Effects of the vehicle fleet on air quality in an urban university environment of Ecuador
DOI:
https://doi.org/10.51798/sijis.v5i4.835Keywords:
air quality, university environment, carbon monoxide, particulate matter, air pollution, environmental monitoring, vehicular emissionsAbstract
Introduction: Air quality in university environments is critical for the health and well-being of students and staff. This study aims to identify the permissible pollutants emitted in the parking lots of the State Technical University of Quevedo (UTEQ) in the canton of Quevedo, province of Los Ríos, Ecuador. Methods: An abductive (inductive-deductive) approach was employed, utilizing statistical methods to analyze primary emission data. In-situ measurements of pollutants, specifically carbon monoxide (CO) and particulate matter (PM2.5 and PM10), were conducted monthly across three parking lots at the university. Each parking lot contained three strategically established monitoring points, where measurements were taken during peak entry and exit hours and on days of highest traffic, using the Temtop M2000C-2º sensor. Results: The findings revealed significant statistical differences in pollutant concentrations among the parking lots. CO levels were notably higher in the main parking lot during midday, while PM2.5 and PM10 exhibited greater dispersion in the rear parking lot. The maximum measured values were 456.71 ppm (CO) in the main parking lot, 22.29 µg/m³ (PM2.5) in the Produteq lot, and 32.54 µg/m³ (PM10) in the rear parking lot, all of which remain within the permissible limits established by Ecuadorian legislation. Conclusion: Although air pollution levels at UTEQ are classified as moderate, the results underscore the necessity for implementing short-term mitigation measures to enhance air quality. It is recommended that university authorities develop projects and actions aimed at reducing vehicular emissions and improving overall environmental conditions on campus.
References
Álvarez-Narvaez, V. M., Quiñones, E., Huertas-Bolaños, M. E., Arciniegas-Suárez, C., Berdugo-Arrieta, J., & Ramírez-Rivas, D. (2016). Metodología para la selección de sitios de monitoreo atmosférico en zonas urbanas afectada por las emisiones de fuentes móviles. Revista UIS Ingenierías, 15(2), 73–84. https://doi.org/10.18273/revuin.v15n2-2016006
Bell, E., Bryman, A., & Harley, B. (2022). Business research methods. Oxford university press.
Brauer, M., Guttikunda, S., Nishad, K. A., Dey, S., Tripathi, S., Weagle, C., & Martin, R. (2019). Examination of monitoring approaches for ambient air pollution: A case study for India. Atmospheric Environment, 216. 116940. https://doi.org/10.1016/j.atmosenv.2019.116940.
Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., & Peters, A. (2010). Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation, 121(21), 2331-2378. https://doi.org/10.1161/CIR.0b013e3181dbece1
Choi, K., & Chong, K. (2022). Modified Inverse Distance Weighting Interpolation for Particulate Matter Estimation and Mapping. Atmosphere, 13, 846. https://doi.org/10.3390/atmos13050846
Chowdhury, S., Dey, S., Di Girolamo, L., Smith, K., Pillarisetti, A., & Lyapustin, A. (2019). Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmospheric Environment, 204, 142-150. https://doi.org/10.1016/j.atmosenv.2019.02.029
Datta, A., Saha, A., Zamora, M. L., Buehler, C., Hao, L., Xiong, F., Gentner, D. R., & Koehler, K. (2020). Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore. Atmospheric Environment, 242, 117761. https://doi.org/10.1016/j.atmosenv.2020.117761
Duyzer, J., Hout, D., Zandveld, P., & Ratingen, S. (2015). Representativeness of air quality monitoring networks. Atmospheric Environment, 104, 88-101. https://doi.org/10.1016/j.atmosenv.2014.12.067
Gonzalez, A., Boies, A., Swanson, J., & Kittelson, D. (2022). Measuring the air quality using low-cost air sensors in a parking garage at University of Minnesota, USA. International journal of environmental research and public health, 19(22), 15223. https://doi.org/10.3390/ijerph192215223
Gu, S., Fu, B., Thriveni, T., Fujita, T., Ahn, J. W. (2019). Coupled LMDI and system dynamics model for estimating urban CO2 emission mitigation potential in Shanghai, China. Journal of Cleaner Production, 240, 118034. https://doi.org/10.1016/j.jclepro.2019.118034
Gulia, S., Prasad, P., Goyal, S. K., & Kumar, R. (2020). Sensor-based Wireless Air Quality Monitoring Network (SWAQMN) - A smart tool for urban air quality management. Atmospheric Pollution Research, 11(9), 1588-1597. https://doi.org/10.1016/j.apr.2020.06.016
Holnicki, P., Nahorski, Z., & Kałuszko, A. (2021). Impact of vehicle fleet modernization on the traffic-originated air pollution in an urban area—A case study. Atmosphere, 12(12), 1581. https://doi.org/10.3390/atmos12121581
Ke, W., Zhang, S., Wu, Y., Zhao, B., Wang, S., & Hao, J. (2017). Assessing the future vehicle fleet electrification: the impacts on regional and urban air quality. Environmental science & technology, 51(2), 1007-1016. https://doi.org/10.1021/acs.est.6b04253
