healthcare waste management a state of the art literature review

Title: Healthcare waste management: a state-of-the-art literature review

Authors : Ankur Chauhan; Amol Singh

Addresses : Department of Operations Management, Indian Institute of Management Rohtak, MD University Campus, 124001 Rohtak, Haryana, India ' Department of Operations Management, Indian Institute of Management Rohtak, MD University Campus, 124001 Rohtak, Haryana, India

Abstract : Enormous amount of waste generation is a critical issue across the world. This issue becomes more vital when various types of hazardous waste gets mixed up with general waste. One such hazardous waste is healthcare waste; this waste is generated in hospitals during different healthcare activities such as pathological diagnostic, surgery, etc. Therefore, to take an account on the issue of healthcare waste management the review of literature has been carried out on the basis of the classification given in Figure 1. This review article provides numerous future research directions including the requirement of more in-depth application of operations management tools and techniques. Additionally, the un-addressed issues related to a healthcare waste management i.e., inventory management, warehousing, bins allocation, routing and transportation, also needs attention for an effective management of healthcare waste.

Keywords : healthcare waste management; HCWM; practices; policies; literature review; waste disposal; optimisation; reverse logistics; recycling; medical waste; hospitals; operations management; inventory management; warehousing; bin allocation; routing; transport.

DOI : 10.1504/IJEWM.2016.080400

International Journal of Environment and Waste Management, 2016 Vol.18 No.2, pp.120 - 144

Received: 31 Dec 2015 Accepted: 02 Jul 2016 Published online: 21 Nov 2016 *

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Open Access

Peer-reviewed

Research Article

Knowledge, attitudes, and practices of health care waste management among Zambian health care workers

Roles Conceptualization, Formal analysis, Writing – original draft

Affiliation National Health Research Authority, Lusaka, Zambia

Roles Data curation, Formal analysis, Software, Writing – review & editing

* E-mail: [email protected]

Roles Conceptualization, Formal analysis

Roles Conceptualization, Writing – review & editing

Roles Validation, Writing – review & editing

Affiliation Ministry of Health, Lusaka, Zambia

Roles Supervision, Writing – review & editing

Roles Supervision, Writing – original draft, Writing – review & editing

• Colleen M. Leonard, 
• Chipwaila Choolwe Chunga, 
• Justine M. Nkaama, 
• Kutha Banda, 
• Chilekwa Mibenge, 
• Victor Chalwe, 
• Godfrey Biemba, 
• Sandra Chilengi-Sakala, 
• Florence Kabinga Mwale

• Published: June 22, 2022
• https://doi.org/10.1371/journal.pgph.0000655
• Reader Comments

Poor management of health care waste poses a serious threat to the health of health care workers, patients and communities. In developing countries, adequate health care waste management (HCWM) is often a challenge. To address this, the Zambian Health Services Improvement Project with HCWM as a component, was implemented in five Zambian provinces (Luapula, Muchinga, Northern, North-Western and Western Provinces), under which this cross-sectional study was conducted to identify the knowledge, attitudes, and practices of health care workers on HCWM. Fifty government hospitals and health posts from five provinces in Zambia were included in the study. Data was collected using a mixed-methods approach, which included surveys with health care workers (n = 394), in-depth interviews (n = 47) with health officials at the provincial, district, and facility levels, and observational checklists (n = 86). Overall, knowledge of proper waste segregation was average (mean knowledge score 4.7/ 7). HCWM knowledge varied significantly by job position (p = 0.02) and not by facility level, years of service, nor prior training. Only 37.3% of respondents recalled having received any sort of HCWM training. Poor waste segregation practice was found as only 56.9% of the facilities used an infectious waste bag (yellow, red or orange bin liner) and a black bag for general waste. This study revealed that only 43% of facilities had a functional incinerator on site for infectious waste treatment. Needle sticks were alarmingly high with 31.3% of all respondents reporting a prior needle stick. The system of HCWM remains below national and international standards in health facilities in Zambia. It is imperative that all health care workers undergo comprehensive HCWM training and sufficient health care waste commodities are supplied to all health facility levels in Zambia.

Citation: Leonard CM, Chunga CC, Nkaama JM, Banda K, Mibenge C, Chalwe V, et al. (2022) Knowledge, attitudes, and practices of health care waste management among Zambian health care workers. PLOS Glob Public Health 2(6): e0000655. https://doi.org/10.1371/journal.pgph.0000655

Editor: Carmen García Peña, Instituto Nacional de Geriatria, MEXICO

Received: January 17, 2022; Accepted: May 26, 2022; Published: June 22, 2022

Copyright: © 2022 Leonard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are in the manuscript and/or supporting information files.

Funding: This work was supported by the World Bank under the Zambian Health Services Improvement Project ( https://projects.worldbank.org/en/projects-operations/project-detail/P145335 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare that no competing interests exist.

Introduction

There has been a rapid growth of health care waste in developing countries over recent years due to increased access to medical services; therefore, proper health care waste management (HCWM) is essential [ 1 ]. Poor management of health care waste can lead to adverse health and environmental effects [ 2 , 3 ]. At each stage of the HCWM system (segregation, storage, transport, treatment and disposal), there is potential to spread infectious diseases [ 2 , 4 ]. Poor HCWM can impact the health of health care workers, patients, and even communities, especially in low- and middle-income countries [ 5 ]. Furthermore, inadequate treatment of medical waste, such as openly burning waste, poses serious environmental risks through harmful emissions to the surrounding community [ 6 – 8 ].

Health care waste may be categorized as either hazardous or non-hazardous. Hazardous waste consists of infectious materials, sharps, chemical waste, pharmaceuticals, and radioactive waste [ 4 ]. Infectious waste includes waste contaminated with blood or other bodily fluids, cultures from laboratory work, and waste items from patients, including but not limited to: bandages, swabs, discarded tissue samples, blood microscopy slides, and disposable medical devices [ 9 ]. Non-hazardous waste, such as plastic packaging, paper and office products, is waste that does not pose any biological, chemical, radioactive or physical harm [ 9 ]. It is estimated that globally about 15% of the total waste generated in Health Care Facilities (HCFs) is hazardous [ 9 ]. Hazardous waste poses occupational health and safety risks, and environmental pollution to the surrounding community if not disposed of properly [ 2 ]. Infectious waste contaminated with Human Immunodeficiency Virus (HIV), Hepatitis B and C viruses can pose harm to health care providers. According to the World Health Organization, of the approximate 35 million health workers worldwide, around 3 million (9.4%) are exposed to blood borne pathogens through a percutaneous injury annually (e.g. contaminated needle stick injuries) [ 10 ]. Percutaneous injuries among health workers can occur as a result of mishandling sharps as well as poor practices like recapping used needles [ 10 ]. Further, in 2000, the WHO indicated that inadequate disposal, handling and reuse/recycling of contaminated syringes and other waste items resulted in 21 million Hepatitis B infections (32% of all new infections), two million Hepatitis C infections (40% of all new infections) and 260,000 HIV infections globally (5% of all new infections) [ 11 ]. Poor handling and disposal of medical waste not only impacts the health of health care workers, but also that of patients, visitors, and non-hospital staff involved in the handling and treatment of infectious health care waste. In addition, many developing countries face HCWM burdens; consequently, approximately 50% or more of the global population is exposed to environmental, occupational and public health risks from poor HCWM [ 5 ].

In sub-Saharan Africa, the state of HCWM is often below international standards [ 12 , 13 ]. A study in Cameroon, found that health care waste collection and handling systems, including containers and bins for segregated wastes, are generally in a poor state [ 3 ]. There is a lack of research on the state of HCWM in Zambia. In Zambia, there are three different health facility levels: health centres/posts, district and regional level hospitals. Each level (and unique facility) has different resources and staff available. Therefore, it is likely that HCWM attitudes and practices vary widely between facilities.

The National Health Care Waste Management Plan seeks to establish a sustainable HCWM system that takes into account environmentally sound practices, principles and commitments, including organizing HCWM options that are technically, socially, and economically appropriate [ 14 ]. This aims to reduce the transmission of communicable diseases through proper disposal of health care waste at health care facilities and disposal centres. Improved waste management practices also have important benefits at the national level, which include improved environmental health due to reduced water and soil pollution of nearby communities; creation of job and livelihood opportunities in the area of waste management, treatment and disposal; and a reduction in the overall costs for waste management.

It is important to understand the gaps in attitudes, knowledge and practice surrounding HCWM in Zambia. This study was conducted on a wide scale investigating health care waste management at three different health facility levels within five provinces of Zambia. We conducted a cross-sectional study to identify the knowledge, attitudes, and practices (KAPs) of health care workers on HCWM and to explore the individual and institutional factors associated with proper HCWM practices. To our knowledge, this is the first published study of its kind assessing the knowledge, attitudes and practices of HCWM in health facilities across various provinces in Zambia.

Materials and methods

This cross-sectional, mixed-methods study was conducted in November 2018 in five Zambian provinces (Luapula, Muchinga, Northern, North-Western and Western Provinces). These provinces were chosen because the Zambian Health Services Improvement Project (ZHSIP) was being implemented there. A two-stage sampling method was used to select the study hospital and health centers in each province. The sampling frame included all public hospitals at each level, including rural and urban health centres and health posts. In the first stage, two districts in each province were selected using simple random sampling (SRS). In the second stage, the main hospital in each district was purposively selected, then four hospitals or health facilities were selected per district using SRS. This amounted to five facilities per district (10 per province) for a total of 50 facilities visited. One health facility in Northern Province and two facilities in Muchinga Province were purposively selected due to logistical challenges attending other facilities.

Study design and participants

This mixed-methods study had three parts: 1) survey with health care workers to determine their knowledge, attitudes and practices surrounding HCWM ( S1 Text ); 2) in-depth interviews with health officials at the health facility, district and provincial levels to uncover the attitudes towards HCWM and supplement the quantitative data collected from the surveys ( S2 Text ); 3) HCWM facility checklist to observe the current practice of HCWM ( S1 Checklist ). For the survey, health care workers, including doctors, nurses, lab technicians, community health workers, clinical officers, and environmental health specialists were targeted. For the hospitals, multiple wards were visited and a sample of at least five health care workers from each visited ward were surveyed. At a minimum, the lab was visited at each hospital. However, for the urban and rural health centers, all health care workers present were surveyed, including the facility in-charge, nurse(s), environmental health technologist, and community health volunteers (if applicable). The targeted sample size was 410 surveys, which includes a 6% non-response rate ( S3 Text ). For the in-depth interviews, key respondents that work directly or oversee waste management in the province, district and facility levels were purposively selected. At the provinces and districts, the Provincial Health Director, District Health Director, Environmental Health Officer and Health Promotion Officers were interviewed. At the hospitals and health centres, the facility in-charge and Environmental Health Specialists were interviewed. A total of 50 interviews were targeted. Lastly, the facility checklist was used in all the 50 targeted health facilities. For all health centers and health posts, the checklist was administered once whereas for larger health facilities it was administered in each ward. In total, 86 checklists were completed.

Data collection and management

Data collection was conducted by five survey teams, one for each province. The survey data was collected using Open Data Kit (ODK) on a portable tablet, which allows for real-time electronic data capture. Each survey was conducted in-person, on site, with one enumerator and one respondent at a time. The survey can be viewed in the Supplementary materials ( S2 Text ). For the in-depth interviews, a paper in-depth interview guide was used ( S3 Text ). All interviews were recorded and transcribed, unless the interviewee declined to be recorded. Lastly, a checklist was used to observe the HCWM practices and HCWM items present at each facility (S4 Text). The checklist was a spot checklist to record if various waste management items were available and/or functional. At each hospital, one checklist was completed for each ward visited. Participant confidentiality was ensured and no names or identifiable markers were recorded on the data collection forms. Each survey team had a data manager. The data manager was responsible for making sure all data from each tablet was uploaded into the secure server at the end of each day. The data manager also conducted random spot checks with the survey data to flag any mistakes or inconsistencies in the data. The original dataset was password protected and stored on a secure computer.

Data analysis

Descriptive statistics were conducted, including chi-square tests. Chi-square tests of independence were used to compare outcomes across various factors including facility type, position, and years of service. If assumptions were not met, Fischer’s exact test were utilized. A composite variable called knowledge on waste management was derived based on the wastes that belong either in the yellow bag, sharp container, or black bag. Respondents that were able to allocate at least six out of the seven hospital waste locations correctly were classified as having high knowledge on waste management, those who could not were classified as having low knowledge. All statistical analyses were performed using Stata 13 (StataCorp, College Station, Texas, USA) and Microsoft Excel (2016).

