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Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy a
b
c
d
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Yidong Guan , Yuan Zhang , Dongye Zhao , Xiaofeng Huang & Haini Li a
Jiangsu Provincial Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Department of Environmental Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China b
School of Environmental Science and Engineering, Suzhou University of Science and Technology, 215009, Suzhou, Peoples R. China c
Environmental Engineering Program, Department of Civil Engineering, Auburn University, Auburn, AL 36849, USA d
Wuxi Taihu Lake Management Company Limited, Wuxi 214063, China Accepted author version posted online: 06 Feb 2015.
To cite this article: Yidong Guan, Yuan Zhang, Dongye Zhao, Xiaofeng Huang & Haini Li (2015): Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy, Journal of the Air & Waste Management Association, DOI: 10.1080/10962247.2015.1010751 To link to this article: http://dx.doi.org/10.1080/10962247.2015.1010751
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Yidong Guan1, †, Yuan Zhang2, Dongye Zhao†, Xiaofeng Huang3, Haini Li3
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Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy 1, †
Yidong Guan (), Corresponding author
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Email: [email protected] 2
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School of Environmental Science and Engineering, Suzhou University of Science and Technology, 215009, Suzhou, Peoples R. China E-mail address: [email protected] (or [email protected]) †
Dongye Zhao (), Co-corresponding author, Environmental Engineering Program, Department of Civil Engineering, Auburn University, Auburn, AL 36849, USA
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Email: [email protected]; 3
Wuxi Taihu Lake Management Company Limited, Wuxi 214063, China
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E-mail address: [email protected] (Xiaofeng Huang) and [email protected] (Haini Li) ABOUT THE AUTHORS
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Yidong Guan is a current visiting scholar in the Department of Civil Engineering, Auburn University, and a lecturer in Nanjing University of Information Science and Technology. Yuan Zhang is a lecturer at the Suzhou University of Science and Technology.
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Jiangsu Provincial Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Department of Environmental Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dongye Zhao is a Professor in the Department of Civil Engineering, Auburn University. Xiaofeng Huang is a research Professor at the Wuxi Taihu Lake Management Company Limited. Haini Li is an engineer at the Wuxi Taihu Lake Management Company Limited.
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Impact statement
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Rural domestic waste (RDW) is affecting 720 million people in China and over 3221 million people worldwide. Consequently, handling and disposal of RDW has serious health implications to rural dwellers and the eco-systems. This study offers a systemantic and quantitative overview and analysis of historical data on RDW production and management practices in a prototype region in China, which is confronted with great environmental challenges associated with RDW. Then we predicted future production of RDW and proposed a sustainable RDW management strategy, which holds the promise to greatly mitigate the mounting environmental pressure associated with RDW and provides a science-based guidance for decision makers and practitioners for assuring rapid yet “green” economic development.
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Lack of access to adequate sanitation facilities has serious health implications for rural dwellers
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and can degrade the eco-systems. This study offers a systemantic and quantitative overview of historical data on rural domestic waste (RDW) production and past and current management practices in a prototype region in China, where rural areas are undergoing rapid urbanization and are confronted with great environmental challenges associated with poor RDW management
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practices. The results indicate that RDW is characterized with a large fraction of kitchen waste (42.9%) and high water content (53.4%). The RDW generation (RDWG) per capita between
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2012 and 2020 is estimated to increase from 0.68 to 1.01 kg/d-cap. The Hill 1 model is able to
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adequately simulate/project the population growth in rural area from 1993 to 2020. The annual RDWG in the region is estimated to double from 6,033,000 tons/year in 2008 to 12,030,000 tons/year by 2020. By comparing three RDW management scenarios based on the life cycle
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ABSTRACT
inventory approach and cost-benefit analysis, it is strongly recommended to upgrade the present Scenario 2 (sanitary landfill treatment) to Scenario 3 (source separation followed by composting and landfill of RDW) to significantly reduce the ecological footprint and to improve the cost-effectiveness and long-term sustainability.
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Keywords: Domestic waste, solid waste, waste management, outdoor burning, landfill, waste disposal, source separation
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INTRODUCTION Over 3221 million people live in rural areas worldwide, accounting for 45.4% of total world
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been associated with various health risk and ecological degradation in the rural areas (Minh et al., 2003; Liu et al., 2005; Li et al., 2011), which is considered a major factor that hampers the launch
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of the Millennium Development Goals (MDGs) of United Nations. China is the largest developing country in the world, and the GDP (gross domestic product) of China has reached the second in the world after more than thirty years of continued rapid economic growth. While China is
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experiencing the sheer transition from an agriculture-based economy to an industry-based economy, the resulting environmental consequences have been challenging, and are believed to
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limit further sustainable development. Of the tremendous amounts of rural domestic waste (RDW) produced annually, it is estimated that about 1×108 tons of RDW was abandoned carelessly
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without proper treatment (Yao et al., 2009), which poses a long-term threat to the hygiene of rural residents and the ecological health. Consequently, more effective strategy for management of RDW is of dire need to sustain the high economic growth rate while mitigating the environmental
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population in 2014 (United Nations, 2014). However, lack of access to sanitation facilities has
impacts.
Traditionally, the social and economic development in rural areas in China is far behind the urban areas. As a result, municipal solid waste management (MSWM), also known as solid waste
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management (SWM), is only practiced in cities, which barely addresses SWM in rural regions (Guan et al., 2011; Guan et al., 2012a; Guan et al., 2012b; Guan et al., 2013). Due to the Hukou (household registration) system, the Chinese population is strictly divided into two categories: the
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urban residents and the rural residents. While MSWM in cities has been extensively researched
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(Wang and Nie, 2001; Li et al., 2009; Chen et al., 2010b; Cheng and Hu, 2010; Zhang et al., 2010; Tai et al., 2011), there has been very little information available on the rural domestic waste
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disposal of RDW (Guan et al., 2012a; Guan et al., 2012b). Moreover, the rapid and mounting
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urbanization further exerts growing demand for more effective management measures for RDW. In this context, we chose Zhejiang Province, a rapidly industrializing region in China, as the representative region. This region hosts a population of 46.8 million, which is close to that of Spain, but with a land area of about 20.1% of Spain. As a frontier in China’s booming economy,
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this region has been confronted with serious environmental consequences and challenges,
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especially with respect to RDW. The objectives of this study were to: 1) investigate and systematically analyze the overall RDW data and RDWM practices in Zhejiang Province,
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including the waste composition, production, disposal and management; and 2) explore a more sustainable mid- to long-term strategy by means of life cycle inventory (LCI) and cost-benefit analyses to meet the challenges posed by the high volume of RDWs and the inappropriate handling
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management (RDWM) in China. This deficiency often leads to inappropriate handling and
and disposal practices. Here, three RDWM scenarios were to be compared through coupled LCI and cost-benefit analyses, including Scenario 1 (unsanitary landfill and outdoor burning), Scenario 2 (sanitary landfill treatment), and Scenario 3 (source separation followed by composting and landfill).
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The information obtained will facilitate the development of a more cost-effective and implementable RDWM strategy in Zhejiang Province, which also serves as a direct guidance for RDWM in the extensive rural areas in China. To our knowledge, this is one of very few systematic
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works in a large-province scale about RDW characterization and production (including prediction
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of future RDW generation) in China, and this is the first study to apply the integrated LCI and
cost-benefit approach to analyze various RDWM practices, which is of broader practical value
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rural areas, accounting for 22.3% of rural population worldwide (United Nations, 2014),
of the United Nations.
