A LINEAR PROGRAMMING APPROACH TO OPTIMIZING ENVIRONMENTAL RESOURCE MANAGEMENT IN URBAN AREAS

Authors

  • Benjamin E. Idisi Delta State University of Science and Technology, Ozoro
  • S. A. Ogumeyo

DOI:

https://doi.org/10.33003/fjs-2024-0806-2770

Keywords:

Environmental Management, Linear Programming, Urban Resources, Sustainability, Optimization

Abstract

This study investigates the optimization of environmental resource management in urban settings using a linear programming approach, focusing on ammonia production and wastewater management. The model addresses uncertainties in resource allocation by evaluating the cost implications of wastewater discharge during production. Key variables include water drawn from rivers, sludge discharge, and energy consumption. The study explores scenarios where zero-discharge policies are implemented, resulting in reduced water usage but increased energy consumption and higher production costs. The methodology employed linear programming to minimize the cost of ammonia production while ensuring water quality through regulated waste disposal. The findings indicate that minimizing river impurities through controlled waste discharge reduces water usage but escalates energy consumption, complicating cost management. Results from the numerical example show that under optimal conditions, 6,666.7 liters of water and 555.6 kg of sludge are needed to produce 41,666.7 kg of ammonia, with zero energy consumption in the process. The significance of these results lies in their potential to inform sustainable urban resource management policies that balance economic and environmental priorities. The study concludes that while achieving high water quality increases costs, it is crucial for sustainable ammonia production and environmental conservation, particularly in minimizing the ecological impact of industrial activities on water resources.

References

Abraham, S., & Li, X. (2014). A cost-effective wireless sensor network system for indoor air quality monitoring applications. Procedia Computer Science, 34, 165-171. https://doi.org/10.1016/j.procs.2014.07.090

Ahuja, T., Jain, V., & Gupta, S. (2016). Smart pollution monitoring for instituting aware travelling. International Journal of Computer Applications, 145(9), 4-11. https://doi.org/10.5120/ijca2016910747

Camarillo-Escobedo, R., Flores, J. L., Marin-Montoya, P., García-Torales, G., & Camarillo-Escobedo, J. M. (2022). Smart multi-sensor system for remote air quality monitoring using unmanned aerial vehicle and LoRaWAN. Sensors, 22(5), 1706. https://doi.org/10.3390/s22051706

Dominici, F., Zanobetti, A., Schwartz, J., Braun, D., Sabath, B., & Wu, X. (2022). Assessing adverse health effects of long-term exposure to low levels of ambient air pollution: implementation of causal inference methods. Research Reports: Health Effects Institute, 2022. https://doi.org/10.1289/isee.2023.vo-020

Evangelopoulos, D., Katsouyanni, K., Keogh, R., Samoli, E., Schwartz, J., Barratt, B., Zhang, H., & Walton, H. (2020). PM2.5 and NO2 exposure errors using proxy measures, including derived personal exposure from outdoor sources: A systematic review and meta-analysis.. Environment international, 137, 105500 . https://doi.org/10.1016/j.envint.2020.105500.

Fan, Z., Zhao, Y., Hu, B., Wang, L., Guo, Y., Tang, Z., ... & Mao, X. (2024). Enhancing urban real-time PM2.5 monitoring in street canyons by machine learning and computer vision technology. Sustainable Cities and Society, 100, 105009. https://doi.org/10.1016/j.scs.2023.105009

Fascista, A. (2022). Toward integrated large-scale environmental monitoring using WSN/UAV/Crowdsensing: A review of applications, signal processing, and future perspectives. Sensors, 22(5), 1824. https://doi.org/10.3390/s22051824

Gordon, J., Bilsback, K., Fiddler, M., Pokhrel, R., Fischer, E., Pierce, J., & Bililign, S. (2023). The Effects of Trash, Residential Biofuel, and Open Biomass Burning Emissions on Local and Transported PM2.5 and Its Attributed Mortality in Africa. GeoHealth, 7. https://doi.org/10.1029/2022GH000673.

Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony search-based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101-109. https://doi.org/10.1016/j.engappai.2017.11.003

Horender, S., Auderset, K., Quincey, P., Seeger, S., Skov, S. N., Dirscherl, K., ... & Vasilatou, K. (2021). Facility for production of ambient-like model aerosols (PALMA) in the laboratory: application in the intercomparison of automated PM monitors with the reference gravimetric method. Atmospheric Measurement Techniques, 14(2), 1225-1238. https://doi.org/10.5194/amt-14-1225-2021

Huang, X., Tang, G., Zhang, J., Liu, B., Liu, C., Zhang, J., Công, L., Cheng, M., Yan, G., Gao, W., Wang, Y., & Wang, Y. (2021). Characteristics of PM2.5 pollution in Beijing after the improvement of air quality.. Journal of environmental sciences, 100, 1-10 .https://doi.org/10.1016/j.jes.2020.06.004.

Johnston, J. D., Collingwood, S. C., LeCheminant, J. D., Peterson, N. E., Reynolds, P. R., Arroyo, J. A., ... & Beard, J. D. (2023). Personal Exposure to Fine Particulate Air Pollution among Brick Workers in Nepal. Atmosphere, 14(12), 1783. https://doi.org/10.3390/atmos14121783

Khan, J., Sun, L., Tian, Y., Shi, G., & Feng, Y. (2021). Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources.. Journal of environmental sciences, 99, 196-209 . https://doi.org/10.1016/j.jes.2020.06.027.