For the qualitative analysis of the in-depth interviews, all interview recordings were transcribed by research assistants. Thematic analysis was conducted to identify key factors that were associated with waste management in the health facilities. Four codes were formed (level of knowledge, attitude and practice; socio-demographic factors associated with HCWM; institutional factors; and adherence to policies) to index and identify key emerging themes of the interviews.

Ethics statement

Ethical clearance was obtained from the University of Zambia Biomedical Research Ethics Committee (UNZABREC) and the National Health Research Authority (NHRA) of Zambia. Written informed consent was obtained from each participant prior to starting the survey or interview. All participants were given a copy of the consent form to keep for their own records.

In total, 50 facilities were visited (26% in urban areas). A total of 394 respondents participated in the KAPs survey ( Table 1 ). Most of the respondents were female (58.6%) and the slightly over half of the respondents (51.3%) were from the district hospitals. Nursing was the most common field represented at 36.3% of those surveyed, and the next most common group was the cleaning staff at 13.2%. The lowest representation was from doctors which comprised 2% of all respondents. Half of the respondents were between 20–29 years old. Lastly, the majority of all respondents (59.1%) had one to four years of experience in their current position.

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https://doi.org/10.1371/journal.pgph.0000655.t001

A survey question was administered asking where seven different waste items should be disposed ( S2 Text ). If the respondent answered six or seven correct, then they were deemed to have “high knowledge” of the proper health care waste management practices. Any score lower than six was deemed “low knowledge.” Luapula Province had the highest proportion of respondents (47%, n = 36) with a high knowledge of proper HCWM practices. On the other hand, North-western and Western Provinces had the lowest proportion of respondents with a high knowledge of proper HCWM practices at 26%. Overall, the knowledge of proper HCWM practices was average. The mean knowledge score was 4.7 out of 7 (SE = 0.07). In terms of individuals having a high knowledge of HCWM practices, only 34% of all respondents had a high level of knowledge. The knowledge of waste segregation by waste item was average for all items except the empty intravenous (IV) bag, for which only 24% of respondents knew that the item should be discarded in the domestic bin (black bin liner) ( Table 2 ). Overall, waste segregation knowledge was found to be associated with the position of the health worker ( Table 3 ). For the health workers, there was a wide range of knowledge with the laboratory staff having the highest knowledge and midwives having the lowest. By health facility type, health workers from the regional hospitals were the most knowledgeable and those from the health centres/ health posts had the lowest level of waste disposal knowledge; however, this trend was not statistically significant. Prior training in HCWM was not found to be associated with knowledge of HCWM.

https://doi.org/10.1371/journal.pgph.0000655.t002

https://doi.org/10.1371/journal.pgph.0000655.t003

In terms of training, only 37.3% of respondents recalled having received any sort of waste management training. Like knowledge of HCWM practices, prior training was found to be associated with the position of the health worker. For example, most environmental health staff and cleaners (71% and 50%, respectively) stated that they had received waste management training in the past. For all other positions, less than half of the workers had received waste management training ( Table 4 ).

https://doi.org/10.1371/journal.pgph.0000655.t004

HCWM practice

From the checklist, we assessed the presence of various HCWM items ( Table 5 ). Eighty-six observational checklists were completed in total. When visiting the health facilities and hospital wards, we found that most had bins for segregating waste (89.5%). Yet, bin liners were less common. Only 61.6% of facilities had yellow or red biohazard bin liners for hazardous waste. Even fewer had black bin liners for domestic waste (41.9%). However, in practice, 94% of the staff interviewed say that they segregate different types of waste ( Table 5 ). Almost all facilities and wards (94.2%) had a sharps box on site for the safe disposal of needles and syringes. For safe treatment and disposal of infectious waste, only 43.2% of facilities had a functional incinerator in a secured area on site. For the waste bins, using containers with a lid and bin liner was generally low. It was found to be associated with the type of facility, with the lowest compliance in the district hospitals at 47.7%. Exclusively disposing of infectious waste in a container with a yellow, red, or orange bin liner was also associated with the type of facility, but the highest rate was among the district hospitals ( Table 6 ). In addition, full personal protective equipment (PPE) for waste management staff, timely emptying of storage facilities, and waste transport methods were found to be associated with the type of facility. The regional hospitals were significantly more likely to have full PPE for their waste management staff than the other facility levels. The majority of health facilities stated that their waste storage facility is emptied within 24 hours, but this percentage was the lowest for the health centres/ health posts. For transporting waste to the disposal site, using a wheelchair was the most common alternative method used.

https://doi.org/10.1371/journal.pgph.0000655.t005

https://doi.org/10.1371/journal.pgph.0000655.t006

Only 4.3% of facilities used the WHO recommended color coding for all health care waste, including a brown bin liner for chemical and pharmaceutical waste. Excluding the brown bin liner, 56.9% of facilities use an infectious waste bag (yellow, red or orange) and a black bag for general waste. However, many people interviewed stated that they often label waste containers if they do not have the correct color-coded bin liners. Health workers cited stock-outs as a reason for not having the correct bin liners all the time.

Attitude and practice

In total, 47 in-depth interviews were conducted with health officials at the provincial, district, and facility levels. Overall, all persons interviewed had a positive attitude towards HCWM. Those who were not as knowledgeable on HCWM usually expressed a willingness to learn. There were differences in HCWM practice between the three different facility levels (province, district, and health post) and facilities with and without an environmental health technician (EHT). Those interviewed at the provincial and district levels stated that they conduct HCWM trainings in the form of an orientation for new staff. One of the district health officers stated:

“We ensure that all the staff are given an in-house orientation as they report to a facility to ensure they know how that facility handles waste. We have deliberate policies within our district…I assume even in other districts they have…to ensure the EHT take a leading role in orientation and monitoring the staff handling of waste.”

Therefore, health officers have policies on HCWM and expect the EHT to take the lead in orienting and monitoring staff at all facility levels to ensure proper waste management. However, when respondents were asked if they had received HCWM training in the past only 37.3% of respondents recalled having received any sort of waste management training. Also, usually only the district and provincial hospitals have EHTs, while the health centres/ posts do not have an EHT due to staffing limitations.

Qualitative interviews suggested that the presence of an EHT in health facilities had a positive influence on HCWM. An in-charge at one health facility indicated that they have an EHT who is very involved, especially in waste management. On the other hand, health workers from facilities that do not have EHTs seemed to be less confident in their HCWM practices One in-charge at a rural health center without an EHT said:

“So we have put a staff to take care of waste management… umm… we are lacking basic training because what we use is just… uh…common knowledge… . in disposing of. So we are using what we have, but I think we are lacking basic, uhh…training.”

At the facility level, when there is no EHT, someone is usually assigned to be in charge of waste management. One concern that was identified was that some staff at the rural health centres feel that they are lacking adequate training in health care waste management.

Consequences of poor HCWM

A history of a needle stick was high with 31.3% of all survey respondents reporting a prior needle stick. Of those who had received a needle stick, 59.5% said that it occurred within the first 24 months on the job. The majority (75.4%) of those who experienced a needle stick stated that they reported the needle stick incident to management for mitigation.

Our study reveals that the system of waste management remains below national and international standards in all health facility levels in Zambia. The in-depth interviews with health officials at the provincial, district, and facility levels provide an understanding of the attitudes and policies in place surrounding HCWM. While the surveys give insight into the individual knowledge, comfort level, and practices of HCWM among health workers working at different health facilities. The observational checklists assessed the availability of essential supplies to implement proper HCWM. Overall, we found that although attitudes towards HCWM were generally positive and policies were available at the provincial levels, HCWM knowledge was average and essential supplies were often lacking.

Almost all facilities had bins for disposing infectious waste; however, waste bins with lids and bin liners were not always present. Bin liners and lids limit the exposure of infectious waste items from getting in contact with health workers and patients [ 4 ]. Poor waste segregation practice was found as only 62% of health workers stated that they exclusively place infectious waste in a hazardous waste colored bin. The vast majority of facilities were not compliant with the WHO color scheme for segregating waste, including a brown bin liner for pharmaceutical waste [ 4 ]. Similar findings have been reported from other HCWM studies in sub-Saharan Africa [ 15 , 16 ]. For example, in one Nigerian hospital, only 54% of the health workers were aware of or had seen color coded bins [ 15 ]. In this study, slightly over half (56.9%) of the facilities use an infectious waste bag (yellow, red or orange bin liner) and a black bag for general waste. The district hospitals and health centres/ posts were less likely to have bin liners and color-coded bins compared to the regional hospitals. Procurement of bin liners and lids for each medical waste bin should be a priority, especially for district hospitals and health centres. The waste transport method also varied by facility level. The preferred method of using a wheeled bin was used in less than half of the facilities and hospital wards. In addition, only 43% of all health facilities had a functional incinerator on site; however, this varied greatly by facility level as all four regional hospitals (100%) had a functional incinerator while only 34% of the health centres had one. The remaining facilities often relied on burning their waste in a brick-and-mortar enclosed area. This can cause damage to the environment and create human health problems. Wastes containing polyvinyl chloride and other plastics in IV and blood bags, tubes, and some syringes when burned produce highly toxic chemicals (dioxins and furans) which can cause cancer, infertility and other serious health problems, such as asthma [ 1 , 2 , 17 , 18 ]. In Zambia, burning biomass has occurred for many years and is a major source of air pollution [ 19 ]. Further environmental damage from improper burning of medical waste should be avoided, and medical waste incinerators that meet WHO standards should made available to the health facilities.

A proper waste management system consists of appropriate segregation, storage, transfer, and disposal of medical waste [ 2 ]. A WHO/UNICEF evaluation found that only 60% of sampled health facilities in the WHO Africa region (with 12 countries represented) had adequate waste management systems in place for the safe disposal of health care waste [ 12 ]. Our study found a lower percentage as only 43% of the health facilities had a functional incinerator on site. A study of urban health clinics in Ethiopia found that 61% of the surveyed clinics had poor HCWM practices [ 13 ]. We found very similar results with regards to HCWM practice variables collected.

Knowledge of HCWM was average (mean score = 4.7/ 7), but it did vary significantly based on the position of the health worker, with the highest knowledge among the laboratory staff. Other staff members, especially the cleaning staff that directly deal with waste disposal, should be targeted for more comprehensive HCWM training. It was interesting to note that prior training in HCWM was not found to be associated with having a high knowledge of HCWM. This suggests that the trainings the health workers have previously received are either not frequent enough or not adequate for lasting retention. Therefore, more comprehensive training should be given to all health workers at orientation and at regular intervals throughout their post.

In terms of training, previous HCWM training was low and ranged from 32–43%, depending on the health facility level. A similar rate of health workers having received HCWM management training (37%) was found in a study in Ethiopia [ 20 ]. In Zambia, an EHT is normally in charge of training health workers in HCWM. The EHT is also responsible for monitoring and implementing a system to correct any errors in disposing health care waste. However, usually only the district and provincial hospitals have EHTs, while the health centres/ posts do not have an EHT due to staffing limitations. An in-charge at the health centre/post may be expected to take on more duties by acting as the environmental health focal point person but they may not have all the knowledge of HCWM that an EHT would. This may explain the lower rates of training observed for those at the health centres/posts compared to the district and regional hospitals. Special effort should be focused on the hospitals and health facilities without EHTs to ensure their staff are properly trained in HCWM. Training health workers is critical for effective waste management as this has a bearing on waste segregation, storage, collection, transportation and disposal [ 21 ]. Prior research has shown that training health workers improves knowledge, attitudes and practice for reducing hazards from infectious wastes [ 21 , 22 ].

With regard to preventing health hazards as a result of handling hazardous waste, many health facilities did not have full PPE for their waste management staff, with the lowest rate reported from the health centres/ posts (34%). Furthermore, needle sticks were alarmingly high as almost one-third (31%) of all health workers reported a needle stick injury while at work. A similar rate of needle stick injuries (43%) was found among health workers in Ethiopia [ 23 ]. A needle stick can expose the health care worker to various infectious diseases, such as HIV, Hepatitis B and C infections [ 10 , 11 ]. In Africa, medical waste handlers are more likely to contract Hepatitis B infection compared to medical waste handlers in non-African settings [ 24 ]. In Zambia, there is currently no policy to vaccinate all health workers, which raises the risk of Hepatitis B and other infections to the health care staff. There is a paucity of research on needle stick injuries in Africa [ 25 ], but our study shows a high rate of needle sticks among health workers in Zambia. In order to prevent needle stick injuries among health workers, sufficient PPE should be provided and training on the handling of sharp instruments should be given at orientation to new staff and at regular intervals thereafter [ 26 ].