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METHODOLOGY
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improving the RDW management in China will greatly facilitate the implementation of the MDGs
The State of Zhejiang Province
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Zhejiang Province is situated on the southeast region of China, covering a land area of 101.8×103
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square kilometers (mountainous and hilly regions amounting to 70.4%). The total population is about 46.8 million, of which 42.4% are rural residents. The GDP for 2012 was 550.2 billion US dollars, of which the agricultural GDP accounted for 4.8%. GDP per capita is about 10059 US
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beyond the studied region. Given that more than half population (720 million) in China live in
dollars (1 US dollar = 6.3 Yuan in 2012), and the net income per capita was about 5484 US dollars for the urban residents and about 2309 US dollars for the rural residents. The annual average precipitation is 1353 mm, and the annual average temperature is about 17.8 oC.
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RDW Data Acquisition and Processing The available RDW data has been limited, and most of the relevant data owned by the relevant
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government departments or the associated agencies are not accessible for the public. In this work,
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the data were collected from three major sources: 1) peer-reviewed credible publications of other researchers, including books, journal articles and relevant research notes and reports; 2) published
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information collected from government sponsored projects e.g.(Guan et al., 2011; Guan et al.,
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2012a; Guan et al., 2012b; Guan et al., 2012c; Guan et al., 2013), which allowed researchers limited access to the government files. To assure data quality and credibility, cautions were exercised by analyzing the reliability of waste-sampling procedures, cross-comparing relevant literatures or data from different sources, and cross-checking with other independent researches. In
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this work, the information on RDW in Zhejiang Province was collected from the following references and bibliographies: (CMEP in China, 2007; MEP of China and GAQSIQ of China,
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2008; Qiu, 2008; Shan et al., 2009; Chen et al., 2010a; Cheng and Hu, 2010; Guan et al., 2011; Cai, 2012; Guan et al., 2012a; Guan et al., 2012b; Ma et al., 2012; Guan et al., 2013). Data on municipal
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solid wastes were obtained from the references: (Wang and Nie, 2001; Xiao et al., 2007; Zhuang, 2007; Prechthai et al., 2008; Li et al., 2009; Chen et al., 2010b; Cheng and Hu, 2010; Zhang et al.,
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government documents from the national and provincial authorities, and 3) our prior work and
2010; Tai et al., 2011). Data processing and preliminary statistical analysis were carried out using Microsoft Excel 2003 (Microsoft Corporation, USA). Non-linear regression analyses were performed to predict the rural population growth in Zhejiang Province using Origin 8.0 (OriginLab Corporation, USA) and Data
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Processing System (DPS) software V13.5 (Tang and Zhang, 2013), which have been considered a reasonable modeling method (Beigl et al., 2008). Model error in the regression analysis is computed as the ratio of absolute difference between the simulation value and the practical value
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divided by the practical value. In this work, the data are presented as mean ± standard deviation
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(SD), and the significance level (p value) is set to 0.05.
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The amount of waste generated is proportional to the population and the mean living standards of
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the people (Daskalopoulos et al., 1998). By combining the RDW generation (RDWG) per capita and the population in Zhejiang Province, we obtain: 4
Annual RDWG in Zhejiang (10 • t/year)
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Average RDWG per capita per day (kg/capita/d) × 365 × Population (10 )
(1)
1000
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=
Average RDWG per capita per day in the next year (kg/capita/d)
(2)
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= (1 + annual growth rate of RDWG (%)) • (Average RDWG in current year (kg/capita/d))
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The RDWG per capita is computed based on the available RDWG data and following the analogy-related method (Beigl et al., 2004; Nie et al., 2005), and such analogy-related estimation has been applied in many fast-growing regions, such as Foshan city in China (Nie et al., 2005) and
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Calculation of Waste Generation
central and east European cities (Beigl et al., 2004). The population growth was estimated by using the historical statistical data, which could be accessed from the Statistical Bureau of Zhejiang Province (1986-2012). Section below gives details on determination of RDWG and the annual growth rate of RDWG.
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CHARACTERISTICS
AND
GENERATION
OF
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RDW
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RDW Characteristics
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waste, plastics, paper and fibers account for 42.9%, 11.2%, 8.1%, 7.8% and 5.6% of the RDW, respectively. In addition, combustible wastes make up 75.5%, recyclable wastes 24.8%, and
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compostable wastes 54.1%. The typical average water content of RDW is 53.4%. Here Table 1
Figure 1 compares the components of RDW and MSW in Zhejiang. It is evident from Table 1 and
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Figure 1 that the kitchen wastes makes up a large part of the total Zhejiang RDW and MSW. This
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can be attributed to the diet habits in Zhejiang, where unprocessed and unpackaged vegetables and fruits are generally consumed, which results in the high level of organic matter content in the
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domestic wastes. The high organic matter content is coupled with the high water content for both RDW and MSW (above 50%), which results in the low calories of the wastes, and thus, prohibits effective waste incineration without the addition of costly fossil fuels. This is generally in
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As shown in Table 1, organic materials account for 70% of the RDW, and the kitchen waste, yard
agreement with the reported data on MSW in China (Wang and Nie, 2001; Zhang et al., 2010).
Here Figure 1
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Nevertheless, there are also remarkable discrepancies of waste characteristics between the RDW and MSW (Figure 1). For instance, the average amounts of yard wastes and inert residue of the Zhejiang RDW are much higher than those of the Zhejiang MSW (He et al., 2004; Zhuang et al.,
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2008), whereas the portion of kitchen wastes in the RDW is about 76.3% of that in the MSW.
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These variations are mainly due to the partial commingling of agricultural wastes with RDW. Additionally, the difference in living standard and habits, and environmental consciousness
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RDW Generation
The waste generation is affected by a number of factors, including population, income, living habits, education levels, cultural and religious beliefs, social and public attitudes, and climate (Bandara et al., 2007). For rapidly developing areas, the prediction of solid waste generation is
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always quite challenging, especially in the fast-growing rural region like Zhejiang Province, where the systematical historical record of tonnage generated has been lacking. Therefore, we employed
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the analogy-related method, which integrates the effects of socio-demographic and economic
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perspectives, to estimate the RDW generation (Beigl et al., 2004; Nie et al., 2005). To this end, we chose the Yangtze River Delta Region (including Zhejiang and Jiangsu Province, and the city of Shanghai) for the estimate. The region shares the similar geographical characteristics, living habits
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between the urban and rural residents may also play a role.
and historical culture. The mean value of RDWG per capita of Zhejiang in 2008 was 0.50 kg/cap/d according to Chen et al. (2010), which has been the only province-wide study on the RDWG in Zhejiang. Consequently, this 2008 RDWG value (0.50 kg/per/d) was utilized as the initial value to calculate the RDWG in
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Zhejiang from 2009 to 2020, using Equations (1) and (2). As stated above, the annual growth rate of RDWG in Zhejiang was first calculated by using the solid waste generation rates in the Yangtze River Delta Region based on the analogy-related method (Beigl et al., 2004; Nie et al., 2005).
