Poster gss
- 1. MONITORING PESTICIDE DRIFT USING PASSIVE MEASUREMENTS OF OPEN-PATH FTIR Oz Kira, Yael Dubowski and Raphael Linker Faculty of Civil and Environmental Engineering, Technion, Haifa, Israel 1 Introduction Pesticide drift, originated from agricultural use, may be a threat for the health of close by population and ecological systems. Airborne pesticides spend significant part of their lifetime as condensed aerosols, which are more difficult to measure and estimate than gaseous compounds. In the present research we are using OpenPath Fourier Transform Infra-Red spectrophotometer (OP-FTIR) for real time monitoring of pesticide drift. Infrared instruments in general have shown significant potential for detecting and identifying airborne compounds. Yet, the use of OP-FTIR has been restricted mainly to measuring gaseous components, and its use for investigating toxic airborne particulates or condensed-phase pollutants has been reported only in very few studies. 2 Radiative transfer – remote sensing principles Passive remote sensing of aerosols in the atmosphere in general and of pesticide drift in particular is based on radiative transfer. The LOS, consists of the ambient air, the background, and the pesticides cloud. Each medium emits radiation according to its optical properties and temperature, and interacts with incoming radiation. Remote sensing by spectral instruments is unable of measuring aerosols size distribution and concentration directly, and in order to estimate these, a suitable model is needed. BenDavid et al. 2003 developed a radiative transfer model to assist the detection and estimation of biological aerosols. 𝟏 𝟐 𝑴 𝝀 = 𝑴 𝟏 𝝀 + 𝟏 − 𝒆𝒙𝒑 −𝜶 𝝀 𝝆 𝟑 𝑩 𝝀, 𝑻 𝒄 𝒕 𝟏 𝝀 + 𝑴 𝟐 𝝀 𝒆𝒙𝒑 −𝜶 𝝀 𝝆 𝒕 𝟏 𝝀 + 𝜺 𝒃 𝝀 𝑩 𝝀, 𝑻 𝒃 𝒕 𝟐 𝝀 𝒆𝒙𝒑 −𝜶 𝝀 𝝆 𝒕 𝟏 𝝀 The atmospheric radiance between the aerosol cloud and the sensor (1). The radiance of the aerosol cloud (2). The atmospheric radiance between the aerosol cloud and the background (3). The radiance of the background surface (4). α(λ) is the mass extinction coefficient ρ is the mass column density 𝒆𝒙𝒑 −𝜶 𝝀 𝝆 is the cloud’s transmission 𝜺 𝒃 𝝀 is the background emissivity 3 𝑩 𝝀, 𝑻 𝒃 is the blackbody radiation 𝑴 𝝀 is the radiance of the ambient atmosphere 𝒕 𝝀 is the atmospheric transmission Figure 1: The geometry of the LOS between the sensor and the background (Harig., 2004). 3 Remote sensing of aerosol - challenges The calculation of scattered radiation from molecules and extra fine particles in the UVVis-IR range is rather simple and straight forward; however this is not the case with larger particles which are calculated using the complicated Mie theory. The signal depends on optical parameters (e.g. refractive index) and quantitative parameters (e.g. size distribution and concentration). One of these parameters and one of the major challenges is to isolate each of the parameters' influence. Figure 2: Signal dependence on the droplets’ diameter. A result of radiative transfer modeling using Modtran 5. 𝟒 Figure 3: Signal dependence on the droplets’ concentration. A result of radiative transfer modeling using Modtran 5. 4 Goals The main goals of this study are the detection and quantification of a pesticide cloud in a contained environment and in the field. First of all experiments where conducted to detect the cloud’s presence in the line of sight. After the successful detection of the cloud, estimation of parameters such as concentration and size distribution will be attempted. Figure 4: The OP-FTIR measurement setup at the Matityahu orchard. Figure 5: The greenhouse measurement setup at the Technion. 5 Results – Greenhouse measurements 6 Results – Outdoor measurements The greenhouse measurements were intended to study the limits of detection using the OP-FTIR. The temperature of the polyethylene sheet is very similar to the background (soil and vegetation), which causes a lower signal extinction due to the cloud’s presence in the line of sight. Detection of a water cloud using Neural Quantification of the flow rate using Neural Networks: The first goal was to detect a Networks: A preliminary attempt was made cloud in the line of sight. An experiment of to quantify the signal using vendor data of 440 measurements using several types of flow rates and the same 440 measurements. nozzles with different size distributions and The RMSE obtained was 9 μm. flow rates was carried out. Outdoor measurement were intended to study the actual abilities of the OP-FTIR during a representative spraying of pesticides. True positive: 94.8% True negative: 82.9% Water cloud signals as a function of nozzles type and number: An outdoor experiment yielded good results in which a clear distinction between nozzle type and quantity was observed. Total true detection : 90.5% Figure 4: The results of the cloud’s detection experiment. Measurements of a standard pesticide cloud created by a common sprayer: An initial attempt was made to detect a cloud in the field in real conditions. There were four spraying events at different distances. 1st event 2nd event Figure 5: The results of the cloud’s quantification experiment. Figure 6: Measurements of a cloud produced by 5,3, and 1 nozzles with d50 of 119 μm . 7 Conclusions The preliminary experiments show promising results. The OP-FTIR was able to detect a cloud in all of the experiment, even in the limited radiative conditions in the greenhouse. The quantitative part of this study is much more difficult due to the large number of parameters influencing on the total signal. Future plans of this study include the incorporation of radiative transfer modeling using Modtran 5 in order to estimate the cloud’s quantitative parameters. Additionally, chemometric methods, such as the mentioned neural network, will be used to quantify these parameters. The use of the OP-FTIR could contribute, in the future, to calibrating and validating models of Aeolian transport and aerosolized pollutants. 3rd event 4th event Figure 7: The results of the field experiment. Supported by: Center for Security Science and Technology (CSST)