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Article

GHG Emissions Assessment of Civil Construction Waste Disposal and Transportation Process in the Eastern Amazon

by
Luiz Maurício Maués
1,*,
Norma Beltrão
2 and
Isabela Silva
1
1
Programa de Pós-Graduação em Engenharia Civil, Instituto de Tecnologia, Campus Universitário Guamá Universidade Federal do Pará (UFPA), 66075-110 Belém, PA, Brazil
2
Programa de Pós-Graduação em Ciências Ambientais, Centro de Ciências Naturais e Tecnologia, Universidade do Estado do Pará (UEPA), 66095-100 Belém, PA, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(10), 5666; https://doi.org/10.3390/su13105666
Submission received: 19 February 2021 / Revised: 13 April 2021 / Accepted: 15 April 2021 / Published: 18 May 2021
(This article belongs to the Section Sustainable Urban and Rural Development)
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Abstract

:
The urbanization process represented by an increased supply of housing and transport infrastructure has taken place at an accelerated rate in several regions of Brazil, especially in the metropolitan areas of the Brazilian Amazon. Despite the existence of environmental policies that guide the proper disposal of civil construction waste (CCW) in Brazil, the impacts of these policies are still negligible, pointing to the need to establish other metrics such as the measurement of greenhouse gas (GHG) emissions in CO2eq associated with civil construction waste. This work aims to evaluate, in the second-largest city in the Brazilian Amazon, the environmental impact generated by the transportation of CCW to disposal sites, having as indicators the volume of this waste and the CO2 emissions produced during a whole year. A literature review on life cycle carbon emissions assessment in building construction and CO2 emissions in transportation are provided to establish the background of the research methodology. Data collection was carried out by searching large generators of construction waste, the companies responsible for transporting construction waste, and the types of vehicles used. Calculation of GHG emissions from CCW transportation was based on the method described in the 2006 IPCC Guidelines. The study identified a volume of waste of around 1244 m3/month, with a generation of 40,440 kgCO2/year, only from small and large generators. Besides the damage identified in this study, there is also the dumping of CCW into urban streams in the city which is causing negative impacts on sanitation and drainage systems. The results point to the need to strengthen local policies to mitigate the impacts of the existing CCW to contribute to a more sustainable city.

1. Introduction

The expansion of construction activities resulting from the rapid population growth and consequent demand for infrastructure, urbanization, and housing work in many parts of the world generates, over time, a large amount of construction waste [1]. Construction waste generally includes materials such as plastics, concrete, wood, bricks, and tiles whose inadequate production and disposal can degrade the environment. These materials can be valuable when recycled or reused, as they are produced through intensive processes using natural resources, many of which are limited and non-renewable [2]. Waste generated by the Brazilian civil construction is at a high rate, between 0.128 m3/m2 per construction area identified in the southern region [3], and 0.21 m3/m2 in the northern region [4]. Despite the growth of this sector, only a small amount of this waste is recycled [5]. To change this scenario, one of the alternatives could be the adoption of mechanisms that foster a circular economy [6], especially opportunities for reductions in energy use, greenhouse gas (GHG) emissions, and waste generation [7,8,9], which remain very deficient in developing countries. This fact is highlighted by the growing number of publications on the topic with the aim of minimizing waste production and its impacts in these countries [10,11,12,13,14,15].
Although many countries have worked to improve waste recycling rates, factors such as weight, low economic value per unit, and legal requirements lead to frequent on-site management of construction waste [16]. In this sense, the solutions can be sorted into actions aimed at the management and reduction of waste generation [17,18], the impact of the waste produced during the execution phase [19], its reuse and final disposal [20,21], and its characteristics [22,23].
The inadequate disposal of construction waste is a problem in many emerging and developing countries [24,25,26,27]. Landfill disposal is the most critical because of the environmental impacts, such as reduction of air quality, destruction of soil structure, risk of fires, water pollution, and visual pollution, in addition to having one of the highest greenhouse gas (GHG) emissions [28,29,30]. In a study conducted in Guangzhou, China, it was estimated that GHG emissions from landfill disposal will account for 75% of the total emissions in 2030, while GHG emissions from recycling disposal will be responsible for only 0.5% [31].
In a scenario of economic growth and urbanization, the disposal of construction waste in landfill remains a reality in many cities in the developing world. In many cases, this is the least costly solution, favored by the fragile environmental management and enforcement systems of these countries, as is the case in Brazil [32]. This is the situation in the city of Belém, located in the eastern part of the Brazilian Amazon, which is the focus of this study. With approximately 1.49 million inhabitants, Belém is the capital of the state of Pará and has for some years maintained an open-air area known as the Aurá landfill for the disposal of urban solid waste (USW) from the city and metropolitan region. As reported by Imbiriba et al. [33], although the Aurá landfill has been deactivated for USW disposal since 2015, it remains the final destination for waste produced by the construction industry.
Since construction waste is mostly inert materials [34], it could be used in various applications, such as in street paving [35,36,37] or in the dosage of mortar and concrete mixtures, replacing natural resources with recycled materials [38]. However, waste reuse initiatives are in the early stages of development in Brazil. Furthermore, as construction waste does not go through a classification process to sort the discarded material types, it may contain reactive substances capable of contaminating soil and water resources, or posing risks to human health [22]. It also contributes to increasing the volume, and accelerating the depletion, of landfill storage areas.
A further consequence of the absence of an effective policy for reducing the volume of construction waste is the amount of gas emitted from vehicles transporting waste from construction sites to the destination (open landfills), contributing to the potential impact on the environment. Despite the relevance of the theme, there have been few studies in the literature that have evaluated GHG emissions from the transportation of construction waste and its environmental impacts. In a survey published by the Climate Observatory, based on the Greenhouse Gas Emissions Estimation System [39], it was shown that Brazil generated about 2.071 billion tons of CO2eq in 2017, and the state of Pará was the main contributor. This was largely due to land use change activities, particularly the deforestation of rainforests. This study also showed that the waste sector emitted 91 million tons of CO2eq, while the transport sector was the main GHG emitter in the energy sector (209 million tons of CO2 eq, or 48%).
Among the many substances that serve as GHGs, the main is CO2. It can be said that there are essentially two categories of energy sources for CO2 emissions, the main one is fossil fuels such as coal, oil, and gas [40], and the second is diesel oil, which is derived from crude oil, in the case of trucks that transport waste.
The objective of this work was to assess the production and inappropriate disposal of construction waste in the city of Belém and to quantify the volume of GHG emissions from vehicles that carry this waste to open landfill. To reach this objective, the GHG emissions inventory methodology will be used.

