High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

Abstract

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In-Situ IoT Sensor Network and Remote Sensing Approaches

“…This modification affects various atmospheric conditions, including temperature, wind patterns, and precipitation. The presence of particulate matter can lead to the formation of fog and acid rain and contributes to the greenhouse effect, as discussed in [5][6][7][8][9][10][11].…”

“…In a separate study, [10], Yu et al, 2022 enhanced the modeling of PM 2.5 concentrations with high spatial-temporal resolution. They incorporated data from the Next Generation Weather Radar (NEXRAD), along with information from the European Centre for Medium-Range Weather Forecasts (ECMWF), AOD measurements from the Geostationary Operational Environmental Satellite (GOES-16), and PM 2.5 concentrations measured by in situ sensors from the Environmental Protection Agency (EPA) across the United States.…”

Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

“…MET data are based on local meteorological station or satellite observations to provide continuous estimates in both space and time, usually at a daily or weekly time scale. Other direct measurement-based MET data, such as wind related measures derived from Next-generation Weather Radar (NEXRAD), has been used for various purposes, such as PM modeling (Yu et al, 2022 ).…”