LoRa Sensor Node Mounted on Drone for Monitoring Industrial Area Gas Pollution

One of the biggest threats to human health is air pollution, which significantly influences people. Due to challenges like industrial and rural locations, where sensing frequently falls short of providing sufficient information about air quality, it is challenging to collect data close to pollution sources. Government-led statically deployed stationary monitors are typically used for air quality monitoring. However, many emissions that contribute to air pollution are erratic and unpredictable. A significant problem for environmental protection will be how to monitor air pollution emissions dynamically and efficiently. It can fulfill two objectives. The first is that if the Unmanned Aerial Vehicle (UAV) is flying to a remotely monitored target, it can relay the detected data back to the server in real-time. This work aims to suggest an innovative mobile wireless air pollution monitoring system comprising UAVs with inexpensive air pollution sensors that transmit data over Long Range (LoRa). The outcomes also demonstrated that LoRa Radio transmitter sx1278 could transmit data for distances up to 5km in urban areas. The system was tested successfully at two sites in the Ewairij industrial area south of Baghdad, and the data was received at the base station from the sensor Node, which is carried by the drone during its flight for a distance of 1.5 km and height of 20 meters, round trip. As a result, the industrial areas were classified according to the Air Quality Index (AQI) between satisfactory to moderate according to gas concentrations. The highest gas carbon monoxide (CO) concentration increase was close to dangerous in both sites, as it recorded 9.75µg/m3 in site#1 and 7.75µg/m3 in site#2. In conclusion, the AQI did not reach a poor level in these tested areas

A Consumer UAV-based Air Quality Monitoring System for Smart Cities

Growing concerns on air pollution in large cities urge the development of new smart cities technologies to monitor and improve air quality of the cities. Unmanned aerial vehicle (UAV) is getting attentions in air monitoring applications due to its high maneuverability in both horizontal and vertical dimensions to obtain high-spatial-resolution and near-surface vertical profiling of atmosphere pollution. Low cost micro-sensors add additional advantages to UAVs to offer numerous advantages for capturing the spatial and temporal variability of air pollutants. Despite all these exciting prospects, challenges need to be studied and addressed to exploit UAV technologies for air quality monitoring. This work designs and develops a consumer UAV-based air quality monitoring system with off-the-shelf consumer components. The design enables a UAV to carry multiple sensors to accomplish real-time monitoring of multiple air pollutants. Our prototype and experiments verify the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment