was predicted. The effects of different scenarios were evaluated. Results show that water quality in Beijing's Sub-center can be considerably improved by controlling emissions of NPS pollution. Reducing agricultural NPS pollution is the key to reducing Ammonia-Nitrogen and Total Phosphorus concentrations and improving water quality. Results also demonstrate that water quality response varies with land use type. This study aims to provide reliable and practical information to support water pollution control in Beijing's Sub-center, especially NPS pollution. This water environment model fills the gap in the NPS pollution simulation in rivers in Beijing's Sub-center and promotes the application of the water environment model in water pollution management.

Identifying Relationship Between Nutrient Contents in Road-Deposited Sediment and Urban Basic Elements Based on Kernel Density Estimation

Understanding the spatial connection between urban basic elements and pollutants in road-deposited sediment (RDS) is beneficial to the quantitative explanation of source areas and the precise management of urban nonpoint source pollution. The objective of this study was to explore the spatial connection between urban basic elements and nutrient contents in RDS, using Hanyang district of Wuhan city as a case study area. Total nitrogen (TN), total phosphorus (TP), exchangeable phosphorus (Ex-P), aluminum-bound phosphorus (Al-P), iron-bound phosphorus (Fe-P), occluded phosphorus (Oc-P), apatite phosphorus (Ca-P), detrital apatite phosphorus (De-P), and organophosphorus (Or-P) were chosen as representative pollutants, using the kernel density of the road network and seven groups of points of interest to represent the spatial distribution of urban basic elements. Through correlation analysis, the relationship between urban basic elements and nutrient contents in RDS was examined. The results showed that nutrient contents in RDS from Hanyang varied significantly with location. Specifically, Al-P, Oc-P, and Or-P had greater spatial variability in contrast with that of Ex-P and Fe-P, and the spatial variabilities of Ca-P and De-P both varied considerably with the particle size of the RDS. All types of urban basic elements and nutrient contents in RDS were correlated to different extents. Ex-P, Al-P, Oc-P, and Or-P were negatively correlated with the kernel density of most urban basic elements, whereas TN, TP, Fe-P, Ca-P, and De-P showed a positive correlation with the kernel density of most urban basic elements. TN, Al-P, Fe-P, Ca-P, and Or-P appeared to have much stronger correlation with the kernel density of urban basic elements compared to other selected pollutants, whereas the extent of correlation between them was influenced by the particle size of the RDS. In general, the correlation between nutrient contents in RDS and the kernel density of urban basic elements was more significant when the particle size of the RDS was under 450 μm and more robust when the particle size of the RDS was under 150 μm.

Photochemical and microbial alteration of chromophoric dissolved organic matter (CDOM) in aquatic ecosystems associated with different sources

城市非点源污染向水生生态系统中输入大量的溶解有机物(DOM)，对生态系统健康产生重要影响. 有色可溶性有机物(CDOM)是广泛分布于自然水体中的一类成分和结构复杂、含有多种高活性化学官能团的大分子聚合物，是DOM的重要组分，对水生生态系统健康、能量流动及生物地球化学循环有重要影响. 光化学反应和微生物代谢过程被认为是控制水体CDOM转化、降解和循环的主要影响因素. 然而，对城市化如何影响CDOM组成以及光化学和微生物如何相互作用影响城市水体CDOM动态的理解是不足的. 因此，为评估光化学过程和微生物代谢对不同城市水体CDOM降解与转化的贡献，解析不同城市水体CDOM光化学/微生物降解作用机理，本研究在英国伯明翰选择3类具有典型DOM来源的水体样本，通过实验室9 d受控培养实验，对比分析光化学以及微生物影响下CDOM来源和组成的变化. 结果表明：(1)城市河流由于接受上游污水排放及较短的水力滞留时间，含有丰富的芳香性碳，其CDOM光化学活性明显高于湖泊，光化学降解率为16.60%;(2)城市湖泊CDOM受人类活动影响，自生源类荧光成分富集，生物活性高，在微生物培养过程中CDOM增加了62.16%，而相较于城市湖泊，非城市湖泊由于接受周围景观土壤输入的大量腐殖质类CDOM，光照对其降解转化作用较为明显; (3)光化学过程促进了陆源CDOM中大分子类腐殖质物质降解成为生物活性高的小分子化合物，刺激微生物代谢生成类蛋白质类有机物; 以类蛋白质组分为主导的CDOM在光照过程中被转化为难降解状态，生物活性降低，CDOM微生物代谢过程被抑制. 研究成果为城市水体不同CDOM来源及活性差异特征研究提供了新的思路，有助于城市河流的可持续开放与管理.;Urban non-point source pollution contributes large amounts of dissolved organic matter (DOM) to aquatic ecosystems and has a significant impact on ecosystem health. Chromophoric dissolved organic matter (CDOM) is a group of macromolecular polymers with complex composition and structure, containing a variety of highly reactive chemical functional groups, which are widely distributed in natural waters. It has an important impact on aquatic ecosystem health, energy flow and biogeochemical cycling. Photochemical reactions and microbial metabolic processes are the main controlling factors on the transformation, degradation and cycling of CDOM in water bodies. However, understanding how urbanization source input impacts CDOM quantity and quality and how photochemical and microbial interact to affect CDOM dynamic are deficient. To assess the contribution of photochemical processes and microbial metabolism to the degradation and transformation of different sources of CDOM in urban waters, and to elucidate the mechanisms of photochemical/microbial degradation of different sources of CDOM in aquatic systems (non-urban lake with humic, mostly terrestrial CDOM; urban lake with autochthonous biological production CDOM; urban river with mixed humic and protein-like CDOM), this study compares and analyses the changes in the sources and composition of CDOM under the influence of photochemistry and microorganisms in a controlled 9-day laboratory incubation experiment in three water bodies with typical urban characteristics, selected in Birmingham, UK. Changes in CDOM concentration, optical properties and chlorophyll concentration were measured at the initial and end of incubations. The results show that: (1) due to the acceptance of upstream sewage discharge and shorter hydraulic retention times, the CDOM of urban rivers is dominated by aromatic substances, with the highest photochemical degradation rate of 16.60%; (2) the CDOM of urban lakes, which is affected by human activities, is dominated by autochthonous substances, and could be increased by 62.16% via microbial metabolism, while compared to urban lakes, non-urban lakes have a more pronounced effect of light on their degradation and transformation due to receiving a large input of humus-like CDOM from the surrounding landscape soils; (3) the photochemical process could degrade terrestrial humic-like substances into small molecular compounds with high biological activity, which could stimulate microbial metabolism to produce protein-like CDOM, however, photochemical degradation could inhibit microbial metabolism by transform the autochthonous protein-like CDOM into a refractory state. The study provides a novel perspective for the differences in the sources and activities of CDOM in different urban aquatic systems and the changes in CDOM characteristics during photochemical and microbial processes.