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Based on the available data, the annual growth rate of MSW generation in Zhejiang from 2001 to
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2010, Jiangsu (represented by Suzhou) from 1995 to 2005, and Shanghai from 2001 to 2010, was
4.1%, 6.4% and 4.0%, respectively (Statistical Bureau of Zhejiang Province, 1986-2012; Zhao and
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calculation, which is very close to the average growth rate in the region (4.8%). The results (Table
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2) indicate that the RDWG per capita in Zhejiang is 0.68 and 1.01 kg/capita/d for 2012 and 2020, respectively. Here Table 2
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The rural population growth in Zhejiang Province was taken as another factor in determining the amount of RDWG, as was the case for MSW generation in urban China (Wang and Nie, 2001;
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Chen et al., 2010b) and in European countries (Daskalopoulos et al., 1998). Figure 2 shows the
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variation of the population from 1986 to 2012. From 1986 to 1996, the rural population experienced an exponential rise followed by a rapid descending process from 1996 to 2007, then a gradual decline from 2007 to 2012. The data in 1996 could be assumed to be the critical point of
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Yang, 2003; Wang et al., 2008). Therefore, a conservative estimate of 5% was used in our
the population dynamics, since this year represented the turning point from the previously increasing trend to the decreasing trend. The following two regression equations were obtained when the population data from 1996 to 2012 were fitted through the Hill 1 non-linear regression and the linear regression, respectively:
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y = 3578.300 − 302.921 •
x 999.283 2002.179999.2831 + x 999.283
(3)
(p Scenario 3.
Analysis of costs and benefits
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(sum of capital, operating and maintenance) on a per-tonnage basis (3.0 US$/ton-RDW), as
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compared to Scenario 2 (10.7 US$/ton) and Scenario 3 (31.1 US$/ton). This is reasonable given the fact that Scenario 1 offers only simplistic disposal facilities and partial treatment of RDW whereas Scenarios 2 and 3 involve more advanced sanitary landfill facilities, more professional O & M, and more labor for sorting out RDW (Scenario 3). On average, three agricultural workers can
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undertake the sorting work of RDW in one village, and the salary is estimated at about 1280 CNY (US $206) per month per capita based on the average minimum wages in Zhejiang Province. In
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terms of revenue, Scenarios 1, 2, and 3 may generate an economic return of 0, 3.7, and 4.2
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US$/ton-RDW, respectively. Upon deduction of the financial returns, the net cost for Scenarios 1, 2, and 3 are 3.0, 8.0, and 16.6 US$/ton-RDW per year. Given the increasingly stringent environmental regulations and pressure, Scenario 1 had been
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Table 4 compares the costs and benefits of the three scenarios. Scenario 1 offers the lowest cost
phased out, and Scenario 2 as a transitional approach is undergoing close scrutiny. In contrast, Scenario 3 may offer a much sounder solution for the disposal of RDW for its superior environmental benefits and affordable cost. Since less than half of RDW is landfilled, Scenario 3 needs less than half energy and material consumption, occupies less than half of land use, and
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renders much less waste stream emissions. It should be noted that the environmental damage caused by the contaminant emissions and the value associated with natural resource conservation is not reflected in the cost and benefit analysis in Table 4, which may undermine the practical value
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of Scenario 3 by underestimating the much higher environmental harm and resource depletion
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associated with the Scenarios 1 and 2. Furthermore, the saving in land usage and in landfill volume is of particular significance in Zhejiang Province as well as other more developed regions in
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costly and challenging to locate and build more landfill facilities. Diverting most of the organic
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waste from landfill to composting results in much reduced landfill volume, which translates into much reduced landfill leachate and less expenditure in the leachate treatment. The compost product is known to enhance the soil fertility and quality and thus can be conveniently used in agricultural applications. As shown in Figure 3, the waste recyclers would work much more
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conveniently when the high water content kitchen wastes and hazardous wastes are separated out, which in turn will promote the recycling activities. In addition, the recycled materials, such as
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bottles, plastics, metals, cardboard, paper products etc., can be reclaimed as a profit, which may
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grow as the government is further promoting recycles.
CONCLUSIONS
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China. Due to the soaring land price and increasing public opposition, it has become increasingly
This study surveyed and systematically analyzed past and current RDW management practices and proposed a more environmentally sound and cost-effective approach in handling and
disposing of RDW in Zhejiang Province. The major findings are summarized as follows:
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(1) The typical RDW in Zhejiang Province include kitchen wastes (42.9%), combustible wastes (75.5%), recyclables (24.8%), and compostable wastes (54.1%), and the average water content is 53.4%.
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(2) The RDWG per capita between 2012 and 2020 is estimated to increase from 0.68 to 1.01 kg/d-cap, and the Hill 1 model can be used to simulate/project the population growth in rural
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thousand tons/year in 2008 to 12030 thousand tons/year by 2020.
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(3) The past (Scenario 1), current (Scenario 2) and future proposed (Scenario 3) RDW management practices are compared based on coupled LCI and cost-benefit analyses. The proposed Scenario 3 (composting and landfill treatment of source separated RDW) represents one of the best RDWM options that represents the best practice for future RDW disposal for its
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economic competitiveness and least environmental impacts. Based on local economic development and environmental impacts of RDW, it is recommended to upgrade the present
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RDWM (sanitary landfill treatment of RDW, scenario 2) to Scenario 3 to minimize the ecological
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footprint at an acceptable expenditure and toward long-term sustainable development. ACKNOWLEDGMENTS
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Zhejiang from 1986 to 2020. The annual RDWG in Zhejiang is estimated to double from 6033
The research was partially supported by a scholarship from Demonstration Base of Water Quality Improvement and Ecosystem Restoration at Lakeside Zone of Taihu Xincheng (2012ZX07101-013-02), Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD), Nanjing University of Information Science and Technology (NUIST, No.
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2013x010, S8111028001). The authors also thank the China Section of the Air & Waste Management Association for generously covering the cost of the page charges, which makes the publication of this paper possible.
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cheng shi sheng huo la ji zong he chu li fang an yan jiu). Zhejiang University, Hangzhou.
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Zhuang, Y., 2007. Life cycle assessment of MSW treatment scenarios in Hangzhou (Hangzhou shi
28
Manag.,
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2022-2030.
Table 1. Descriptive statistical characteristics of rural solid waste (RDW) composition in Zhejiang Province, China (n = 24)
20.0
11.2
8.5
8.1
±19.3
±8.0
±6.9 ±6.0
±3.0
Median
42.7
18.7
25 Percentiles
26.6
12.2
75 Percentiles
63.1
5.6
2.4
0.9
0.2 53.4±9.
waste
±2.1
content
±4.4 ±1.3 ±1.0 ±0.2
0
7.4
8.0
8.0
5.4
2.0
0.8
0.2
55.8
7.7
2.2
5.9
6.4
1.6
1.5
0.1
0.0
47.8
9.4
8.8
8.0
3.2
1.3
0.3
58.3
ed 27.1
s
9.9
12.5 14.4
pt
Mean ± S.D.
