Desenvolvimento de sistema de baixo custo para estimativas de material fino em uma área urbana no leste da Amazônia oriental

Abstract

In Brazil, the systematic monitoring of environmental variables, mainly ambient air quality, is classically performed by stations densely distributed along the south-southeast axis. High-tech instrumentation and manual monitoring methods are frequently used, often requiring expensive infrastructures, raising the cost of their use. A problem arising from this scenario is the scarce environmental monitoring of air quality, especially in the Legal Amazon. Limited by expensive costs, there is a few sparsely located and financially monitoring stations, making their use in multipoint in the territory unfeasible, reducing the amount of sampling within an urban area when faced with projects with limited resources. Here, we seek to develop a monitoring platform for Fine Particulate Material (MP2.5) with an interface for viewing and storing data. To this end, an intensive period of field observation is carried out in the municipality of Barcarena-PA, between Sep2021 and Jan2022. A platform with an LCS sensor, SPS30, is evaluated as a function of polynomial-fit curves and statistical parameters such as EMA (Mean Absolute Error), Percent Relative Deviation (RD%), Mean Square Error (EMQ), and Coefficient of Determination (R²), using a conventional system for standard and continuous monitoring of the MP2.5 (Tapered Element Oscillating Microbalance (TEOM). The scientific findings show MP2.5 estimates comparable to TEOM after multiple adjustments, considering the data on an hourly measurement basis. Under global (unadjusted) conditions, we ve found values often chose to 3.7 µgm³ and 5.4 µgm³ for the SPS30 during the daytime and nighttime periods, respectively, indicating a statistically insignificant relationship with the standard sensor, that is systematic underestimations of ~57% (DR%) and equal EMA, EMQ and R² values ​​of 7.2 µgm³, 73.4 µgm³, and 0.21, respectively. The sensitivities of the SPS30 sensor in response to ambient conditions (rainfall, temperature, and humidity) revealed adjustment curves which give improvements in the sensor for temperature ranges ranging from 23-33 °C and 60-99%. Under these conditions, we applied first-order polynomial adjustments to the SPS30, and significant improvements in the statistical parameters, such as reduction in EMA, DR%, EMQ, and R² values to 3.8 µgm³, 12.0%, 23.4 µgm³, and 0.21, respectively. These improvements, however, do not imply the calibration of data for other environments in the Amazon, different from urban zones of the studied area, due to the systematic average increase of 6.6 µgm³ in the base-line of SPS30 estimates (MP2.5 ≤ 10th percentile) after calibrations; an overestimate of ~4 µgm³ concerning TEOM. The developed LCS reveals a potential for expanding methodological solutions measuring MP2.5 in the Amazon. This sensor can work in different applications and studies, such as monitoring personal exposure. However, sophisticated corrections and calibration techniques are required in the data yet. For that, new improvements to this prototype are fundamental steps to the MP2.5 estimates in the Amazon. In future studies, comparisons using a few LSC sensors from other models, SPS30 and standard sensor types, will be a critical step towards a more robust and applicable development of LSC in the Amazon, thus improving the coverage of quality monitoring of the air in urban and rural areas with relevant information for public management policies focused on the environment in the region.