It is particularly difficult to implement a safe and environmentally-friendly HCWM system in developing countries [ 2 ]. Inadequate funding, poor training and lack of awareness of policies and guidelines on HCWM have led to poor handling and disposal of medical waste in health facilities throughout Africa [ 2 ]. Zambian health facilities face these same challenges, especially the health centres/ posts. The Zambian Ministry of Health has developed HCWM guidelines and policies that are available at the provincial and district offices, but they were not always present at the health facilities, especially the health centres/ posts. In a study conducted in two South African hospitals, gaps were identified between the policy and implementation of HCWM practices, which ultimately led to poor waste management [ 27 ]. Having the Ministry of Health’s HCWM guidelines available at each health facility would allow health workers to review the guidelines and help them carry out proper health care waste handling, transport and disposal.

There were a few limitations in this study. One limitation is that we had to resort to a non-random sampling of health facilities during the data collection period due to logistical challenges accessing a few facilities. Also, some key-informants for the qualitative interviews were not available at the time of the visit for an interview, so we did not complete all targeted interviews. A fundamental limitation of this analysis is the inability to directly assess the individual practice of the health care workers. The practice analysis was based on self-report or the presence of certain HCWM items at the facilities rather than observing the actual practice of health workers, which may have biased our results. Lastly, there was a small representation of some health professionals surveyed, including doctors and midwives, which precludes generating conclusive findings regarding differences in HCWM training and knowledge between different health professions.

Conclusions

Management of health care waste remains below national and international standards in various levels of health facilities in Zambia. The findings of this study indicate poor health care waste segregation, treatment and disposal, mainly due to a lack of proper HCWM tools, especially at the health centres/posts. Additionally, a high rate of needle sticks among health care workers was found. Based on the present study, we have identified three recommendations to improve HCWM in Zambia. The Ministry of Health should ensure that all health care workers undergo comprehensive training in the basics of health care waste management. Second, there is a need to include health care commodities (bins, bin liners, personal protective equipment, sharp containers/boxes) in the procurement process for medical and non-medical logistics. Lastly, we advocate for mainstreaming health care waste management and infection prevention, and control practices in all the different health programs.

Supporting information

S1 checklist. health care waste management assessment checklist..

https://doi.org/10.1371/journal.pgph.0000655.s001

S1 Text. Questionnaire for knowledge, attitude, and practice survey for health care waste management study.

https://doi.org/10.1371/journal.pgph.0000655.s002

S2 Text. Interview guide for KAP survey for health care waste management.

https://doi.org/10.1371/journal.pgph.0000655.s003

S3 Text. Sample size calculation.

https://doi.org/10.1371/journal.pgph.0000655.s004

Acknowledgments

We are grateful to all the health facilities and health care workers who participated in this survey study. We appreciate the support of the Ministry of Health and the World Bank.

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• Microbial communities
• Industrial microbiology

Wastewater treatment plants (WWTPs) are considered to be hotspots for the spread of antibiotic resistance genes (ARGs). We performed a metagenomic analysis of the raw wastewater, activated sludge and treated wastewater from two large WWTPs responsible for the treatment of urban wastewater in Moscow, Russia. In untreated wastewater, several hundred ARGs that could confer resistance to most commonly used classes of antibiotics were found. WWTPs employed a nitrification/denitrification or an anaerobic/anoxic/oxic process and enabled efficient removal of organic matter, nitrogen and phosphorus, as well as fecal microbiota. The resistome constituted about 0.05% of the whole metagenome, and after water treatment its share decreased by 3–4 times. The resistomes were dominated by ARGs encoding resistance to beta-lactams, macrolides, aminoglycosides, tetracyclines, quaternary ammonium compounds, and sulfonamides. ARGs for macrolides and tetracyclines were removed more efficiently than beta-lactamases, especially ampC , the most abundant ARG in the treated effluent. The removal efficiency of particular ARGs was impacted by the treatment technology. Metagenome-assembled genomes of multidrug-resistant strains were assembled both for the influent and the treated effluent. Ccomparison of resistomes from WWTPs in Moscow and around the world suggested that the abundance and content of ARGs depend on social, economic, medical, and environmental factors.

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Introduction.

The spread of antimicrobial resistance (AMR) in the environmental microbiome has become one of the frequently noted problems in recent years, along with global climate change, food security and other technological challenges. Numerous studies show that from year to year, in addition to increasing the cost of hospitalization and treatment of patients infected with multidrug-resistant bacteria, the number of deaths of such patients is growing 1 , 2 . Understanding the mechanisms underlying the emergence, selection and dissemination of AMR, and antibiotic resistance genes (ARGs), is required for the development of sustainable strategies to control and minimize this threat. The dissemination of antibiotic resistant bacteria (ARB) and ARGs occurs differently and this process is more active in urban territories rather than in rural ones. The rate of spread of ARGs and ARB in urban areas is obviously determined by the high population density and, as a rule, wastewater which flows from these areas contains both ARG and ARB. Most antibiotics used in medicine, agriculture and the food industry, as well as resistant bacteria, end up in wastewater. Wastewater treatment plants (WWTPs) therefore could provide a comprehensive overview of ARG abundance, diversity and genomic backgrounds in particular region 3 . Moreover, wastewater and WWTPs are places where ARGs and ARB are particularly abundant and are often considered “hotspots” for the formation of strains with multiple resistance and one of the main sources of the spread of AMR in the environment 4 .

Despite numerous studies on the role of WWTPs in resistome diversity and dissemination, each new study is, in terms of time and geography, unique, as many urban areas and countries have not yet been studied. In addition, some studies are dedicated to explore only one component of the wastewater treatment system, such as wastewater, activated sludge or treated effluent, and there is a lack of research that would give a comprehensive view of the diversity and change in the composition of the resistome at different stages of water cleaning, from wastewater to treated effluent, released into the environment.

Usually, wastewater treatment in large facilities takes place in three stages. The first stage includes physical methods of water cleaning, the second stage is microbiological treatment in bioreactors with activated sludge (AS), and the third stage is the final treatment of water and its disinfection. At the second stage, than could be performed using several technologies, microorganisms of AS are used to remove organic matter, ammonium, and (in more complex processes) phosphorus 5 . At this stage, the removal of microorganisms present in the wastewater, including ARB, occurs due to their adsorption on AS particles, which are removed along with excess AS. The efficiency of this process differs for various bacteria and depends on the purification technology used. Therefore, purification technologies directly affect the removal of particular ARGs and ARB, however, this issue was poorly studied 6 .

ARGs representing all known resistance mechanisms have been found in WWTP environments 7 . ARGs for beta-lactams, macrolides, quinolones, tetracyclines, sulfonamides, trimethoprim, and multidrug efflux pump genes have been found in the incoming wastewater, AS, and treated effluent in various countries 7 , 8 . Recently, Munk and coauthors (2022) using metagenomics methods characterized resistomes of 757 urban wastewater samples from 243 cities in 101 countries covering 7 major geographical regions. They reported regional patterns in wastewater resistomes that differed between subsets corresponding to drug classes and were partly driven by taxonomic variation 3 . Although this study did not analyzes the composition of the wastewater resistome after treatment, there are numerous evidences that the prevalence of ARB and ARG in rivers may increase downstream from the point of discharge of treated wastewater into them 9 , 10 . In a study of WWTPs in Germany, 123 types of clinically significant antibiotic resistance genes were found in treated wastewater discharged into water bodies 11 . An analysis of the presence of 30 ARGs at different stages of wastewater treatment at WWTPs in Northern China showed that the content of most ARGs in the treated effluent was lower compared to the influent entering the treatment, although an increase in the abundance of some ARGs and their release into the environment was also observed 12 . A metagenomic analysis of WWTP in Hong Kong revealed seasonal changes in the content of several types of ARG and its decrease in the treated effluent 13 , 14 . Most ARGs were reduced by more than 98% in the treated effluent compared to the wastewater entering the treatment 14 . Some other studies have also reported a decrease in ARGs after wastewater treatment 15 , 16 , 17 . However, in other studies, no changes in the ARG content or even an increase were observed 17 , 18 , 19 . Although there are numerous studies of resistomes in WWTP-related environments the distribution of samples was geographically biased and covered mostly North America, Western Europe, Eastern Asia (mostly China), Australasia, and few places in South America/Caribbean and Sub-Saharan Africa 3 .

In order to expand the geographical coverage and our knowledge about global resistome abundance and diversity, we analyzed resistomes of wastewater before and after treatment at large-scale WWTPs in the city of Moscow (Russia). Although Moscow WWTPs are among the largest in the world and may play an important role in the spread of antibiotic resistance, the resistomes of municipal wastewaters in Moscow have not previously been studied by modern molecular genetic methods. Previously we performed 16S rRNA gene profiling of AS microbial communities at large-scale WWTPs responsible for the treatment of municipal wastewater ion Moscow 5 . Comparison of microbial communities of AS samples from WWTPs in Moscow and worldwide revealed that Moscow samples clustered together indicating the importance of influent characteristics, related to local social and environmental factors, for wastewater microbiomes 5 . For example, due to the relatively low cost of water for household consumption, wastewater in Moscow has a relatively low content of organic matter. Apparently the presence of ARB and ARGs in communal wastewater depends on the frequency of antibiotic use and the range of drugs used. These factors differ in different countries and cities. Therefore, the characterization of the resistome and the role of Moscow WWTPs in the dispersion of ARGs is an important goal. Of particular interest is also the assessment of the impact of wastewater treatment technology on the composition of the resistome and the degree of ARG removal.

Here we present the first metagenomic overview of the composition of resistome of influent wastewater, AS and treated effluent released into the environment at two Moscow WWTPs employing different treatment technologies.

Characteristics of WWTPs and water chemistry

The Lyuberetskiy WWTP complex of JSC “Mosvodokanal” carry out the treatment of wastewater in the city of Moscow with a capacity of about 2 million m 3 per day. This complex consists of several wastewater treatment units (hereafter referred to as WWTPs). They purify the same inflow wastewater but otherwise are independent installations between which there is no transfer and mixing of AS. Two WWTPs implementing different technologies for wastewater treatment were chosen as the objects of study. The first one (LOS) is operated using anaerobic/anoxic/oxic process, also known as the University of Cape Town (UCT) technology. There the sludge mixture first enters the anaerobic zone, where phosphate-accumulating microorganisms (PAO) consume easily degradable organics, then to the anoxic zone, where denitrification and accumulation of phosphates by denitrifying PAO occur, and finally to the aerobic zone, where organic matter and ammonium are oxidized while PAO accumulate large quantities of polyphosphate. The second WWTP (NLOS2) uses a simpler nitrification–denitrification technology (N-DN). In the aerobic zones organics and ammonium are oxidized, while in the anoxic zone nitrate is reduced to gaseous nitrogen. This treatment technology removes organic matter and nitrogen, but was not specially aimed to remove phosphates. The production capacity of LOS is approximately 2 times more than that of NLOS2; there are no other important differences between these WWTPs besides treatment technology.

Sampling and chemical analysis

Wastewater and AS samples were collected in September 2022 and kindly provided by “Mosvodokanal” JSC. The temperature of water samples was about 24 °C. Samples of AS from bioreactors of two WWTPs were taken in 50 ml Falcon tubes (BD Biosciences). Wastewater samples (influent and effluents from two WWTPs) were taken in 5 L plastic bottles. The cells were collected by centrifugation at 3000 g for 20 min at 4 °C.

Wastewater quality values, namely, biochemical oxygen demand (five days incubation) (BOD 5 ), chemical oxygen demand (COD), total suspended solids (TSS), sludge volume index (SVI), ammonium nitrogen (N-NH 4 ), nitrate nitrogen (N-NO 3 ), nitrite nitrogen (N-NO 2 ) and phosphorus (P-PO 4 ) in the influent and effluents of two WWTPs were measured by the specialized laboratory “MSULab” according to the Federal inspection of environmental management’s protocols for chemical analyses of water.