New framework for assessing urban stormwater management measures in the context of climate change

Global climate change has necessitated the update of urban stormwater management measures (SMMs), but this task is extremely difficult due to the deficiency of evaluation caused by discreteness and the limitation of selected storm events, the ignorance of antecedent dry day (ADD) and lack of suitable stochastic storm generation method. In this study, a new framework that considers both stochastic precipitation and ADD is introduced to evaluate urban SMMs more adequately. Gamma distribution fitting, the space discarding method, the production of probability density distribution maps and multiple nonlinear regression were combined with a physical-based model to assess the effectiveness of SMMs under changing climates. Taking low impact development practices (LIDs) as an example of SSMs, the case study showed that the proposed framework provided effectiveness probability density distribution map and regression equations with more evaluation details by increasing the number and type of storm events compared with current monitoring. Moreover, it is demonstrated that ADD should be considered as one important factor in the design of LIDs, especially for controlling urban non-point source pollution. The value of ADD will significantly affect the control effect of LIDs on pollutant loads and event mean concentration in runoff, which varied for different pollutants. Through case study, it shows there is a risk that LIDs would be less effective at controlling runoff and non-point source pollution in future climate scenarios, especially for RCP 8.5 which is more extreme. Therefore, adaptation capacity of climate change should be considered in the design of SMMs. The proposed framework will be a useful tool in the assessment, design and planning of urban SMMs considering climate change.

Influence of Impervious Surface Roughness on Accumulation and Erosion of Urban Non-Point Source Particles

At present, there are few reports about how impervious surface microstructure characteristics affect the runoff output process of street dust. Based on field observations of 12 rainfall events, this study quantified the microstructure characteristics of impervious surfaces by structural depth (roughness) and analyzed the correlation between roughness and accumulation characteristics of street dust on sunny days as well as scouring characteristics in rainy days. The results show that the roughness of the underlying surface notably affects dust accumulation on sunny days and scouring in rainy days. The correlation between roughness and street dust accumulation (r=0.664, P<0.01) was enhanced on sunny days, and the correlation between roughness and street dust erosion (r=0.527, P<0.01) was enhanced by rainfall. The correlation of street dust accumulation and roughness of each particle size segment increased as particle size increased (0.529 ≤ r<0.757), and the correlation between street dust scouring amount and roughness decreased as particle size increased (0.603 > R > 0.209). By establishing the linear regression model of roughness and rainfall, the cumulative pollution load of TSS in rainfall runoff can be well predicted. The effects of roughness and rainfall on the cumulative load of grain sizes<20 μm and >250 μm are significant. These results elucidate the role of roughness and rainfall analysis in predicting surface runoff pollution load characteristics, which can provide new information for predicting and evaluating urban non-point source pollution.