Evaluation of Biogas Potential from Livestock Manures and Multicriteria Site Selection for Centralized Anaerobic Digester Systems: The Case of Jalisco, México
Abstract
:1. Introduction
1.1. Livestock Waste Management
1.2. Livestock Waste Importance and Pollution in Jalisco, Mexico
1.3. Cluster Spatial Analysis for the Anaerobic Treatment of Livestock Waste
2. Methods
2.1. Data Collection and Processing
2.2. Identification of Critical Clusters
2.2.1. Total Livestock Waste Produced by the LPU Closest to the Potential Sites
2.2.2. Distance between the Potential Sites and the Nearest Livestock Production Unit
2.2.3. Distance between the Potential Sites and the Nearest Urban Community
2.2.4. Distance between the Potential Sites and the Nearest Superficial Water Body
2.2.5. Slope of Potential Sites
2.3. Critical Cluster Analysis
2.3.1. Nitrogen and Phosphorus Recovery
2.3.2. Energetic Potential Calculation
2.3.3. Greenhouse Gas Emission Calculation and Potential Reduction
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Authors | Year | Total Livestock Waste Production at Nearest LPU to the Site | Distance between the Site and the Nearest LPU | Distance between the Site and the Nearest Urban Community | Distance between the Site and the Nearest Superficial Water Body | Slope of the Site | Study Region |
---|---|---|---|---|---|---|---|
Mukherjee et al. [73] | 2015 | X | X | X | X | Connecticut, USA | |
Comber et al. [74] | 2015 | X | X | East Midlands, UK | |||
Ma et al. [22] | 2004 | X | X | X | X | X | New York, USA |
Venier and Yabar [56] | 2017 | X | X | X | X | Buenos Aires, Argentina | |
Zareei [75] | 2018 | X | X | X | X | Iran | |
Dagnall, Hill, and Pegg [58] | 2000 | X | X | X | X | UK | |
Thompson, Wang, and Li [57] | 2013 | X | X | X | X | X | Vermont, USA |
Höhn et al. [59] | 2014 | X | X | X | Southern Finland | ||
Batzias, Sidiras, and Spryou [21] | 2005 | X | X | Greece | |||
Zubaryeva et al. [76] | 2012 | X | X | X | X | X | Apulia Region, Italy |
Waste Production Rates per Species (Ri) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Livestock Species | kg head−1 day−1 | kg head−1 month−1 | kg head−1 year−1 | |||||||
Value | Average | Range | Value | Average | Range | Value | Average | Range | Source | |
Cattle | 31.6 | 35.37 | 30.74–47.70 | 960.96 | 1075.73 | 934.80–1450.55 | 11,534.00 | 12,911.26 | 11,220.10–17,410.5 | [79] |
36.98 | 1124.56 | 13,497.70 | [80] | |||||||
30.74 | 934.80 | 11,220.10 | [81] | |||||||
32.50 | 988.33 | 11,862.50 | [82] | |||||||
47.70 | 1450.56 | 17,410.50 | [83] | |||||||
32.72 | 995.02 | 11,942.80 | [84] | |||||||
Swine | 5.72 | 8.03 | 4.80–15.15 | 173.95 | 244.39 | 145.96–460.71 | 2087.80 | 2933.38 | 1752.00–5529.75 | [85] |
11.47 | 348.80 | 4186.55 | [79] | |||||||
4.80 | 145.97 | 1752.00 | [80] | |||||||
15.15 | 460.71 | 5529.75 | [82] | |||||||
6.03 | 183.37 | 2200.95 | [84] | |||||||
5.05 | 153.57 | 1843.25 | [86] | |||||||
Poultry | 0.35 | 0.19 | 0.09–0.35 | 10.64 | 5.73 | 2.73–10.64 | 127.75 | 68.74 | 32.85–127.75 | [85] |
0.10 | 3.04 | 36.50 | [79] | |||||||
0.25 | 7.60 | 91.25 | [80] | |||||||
0.17 | 5.17 | 62.05 | [82] | |||||||
0.17 | 5.17 | 62.05 | [84] | |||||||
0.09 | 2.74 | 32.85 | [86] |
Parameter | Unit | Weight (%) | Value | ||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
