Urban Black-odor Water Research Articles

Coupling the measures of pollution source control and water replenishment to improve water quality in the catchment scale of Qianshan River Basin

With the development of the economy, the problem of urban black odorous water bodies has become increasingly significant, having a serious impact on the environment. As important means of remediating aquatic environments, pollution source control and water replenishment are of great significance in improving water quality. This study takes the Qianshan River Basin in Zhuhai City as its study area to simulate their effects on the improvement of water quality. A coupled model of water quantity and quality in Qianshan River Basin was constructed using MIKE11to analyze the water quality compliance rate, with sewage interception rates of 85%, 90%, and 95%, and to investigate the effect of pollution source control on the improvement of the aquatic environment. Using different sewage interception rates, the amount of water replenishment was calculated in order to meet water quality standards, the water replenishment scheme was determined via river-specific and time-specific methods, and the model was used to analyze the replenishment effect of the scheme. The results show that increasing the sewage interception rate can significantly improve the COD compliance rate, and improve the NH3–N and TP compliance rate; however, the enhancement effect is not sufficiently significant. When a sewage interception rate of 95% is implemented, there are still five rivers with a low NH3–N compliance rate, and six rivers with low a TP compliance rate. Comparing the water replenishment effect under different sewage interception rates of 85% and 95%, the water replenishment program alongside a sewage interception rate of 95% can effectively improve the aquatic environment and the water quality essentially meets the standard under different rainfall conditions; this demonstrates that the program presented herein can be used as the aquatic environment remediation program of choice for the Qianshan River Basin.

Black-odorous water bodies annual dynamics in the context of climate change adaptation in Guangzhou City, China

Black-odorous water (BOW) in urban areas has brought detrimental ecological effects and posed a threat to the health of surrounding residents. Identifying BOWs in urban areas is difficult because they are usually small in area, and discontinuous in spatial distribution. The efforts to adapt to climate change in cities have a direct connection to urban environment and may affect the dynamics of BOWs, but their relationship has seldom been addressed in previous research. This research builds a new urban BOW detection model using Gaofen (GF) images and ground-level in-situ water quality data to detect the spatiotemporal dynamics of BOWs in Guangzhou City's main urban area from 2016 to 2020, when comprehensive climate adaptation strategy has been implemented as a pilot metropolitan area in China. Spatial analysis in the study area with a total of 97 focused rivers revealed a decreasing trend in BOW occurrence (from 85.57% in 2016 to 21.65% in 2020) in the context of climate change adaptation efforts. Redundancy analysis between BOWs occurrence and environmental factors showed that across the entire study area, the contributions of anthropogenic factors (highest proportion at 14.3% for the area percentage of built-ups) to BOW, such as population density, agricultural water use, domestic water use, and so on, distinctly stronger than climatic drivers (largest contribution of 4.4% for temperature). The results suggested that climate change adaptation efforts help to decrease BOW occurrence in the study area, while exploring the response mechanism between climate change adaptation measures and the changes of BOWs is necessary in the future research. The findings were conducive to the development of targeted measures to decrease the occurrence of urban BOWs while improving adaptability of the city to climate change.

Effective removal of nitrogen and phosphorus from a black-odorous water by novel oxygen-loaded adsorbents

Black-odorous water, caused by hypoxia and overloading of nitrogen and phosphorus, is ubiquitous in global urban rivers. In this study, we developed oxygen-loaded adsorbents by loading oxygen into activated carbon, Attapulgite, Phoslock, and Muscovite via vacuum-pressure swing method, and investigated their aeration and removal efficiency of phosphate, ammonia nitrogen, and total nitrogen in black-odorous water. Results showed that the addition of oxygen-loaded coal-based columnar activated carbon (OCC) or oxygen-loaded Muscovite (OM) alone could increase the dissolved oxygen (DO) concentration at the sediment–water interface to more than 6 mg·L-1 on the first day, and OCC could maintain the high level of oxidation–reduction potential (ORP) (up to + 327 mV) for 15 days. Most oxygen-loaded adsorbents remarkably reduced phosphate in water, essentially from 0.27 mg·L-1 to < 0.05 mg·L-1. Additionally, ammonia nitrogen and total nitrogen were reduced by more than 50 % by adding materials after oxygen loading treatment. The 16 s RNA results showed that Dechloromonas was dominant in abundance, and the reduction of nitrogen was mainly affected by microbial activity. Our results provided a series of oxygen-loaded adsorbent materials with potential engineering applications for rapid treatment of urban black-odorous water.