2. Literature Review

2.1. Construction Waste

The construction industry is one of the largest waste producers in the world, producing one of the largest amounts of solid urban waste. The volume of construction waste produced in China has been estimated at 1.13 billion tons [41], while nearly 890 million tons are produced annually in the European continent [42]. In Brazil, the Institute of Applied Economic Research (IPEA) estimates that 31 million tons of construction waste are generated annually [43]. According to Diniz et al. [44] in the target city of this research, approximately 52,000 m3 of USW are collected per month and it is estimated that 60% of this total comes from the construction sector.
Another problem related to construction waste refers to the segregation of waste according to the type of material; the different characteristics of the construction projects determine the classification of the type of cumbersome waste generated. In their research conducted in China, Lu et al. [45] concluded that construction waste consisted of various types of materials with the predominance of wood and concrete. Non-realization of on-site waste sorting hinders the proper reuse of this material.
According to Wang et al. [10], construction waste in Hong Kong is sent to different locations depending on waste sorting, which may consist of inert materials or materials unsuitable for landfilling. Inert components are materials that can be reused for remediation and ground improvement works (e.g., soil, rubble, and concrete). The non-inert part includes materials such as wood and timber, packaging, and other organic materials. In Hong Kong, it was suggested that each category of waste should be delivered to different waste disposal facilities. In Brazil, to minimize the impact generated by these residues, the National Environment Council (CONAMA) resolution 307 established that construction waste must not be disposed of together with household waste or in dump areas, slopes, or close to water bodies [46]. Even though the legislation has undergone effective changes, there is still a need to highlight further the issue of waste. As stated by Debrah, Vidal, and Dinis [47], it is necessary to instigate environmental education policies in developing countries in order to achieve sustainable development and to trigger a full social transformation.

2.2. Life Cycle Carbon Emissions Assessment (LCCO2A) in Building Construction

The environmental impact of construction activities, from raw material extraction to waste disposal, has been discussed and is controversial. In this sense, agents all over the world involved in the production chain are seeking alternatives to make their products more sustainable [48]. The strategies used range from environmental certifications, i.e. qualitative evaluations, to life cycle assessment (LCA) with quantitative evaluations [49].
Concerning LCA, it can be said that the objective is to quantify the total environmental impacts of buildings over their entire life cycles, considering processes or products, to enable the implementation of opportunities to bring improvements in the environmental sphere [50]. Besides LCA, two other methodologies are also employed in the assessment of the environmental impacts of building construction, namely: (a) the life cycle energy assessment (LCEA), which measures the energy use as resource input; and (b) the life cycle carbon emissions assessment (LCCO2A), which focuses on evaluating CO2 emissions as output over the whole life cycle of a building.
These methodologies have been used by several authors in a variety of applications. In this context, the work developed in Brazil by Maués et al. [51] stands out. These authors carried out an LCCO2A to quantify the amount of CO2 emitted in the main construction processes of social housing projects. In addition, Caldas and Sposto [48] calculated the volume of CO2 generated in the transportation of ceramic and structural blocks. Another research developed by Campos, Punhagui, and John [52] quantified CO2 emissions in the transportation of native wood from the Amazon region.
Among the international studies, the research by Dossche, Boel, and De Corte [53] conducted an LCA into a beam-floor system representative of the current practice in Belgian building. In a comparative study of different types of slabs, the researchers stated that the transport phase and the waste material generated in the production process were the factors that brought about the greatest variations in environmental impact; waste material alone was estimated to generate an increase in the impact of up to 9.78% and transportation of up to 18.24% [19].

2.3. CO2 Emission in Transportation

In order to stabilize average global warming and reduce GHG emissions, the Paris Climate Agreement establishes that the global temperature should not rise by more than 2 ºC above pre-industrial levels, and for that, changes are necessary for several sectors, among them transportation [54]. The transportation sector includes air, ground, and marine vehicles. All share the common characteristic of GHG emissions from burning fossil fuels, causing environmental and public health impacts [55,56]. In the transportation sector, CO2 is the main greenhouse gas associated with emissions [57,58]. Data from the seven top transport CO2 emitter countries (United States, China, India, Russia, Brazil, Japan, and Canada) concluded that they contribute to 45.6% of the global CO2 emissions in this sector [59].
The logistics of product transport represents the major source of CO2 emissions [60], as the transportation of defective materials or residues is part of the production process. Greenhouse gas, and especially CO2, emissions from transport trucks are dependent on the weight of the vehicle [61]. In this sense, construction projects that generate a large volume of tailings require heavy transport activity to the destination and therefore generate more CO2 emissions. In this context, studies quantifying all stages of the process, including road transportation of construction waste, are essential. In the report on the Sector Emissions and Land Use Change—Brazil 2018 [39], the transport sector was the main CO2 emitter in the energy sector, having generated 209 million tons of CO2eq. Thus, it is important to establish inventories to identify and map the profile of CO2 emissions in order to capture opportunities to reduce or even eliminate these emissions.

