Geohealth最新文献

Planetary Health (PH) is a relatively new field that studies the sciences behind the interaction between the environment, living organisms, and human health. The number of publications in this field has increased exponentially in the past few years. This study analyzed the trend of using the term “PH” in literature. We searched PubMed for all publications (APP) and those mentioning Planetary Health (APmPH) without limitations on text availability, dates, or species. The publication trend was estimated using the average annual percent change (AAPC). Joinpoint Regression Program 4.9.1.0 identified periods with statistically distinct log-linear trends in publication numbers over time. Bonferroni adjustment determined significant trend shifts. The time frame of the retrieved APP and APmPH results ranged from 2002 to 2022 with a publication boom since 2017. The most common study designs were reviews, followed by comments and editorials. The APP, APmPH, and the proportion of APmPH to APP steadily increased from 2002 to 2022, with an AAPC of 6.0% (95% CI: 4.4%–7.6%, p < 0.001), 35.7 (95% CI: 21.3%–51.9%, p < 0.001), and 28.1% (95% CI: 15.2–42.5, p < 0.001), respectively. The term “PH” is increasingly prominent in academic literature, underscoring the need for interdisciplinary efforts. Its growing usage also highlights the need for recognition in Medical Subject Headings by the National Library of Medicine.

Higher concentrations of ambient air pollutants, including PM_2.5 and NO₂, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID-19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID-19 and related mortality using a population-based ecological study in California. Using gridded population estimates, we estimated area-level exposure to annual average oil and gas production volume from active wells within 1 kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID-19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4 months of the study period (February–May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area-level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates.

There is limited evidence regarding the effects of long-term exposure to PM_2.5 constituents on the risk of arthritis and rheumatoid arthritis, and the interaction between PM_2.5 and green space remains unclear. This study examined the relationship between long-term exposure to PM_2.5 constituents and the risk of arthritis and rheumatoid arthritis, with the exposure period extending from recruitment until self-reported outcomes, death, loss to follow-up, or end of follow-up. Additionally, the study assessed whether there was an interactive effect between PM_2.5 and green space on these risks. We gathered cohort data on 18,649 individuals aged ≥45 years. We applied generalized linear mixed-effects models to estimate the effects of PM_2.5 constituents, NDVI, and their interaction on arthritis and rheumatoid arthritis. The quantile g-computation and weighted quantile sum regression model were applied to estimate the combined effect of PM_2.5 constituents. Our results showed that exposure to single and mixed PM_2.5 constituents adversely affected arthritis and rheumatoid arthritis, and was mainly attributed to the black carbon component. We observed “U” or “J” shaped exposure-response curves for the effects of PM_2.5, OM, NO₃⁻ and NH₄⁺ exposure on the development of arthritis/rheumatoid arthritis. Additionally, the odds ratio of arthritis for per interquartile range (IQR) increase in PM_2.5 was 1.209 (95% CI:1.198, 1.221), per 0.1-unit decrease in NDVI was 1.091 (95% CI:1.033, 1.151), and the interaction term was 1.005 (95% CI:1.002, 1.007). These findings flesh out the existing evidence for PM_2.5 constituents, NDVI and arthritis, rheumatoid arthritis, but the underlying mechanisms still require further exploration.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult—since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Indonesia accounts for more than one third of the world's tropical peatlands. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought and El Niño events. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. Measurements of air pollutant concentrations are sparse in both regions contributing to large uncertainties in both fire emissions and air quality degradation. We deployed a network of 13 low-cost PM_2.5 sensors across urban and rural locations in Central Kalimantan and measured indoor and outdoor PM_2.5 concentrations during the onset of an El Niño dry season in 2023. During the dry season (September 1st to October 31st), mean outdoor PM_2.5 concentrations were 136 μg m⁻³, with fires contributing 90 μg m⁻³ to concentrations. Median indoor/outdoor (I/O) ratios were 1.01 in rural areas, considerably higher than those reported during wildfires in other regions of the world (e.g., USA), indicating housing stock in the region provides little protection from outdoor PM_2.5. We combined WRF-Chem simulated PM_2.5 concentrations with the median fire-derived I/O ratio and questionnaire results pertaining to participants' time spent I/O to estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m⁻³) during the dry season. Our work provides new information on the exposure of people in Central Kalimantan to smoke from fires and highlights the need for action to help reduce peatland fires.

