Wastewater reclamation and reuse in China: Opportunities and challenges

Lyu, Sidan; Chen, Weiping; Zhang, Weiling; Fan, Yupeng; Jiao, Wentao

doi:10.1016/j.jes.2015.11.012

Cited by 294 publications

(142 citation statements)

References 45 publications

(44 reference statements)

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“…Market proximity, high opportunities for income generation, reliable and free irrigation water supply, and minimum artificial fertilizer requirement are the often cited benefits of irrigation within cities (Drechsel et al 2006;Raschid-Sally and Jayakody 2008;Qadir et al 2010;Lyu et al 2016). However, the same studies also report high levels of pathogen indicator organisms or also heavy metals in vegetables grown with polluted urban water sources.…”

Section: Introductionmentioning

confidence: 99%

Microbiological quality of lettuce (Lactuca sativa) irrigated with wastewater in Addis Ababa, Ethiopia and effect of green salads washing methods

Woldetsadik

Drechsel

Keraita³

et al. 2017

FoodContamination

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Background: In Addis Ababa, where irrigation water for vegetable production is commonly derived from the highly polluted Akaki river, information on microbial contamination of water and irrigated vegetable is scanty. An assessment was done to determine the microbiological quality of irrigation water and lettuce harvested from 10 urban farming sites of Addis Ababa. The efficacy of 5 lettuce washing methods were also assessed. A total of 210 lettuce and 90 irrigation water samples were analyzed for faecal coliform and helminth eggs population levels. Results: The mean faecal coliform levels of irrigation water ranged from 4.29-5.61 log 10 MPN 100 ml −1 , while on lettuce, the concentrations ranged from 3.46-5.03 log 10 MPN 100 g

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Section: Introductionmentioning

confidence: 99%

Microbiological quality of lettuce (Lactuca sativa) irrigated with wastewater in Addis Ababa, Ethiopia and effect of green salads washing methods

Woldetsadik

Drechsel

Keraita³

et al. 2017

FoodContamination

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“…At the same time, there are a lot of low quality water didn't be used of safe, effective and standard in China. According to the China's water resources in 2014, the national wastewater emissions was 77.1 billion m 3 ,and municipal wastewater emissions was 43.8 billion m 3 , accounted for 56.81%. In order to ensure agricultural production, low quality water was spontaneous used for irrigation in the arid and semi-arid area.…”

Section: Introductionmentioning

confidence: 99%

“…Reclaimed municipal wastewater was mainly irrigation for agriculture, Australia's Werribee farm irrigated with reclaimed wastewater since 1897 (McPherson, 1979), 42% of the reclaimed water consumption was used for agricultural irrigation (Valentina, 2005). Crop area irrigated with reclaimed wastewater in China was increasing year by year, such as reclaimed water consumption of Beijing was 680 million m 3 in 2010, 44.11% of the reclaimed water consumption was used for agricultural irrigation (Lyuet al 2016). Compared with sewage quality, the quality of reclaimed wastewater has been improved, but still contained high concentration of organics, nitrogen, phosphorus and microbe, so there were three aspects of the problem should be emphasis.…”

Section: Introductionmentioning

confidence: 99%

Effect of reclaimed municipal wastewater irrigation on greenhouse soil mineral nitrogen dynamic and fruit quality of tomato

Li¹,

Qi²,

Du³

et al. 2017

2nd Annual International Conference on Energy, Environmental &Amp; Sustainable Ecosystem Development (EESED 2016)

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To examine the effects of nitrogen and reclaimed municipal wastewater(RMW) irrigation on fruit quality of greenhouse tomato and soil nitrogen utilization, field experiments were carried out. Root-layer mineral nitrogen, total nitrogen, tomato yield, nitrogen in fruit, partial factor productivity from applied N, apparent N loss, nitrogen supplying capacity and fruit quality(flavor quality, nutritional quality) were analyzed. The results indicated that RMW irrigation leaded to an average of 1.72 -40.39% increase in mineral nitrogen and total nitrogen of rhizosphere soil in 1 st , 2 nd and 4 th cluster fruit expanding stage. Compared with CK, nitrogen in fruit, partial factor productivity from applied N and nitrogen supplying capacity of RMW irrigation were obvious increased. Furthermore, compared with CK, nutritional quality of tomato fruits were improved significantly. Maintaining soil fertility, the yield and nutritional quality of fruits were therefore notable improved under RMW irrigation, and which was an effectively management strategy of soil sustainable production and alleviating soil environment.