7.8
Water
t
42.9
ul
us
waste waste ue
M an
waste
Metal
cr ip
Plastics Paper Fibers Glass
Maximum—Min 70.3—1 35.2— 24.9 19.1 14.4— 13.2— 17.1 4.9— 2.9— 0.3— 35.9—6 0.9
ce
imum
7.8
—0
—0
3.3
3.7
—0
0.3
Note:
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Kitchen Inert Yard Resid
Harmf
1. The components are calculated on the mass ratio of wet weight (wt, %). 2. S.D. denotes Standard Deviation.
29
0.1
0
8.0
3. The data is summarized from the following references (Cai, 2012; Chen et al. 2010; Ma et al., 2012; Qiu, 2008). 4. Inert waste is mainly composed of the abandoned brick, furnace cinder and pebble, etc. Residue
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t
is the leftover waste. Harmful waste includes the paint bottles, batteries and pesticides bottles, etc.
30
Table 2. Estimation of annual RDW generated in Rural Zhejiang 1
RDW RDW generated per 4
2008
603.3
a
2009
631.4
0.527
2010
661.9
2011
778.0
2012
815.9
2013
855.6
(104)
3292
us
0.502
M an
3282
3279
0.650
3279
ed
0.553
3278
pt
0.682
3274
898.3
0.752
3273
ce
0.716
2014
Province
cr ip
generated (10 ·t/year) capita (kg/capita/d)
Zhejiang
t
Annual
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Year
Population in Rural
2015
943.5
0.790
3272
2016
989.9
0.829
3272
31
1040
0.871
3271
2018
1091
0.914
3271
2019
1146
0.960
3271
2020
1203
1.01
3270
cr ip
us
M an
Note: a indicates the value of annual RDW generation in 2008 is recalculated from Chen et al. (2010). 1 means the rural population in Zhejiang Province from 2008-2012 based on data from the
ce
pt
ed
Statistical Bureau of Zhejiang Province (1986-2012).
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t
2017
32
Table 3. LCI of three scenarios Input flow (per ton of Unit
Scenario 1
Scenario 2
Clay a1 g
87615
227800
Diesel kg
0.90
2.01
Scenario 3
ce
Electricity recovery kwh
us 2.53
2.01E-02
8.72E-03
0
4.03E-05
1.74E-05
2.24E-01
8.25
0
59.77
25.88
102965
113994
49359
0
pt
Electricity consumption kwh
98637
0
ed
LDPE m3
M an
Pesticide kg
Gas emissions b
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Material consumption a
cr ip
t
waste)
CH 4 g
33
354739
242265
104904
NO x g
333
37
16.08
VOC g
256
11
H2S g
546
607
SO 2 g
56
7
7.67
pt
SS g
cr ip 4.77
262.86
us
64.31
ed
COD g
M an
Water emissions c
2.95
24.34
10.54
7.30
3.16
NH 3 -N g
28.72
6.09
2.64
TN g
92.88
9.74
4.22
TP g
0.33
0.73
0.32
ce Ac
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t
CO 2 g
34
NA
2.43E-04
1.05E-04
T-Cd g
5.92E-02
2.43E-03
1.05E-03
T-Cr g
1.46E+00
2.43E-02
T-As g
NA
2.43E-02
T-Pb g
8.07E-01
2.43E-02
cr ip 1.05E-02
us
1.05E-02
M an
1.05E-02
Note:
2.
a1
ed
1. a (Hong et al. 2010; Wei et al., 2009; Zhou et al., 2012).
Clay is utilized for constructing the top cover, daily cover, and bottom layer of sanitary
pt
landfill, and their corresponding thickness is 100 cm, 60 cm (4 landfill cells on each vertical height), and 100 cm, respectively. For the landfills in scenario 1, 2, and 3, their clay height is
ce
assumed to be 100 cm, 260 cm, and 260 cm, respectively. 3. a2 The diesel consumption used in the unsanitary landfills in scenario 1 is assumed to be half of
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t
T-Hg g
the sanitary landfills (scenario 2-3). 4. b (Wei et al., 2009; Zhou et al., 2012), and the gas emissions of the sanitary landfill is computed with the LandGem model (Environmental Protection Agency (EPA) et al., 2005).
35
5. c (Guan et al., 2012a; Guan et al., 2012b; MEP of China and GAQSIQ of China, 2008; Prechthai et al., 2008; Wei et al., 2009).
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cr ip
t
6. NA means not available.
36
Table 4. Financial cost and benefit of scenarios a Scenario
1
2
3
t 4.3
Electricity generation —
ed
Revenue
pt
Compost —
ce
Electricity —
Recyclable commodities —
Net cost
cr ip
Operation & maintenance —
8.5
us
5.7
M an
Civil construction work 3.0
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Investment cost
-3.0
22.6
1.7
0.7
—
4.2
3.7
1.6
—
10.3
-8.0
-15.7
Note: 1. The unit is US$/ton of RDW/year at 1 US$=6.20 Chinese yuan (CNY).
37
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t
2. a The data on costs and benefits of landfill are referred from Zhuang (2007).
38
Figure 1. Comparison of waste components between RDW and MSW. Note:
t
1. The harmful waste is not accounted in this comparison, whose value is usually too small (less
cr ip
than 0.1%).
2. The data of Zhejiang RDW are summarized from the following references (Cai, 2012; Chen et
us
M an
al., 2011; Ma et al., 2012; CMEP of China, 2007; Qiu, 2008; Shan et al., 2009). 3. The data of Zhejiang MSW are summarized from the references (He et al., 2004; Zhuang et al.,
ce
pt
ed
2008).
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al., 2010; Cheng and Hu, 2010; Guan et al., 2013; Guan et al., 2012a; Guan et al., 2012b; Guan et
39
Figure 2. Rural population in Zhejiang Province Note: The Hill 1 Fit is the built-in equation of Origin 8.0. The red squares refer to these points that
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cr ip
t
are not included in the statistical regression.
40
Figure 3. Rural domestic waste management in Zhejiang Province, China. Note:
t
1. The current situation of rural domestic waste management is based on the following references
Liu et al., 2005; Luo, 2006; Wu et al., 2006; Ye and Qin, 2008).
cr ip
(Guan et al., 2013; Guan et al., 2012a; Guan et al., 2012b; Guan et al., 2012c; Guan et al., 2011;
us
ce
pt
ed
M an
second recycling point, R3=the third recycling point, and R4=the fourth recycling point.
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2. The R1 to R4 indicate the recycling point of RDW. R1=the first recycling point, R2=the
41
Figure 4. Relationship between disposal ratio and income per capita of RDW in Zhejiang Province, China
t
Note:
cr ip
(a): 1) The number in bracket is the total disposal ratio, which is the sum of the disposal ratios of village, town and county level. 2) The mean value of disposal ratio of village, town and county is
us
summarized from the reference (Shan et al., 2009).
ce
pt
ed
Zhejiang Province, 1986-2012).
M an
(b): The data is summarized from the following references (Shan et al., 2009; Statistical Bureau of
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20.7±17.5%, 16.8±15.5% and 17.3±8.0%, respectively. 3) The data is restructured and
42
Figure 5. Schematic diagram boundaries for the Scenario 1, 2 and 3 Note:
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t
1. The waste is measure with wet weight (water content 53.4%). 2. OM (organic material).
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3. HMs (hazardous materials).
