The classification of wetland plants using unmanned aerial vehicle (UAV) and satellite synergies has received increasing attention in recent years. In this study, UAV-derived training and validation data and WorldView-3 satellite imagery are integrated in the classification of five dominant wetland plants in the Old Woman Creek (OWC) estuary, USA. Several classifiers are explored: (1) pixel-based methods: maximum likelihood (ML), support vector machine (SVM), and neural network (NN), and (2) object-based methods: Naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (k-NN). The study evaluates the performance of the classifiers for different image feature combinations such as single bands, vegetation indices, principal components (PCs), and texture information. The results showed that all classifiers reached high overall accuracy (>85%). Pixel-based SVM and object-based NB exhibited the best performance with overall accuracies of 93.76% and 93.30%, respectively. Insignificantly lower overall accuracy was achieved with ML (92.29), followed by NN (90.95) and object-oriented SVM (90.61). The k-NN method showed the lowest (but still high) accuracy of 86.74%. All classifiers except for the pixel-based SVM required additional input features. The pixel-based SVM achieved low errors of commission and omission, and unlike the other classifiers, exhibited low variability and low sensitivity to additional image features. Our study shows the efficacy of combining very high spatial resolution UAV-derived information and the super spectral observation capabilities of WorldView-3 in machine learning for mapping wetland vegetation.

Linking Per Capita GDP to Energy Consumption, Ecological Footprint, and Carbon Dioxide Emission in a Developing Economy in the World: The Case of Bangladesh

Identifying sources of air pollutants is essential for informing actions to reduce emissions, exposures, and adverse health impacts. This study updates and extends apportionments of particulate matter (PM_2.5) in Detroit, MI, USA, an area with extensive industrial, vehicular, and construction activity interspersed among vulnerable communities. We demonstrate an approach that uses positive matrix factorization models with combined spatially and temporally diverse datasets to assess source contributions, trend seasonal levels, and examine pandemic-related effects. The approach consolidates measurements from 2016 to 2021 collected at three sites. Most PM_2.5 was due to mobile sources, secondary sulfate, and secondary nitrate; smaller contributions arose from soil/dust, ferrous and non-ferrous metals, and road salt sources. Several sources varied significantly by season and site. Pandemic-related changes were generally modest. Results of the consolidated models were more consistent with respect to trends and known sources, and the larger sample size should improve representativeness and stability. Compared to earlier apportionments, contributions of secondary sulfate and nitrate were lower, and mobile sources now represent the dominant PM_2.5 contributor. We show the growing contribution of mobile sources, the need to update apportionments performed just 5–10 years ago, and that apportionments at a single site may not apply elsewhere in the same urban area, especially for local sources.

Fugitive dust (FD) is a nuisance and potential health issue, particularly in environmental justice communities that can experience high levels of contaminated FD. This community-initiated study examined FD from a scrap metal processor in Detroit, Michigan, to determine whether the FD was contaminated, how it migrated through the community, whether wipe or composite road dust samples were preferable, and whether literature profiles adequately characterized this source. The study was motivated by community concerns, as well as a massive subsidence/upheaval event resulting from excessive accumulation of mill scale, which is a type of scrap metal, at the facility. We collected 57 wipe samples from windows and other surfaces, and 20 composite road dust and surface soil samples, which were analyzed by X-ray fluorescence. Concentrations were expressed using the fraction of the reconstructed mass. We also compared results to air quality monitoring data and calculated pollution indices and enrichment factors. Samples collected near the processor had high levels of Cr, Cu, Fe, Ni, Sr, and Zn compared with background soils, and levels remained elevated in residential areas several blocks distant. Composite road dust/sediment samples appeared preferable to wipe samples for chemical characterization. The available chemical profiles did not match the FD composition, suggesting the need for local profiles. The high level of Fe, which is consistent with mill scale, was a novel finding and caused the road dust to exceed health protection screening levels. Numerous metal scrap facilities operate locally and nationally, and our results show the need to improve controls to limit or eliminate FD emissions from industrial sources using enforced policies that reduce dust generation and truck track-out.

Cyanobacterial harmful algal blooms (cyanoHABs) occur worldwide and can cause ingestion and inhalation exposure to microcystin and other potent toxins. This study develops life course exposure measures for cyanobacteria for application in population studies and then associates these measures with the survival of individuals with amyotrophic lateral sclerosis (ALS). The exposure measures utilize an individual’s residence history, date of disease onset, and satellite data from the Cyanobacteria Assessment Network. Residence duration for selected exposure windows referenced to disease onset date was used to weight cyanobacteria concentrations in water bodies within 0.25 to 10 km of each residence. Different concentration metrics, buffer sizes, and exposure windows were evaluated. The 2.5 and 5 km buffers best balanced the likelihood and plausibility of exposure while still resolving exposure contrasts. Over their lifetime, most study participants lived within 5 km of cyanobacteria blooms, and the exposure was associated with up to 0.89 years shorter survival, with significant interactions for individuals reporting swimming, fishing, and private wells. Our findings suggest a new and modifiable risk factor for ALS survival, and a need to confirm exposures and epidemiological findings. These cyanoHAB exposure estimates can facilitate population studies that can discover new relationships with neurodegenerative and other diseases.

Life course exposure estimates developed using geospatial datasets must address issues of individual mobility, missing and incorrect data, and incompatible scaling of the datasets. We propose methods to assess and resolve these issues by developing individual exposure histories for an adult cohort of patients with amyotrophic lateral sclerosis (ALS) and matched controls using residence history and PM_2.5, black carbon, NO₂, and traffic intensity estimates. The completeness of the residence histories was substantially improved by adding both date and age questions to the survey and by accounting for the preceding and following residence. Information for the past five residences fully captured a 20-year exposure window for 95% of the cohort. A novel spatial multiple imputation approach dealt with missing or incomplete address data and avoided biases associated with centroid approaches. These steps boosted the time history completion to 99% and the geocoding success to 92%. PM_2.5 and NO₂, but not black carbon, had moderately high agreement with observed data; however, the 1 km resolution of the pollution datasets did not capture fine scale spatial heterogeneity and compressed the range of exposures. This appears to be the first study to examine the mobility of an older cohort for long exposure windows and to utilize spatial imputation methods to estimate exposure. The recommended methods are broadly applicable and can improve the completeness, reliability, and accuracy of life course exposure estimates.