3. Research Methodology

To achieve the objective of this work, which is to assess the environmental impact of transporting CCW to the disposal site, exploratory research of the quantitative approach was carried out in the second largest city in the Brazilian Amazon, by evaluating the following indicators: the volume of waste and the CO2 emissions produced over a whole year. The steps used in this research are detailed below.

3.1. Data Collection

Data collection started by searching the largest generators of construction waste, i.e., the construction projects that were being carried out in the city and that generated more than 5 m3 of waste. For this, images from the Google Maps® and Google Earth® platforms were used. However, the resolution and the date of the images from such platforms and the afforestation of the city prevent the exact identification of irregular dumping sites within the city. To overcome these barriers, navigation through the city streets using bicycles was planned in order to carry out field observations, making photographic records, and mark points with a GPS.
The second stage of the work was to identify the number of companies registered at the Sanitation Secretariat in the municipality of Belém with clearance to carry out the transportation of construction and demolition waste. With this information, it was possible to contact the companies to identify the types of vehicles used in their transport operations, as well as characteristics such as: brand, manufacture year, model, dumpster volume, number of roll-off containers transported by the vehicle, and number of trips made per vehicle on average per day. Based on this, the vehicles were classified (light, heavy) according to the guidelines drawn up by the National Traffic Council (CONTRAN) Resolution No. 396 of 13/12/2011.

3.2. Quantification of Greenhouse Gases in the Transportation of Waste

The third stage involved calculating GHG emissions from vehicles using the methodology outlined in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. For this purpose, the distances to be traveled by trucks from the production site to the place where the waste was discharged were defined. Subsequently, for each of the calculated distances, the amount of fuel consumed in this operation was identified. Thereafter, the volume of greenhouse gases generated by trucks was calculated, multiplying the amount of fuel consumed in transport by the greenhouse gas generation factors of the gases: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). To facilitate the analysis to be carried out in this research, the quantities of gases were grouped into a single indicator, expressed in carbon dioxide equivalents (CO2eq), according to the Global Warming Potential (GWP) methodology, where the quantity of gases emitted (CO2, CH4, and N2O, among others) are transformed (see Table 1 with the conversion values of each of the gases for their CO2 equivalents).

3.3. Identification of Impacts Caused by Waste in the City

At this stage, we sought to identify the impact of construction waste on the urban environment. Firstly, the volume of gases (CO2eq) generated in the transportation of waste (monthly and annual) was identified, using the total distance of transportation of the waste and the amount of GHG emitted by the transportation vehicles. In the second analysis of the impacts, we sought to identify the type of impact that construction waste generates on the city, such as: the impact on the circulation routes of people and vehicles, the segregation of streams and channels, and the degradation of mangroves, among others.
For that, Google Earth® images from the month of November 2019, with information captured by several satellites, were used [62]. Data on location and affected area were then used to identify, through the Street View mode, sites where irregular dumping of construction waste was practiced.

4. Case Description

The study focuses on the administrative region of Belém, the capital of the state of Pará. Geographically, the city of Belém lies on the Eastern Amazon and has an area of approximately 1059 km2 with around 1.49 million inhabitants [63]. Data were collected in November 2019. The large generators of construction waste were surveyed in the 41 districts of the city using the Google Maps® platform, and when identified, their coordinates were recorded. Subsequently, the (shortest) routes travelled by trucks to transport the waste from the construction sites to the Aurá landfill were drawn, providing information on the distances traveled, as shown in Figure 1.
After identifying the generation sites, as well as the volume of waste generated at each location, the Google Maps® platform was used to estimate the distance between each of the generation sites to the final destination of the waste at the Aurá landfill. For the sake of simplicity, the construction sites were pooled and a total distance value was calculated for each district, as shown in the table with the calculation of “Transport distance between collection points and the Aurá landfill” (see Appendix A, Appendix B, Appendix C and Appendix D).
In order to gather the information needed to estimate the GHGs emitted in the transportation of construction waste from small generators (producing less than 5 m3) in November 2019, approximately 937 km of roads were explored in the city. To do this, routes were recorded using the Strava Running and Cycling® application to track cycling routes and record distances travelled using smart phones equipped with GPS technology. Some examples of routes navigated for data collection are presented in Figure 2. This survey did not aim for a comprehensive sampling of dumpsters in the city, but rather at the production of a representative sample of the disposal of construction waste, as it is known that such tailing dump sites and dumpsters are dynamic, constantly moved from one location to another. During the mapping, it was also possible to quantify the waste produced by small generators along the streets of the target city.
In the survey across the city streets, two classic types of construction waste disposal were identified (all considered to be small generators): those in which a company is hired to provide roll-off containers for waste collection, and those in which the waste is dumped on the roadway, sidewalks, or by curbs (see Figure 2).
With the mapping of irregular waste generation on public streets in the city, it was possible to estimate a volume of about 59 m3 (during the data collection period). Some aspects must be highlighted: (i) this quantification of waste volume is difficult and, therefore, the estimated value is an approximation provided mainly to report the existing problem, primarily linked to the poor environmental education of the population; (ii) the estimated volume of waste was not included in the calculation of GHG emissions because it was found that the dynamics of waste transport to the Aurá landfill adopted by the municipal service was totally different from that of roll-off trucks.
The calculated values of GHGs emitted into the atmosphere by small generators are shown below (see table with “Transport distance between collection points and the Aurá landfill” in the Appendix A, Appendix B, Appendix C and Appendix D).