43
Journal of the Air & Waste Management Association Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uawm20
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Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy a
b
c
d
d
Yidong Guan , Yuan Zhang , Dongye Zhao , Xiaofeng Huang & Haini Li a
Jiangsu Provincial Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Department of Environmental Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China b
School of Environmental Science and Engineering, Suzhou University of Science and Technology, 215009, Suzhou, Peoples R. China c
Environmental Engineering Program, Department of Civil Engineering, Auburn University, Auburn, AL 36849, USA d
Wuxi Taihu Lake Management Company Limited, Wuxi 214063, China Accepted author version posted online: 06 Feb 2015.
To cite this article: Yidong Guan, Yuan Zhang, Dongye Zhao, Xiaofeng Huang & Haini Li (2015): Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy, Journal of the Air & Waste Management Association, DOI: 10.1080/10962247.2015.1010751 To link to this article: http://dx.doi.org/10.1080/10962247.2015.1010751
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Yidong Guan1, †, Yuan Zhang2, Dongye Zhao†, Xiaofeng Huang3, Haini Li3
t
Rural domestic waste management in Zhejiang Province, China: Characteristics, current practices, and an improved strategy 1, †
Yidong Guan (), Corresponding author
us
Email: [email protected] 2
M an
School of Environmental Science and Engineering, Suzhou University of Science and Technology, 215009, Suzhou, Peoples R. China E-mail address: [email protected] (or [email protected]) †
Dongye Zhao (), Co-corresponding author, Environmental Engineering Program, Department of Civil Engineering, Auburn University, Auburn, AL 36849, USA
ed
Email: [email protected]; 3
Wuxi Taihu Lake Management Company Limited, Wuxi 214063, China
pt
E-mail address: [email protected] (Xiaofeng Huang) and [email protected] (Haini Li) ABOUT THE AUTHORS
ce
Yidong Guan is a current visiting scholar in the Department of Civil Engineering, Auburn University, and a lecturer in Nanjing University of Information Science and Technology. Yuan Zhang is a lecturer at the Suzhou University of Science and Technology.
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Jiangsu Provincial Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Department of Environmental Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dongye Zhao is a Professor in the Department of Civil Engineering, Auburn University. Xiaofeng Huang is a research Professor at the Wuxi Taihu Lake Management Company Limited. Haini Li is an engineer at the Wuxi Taihu Lake Management Company Limited.
1
Impact statement
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t
Rural domestic waste (RDW) is affecting 720 million people in China and over 3221 million people worldwide. Consequently, handling and disposal of RDW has serious health implications to rural dwellers and the eco-systems. This study offers a systemantic and quantitative overview and analysis of historical data on RDW production and management practices in a prototype region in China, which is confronted with great environmental challenges associated with RDW. Then we predicted future production of RDW and proposed a sustainable RDW management strategy, which holds the promise to greatly mitigate the mounting environmental pressure associated with RDW and provides a science-based guidance for decision makers and practitioners for assuring rapid yet “green” economic development.
us
Lack of access to adequate sanitation facilities has serious health implications for rural dwellers
M an
and can degrade the eco-systems. This study offers a systemantic and quantitative overview of historical data on rural domestic waste (RDW) production and past and current management practices in a prototype region in China, where rural areas are undergoing rapid urbanization and are confronted with great environmental challenges associated with poor RDW management
ed
practices. The results indicate that RDW is characterized with a large fraction of kitchen waste (42.9%) and high water content (53.4%). The RDW generation (RDWG) per capita between
pt
2012 and 2020 is estimated to increase from 0.68 to 1.01 kg/d-cap. The Hill 1 model is able to
ce
adequately simulate/project the population growth in rural area from 1993 to 2020. The annual RDWG in the region is estimated to double from 6,033,000 tons/year in 2008 to 12,030,000 tons/year by 2020. By comparing three RDW management scenarios based on the life cycle
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ABSTRACT
inventory approach and cost-benefit analysis, it is strongly recommended to upgrade the present Scenario 2 (sanitary landfill treatment) to Scenario 3 (source separation followed by composting and landfill of RDW) to significantly reduce the ecological footprint and to improve the cost-effectiveness and long-term sustainability.
2
Keywords: Domestic waste, solid waste, waste management, outdoor burning, landfill, waste disposal, source separation
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INTRODUCTION Over 3221 million people live in rural areas worldwide, accounting for 45.4% of total world
us
been associated with various health risk and ecological degradation in the rural areas (Minh et al., 2003; Liu et al., 2005; Li et al., 2011), which is considered a major factor that hampers the launch
M an
of the Millennium Development Goals (MDGs) of United Nations. China is the largest developing country in the world, and the GDP (gross domestic product) of China has reached the second in the world after more than thirty years of continued rapid economic growth. While China is
ed
experiencing the sheer transition from an agriculture-based economy to an industry-based economy, the resulting environmental consequences have been challenging, and are believed to
pt
limit further sustainable development. Of the tremendous amounts of rural domestic waste (RDW) produced annually, it is estimated that about 1×108 tons of RDW was abandoned carelessly
ce
without proper treatment (Yao et al., 2009), which poses a long-term threat to the hygiene of rural residents and the ecological health. Consequently, more effective strategy for management of RDW is of dire need to sustain the high economic growth rate while mitigating the environmental
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population in 2014 (United Nations, 2014). However, lack of access to sanitation facilities has
impacts.
Traditionally, the social and economic development in rural areas in China is far behind the urban areas. As a result, municipal solid waste management (MSWM), also known as solid waste
3
management (SWM), is only practiced in cities, which barely addresses SWM in rural regions (Guan et al., 2011; Guan et al., 2012a; Guan et al., 2012b; Guan et al., 2013). Due to the Hukou (household registration) system, the Chinese population is strictly divided into two categories: the
t
urban residents and the rural residents. While MSWM in cities has been extensively researched
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(Wang and Nie, 2001; Li et al., 2009; Chen et al., 2010b; Cheng and Hu, 2010; Zhang et al., 2010; Tai et al., 2011), there has been very little information available on the rural domestic waste
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disposal of RDW (Guan et al., 2012a; Guan et al., 2012b). Moreover, the rapid and mounting
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urbanization further exerts growing demand for more effective management measures for RDW. In this context, we chose Zhejiang Province, a rapidly industrializing region in China, as the representative region. This region hosts a population of 46.8 million, which is close to that of Spain, but with a land area of about 20.1% of Spain. As a frontier in China’s booming economy,
ed
this region has been confronted with serious environmental consequences and challenges,
pt
especially with respect to RDW. The objectives of this study were to: 1) investigate and systematically analyze the overall RDW data and RDWM practices in Zhejiang Province,
ce
including the waste composition, production, disposal and management; and 2) explore a more sustainable mid- to long-term strategy by means of life cycle inventory (LCI) and cost-benefit analyses to meet the challenges posed by the high volume of RDWs and the inappropriate handling
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management (RDWM) in China. This deficiency often leads to inappropriate handling and
and disposal practices. Here, three RDWM scenarios were to be compared through coupled LCI and cost-benefit analyses, including Scenario 1 (unsanitary landfill and outdoor burning), Scenario 2 (sanitary landfill treatment), and Scenario 3 (source separation followed by composting and landfill).
