Characteristics and Influencing Factors of Spatial Differentiation of Urban Black and Odorous Waters in China

A review of the formation conditions and assessment methods of black and odorous water

Black and odorous water is an extreme pollution phenomenon. This article reviews the formation process, formation conditions, and evaluation methods of black and odorous water. The results indicate that N, P, and TOC are the key nutrients inducing black and odorous water while S, Fe, and Mn are key elements forming blackening and odorizing pollutants. In addition, Cyanobacteria, Proteobacteria, Firmicutes, Verrucomicrobia, Planctomycetes, and Actinobacteria participate in the biogeochemistry cycles of key elements and play important roles in the blackening and odorizing process of water. The black and odorous thresholds that need further verification are as follows: 1.0 g/L of organic matrix, 2.0-8.0 mg/L of NH3-N, 0.6-1.2 mg/L of TP, 0.05 mg/L of Fe2+, 0.3 mg/L of Mn2+, 1.2-2.0 mg/L of DO, and -50 to 50 mV of the ORP. In order to propose a universal assessment method, it is suggested that NH3-N, DO, COD, BOD, and TP serve as the assessment indicators, and the levels of pollutions are I (not black odor), II (mild black odor), III (moderate black odor), IV (severe black odor), and inferior IV (extremely black odor).

A comparison study on the role of urbanization in altering the short-duration and long-duration intense rainfall

Urbanization has significantly changed the regional hydrological cycle and energy balance. However, the different roles of urbanization on intense rainfall with short and long persistence need a better understanding, particularly in coastal mega cities with complex terrains. In this study, we compared the spatial and diurnal characteristics of intense rainfall across two coastal cities with different degrees of urbanization with 70 meteorological stations. The effects of anthropogenic and geographical factors on short-duration intense rainfall (SDIR) and long-duration intense rainfall (LDIR) were investigated using the statistical method. SDIR and LDIR events show different spatial patterns that SDIR events center in the highly urbanized regions while LDIR events center in the coastal mountainous regions. In terms of diurnal features, it is found that the higher occurrence frequency of SDIR in the dense urban region than that in suburbs occurs over the period with strong urban heat island intensity (UHII). It indicated that thermal contrast between urban and suburbs breeds an atmospheric environment for inspiriting the convection and SDIR events. The urban-induced increase in SDIR events depended on urbanization stages, with only dense urban regions showing a significant influence. The results of geographical detector model (GDM) also demonstrated that the synergy of build-up area and population explained 48 %-51 % of spatial heterogeneity of SDIR. Nevertheless, urbanization has little effect in modifying the diurnal features of LDIR events, while it might influence spatial rainfall patterns by enhancing rainfall peaks. The GDM results indicated that terrain positively dominates the spatial distribution for LDIR events, and the interactions of multi-factors (terrain, urbanization, or distance to the coast et al.) have enhanced explanatory power (q values up to 0.70). The results provide a fundamental understanding for the effects of anthropogenic and geographical factors on different types of rainfall events in coastal mega cities.

Analysis of the Characteristics and Spatial Pattern of the Catering Industry in the Four Central Cities of the Yangtze River Delta

The development of the catering industry in big cities is of great significance for countries to improve the quality of development and improve people’s living standards. In recent years, the urban catering industry has effectively promoted the process of urbanization, and it is significant to study the development characteristics and spatial distribution of the catering industry for the urban pattern. Taking the four central cities (Shanghai, Hangzhou, Nanjing, and Hefei) of China’s Yangtze River Delta (YRD) urban agglomeration as examples, first, the point-of-interest (POI) data of various catering facilities in the city’s main urban area were crawled from the Amap (AutoNavi map) open platform through Python. Second, for the first time, three quantitative indicators were constructed to characterize the development and urbanization of the urban catering industry, namely cuisine localization index (CLI), cuisine diversity index (CDI), and cuisine geographical preference index (CGPI). Third, the overall spatial characteristics of the catering industry in the four central cities and administrative districts were obtained using the methods of kernel density and spatial autocorrelation analysis. The spatial distribution of the catering industry development in each city is displayed through GIS visualization, and its influencing factors are discussed preliminarily through geographically weighted regression (GWR) model. The research shows that: (1) the four central cities in the YRD have formed core catering areas with different agglomeration levels. Different cuisines in the city have the characteristics of partial spatial overlap. (2) In the four central cities of the YRD, there is a significant positive spatial correlation between the catering industry as a whole and individuals. Hangzhou and Hefei have higher CLI (0.38), but the cuisine structure is relatively simple. The CLI of Shanghai and Nanjing is at a low level, but the overall cuisine structure is relatively balanced. (3) The catering industry in the four central cities has a high degree of agglomeration, a wide range of agglomeration, and many agglomeration points. Only Shanghai cuisine, Jiangsu and Zhejiang cuisine, Anhui cuisine, Fujian cuisine, Shandong cuisine, and Hunan cuisine have significant positive correlations in space, and the correlations weaken in turn. (4) The influence intensity of the influencing factors on CLI and CDI is in the order of degree of openness, regional economic level, market vitality, population concentration level, industrial structure. The spatial pattern of catering in a city is greatly affected by the regional economy and population density. This study can provide a reference for research on the spatial distribution of the catering industry in similar urban agglomerations around the world.