5. Results and Findings

Greenhouse Gas Emissions

The first analysis consisted of calculating the volume of CO2 emitted by the trucks carrying the waste to the Aurá landfill. Twenty-one firms with containers on the city streets were identified in the survey. They were contacted and they provided specifications for the vehicles that formed their fleet. See the table with “Transport distance between collection points and the Aurá landfill” in the Appendix A, Appendix B, Appendix C and Appendix D.
The survey allowed the calculation of the volume of waste generated by the construction activity in the city, and this data is relevant for the development of adequate public policies. During the month of the survey (November 2019), approximately 1244 m3 of waste was generated by small and large generators in the housing construction and renovation sector.
After identifying the vehicles, the GHG emission quantities for each vehicle were estimated. This survey was carried out according to the information provided in the Vehicle Emissions report in the state of São Paulo as information of this nature is not available for the city of Belém. Table 1 presents the baseline factors, i.e., the amount of greenhouse gases produced (in grams) by vehicles during transporting by kilometer.
Greenhouse gas emissions were calculated through the method described in the IPCC guidelines [64] (as it is an internationally known and accepted methodology, we will not reproduce the method used to perform the calculation, but we recommend reading it for those who wish to obtain more details on how to calculate the emission of greenhouse gases). The average values of the fuel consumed by the trucks were used in the calculation, and this consumption was applied to the distances recorded for each waste generator. Next, the total CO2eq generated by the transportation activity was estimated over the one-month period. Tables with the factors were used as parameters in the calculation of GHG emissions (the Appendix A, Appendix B, Appendix C and Appendix D shows the calculations for small and large waste generators for the period of one month, respectively).
The data presented in the tables show monthly values of 920 kg of CO2eq emitted by trucks for small generators and 2450 kg for large generators. Projections indicate that this activity is capable of generating about 40,440 kg of CO2eq per year. It should be noted that the greenhouse gases emitted refers to the route between the place where the waste is generated and the Aurá landfill. As it is not possible to identify the return route of the truck between the Aurá landfill and the next waste collection point, the return value of the empty truck has not entered into the calculation. However, it can be inferred that this amount is a little smaller than the route calculated in this research, which would significantly increase the amount of gases emitted with this activity.
One of the problems generated by the lack of a more effective policy for the reuse of construction waste is the depletion of landfill areas in large cities, as also pointed out by Ulsen and John [23]. In this context, the Aurá landfill in Belém continued to receive construction waste despite having been deactivated in 2015.
Another important aspect observed was that approximately 23% of the containers of small generators were in front of newly constructed residential buildings with recent owner occupancy, which means that residents were renovating their housing units before occupying the property. It is noteworthy that construction companies do not have a policy of offering greater flexibility to their customers regarding adaptations during the execution of the construction project, taking into account that this would certainly generate a significant reduction in the waste produced as there would not be demolition and removal of finishes by the construction company. Further worsening the scenario, a portion of the construction waste generated in the city ends up being dumped in mangroves (see Figure 3a) or even transported to streams flowing throughout the city (see Figure 3b). Many cities located in the Amazon are near the rivers that form part of the Amazon River Basin. The city of Belém, in addition to being located between a river and a bay, is intersected by a network of canals, many of them in the open, which were old small rivers that were channeled as the city grew. Civil construction and demolition waste, when not removed by accredited companies for proper treatment, is at risk of being dumped in these channels, contributing to the silting up and the occurrence of flooding in the rainy season due to the obstruction of the drainage network.

6. Conclusions and Future Work

The production and inappropriate disposal of civil construction waste (CCW) in the city of Belém, a city with an expanding urban population located in the Brazilian Eastern Amazon, and the subsequent GHG emissions assessment from the vehicles that carry this waste to open landfill were the topics explored in this study. It was found that, if the construction companies had implemented policies to reduce waste during the construction process, several structural problems observed during the research could be mitigated or eliminated. The life cycle carbon emissions assessment (LCCO2A) in building construction was considered as a valid methodology for estimating the CO2 emissions from the observed CCW and its transportation to the final disposal site, the Aurá landfill. This methodology has been applied in high-profile publications, leading to a high level of confidence in the results obtained.
Data collection involved searching for large generators of construction waste, the companies responsible for transporting construction waste, and the types of vehicles used. Data such as the number of dumpsters, their location, average distance to the final disposal site, characteristics of the transport vehicle, number of trips, and GHG emission factors per unit of fuel consumed and distance traveled made it possible to quantify the total CO2eq emitted in the period of one month, namely, November 2019. Overall, our results identified a volume of waste of around 1244 m3/month, with a generation of 40,440 kgCO2/year, only from small and large generators. Our data indicated that the volume of CO2eq (about 917,715.3 kgCO2) released into the atmosphere by motor vehicles powered by fossil fuels is significant and contributes considerably to the environmental impacts. These findings indicated that the impacts of the CCW generation and its transportation to the final disposal site cannot be ignored, and if actions are not taken, this will become a bigger and more frequent problem in the near future as the urbanization process takes place.
More generally, among the impacts observed in the city of Belém, visual pollution on urban roads stands out. Another consequence of the waste being irregularly deposited on public roads was the impairment of the flow of pedestrians on the sidewalks, of bicycles on cycle paths, and even of vehicle traffic, in addition to the obstruction of parking spaces. It was also noticed that the irregular dumping of CCW causes silting in the open channels that run through the city, resulting in a reduction in rainwater retention and absorption capacity, and ultimately causing the frequent flooding of roads and houses, especially in the lower areas.
This study case adds to a growing corpus of research showing that the quantification of these emissions can be considered as a starting point for the adoption of strategies to reduce GHG emissions generated by construction activities, with actions from both the private and public sectors. Economic instruments in environmental policies, such as a reduction of taxes per volume produced and environmental certifications of “zero waste”, among others, could be implemented to reduce the generation of waste and inadequate waste disposal. These instruments tend to be more effective when command and control policies are fragile, as is the case in many medium and large Brazilian cities.
Finally, future studies and models incorporating scenarios based on estimates of the volume of waste generated in the current context are recommended. Subsequently, the results obtained from the simulation of various interventions and actions could be used to minimize waste production and environmental impacts, whether through the optimization of transport routes or the replacement of currently used fossil fuels with more sustainable ones.