4
The information obtained will facilitate the development of a more cost-effective and implementable RDWM strategy in Zhejiang Province, which also serves as a direct guidance for RDWM in the extensive rural areas in China. To our knowledge, this is one of very few systematic
t
works in a large-province scale about RDW characterization and production (including prediction
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of future RDW generation) in China, and this is the first study to apply the integrated LCI and
cost-benefit approach to analyze various RDWM practices, which is of broader practical value
us
rural areas, accounting for 22.3% of rural population worldwide (United Nations, 2014),
of the United Nations.
ed
METHODOLOGY
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improving the RDW management in China will greatly facilitate the implementation of the MDGs
The State of Zhejiang Province
pt
Zhejiang Province is situated on the southeast region of China, covering a land area of 101.8×103
ce
square kilometers (mountainous and hilly regions amounting to 70.4%). The total population is about 46.8 million, of which 42.4% are rural residents. The GDP for 2012 was 550.2 billion US dollars, of which the agricultural GDP accounted for 4.8%. GDP per capita is about 10059 US
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beyond the studied region. Given that more than half population (720 million) in China live in
dollars (1 US dollar = 6.3 Yuan in 2012), and the net income per capita was about 5484 US dollars for the urban residents and about 2309 US dollars for the rural residents. The annual average precipitation is 1353 mm, and the annual average temperature is about 17.8 oC.
5
RDW Data Acquisition and Processing The available RDW data has been limited, and most of the relevant data owned by the relevant
t
government departments or the associated agencies are not accessible for the public. In this work,
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the data were collected from three major sources: 1) peer-reviewed credible publications of other researchers, including books, journal articles and relevant research notes and reports; 2) published
us
information collected from government sponsored projects e.g.(Guan et al., 2011; Guan et al.,
M an
2012a; Guan et al., 2012b; Guan et al., 2012c; Guan et al., 2013), which allowed researchers limited access to the government files. To assure data quality and credibility, cautions were exercised by analyzing the reliability of waste-sampling procedures, cross-comparing relevant literatures or data from different sources, and cross-checking with other independent researches. In
ed
this work, the information on RDW in Zhejiang Province was collected from the following references and bibliographies: (CMEP in China, 2007; MEP of China and GAQSIQ of China,
pt
2008; Qiu, 2008; Shan et al., 2009; Chen et al., 2010a; Cheng and Hu, 2010; Guan et al., 2011; Cai, 2012; Guan et al., 2012a; Guan et al., 2012b; Ma et al., 2012; Guan et al., 2013). Data on municipal
ce
solid wastes were obtained from the references: (Wang and Nie, 2001; Xiao et al., 2007; Zhuang, 2007; Prechthai et al., 2008; Li et al., 2009; Chen et al., 2010b; Cheng and Hu, 2010; Zhang et al.,
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government documents from the national and provincial authorities, and 3) our prior work and
2010; Tai et al., 2011). Data processing and preliminary statistical analysis were carried out using Microsoft Excel 2003 (Microsoft Corporation, USA). Non-linear regression analyses were performed to predict the rural population growth in Zhejiang Province using Origin 8.0 (OriginLab Corporation, USA) and Data
6
Processing System (DPS) software V13.5 (Tang and Zhang, 2013), which have been considered a reasonable modeling method (Beigl et al., 2008). Model error in the regression analysis is computed as the ratio of absolute difference between the simulation value and the practical value
t
divided by the practical value. In this work, the data are presented as mean ± standard deviation
cr ip
(SD), and the significance level (p value) is set to 0.05.
us
The amount of waste generated is proportional to the population and the mean living standards of
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the people (Daskalopoulos et al., 1998). By combining the RDW generation (RDWG) per capita and the population in Zhejiang Province, we obtain: 4
Annual RDWG in Zhejiang (10 • t/year)
4
Average RDWG per capita per day (kg/capita/d) × 365 × Population (10 )
(1)
1000
ed
=
Average RDWG per capita per day in the next year (kg/capita/d)
(2)
pt
= (1 + annual growth rate of RDWG (%)) • (Average RDWG in current year (kg/capita/d))
ce
The RDWG per capita is computed based on the available RDWG data and following the analogy-related method (Beigl et al., 2004; Nie et al., 2005), and such analogy-related estimation has been applied in many fast-growing regions, such as Foshan city in China (Nie et al., 2005) and
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Calculation of Waste Generation
central and east European cities (Beigl et al., 2004). The population growth was estimated by using the historical statistical data, which could be accessed from the Statistical Bureau of Zhejiang Province (1986-2012). Section below gives details on determination of RDWG and the annual growth rate of RDWG.
7
CHARACTERISTICS
AND
GENERATION
OF
t
RDW
cr ip
RDW Characteristics
us
waste, plastics, paper and fibers account for 42.9%, 11.2%, 8.1%, 7.8% and 5.6% of the RDW, respectively. In addition, combustible wastes make up 75.5%, recyclable wastes 24.8%, and
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compostable wastes 54.1%. The typical average water content of RDW is 53.4%. Here Table 1
Figure 1 compares the components of RDW and MSW in Zhejiang. It is evident from Table 1 and
ed
Figure 1 that the kitchen wastes makes up a large part of the total Zhejiang RDW and MSW. This
pt
can be attributed to the diet habits in Zhejiang, where unprocessed and unpackaged vegetables and fruits are generally consumed, which results in the high level of organic matter content in the
ce
domestic wastes. The high organic matter content is coupled with the high water content for both RDW and MSW (above 50%), which results in the low calories of the wastes, and thus, prohibits effective waste incineration without the addition of costly fossil fuels. This is generally in
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As shown in Table 1, organic materials account for 70% of the RDW, and the kitchen waste, yard
agreement with the reported data on MSW in China (Wang and Nie, 2001; Zhang et al., 2010).
Here Figure 1
8
Nevertheless, there are also remarkable discrepancies of waste characteristics between the RDW and MSW (Figure 1). For instance, the average amounts of yard wastes and inert residue of the Zhejiang RDW are much higher than those of the Zhejiang MSW (He et al., 2004; Zhuang et al.,
t
2008), whereas the portion of kitchen wastes in the RDW is about 76.3% of that in the MSW.
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These variations are mainly due to the partial commingling of agricultural wastes with RDW. Additionally, the difference in living standard and habits, and environmental consciousness
us M an
RDW Generation
The waste generation is affected by a number of factors, including population, income, living habits, education levels, cultural and religious beliefs, social and public attitudes, and climate (Bandara et al., 2007). For rapidly developing areas, the prediction of solid waste generation is
ed
always quite challenging, especially in the fast-growing rural region like Zhejiang Province, where the systematical historical record of tonnage generated has been lacking. Therefore, we employed
pt
the analogy-related method, which integrates the effects of socio-demographic and economic
ce
perspectives, to estimate the RDW generation (Beigl et al., 2004; Nie et al., 2005). To this end, we chose the Yangtze River Delta Region (including Zhejiang and Jiangsu Province, and the city of Shanghai) for the estimate. The region shares the similar geographical characteristics, living habits
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between the urban and rural residents may also play a role.
and historical culture. The mean value of RDWG per capita of Zhejiang in 2008 was 0.50 kg/cap/d according to Chen et al. (2010), which has been the only province-wide study on the RDWG in Zhejiang. Consequently, this 2008 RDWG value (0.50 kg/per/d) was utilized as the initial value to calculate the RDWG in
9
Zhejiang from 2009 to 2020, using Equations (1) and (2). As stated above, the annual growth rate of RDWG in Zhejiang was first calculated by using the solid waste generation rates in the Yangtze River Delta Region based on the analogy-related method (Beigl et al., 2004; Nie et al., 2005).