Total livestock waste production at nearest LPU to the site | Ton day−1 | 30 | 0–1 | 1–5 | 5–10 | 10–20 | >20 |
Distance between the site and the nearest LPU | m | 30 | >2000 | 1500–2000 | 1000–1500 | 500–1000 | 0–500 |
Distance between the site and the nearest urban community | m | 10 | >2000 | 1500–2000 | 1000–1500 | 500–1000 | 0–500 |
Distance between the site and the nearest superficial water body | m | 20 | >500 | 300–500 | 100–300 | 50–100 | 0–50 |
Slope of the site | % | 10 | 0–2 | 2–4 | 4–6 | 8–10 | >10 |
Volatile Solid Fraction (Vs) (%) | Methane Production (Mp) (Nm3 CH4 kg Volatile Solids−1) | |||||||
---|---|---|---|---|---|---|---|---|
Livestock Species | Value | Average | Range | Source | Value | Average | Range | Source |
Cattle | 10.79 | 16.80 | 10.79–28.8 | [99] | 0.24 | 0.19 | 0.13–0.24 | [28] |
16 | [100] | |||||||
28.8 | [101] | 0.13 | [28] | |||||
10.86 | [84] | |||||||
13.12 | [102] | 0.21 | [103] | |||||
21.7 | [104] | |||||||
Swine | 12.23 | 14.80 | 6.22–22 | [99] | 0.29 | 0.34 | 0.29–0.45 | [28] |
20 | [100] | |||||||
22 | [101] | 0.45 | [28] | |||||
8.59 | [84] | |||||||
20.02 | [102] | 0.29 | [103] | |||||
6.22 | [28] | |||||||
Poultry | 19.38 | 24.80 | 17–37.21 | [99] | 0.157 | 0.19 | 0.02–0.39 | [28] |
17 | [100] | |||||||
19.5 | [101] | 0.023 | [28] | |||||
19.11 | [84] | |||||||
37.21 | [102] | 0.39 | [105] | |||||
36.7 | [106] |
Scenario | Energetic Potential Calculation | Greenhouse Gas Emission Calculation |
---|---|---|
A | The methane produced by the anaerobic digestion (AD) process is transformed into electric energy by low efficiency small farm-scale generators = 25%) [109] | Waste generated by the individual LPUs is disposed of directly by field application, which is the most common practice in Jalisco. N2O is reportedly generated at a rate of 0.2 kg of N2O per kg of the nitrogen content of the waste. The CH4 generated by the uncontrolled degradation of organic matter in soil is also considered. N2O and CH4 present values of 298 and 25 kg CO2eq per kg, respectively [111,115]. |
B | The methane produced by the AD process is transformed into electric energy by high efficiency centralized generators ( = 40%) [109]. | Waste generated by each specific LPU is treated by anaerobic digestion in a CADU and the biogas obtained through this process is used for the production of electric energy. Since N2O generation can be negligible during controlled AD only CH4 generated during the AD process is taken into account. Additionally, as it is used for energy generation, the CH4 is transformed into CO2 by combustion at a rate of 2.75 kg of CO2 per kg of CH4 [111,115]. |
Cluster | Municipality | Livestock Waste (Gg year−1) | Total Nitrogen Recovery (Mg year−1) | Total Phosphorus Recovery (Mg year−1) | CH4 (Mg year−1) | Electricity Potential A (MW) | Electricity Potential B (MW) | CO2eq A (Gg year−1) | CO2eq B (Gg year−1) | Emission Reduction CO2eq (Gg year−1) | |
---|---|---|---|---|---|---|---|---|---|---|---|
A | Lagos de Moreno | Min | 343.08 | 3930.44 | 2722.87 | 3398.79 | 0.97 | 1.55 | 108.39 | 9.35 | 99.05 |
Mean | 594.53 | 7314.80 | 4865.64 | 19,266.00 | 8.21 | 13.13 | 512.29 | 52.98 | 459.31 | ||
Max | 1099.95 | 13,571.19 | 9085.67 | 76,328.91 | 21.78 | 34.85 | 1989.11 | 209.91 | 1779.20 | ||
Unión de San Antonio | Min | 22.36 | 152.06 | 127.21 | 250.62 | 0.07 | 0.11 | 7.17 | 0.69 | 6.48 | |