DNA isolation, 16S rRNA gene sequencing and analysis

Total genomic DNA was isolated using a Power Soil DNA isolation kit (Qiagen, Germany). DNA for each sample was isolated in four parallel replicates, which were then pooled. PCR amplification of 16S rRNA gene fragments comprising the V3–V4 variable regions was performed using the universal primers 341F (5′-CCTAYG GGDBGCWSCAG) and 806R (5′-GGA CTA CNVGGG THTCTAAT) 20 . The obtained PCR fragments were bar-coded and sequenced on Illumina MiSeq (2 × 300 nt reads). Pairwise overlapping reads were merged using FLASH v.1.2.11 21 . All sequences were clustered into operational taxonomic units (OTUs) at 97% identity using the USEARCH v.11 program 22 . Low quality reads were removed prior to clustering, chimeric sequences and singletons were removed during clustering by the USEARCH algorithms. To calculate OTU abundances, all reads obtained for a given sample were mapped to OTU sequences at a 97% global identity threshold by USEARCH. The taxonomic assignment of OTUs was performed by searching against the SILVA v.138 rRNA sequence database using the VSEARCH v. 2.14.1 algorithm 23 .

The diversity indices at a 97% OTU cut-off level were calculated using USEARCH v.11 22 . To avoid sequencing depth bias, the numbers of reads for each sample were randomly sub-sampled to the size of the smallest set.

Sequencing of metagenomic DNA, contigs assembly and binning of MAGs

Metagenomic DNA was sequenced using the Illumina HiSeq2500 platform according to the manufacturer’s instructions (Illumina Inc., San Diego, CA, USA). The sequencing of a paired-end (2 × 150 bp) NEBNext Ultra II DNA Library prep kit (NEB) generated from 145 to 257 million read pairs per sample. Adapter removal and trimming of low-quality sequences (Q < 30) were performed using Cutadapt v.3.4 24 and Sickle v.1.33 ( https://github.com/najoshi/sickle ), respectively. The resulting Illumina reads were de novo assembled into contigs using SPAdes v.3.15.4 in metagenomic mode 25 .

The obtained contigs were binned into metagenome-assembled genomes (MAGs) using 3 different programs: MetaBAT v.2.2.15 26 , MaxBin v.2.2.7 27 and CONCOCT v.1.1.0 28 . The results of the three binning programs were merged into an optimized set of MAGs using DAS Tool v.1.1.4 29 . The completeness of the MAGs and their possible contamination (redundancy) were estimated using CheckM v.1.1.3 30 with lineage-specific marker genes. The assembled MAGs were taxonomically classified using the Genome Taxonomy Database Toolkit (GTDB-Tk) v.2.0.0 31 and Genome Taxonomy database (GTDB) 32 .

ARG identification

Open reading frames (ORFs) were predicted in assembled contigs using Prodigal v.2.6.3 33 . ARGs were predicted using the NCBI AMRFinderPlus v.3.11.4 ( https://github.com/ncbi/amr/wiki ) command line tool and its associated database 34 . The predicted protein sequences of all ORFs were analyzed in this tool with parameter “-p”.

Efficiency of wastewater treatment

Two wastewater treatment technologies were used in the investigated WWTPs,—nitrification/denitrification at NLOS2 and more advanced anaerobic/anoxic/oxic UCT process at LOS. LOS removed more than 99.5% of organic matter (according to the BOD5 data) and more than 99.9% of ammonium while the performance of NLOS2 was poorer (Table 1 ). Particularly noticeable differences were observed in nitrate and nitrite concentrations in the effluents suggesting the lower efficiency of denitrification in the NLOS2. Interestingly, although the NLOS2 unit was not designed to remove phosphorus, the concentration of phosphates in the treated effluent at this WWTP is only slightly higher than at LOS. The treated influent at LOS contains fewer solids consistently with lower SVI. Overall, the technology used at LOS plant is more efficient.

Microbiomes of the influent wastewater, activated sludge and treated effluent

The 16S rRNA gene profiling of microbial communities revealed 1013 species-level OTUs (97% identity) in the influent and 1.2–1.7 times more OTUs in the AS and treated effluent samples (Supplemental Table S1 ). The Shannon diversity indices correlated with the number of detected OTUs and increased in the series “influent” – “activated sludge” – “effluent” at each WWTP (Supplemental Table S2 ).

Analysis of the microbiome of wastewater supplied for biological treatment showed that that the most numerous phyla in the microbial community were Firmicutes (28.4% of all 16S rRNA gene sequences), Campylobacterota (28.0%), Proteobacteria (20.9%), and Bacteroidota (10.5%) (Fig.  1 ). These were mainly representatives of the fecal microbiota, which are often found in wastewater. The phylum Firmicutes was dominated by Streptococcaceae (9.7%, mostly S treptococcus sp.), Lachnospiraceae (5.9%), Ruminococcaceae (3.0%), Carnobacteriaceae (1.7%), Peptostreptococcaceae (1.6%) and Veillonellaceae (1.4%). Most of Campylobacterota belonged to the family Arcobacteraceae (26.8%) of the genera Arcobacter (19.9%), Pseudarcobacter (2.5%) and uncultured lineage (4.3%), as well as by sulfur-oxidizing Sulfurospirillum (1.0%). Among the Proteobacteria the most abundant genera were Acinetobacter (7.8%) , Aeromonas (1.8%) and Pseudomonas (1.1%). Most of the identified Bacteroidota were typical fecal contaminants such as members of the genera Bacteroides (2.6%), Macellibacteroides (1.5%), Prevotella (1.4%), and Cloacibacterium (1.2%).

Microbial community composition in the influent, AS and treated effluent samples according to 16S rRNA gene profiling. The composition is displayed at the phylum level. INFL, influent wastewater; AS-LOS, AS at LOS plant; CW-LOS, treated effluent at LOS plant; AS-NLOS2, AS at NLOS2 plant; CW-NLOS2, treated effluent at NLOS2 plant.

Activated sludge of WWTP bioreactors is a complex microbial community consisting of physiologically and phylogenetically heterogeneous groups of microorganisms involved in the removal of major contaminants from wastewater. The composition of AS microbiomes was very different from the microbiome of incoming wastewater (Fig.  1 ). The phyla Campylobacterota (less than 0.5%) and Firmicutes (2–4%) were much less abundant in AS microbiomes. Proteobacteria was the dominant group in the microbiomes of AS (23–40%), but its composition differed from the microbiome of influent wastewater: instead of the fecal microflora (Enterobacterales and others) the AS community harbored lineages involved in the purification processes ( Competibacteraceae , Rhodocyclaceae , Nitrosomonadaceae , etc.). Likewise, Bacteroidota were among the most numerous phyla in AS microbiomes at both LOS (6.5%) and NLOS2 (14.1%), but instead of Bacteroidales mostly comprised Chitinophagales and Sphingobacteriales typical for AS communities. The numerous groups of AS community also included Chloroflexi (22% and 10% in LOS and NLOS2, respectively), Patescibacteria (1.8% and 9.9%), Nanoarchaeota (4.3% and 9.1%), Nitrospirota (3.9% and 7.3%), Verrucomicrobiota and Myxococcota (about 4% in both WWTPs). Bacteria that play an important role in the removal of nitrogen ( Nitrospira and Nitrosomonas ) and phosphorus ( Dechloromonas ), as well as glycogen-accumulating Ca . Competibacter, have been found in large numbers. The abundance of these functional groups is consistent with the high efficiency of nitrogen and phosphorus removal.

The main source of microorganisms in treated effluent is the AS, from which they are washed out; bacteria from the influent water may also be present in minor amounts. Therefore, as expected, the microbiome composition of treated wastewater was similar to that of activated sludge. Consistently, compositions of microbiomes of treated effluent were similar to that of AS samples. However, some differences were observed, in particular, the microbiomes of the treated effluent contained many Cyanobacteria (7.74% and 3.49% for LOS and NLOS2, respectively) which were found in minor amounts both in the influent water and in the ASs (< 0.5%). Probably, these light-dependent bacteria proliferate in the final clarifier and then can be easily washed out with the effluent.

Diversity of resistomes

The results of metagenomic analysis of incoming wastewater revealed 544 ARGs in the assembled contigs, classified into 33 AMR gene families (Table 2 and Supplemental Table S3 ). Among the most numerous were classes A, C, D and metallo- beta-lactamases, rifampin ADP-ribosyltransferase, Erm 23S ribosomal RNA methyltransferase, aminoglycoside nucleotidyl-, acetyl- and phospho-transferases, the ABC-F type ribosomal protection proteins, chloramphenicol acetyltransferase, trimethoprim-resistant dihydrofolate reductase, quaternary ammonium compound efflux SMR transporters, lincosamide nucleotidyltransferases, tetracycline efflux MFS transporters and tetracycline resistance ribosomal protection proteins (Table 2 ). These genes may enable antibiotic inactivation (373 genes), target protection (85 genes), efflux (44 genes) and target replacement (25 genes).

The abovementioned genes confer resistance to most of commonly used drugs: beta-lactams (198 genes), macrolides (74 genes), rifamycin (60 genes), aminoglycosides (51 genes), tetracycline (27 genes), phenicols (27 genes), diaminopyrimidines (19 genes), quaternary ammonium compounds (16 genes), glycopeptides (15 genes), lincosamide (13 genes), fosfomycine (12 genes) and drugs of 11 others classes (Fig.  2 ).

ARGs identified in wastewater and AS samples categorized by drug classes.

About twice less ARGs were identified in AS samples from both WWTPs. Like in the influent, beta-lactamases of classes A, D, and metallo-beta-lactamases were the most numerous, while only a few genes for class C enzymes were found (Table 2 ). Other families of ARGs, numerous in the influent, were also numerous in AS microbiomes. A notable difference between the resistomes of the AS samples is the greater number of rifampin-ADP-ribosyltransferase genes ( arr ) in NLOS2 compared to LOS (63 vs 33). The largest number of arr genes was assigned to Bacteroidota, and the lower relative abundance of this phylum in AS at LOS likely explains these differences. Like in the wastewater, resistance to beta-lactams, macrolides, rifamycin, aminoglycosides, and tetracyclines was the most common (Fig.  2 ). On the contrary, genes for some drug classes were underrepresented in AS resistomes, especially for diaminopyrimidines (3 and 2 genes for LOS and NLOS2, respectively) and glycopeptide antibiotics (2 and 0 genes).

The results of metagenomic analysis of treated effluent showed that the diversity of these resistomes was only slightly higher than that of the corresponding AS samples. This result was expected since the main source of microorganisms in the effluent is activated sludge, from which they are partially washed. However, resistomes of treated effluent at both WWTPs contains about twice more class A beta-lactamase genes than AS samples suggesting less efficient absorption of their host bacteria at AS particles (Table 2 ).

Quantitative analysis of antibiotic resistance genes of WWTP

The results described above provide information on the diversity of resistance genes, but not on their abundance in the metagenomes, which depends on the abundance of corresponding bacterial hosts. To quantify the shares of individual ARGs in the metagenome and resistome, the amounts of metagenomic reads mapped to the corresponding ARGs in contigs were determined. In total, the resistome accounted for about 0.05% of the metagenome of wastewater supplied for treatment, while the shares of resistomes in the metagenomes of AS and treated effluent samples were 0.02% and 0.014% at the LOS and NLOS2 WWTPs, respectively.

Quantitative analysis of the content of individual ARGs in metagenomes showed that the structure of the influent resistome was very different from that of AS and treated effluent. The relative content of ARGs accounting for more than 1% in at least one analyzed resistome is shown in Fig.  3 . The LOS and NLOS2 WWTPs differed significantly from each other, and the differences between the AS and effluent resistomes at each WWTP were much less pronounced.

The relative abundancies of particular ARGs in the resistomes. Only ARGs with shares greater than 1% in at least one sample are shown, all other ARGs are shown as “others”.

The resistome of the influent was not only the most diverse, but also the most even in composition. The shares of none of the ARGs exceeded 5% of the resistome, and the 23 most common ARGs accounted for a half of the resistome. The most abundant ten ARGs were qacE, sul1, ampC, blaOXA, msr(E), erm(B), mph(E), tet(C), aph(3'')-Ib and aph(6)-Id, conferring resistance to antiseptics, sulfonamides, beta-lactams, macrolides, aminoglycosides (streptomycin), and tetracyclines.

AS and treated effluent at LOS plant was strongly dominated by a single AGR type, class C beta-lactamase ampC , accounting for about 45% of their resistomes. This gene was also the most abundant one in the resistomes of AS and effluent at NLOS2 (14.8% and 18.2%, respectively). Apparently it originates from the influent wastewater supplied for treatment where its share in the resistome was 3.2%. AmpC β-lactamases are considered clinically important cephalosporinases encoded on the chromosomes and plasmids of various bacteria (especially Enterobacteriaceae ), where they mediate resistance to cephalothin, cefazolin, cefoxitin and most penicillins 35 . Close homologues of this gene, with a nucleotide sequence identity of 99.8–100%, have been found in plasmids and chromosomes of various Proteobacteria ( Thauera, Sphingobium, Aeromonas etc.). Since in all samples ampC was found in short contigs with very high coverage, it is likely widespread in the genomes of various bacteria in different genetic contexts.

