Urban Surface Water Quality, Flood Water Quality and Human Health Impacts in Chinese Cities. What Do We Know?

Diversity and antimicrobial susceptibility patterns of clinical and environmental Salmonella enterica serovars in Western Saudi Arabia

The diverse environmental distribution of Salmonella makes it a global source of human gastrointestinal infections. This study aimed to detect Salmonella spp. and explore their diversity and antimicrobial susceptibility patterns in clinical and environmental samples. Pre-enrichment, selective enrichment, and selective plating techniques were adopted for the Salmonella detection whereas the API 20E test and Vitek Compact 2 system were used to confirm the identity of isolates. Salmonella serovars were subjected to molecular confirmation by 16S rDNA gene sequencing. Disc diffusion method and Vitek 2 Compact system determined the antibiotic susceptibility of Salmonella serovars. Multiple antibiotic resistance index (MARI) was calculated to explore whether Salmonella serovars originate from areas with heavy antibiotic usage. Results depicted low Salmonella prevalence in clinical and environmental samples (3.5%). The main detected serovars included Salmonella Typhimurium, S. enteritidis, S. Infantis, S. Newlands, S. Heidelberg, S. Indian, S. Reading, and S. paratyphi C. All the detected Salmonella serovars (27) exhibited multidrug resistance to three or more antimicrobial classes. The study concludes that the overall Salmonella serovars prevalence was found to be low in environmental and clinical samples of Western Saudi Arabia (Makkah and Jeddah). However, antimicrobial susceptibility patterns of human and environmental Salmonella serovars revealed that all isolates exhibited multidrug-resistance (MDR) patterns to frequently used antibiotics, which might reflect antibiotic overuse in clinical and veterinary medicine. It would be suitable to apply and enforce rules and regulations from the One Health approach, which aim to prevent antibiotic resistance infections, enhance food safety, and improve human and animal health, given that all Salmonella spp. detected in this investigation were exhibiting MDR patterns.

Effect of Different Ameliorants on the Infiltration and Decontamination Capacities of Soil

The expansion of urban construction areas can reduce the infiltration rate of rainwater in permeable land, and a large amount of runoff rainwater cannot penetrate the soil. In extreme rainstorm weather, it is easy to cause serious urban waterlogging problems. To improve the infiltration and decontamination ability of green space soil, two types of inorganic ameliorants (i.e., sand and grain shell) and structural ameliorants (i.e., desulfurization gypsum and polyacrylamide) were utilized as amendments in the soil. The influence of the selected ameliorants on the infiltration and decontamination ability was analyzed through a soil infiltration test, soil pore distribution determination and a soil decontamination test. Three parameters including the soil infiltration rate, pore distribution characteristics and pollutant removal rate were proposed. The results showed that sand, grain shells and desulfurization gypsum (FGD gypsum) all enhanced the infiltration capacity of soil, while PAM decreased the infiltration capacity. Meanwhile, mixed sand and grain shell with the FGD gypsum and polyacrylamide can effectively improve the decontamination capacity of the soil. Comprehensive analysis showed that the better improvement combination is 10% sand + 20% grain hull + 0.5 g/kg FGD gypsum + 0.1 g/kg PAM.

Comments and recommendations on Sponge City - China's solutions to prevent flooding risks

Background

/Objective: Flooding risk is a global issue, and various approaches have been established to prevent flooding risk around the world. China is one of the heavily flood-affected countries and has been implementing the Sponge City program since 2015 to defend against flooding. Unfortunately, flooding has been common in China in recent years, causing severe health risks to citizens. This research mainly focuses on (a) evaluating the implementation of China's Sponge City program and the associated impacts on human health and (b) exploring the future improvement of the Sponge City program in China.

Methods

The Interpretive Document Approach was used to explore an inclusive review of the Sponge City program and its implications on human health.