Mean | 33.53 | 240.27 | 208.07 | 1039.09 | 0.44 | 0.71 | 26.73 | 2.86 | 23.88 | ||
Max | 58.58 | 436.04 | 386.44 | 3373.17 | 0.96 | 1.54 | 86.93 | 9.28 | 77.65 | ||
Total A | Min | 365.45 | 4082.50 | 2850.08 | 3649.41 | 1.04 | 1.67 | 115.57 | 10.04 | 105.53 | |
Mean | 628.05 | 7555.07 | 5073.72 | 20,305.09 | 8.65 | 13.84 | 539.03 | 55.84 | 483.19 | ||
Max | 1158.54 | 14,007.23 | 9472.11 | 79,702.08 | 22.75 | 36.39 | 2076.04 | 219.18 | 1856.85 | ||
B | Atotonilco el Alto | Min | 29.83 | 226.62 | 203.23 | 347.59 | 0.10 | 0.16 | 10.04 | 0.96 | 9.08 |
Mean | 47.36 | 369.96 | 337.03 | 1547.4 | 0.66 | 1.05 | 39.88 | 4.26 | 35.63 | ||
Max | 86.23 | 686.25 | 631.50 | 5097.45 | 1.45 | 2.33 | 131.53 | 14.02 | 122.44 | ||
La Barca | Min | 32.36 | 271.18 | 256.20 | 391.20 | 0.11 | 0.18 | 11.40 | 1.08 | 10.32 | |
Mean | 54.2 | 454.03 | 428.97 | 1850.5 | 0.79 | 1.26 | 47.76 | 5.09 | 42.67 | ||
Max | 102.16 | 855.90 | 808.66 | 6166.73 | 1.76 | 2.82 | 159.27 | 16.96 | 148.95 | ||
Ocotlán | Min | 57.86 | 344.46 | 260.06 | 621.18 | 0.18 | 0.28 | 17.58 | 1.70 | 15.87 | |
Mean | 81.31 | 519.81 | 415.88 | 2356.38 | 1 | 1.61 | 60.48 | 6.48 | 54 | ||
Max | 134.93 | 912.77 | 760.93 | 7496.59 | 2.14 | 3.42 | 192.85 | 20.62 | 176.98 | ||
Tototlán | Min | 184.96 | 721.85 | 296.16 | 1774.70 | 0.50 | 0.81 | 48.67 | 4.88 | 43.79 | |
Mean | 218.18 | 872.71 | 380.54 | 4960.16 | 2.11 | 3.38 | 126.02 | 13.64 | 112.38 | ||
Max | 302.39 | 1249.00 | 581.33 | 14,428.91 | 4.12 | 6.59 | 368.17 | 39.68 | 324.38 | ||
Zapotlán del Rey | Min | 11.37 | 95.23 | 89.97 | 137.37 | 0.04 | 0.06–0.99 | 4.00–55.93 | 0.38–5.96 | 3.62–52.30 | |
Mean | 19.03 | 159.44 | 150.64 | 649.81 | 0.28 | 0.44 | 16.77 | 1.79 | 14.98 | ||
Max | 35.88 | 300.55 | 283.97 | 2165.48 | 0.62 | 0.99 | 55.93 | 5.96 | 52.30 | ||
Total B | Min | 316.39 | 1659.32 | 1105.63 | 3272.04 | 0.93 | 1.49 | 91.69 | 9.00 | 82.69 | |
Mean | 420.08 | 2375.94 | 1713.05 | 11,364.25 | 4.84 | 7.75 | 290.91 | 31.25 | 259.66 | ||
Max | 661.60 | 4004.47 | 3066.39 | 35,355.16 | 10.09 | 16.14 | 907.75 | 97.23 | 825.05 |
Cluster | Municipality | Livestock Waste (Gg year−1) | Total Nitrogen Recovery (Mg year−1) | Total Phosphorus Recovery (Mg year−1) | CH4 (Mg year−1) | Electricity Potential A (MW) | Electricity Potential B (MW) | CO2eq A (Gg year−1) | CO2eq B (Gg year−1) | Emission Reduction CO2eq (Gg year−1) | |
---|---|---|---|---|---|---|---|---|---|---|---|
C | Encarnación de Díaz | Min | 206.60 | 2435.94 | 1538.43 | 1906.13 | 0.54 | 0.87 | 62.17 | 5.24 | 56.93 |
Mean | 352.66 | 4589.53 | 2791.53 | 11,063.63 | 4.71 | 7.54 | 296.45 | 30.42 | 266.02 | ||
Max | 640.03 | 8456.62 | 5186.68 | 45,612.32 | 13.02 | 20.83 | 1190.71 | 125.43 | 1133.78 | ||
San Juan de Los Lagos | Min | 580.95 | 7964.80 | 4898.05 | 5078.97 | 1.45 | 2.32 | 174.44 | 13.97 | 160.48 | |
Mean | 1049.83 | 15,431.17 | 9070.54 | 33,601.92 | 14.31 | 22.9 | 909.04 | 92.41 | 816.63 | ||
Max | 1947.94 | 28,585.88 | 16,893.80 | 145,769.25 | 41.60 | 66.56 | 3814.60 | 400.87 | 3654.13 | ||
Teocaltiche | Min | 7.10 | 138.41 | 66.87 | 39.02 | 0.01 | 0.02 | 1.80 | 0.10 | 1.70 | |
Mean | 14.05 | 284.53 | 133.79 | 431.13 | 0.18 | 0.29 | 12.17 | 1.19 | 10.98 | ||
Max | 26.02 | 525.02 | 247.52 | 2278.55 | 0.65 | 1.04 | 60.09 | 6.27 | 58.40 | ||
Jalostotitlán | Min | 149.33 | 2515.21 | 1353.20 | 1056.25 | 0.30 | 0.48 | 41.40 | 2.90 | 38.49 | |