Evaluation of aquatic phytoremediation technologies in urban black–odorous water remediation technology using a multi-level fuzzy analysis method

Abstract The comprehensive analysis and research on the selection and evaluation of technology for urban black and odorous water remediation have not yet formed a complete system, and the technical analysis and comparison of the remediation technology are not combined with the characteristics of the river and the pollution status, which often could not achieve the expected effect. Combining the multi-level index division and fuzzy weight matrix calculation method, this paper establishes a comprehensive evaluation method for urban black–odorous water treatment technology based on multi-level fuzzy analysis. The proposed method was applied to assess four kinds of in situ purification-aquatic plant remediation technologies. The results showed that the pollutants removal efficiency of a submerged plant was much higher than that of an emergent aquatic plant, floating-leaved water plant and floating plant. Meanwhile, according to the evaluation model, the comprehensive grading order of in situ purification-aquatic plants was as follows: emergent aquatic plant (84.2044) &amp;gt; submerged plant (78.838) &amp;gt; floating-leaved water plant (72.7596) &amp;gt; floating plant (66.4312). The calculations indicated that the ecological restoration project of black and odorous water should build an ecological restoration technology system based on in situ purification-submerged plant remediation technology, supplemented by other three types of aquatic phytoremediation technologies.

Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting

During accelerated urbanization, the lack of attention to environmental protection and governance led to the formation of black-odor water. The existence of urban black-odor water not only affects the cityscape, but also threatens human health and damages urban ecosystems. The black-odor water bodies are small and hidden, so they require large-scale and high-resolution monitoring which offers a temporal and spatial variation of water quality frequently, and the unmanned aerial vehicle (UAV) with a multispectral instrument is up to the monitoring task. In this paper, the Nemerow comprehensive pollution index (NCPI) was introduced to assess the pollution degree of black-odor water in order to avoid inaccurate identification based on a single water parameter. Based on the UAV-borne multispectral data and NCPI of sampling points, regression models for inverting the parameter indicative of water quality were established using three artificial intelligence algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The result shows that NCPI is qualified to evaluate the pollution level of black-odor water. The XGBoost regression (XGBR) model has the highest fitting accuracy on the training dataset (R2 = 0.99) and test dataset (R2 = 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion.

Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data.

To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L’Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650–850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.

Characterization, risk assessment and resource potential of sediments in the black-odor water in Hunan, China.

The black-odor water in urban water bodies is increasingly found, resulting in serious atrophy of water bodies and degradation of water ecosystems. At present, the pollution status of the sediments as an internal source still remains much unknown. In order to assess the pollution status of black-odor water sediments, the content of total nitrogen, total phosphorus, organic matter and heavy metals in the sediments were determined by taking the black-odor water sediments in urban areas of Hunan Province as the research object. Geoaccumulation and potential ecological risks index methods were used to evaluate heavy metal pollution in the sediments, and comprehensive pollution index method was used to evaluate nitrogen and phosphorus nutrient pollution in the sediments so as to provide pertinent information for urban black-odor water treatment. Finally, resource recovery potentials of sediments in black-odor water were revealed. The results could be beneficial for evaluation of sediments in black-odor water bodies.