The second most abundant ARG in the resistomes of AS samples was sulfonamide-resistant dihydropteroate synthase ( sul1 ). It accounted for 4–5% of AS and treated effluent resistomes in LOS and for about 11% in NLOS2, while its share in the influent water resistome was about 5%. The sul1 gene is usually found in class 1 integrons being linked to other resistance genes, including qacE 36 . Consistently, sul1 and qacE were found in one contig assembled for the influent water samples and assigned to Gammaproteobacteria. Another sulfonamide-resistance gene, sul2 , was also numerous, accounting for about 2% of the resistomes in the influent and LOS samples, and for about 4% in the AS and water treated at NLOS2.

Since ARGs entering the activated sludge and then into the treated effluent originate mostly from wastewater supplied for treatment, the absolute majority of ARGs present in the influent in significant amounts (more than 0.2% resistome) in were also found in AS and effluent samples. The only exception macrolide 2′-phosphotransferase gene mph(B) accounting for 0.51% in the influent resistome. Likewise, all ARGs accounting for more than 0.2% of resistomes in the treated effluent were present also in the influent.

Potential multidrug resistant strains

One of the most important public health problems is the spread of multidrug resistant pathogens (MDR), which refers to resistance to at least one agent in three or more chemical classes of antibiotic (e.g. a beta-lactam, an aminoglycoside, a macrolide) 37 . Such strains can arrive with wastewater entering the treatment, and also form in AS communities. AS are dense and highly competitive microbial communities, which, along with the presence of sublethal concentrations of antibiotics and other toxicants in wastewater, creates ideal conditions not only for the selection of resistant strains, but also for the formation of multiple resistance through horizontal gene transfer 4 . To identify MDR bacteria, we binned metagenomic contigs into metagenome-assembled genomes (MAGs) and looked for MAGs comprising several ARGs. Only MAGs with more than 70% completeness and less than 15% contamination were selected for analysis: 117, 56, 72, 94 and 121 for influent, AS of LOS, effluent of LOS, AS of NLOS2 and effluent of NLOS2, respectively. Five MAGs of MDR bacteria were identified in the metagenome of the influent, one—in AS of LOS, two—in the LOS effluent and one in the NLOS2 effluent (Table 3 ). These MAGs were assigned to unclassified genus-level lineages of Ruminococcaceae and Cyclobacteriaceae, Phocaeicola vulgatus, Streptococcus parasuis, Ancrocorticia sp., Enterococcus sp., Bacillus cereus and Undibacterium sp.

Disscussion

We characterized the composition of microbial communities and the resistomes of influent wastewater, activated sludge and treated effluent from two WWTPs in city of Moscow, where various biological water treatment technologies are used. Among the predominant bacteria in the influent wastewater we found mainly fecal contaminants of the genera Collinsella , Bacteroides , Prevotella , Arcobacter , Arcobacteraceae , Blautia , Faecalibacterium, Streptococcus , Acinetobacter , Aeromonas and Veillonella 38 , 39 , 40 , 41 , 42 , 43 . Previously, we performed 16S rRNA gene profiling of wastewater before and after treatment at one WWTP (LOS) and revealed that all abovementioned potential pathogens were efficiently removed and their relative abundance in the water microbiome decreased by 50‒100 times 44 . Similar pattern of removal of potential pathogenic bacteria was observed here for NLOS2 where another water treatment technology is used.

An important indicator of the dissemination of ARG is the proportion of the resistome in the entire metagenome before and after wastewater treatment. In the influent, the resistome accounted for about 0.05% of the metagenome, which corresponds to approximately two ARGs per bacterial genome. Approximately the same values are typical for most countries 3 . After treatment, the fraction of the resistome in the wastewater metagenomes decreases, but, surprisingly, only by 2–4 times. However, since the total concentration of microorganisms in treated effluent is approximately two orders of magnitude lower than in raw wastewater, it is likely that the total abundance of ARGs in the treated effluent is significantly reduced.

Apparently, fecal contaminants effectively removed during treatment are not the only carriers of ARG in wastewater, which are also found in bacteria characteristic of activated sludge and thus appearing in the effluents. Unfortunately, due to the high diversity of microbiomes and the tendency of ARG to be present in multiple copies in different genomic environments, most of the contigs containing ARG turned out to be short, which did not allow to define their taxonomic affiliation.

The resistome of influent water includes 26 ARGs, the share of which is more than 1%. Among of them the prevalence of ampC, aadA, qacE, bla, qacF and qacL is specific for Moscow WWTPs, since these genes were not among the 50 most common ARGs according to the results of a worldwide analysis of wastewater resistomes in large cities 3 . Different ARGs were most “evenly” represented in the influent wastewater while in the AS and treated effluent, a clear selection of particular types of ARGs was observed, which obviously reflects a change in the composition of microbiomes. A vivid example is the increase in the proportion of ampC in the resistomes, especially at LOS.

The discovered ARGs can confer resistance to most classes of antibiotics and among the resistomes of the studied WWTPs in the city of Moscow, genes conferring resistance to beta-lactam antibiotics were the most common, they accounted for about 26% of the resistome in the water supplied for treatment (Fig.  4 ). Similar values have been observed for wastewater in some other countries, particularly in Eastern Europe and Brazil, where 20 to 25% of reads were assigned to ARGs conferring resistance to beta-lactams 3 . According to data for 2021, beta lactams accounted for about 40% of the total antibiotic consumption in Russia in the medical sector 45 .

The relative abundancies of ARGs in the resistomes categorized by drug classes.

Like in most wastewater resistomes in different countries, ARGs conferring resistance to macrolides, aminoglycosides and tetracycline were also among the most abundant in wastewater from Moscow (Fig.  4 ). Resistance to macrolides, rather than beta-lactams, was most common in wastewater from most countries in Europe and North America, while in Moscow ARGs to macrolide were the second most common. Macrolides and tetracyclines are also widely used in medicine in Russia (20% and 5% of total antibiotic consumption in 2021, respectively). On the contrary, medical consumption of aminoglycosides in Russia is rather low (< 1% of the total), therefore, the high abundance of relevant ARGs was unexpected. The opposite pattern was observed for quinolones, which make up about 22% of the antibiotics used in medicine, but their ARGs accounted for only about 1% of the resistome. However the main mechanisms of resistance to quinolones, mutations in the target enzymes, DNA gyrase and DNA topoisomerase IV, and increased drug efflux 46 , were not addressed in our study.

A peculiar feature of Moscow wastewater resistome was the high content of resistance genes to sulfonamides (about 9%), which were not among the major genes in wastewater resistomes worldwide 3 . Sulfonamides are synthetic antimicrobial agents that currently have limited use in the human medicine, alone or mainly in combination with trimethoprim (a dihydrofolate reductase inhibitor), in the treatment of uncomplicated respiratory, urinary tract and chlamydia infections 7 , 47 . Different sulfonamide ARGs ( sul1, sul2 and sul3 ) were detected in the wastewater in the some countries, including Denmark, Canada, Spain and China, applying culture dependent, independent and qPCR methods 7 . The opposite picture was observed for streptogramin resistance genes, which were among the ARGs in the majority of resistomes worldwide, but in Moscow wastewater they accounted for less than 1%. This is probably due to the limited use of this drug in Russia.

Another distinguishing feature of the resistome of wastewater in Moscow is the high content of ARGs conferring resistance to quaternary ammonium compounds (QAC), about 9%. It can be explained by the frequent use of these antiseptics in medicine. QACs are active ingredients in more than 200 disinfectants currently recommended for inactivation the SARS-CoV-2 (COVID-19) virus 48 . A recent study showed that the number of QACs used to inactivate the virus in public facilities, hospitals and households increased during the COVID-19 pandemic 49 . Indeed, the results of a study dedicated to the study of wastewater resistome worldwide 3 did not reveal the presence of QAC ARGs in the wastewater, since the samples for this study were collected before the pandemic.

An important issue is the extent to which different water treatment technologies remove ARGs. The effective removal of ARG was primary due to a decrease in the concentration of microorganisms in treated effluent, since the share of resistome in the metagenome after treatment decreased by only 2.6 –3.7 times and the NLOS2 plant appeared to be more effective in this respect. However, compared to LOS, treated effluent at NLOS2 contains approximately twice as much suspended solids, probably due to poorer settling characteristics of the sludge indicated by the higher SVI. Therefore, the overall efficiency of removing ARGs from wastewater at two WWTPs may be similar.

Considering the relative abundances of ARGs in the resistomes, genes conferring resistance to macrolides and tetracyclines were removed more efficiently than beta lactamases, especially ampC , and rifampin ADP-ribosyltransferase genes. The low efficiency of removal of the ampC gene and the increase in its abundance in the resistome after wastewater treatment were previously reported for WWTPs in Germany 50 . Efficient removal of ARGs to macrolides ( ermB, ermF, mph(A), mef(A) ) and tetracyclines ( tet(A), tet(C), tet(Q), tet(W) ) has been reported in a number of studies worldwide 51 . ARGs enabling resistance to sulfonamides, tetracyclines and chloramphenicol were more efficiently removed at LOS than at NLOS2, while the opposite was observed for beta lactamases (Fig.  4 ). The later became the most abundant class of ARGs in the treated effluent.

Metagenomic analysis not only identified resistance genes, but also revealed probable MDR strains based on the analysis of assembled MAGs. We identified 9 such strains in both influent, AS and treated effluent. The real number of MDR strains is probably higher, since only a small fraction of all metagenomic contigs was included in the assembled high quality MAGs.

Phocaeicola vulgatus , (formerly Bacteroides vulgatus ), is a mutualistic anaerobic bacteria commonly found in the human gut microbiome and frequently involved in human infections. The results of whole genome analysis showed presence of blaTEM-1 and blaCMY-2 ARGs, which confers resistant to beta-lactams 52 , 53 . P. vulgatus was also identified as potential host for the transmission of tetracycline ARGs 54 . Streptococcus parasuis is an important zoonotic pathogen that causes primarily meningitis, sepsis, endocarditis, arthritis, and pneumonia in both pigs and humans 55 . A variety of MDR strains of this bacterium have been described. For instance, S. parasuis strain H35 was isolated from a lung sample of a pig in China; several ARGs, including optrA , catQ , erm(B), lsa(E), msr(D), mef(A), mdt(A), tet(M), lnu(B), aadE and two copies of aacA-aphD , were found in the chromosome and cfr(D) was detected on plasmid pH35-cfrD 56 . MDR strain of Bacillus cereus was identified in the effluent water microbiome. This bacterium is known as human pathogen and a common cause of food poisoning with toxin-producing property 57 . Bacillus cereus was isolated from drinking water treatment plant in China and antimicrobial susceptibility testing revealed that it was resistant to cefoxitin, penicillin tetracycline 58 , macrolide-lincosamide-streptogramin (MLSB), aminoglycoside and tetracycline antibiotics 59 . Assembled MAG B.cereus from effluent water contained ARGs conferring to macrolides, beta-lactams, fosfomycin and streptogramin and may be considered as MDR strain. Genomes of members of the genera Streptococcus (AS of LOS) and Enterococcus (influent), not identified at the species level, were found to contain multiple ARGs. Most of species of these genera are opportunistic and true pathogens known for their drug resistance 60 , 61 . One MAG from the influent water metagenome was assigned to uncultured lineage of the family Ruminococcaceae. Members of this family are typical non-pathogenic gut inhabitants, although genomes of some strains could harbor ARGs 62 .

Three MAGs retrieved from influent wastewater microbiome ( Ancrocorticia ) and treated effluent water ( Cyclobacteriaceae and Undibacterium ) were found to contain several ARGs. However, we found no evidences about pathogenic and MDR strains in these taxa. It is possible that these environmental bacteria acquired ARGs via horizontal gene from outside their lineages. WWTPs are an ideal environment for horizontal gene transfer (HGT), since when bacteria are exposed to strong selective pressures, such as the presence of antimicrobials, the horizontal acquisition of ARGs enables genetic diversification and create the potential for rapid gains in fitness 63 .

Conclusions

Metagenome sequencing of the raw wastewater, activated sludge and treated wastewater at two large WWTPs of the Moscow city revealed several hundreds of ARGs that could confer resistance to most commonly used classes of antibiotics.

Resistome accounted for about 0.05% of the wastewater metagenome and after wastewater treatment its share decreased by 3–4 times.