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“…Various ecological regions have been seriously destroyed by the rapid growth of the social economy through the occupations of wetlands for agricultural land (Bai et al, ; Murray, Clemens, Phinn, Possingham, & Fuller, ), community facilities (Kirwan & Megonigal, ; Zhang et al, ), tourism development (Lee & Hsieh, ; Wu, Gao, Wang, Wang, & Xu, ), and so forth in recent decades all over the world. Moreover, the uncontrollable uses of wetland water resources for those developments lead to a further threat (G. Liu, Tian, Sun, Xiao, & Yuan, ; Lyu, Chen, Zhang, Fan, & Jiao, ). More than 65% of water resources are extracted from the wetlands in industrial and agricultural production, which seriously affects the physical and chemical properties of soil (Leung et al, ; Meng et al, ) and alters the wetland vegetation drastically (Feng, Han, Hu, & Chen, ; Roberts, Hunt, Arroyo‐Kalin, Evans, & Boivin, ).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Lin

et al. 2019

Natural Resource Modeling

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In real-world wetland vegetation morphology (WVM) detection, large scene variations such as those due to landform, vegetation, sunlight, weather, and sky, as well as camera parameter settings such as focal length and shooting angle, require systematic and complicated artificial intelligence technology to accurately discriminate inter and intra-class wetland objections. To deal with these challenges, we introduced a deep-level discriminative model based on convolutional neural networks (CNN) for classifying the images of DongZhai Harbor intertidal, Lashi Lake alpine, Yancheng coastal and Zoige plateau wetlands in China. A 96-dimensional convolution operation with kernel sizes of 11 × 11 × 3 first applied to the resized 227 × 227 WVM input pictures to acquire the effective morphologic features. The perceptron layers of the rectified linear unit and the batch normalization were used in the middle layer to achieve the better gradient propagation property during the training process. The WVM features were down-sampled by the pooling networks to reduce the neuron dimensions. The fully connected layer linked to the output of the convolutional and pooling layers to obtain the high-level WVM species information for the final WVM classification purpose. The deep-level CNN-based method was compared with the traditional shallow-level feature-designed algorithms of conditional maximum entropy regression, multilayer perceptron and support vector machine. The deep-level algorithm showed the superior performance for detecting the WVM species, which provided a superior alternative routine for accurate artificial intelligence classification of WVM in the ecological engineering application. Recommendations for Resource Managers• This proposed artificial intelligence model can be used to effectively help people understand the macroscopic scene changes of ecological elements from the wetland vegetation morphology images. • The detection results show the robustness of the discriminative model, for it is not affected by the complex natural factors such as landform, vegetation, sunlight, weather, and sky, as well as human factors such as camera parameter settings of focal length and shooting angle to some extent. • The deep-level model based on the cascade convolution framework can be used to extract more distinguishing detailed ecological features from the original wetland vegetation morphology images compared with the shallow-level algorithms using a single convolutional layer. K E Y W O R D S classification, convolutional neural network, ecological engineering, vegetation morphology, wetland

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Wastewater reclamation and reuse in China: Opportunities and challenges

Cited by 294 publications

References 45 publications

Microbiological quality of lettuce (Lactuca sativa) irrigated with wastewater in Addis Ababa, Ethiopia and effect of green salads washing methods

Microbiological quality of lettuce (Lactuca sativa) irrigated with wastewater in Addis Ababa, Ethiopia and effect of green salads washing methods

Effect of reclaimed municipal wastewater irrigation on greenhouse soil mineral nitrogen dynamic and fruit quality of tomato

Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

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