Results

/Findings: The Sponge City program in China is still insufficient to prevent flooding risks effectively. In the past eight years, 24/34 provinces have recorded flooding, which caused a total of 4701 deaths and over 525.5 billion RMB (around 72.9 billion US$) in economic loss. Till now, only 64/654 cities have promulgated local legislation to manage sponge city construction, although the Sponge City was implemented in 2015. Besides, the completed Sponge City program constructions cannot fully prevent flooding risks, the flood prevention capacity is limited. The Sponge City program is not granted priority, lacking national legislation hinders Sponge City program implementation in China.

Conclusions

China needs to make national legislation on the Sponge City program and update the Sponge City program technology guidelines. Local governments should implement Sponge City construction according to local geographic environments.

Watershed Ecohydrological Processes in a Changing Environment: Opportunities and Challenges

Basin ecohydrological processes are essential for informing policymaking and social development in response to growing environmental problems. In this paper, we review watershed ecohydrology, focusing on the interaction between watershed ecological and hydrological processes. Climate change and human activities are the most important factors influencing water quantity and quality, and there is a need to integrate watershed socioeconomic activities into the paradigm of watershed ecohydrological process studies. Then, we propose a new framework for integrated watershed management. It includes (1) data collection: building an integrated observation network; (2) theoretical basis: attribution analysis; (3) integrated modeling: medium- and long-term prediction of ecohydrological processes by human–nature interactions; and (4) policy orientation. The paper was a potential solution to overcome challenges in the context of frequent climate extremes and rapid land-use change.

Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming

In China, the notion of a water sensitive city has gained popularity in urban water management as a result of the detrimental effects of flooding and pollution caused by developmental activities. Urban systems and their interrelationships are critical for long-term urban water management and water sensitivity. This article is a case study considering how a strength, weakness, opportunities, and threat (SWOT) analysis-based approach to urban water management interventions in Guangzhou and Kunming cities (China) enables decision makers to identify solutions for cities to become more water-sensitive and resilient. The similar difficulties and rewards with respect to the contexts of both cities were synthesized using SWOT analysis. The contextual SWOT analysis, in conjunction with the comprehensive inclusion of Sustainable Development Goals (SDGs) in intervention planning in these cities, revealed that a water-sensitive-cities approach requires the establishment of a comprehensively multi-objective rainwater management system; this approach would have the goals of reducing rainwater draining sources, controlling processes and adaptive measures, and governing the system to make it more resilient. The water strategy should be holistic and adaptive, capable of providing a broad range of ecological services and other social benefits consistent with the fulfilment of the Sustainable Development Goals, and adaptable to other Chinese cities seeking to achieve water sensitivity.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Pollution Source Apportionment and Water Quality Risk Evaluation of a Drinking Water Reservoir during Flood Seasons

Reservoirs play an important role in the urban water supply, yet reservoirs receive an influx of large amounts of pollutants from the upper watershed during flood seasons, causing a decline in water quality and threatening the water supply. Identifying major pollution sources and assessing water quality risks are important for the environmental protection of reservoirs. In this paper, the principal component/factor analysis-multiple linear regression (PCA/FA-MLR) model and Bayesian networks (BNs) are integrated to identify water pollution sources and assess the water quality risk in different precipitation conditions, which provides an effective framework for water quality management during flood seasons. The deterioration of the water quality of rivers in the flood season is found to be the main reason for the deterioration in the reservoir water quality. The nonpoint source pollution is the major pollution source of the reservoir, which contributes 53.20%, 48.41%, 72.69%, and 68.06% of the total nitrogen (TN), phosphorus (TP), fecal coliforms (F.coli), and turbidity (TUB), respectively. The risk of the water quality parameters exceeding the surface water standard under different hydrological conditions is assessed. The results show that the probability of the exceedance rate of TN, TP, and F.coli increases from 91.13%, 3.40%, and 3.34%, to 95.75%, 25.77%, and 12.76% as the monthly rainfall increases from ≤68.25 mm to >190.18 mm. The risk to the water quality of the Biliuhe River reservoir is found to increase with the rising rainfall intensity, the water quality risk at the inlet during the flood season is found to be much greater than that at the dam site, and the increasing trend of TP and turbidity is greater than that of TN and F.coli. The risk of five-day biochemical oxygen demand (BOD₅) does not increase with increasing precipitation, indicating that it is less affected by nonpoint source pollution. The results of this study can provide a research basis for water environment management during flood seasons.