Author Contributions

Conceptualization, L.M.M.; data curation, L.M.M.; formal analysis, L.M.M.; funding acquisition, L.M.M.; investigation, I.S.; methodology, L.M.M.; visualization, N.B.; writing—original draft, L.M.M., N.B., and I.S.; writing—review and editing, L.M.M. and N.B. All authors have read and agreed to the published version of the manuscript.

Funding

The authors are thankful to the PROPESP/UFPA for providing the required funds to support the publication of this article.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are thankful to the Coordination of Superior Level Staff Improvement—Brazil (CAPES)—Finance Code 001.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Transport distance between collection points and the Aurá landfill.
Table A1. Transport distance between collection points and the Aurá landfill.
Transport Distance to Aurá Landfill—Small GeneratorsTransport Distance to Aurá Landfill—Large Generators
Container (Units)DistrictGeographic CoordinatesDistance (km)Container (Units)DistrictGeographic CoordinatesDistance (km)
2Batista Campos1°27′35.6″ S 48°29′19.8″ W27.74Guamá1°28′15.5″ S 48°27′32.7″ W27.1
1Batista Campos1°27′29.5″ S 48°29′52.9″ W286Guamá1°28′14.0″ S 48°27′19.4″ W27.6
1Batista Campos1°27′21.3″ S 48°29′40.2″ W27.62Guamá1°28′05.2″ S 48°27′15.0″ W27.3
1Fátima1°27′18.7″ S 48°28′14.4″ W25.71Guamá1°28′06.4″ S 48°28′03.0″ W28.6
1Guamá1°27′40.2″ S 48°28′07.8″ W25.74Guamá1°27′35.4″ S 48°28′13.2″ W24.3
1Marambaia1°23′59.0″ S 48°27′13.0″ W19.68Terra firme1°28′04.6″ S 48°27′10.6″ W27.1
1Marambaia1°23′57.5″ S 48°26′54.7″ W20.31Terra firme1°27′06.6″ S 48°27′02.1″ W24.2
1Marco1°25′42.7″ S 48°27′47.9″ W21.81Terra firme1°27′04.69″ S 48°27′02.26″ W24.3
1Nazaré1°27′07.9″ S 48°29′04.7″ W26.41Terra firme1°26′56.5″ S 48°27′10.3″ W24.6
2Nazaré1°26′56.1″ S 48°28′45.3″ W26.61Marco1°26′33.1″ S 48°27′07.4″ W23
1Nazaré1°26′58.7″ S 48°29′11.5″ W271Marco1°25′55.0″ S 48°27′05.8″ W21.4
1Nazaré1°26′45.5″ S 48°28′41.3″ W25.91Marco1°26′33.9″ S 48°27′31.8″ W22.8
1Nazaré1°26′45.6″ S 48°28′33.6″ W25.62Marco1°26′11.2″ S 48°27′25.4″ W22.6
1Pedreira1°24′58.3″ S 48°27′50.2″ W21.32Pedreira1°26′42.0″ S 48°27′55.1″ W22.9
2Pedreira1°25′38.6″ S 48°27′44.4″ W222Marco1°26′07.2″ S 48°27′43.0″ W21.8
2Sacramenta1°24′47.0″ S 48°28′12.8″ W21.41Marco1°25′48.6″ S 48°27′10.6″ W20.3
1São Brás1°27′32.0″ S 48°28′14.2″ W25.94Pedreira1°25′53.3″ S 48°27′42.1″ W21.8
3São Brás1°27′12.0″ S 48°28′26.7″ W25.71Castanheira1°25′46.1″ S 48°26′53.6″ W22.4
2São Brás1°27′20.5″ S 48°28′26.3″ W26.21Castanheira1°24′13.7″ S 48°25′55.3″ W17.7
1São Brás1°27′17.2″ S 48°28′23.1″ W25.42Curió utinga1°25′10.5″ S 48°26′20.6″ W21.5
1São Brás1°27′16.8″ S 48°28′17.3″ W25.31Marambaia1°23′54.6″ S 48°26′11.1″ W18.1
1São Brás1°27′11.2″ S 48°28′20.6″ W25.24Maracangalha1°24′27.1″ S 48°28′43.6″ W23.5
1São Brás1°27′13.6″ S 48°28′32.4″ W25.68Bonfim1°06′49.1″ S 48°23′17.1″ W48
1São Brás1°27′06.8″ S 48°28′33.5″ W25.46Itaiteua1°15′48.3″ S 48°27′01.3″ W36
2São Brás1°27′15.8″ S 48°28′20.5″ W25.31Campina de icoaraci1°17′37.8″ S 48°28′00.3″ W28.9
