PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

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

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Urban configuration and PM2.5 concentrations: Evidence from 330 Chinese cities

Preventing high concentrations of fine particulate (PM_2.5) to realize the goal of sustainable development is becoming a challenge for rapidly urbanized cities. Increasing vehicle emission due to inefficient urban form is thought to be the main cause of traffic congestion and increased PM_2.5 concentrations. Previous efforts attributing PM_2.5 concentrations to urban forms are yet to reach consistent conclusions on practical environmental protection strategies. In this study, we considered urban compositions and their spatial configuration to propose a new measurement-urban configuration-and document the effects of urban configuration on PM_2.5 concentrations. Using 330 Chinese cities as our sample, we found that the areas of two types of urban facilities, namely, residence and industry, are positively related to PM_2.5 concentrations, and the area of public service facilities is negatively related to PM_2.5 concentrations. Regarding the spatial configuration of different urban compositions, we documented that residence-industry accessibility is a key factor of PM_2.5 concentrations and plays a more important role than the residence-commerce accessibility. We also compared the influence of two accessibility indices (distance- and gravity-based accessibilities) and further found that the effect of reducing the residence-industry distance is more remarkable than the effect of increasing residential or industrial area on reducing PM_2.5 concentrations. Our results indicate that the key to reaching sustainable urban expansion is to synchronize urban constructions with spatial configuration optimization. For Chinese cities, a 7.52% increase in residence area requires at least 1% decrease in the average residence-industry distance to eliminate the incremental effects of newly constructed residential region on PM_2.5 concentrations. This study casts new light on the relationship between urban configuration and PM_2.5 concentration and provides decision makers practical and realistic approaches in realizing sustainable development goals.

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

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.