Mean | 284.71 | 5056.35 | 2615.32 | 8943.86 | 3.81 | 6.1 | 247.48 | 24.6 | 222.88 | ||
Max | 529.26 | 9350.14 | 4855.01 | 43,283.50 | 12.35 | 19.76 | 1137.81 | 119.03 | 1099.32 | ||
Total C | Min | 943.97 | 13,054.37 | 7856.55 | 8080.37 | 2.30 | 3.69 | 279.81 | 22.22 | 257.59 | |
Mean | 1701.26 | 25,361.58 | 14,611.18 | 54,040.53 | 23.02 | 36.83 | 1465.13 | 148.61 | 1316.52 | ||
Max | 3143.25 | 46,917.67 | 27,183.01 | 236,943.62 | 67.62 | 108.19 | 6203.22 | 651.59 | 5945.63 | ||
D | Acatic | Min | 54.51 | 492.62 | 436.33 | 637.50 | 0.18 | 0.29 | 18.87 | 1.75 | 17.12 |
Mean | 92.25 | 848.83 | 740.5 | 3127.88 | 1.33 | 2.13 | 81.2 | 8.6 | 72.6 | ||
Max | 173.70 | 1594.98 | 1393.79 | 10,806.85 | 3.08 | 4.93 | 279.68 | 29.72 | 262.56 | ||
Tepatitlán de Morelos | Min | 201.33 | 9024.47 | 1686.64 | 1872.81 | 0.53 | 0.86 | 62.25 | 5.15 | 57.10 | |
Mean | 359.93 | 4943.44 | 3076.59 | 11,652.53 | 4.96 | 7.94 | 312.87 | 32.04 | 280.82 | ||
Max | 670.07 | 9175.12 | 5740.97 | 48,635.18 | 13.88 | 22.21 | 1270.56 | 133.75 | 1213.46 | ||
Zapotlanejo | Min | 32.78 | 306.42 | 167.07 | 290.22 | 0.08 | 0.13 | 9.08 | 0.80 | 8.28 | |
Mean | 49.66 | 554.22 | 298.94 | 1394.39 | 0.59 | 0.95 | 37.22 | 3.83 | 33.39 | ||
Max | 82.61 | 993.04 | 544.69 | 5601.77 | 1.60 | 2.56 | 145.96 | 15.40 | 137.68 | ||
Total D | Min | 288.61 | 3387.79 | 2290.05 | 2800.53 | 0.80 | 1.29 | 90.20 | 7.70 | 82.50 | |
Mean | 501.85 | 6346.49 | 4116.03 | 16,174.8 | 6.89 | 11.02 | 431.29 | 44.48 | 386.81 | ||
Max | 926.38 | 11,763.14 | 7679.45 | 65,043.82 | 18.56 | 29.70 | 1696.20 | 178.87 | 16,13.70 |
Livestock Waste (Gg year−1) | Total Nitrogen Recovery (Mg year−1) | Total Phosphorus Recovery (Mg year−1) | CH4 (Mg year−1) | Electricity Potential A (MW) | Electricity Potential B (MW) | CO2eq A (Gg year−1) | CO2eq B (Gg year−1) | Emission Reduction CO2eq (Gg year−1) | ||
---|---|---|---|---|---|---|---|---|---|---|
Sum Critical Clusters | Min | 1914.42 | 22,183.98 | 14,102.31 | 17,802.36 | 5.08 | 8.13 | 577.28 | 48.96 | 528.32 |
Mean | 3251.23 | 41,639.08 | 25,513.98 | 101,884.68 | 43.4 | 69.44 | 2726.36 | 280.18 | 2446.18 | |
Max | 5889.77 | 76,692.50 | 47,400.96 | 417,044.68 | 119.02 | 190.43 | 10,883.20 | 1146.87 | 10,354.89 | |
Jalisco | Min | 2370.47 | 26,482.52 | 17,365.25 | 22,660.38 | 6.47 | 10.35 | 724.34 | 62.32.33 | 662.03 |
Mean | 4002.62 | 49,240.41 | 31,153.68 | 126,025.78 | 53.63 | 85.88 | 3354.19 | 346.57 | 3012.62 | |
Max | 7256.18 | 90,752.42 | 57,931.67 | 505,210.76 | 144.18 | 230.69 | 13,171.15 | 1389.33 | 12,509.12 |
Favorable | Unfavorable | |
---|---|---|
Strengths | Weaknesses | |
Internal | It is a quantitative methodology that makes it possible to identify and classify LPU clusters in a given region in areas of high environmental risk and with the most viability for CADU implementation as a result of the weighted overlay analysis. The results not only indicate where resources are available based on spatial distribution, but also show where the best potential and priority locations should be located in the future. | The application of the methodology may be subjective (i.e., it requires the judgment of experts to select the parameters and to determine their weights). The analytic hierarchy process (AHP) may be used for selecting and weighting the parameters, thus reducing bias in decision-making. |