3Umarizal1°26′34.6″ S 48°28′38.3″ W262Cruzeiro1°18′16.8″ S 48°28′39.2″ W29
2Umarizal1°26′45.3″ S 48°28′25.6″ W25.18Tenoné1°17′43.5″ S 48°26′15.3″ W30.2
1Umarizal1°26′16.7″ S 48°28′50.1″ W26.41Tapanã1°21′09.4″ S 48°27′49.3″ W23.3
1Nazaré1°27′10.9″ S 48°28′57.9″ W26.81Tapanã1°21′05.9″ S 48°28′07.3″ W23.7
1São Brás1°27′09.1″ S 48°28′07.9″ W23.33Maguari1°21′06.1″ S 48°22′44.0″ W10.7
1Nazaré1°26′56.6″ S 48°28′45.5″ W25.15Coqueiro1°21′13.7″ S 48°25′06.9″ W17.9
1Campina1°26′41.9″ S 48°29′43.8″ W26.4440 horas1°21′25.5″ S 48°25′10.7″ W17.5
1Campina1°26′55.2″ S 48°29′53.7″ W26.74Coqueiro1°21′37.7″ S 48°25′06.9″ W18.1
1Campina1°26′59.2″ S 48°30′02.0″ W27.1440 horas1°21′50.2″ S 48°25′13.0″ W16.7
1Umarizal1°26′31.8″ S 48°29′02.7″ W25.62Maguari1°20′28.2″ S 48°23′37.5″ W14.1
1São Brás1°27′12.4″ S 48°28′20.3″ W23.71Paar1°20′30.5″ S 48°23′43.7″ W15.1
1Umarizal1°26′08.8″ S 48°29′28.7″ W25.32Icuí guajará1°20′17.8″ S 48°24′40.4″ W16.5
1Reduto1°26′48.0″ S 48°29′31.9″ W26.11Cidade nova1°21′20.8″ S 48°24′18.3″ W15.2
1Reduto1°26′52.3″ S 48°29′33.8″ W271Pedreirinha1°21′31.5″ S 48°20′55.9″ W7.6
1Nazaré1°27′07.7″ S 48°28′42.6″ W24.21Novo horizonte1°21′35.8″ S 48°19′55.4″ W6.4
1Umarizal1°26′33.0″ S 48°29′06.1″ W25.71Bairro novo1°21′27.3″ S 48°20′19.4″ W6.3
1Umarizal1°26′29.7″ S 48°29′07.7″ W25.61Tapanã1°21′38.1″ S 48°27′44.6″ W24.2
1Canudos1°27′06.6″ S 48°27′32.0″ W23.32Parque verde1°21′56.9″ S 48°26′06.0″ W20.1
1Souza1°24′46.0″ S 48°26′33.6″ W17.91Cabanagem1°21′39.8″ S 48°26′26.4″ W20.4
1Coqueiro1°20′45.5″ S 48°26′55.7″ W20.71Cabanagem1°21′39.8″ S 48°26′26.4″ W20.4
2Marambaia1°24′27.9″ S 48°27′39.1″ W22.61Águas lindas1°23′55.2″ S 48°23′04.1″ W13.7
1Julia seffer1°23′23.2″ S 48°23′27.1″ W12.5
1Atalaia1°23′05.2″ S 48°26′03.0″ W18
1Guanabara1°23′36.1″ S 48°24′43.0″ W14.1
1Souza1°24′37.2″ S 48°26′36.1″ W18.9
2Pedreira1°26′09.0″ S 48°28′39.2″ W24.6
1Pedreira1°25′58.8″ S 48°28′12.0″ W23
3Pedreira1°25′32.6″ S 48°28′32.1″ W23.8
4Telégrafo1°25′42.1″ S 48°29′28.4″ W25.1
1Marco1°26′26.5″ S 48°28′04.4″ W23.1
1Umarizal1°26′44.0″ S 48°28′53.4″ W24.8
1Umarizal1°26′48.8″ S 48°29′01.5″ W25.1
2Umarizal1°26′43.2″ S 48°29′07.1″ W25.5
2Umarizal1°26′20.4″ S 48°29′06.6″ W25
3Umarizal1°26′26.1″ S 48°29′35.0″ W25.9
1Reduto1°26′36.1″ S 48°29′41.2″ W26.1
2São braz1°26′54.0″ S 48°28′11.8″ W23.8
1São braz1°26′57.1″ S 48°29′06.1″ W25.9
2Umarizal1°26′52.4″ S 48°29′04.3″ W25.3
2Umarizal1°26′51.8″ S 48°28′47.7″ W25.1
2Nazaré1°26′47.4″ S 48°28′48.2″ W24.8
1Umarizal1°26′48.0″ S 48°28′50.4″ W24.8
1Cidade velha1°28′01.8″ S 48°29′49.9″ W27.6
2Umarizal1°26′48.0″ S 48°29′05.8″ W18.7
2Umarizal1°26′25.6″ S 48°29′07.8″ W18.9
2Umarizal1°26′35.9″ S 48°28′46.4″ W19.1
2Umarizal1°26′34.7″ S 48°29′17.7″ W19.1
2Marco1°26′07.1″ S 48°27′43.5″ W16.8
2Jurunas1°27′57.7″ S 48°29′18.7″ W22.1
2Val-de-caes1°23′21.5″ S 48°28′09.6″ W17.1
2Val-de-caes1°23′21.9″ S 48°28′07.9″ W17.1
2Val-de-caes1°23′28.4″ S 48°28′08.3″ W17.2
2Val-de-caes1°23′28.6″ S 48°28′10.8″ W17.2
4Val-de-caes1°23′28.8″ S 48°28′09.7″ W17.2