The resistomes were dominated by ARGs encoding resistance to beta-lactams, macrolides, aminoglycosides, tetracycline, QAC, and sulfonamides. A peculiar feature of Moscow wastewater resistome was the high content of ARGs to sulfonamides and limited occurrence of resistance to streptogramins.

ARGs for macrolides and tetracyclines were removed more efficiently than ARGs for beta-lactamases.

A comparison of wastewater resistomes from Moscow and around the world suggested that the abundance and content of ARG in wastewater depend on social, medical, and environmental factors.

Data availability

The raw data generated from 16S rRNA gene sequencing and metagenome sequencing have been deposited in the NCBI Sequence Read Archive (SRA) and are available via the BioProject PRJNA945245.

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Acknowledgements

This work was partly supported by the Russian Science Foundation (Project 22-74-00022 to S.B.).

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S.B. and N.V.R. designed and supervised the research project; A.G.D. collected the samples and analysed chemical composition of wastewater; A.V.M. performed 16S rRNA gene profiling and metagenome sequencing; S.B., A.V.B., N.V.P., and N.V.R. analysed the sequencing data; S.B. and N.V.R. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

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Begmatov, S., Beletsky, A.V., Dorofeev, A.G. et al. Metagenomic insights into the wastewater resistome before and after purification at large‑scale wastewater treatment plants in the Moscow city. Sci Rep 14 , 6349 (2024). https://doi.org/10.1038/s41598-024-56870-0

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Municipal solid waste management in Russia: potentials of climate change mitigation

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The goal of this study was to assess the impact of the introduction of various waste management methods on the amount of greenhouse gas emissions from these activities. The assessment was carried out on the example of the Russian waste management sector. For this purpose, three scenarios had been elaborated for the development of the Russian waste management sector: Basic scenario, Reactive scenario and Innovative scenario. For each of the scenarios, the amount of greenhouse gas emissions generated during waste management was calculated. The calculation was based on the 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories. The results of the greenhouse gas net emissions calculation are as follows: 64 Mt CO 2 -eq./a for the basic scenario, 12.8 Mt CO 2 -eq./a for the reactive scenario, and 3.7 Mt CO 2 -eq./a for the innovative scenario. An assessment was made of the impact of the introduction of various waste treatment technologies on the amounts of greenhouse gas emissions generated in the waste management sector. An important factor influencing the reduction in greenhouse gas emissions from landfills is the recovery and thermal utilization of 60% of the generated landfill gas. The introduction of a separate collection system that allows to separately collect 20% of the total amount of generated municipal solid waste along with twofold increase in the share of incinerated waste leads to a more than threefold reduction in total greenhouse gas emissions from the waste management sector.

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Introduction

Population growth, urbanization and changing life style have resulted in increased amounts of generated solid waste, which poses serious challenges for many cities and authorities around the world (Abu Qdais et al. 2019 ; Chen 2018 ; Dedinec et al. 2015 ). In 2011, world cities generated about 1.3 Gt of solid waste; this amount is expected to increase to 2.2 Gt by 2025 (Hoornweg and Bhada-Tata 2012 ). Unless properly managed on a national level, solid waste causes several environmental and public health problems, which is adversely reflected on the economic development of a country (Abu Qdais 2007 ; Kaza et al. 2018 ).

One of the important environmental impact of the waste management sector are the generated greenhouse gas (GHG) emissions. These emissions come mostly from the release of methane from organic waste decomposition in landfills (Wuensch and Kocina 2019 ). The waste management sector is responsible for 1.6 Gt carbon dioxide equivalents (CO 2 -eq.) of the global GHG direct anthropogenic emissions per year (Fischedick et al. 2014 ), which accounts for approx. 4% of the global GHG emissions (Papageorgiou et al. 2009 ; Vergara and Tchobanoglous 2012 ). The disposal of municipal solid waste (MSW) contributes to 0.67 Gt CO 2 -eq./a worldwide (Fischedick et al. 2014 ), which is approx. 1.4% of the global GHG emissions. Per capita emissions in developed countries are estimated to be about 500 kg CO 2 -eq./a (Wuensch and Kocina 2019 ), while in the developing and emerging countries, it is around 100 kg CO 2 -eq./a per person. This low contribution of waste management sector comparing to other sectors of the economy, such as energy and transportation, might be the reason for the small amount of research that aims to study GHG emissions from the waste management sector (Chung et al. 2018 ).

However, it is important to consider that the mitigation of GHG emissions from waste management sector is relatively simple and cost-effective as compared to other sectors of the economy. Several studies proved that separate waste collection and composting of biowaste as well as landfilling with landfill gas recovery is currently found to be one of the most effective and economically sound GHG emissions mitigation options (Chen 2018 ; EI-Fadel and Sbayti 2000 ; Yedla and Sindhu 2016 ; Yılmaz and Abdulvahitoğlu 2019 ). Metz et al. 2001 estimated that 75% of the savings of methane recovered from landfills can be achieved at net negative direct cost, and 25% at cost of about 20 US$/Mg CO 2 -eq./a. In any country of the world, the potential of the waste management sector is not yet fully utilized; the implementation of relatively simple and inexpensive waste treatment technologies might contribute to national GHG mitigation goals and convert the sector from a net emitter into a net reducer of GHG emissions (Crawford et al. 2009 ; Voigt et al. 2015 ; Wuensch and Simon 2017 ).

While there are many well-established solutions and technologies for the reduction in GHG emitted from the waste sector, there is no universal set of options that suits all the countries. When thinking to adapt certain solutions of GHG mitigation, it is important to take into account local circumstances such as waste quantities and composition, available infrastructure, economic resources and climate (Crawford et al. 2009 ).

It is expedient to assess how the introduction of modern waste management methods affects the amount of GHG emissions from the waste management process by the example of those countries in which the waste management sector is undergoing reform. These countries include the Russian Federation, where the values of targets for the waste management industry until 2030 are legally established (Government of the Russian Federation 2018 ). In addition, on February 8, 2021, Russia issued a Presidential Decree “On Measures to Implement State Scientific and Technical Policy in the Field of Ecology and Climate,” which prescribes the creation of a Federal Program for the Creation and Implementation of Science-Intensive Technologies to Reduce Greenhouse Gas Emissions (Decree of the President of the Russian Federation 2021 ).

The goal of this study was to quantify the impact of the introduction of various modern waste treatment methods on the volume of GHG emissions from the waste management sector using the example of Russia. To achieve this goal, the following objectives were set and solved:

Elaborate scenarios for the development of the waste management industry, based on the established Industry Development Strategy for the period up to 2030 (Government of the Russian Federation 2018 )

Determine the weighted average morphological composition of MSW;

Select emission factors for various waste treatment methods;

Calculate GHG emissions under each scenario and analyze the calculation results.

The study was conducted from November 2019 to May 2020; the text was updated in March 2021 in connection with the changed situation, as climate change issues began to play an important role on the agenda in Russia. The study and its calculations are theoretical in nature and did not involve experimental research. It was carried out by the authors at their place of work—in Germany (Technische Universität Dresden, Merseburg University of Applied Sciences) and in Russia (Perm National Research Polytechnic University).

Greenhouse gas emissions related to municipal solid waste management sector in Russia

According to the State Report on the Status of Environmental Protection of the Russian Federation of 2018 (Ministry of Natural Resources and Ecology of the Russian Federation 2019 ), the volume of generated MSW has increased by 17% from 235.4 to 275.4 m 3 (49.9 to 58.4 Mt) during the time period 2010 to 2018. With approx. 147 million inhabitants, the annual per capita generation rate is about 400 kg. Until now, MSW management in Russia has been disposal driven. More than 90% of MSW generated is transported to landfills and open dump sites; 30% of the landfills do not meet sanitary requirements (Korobova et al. 2014 ; Tulokhonova and Ulanova 2013 ). According to the State Register of the Waste Disposal Facilities in Russia, there were 1,038 MSW landfills and 2,275 unregistered dump sites at the end of 2018 (Rosprirodnadzor 2019 ). Such waste management practices are neither safe nor sustainable (Fedotkina et al. 2019 ), as they pose high public health and environmental risks and lead to the loss of valuable recyclable materials such as paper, glass, metals and plastics which account for an annual amount of about 15 Mt (Korobova et al. 2014 ).

According to the United Nations Framework Convention on Climate Change (UNFCCC) requirements, the signatory parties of the convention need to prepare and submit national communication reports that document GHG emissions and sinks in each country by conducting an inventory based on Intergovernmental Panel on Climate Change (IPCC) guidelines (UNFCCC 2006 ). Being the fourth biggest global emitter of GHG emissions, Russia submitted its latest National Inventory Report (NIR) to UNFCCC in April 2019. The report documents national GHG emissions by source and removals by sink (Russian Federation 2019 ). The total emissions had been decreased from 3.2 Gt in 1990 to about 2.2 Gt of CO 2 -eq. in 2017, which implies 30% reduction over a period of 27 years. At the same time, the emissions from the disposal of solid waste increased from 33 Mt in 1990 by more than 100% to 69 Mt CO 2 -eq. in 2017. In terms of methane emissions, Russian solid waste disposal sector is the second largest emitter in the country and accounts for 18.1% of the total emitted methane mostly in the form of landfill gas, while the energy sector is responsible for 61.2% of methane emissions (Russian Federation 2019 ).

Landfill gas recovery from MSW landfills is not widely practiced in the Russian Federation. According to the statistics of the Russian Ministry of Natural Resources and Ecology, the share of landfill gas energy in the total renewable energy produced in Russia was 8.61%, 5.43%, 2.77% and 2.59% in 2011, 2012, 2013 and 2014, respectively (Arkharov et al. 2016 ). Different studies show that the potential of recovering energy from landfill gas in the Russian Federation is high (Arkharov et al. 2016 ; Sliusar and Armisheva 2013 ; Starostina et al. 2018 ; Volynkina et al. 2009 ).

Waste-to-energy technology is still in its infancy in Russia; the country is lagging in this area (Tugov 2013 ). Despite that, there is a great interest among the public as well as the private sector in the possibilities of the recovery of energy from MSW. In April 2014, the State Program “Energy Efficiency and Energy Development” was approved, which includes a subprogram on the development of renewable energy sources in the Russian Federation (Government of the Russian Federation 2014 ). In this program, MSW was considered as a source of renewable energy. Until the year 2017, there were only four waste incineration plants in Moscow region processing 655,000 Mg MSW per year, with only one incinerator recovering energy in form of heat and electricity (Dashieva 2017 ). In the nearest future, the construction of four additional incinerators in Moscow region and one in the city of Kazan is planned. The annual total combined capacity of the four new plants in Moscow will be about 2.8 Mt (Bioenergy International 2019 ). In the Kazan incinerator, 0.55 Mt of MSW will be treated annually, which eventually will allow ceasing of landfilling of solid waste in the Republic of Tatarstan (Bioenergy International 2019 ; Regnum 2017 ). The construction of these five new incineration plants is part of the Comprehensive Municipal Solid Waste Strategy adopted by the Russian government in 2013 (Plastinina et al. 2019 ). The focus of this strategy is the reduction in the amount of landfilled waste by creating an integrated management system and industrial recycling of waste.

Separate collection of MSW and the recycling of different waste fractions at the moment plays only a negligible role in the Russian Federation.

Materials and methods

Scenarios of the development of municipal solid waste management system.

To assess the current situation and the potential for reducing GHG emissions from the MSW management industry, three scenarios of the development of the Russian waste management system had been elaborated. The developed scenarios are based on the official statistics data on the amount of waste generated and treated, and also on the adopted legislative acts that determine the development directions of the Russian waste management system and set targets in these areas (Council for Strategic Development and National Projects 2018 ). That is why the developed scenarios include such measures to improve the waste management system as elimination of unauthorized dump sites, introduction of landfill gas collection and utilization systems at the landfills, incineration of waste with energy recovery, separate collection of waste, and recycling of utilizable waste fractions, and do not include other waste-to-energy technologies and waste treatment strategies contributing to climate change mitigation. Separate collection and treatment of biowaste is not applied in the national waste management strategy of the Russian Federation (Government of the Russian Federation 2018 ) and therefore was beyond the scope of the elaborated scenarios. For the purpose of the current study, three scenarios had been developed.