t
Based on the available data, the annual growth rate of MSW generation in Zhejiang from 2001 to
cr ip
2010, Jiangsu (represented by Suzhou) from 1995 to 2005, and Shanghai from 2001 to 2010, was
4.1%, 6.4% and 4.0%, respectively (Statistical Bureau of Zhejiang Province, 1986-2012; Zhao and
us
calculation, which is very close to the average growth rate in the region (4.8%). The results (Table
M an
2) indicate that the RDWG per capita in Zhejiang is 0.68 and 1.01 kg/capita/d for 2012 and 2020, respectively. Here Table 2
ed
The rural population growth in Zhejiang Province was taken as another factor in determining the amount of RDWG, as was the case for MSW generation in urban China (Wang and Nie, 2001;
pt
Chen et al., 2010b) and in European countries (Daskalopoulos et al., 1998). Figure 2 shows the
ce
variation of the population from 1986 to 2012. From 1986 to 1996, the rural population experienced an exponential rise followed by a rapid descending process from 1996 to 2007, then a gradual decline from 2007 to 2012. The data in 1996 could be assumed to be the critical point of
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Yang, 2003; Wang et al., 2008). Therefore, a conservative estimate of 5% was used in our
the population dynamics, since this year represented the turning point from the previously increasing trend to the decreasing trend. The following two regression equations were obtained when the population data from 1996 to 2012 were fitted through the Hill 1 non-linear regression and the linear regression, respectively:
10
y = 3578.300 − 302.921 •
x 999.283 2002.179999.2831 + x 999.283
(3)
(p Scenario 3.
Analysis of costs and benefits
us
(sum of capital, operating and maintenance) on a per-tonnage basis (3.0 US$/ton-RDW), as
M an
compared to Scenario 2 (10.7 US$/ton) and Scenario 3 (31.1 US$/ton). This is reasonable given the fact that Scenario 1 offers only simplistic disposal facilities and partial treatment of RDW whereas Scenarios 2 and 3 involve more advanced sanitary landfill facilities, more professional O & M, and more labor for sorting out RDW (Scenario 3). On average, three agricultural workers can
ed
undertake the sorting work of RDW in one village, and the salary is estimated at about 1280 CNY (US $206) per month per capita based on the average minimum wages in Zhejiang Province. In
pt
terms of revenue, Scenarios 1, 2, and 3 may generate an economic return of 0, 3.7, and 4.2
ce
US$/ton-RDW, respectively. Upon deduction of the financial returns, the net cost for Scenarios 1, 2, and 3 are 3.0, 8.0, and 16.6 US$/ton-RDW per year. Given the increasingly stringent environmental regulations and pressure, Scenario 1 had been
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Table 4 compares the costs and benefits of the three scenarios. Scenario 1 offers the lowest cost
phased out, and Scenario 2 as a transitional approach is undergoing close scrutiny. In contrast, Scenario 3 may offer a much sounder solution for the disposal of RDW for its superior environmental benefits and affordable cost. Since less than half of RDW is landfilled, Scenario 3 needs less than half energy and material consumption, occupies less than half of land use, and
19
renders much less waste stream emissions. It should be noted that the environmental damage caused by the contaminant emissions and the value associated with natural resource conservation is not reflected in the cost and benefit analysis in Table 4, which may undermine the practical value
t
of Scenario 3 by underestimating the much higher environmental harm and resource depletion
cr ip
associated with the Scenarios 1 and 2. Furthermore, the saving in land usage and in landfill volume is of particular significance in Zhejiang Province as well as other more developed regions in
us
costly and challenging to locate and build more landfill facilities. Diverting most of the organic
M an
waste from landfill to composting results in much reduced landfill volume, which translates into much reduced landfill leachate and less expenditure in the leachate treatment. The compost product is known to enhance the soil fertility and quality and thus can be conveniently used in agricultural applications. As shown in Figure 3, the waste recyclers would work much more
ed
conveniently when the high water content kitchen wastes and hazardous wastes are separated out, which in turn will promote the recycling activities. In addition, the recycled materials, such as
pt
bottles, plastics, metals, cardboard, paper products etc., can be reclaimed as a profit, which may
ce
grow as the government is further promoting recycles.
CONCLUSIONS
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China. Due to the soaring land price and increasing public opposition, it has become increasingly
This study surveyed and systematically analyzed past and current RDW management practices and proposed a more environmentally sound and cost-effective approach in handling and
disposing of RDW in Zhejiang Province. The major findings are summarized as follows:
20
(1) The typical RDW in Zhejiang Province include kitchen wastes (42.9%), combustible wastes (75.5%), recyclables (24.8%), and compostable wastes (54.1%), and the average water content is 53.4%.
cr ip
t
(2) The RDWG per capita between 2012 and 2020 is estimated to increase from 0.68 to 1.01 kg/d-cap, and the Hill 1 model can be used to simulate/project the population growth in rural
us
thousand tons/year in 2008 to 12030 thousand tons/year by 2020.
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(3) The past (Scenario 1), current (Scenario 2) and future proposed (Scenario 3) RDW management practices are compared based on coupled LCI and cost-benefit analyses. The proposed Scenario 3 (composting and landfill treatment of source separated RDW) represents one of the best RDWM options that represents the best practice for future RDW disposal for its
ed
economic competitiveness and least environmental impacts. Based on local economic development and environmental impacts of RDW, it is recommended to upgrade the present
pt
RDWM (sanitary landfill treatment of RDW, scenario 2) to Scenario 3 to minimize the ecological
ce
footprint at an acceptable expenditure and toward long-term sustainable development. ACKNOWLEDGMENTS
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Zhejiang from 1986 to 2020. The annual RDWG in Zhejiang is estimated to double from 6033
The research was partially supported by a scholarship from Demonstration Base of Water Quality Improvement and Ecosystem Restoration at Lakeside Zone of Taihu Xincheng (2012ZX07101-013-02), Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD), Nanjing University of Information Science and Technology (NUIST, No.
21
2013x010, S8111028001). The authors also thank the China Section of the Air & Waste Management Association for generously covering the cost of the page charges, which makes the publication of this paper possible.
cr ip
t
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Table 1. Descriptive statistical characteristics of rural solid waste (RDW) composition in Zhejiang Province, China (n = 24)
20.0
11.2
8.5
8.1
±19.3
±8.0
±6.9 ±6.0
±3.0
Median
42.7
18.7
25 Percentiles
26.6
12.2
75 Percentiles
63.1
5.6
2.4
0.9
0.2 53.4±9.
waste
±2.1
content
±4.4 ±1.3 ±1.0 ±0.2
0
7.4
8.0
8.0
5.4
2.0
0.8
0.2
55.8
7.7
2.2
5.9
6.4
1.6
1.5
0.1
0.0
47.8
9.4
8.8
8.0
3.2
1.3
0.3
58.3
ed 27.1
s
9.9
12.5 14.4
pt
Mean ± S.D.