It is a flexible methodology that allows for the integration of a wide variety of environmental, economic, and social parameters to the spatial model to help determine the optimal sites for installing CADUs from a holistic perspective. | Other parameters may be considered for the analysis such as the proximity to the electric grid or to the road network, the preparedness of farmers to participate, spatial water pollution levels, and a number of local factors not included in this study. Additionally, the financial viability could be assessed with the use of an economic optimization framework. | |
The mathematical models rely on actual farm data of location and headcount of LPUs as well as actual data of the hydrographic structure of Jalisco, the digital elevation model, and the location of urban communities. | Euclidean distance between the potential sites and the LPUs was used rather than the road network distance. | |
This methodology is rooted in multicriteria evaluation integrated with a geographical information system (GIS). The methodology can be easily implemented with a medium level of GIS understanding. | Only livestock manure was used for the calculation of the biogas potential. Information regarding the spatial distribution of a wider range of substrates available in Jalisco for co-digestion could improve the estimation of the state’s potential for biogas production. | |
The application of this methodology has favorable repercussions in decision-making on environmental, social, health, and economic issues. | In this study only bibliographical considerations were used to determine the precise methanogenic potential and nitrogen and phosphorus digestate composition. Experimental studies using biodigesters at the laboratory and pilot levels must be carried out because substrates tend to be highly site-specific. | |
The resulting graphical display is easily understandable for state/local governments and other parties interested in biogas energy potential, and it serves as guide for planning investment in any local region. | The amount of energy produced by the CADUs represents only 3.4–5.5% of the state’s energy consumption, so its scope will probably only satisfy local energy demands. The results of this current study may be underestimated, being that the total headcount according to SADER (Agriculture and Rural Development Agency) is 3.22, 89.10, and 7.47 times higher for swine, cattle, and poultry, respectively. However, the data estimated by SADER lacks the LPU location information necessary for the spatial analysis. | |
With the implementation of CADUs, more farms can use a large facility and economies of scale can be achieved. Farmers need new ways to comply with increasing federal and state regulation of animal wastes. | Potential stakeholders interested in the implementation will need the CADUs to be connected to the grid. Such a connection can be costly if locations are not close enough to the existing electrical grid. This parameter should be included for the weighted overlay analysis in future studies. | |
Opportunities | Threats | |
External | This methodology allows for a first approach to a future implementation. However, the participation of different stakeholders would be necessary for a refining phase. | A poor selection of the panel of experts defining and weighing the parameters can translate into misleading results with economic, social, and environmental consequences. |
