Environmental Research Letters最新文献

Recent global temperature increases and extreme heat events have raised concerns about their impact on health, particularly in vulnerable regions like Africa. This study assesses future heat stress and population exposure in the Lake Victoria region under the high-emission SSP5-8.5 climate change scenario, using a convection-permitting climate model, heat stress indices (humidex and heat index), and high-resolution population projections under the high-emission SSP5-8.5 scenario, interpreted here as the high-end of the climate change signal. Results indicate a substantial increase in the duration of dangerous heat stress. By the end of the century, up to 122 million people, or around 44 $\%$ of the population may experience dangerous heat stress for more than 5 $\%$ of the time annually (i.e. ∼18 days), compared to 1 $\%$ of the population or around 1 million people for the period 2005-2016. Up to 28 $\%$ of the population (∼78 million people) would even experience dangerous heat for 15 $\%$ of the time (i.e. ∼55 days). 66 $\%$ of this increased population exposure can be attributed to the combined effect of increasing temperatures and total population in the region. High heat-risk areas include the northern and southern shores of Lake Victoria and urban areas. The study highlights the need to consider both climate and population dynamics when assessing heat stress, and underscores the urgency of adaptation in the Lake Victoria region.

Many studies link average residential greenspace exposure during pregnancy to birthweight changes, but evidence on critical timing for low birthweight is limited. Furthermore, coarse aggregations of exposure levels throughout pregnancy may obscure complex exposure-response relationships. This case-control study using the birth data (n = 788,275) in three US states examined the associations between the ZIP code-level weekly enhanced vegetation index (EVI) levels during gestational weeks 0-39 and term low birthweight (TLBW). The logistic regression with distributed lag non-linear functions, adjusted for maternal characteristics and season, estimated odds ratios (OR) of TLBW per interquartile range increase (0.200) in weekly EVI. Week-specific ORs showed an inverted U-shape. Significant ORs were observed in weeks 0-7 and 30-39, ranging from 0.989 (95% CI: 0.978-0.999) to 0.996 (95% CI: 0.992-1.000). Results highlight the importance of higher greenspace exposure in early and late pregnancy for reducing TLBW risk, informing policy and future research.

Wildfire activity is increasing globally due to climate change, with implications for air quality and public health. Fine particulate matter (PM_2.5) from wildfire smoke contributes to cardiorespiratory morbidity and mortality, adverse birth outcomes, mental health stressors, and disruptions to food security and traditional livelihoods. However, quantifying health risks remains difficult due to sparse monitoring, challenges in isolating wildfire-specific pollution, and limited long-term exposure assessments. We developed a historical air quality dataset for Alaska using a hybrid approach that integrates GEOS-Chem atmospheric modeling with ground-based data to estimate daily wildfire-attributable PM_2.5 at a 0.625° × 0.5° resolution from 2003 to 2020. We aggregated these estimates by census tract and derived metrics to quantify long-term wildfire smoke exposure, then combined these estimates with social vulnerability data to identify populations disproportionately affected. Alaskans experienced an average of 3.5 million person-days of moderate and >800 000 person-days of dense smoke exposure annually. In years when over 2 million acres burned, 86%-98% of census tracts recorded at least 1 d of moderate smoke, and up to 73% experienced dense smoke. Northern Interior Alaska had over 300 cumulative days of poor air quality (∼10% of summer days) over the 18 year period, with smoke waves lasting as long as 43 d. Tracts identified as having high smoke exposure and high smoke vulnerability were generally in rural Interior Alaska; however, urban tracts in Interior and Southcentral were also identified. High-exposure census tracts had statistically greater proportions of housing cost-burdened residents and women of childbearing age. This study highlights the need to move beyond traditional fire metrics and adopt measures that better capture the full scope of human exposure. Our approach provides a framework for assessing health risks and integrating public health into climate adaptation and fire management especially in wildfire-prone regions where observations are sparse.

Despite the growing evidence on the associations between greenspace and violent crime, there is a lack of research on the urban greenspace's influence on the associations between ambient temperature and violent crime. This observational study examined the risk differences by community's greenspace level using various greenspace indicators. Our time-series analysis modeled the associations between daily mean temperature (°C) over two lag days (lag0-1) and daily counts of violent crime during summer (May-September) in each ZIP code in Chicago, IL (2001-2023), adjusting for confounding factors. Our random-effects meta analysis analyzed estimated the pooled relative risk (RR) at the 80th summer temperature percentile compared to the reference temperature (10th percentile) across the ZIP codes. Our meta-regressions analyzed how the ZIP code-specific relative risks (RRs) differ by the number of parks, sum of park areas, percentage of vegetated area, percentage of recreational vegetated area, vegetation density (30 m), percent tree coverage, and percent street-level tree coverage aggregated at the ZIP code level. A total of 1075 959 counts of violent crime were included in our analysis. We found 8% (95% CI: 7%-10%) higher risk of violent crime incidents when the daily mean temperature was at the 80th percentile (25.9 °C) compared to the reference temperature (8.6 °C). The pooled RR was significantly lower in ZIP codes with the highest vegetation density (RR = 1.085 [95% CI: 1.040-1.131]) compared to those with the lowest vegetated density (RR = 1.124 [1.088-1.162]). The RR was significantly lower in ZIP codes with the highest percentage of tree coverage (RR = 1.088 [1.046-1.132]) compared to the ZIP codes with the lowest percentage of tree coverage (RR = 1.123 [1.086-1.162]). The observed results indicate that greenspace can be beneficial in reducing the associations between heat and violent crime. The results should be considered in urban greenery planning and policies to reduce violent crime.