The relationship between exposure to ambient air pollutants and emergency attendance for upper gastrointestinal bleeding (UGIB) remains inconclusive. This study examines the association between short-term exposure to various ambient pollutants and the risk of UGIB emergency attendance. Data on daily UGIB emergency attendance, ambient pollutants, and meteorological conditions in Hong Kong were collected from 2017 to 2022. A time-series study using a distributed lag non-linear model to analyze the data, considering lag days. Stratified analysis was performed based on sex, seasons, and the COVID-19 pandemic period. The burden was quantified using attributable fraction (AF) and number (AN). The study included 31,577 UGIB emergency records. Exposure to high levels of PM_2.5 significantly increased the risk of UGIB emergency attendance from lag day 3 (RR: 1.012) to day 6 (RR: 1.008). High NO₂ exposure also posed a significant risk from lag day 0 (RR: 1.026) to day 2 (RR: 1.014), and from lag day 5 (RR: 1.013) to day 7 (RR: 1.024). However, there was no association between UGIB and high O₃ levels. The attributable burden of high-concentration NO₂ exposure was higher compared to those of PM_2.5. Males and elderly individuals (≥65 years) faced a higher risk of UGIB emergencies, particularly during cold seasons. Our study suggests that both PM_2.5 and NO₂ exposure are associated with an increased risk of emergency attendance for UGIB. Ambient pollutant exposure has a stronger effect on UGIB in males and the elderly, particularly during cold seasons.

The COVID-19 outbreak in 2020 resulted in rapidly rising infection rates with high associated mortality rates. In response, several epidemiological studies aimed to define ways in which the spread and severity of COVID-19 can be curbed. As a result, there is a steady increase in the evidence linking greenspaces and COVID-19 impact. However, the evidence of the benefits of greenspaces or greenness to human wellbeing in the context of COVID-19 is fragmented and sometimes contradictory. This calls for a meta-analysis of existing studies to clarify the matter. Here, we identified 621 studies across the world on the matter, which were then filtered down to 13 relevant studies for meta-analysis, covering Africa, Asia, Europe, and the USA. These studies were meta-analyzed, with the impacts of greenness on COVID-19 infection rate quantified using regression estimates whereas impacts on mortality rates were measured using mortality rate ratios. We found evidence of significant negative correlations between greenness and both COVID-19 infection and mortality rates. We further found that the impacts on COVID-19 infection and related mortality are moderated by year of publication, greenness metrics, sample size, health and political covariates. This clarification has far-reaching implications for policy development toward the establishment and management of green infrastructure for the benefit of human wellbeing.

Wildfire smoke exposure leads to poorer health among those with pre-existing conditions such as asthma. Particulate matter in wildfire smoke can worsen asthma control, cause acute exacerbations, and increase health resource utilization (HRU) and costs. Research to date has been retrospective with few opportunities to project changes in underlying asthma control and HRU given exposure to wildfire smoke. Using a microsimulation of 5,000 Californians with asthma, we calculated changes in asthma control distribution, risk of exacerbation, and HRU and cost outcomes in the 16 weeks during and after a wildfire. The model was calibrated against empirical values on asthma control distribution and increased HRU after exposure to wildfire smoke. Without smoke exposure, 48% of the cohort exhibited complete or well control of asthma, and 8% required acute healthcare per cycle. Following two consecutive weeks of wildfire smoke, complete or well control of asthma fell to 27%, with an additional 4% HRU. This corresponds to total additional $601,250 in all-cause medical costs and eight fewer quality-adjusted life years over 16 weeks of model time. Our model found increased asthma health and cost burden due to wildfire smoke that were aligned with empirical evidence from a historic wildfire event. This study establishes a framework for a more nuanced understanding of asthma impacts from wildfire smoke that can help inform the development of public health policies to mitigate harm and promote resilience among asthma patients in the face of climate change.

The Atacama Desert’s naturally elevated metal(loid)s pose a unique challenge for assessing the environmental impact of mining, particularly for indigenous communities residing in these areas. This study investigates how copper mining influences the dispersion of these elements in the wind-transportable fraction (<75 μm) of surface sediments across an 80 km radius. We employed a multi-pronged approach, utilizing spatial modeling to map element distributions, exponential decay analysis to quantify concentration decline with distance, regime shift modeling to identify dispersion pattern variations, and pollution assessment to evaluate impact. Our results reveal significant mining-driven increases in surface concentrations of copper (Cu), molybdenum (Mo), and arsenic (As). Notably, within the first 20 km, concentrations peaked at 1,016 mg kg⁻¹ for Cu, 31 mg kg⁻¹ for Mo, and a remarkable 165 mg kg⁻¹ for As. Cu and Mo displayed significant dispersion, extending up to 50 km from the source. However, As exhibited the most extensive reach, traveling up to 70 km downwind, highlighting the far-reaching ecological footprint of mining operations. Mineralogical analyses corroborated these findings, identifying mining-related minerals in surface sediments far beyond the immediate mining area. Although pollution indices based on the proposed Local Geochemical Background reveal significant contamination across the study area, establishing accurate pre-industrial baseline values is essential for a more reliable assessment. This study challenges the concept of “natural pollution” by demonstrating that human activities exacerbate baseline metal(loid)s levels. Expanding monitoring protocols is imperative to comprehensively assess the combined effects of multiple emission sources, including mining and natural processes, in safeguarding environmental and human health for future generations.