A wider range of substrates could be included in the analysis, although a comprehensive assessment is required to understand how a wider range of local substrates and substrate mixtures would affect the overall biodigester operation. | If the information collected in the databases is not reliable, the level of uncertainty in the results increases markedly. | |
The methodology can be easily adapted to other fields of application such as identifying sites for managing urban solid waste, siting collection centers and processing food surpluses, and locating sites for managing waste from the tequila industry, among others. | Transporting manure poses certain risks to the environment and public health such as spillage of liquid or solid manure due to filtering, overloading, blowing winds, or equipment breaking. Appropriate transportation techniques should be applied. | |
The best practices for waste management should be encouraged at the municipal and intermunicipal level for recycling and energy recovery, to promote farmers to become more interested in its implementation. | It is important to visualize possible changes in energy policy at the federal level before deriving policy recommendations. Unfavorable electricity sale tariffs may become a disincentive for future development of CADUs. | |
A broader benefit–cost analysis to determine the optimal locations based on the capacity of the CADUs should involve the comparison of benefits and costs associated with pollution, to compare the economic costs of implementing CADUs against the environment, and the health costs of not implementing them. | Biogas production requires facilities with personnel with medium to high levels of technological skills. A lack of trained personnel could be a problem for further project implementation. | |
The state of Jalisco is the third largest consumer of electrical energy in Mexico with 13,476.20 GW/h and it generates only 12% of that amount. The production of energy through CADUs contributes to improving the energy sufficiency of the state [117]. |
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Díaz-Vázquez, D.; Alvarado-Cummings, S.C.; Meza-Rodríguez, D.; Senés-Guerrero, C.; de Anda, J.; Gradilla-Hernández, M.S. Evaluation of Biogas Potential from Livestock Manures and Multicriteria Site Selection for Centralized Anaerobic Digester Systems: The Case of Jalisco, México. Sustainability 2020, 12, 3527. https://doi.org/10.3390/su12093527
Díaz-Vázquez D, Alvarado-Cummings SC, Meza-Rodríguez D, Senés-Guerrero C, de Anda J, Gradilla-Hernández MS. Evaluation of Biogas Potential from Livestock Manures and Multicriteria Site Selection for Centralized Anaerobic Digester Systems: The Case of Jalisco, México. Sustainability. 2020; 12(9):3527. https://doi.org/10.3390/su12093527
Chicago/Turabian StyleDíaz-Vázquez, Diego, Susan Caroline Alvarado-Cummings, Demetrio Meza-Rodríguez, Carolina Senés-Guerrero, José de Anda, and Misael Sebastián Gradilla-Hernández. 2020. "Evaluation of Biogas Potential from Livestock Manures and Multicriteria Site Selection for Centralized Anaerobic Digester Systems: The Case of Jalisco, México" Sustainability 12, no. 9: 3527. https://doi.org/10.3390/su12093527