Simulation of Denitrification Process of Calcium Nitrate Combined with Low Oxygen Aeration Based on Double Logarithm Mode

In situ remediation of sediment pollution is an important measure for the treatment of urban black-odorous water. In this study, the process of calcium nitrate dosing and low oxygen aeration was used to repair the sediment of black-odorous water body in a glass container. The variation trend and removal efficiency of ammonia nitrogen and nitrate nitrogen in sediment and overlying water were investigated during the process. By establishing the double logarithm model of calcium nitrate sediment repair process, the change law of ammonia nitrogen and nitrate nitrogen content in sediment under different calcium nitrate dosing conditions was studied, and the denitrification process of different calcium nitrate dosing and low oxygen aeration was simulated. The results showed that by establishing the double logarithm model of calcium nitrate sediment remediation process, when the dosage of calcium nitrate was 6%, the inhibition rate of calcium nitrate on nitrate nitrogen release was the largest. The stable inhibitory concentration of nitrate nitrogen was 11.65 mg/g, and the stable inhibited concentration of ammonia nitrogen was 382.95 mg/kg. The stable inhibitory concentration of nitrate nitrogen and ammonia nitrogen in overlying water was 8.34 mg/L and 16.47 mg/L. Moreover, excessive calcium nitrate (8%) may increase the risk of microbial ecological environment in sediment and weaken the inhibitory effect. The optimum parameters were the calcium nitrate dosage of 6%, the reaction time of 21 days, and the aeration rate of 30 mL/min. Under these conditions, the removal effect of ammonia nitrogen in sediment and overlying water was further improved, and the concentration of nitrate nitrogen was effectively controlled. The stable inhibitory content of nitrate nitrogen in sediment was 5.55 mg/g, and the stable inhibitory content of ammonia nitrogen was 982.79 mg/kg. The stable inhibitory concentration of nitrate nitrogen and ammonia nitrogen in overlying water was 6.55 mg/L and 118.20 mg/L. Based on a simulation, this study provides important technical support for the formulation of a refined endogenous pollution control scheme by controlling the process of calcium nitrate remediation and low oxygen aeration.

Study on the Spectral Characteristics of Black-Odorous Water: A Case Study of Shenzhen City, China

ABSTRACT The occurrence of black-odorous water (BOW) in urban waters is a common problem in China that has occasional, seasonal, and perennial rules. The treatment of urban has become an important task for water managers in China. Based on field BOW spectra and 13 types of water quality parameters measured in Shenzhen City, in this study, we processed three types of spectra, including normalized, difference, and ratio spectra, to reveal the correlation between the spectra and the water quality parameters, especially for the spectra and four indicators that are used to distinguish BOW. The four indicators include Secchi depth (SD), dissolved oxygen (DO), oxidation-reduction potential (ORP), and ammonia nitrogen (NH3-N). Furthermore, we analysed the spectral sensitivity of the different water quality parameters to build a quantitative regression model between the water quality parameters and the portrait spectrum. The results showed the following: (1) Of the four indicators, both SD and NH3-N were significantly related to the spectrum of mild and severe BOW, and the ORP was only significantly related to the spectrum of severe BOW. The correlation between DO and BOW was not significant. (2) For mild BOW, the sensitive bands were 404 to 572 nm and 704 to 864 nm for the SD and 705 to 717 nm for NH3-N. For severe BOW, they were 499 to 606 nm and 730 to 900 nm for the SD, 556 to 664 nm for NH3-N, and 642 to 709 nm for the ORP. (3) The SD had the strongest correlation with the BOW spectrum. The correlation between the SD and the BOW spectrum was symmetrically distributed in the range of 404 to 572 nm near 489 nm, whereas 545 nm appeared as the centre in the range of 499 to 606 nm. (4) The best regression relationship between the SD and BOW was R 489/ R 714 (coefficient of determination R 2 = 0.8806) and R 545 (R 2 = 0.4868) for the mild and severe grades, respectively. The best regression relationship was R 489- R 607 (R 2 = 0.7598) between NH3-N and mild BOW. The results of this study provide important guidance significance and application value regarding the selection of bands, model construction, and result verification for urban BOW using remote sensing.

