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    Rescue robot against risks in natural disasters using Arduino
    (Springer, 2022-01-01)
    Nowadays, nobody can deny that advanced technology is widespread in almost every aspect of our daily lives. Our main objective in this research is to provide a tool that is complementary to the work of rescuers in the face of a natural disaster that minimizes the risks of loss of life during the SAR (Search And Rescue) process. The main function of the designed prototype, named “Rescue Bot”, is the search of missing persons after collapses of large-scale structures of any kind, where it is difficult to locate people caught in a landslide. The rescue bot has several advantages: it is lightweight, it has small dimensions, it is cheap, and it uses a simple and low-level programming language through the free hardware platform Arduino. Its mission is to reduce the number of fatalities and rescue time and take the risks that rescuers face in every natural disaster. We have used several sensors and an infrared camera as indispensable accessories in the Rescue Bot. The information collected is sent to the control centre in real-time.
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    Spatial and temporal mapping of transport emissions and application of air quality models using low cost sensor data
    (Elsevier BV, 2025-10-10)
    Traffic-related atmospheric emissions of greenhouse gases (GHG) and toxic air pollutants (AP) are a serious environmental problem that affects climate change and air quality in megacities. About 80 % of air pollution in São Paulo comes from vehicles. This work aimed to develop a methodology using a traffic demand model for GHG and AP inventories of vehicular emissions and demonstrate its applicability to the Metropolitan Area of São Paulo (MASP) as a part of regional air quality and climate change modelling. These high-resolution emission inventories also allow identifying hot spots of air pollution and poor air quality with a spatial resolution of 0.5 km and temporal resolution of 1 h. With this, we also intend to develop an approach for the validation of the emission model through low cost sensor measurements. These sensors will be placed through the MASP close to the identified vehicle emission hot spots to continuously measure over one-year duration to address a novel question on how the low-cost sensors data can be applied for improving the model performance and air quality monitoring. This paper integrates two approaches: the vehicle emission and air quality modeling and the use of low-cost sensors for model validation and develop novel approaches for high-resolution spatial mapping. This work provided a basis for establishing sound climate change policies in other areas such as public health and urban planning. These high-resolution emission inventories also allowed identifying hot spots of air pollution and poor air quality with a spatial resolution of 0.5 km and temporal resolution of 1 h. Data from sensors NOTS were compared with reference data obtained from the Osasco monitoring network website and data from devices at other nearby air quality monitoring stations. This comparison made it possible to determine the errors for adjusting the calibration model in the field.The calibration of the NOTS platforms considered the co-location between the NOTS devices and the CETESB monitor platform.
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    Relationship between forest fires and air quality: a study of particulate matter in localities distant from the main emission source
    (Instituto Internacional de Ecologia (Brazil), 2025-01-01)
    Wildfires represent a growing threat to air quality in fire-prone regions; however, the spatiotemporal dynamics of pollutant dispersion to distant localities remain inadequately characterized. This study quantifies particulate matter concentrations (PM1.0, PM2.5, PM10) in localities distant from the February 2024 Valparaíso wildfire, evaluating their spatiotemporal variation and association with fire intensity and meteorological parameters using a network of low-cost sensors. Four PurpleAir sensors distributed along the Valparaíso-Santiago corridor (70-120 km) were integrated with meteorological stations and satellite-derived Fire Radiative Power (FRP) data. Analysis of variance revealed significant differences in PM concentrations between pre-fire, during-fire, and post-fire phases, with the proximal station (S1) showing the strongest response. Pearson correlations with False Discovery Rate adjustment demonstrated immediate associations between FRP and PM at S1 (r = 0.71-0.72, p < 0.05), while distal stations (S2-S4) exhibited significant correlations only after applying 1-2 day temporal lags (r = 0.44-0.49, p < 0.05), providing quantitative evidence of regional-scale pollutant transport. These findings establish an empirical foundation for developing early warning systems in fire-prone regions and underscore the necessity of considering temporal lags in air quality management strategies during wildfire events.
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