Characteristics and Influencing Factors of Spatial Differentiation of Market Service Industries in Rural Areas around Metropolises—A Case Study of Wuhan City’s New Urban Districts

Market services industries are closely related to residents’ lives, and its spatial distribution has an important impact on satisfying residents’ consumption needs and promoting economic development. In recent years, with the rapid development of urban–rural integration and the gradual implementation of a rural revitalization strategy, rural areas around metropolises have become the “frontier” of urban geographic expansion, causing the rural market services industries to specialize, commercialize, and modernize. Taking Wuhan as a case study, the spatial distribution characteristics and influencing factors of the market services industries were measured using the average nearest neighbor index, the kernel density estimate, the spatial correlation analysis, and the Geodetector method. The results are as follows. (1) The market services industries in Wuhan’s new urban districts, as a whole and individually, showed characteristics of agglomeration. The market services industries overall formed two high-density areas and multiple agglomeration areas, and the high-density areas of different types of market services industries showed characteristics of partial spatial overlap. (2) There was a significant positive spatial correlation in market services industries, as a whole and individually. Among them, the life services industry had the strongest spatial correlation, while the accommodation services industry had the weakest. (3) Market demand and traffic conditions were the core factors influencing the distribution of the market services industries in Wuhan’s new urban districts, followed by urbanization and economic levels; and tourism conditions had a lower impact. Each detector had a different impact on the spatial distribution of different market services industries, and the interaction research showed that the spatial distribution of the market services industries was the result of a combination of multiple factors. This research provides a future development direction for market service industries in rural areas.

A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

DNA metabarcoding reveals the significant influence of anthropogenic effects on microeukaryotic communities in urban waterbodies

Biological monitoring and assessment are the first and most fundamental steps towards diagnosing ecological or environmental quality. Increasing anthropogenic impact on urban ecosystems has prompted the development of less expensive and more efficient bioassessment approaches. Generally, a morphospecies based approach is effective for plants and large organisms but challenging for the microbial biosphere. To overcome this challenge, we used high-throughput DNA sequencing for predicting anthropogenic effects on microeukaryotic communities in urban waterbodies along a pollution gradient in Wuhan City, central China in summer 2019. Our results indicated that microeukaryotic community structure was distinct between non-urban polluted reservoir and urban polluted waterbodies. The heterogeneity of environmental condition significantly affected the microeukaryotic diversity, community structure, and species interactions. Integrated co-occurring network analysis revealed that the pollution gradient has a significant adverse impact on network complexity and network dissimilarity. These results revealed that the significant variation in anthropogenically-driven environmental condition shaped microeukaryotic communities in urban freshwater ecosystems. Furthermore, we observed that the relative abundance of indicative OTUs were significantly and negatively correlated with pollution level and these indicative OTUs could be used to predict the water quality status with up to 77% success. Thus, our multiple approaches combining 18S rDNA amplicon sequencing, co-occurring network and indicator species analyses suggest that this study gives a novel approach based on microeukaryotic communities to assess and predict the water quality status of urban aquatic environments.

Influence of a new ornamental species (Spathiphyllum blandum) on the removal of COD, nitrogen, phosphorus and fecal coliforms: a mesocosm wetland study with PET and tezontle substrates

The objective of this study was to evaluate the influence of a new species of plant (Spathiphyllum blandum) in the elimination of chemical oxygen demand (COD), nitrogen, phosphorus and fecal coliforms (FCs) in mesocosms of wetlands with polyethylene terephthalate (PET) and tezontle substrates under a tropical climate. The experiments were developed at the mesocosm level in 20 experimental units; 10 were planted with Spathiphyllum blandum, five in PET substrates and five in tezontle, and 10 more were used as controls without vegetation, of which five contained tezontle and five contained PET. The systems were fed with contaminated water from the river Sordo, with a hydraulic retention time of 3 days for 12 months; samples were taken in the influent and effluents of the mesocosms every 2 weeks, with the purpose of evaluating the removal of contaminants. The results showed that presence of this species tended to improve or significantly improved the removal of COD, NH₄-N, PO₄-P, and FCs by 7%, 16%, 29% and 12%, respectively. It was also possible to confirm that the presence of this species reduced the rate of denitrification. These results confirm that in developing countries it is feasible to find new wetland species to be used for wastewater phytoremediation.

Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Ecological Red Line Areas

Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing’s ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas.

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.