Urban Black-Odor Water Remote Sensing Mapping Based on Shadow Removal: A Case Study in Nanjing

Urban black-odor water is a serious water environmental problem worldwide that seriously affects the living environment and the health of residents. The detection of black-odor water is a primary problem of centralized governance. Remote sensing is an effective means for water-quality monitoring. However, in most urban areas, with their complex underlying conditions, black-odor water always shows an uncertain distribution and small area. Furthermore, because of similar spectral characteristics, it is easily confused with dark objects such as asphalt roads, dark roofs, bridges, river levees, and building shadows. In this article, we propose a new black-odor water detection method, without shadow misjudgment, for complex urban conditions. A remote sensing image from the Chinese high-resolution satellite Gaofen-2 (GF-2) was used for detection. Using in situ water-quality measurement data for verification, the results showed that the proposed method achieved an accuracy of 85.7% while reducing the false alarm rate to 3.5%. Compared to four other existing methods, the proposed method proved to be more practical because it can reduce the false extraction of nonblack-odor water to the greatest extent possible while ensuring accuracy The method proposed in this article has a good effect on the extraction of black-odor water in Nanjing City. Therefore, our method can be applied widely to screening and management of large-scale urban black-odor water and can provide reliable data for centralized treatment.

Monitoring urban black-odorous water by using hyperspectral data and machine learning.

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.

Remote Sensing Classification of Urban Black-odor Water Based on Decision Tree

Remote sensing monitoring of black-odor water is an important method for understanding the current status of urban water quality, and comprehensively evaluating the effect of urban water environment treatment. A total of 171 samples were collected in Nanjing, Changzhou, Wuxi, and Yangzhou cities and water quality parameters and optical parameters were measured simultaneously. Based on the analysis of the water color and optical characteristics of the black-odor water and non-black-odor water (denoted as general water), a decision tree was constructed to identify the severe, mild black-odor water, and general water as green and yellow water. The results found that:①According to the water color, the water bodies can be divided into six types. Among them, type 1 to 4 water bodies are black-odor water, which are gray black, dark gray, gray, and light gray water, respectively, and type 5 and 6 water bodies are general water, which are green and yellow water, respectively; ②Type 1 water body contains high contents of non-pigmented particulate matter and colored dissolved organic matter(CDOM), however, the absorption of pigmented particulate matter is not dominant. Type 2 and 5 water bodies are dominated by pigmented particulate matter. Type 3, 4, and 6 water bodies are dominated by non-pigmented particulate matter; ③After water color classification, and according to the differences of the reflection spectrums of the six types of water bodies, the difference of black-odorous water index (DBWI), green-red-nir area water index (G-R-NIR AWI), the green band reflectance and the normalized difference black-odorous water index (NDBWI) were used to construct a decision tree to identify the severe, mild black-odor water, and general water; ④The decision tree was applied to the PlanetScope satellite image of Yangzhou City on April 9, 2019, and 10 synchronous sampling points were used for verification. The overall recognition accuracy reached 80.00%, and the K value reached 0.67. The urban water classification model, after water color classification, can be applied to other similar water bodies, and provides a technical method for the supervision of black-odor water bodies.

Characteristics of the chromophoric dissolved organic matter of urban black-odor rivers using fluorescence and UV-visible spectroscopy.

