Geohealth最新文献

Access to urban natural space, including blue and greenspace, is associated with improved health. In 2021, the C40 Cities Climate Leadership Group set 2030 Urban Nature Declaration (UND) targets: “Quality Total Cover” (30% green area within each city) and “Equitable Spatial Distribution” (70% of the population living close to natural space). We evaluate progress toward these targets in the 96 C40 cities using globally available, high-resolution data sets for landcover and normalized difference vegetation index (NDVI). We use the European Space Agency (ESA)'s WorldCover data set to define greenspace with discrete landcover categories and ESA's Sentinel-2A to calculate NDVI, adding the “open water” landcover category to characterize total natural space. We compare 2020 levels of urban green and natural space to the two UND targets and predict the city-specific NDVI level consistent with the UND targets using linear regressions. The 96-city mean NDVI was 0.538 (range: 0.148, 0.739). Most (80%) cities meet the Quality Total Cover target, and nearly half (47%) meet the Equitable Spatial Distribution target. Landcover-measured greenspace and total natural space were strong (mean R² = 0.826) and moderate (mean R² = 0.597) predictors of NDVI and our NDVI-based natural space proximity measure, respectively. The 96-city mean predicted NDVI value of meeting the UND targets was 0.478 (range: 0.352–0.565) for Quality Total Cover and 0.660 (range: 0.498–0.767) for Equitable Spatial Distribution. Our translation of the area- and access-based metrics common in urban natural space targets into the NDVI metric used in epidemiology allows for quantifying the health benefits of achieving such targets.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%–79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%–80% false positive rate and 0%–38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

In 2023 human populations experienced multiple record-breaking climate events, with widespread impacts on human health and well-being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy-relevant investigations into the detrimental health effects of continued climate change.

Climate change is causing more frequent and severe extreme heat events (EHEs) in Canada, resulting in significant loss of life. However, patterns across mortality reporting for historical EHEs have not been analyzed. To address this gap, we studied deaths in Canadian EHEs from 1936 to 2021, identifying trends and challenges. Our analysis revealed inconsistencies in mortality data, discrepancies between vulnerable populations identified, difficulties in determining the cause of death, and inconsistent reporting on social vulnerability indicators. We provide some observations that could help inform solutions to address the gaps and challenges, by moving toward more consistent and comprehensive reporting to ensure no population is overlooked. Accurately accounting for affected populations could help better target evidence-based interventions, and reduce vulnerability to extreme heat.

The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM_2.5, NO₂, and O₃ concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM_2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO₂ concentrations and summer O₃ concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.

To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM_2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM_2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM_2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM_2.5 AQI only, HMS only, and a multivariate feature set including PM_2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM_2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

The study proposes a dynamic spatio-temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012–2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated. By modeling the geographical distribution of tuberculosis, the spatial correlation with particulate matter (PM_2.5) pollution was revealed. The identification of clusters of infected persons is an indispensable step in the construction of efficient tuberculosis management systems. The results highlight the link between the distribution of tuberculosis, air pollution and socio-economic development, which requires a detailed analysis of the epidemiological data obtained in the national tuberculosis surveillance and control program from the perspective of geographical distribution.

During NASA's Apollo missions, inhalation of dust particles from lunar regolith was identified as a potential occupational hazard for astronauts. These fine particles adhered tightly to spacesuits and were unavoidably brought into the living areas of the spacecraft. Apollo astronauts reported that exposure to the dust caused intense respiratory and ocular irritation. This problem is a potential challenge for the Artemis Program, which aims to return humans to the Moon for extended stays in this decade. Since lunar dust is “weathered” by space radiation, solar wind, and the incessant bombardment of micrometeorites, we investigated whether treatment of lunar regolith simulants to mimic space weathering enhanced their toxicity. Two such simulants were employed in this research, Lunar Mare Simulant-1 (LMS-1), and Lunar Highlands Simulant-1 (LHS-1), which were added to cultures of human lung epithelial cells (A549) to simulate lung exposure to the dusts. In addition to pulverization, previously shown to increase dust toxicity sharply, the simulants were exposed to hydrogen gas at high temperature as a proxy for solar wind exposure. This treatment further increased the toxicity of both simulants, as measured by the disruption of mitochondrial function, and damage to DNA both in mitochondria and in the nucleus. By testing the effects of supplementing the cells with an antioxidant (N-acetylcysteine), we showed that a substantial component of this toxicity arises from free radicals. It remains to be determined to what extent the radicals arise from the dust itself, as opposed to their active generation by inflammatory processes in the treated cells.

Beneficial effects on health outcomes have been observed from exposure to spaces with substantial green vegetation (“greenspace”). This includes studies of greenspace exposure on birth outcomes; however, these have been conducted largely in urban regions. We characterized residential exposure to greenspace and land cover diversity during pregnancy in rural northern New England, USA, investigating whether variation in greenspace or diversity related to newborn outcomes. Five landscape variables (greenspace land cover, land cover diversity, impervious surface area, tree canopy cover, and the Normalized Difference Vegetation Index) were aggregated within six circular zones of radii from 100 to 3,000 m around residential addresses, and distance to conservation land was measured, providing a total of 31 greenspace and diversity metrics. Four birth outcomes along with potentially confounding variables were obtained from 1,440 participants in the New Hampshire Birth Cohort Study. Higher greenspace land cover up to 3,000 m was associated with larger newborn head circumference, while impervious surface area (non-greenspace) had the opposite association. Further, birth length was positively associated with land cover diversity. These findings support beneficial health impacts of greenspace exposure observed in urban regions for certain health outcomes, such as newborn head circumference and length but not others such as birthweight and gestational age. Further our results indicate that larger radius buffer zones may be needed to characterize the rural landscape. Vegetation indices may not be interchangeable with other greenspace metrics such as land cover and impervious surface area in rural landscapes.

Climate change is escalating the threat of heat stress to global public health, with the majority of humans today facing increasingly severe and prolonged heat waves. Accurate weather data reflecting the complexity of measuring heat stress is crucial for reducing the impact of extreme heat on health worldwide. Previous studies have employed Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) metrics to understand extreme heat exposure, forming the basis for heat stress guidelines. However, systematic comparisons of meteorological and climate data sets used for these metrics and the related parameters, like air temperature, humidity, wind speed, and solar radiation crucial for human thermoregulation, are lacking. We compared three heat measures (HI_max, WBGT_Bernard, and WBGT_Liljegren) approximated from gridded weather data sets (ERA5-Land, PRISM, Daymet) with ground-based data, revealing strong agreement from HI and WBGT_Bernard (R² 0.76–0.95, RMSE 1.69–6.64°C). Discrepancies varied by Köppen-Geiger climates (e.g., Adjusted R² HI_max 0.88–0.95, WBGT_Bernard 0.79–0.97, and WBGT_Liljegren 0.80–0.96), and metrological input variables (Adjusted R² T_max 0.86–0.94, T_min 0.91–0.94, Wind 0.33, Solar_max 0.38, Solar_avg 0.38, relative humidity 0.51–0.74). Gridded data sets can offer reliable heat exposure assessment, but further research and local networks are vital to reduce measurement errors to fully enhance our understanding of how heat stress measures link to health outcomes.