Kumar, P., Druckman, A., Gallagher, J., Gatersleben, B., Allison, S., Eisenman, T., Hoang, U., Hama, S., Tiwari, A., Sharma, A., Abhijith, K. V., Adlakha, D., McNabola, A., Astell-Burt, T., Feng, X., Skeldon, A. C., Lusignan, S., Morawska, L. (2019). The nexus between air pollution, green infrastructure and human health. Environment International, 133(A), 105181. https://doi.org/10.1016/j.envint.2019.105181
Li, H. Z., Gu, P., Ye, Q., Zimmerman, N., Robinson, E. S., Subramanian, R., Apte, J. S., Robinson, A. L., & Presto, A. A. (2019). Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors. Atmospheric Environment: X, 2, 100012. https://doi.org/10.1016/j.aeaoa.2019.100012
Liu, Y., & Cirillo, C. (2018). Modeling green vehicle adoption: An integrated approach for policy evaluation. International Journal of Sustainable Transportation, 12(7), 473-483. https://doi.org/10.1080/15568318.2017.1393584
Longo, M., Yaïci, W., & Zaninelli, D. (2015). “Team play” between renewable energy sources and vehicle fleet to decrease air pollution. Sustainability, 8(1), 27. https://doi.org/10.3390/su8010027
Lopez-Arboleda, E., Sarmiento, A.T. & Cardenas, L.M. (2021). Systemic approach for integration of sustainability in evaluation of public policies for adoption of electric vehicles. Systemic Practice and Action Research, 34, 399-417. https://doi.org/10.1007/s11213-020-09540-x
Marć, M., Tobiszewski, M., Zabiegała, B., Guardia, M., & Namieśnik J. (2015). Current air quality analytics and monitoring: A review. Analytica Chimica Acta, 853, 116-126. http://dx.doi.org/10.1016/j.aca.2014.10.018
Ministry of Environment of Ecuador [MAE]. (2015). Acuerdo Ministerial N° 028, Reforma al Texto Unificado de Legislación Secundaria Medio Ambiente.
Ministry of Industries and Productivity of Ecuador [MIPRO]. (2017). Registro Oficial No. 919, Control de emisiones contaminantes de Móviles Terrestres.
Montalvo, L., Fosca, D., Abarca, M., Saito, C., & Villanueva, E. (2022). An Air Quality Monitoring and Forecasting System for Lima City With Low-Cost Sensors and Artificial Intelligence Models. Frontiers in Sustainable Cities, 4, 849762. https://doi.org/10.3389/frsc.2022.849762
Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G., Jayaratne, R., Kumar, P., Lau, A., Louie, P., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., & Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?. Environment International, 116, 286-299. https://doi.org/10.1016/j.envint.2018.04.018
Nagendra, S., Yasa, P. R., Narayana, M. V., Khadirnaikar, S., & Rani, P. (2019). Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots. Sustainable Cities and Society, 44, 520-535. https://doi.org/10.1016/j.scs.2018.10.006
Pope, C. A., 3rd, & Dockery, D. W. (2006). Health effects of fine particulate air pollution: lines that connect. Journal of the Air & Waste Management Association, 56(6), 709-742. https://doi.org/10.1080/10473289.2006.10464485
Rebs T, Brandenburg M, & Seuring, S. (2019). System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach. Journal of Cleaner Production, 208, 1265-1280. https://doi.org/10.1016/j.jclepro.2018.10.100
Ren, J., Tan, S., Yang, L., Goodsite, M. E., Pang, Ch., Dong, L. (2015). Optimization of emergy sustainability index for biodiesel supply network design. Energy Conversion and Management, 92, 312-321. https://doi.org/10.1016/j.enconman.2014.12.066
Shahbazi, H., Hosseini, V., Torbatian, S., & Hamedi, M. (2019). Assessment of emission reduction scenarios with a focus on the impact of vehicle fleets on Tehran air quality: Case study. Transportation Research Record, 2673(5), 197-207. https://doi.org/10.1177/0361198119836770
Sobrino, N., & Arce, R. (2021). Understanding per-trip commuting CO2 emissions: A case study of the Technical University of Madrid. Transportation Research Part D: Transport and Environment, 96, 102895. https://doi.org/10.1016/j.trd.2021.102895
Tan, Y., Jiao, L., Shuai, Ch., Shen, L. (2018). A system dynamics model for simulating urban sustainability performance: A China case study. Journal of Cleaner Production, 199, 1107-1115. https://doi.org/10.1016/j.jclepro.2018.07.154
Vu, B. N., Sánchez, O., Bi, J., Xiao, Q., Hansel, N. N., Checkley, W., Gonzales, G. F., Steenland, K., & Liu, Y. (2019). Developing an Advanced PM2.5 Exposure Model in Lima, Peru. Remote Sensing, 11(6), 641. https://doi.org/10.3390/rs11060641
Weissert, L. F., Alberti, K., Miskell, G., Pattinson, W., Salmond, J. A., Henshaw, G., & Williams, D. E. (2019). Low-cost sensors and microscale land use regression: Data fusion to resolve air quality variations with high spatial and temporal resolution. Atmospheric Environment, 15, 285-295. https://doi.org/10.1016/j.atmosenv.2019.06.019
Zhang, Y. L., & Cao, F. (2015). Fine particulate matter (PM2.5) in China at a city level. Scientific Reports, 5, 14884. https://doi.org/10.1038/srep14884
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