Urban black-odor water (BOW) is a typical phenomenon seen in the urban water environment; it is caused by excessive pollution by organic matter and other pollutants, such as nitrogen and phosphorous. Chromophoric dissolved organic matter (CDOM) is a major optical fraction of dissolved organic matter. In this study, optical properties and components of CDOM were obtained from 178 river samples collected from five cities in China, the sample were investigated using absorption and fluorescence spectroscopy. The collected included 89 ordinary water (OW) samples, 63 mild BOW (MBOW), and 26 heavy BOW (HBOW) samples. Significant differences were found in the absorption spectra of the HBOW, MBOW, and OW samples, particularly in their optical parameters (the slope of the spectrum (S275-295), and the ratio of two absorption coefficients of CDOM (E2:E3)). Additionally, the fluorescence intensity of the humic acid-like component (F5) and soluble microbial by product-like component (F4) obtained via the fluorescence regional integration (FRI) method were 3 and 4.2 times higher in HBOW than in OW, respectively; this could be used as an indicator to distinguish OW from BOW in urban rivers. The results obtained using the redundancy method and the strong negative correlation between F4 and dissolved oxygen (DO) (r=- 0.56) suggested that the composition of CDOM could change significantly under different urban water environments (p<0.01). Different correlations were also found between F5, and a355, E2:E3, S275-295 in different BOW levels, suggesting that the optical parameters of CDOM were mainly determined by the polluted organic matter originating from terrestrial sources with large molecular humic acid-like compounds; optical parameter a355 could distinguish BOW from OW. These findings are conducive in understanding the dynamics of organic matter pollution and to discover the composition and optical properties of the CDOM in urban BOW and OW, thereby providing an effective method for tracking the spatial characteristics of BOW in urban rivers using remote sensing technologies in areas with multiple sources of pollution.

Mechanism of Urban Black Odorous Water Based on Continuous Monitoring: A Case Study of the Erkeng Stream in Nanning

Black odorous water seriously endangers urban ecological functions. The "Water Pollution Prevention Action Plan" promulgated by the State Council has attached great importance to this issue and set a timetable for achieving the goal of pollution remediation of the urban black odorous water problem. However, in the process of managing the city's black odorous water, we found that the apparent governance effect is not sustainable. Many of the urban waters that have been treated to become clear have returned to a black odorous state. This problem has constrained the completion of the black odorous water control plan, and urgently needs to be resolved. To explain the reason for this phenomenon, we chose the Erkeng Stream in Nanning as the research object, which is a water body that returns to a black odorous state after treatment. We used a multi-parameter water quality tester and chemical analysis method to carry out daily continuous monitoring for 24 h and monthly dynamic monitoring of the water body. The results showed that the rainfall process was significantly correlated with the ammonia nitrogen concentration in the water (P<0.01), and the temperature was positively correlated with the trend of ammonia nitrogen concentration in the water (r=0.23, P<0.05), which in turn was negatively correlated with the change trend of water transparency (r=-0.33, P<0.01). The above results show that the return of the black odorous state may be related to the microbial degradation of endogenous pollutants and the input of external pollutants. The reason may be:① The microorganisms are driven by light and temperature to promote the development of water in the direction of the black odorous state; ② Contaminants carried by rainfall promote the formation of black odor in water bodies. In short, in the context that internal pollution cannot be completely eradicated and external pollutants are difficult to control effectively, to prevent the treated urban water body from returning to a black odorous state, attention should be paid to endogenous pollutants such as river sediment and its control technology. Moreover, ecological control measures should be comprehensively adopted to reduce the input of external source indicators.

Discussion on Urban Black Odor Water Body Treatment and Long-term Management and Maintenance

In view of the increasingly serious pollution of urban rivers, seasonal or end-age black odor, not only affects the urban landscape, but also affects the daily life of the surrounding residents, in order to eliminate urban black and smelly rivers and improve the quality of urban water environment. This paper proposes the treatment technology of urban black and odorous water from the aspects of controlling source pollution, water pollution control and ecological restoration technology. Strengthen management from the long-term improvement system of water quality and policy guarantees to achieve the long-term clear effect of urban water bodies.

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.