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful.

Flood Classification Based on a Fuzzy Clustering Iteration Model with Combined Weight and an Immune Grey Wolf Optimizer Algorithm

Flood classification is an important basis for flood forecasting, flood risk identification, flood real-time scheduling, and flood resource utilization. However, flood classification results may be not reasonable due to uncertainty, the fuzziness of evaluation indices, and the demerit of not comprehensively considering the index weight. In this paper, based on the fuzzy clustering iterative model, a sensitivity coefficient was applied to combine the subjective and objective weights into a combined weight, then the fuzzy clustering iterative model with combined weight (FCI-CW) was proposed for flood classification. Moreover, an immune grey wolf optimizer algorithm (IGWO) based on the standard grey wolf optimizer algorithm and an immune clone selection operator was proposed for the global search of the optimal fuzzy clustering center and the sensitivity coefficient of FCI-CW. Finally, simulation results at Nanjing station and Yichang station demonstrate that the proposed methodology, i.e., FCI-CW combined with IGWO, is reasonable and reliable, can effectively deal with flood classification problems with better fitness and a comprehensive consideration of the subjective and objective aspects, and has great application potential in sorting, evaluation, and decision-making problems without evaluation criteria.

Effect of Artificial Solar Radiation on the Die-Off of Pathogen Indicator Organisms in Urban Floods

In the last decade, flooding has caused the death of over 60,000 people and affected over 900 million people globally. This is expected to increase as a result of climate change, increased populations and urbanisation. Floods can cause infections due to the release of water-borne pathogenic microorganisms from surcharged combined sewers and other sources of fecal contamination. This research contributes to a better understanding of how the occurrence of water-borne pathogens in contaminated shallow water bodies is affected by different environmental conditions. The inactivation of fecal indicator bacteria Escherichia coli was studied in an open stirred reactor, under controlled exposure to simulated sunlight, mimicking the effect of different latitudes and seasons, and different concentrations of total suspended solids (TSS) corresponding to different levels of dilution and runoff. While attachment of bacteria on the solid particles did not take place, the decay rate coefficient, k (d^-1), was found to depend on light intensity, I (W m^-2), and duration of exposure to sunlight, T (h d^-1), in a linear way (k = k _D+ 0.03·I and k = k _D+ 0.65·T, respectively) and on the concentration of TSS (mg L^-1), in an inversely proportional exponential way (k = k _D+ 14.57·e^-0.02·[TSS] ). The first-order inactivation rate coefficient in dark conditions, k _D= 0.37 d^-1, represents the effect of stresses other than light. This study suggests that given the sunlight conditions during an urban flood, and the concentration of indicator organisms and TSS, the above equations can give an estimate of the fate of selected pathogens, allowing rapid implementation of appropriate measures to mitigate public health risks.

Objectives and Indexes for Implementation of Sponge Cities—A Case Study of Changzhou City, China

This paper presents a framework of objectives and indexes for sponge cities implementation in China. The proposed objectives and indexes aims to reflect whether the city is in accord with the sponge city. Different cities have different objectives and indexes as each city has its own geologic and hydrogeological conditions. Therefore, the main problems (e.g., water security and flood risks) in the central urban area of Changzhou city, China were evaluated scientifically. According to the local conditions, four objectives and eleven indexes have been made as a standard to estimate the sponge city and set a goal for the city development to reach the goal of sustainable urban development. The strategy of process control was implemented to improve the standard of urban drainage and flood control facilities, regulate total runoff and reduce storm peak flow, and the ecological monitoring of the function of the rivers and lakes. The objectives of sponge cities include water security, water quality improvement, healthy water ecosystems, and water utilization efficiency. Urban flood prevention capacity, river and lake water quality compliance, and annual runoff control are the key objectives to encourage the use of non-conventional water resources.