Appendix B

Table
Table A2. Characteristics of the vehicles that made up the fleet that wastes transport
Table A2. Characteristics of the vehicles that made up the fleet that wastes transport
CompanyTruck ModelPBTCategory–CONTRANYear of ManufactureWaste Volume of the Container-m3Load per Container per TripNumber of Trips per Day per TruckNumber of Containers per Truck per DayPhase Proconave *Autonomy (km/L) *
1Ford Cargo 623223Heavy20115236P53.4
Ford 1318016Heavy20124124P73.6
2Volkswagen 13.19016Heavy20125248P73.6
Volkswagen 13.19016Heavy20125248P73.6
3Mercedes Benz ATEGO 171816Heavy20124248P73.6
Mercedes Benz L-131923Heavy20125248P73.6
4Ford 1318016Heavy20121524P73.6
Volkswagen 13.19016Heavy20125248P73.6
5Volkswagen 1719016Heavy20155133P73.6
Volkswagen 1719116Heavy20155236P73.6
6Volkswagen 1719016Heavy20155133P73.6
Volkswagen 1719016Heavy20155133P73.6
7Volkswagen 2428016Heavy20135236P73.6
Volkswagen 2428016Heavy20135236P73.6
8Volkswagen 13.19016Heavy20125248P73.6
Volkswagen 13.19016Heavy20125248P73.6
9Ford Cargo 141823Heavy19895236P2/P3/P43.5
Volkswagen 1719016Heavy20114236P53.4
10C- 1119, FORD16Heavy20145236P73.6
C- 1119, FORD16Heavy20145236P73.6
11Volkswagen 2428016Heavy20135236P73.6
Volkswagen 1318016Heavy20085236P53.4
Iveco /EUROCARGO 170E2223Heavy20195236P73.6
12Volkswagen 171023Heavy20055133P53.5
Mercedes 111316Heavy19705236P2/P3/P43.5
Atego 1419 Mercedes Benz16Heavy20135236P73.6
13Atego 1419 Mercedes Benz16Heavy20195236P73.6
Mercedes Benz 131916Heavy20135236P73.6
Mercedes Benz 132016Heavy20135236P73.6
14Mercedes Benz 131916Heavy20135111P73.6
15Volkswagen 1719016Heavy20175236P73.6
Mercedez Benz Atego 171916Heavy20165236P73.6
Mercedez Benz Atego 172016Heavy20195236P73.6
Mercedez Benz Atego 172116Heavy20195236P73.6
16Mercedez Benz Atego 141823Heavy20054248P53.5
Mercedez Benz Atego 171816Heavy20094248P53.4
17Volkswagen 1415016Heavy19965133P2/P3/P43.5
18FORD/CARGO 121516Heavy19885236P2/P3/P43.5
19Mercedes Benz 132016Heavy20135236P73.6
Mercedes Benz 132016Heavy20135236P73.6
20Volkswagen 2428016Heavy20135236P73.6
Volkswagen 2428016Heavy20135236P73.6
21Mercedez Benz Atego 171816Heavy20094248P53.4
Mercedez Benz Atego 171816Heavy20094248P53.4
* Information retrieved from the series of Vehicle Emission reports in the state of São Paulo (2018).

Appendix C

Table
Table A3. Factors used as parameters in the calculation of greenhouse gas (GHG) emissions.
Table A3. Factors used as parameters in the calculation of greenhouse gas (GHG) emissions.
InputsAtmosphere Gas Emissions (g)
Container (und.)DistrictAverage Transport Distance (km)Number of Trips (und.)Fuel Consumption (L/KM)Total Fuel Consumption (L)CO2COHCNOxMPCH4N2OTotal CO2eq (kg)
4Batista Campos27.820.4223.3560,78114.823.2889.022.561.670.8361
1Fátima25.710.4210.7928,08713.73.0382.292.361.540.7728
1Guamá25.710.4210.7928,08713.73.0382.292.361.540.7728
4Marambaia20.820.4217.4745,47511.092.4566.61.911.250.6246
1Marco21.810.429.1623,84411.622.5769.82.011.310.6524
9Nazaré2650.4254.6142,12413.863.0783.252.391.560.78142
3Pedreira21.720.4218.2347,45311.572.5669.4821.30.6548
2Sacramenta21.410.428.9923,40111.412.5368.521.971.280.6424
15São Brás25.280.4284.67220,39713.432.9780.692.321.510.76221
10Umarizal25.750.4253.97140,48413.73.0382.292.361.540.77141
2Reduto26.610.4211.1729,07614.183.1485.172.451.60.829
1Canudos23.310.429.7925,48412.422.7574.612.141.40.726
1Souza17.910.427.5219,5759.542.1157.321.651.070.5420
1Coqueiro20.710.428.6922,62111.032.4466.281.91.240.6223
3Campina26.720.4222.4358,38614.233.1585.492.461.60.859
Total357.0 351.6915,275.0190.342.11143.132.821.410.7920.0

