Geohealth最新文献

Air quality has improved in recent decades across most of the United States. However, decreases in pollution have not been uniform, potentially exacerbating inequalities in air pollution exposure by race and ethnicity. These inequalities exist, in part, because of spatial differences in source(s), for example, power plants or roadways. Determining which sources are driving inequality across racial and ethnic groups is critical to determining which policies (e.g., targeting power plant vs. vehicle emissions) would reduce inequalities. Our study determines which pollutant sources should be decreased to address inequalities in four pollutants (NO_x, SO₂, VOCs, and PM_2.5) in the Commonwealth of Virginia. We derived emissions from eight source categories for 134 Virginia counties from the National Emissions Inventory and the MOtor Vehicle Emissions Simulator mobile source emissions model. We used race and ethnicity data from the American Community Survey from 2011 to 2020. We applied the Atkinson Index to obtain a single summary of inequality for each source-pollutant pair (e.g., NO_x from electricity generation) across all race and ethnic groups. Most source category emissions were unequally distributed for at least once pollutant. Compared to other sources, electricity generation resulted in the largest inequalities across pollutants. Mobile sources increased in inequality from 2011 to 2020 even as emissions decreased. These results show the importance of identifying sources that contribute most to inequalities when developing policies to promote environmental justice.

The mosquito-borne disease dengue is sensitive to climate, in part because of the influence climate has on breeding habitats of dengue's Aedes mosquito vectors. Dengue risk assessment models currently leverage climate-dengue statistical associations, yet what remain understudied are the mechanistic pathways that yield different statistical relationships in different locations. We hypothesize that elucidating the mechanisms by which spatiotemporal variability in climate influences dengue incidence will improve dengue dynamics predictions across climatically distinct locations and beyond dengue's well-known seasonal cycles. We test this hypothesis by investigating a key pathway in the climate-dengue process chain: climate impacts on Aedes breeding habitats. We have implemented a mechanistic modeling pipeline that simulates climatic influence on habitat water dynamics and thereby on relative population size of the vector. We use this modeling pipeline, driven by meteorological data, to simulate monthly Aedes populations for three climatically distinct cities in Sri Lanka. We find that simulated vector abundance is plausibly associated with climate conditions and that climate drivers of vector abundance vary among locations. Moreover, tercile-tercile comparisons of dengue incidence against model variables indicate that risk assessments based on predicted vector abundance perform similarly to those based on meteorology alone—the signal of weather variability and its relationship to dengue propagates through the modeling pipeline. These results justify future testing of this modeling pipeline within a dengue risk assessment framework, where its process-based structure may be leveraged to guide proactive dengue control efforts in high-risk years and to simulate impacts of future climate conditions on dengue dynamics.

The widespread concern surrounding the enhanced spillover risk of infectious diseases due to dramatic global land use changes has sparked significant discussion. However, the specific implications of these changes on scrub typhus, a vector-borne infectious disease facing increasing incidence and substantial expansion, remain unclear. Here, we constructed a comprehensive landscape fragmentation index (LFI), which reflects the interaction between human activities and natural habitats. Then we utilized a generalized additive model (GAM) to estimate the comprehensive and segmented impacts of LFI on scrub typhus incidence in China, grouping the results by year, land use type and fragmentation level. Additionally, we projected changes in such impacts under four shared socioeconomic pathways (SSPs), including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Key results were: (a) The LFI exhibited a nonlinear positive correlation with scrub typhus incidence. Each 0.1 increase in the LFI was associated with a relative risk of 1.20 (95% CI:1.19–1.21) for scrub typhus. Notably, at higher fragmentation levels, scrub typhus incidence tended to decrease. (b) Forest fragmentation had the most significant impact on scrub typhus, followed by cropland fragmentation, whereas construction land fragmentation was negatively associated. (c) The future areas of elevated scrub typhus risk varied among the SSPs, but they were mainly concentrated at the interface between urban expansion and natural habitats. Our results indicate that human interference with the natural ecosystem is a critical factor for the incidence of scrub typhus. These findings are conducive to promoting ecological protection and the prevention and control of scrub typhus.

