Application of Low-cost Sensors for Environmental Exposure Assessment and Mitigation

A suitable and quick determination of air quality allows the population to be alerted with respect to high concentrations of pollutants. Recent advances in computer science have led to the development of a high number of low-cost sensors, improving the spatial and temporal resolution of air quality data while increasing the effectiveness of risk assessment. The main objective of this work is to perform a validation of a particulate matter (PM) sensor (HM-3301) in indoor and outdoor environments to study PM_2.5 and PM₁₀ concentrations. To date, this sensor has not been evaluated in real-world situations, and its data quality has not been documented. Here, the HM-3301 sensor is integrated into an Internet of things (IoT) platform to establish a permanent Internet connection. The validation is carried out using a reference sampler (LVS3 of Derenda) according to EN12341:2014. It is focused on statistical insight, and environmental conditions are not considered in this study. The ordinary Linear Model, the Generalized Linear Model, Locally Estimated Scatterplot Smoothing, and the Generalized Additive Model have been proposed to compare and contrast the outcomes. The low-cost sensor is highly correlated with the reference measure ( $R^2$ greater than 0.70), especially for PM_2.5, with a very high accuracy value. In addition, there is a positive relationship between the two measurements, which can be appropriately fitted through the Locally Estimated Scatterplot Smoothing model. Full article

In the urban environment, the urban heat island effect, the phenomenon of high temperature in the city relative to the suburbs, has become significant due to a large amount of artificial heat dissipation, rare green spaces, high building density, and a large surface material heat capacity. The study of the urban heat island effect has been carried out for many years. Even though many studies have evolved from the measurement and analysis stage to the improvement of the urban heat island effect, the measurement method is still the most important issue of the studies in this field. Basically, the measurement method of the urban heat island effect intensity has three types: remote sensing, mobile transect observation, and fixed station. In order to achieve the dual purpose of reducing research funding requirements and maintaining the accuracy of research results, this study proposes a way to combine mobile transect observation and fixed station. This study exploits the advantages of mobile transect observation and fixed station, and uses low-cost sensors to achieve the basic purpose of urban heat island effect research. First, in this study, low-cost sensors were mounted on mobile vehicles for more than ten mobile transect observations to identify relatively high temperature and low temperature regions in the city; meanwhile, the low-cost sensors were also placed in a simple fixed station to obtain long-term instantaneous urban temperature data. Furthermore, it is possible to analyze the 24-hour full-time variation of the urban heat island effect. Therefore, the results of this study can not only provide a reference for relevant researchers, but can also serve as an important criterion for government departments to establish an “urban heat island effect monitoring system” to achieve the goal of efficient use of the public budget. Full article

Citizens in urban areas are exposed to multiple environmental stressors like noise, heat, and air pollution, with impact on human health. There is a great deal of evidence that connects human health, objective environmental exposure, and place of residence. However, little is known about subjective and objective multiple personal exposures while being mobile. To address this research gap, this paper presents results from a mixed-methods exploratory study with cyclists in the City of Leipzig, Germany. In the summer of 2017, cyclists (n = 66) wore a unique combination of sensors that measured particle number counts (PNC), noise, humidity, temperature, geolocation, and the subjective perception of each exposure on everyday routes for one week (n = 730). A smartphone application was developed to question participants about their perception of subjective exposure. The data were analyzed with three aims: (i) to compare the multiple exposure profiles of the cyclists, (ii) to contrast the objective data and subjective individual perception, and (iii) to examine the role of route decision-making and awareness of health impacts for healthier route choices. The results indicate distinct differences between the exposure profiles of cyclists. Over 80% of the cyclists underestimated their exposure to noise and air pollution. Except for heat, no significant associations between the objective and subjective data were found. This reveals an exposure awareness gap that needs to be considered in urban health planning and risk communication. It is argued that knowledge about health impacts and route characteristics plays a crucial role in decision-making about route choices. The paper concludes with suggestions to harness smart sensing for exposure mitigation and research in health geography. Full article

Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud. Full article

Green wall irrigation procedures are a particularly important and hard task, given that the quality of the green wall depends on them. There is currently a wide variety of irrigation programmers available, with a range of functions and prices, thereby replacing manual activities and making it easier to maintain green walls. This paper proposes the use of low-cost automated irrigation programmers via a freeware called Arduino. The system is based on air and substrate measurements to ensure optimal plant growth and high water-use efficiency. At certain thresholds, the irrigation system is activated. This not only makes irrigation more convenient but also helps to reduce energy consumption, increases irrigation efficiency and saves time. The data is then sent via Transmission Control Protocol using Internet of Things technology, in this case ThingSpeak. The platform compiles the data and presents them in simple graphical format, thus enabling real-time monitoring from wherever there is Internet access. Together with Arduino, the project incorporates the Raspberry pi system that operates like a database via Hypertext Transfer Protocol Wi-Fi received by a Structured Query Language (MySQL) server using Hypertext Preprocessor. These data are used for the subsequent analysis of green wall performance. Full article