Urban black-odor water body dynamic analysis with high-resolution remote sensing image

The black-odor water has significant negative impacts on the sustainable development of society and survival of mankind. The paper presents a new Urban Black-Odor Water (UBOW) model to identify the urban black-odor water body from high-resolution remote sensing image Gaofen-2(GF-2), to help monitor and control urban black-odor water bodies. The new UBOW model is derived from the analysis of the in-situ observation data and the spectral characteristics of GF-2. It takes advantage of the spectral difference at the blue band, the green band and the red band and has certain theoretical ground. The UBOW model can effectively identify the severe black-odor water and the mild black-odor water. The model is validated by the observation and the overall accuracy can be up to 81%. The UBOW model is used to analyze the dynamics of the urban black-odor water bodies of Beijing China, with monthly GF-2 images from March 2017 to October 2018. Results show that the urban black-odor water bodies, whether it is mild black-odor water bodies or severe black-odor water bodies, have been reduced significantly, which is consistent with the governmental report. This confirms that the Beijing government takes effective measurements to remedy the urban black-odor water bodies.

Analysis of Absorption Characteristics of Urban Black-odor Water

The urban black-odor water body has become a serious problem of urban water environment and identifying their optical characteristics in urban areas is the prerequisite and basis for their detection by remote sensing. A total of 85 samples of urban black-odor water, from Changsha, Nanjing, and Wuxi, were collected from 2016 to 2017, and a total of 80 samples were collected from non-black-odor (smelly) water samples. The water quality parameters such as suspended matter and the absorption coefficient were measured. The results showed that:①The total black-odor water body particulate matter absorption coefficients and non-pigmented particulate matter absorption coefficients were generally higher than those for non-black-odor water bodies and they had a certain degree of discrimination at 440 nm, but their effect was not significant. The absorption of relatively high levels of particulate matter in black-odor water was mainly due to high absorption of non-pigmented particles. In the data collected, non-pigmented particles in black-odor water accounted for more than 50% of the total particles. ② The Colored Dissolved Organic Matter (CDOM) absorption coefficients for black-odor water bodies and non-black-odor water bodies differed. The average absorption coefficient of CDOM at 440 nm for black-odor water bodies was 1.7 times higher than that for non-black-odor water bodies. Thus, the black-odor water body can be distinguished by using the slope of the absorption coefficient curve fitted in the characteristic wavelength band of 440 nm, as well as in different wavelength bands; the overall effect is good. However, as the wavelength increases, the discrimination effect decreases. Analysis of the absorption characteristics of urban black-odor water bodies will provide effective technical support for their detection by remote sensing and supervision, and the density of CDOM in black-odor water was generally high, which could be used as an important reference for identification.

Remote Sensing Identification of Urban Black-Odor Water Bodies Based on High-Resolution Images:A Case Study in Nanjing

The identification of urban black-odor water bodies plays an important role in monitoring and controlling black-odor water bodies. In 2016, a ground survey was conducted on the urban reach of Nanjing, and 55 samples from the West Shazhou River, Tuwei River, Xuanwu Lake, and Jinchuan River were obtained. The spectral characteristics of urban black-odor water bodies and other water bodies were analyzed. Recognition algorithms for GF-2 data were proposed in order to analyze the spatial distribution and environmental factors of urban black-odor water bodies. These algorithms were single-band thresholds based on reflectance of the green band, the difference between the blue band and green band, the ratio of the green band and red band, and the chromaticity value. The results indicate that:① compared with other types of water, the urban black-odor water has the smallest spectral slope in the range of 400-500 nm and lowest reflectivity, and the peaks and valleys are not prominent in the whole visible range; ② based on the verification, the accuracy of the ratio algorithm is the highest; and ③ using the ratio algorithm to calculate the GF-2 data of November 3, 2016, a total of 11 black river sections are identified. The entire length is 40.7 km, and the area is 0.749 km2.The black-odor water sections are distributed over a wide range but are not continuous, and they are concentrated in the densely populated areas. Domestic sewage, industrial waste water, and broken river channel are the main reasons.