Agri-Environment Atmospheric Real-Time Monitoring Technology Based on Drone and Light Scattering

Abstract

The emission of particulate matter (PM) from agricultural activities, such as concentrated animal feeding, straw combustion, and mechanized harvest, is a hot issue in the sustainable development of agriculture, which has attracted more and more attention from government departments and researchers. However, the research on the transport of particulate matter in the agri-environment still lacks flexible and efficient measurement methods to obtain real-time and accurate spatial distribution data. The objective of our study is to produce a new intelligent platform for agri-environment atmospheric monitoring with high mobility, temporal and spatial resolution, and remote data transmission function to overcome the shortcomings of traditional atmospheric particulate matter monitoring stations, such as small particle size range, immovability, and high cost. Through the light scattering sensor, microcontroller, and wireless data transmission device assembled on the high-mobility drone, the platform could measure the mass concentration of PM2.5, PM10, and TSP at different spatial points in the agri-environment and transmit the measurement data to the receiving device on the ground through three modes: CLOUD, TCP, and UDP. We also developed monitoring software based on the Android platform, which could complete the connection of device and real-time monitoring of measurement data on the ground. Compared with stationary measurement devices, the biggest advantage of our mobile monitoring system is that it has the ability to measure the concentration of TSP and the vertical distribution of PM, which is very important for the research of agricultural environmental particulate matter emission characteristics. After the sensor and communication performance experiments, the sensors had high consistency in the overall change trend, and the communication accuracy rate was high. We carried out a flight measurement comparison experiment at the Wenhua Road Campus of Henan Agricultural University, and the measurement data were highly consistent with the data from the national monitoring stations. We also conducted an agri-environmental atmospheric measurement experiment in Muzhai Village and obtained the vertical distribution data of PM concentration at the nearby measuring point when the harvester was working. The results showed that after the harvester worked for a period of time, the PM2.5, PM10, and TSP concentrations reached the maximum at the altitude of 20 m at the measurement point, which were 80, 198, and 384 μg/m3, respectively, 2.64~3.10 times the particle concentration in the environment before the harvester began to work. Our new platform had high mobility, sensitive reading, and stable communication during the experiment, and had high application value in agricultural environmental monitoring.