Scenario 1: BASIC (business as usual)

This scenario is based on the current waste management practices, under which 90% of the generated mixed MSW is disposed of on landfills and dump sites. According to the 6th National Communication Report of the Russian Federation to UNFCCC, the total MSW generated that found its way to managed landfills Footnote 1 was 49.209 Mt in 2009, while the amount of MSW disposed in unmanaged disposal sites (dumps) was 5.067 Mt. In 2017, the amount of MSW generated was 58.4 Mt with 10% being diverted from landfills: 3% incinerated and 7% recycled (Ministry of Natural Resources and Ecology of the Russian Federation 2019 ). According to Russian Federation 2019 , landfill gas recovery is not taking place at Russian landfills. This scenario implies the closure of unorganized dump sites, with all the waste to be disposed of on managed dump sites or landfills only.

Scenario 2: REACTIVE (moderate development)

The reactive scenario implies a moderate development of the waste management sector, based on the construction of several large incinerators, a small increase in the share of waste to be recycled and the disposal of remaining waste at sanitary landfills, Footnote 2 with the closure of all the existing unorganized dump sites. In this scenario, all Russian regions were divided into two clusters: the first cluster included the city of Moscow and the Republic of Tatarstan, where new waste incinerators are being built, and the second cluster which includes — all the other cities and regions.

Moscow and the Republic of Tatarstan

In Moscow and Tatarstan together, 8.586 Mt of mixed MSW is generated annually (Cabinet of Ministers of the Republic of Tatarstan 2018 ; Department of Housing and Communal Services of the city of Moscow 2019 ). In an attempt to introduce the waste-to-energy technology in Russia, an international consortium that consists of Swiss, Japanese and Russian firms is currently involved in constructing five state-of-the-art incineration plants in these two areas. Four incinerators are to be built in the Moscow region and one in Kazan, the capital of the Republic of Tatarstan. The annual combined capacity of the four plants in Moscow will be about 2.8 Mt of MSW, and the one of Kazan 0.55 Mt (Bioenergy International 2019 ; Regnum 2017 ). In this scenario, it is assumed that compared to the basic scenario, the share of waste undergone recycling is increased to 10%, i.e., 0.859 Mt annually. Furthermore, these 10% would be transferred to recycling plants to recover secondary raw materials. The remaining 4.377 Mt of mixed MSW would be disposed of in sanitary landfills.

Other cities and regions

In the other cities and regions of Russia, in accordance with the Development Strategy of Waste Recycling Industry until 2030 (Government of the Russian Federation 2018 ), over two hundred new eco-techno parks (i.e., waste recycling complexes) will be built. These facilities will receive mixed MSW that will be sorted there for recycling purposes. Under this scenario, it is also assumed that compared to the basic scenario, the share of waste undergone recycling is increased to 10%, thus transferring 4.982 Mt annually of the mixed MSW to recycling plants. The remaining 44.932 Mt of MSW are disposed of in sanitary landfills.

Scenario 3: INNOVATIVE (active development)

This scenario is based on the legally established priority areas for the development of the industry (Council for Strategic Development and National Projects 2018 ; Government of the Russian Federation 2018 ). The scenario implies deep changes in the industry with the introduction of technologies for incineration, separate collection and recycling of waste. In this scenario, the regions of Russia are divided into three clusters, in accordance with the possibilities of improving the infrastructure for waste management and the need for secondary resources and energy received during the processing of waste. When determining the share of waste to which this or that treatment method is applied, federal targets (Council for Strategic Development and National Projects 2018 ; Government of the Russian Federation 2018 ) and estimates made by the World Bank (Korobova et al. 2014 ) were used.

The first cluster includes two huge, densely populated urban agglomerations in which large incineration plants are under construction: Moscow and Tatarstan. With the construction of new waste incinerators, 3.35 Mt of mixed MSW will be incinerated annually. It is assumed that some 10% of mixed MSW (0.859 Mt) generated in these two regions is to be transferred to eco-techno parks for secondary raw material recovery. Some 20% of the MSW (1.712 Mt) is to be recovered from separately collected waste, and the rest of 2.66 Mt (31%) to be disposed of in sanitary landfills.

Cities with more than 0.5 million inhabitants

This cluster includes large urban agglomerations with developed industry and high demand for materials and energy resources. In this cluster, approx. 28 Mt of MSW is generated annually (Korobova et al. 2014 ). Under this scenario, it is assumed that waste incineration plants are also built in some larger cities, besides Moscow and Kazan. However, the exact quantity and capacity of these plants is yet unknown; it was assumed that in comparison with the basic scenario, in this scenario, the share of incinerated waste increased to 10%, the share of recycled waste to 15%, and a separate waste collection system is partially implemented. Hereby, 10% of the generated mixed MSW (2.79 Mt) is undergoing incineration, 15% (4.185 Mt) is transferred to sorting facilities for secondary raw material recovery, some 20% of the MSW (5.58 Mt) is recovered from separately collected waste and the rest 55% (15.345 Mt) is disposed of in sanitary landfills.

Smaller cities with less than 0.5 million inhabitants and rural areas

This cluster includes smaller cities and towns with some industrial enterprises, as well as rural areas. The amount of waste generated annually in this group of settlements is 21.914 Mt. It is assumed that no waste is incinerated, 15% of the mixed MSW (3.287 Mt) is transferred to sorting facilities for secondary raw material recovery, 10% (2.191 Mt) is recovered from separately collected waste, and the rest 75% (16.435 Mt) is disposed of in sanitary landfills.

Waste flow diagrams corresponding to the three scenarios with their input and output flows are shown in Fig.  1 .

MSW management scenarios with model inputs and outputs

In all the three scenarios, mixed MSW is transferred to sorting facilities where the recovery of valuable materials by mostly hand sorting takes place. Detailed accounts of process efficiency for material recovery facilities, in terms of recovery rates and quality of recovered materials, are scarce in the published literature (Cimpan et al. 2015 ). In the study of Cimpan et al., 2015 , at least three data sets were evaluated with the result that 13–45% of paper, 3–49% of glass, 35–84% of metals and 1–73% of plastics were recovered from the plant input of these materials. Two other studies report similar recovery rates between 60 and 95% for paper, glass, plastic and aluminum for hand and automatic sorting test trials (CalRecovery, Inc and PEER Consultants 1993 ; Hryb 2015 ). Based on this data and the results of the authors’ own experimental studies on manual waste sorting in Russia, the recovery rates for the most valuable waste fractions, including paper/cardboard, glass, metals and plastics had been calculated (Table 1 ). In the Scenario 3, separate collection of paper/cardboard, glass and plastic is introduced. Recovery rates related to the input of the corresponding waste type into each waste management cluster (see Table 1 ) for Moscow and Tatarstan as well as for the cities with more than 0.5 million inhabitants are considered to be higher than for the settlements with less than 0.5 million inhabitants.

For the comparison of GHG emissions of the three elaborated scenarios, a specific assessment model was elaborated.

Model structure

The calculation of the amounts of released and avoided GHG emissions for the different considered waste treatment technologies are based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The IPCC methodology is scientifically widely recognized and used internationally, which makes the results easy comprehensible and easier to compare with other studies.

For the elaboration of the model that would allow calculating the GHG balance emissions, the upstream-operating-downstream (UOD) framework (Gentil et al. 2009 ) was used, where direct emissions from waste management procedures and indirect emissions from upstream and downstream activities are differentiated. On the upstream side, the indirect GHG emissions, like those related to fuel and material extraction, processing and transport as well as plant construction and commissioning, are excluded from the consideration. Indirect emissions from infrastructure construction on the downstream side are outside the system boundaries and not accounted for as they are relatively low (Boldrin et al. 2009 ; Mohareb et al. 2011 ). Direct GHG emissions from the waste transport are also excluded from the system boundaries since they are negligible comparing to the direct emissions from the waste processing/treatment (Weitz et al. 2002 ; Wuensch and Simon 2017 ). Since indirect GHG emissions avoided due to energy and material substitution, as well as carbon sequestration in the downstream processes is significant, they are included into the model. The conceptual framework of the model and its boundaries are shown in Fig.  2 .

Conceptual framework of the model showing upstream and downstream processes along with the system boundaries [derived from Abu Qdais et al. ( 2019 )]

The inputs to the model are waste (its quantity, composition, carbon content fixed in biomass and no-biomass), as well as energy and fuel that are used in the waste treatment processes (see Table 2 and Figs.  1 , 2 and 3 ). The outputs include generated and delivered electricity, recovered secondary materials and sequestrated carbon.

Compensatory system for the substitution of primary materials and energy [derived from Abu Qdais et al. ( 2019 )]

The analysis of MSW composition is not regularly done in Russia, and only a limited number of studies on this subject are published. Since waste composition is the basis for the determination of direct GHG emissions from waste management activities, accurate data is desirable. The Russian Federation is a huge country with both densely populated urban areas and sparsely populated rural areas. Due to the different settlement structures, the waste compositions also differ a lot. It is not expedient to assume an average composition for the entire country. Therefore, hereinafter three clusters had been considered to define waste compositions. The first cluster includes Moscow and the Republic of Tatarstan, since in these regions, a larger amount of mixed MSW is/will be incinerated in the nearest future. The second cluster includes the cities with the population of more than 0.5 million people, and the third cluster includes the settlements with the population of less than 0.5 million people. The waste compositions for these three clusters given in Table 2 are weighted averages of the results of a number of experimental studies of waste composition which were found in sources of the literature published after 2010 and further analyzed. Weighted average here means that the respective data on waste composition that was found for a city or region was included in the weighted average with the proportion that the amount of MSW generated in the city or region takes up as part of the total mass of MSW generated in the respective cluster.

To determine the avoidance of GHG emissions in the downstream processes by means of energy and material substitution as well as carbon sequestration, a compensatory system must be used. In Fig.  3 , the compensatory system for the substitution of energy and primary materials is shown.

Emission factors

Waste incineration.

It is necessary to know the emission factors when calculating GHG emissions from thermal treatment of waste, and also when compiling national emissions inventories (Larsen and Astrup 2011 ). Information on GHG emission factors of various solid waste treatment technologies for each country is of great importance for the assessment of GHGs emitted as a result of adopting a certain technology. However, such factors are not available for the Russian Federation, which implies using the data available in the literature for the countries with the conditions similar to the Russian ones, examining local circumstances of solid waste management system (Friedrich and Trois 2013 ; Larsen and Astrup 2011 ; Noya et al. 2018 ).

There are different factors affecting GHG emission levels from waste incineration. One of the most important factors in determining CO 2 emissions is the amount of fossil carbon in the waste stream meant for incineration. Non-CO 2 emissions are more dependent on the incineration technology and conditions, and for modern waste incinerators, the amounts of non-CO 2 emissions are negligible (Johnke 2001 ; Sabin Guendehou et al. 2006 ).

The amount of fossil carbon was calculated based on waste composition, carbon content and share of fossil carbon given in Table 2 ; the resulting fossil carbon content in wet waste was 0.117 kg C/kg. For the indirectly avoided GHG emissions, the recovery of electricity with a net efficiency of 24% for all the scenarios and for the Scenario 3 also from metals contained in the incinerator slag to substitute primary metals was considered. The recovery of heat in form of process steam or district heat was not considered in the scenarios (Dashieva 2017 ). Further parameters for the calculation of GHG emissions from waste incineration are given in Table 3 .

For the calculation of the impact of the methane released from landfills to climate change over a 100 years’ time horizon, the first-order decay kinetics model was used. Almost 80% of the Russian MSW landfills occupy an area larger than 10 ha (Volynkina and Zaytseva 2010 ). Here, it is assumed that all the MSW is highly compacted and disposed of in deep landfills under anaerobic conditions without the recovery of landfill gas (Govor 2017 ). Since no landfill gas is recovered, in Scenario 1, only the sequestrated non-biodegradable biogenic carbon in the landfill results in avoided GHG emissions. There is an intention in Russia to introduce the collection of landfill gas as the primary measure to reduce GHG emissions from the waste management sector (Government of the Russian Federation 2018 ; Ministry of Natural Resources and Ecology of the Russian Federation 2013 ) within the next years. In the literature, methane recovery rates between 9% (Scharff et al. 2003 ) and 90% (Spokas et al. 2006 ) are reported. For example, most US landfills are well-controlled and managed; in particular, in California, gas collection efficiencies are as high as 82.5% (Kong et al. 2012 ). Based on these values, for both Scenario 2 and Scenario 3, landfill gas recovery is introduced with a recovery rate of 60%. Under these two scenarios, in addition to carbon sequestration, the recovered landfill gas is used to produce electricity, which results in avoided indirect GHG emissions. Other parameters used for the calculation are mainly taken from the latest Russian National Inventory Report where IPCC default parameters were used (Pipatti et al. 2006 ; Russian Federation 2019 ). The parameters used for the calculation of GHG emissions from landfills for all the three scenarios are shown in Table 4 .