Most of the United States (US) population resides in cities, where they are subjected to the urban heat island effect. In this study, we develop a method to estimate hourly air temperatures at $��0.01�°�\times �0.01�°�$ resolution, improving exposure assessment of US population when compared to existing gridded products. We use an extensive network of personal weather stations to capture the intra-urban variability. The uncertainty associated with this crowdsourced data set is addressed through a spatiotemporal Bayesian model implemented with the Integrated Nested Laplace Approximation-Stochastic Partial Differential Equation approach. We evaluate the model on Philadelphia (PA), New York City (NY), Phoenix (AZ), and the Triangle area (NC). These case studies span different climatic zones and urban landscapes. They cover several meteorological events including a deadly heatwave in Phoenix and a snowstorm hitting part of the US in winter 2021. We obtain an overall root mean square error of $��1.06�°�C�$, demonstrating the versatility of our model, and its applicability across various regions in the US. The high granularity of our model allows for the precise identification of hotspots that were previously undetected with daymet and gridMET products. Using the data generated by our method, we show that neighborhoods with high population concentration are more likely to experience elevated temperatures and prolonged hot nights, thus encouraging the use of our model for further epidemiological investigations on the impact of heat or cold stress on human health.

Dengue fever is a mosquito-borne viral disease rapidly creating a significant global public health burden, particularly in urban areas of tropical and sub-tropical countries. Hydroclimatic variables, particularly local temperature, precipitation, relative humidity, and large-scale climate teleconnections, can influence the prevalence of dengue by impacting vector population development, viral replication, and human-mosquito interactions. Leveraging predictions of these variables at lead times of weeks to months can facilitate early warning system preparatory actions such as allocating funding, acquisition and preparation of medical supplies, or implementation of vector control strategies. We develop hydroclimate-based statistical forecast models for dengue virus (DENV) at 1-, 3-, and 6- month lead times for four cities across Colombia (Cali, Cúcuta, Medellín, and Leticia) and compare with standard autoregressive models conditioned on dengue case counts. Our results indicate that (a) hydroclimate-based models are particularly skillful at 3- and 6- month lead times when autoregressive models often fail, (b) sea surface temperatures are the most skillful predictor at 3- and 6- month leads and (c) application of hydroclimate models are most beneficial when average DENV incidence is low, autoregressive relationships are weak, but outbreaks may still occur.

Satellite observations provide continuous and global coverage observations of air pollutants, widely used to inform health impacts and air pollution disparities. Linking satellite retrievals with socioeconomic or health data involves matching the irregularly shaped satellite observations with administrative units. Here, we develop a physics-based approach to spatially oversample nitrogen dioxide (NO₂) retrievals from TROPOspheric Monitoring Instrument (TROPOMI) directly to United States (US) neighborhoods (i.e., block groups). The physics-based oversampling approach considers each satellite pixel as a sensitivity distribution, meaning that satellite instruments are more sensitive to the neighborhoods at the center than at the edge of the observations. We show that directly oversampling satellite observations to administrative shapes is a more accurate and computationally efficient approach than the commonly used gridding approaches, and it is advantageous for shorter temporal windows. Combining the newly developed NO₂ data set with demographic data, we find widespread racial/ethnic and income-related NO₂ disparities across the US. NO₂ disparities are even more pronounced during the most polluted days, suggesting greater acute health effects for overburdened communities. We expect that the resolution-adaptive, neighborhood-level, and GIS-compatible NO₂ data set would lower barriers of the public to access and interpret satellite observations, facilitating the actionable applications of satellite observations.

The flooded soil conditions under which rice is typically grown are beneficial for boosting yield and decreasing herbicide inputs but may pose a food safety and environmental health risk. Flooded soils lead to reducing conditions and anaerobic metabolisms of soil microorganisms, which mobilizes arsenic from soil into soil solution, where it can be absorbed by rice roots and transported to grain. These conditions also promote the production and emission of methane (CH₄)—a potent greenhouse gas. To evaluate how water management affects metal(loid) grain concentrations and CH₄ emissions, we conducted a 2-year field study in which rice paddy water was managed under a range of soil redox conditions that spanned from flooded to non-flooded. We observed that growing rice under less flooded conditions decreased CH₄ emissions and concentrations of grain total As, grain inorganic As, grain total Hg, and grain inorganic Hg relative to flooded conditions, with more reductions observed as conditions were drier; grain organic As and Hg (MeHg) species also decreased with drier conditions particularly in Year 1. However, the driest conditions tested led to a 50%–97% increase in grain Cd concentrations that exceeded the CODEX limit and grain yield reductions as high as 25% and 40% in Year 1 and 2, respectively. While concentrations of toxic metal(loid)s could be manipulated by water management, micronutrient concentrations were similar or decreased with drier conditions, potentially increasing grain Cd bioaccessibility to humans. Because practices for rice water management are gaining momentum, more research should monitor grain Cd levels along with micronutrients.