Kim, G. S., Son, Y. S., Lee, J. H., Kim, I. W., Kim, J. C., Oh, J. T., & Kim, H. (2016). Air pollution monitoring and control system for subway stations using environmental sensors. Journal of Sensors, 2016. https://doi.org/10.1155/2016/1865614

Kothandaraman, D., Praveena, N., Varadarajkumar, K., Madhav Rao, B., Dhabliya, D., Satla, S., & Abera, W. (2022). Intelligent forecasting of air quality and pollution prediction using machine learning. Adsorption Science & Technology, 2022.

https://doi.org/10.1155/2022/5086622

Li, S., Shafi, S., Zou, B., Liu, J., Xiong, Y., & Muhammad, B. (2022). PM2.5 Concentration Exposure over the Belt and Road Region from 2000 to 2020. International Journal of Environmental Research and Public Health, 19. https://doi.org/10.3390/ijerph19052852.

Lung, S. C. C., Thi Hien, T., Cambaliza, M. O. L., Hlaing, O. M. T., Oanh, N. T. K., Latif, M. T., ... & Agustian, D. (2022). Research priorities of applying low-cost PM2. 5 sensors in Southeast asian countries. International Journal of Environmental Research and Public Health, 19(3), 1522. https://doi.org/10.3390/ijerph19031522

Lyu, R., Zhang, J., Pang, J., & Zhang, J. (2024). Modeling the impacts of 2D/3D urban structure on PM2.5 at high resolution by combining UAV multispectral/LiDAR measurements and multi-source remote sensing images. Journal of Cleaner Production, 437, 140613. https://doi.org/10.1016/j.jclepro.2024.140613

Mansour, S., Nasser, N., Karim, L., & Ali, A. (2014, February). Wireless sensor network-based air quality monitoring system. In 2014 international conference on computing, networking and communications (ICNC) (pp. 545-550). IEEE. https://doi.org/10.1109/iccnc.2014.6785394

McCarron, A., Semple, S., Braban, C. F., Swanson, V., Gillespie, C., & Price, H. D. (2023). Public engagement with air quality data: Using health behaviour change theory to support exposure-minimising behaviours. Journal of Exposure Science & Environmental Epidemiology, 33(3), 321-331. https://doi.org/10.1038/s41370-022-00449-2

Mitreska Jovanovska, E., Batz, V., Lameski, P., Zdravevski, E., Herzog, M. A., & Trajkovik, V. (2023). Methods for urban Air Pollution measurement and forecasting: Challenges, opportunities, and solutions. Atmosphere, 14(9), 1441. https://doi.org/10.3390/atmos14091441

Montrucchio, B., Giusto, E., Vakili, M. G., Quer, S., Ferrero, R., & Fornaro, C. (2020). A densely-deployed, high sampling rate, open-source air pollution monitoring WSN. IEEE Transactions on Vehicular Technology, 69(12), 15786-15799. https://doi.org/10.1109/tvt.2020.3035554

Nabizadeh, R., Yousefian, F., Moghadam, V., & Hadei, M. (2019). Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review. Environmental Science and Pollution Research, 26, 30755 - 30771. https://doi.org/10.1007/s11356-019-06382-6.

Onaiwu, G. E., & Eferavware, S. A. (2023). The potential health risk assessment of PM2. 5-bound polycyclic aromatic hydrocarbons (PAHs) on the human respiratory system within the ambient air of automobile workshops in Benin City, Nigeria. Air Quality, Atmosphere & Health, 16(12), 2431-2441. https://doi.org/10.1007/s11869-023-01415-z

Onaiwu, G. E., & Ifijen, I. H. (2024). PM2. 5-bound polycyclic aromatic hydrocarbons (PAHs): quantification and source prediction studies in the ambient air of automobile workshop using the molecular diagnostic ratio. Asian Journal of Atmospheric Environment, 18(1), 6. https://doi.org/10.1007/s44273-024-00027-y

Onaiwu, G. E., & Okuo, J. M. (2023). Quantification of PM2. 5 Bound Polycyclic Aromatic Hydrocarbons (PAHs) and Modelling of Benzo [a] pyrene in the Ambient Air of Automobile Workshops in Benin City. Aerosol Science and Engineering, 7(3), 380-395. https://doi.org/10.21203/rs.3.rs-1727100/v1

Patel, P., & Aggarwal, S. (2022). On the techniques and standards of particulate matter sampling. Journal of the Air & Waste Management Association, 72, 791 - 814. https://doi.org/10.1080/10962247.2022.2048129.

Pavani, M., & Rao, P. T. (2017). Urban air pollution monitoring using wireless sensor networks: A comprehensive review. International Journal of Communication Networks and Information Security, 9(3), 439-449. https://doi.org/10.17762/ijcnis.v9i3.2708

Prill, R., Karlsson, J., Ayeni, O. R., & Becker, R. (2021). Author guidelines for conducting systematic reviews and meta-analyses. Knee Surgery, Sports Traumatology, Arthroscopy, 29, 2739-2744. https://doi.org/10.1007/s00167-021-06631-7

Sassi, M. S. H., & Fourati, L. C. (2022). Comprehensive survey on air quality monitoring systems based on emerging computing and communication technologies. Computer Networks, 209, 108904. https://doi.org/10.1016/j.comnet.2022.108904

Published

2024-12-14

How to Cite

Idisi, B. E., & Ogumeyo, S. A. (2024). A LINEAR PROGRAMMING APPROACH TO OPTIMIZING ENVIRONMENTAL RESOURCE MANAGEMENT IN URBAN AREAS. FUDMA JOURNAL OF SCIENCES, 8(6), 277 - 284. https://doi.org/10.33003/fjs-2024-0806-2770