HOTSPOT ANALYSIS AND COMPARISON BETWEEN SATELLITE-DERIVED AEROSOL OPTICAL DEPTH AND GROUND-BASED PARTICULATE MATTER MEASUREMENTS IN METRO MANILA

Highly urbanized regions such as the Metro Manila area in the Philippines contribute to the deterioration of air quality through overpopulation, excessive vehicle emissions, and industrialization. However, the limited number of ground monitoring stations hinders the detailed estimation of the region’s overall air quality. Satellite-derived air pollutant concentrations have been used in several research studies as a substitute or supplementary to ground-based data due to their extensive spatial and temporal coverage. Using the aerosol optical depth (AOD) from the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and ground measurements of coarse particulate matter (PM10), this study explores the comparison between satellite-derived and ground-based air pollutant concentrations measured from 2017 to 2020 through trend analysis of monthly average values per city. With 16 stations located in different cities, the monthly average values of AOD vs PM10 showed inconsistent results due to significant gaps in the ground data. Through optimized hotspot analysis, it was found that 7.24% of the Metro Manila region are considered hotspots using the MAIAC AOD values from 2017 to 2019 (pre-pandemic). From 2018 to 2020 (pandemic), 23.86% of Metro Manila are counted as hotspots. The AOD derived from satellite imagery and hotspot analysis can be used for future studies that focus on the development of models to predict ground pollutant values and the designation of non-attainment areas

Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

The estimation of PMx (incl. PM10 and PM2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (incl. PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 μg/m3 and 31.27 μg/m3. Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China

Estimación de la concentración de material particulado mediante sensoramiento remoto en la provincia de Lima, 2020

La contaminación del aire es una de las mayores preocupaciones, ya que, genera afectaciones en la salud y el ambiente, por otro lado, el monitoreo mediante estaciones convencionales tiene un alto costo y requiere constante mantenimiento generando brechas temporales a largo plazo. En tal sentido, la provincia de Lima por su gran expansión urbana tiene una alta contaminación por material particulado y las estaciones actuales tienen desventajas. Es por ello, que el objetivo de esta investigación fue estimar la concentración de material particulado mediante sensoramiento remoto en la provincia de Lima. Para ello, se utilizaron las imágenes multiespectrales del sensor MSI a bordo de los satélites Sentinel 2A y 2B, por otro lado, se solicitaron a las estaciones automáticas de SENAMHI los datos de material particulado (PM10 y PM2.5) a escala diaria y horaria para el periodo conformado por los años 2017 al 2020. Las imágenes multiespectrales se dividieron según el porcentaje de nubosidad (20% <= NUBOSIDAD < 20%), así mismo, se calculó la reflectancia en la parte superior de la atmosfera (TOA). De esta manera, en función a los datos de material particulado solicitados se identificaron las bandas espectrales que influyeron significativamente en la estimación de estos contaminantes, adicionalmente, mediante el análisis de varianza se validaron las ecuaciones obtenidas (p-valor < 0.05), finalmente al contrastar los valores medidos con los estimados se obtuvo como resultado que el poder estimador para las concentraciones de PM10 a escala diaria fueron mayores con coeficientes de determinación de 0.63 (20% <= NUBOSIDAD) y de 0.65 (NUBOSIDAD < 20%), para el caso de las concentraciones horarias se obtuvieron coeficientes de determinación de 0.52 (20% <= NUBOSIDAD) y 0.35 (NUBOSIDAD < 20%). En el caso de las concentraciones de PM2.5 el poder estimador fue mínimo, puesto que, se obtuvieron valores de 0.41 (20% <= NUBOSIDAD) y 0.45 (NUBOSIDAD < 20%) a escala diaria y de 0.30 (20% <= NUBOSIDAD) y 0.34 (NUBOSIDAD < 20%) a escala horaria