Highly pathogenic avian influenza (HPAI) virus presents a serious threat to poultry and public health worldwide, with transmission dynamics shaped by avian migration patterns and environmental conditions. Recent outbreaks in the Philippines highlight the urgent need for effective control measures. While previous studies have shown the importance of waterfowl-to-poultry transmission and farm-to-farm spread, the spillover risk to local avian species remains underexplored. This study aimed to examine H5N1-HPAI outbreaks in poultry in relation to environmental factors and local avian species in the Philippines. We applied a two-step ecological niche modelling approach using maximum entropy algorithms. First, environmental variables from remote sensing images were used to predict the distribution of 10 common avian species based on citizen science data from the eBird platform. Next, these avian distribution data were combined with environmental variables to create a risk map for H5N1-HPAI outbreaks in the Philippines. The H5N1-HPAI risk model demonstrated strong predictive performance, with an AUCROC value of 0.936 ± 0.026. Key factors contributing to predicted H5N1-HPAI risk included precipitation levels, population density, and avian species such as the Eurasian Tree Sparrow and Zebra Dove. The higher risk of spillover for the two local avian species may be due to their shared similar environmental signatures with outbreak poultries. The risk map highlighted Metro Manila and Central Luzon as high-risk regions of H5N1-HPAI. This study identified the main clusters and environmental factors associated with avian influenza outbreaks in poultry in the Philippines. Additionally, the transmission risk may threaten the local avian population.

Communities and ecosystems of northern Utah, USA receive particulate pollution from anthropogenic activity and dust emissions from sources including the Great Salt Lake (“the Lake”) playa. In addition to affecting communities, anthropogenic pollution is delivered to the Lake's playa sediments, which are eroded during dust events. Yet, spatial variability in dust flux and composition and their risks to human health are poorly understood. We analyzed dust in 17 passive samplers proximal to the Lake during fall 2022 for dust flux, the dust fraction of particulate matter, ⁸⁷Sr/⁸⁶Sr, and elemental geochemistry. We evaluated spatial patterns of 11 priority pollutant metals and estimated the hypothetical non-cancer dust and soil ingestion health hazard for six age cohorts. We observed the highest dust fluxes proximal to the Lake's playa. The highest concentrations of and greatest number of metals occurred in and south of Ogden, UT. Sites to the northeast of Farmington Bay had the highest fluxes. Metal concentrations and ⁸⁷Sr/⁸⁶Sr suggest that the dust composition near Bountiful represents contributions from anthropogenic sources, whereas the dust composition to the northeast of Farmington Bay reflects the Lake's playa emissions. Evaluations of potential health hazards from dust ingestion suggest that children between birth and 6 years are vulnerable at higher ingestion rates. Thallium, As, Pb, Co and Cr contributed most to the estimated hazard. Among these, As and sometimes Pb are likely derived from the Lake's playa emissions. Thus, suppression of dust emissions from the Lake's playa may decrease possible health risks for children in northern Utah.

Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne zoonotic disease, which are classified by the World Health Organization as a priority disease for research and development in emergency situations due to the high mortality rate. Previous studies indicated that the complex nonlinear and delayed association was observed between meteorological factors and SFTS. However, these did not consider the short-term effect of meteorological factors on the incidence of SFTS. In this study, we used generalized additive models (GAM) and distributed lag nonlinear models (DLNM) to investigate the short-term correlation between meteorological factors and SFTS incidence. From 1 January 2013 to 31 December 2023 a total of 6,601 cases of SFTS were reported in Xinyang. Females constituted the majority with a male-to-female ratio of 0.68 and the average age of cases being approximately at around 61.52 years old. The multivariate GAM analysis revealed that mean temperature exerted the greatest influence on the incidence of SFTS compared to other meteorological factors and interacted with these factors. After accounting for lag period of 0–14 days, the DLNM analysis indicated that specific range of temperature (18–23°C), a certain range atmospheric pressure (1,006–1,017 hPa), extreme high wind speed (>11.6 m/s), and prolonged sunshine duration (>9h) were associated with SFTS, while there was no significant correlation between relative humidity and the incidence of SFTS. This study investigates the non-linear trend and lagged exposure effect of various meteorological factors on short-term SFTS incidence, thereby enhancing our comprehensive understanding of the effect of meteorological factors on SFTS.