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Item type:Publication, Air quality prediction (PM2.5 and PM10) at the upper Hunter town - Muswellbrook using the long-short-term memory method(Science and Information Organization, 2020-01-01)Air quality is crucial for the environment and the life quality of citizens. Therefore, in the present study a software application is developed to predict air quality on the basis of 2.5 particulate matter (〖PM〗_(2.5)) and 10particulate matter (〖PM〗_10), in the city of Upper Hunter, Australia, as it is considered to be one of the cities with the lowest air quality levels worldwide. For this purpose, it has been decided to use the methodology of long-short term memory (LSTM) from data collected by NSW department of planning industry and environment during the period of 30 September 2012 to 30 September 2019, to predict the behavior of the mentioned particulate matter during the month of October 2019. A comparison between the average and maximum values suggested by the software and the actual values has been made and it is shown that the predicted results of the study are quite close to reality. Finally, the results obtained in this study may serve as a basis for local authorities to proceed with the necessary protocols and measures in case an alarming prediction occurs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Environmental impacts of introducing cable cars in the Andean landscape: A case study for Kuelap, Peru(Elsevier, 2020-05-20)Cable cars have slowly become a popular means of transport beyond their classical use at ski resorts. In Latin America their use has thrived to access archaeological sites in the Andes, but also in urban environments for mass transit. Despite some apparent benefits of these systems, the current literature is scarce in terms of quantifying the environmental profile of cable cars. Hence, their environmental performance as compared to other means of transport remains essentially unexplored. Therefore, the main objective of this study was to provide a comparative environmental analysis, using Life Cycle Assessment (LCA) methodology, of the two existing transport methods to visit the Kuelap Archaeological Complex, in northern Peru: a recently built cableway system and the alternative unpaved winding road. An attributional LCA perspective was performed for several impact categories, including global warming and particulate matter formation. In addition, a scenario analysis and an uncertainty analysis, using Monte Carlo simulation, were conducted to account for deterministic and stochastic results interpretation. Results demonstrated that succulent environmental benefits are attained when cable cars substitute road transport in complex Andean orographic conditions. However, the rebound effects of reducing traveling times significantly, as well as social and biodiversity aspects, should be analyzed in further depth to complement the environmental analysis. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An air quality monitoring and forecasting system for Lima city with low-cost sensors and artificial intelligence models(Frontiers Media S.A., 2022-07-07)Monitoring air quality is very important in urban areas to alert the citizens about the risks posed by the air they breathe. However, implementing conventional monitoring networks may be unfeasible in developing countries due to its high costs. In addition, it is important for the citizen to have current and future air information in the place where he is, to avoid overexposure. In the present work, we describe a low-cost solution deployed in Lima city that is composed of low-cost IoT stations, Artificial Intelligence models, and a web application that can deliver predicted air quality information in a graphical way (pollution maps). In a series of experiments, we assessed the quality of the temporal and spatial prediction. The error levels were satisfactory when compared to reference methods. Our proposal is a cost-effective solution that can help identify high-risk areas of exposure to airborne pollutants and can be replicated in places where there are no resources to implement reference networks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of an air pollution measurement system in mining areas of Cerro de Pasco-Peru(IJETAE Publication House, 2022-01-01)One of the big problems in the city of Cerro de Pasco is the air pollution caused by the mining activity that occurs in this area, this activity generates a total of 3737 metric tons per year of particulate matter, which are thrown into the environment in an alarming way, thus reaching that this particulate matter can lodge inside the organism of the inhabitants permanently, causing in them diseases in the respiratory system, thus affecting the most vulnerable population, producing in them infections in the respiratory tract that can even carry those to death. Nowadays the teams that carry out the monitoring of air quality are restricted only to the academic and governmental sphere, so much so that the population does not know the degree of air pollution. For this reason, it is proposed to carry out a system to measure the level of air pollution taking as main data, the measurement of particulate matter and the emission of carbon monoxide, the data that can be recovered from these measurements to be able to compare them with the parameters dictated by the Ministry of the Environment. As a result, an autonomous system was obtained, with which the level of particulate matter, possible toxic gases, and the measurement of the level of carbon monoxide can be measured, all these pollutants that could be in the environment, all these measurements are constant, thus leading the population to become aware of the level of quality of the environment where they live. Keywords- Environmental pollution, articulated matter, carbon monoxide, toxic gases, mining activity, air quality - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a monitoring system for the identification of gas concentration in the district of Mi Perú - Callao, Perú(IJETAE Publication House, 2022-01-01)In recent years, the contamination of toxic gases in society is a very controversial problem because of the various consequences and effects they generate on health. There are several studies which highlight that the main polluting gases to the environment such as carbon monoxide, lead, cadmium, among others are the main causes of the increase in various acute diseases, such as pneumonia, or chronic diseases, such as cardiovascular diseases or lung cancer. That is why, faced with this problem, this research work proposes a monitoring system that determines the concentration of toxic gases, in such a way that it can contribute as an informative medium which can be implemented in various focused points where high rates of pollution are concentrated. The system has a very efficient MQ 135 gas sensor which includes a reading of 5 to 600 ppm (parts per million) at 3m with a temperature of 25ºC. It should be noted that it is important to keep the sensor on 24 hours beforehand to obtain a correct reading. Thus, concluding that this system of toxic gases can be easily applied to society because it is economical and because it has a free software which will be responsible for informing through a web server the level of ppm that you want to analyze at a certain point. Keywords- Toxic Gases, Pollution, Gas Sensor, Environment, Air Quality - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors(MDPI, 2023-04-01)A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.