7.8
Water
t
42.9
ul
us
waste waste ue
M an
waste
Metal
cr ip
Plastics Paper Fibers Glass
Maximum—Min 70.3—1 35.2— 24.9 19.1 14.4— 13.2— 17.1 4.9— 2.9— 0.3— 35.9—6 0.9
ce
imum
7.8
—0
—0
3.3
3.7
—0
0.3
Note:
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Kitchen Inert Yard Resid
Harmf
1. The components are calculated on the mass ratio of wet weight (wt, %). 2. S.D. denotes Standard Deviation.
29
0.1
0
8.0
3. The data is summarized from the following references (Cai, 2012; Chen et al. 2010; Ma et al., 2012; Qiu, 2008). 4. Inert waste is mainly composed of the abandoned brick, furnace cinder and pebble, etc. Residue
us M an ed pt ce Ac
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cr ip
t
is the leftover waste. Harmful waste includes the paint bottles, batteries and pesticides bottles, etc.
30
Table 2. Estimation of annual RDW generated in Rural Zhejiang 1
RDW RDW generated per 4
2008
603.3
a
2009
631.4
0.527
2010
661.9
2011
778.0
2012
815.9
2013
855.6
(104)
3292
us
0.502
M an
3282
3279
0.650
3279
ed
0.553
3278
pt
0.682
3274
898.3
0.752
3273
ce
0.716
2014
Province
cr ip
generated (10 ·t/year) capita (kg/capita/d)
Zhejiang
t
Annual
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Year
Population in Rural
2015
943.5
0.790
3272
2016
989.9
0.829
3272
31
1040
0.871
3271
2018
1091
0.914
3271
2019
1146
0.960
3271
2020
1203
1.01
3270
cr ip
us
M an
Note: a indicates the value of annual RDW generation in 2008 is recalculated from Chen et al. (2010). 1 means the rural population in Zhejiang Province from 2008-2012 based on data from the
ce
pt
ed
Statistical Bureau of Zhejiang Province (1986-2012).
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t
2017
32
Table 3. LCI of three scenarios Input flow (per ton of Unit
Scenario 1
Scenario 2
Clay a1 g
87615
227800
Diesel kg
0.90
2.01
Scenario 3
ce
Electricity recovery kwh
us 2.53
2.01E-02
8.72E-03
0
4.03E-05
1.74E-05
2.24E-01
8.25
0
59.77
25.88
102965
113994
49359
0
pt
Electricity consumption kwh
98637
0
ed
LDPE m3
M an
Pesticide kg
Gas emissions b
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Material consumption a
cr ip
t
waste)
CH 4 g
33
354739
242265
104904
NO x g
333
37
16.08
VOC g
256
11
H2S g
546
607
SO 2 g
56
7
7.67
pt
SS g
cr ip 4.77
262.86
us
64.31
ed
COD g
M an
Water emissions c
2.95
24.34
10.54
7.30
3.16
NH 3 -N g
28.72
6.09
2.64
TN g
92.88
9.74
4.22
TP g
0.33
0.73
0.32
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t
CO 2 g
34
NA
2.43E-04
1.05E-04
T-Cd g
5.92E-02
2.43E-03
1.05E-03
T-Cr g
1.46E+00
2.43E-02
T-As g
NA
2.43E-02
T-Pb g
8.07E-01
2.43E-02
cr ip 1.05E-02
us
1.05E-02
M an
1.05E-02
Note:
2.
a1
ed
1. a (Hong et al. 2010; Wei et al., 2009; Zhou et al., 2012).
Clay is utilized for constructing the top cover, daily cover, and bottom layer of sanitary
pt
landfill, and their corresponding thickness is 100 cm, 60 cm (4 landfill cells on each vertical height), and 100 cm, respectively. For the landfills in scenario 1, 2, and 3, their clay height is
ce
assumed to be 100 cm, 260 cm, and 260 cm, respectively. 3. a2 The diesel consumption used in the unsanitary landfills in scenario 1 is assumed to be half of
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t
T-Hg g
the sanitary landfills (scenario 2-3). 4. b (Wei et al., 2009; Zhou et al., 2012), and the gas emissions of the sanitary landfill is computed with the LandGem model (Environmental Protection Agency (EPA) et al., 2005).
35
5. c (Guan et al., 2012a; Guan et al., 2012b; MEP of China and GAQSIQ of China, 2008; Prechthai et al., 2008; Wei et al., 2009).
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cr ip
t
6. NA means not available.
36
Table 4. Financial cost and benefit of scenarios a Scenario
1
2
3
t 4.3
Electricity generation —
ed
Revenue
pt
Compost —
ce
Electricity —
Recyclable commodities —
Net cost
cr ip
Operation & maintenance —
8.5
us
5.7
M an
Civil construction work 3.0
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Investment cost
-3.0
22.6
1.7
0.7
—
4.2
3.7
1.6
—
10.3
-8.0
-15.7
Note: 1. The unit is US$/ton of RDW/year at 1 US$=6.20 Chinese yuan (CNY).
37
us M an ed pt ce Ac
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cr ip
t
2. a The data on costs and benefits of landfill are referred from Zhuang (2007).
38
Figure 1. Comparison of waste components between RDW and MSW. Note:
t
1. The harmful waste is not accounted in this comparison, whose value is usually too small (less
cr ip
than 0.1%).
2. The data of Zhejiang RDW are summarized from the following references (Cai, 2012; Chen et
us
M an
al., 2011; Ma et al., 2012; CMEP of China, 2007; Qiu, 2008; Shan et al., 2009). 3. The data of Zhejiang MSW are summarized from the references (He et al., 2004; Zhuang et al.,
ce
pt
ed
2008).
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al., 2010; Cheng and Hu, 2010; Guan et al., 2013; Guan et al., 2012a; Guan et al., 2012b; Guan et
39
Figure 2. Rural population in Zhejiang Province Note: The Hill 1 Fit is the built-in equation of Origin 8.0. The red squares refer to these points that
us M an ed pt ce Ac
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cr ip
t
are not included in the statistical regression.
40
Figure 3. Rural domestic waste management in Zhejiang Province, China. Note:
t
1. The current situation of rural domestic waste management is based on the following references
Liu et al., 2005; Luo, 2006; Wu et al., 2006; Ye and Qin, 2008).
cr ip
(Guan et al., 2013; Guan et al., 2012a; Guan et al., 2012b; Guan et al., 2012c; Guan et al., 2011;
us
ce
pt
ed
M an
second recycling point, R3=the third recycling point, and R4=the fourth recycling point.
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2. The R1 to R4 indicate the recycling point of RDW. R1=the first recycling point, R2=the
41
Figure 4. Relationship between disposal ratio and income per capita of RDW in Zhejiang Province, China
t
Note:
cr ip
(a): 1) The number in bracket is the total disposal ratio, which is the sum of the disposal ratios of village, town and county level. 2) The mean value of disposal ratio of village, town and county is
us
summarized from the reference (Shan et al., 2009).
ce
pt
ed
Zhejiang Province, 1986-2012).
M an
(b): The data is summarized from the following references (Shan et al., 2009; Statistical Bureau of
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20.7±17.5%, 16.8±15.5% and 17.3±8.0%, respectively. 3) The data is restructured and
42
Figure 5. Schematic diagram boundaries for the Scenario 1, 2 and 3 Note:
cr ip
t
1. The waste is measure with wet weight (water content 53.4%). 2. OM (organic material).
us M an ed pt ce Ac
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3. HMs (hazardous materials).
43