• Material recovery

In all the scenarios, some part of mixed MSW is treated in eco-techno parks, where valuable secondary raw materials like metals, paper, glass and plastics are recovered, and the sorting residues are forwarded to landfills. In addition, separate collection of some amounts of paper, glass, and plastics in the Scenario 3 is presumed. The corresponding recovery rates are already given in Table 1 . Each recovered secondary material substitutes a certain amount of primary material. Since the production of primary materials is usually connected with higher energy and raw material consumption than that of the secondary materials, more GHGs are released during the production of the former ones. Therefore, every unit of recovered secondary material obtained leads to a reduction in released GHGs.

GHG emission or substitution factors are developed for specific geographical areas and technologies, and their appropriateness to other circumstances may be questionable (Turner et al. 2015 ). The application of one specific emission factor for a recovered material in the whole Russian Federation would already be debatable due to the size of the country. Perhaps that is why emission factors for Russia cannot be found in the literature. For this study, the average values of GHG emission/substitution factors determined for other industrial countries from the study of (Turner et al. 2015 ) were used. The amounts of avoided GHG, i.e., the values of the emission factors in CO 2 equivalents for the recovered valuable waste fractions, including steel, aluminum, paper/cardboard, glass and plastic, are given in Table 5 .

In Table 5 , the used equivalent factor (Global Warming Potential over a time horizon of 100 years) of released methane versus carbon dioxide, the emission factor of the use of fuel oil in the waste incineration process and the substitution factor of delivered electrical power are shown. The emission factor of the generated electricity in the Russian Federation is relatively low, since approx. half (52%) of the electricity is produced by natural gas and approx. 13% by hydro- and nuclear power, while only 13% is produced by coal (British Petrolium 2019 ; U.S. Energy Information Administration 2017 ). The electricity mix factor is therefore only 0.358 Mg CO 2 -eq./MWh generated electricity (Gimadi et al. 2019 ).

Results and discussion

The population of the Russian Federation is expected to decrease in the next decades (United Nations 2019 ), but due to the economic growth, the amount of waste generated per capita is expected to increase in the same ratio; that is why the calculation of the GHG emissions for all the three scenarios was based on an assumed fixed annually amount of 58.4 Mt of MSW. Average waste compositions were calculated for this study on the basis of eleven waste analyses conducted in different Russian cities between 2010 and 2017 and grouped into three clusters (Moscow and Tatarstan, cities with more than 0.5 million inhabitants and cities/settlements with less than 0.5 million inhabitants). From the available literature data for the countries with conditions similar to Russian ones, emission factors were adopted to be further used in calculations of GHG emissions from waste disposal on managed and sanitary landfills, waste incineration and waste recycling with the recovery of secondary raw materials.

In Fig.  4 , the amounts of CO 2 -equivalent emissions per year that contribute to global warming for each of the three scenarios considered in the study are shown. Since the emissions related to the collection and transportation of waste, as well as energy consumption in the upstream side, are almost similar for all the treatment processes (Komakech et al. 2015 ), and as they are relatively small compared to the operational and downstream emissions (Boldrin et al. 2009 ; Friedrich and Trois 2011 ), they were not considered in the model. Avoided and sequestrated emissions were subtracted from the direct emissions to calculate GHG net emission values.

Global warming contribution of the three considered scenarios

The basic scenario (mostly managed landfilling without landfill gas recovery) gives the highest GHG net emissions among all the analyzed scenarios of approx. 64 Mt CO 2 -eq./a, followed by the reactive scenario (mostly sanitary landfilling with landfill gas recovery) with approx. 12.8 Mt CO 2 -eq./a of GHG net emissions. The innovative scenario (sanitary landfilling with landfill gas recovery and increased shares of MSW incineration, separate collection and material recovery) had shown an almost neutral GHG balance with approx. 3.7 Mt CO 2 -eq./a of GHG net emissions.

To assess the impact of the introduction of various waste treatment methods on the amount of GHG emissions from the waste management sector, the specific GHG emissions for each scenario as a whole was calculated, as well as “within” scenarios for each considered waste management process/method (Table 6 ).

The amount of specific total GHG emissions under Scenario 2 is five times less than under Scenario 1. Such a large difference is due to the modernization of existing managed dumpsites (Scenario 1), instead of which MSW is disposed of at sanitary landfills equipped with landfill gas and leachate collection systems, with intermediate insulating layers and top capping (Scenario 2). Such a transition from managed dumpsites to sanitary landfills leads not only to a decrease in the amount of specific released GHG emissions by approx. 1 Mg CO 2 -eq./Mg MSW, but also to a decrease in total emissions due to avoided emissions in the amount of 0.053 Mg CO 2 -eq./Mg MSW generated by energy recovery.

The amount of specific total GHG emissions under Scenario 3 is 3.4 times less than under Scenario 2. This reduction is mainly due to an almost twofold increase in the volume of waste incinerated, along with the introduction of a separate waste collection system (Scenario 3). At the same time, in Scenario 3, the share of plastic in the mixed waste stream sent to incineration is less than in Scenarios 1 and 2 (see Fig.  1 ). Climate-related GHG from waste incineration are generated mainly due to the plastic contained in the waste. Therefore, in Scenario 3, less GHG emissions are released during waste incineration. Reduction in GHG emissions from waste incineration is also facilitated by the recovery of metals from the bottom ash, which occurs only in Scenario 3.

In Scenario 3, the total amount of recycled material is larger than in Scenario 2, since not only part of the mixed waste is recycled, but also separately collected. According to the Scenario 3, metals are not included in the waste fractions collected separately. Metals have a comparably high GHG substitution factor (see Table 5 ); this explains the slight decrease in avoided GHG emissions due to material recovery in Scenario 3 compared to Scenario 2 because of a decreased share of metals in the total waste stream sent for recycling.

Many studies confirm GHG emissions reduction by the application of these waste treatment concepts. It is shown that the recovery of landfill gas from managed landfills has a high potential to reduce GHG emissions from landfills (EI-Fadel and Sbayti 2000 ; Friedrich and Trois 2016 ; Lee et al. 2017 ; Starostina et al. 2014 ). The transfer from the disposal of mixed MSW on landfills to the incineration on waste incineration or waste-to-energy plants leads to further reduction in GHG emissions (Bilitewski and Wuensch 2012 ; Chen 2018 ; Voigt et al. 2015 ). The recovery of secondary materials from MSW allows avoiding additional amounts of GHG emissions (Björklund and Finnveden 2005 ; Franchetti and Kilaru 2012 ; Turner et al. 2015 ; Wuensch and Simon 2017 ).

It should be noted that the calculated results of the direct GHG emissions from landfilling and waste incineration are subject to uncertainties. Waste composition (Table 2 ) and the parameters set/assumed for the landfills (Table 4 ) and waste incineration (Table 3 ) affect the level of the results. Indirect downstream emissions from recovered secondary materials and substituted energy cannot be provided with accuracy, as indicated by missing data for the substitution factors of recovered secondary materials in Russia and the variability of the scenarios for substituted electricity. To get an impression about the possible fluctuation range of the determined results, a sensitivity analysis was carried out. Therefore, all values shown in Tables 1 , 3 , 4 and 5 were ones decreased by 10% and once increased by 10%. The impact of the sensitivity analysis on the GHG net emissions is shown as error bars in Fig.  4 . The results of the sensitivity analysis show a range for the GHG net emissions of the basic scenario between 35.129 and 91.446 Mt CO 2 -eq./a, for the reactive scenario between 5.133 and 16.324 Mt CO 2 -eq./a and for the innovative scenario from − 1.516 to 4.871 Mt CO 2 -eq./a.

All the exact values of the final results shown in Fig.  4 as well as the graphical representation of the results of the sensitivity analysis can be checked in the provided supplementary materials.

The most recent data about global GHG emissions from solid waste disposal shows that direct emissions contribute with 0.67 Gt CO 2 -eq./a (Fischedick et al. 2014 ) to about 1.4% of the total anthropogenic GHG emissions of 49 Gt CO 2 -eq./a (Edenhofer et al. 2015 ). For the Russian Federation, the contribution of the direct emissions from the MSW management accounts for approx. 3.7% of the total GHG emissions of the country of around 2.2 Gt CO 2 -eq./a (Russian Federation 2019 ). In this study, the potential of different waste management methods in relation to climate change impact was assessed using the example of the Russian waste management industry. For this purpose, three scenarios had been developed and analyzed:

Basic scenario (business as usual), based on the existing waste management practices. The scenario implies that 90% of the generated mixed MSW is disposed of on managed dumpsites, 7% is undergone material recovery and 3% incinerated. All the unorganized dumpsites are closed; on managed dumpsites, there is no landfill gas recovery.

Reactive scenario (moderate development). This scenario implies construction of a number of large waste incineration plants and an increase in the share of waste to be recycled so that 84.3% of generated MSW is disposed of in sanitary landfills, 10% is sent to recycling plants for material recovery, and 5.7% is incinerated.

Innovative scenario (active development). This scenario assumes partial implementation of a separate waste collection system and broader introduction of waste processing technologies. As a result, 20% of the total generated MSW is collected separately and then recycled, 14.3% undergoes material recovery, 55.2% is disposed of in sanitary landfills, and 10.5% is incinerated.

For determining weighed average morphological composition of MSW, three clusters of human settlements had been considered, and the respective data on waste compositions had been analyzed. The first cluster includes Moscow and the Republic of Tatarstan, the second cluster includes the major cities (those with the population of more than 0.5 million people), and the third cluster includes the minor cities and rural areas.

For determining emission factors, both own calculation results and reference data from the National Inventory Report and other sources were used. Thus, the amount of fossil carbon, being one of the most important factors determining CO 2 emissions from waste incineration, was calculated based on the waste composition, carbon content and the share of fossil carbon in the waste. For the calculation of the amount of CH 4 released from MSW landfills, the first-order decay kinetics model was used. Avoided GHG emissions are the result of sequestrated non-biodegradable biogenic carbon in landfills (all the scenarios) and recovered landfill gas used to produce electricity (Scenarios 2 and 3). With the use of emission factors for material recovery included those for the recovered valuable waste fractions steel, aluminum, paper and cardboard, glass and plastic, GHG emissions were calculated under each scenario. As it was expected, the basic scenario gives the highest amount of total GHG net emissions of approx. 64 Mt CO 2 -eq./a (1.096 Mg CO 2 -eq./Mg MSW). Under the reactive scenario, the amount of total GHG net emissions is approx. 12.8 Mt CO 2 -eq./a (0.219 Mg CO 2 -eq./Mg MSW), and under the innovative scenario, it is about 3.7 Mt CO 2 -eq./a (0.064 Mg CO 2 -eq./Mg MSW).

The calculation of specific GHG emissions made it possible to assess the extent to which the introduction of various waste treatment methods makes it possible to reduce GHG emissions resulting from the respective waste treatment processes. Analysis of the results of these calculations showed that the transition from managed dumpsites to sanitary landfills can reduce total GHG emissions from the Russian waste management sector by up to 5 times. The introduction of a separate collection system (in which 20% of waste is collected separately) with a simultaneous twofold increase in the share of waste incinerated has led to a more than threefold reduction in total GHG emissions from the sector of Russian waste management. Another factor influencing the reduction in GHG emissions from waste incineration is the recovery of metals from the bottom ash.

Direct GHG emissions can be further reduced with a shift from landfilling to treatment of mixed MSW in material recovery facilities and waste incinerators or even to separate collection and treatment of MSW. In addition, indirect downstream emissions can be avoided by a significant amount via energy and material recovery. With a separate collection and treatment of biowaste and the recovery of district heat from waste incineration process, further GHG mitigation can be obtained. With these additional measures, the MSW industry of the Russian Federation could become a net avoider from a net emitter.

For this study, a number of parameters and emission factors from the literature where used, which does not precisely reflect the situation in Russia. Conducting further research for determining country specific, for a huge country like Russia, possibly even region-specific data and emission factors resulting in the development of a corresponding database would be useful to minimize these uncertainties.

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Wünsch, C., Tsybina, A. Municipal solid waste management in Russia: potentials of climate change mitigation. Int. J. Environ. Sci. Technol. 19 , 27–42 (2022). https://doi.org/10.1007/s13762-021-03542-5

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Received : 17 September 2020

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Accepted : 09 July 2021

Published : 29 July 2021

Issue Date : January 2022

DOI : https://doi.org/10.1007/s13762-021-03542-5

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