Appendix D

Table
Table A4. Factors used as parameters in the calculation of GHG emissions.
Table A4. Factors used as parameters in the calculation of GHG emissions.
InputsAtmosphere Gas Emissions (g)
Container (und.)DistrictAverage Transport Distance (km)Number of Trips (und.)Fuel Consumption (L/KM)Total Fuel Consumption (L)CO2COHCNOxMPCH4N2OTotal CO2eq (kg)
17Guamá26.9890.42101.98265,45414.383.1886.392.481.620.81266
11Terra Firme25.0560.4263.13164,32713.352.9680.212.31.50.75165
11Marco21.4860.4254.13140,90011.452.5368.781.981.290.64141
12Pedreira23.2260.4258.51152,30212.382.7474.352.141.390.7153
2Castanheira20.0510.428.4221,91710.692.3764.21.841.20.622
2Curió Utinga21.510.429.0323,50511.462.5468.841.981.290.6524
1Marambaia18.110.427.619,7839.652.1457.961.671.090.5420
4Maracangalha23.520.4219.7451,38312.532.7775.252.161.410.7152
8Bonfim4840.4280.64209,90625.585.66153.74.422.881.44210
6Itaiteua3630.4245.36118,07219.194.25115.273.312.161.08118
1Campina Icoaraci28.910.4212.1431,60015.43.4192.542.661.730.8732
2Cruzeiro2910.4212.1831,70515.463.4292.862.671.740.8732
8Tenoné30.240.4250.74132,07616.13.5696.72.781.810.91132
9Coqueiro1850.4237.898,3939.592.1257.641.661.080.5499
8Quarenta Horas17.140.4228.7374,7849.112.0254.751.571.030.5175
5Maguari12.430.4215.6240,6596.611.4639.71.140.740.3741
1Paar15.110.426.3416,5038.051.7848.351.390.910.4517
2Icuí Guajará16.510.426.9318,0398.791.9552.831.520.990.518
1Cidade Nova15.210.426.3816,6078.11.7948.671.40.910.4617
1Pedreirinha7.610.423.1983044.050.924.340.70.460.238
1Novo Horizonte6.410.422.6970023.410.7620.490.590.380.197
1Bairro Novo6.310.422.6568983.360.7420.170.580.380.197
3Tapanã23.7320.4219.9351,87812.652.875.982.181.420.7152
2Parque Verde20.110.428.4421,96910.712.3764.361.851.210.622
2Cabanagem20.410.428.5722,30810.872.4165.321.881.220.6123
1Águas Lindas13.710.425.7514,9677.31.6243.871.260.820.4115
1Julia Seffer12.510.425.2513,6666.661.4840.031.150.750.3814
1Atalaia1810.427.5619,6799.592.1257.641.661.080.5420
1Guanabara14.110.425.9215,4107.521.6645.151.30.850.4216
1Souza18.910.427.9420,66810.072.2360.521.741.130.5721
4Telégrafo25.120.4221.0854,87113.382.9680.372.311.510.7555
1Reduto26.110.4210.9628,52913.913.0883.572.41.570.7829
3São Braz24.8520.4220.8754,32513.252.9379.572.291.490.7555
2Nazaré24.810.4210.4227,12313.222.9379.412.281.490.7427
1Cidade Velha27.610.4211.5930,16914.713.2688.382.541.660.8330
22Umarizal23.11110.42106.77277,92212.322.73742.131.390.69278
2Jurunas22.110.429.2824,15611.782.6170.762.031.330.6624
12Val-De-Cães17.1660.4243.24112,5549.152.0254.951.581.030.51113
TOTAL799 9382,440,313425.7894.262557.8773.5247.9423.962450

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Figure 1. Location of the large waste generators and example of routes from the origin site to the Aurá landfill (Brazil).
Figure 1. Location of the large waste generators and example of routes from the origin site to the Aurá landfill (Brazil).
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Figure 2. Example of routes for identifying small waste generators (Brazil).
Figure 2. Example of routes for identifying small waste generators (Brazil).
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Figure 3. Environmental impacts generated by construction waste (Brazil). (a) Construction waste dumped in mangroves (Brazil). (b) Construction waste carried to streams (Brazil).
Figure 3. Environmental impacts generated by construction waste (Brazil). (a) Construction waste dumped in mangroves (Brazil). (b) Construction waste carried to streams (Brazil).
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Table
Table 1. Factors used as parameters in the calculation of GHG emissions.
Table 1. Factors used as parameters in the calculation of GHG emissions.
Emission Factors of GHG Generated by Vehicles
CO2 (g/l)CO(g/km)HC (g/km)NOx (g/km)MPCH4 (g/km)N2O (g/km)
26030.5330.1183.2020.0920.060.03
Global Warming Factors
CO2 (g/l)CO(g/km)HC (g/km)NOx (g/km)MPCH4 (g/km)N2O (g/km)
1000025298
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Maués, L.M.; Beltrão, N.; Silva, I. GHG Emissions Assessment of Civil Construction Waste Disposal and Transportation Process in the Eastern Amazon. Sustainability 2021, 13, 5666. https://doi.org/10.3390/su13105666

AMA Style

Maués LM, Beltrão N, Silva I. GHG Emissions Assessment of Civil Construction Waste Disposal and Transportation Process in the Eastern Amazon. Sustainability. 2021; 13(10):5666. https://doi.org/10.3390/su13105666

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Maués, Luiz Maurício, Norma Beltrão, and Isabela Silva. 2021. "GHG Emissions Assessment of Civil Construction Waste Disposal and Transportation Process in the Eastern Amazon" Sustainability 13, no. 10: 5666. https://doi.org/10.3390/su13105666

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