Urban informal settlement residents are vulnerable to mosquito-borne diseases, but little is known about the specific drivers of risk, or how they differ, within the diversity of informal settlements globally. Here we aimed to identify key drivers of mosquito abundance in different urban informal settlements to inform upgrading programs. We developed a causal framework of mosquito risk and tested it in two distinct geographic settings: Makassar, Indonesia and Suva, Fiji. Using longitudinal mosquito trapping surveys in 24 informal settlements between 2018 and 2024 (totalling 1534 successful trap sets in Makassar and 1216 in Suva), we fitted causal models to infer the relationships between climatic, environmental and socioeconomic drivers and the abundance of two dominant mosquito species: Aedes aegypti and Culex quinquefasciatus. Water supply and access, and variation in temperature and precipitation were key drivers of mosquito abundance in both informal settlement locations, but the direction of effects differed between vector species. Piped water supply in a settlement reduced the abundance of the dengue vector, Ae. aegypti but increased the abundance of Cx. quinquefasciatus. Higher temperature and precipitation were associated with more Ae. aegypti in both geographic locations. By identifying the pathways through which changes in informal settlement environments are likely to alter mosquito risk we provide essential information to guide upgrading and resilience programs.

Despite growing evidence of health risks posed by animal feeding operations (AFOs) including concentrated AFOs (CAFOs), few studies have explored the associated disproportionate health burdens. We investigated risk of cause-specific mortality associated with AFO/CAFOs and related disparities for North Carolina, Pennsylvania, and Virginia (2000-2020). We estimated associations between AFO/CAFO exposure and mortality (anemia, asthma, COPD, diabetes mellitus, cerebrovascular disease, and kidney disease) using logistic regression. For each participant, we applied two exposure metrics based on buffers around population-weighted ZIP-code centroids: (1) binary exposure based on presence or absence of AFOs/CAFOs, and (2) exposure intensity (no exposure, low, medium, and high). We investigated health disparities by individual-level (sex, race/ethnicity, age, education, marital status) and community-level (race, income, poverty, education, racial isolation, educational isolation) characteristics. Presence of AFO/CAFOs was associated with higher risks of cause-specific mortality, particularly for diabetes mellitus or cerebrovascular disease, across all states. People in ZIP codes within ⩽10 km of AFO/CAFO were 1.028 (95% Confidence Interval 1.014, 1.042), 1.039 (1.025, 1.053), and 1.053 (1.031, 1.075) times more likely to die from cerebrovascular disease compared to those in ZIP codes without AFO/CAFO exposure for North Carolina, Pennsylvania, and Virginia, respectively. We found disproportionate health burden associated with AFO/CAFO exposure in some subpopulations, however results varied by state. Our findings provide evidence of higher mortality risk with high AFO/CAFO exposure, with some populations facing disproportionate health burden, although such relationships differed by location.

Livestock are a critical part of our food systems, yet their abundance globally has been cited as a driver of many environmental and human health concerns. Issues such as soil, water, and air pollution, greenhouse gas emissions, aquifer depletion, antimicrobial resistance genes, and zoonotic disease outbreaks have all been linked to livestock operations. While many studies have examined these issues at depth at local scales, it has been difficult to complete studies at regional or national scales due to the dearth of livestock data, hindering pollution mitigation or response time for tracing and monitoring disease outbreaks. In the U.S. the National Agricultural Statistics Service completes a Census once every 5 years that includes livestock, but data are only available at the county level leaving little inference that can be made at such a coarse spatiotemporal scale. While other data exist through some regulated permitting programs, there are significant data gaps in where livestock are raised, how many livestock are on site at a given time, and how these livestock and, importantly, their waste emissions, are managed. In this perspective, we highlight the need for better livestock data, then discuss the accessibility and key limitations of currently available data. We then feature some recent work to improve livestock data availability through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.

The extent and robustness of the interaction between exposures to heat and ambient PM_2.5 is unclear and little is known of the interaction between exposures to cold and ambient PM_2.5. Clarifying these interactions, if any, is crucial due to the omnipresence of PM_2.5 in the atmosphere and increasing scope and frequency of extreme temperature events. To investigate both of these interactions, we merged 6 073 575 individual-level mortality records from thirteen states spanning seventeen years with 1 km daily PM_2.5 predictions from sophisticated prediction model and 1 km meteorology from Daymet V4. A time-stratified, bidirectional case-crossover design was used to control for confounding by individual-level, long-term and cyclic weekly characteristics. We fitted conditional logistic regressions with an interaction term between PM_2.5 and extreme temperature events to investigate the potential interactive effects on mortality. Ambient PM_2.5 exposure has the greatest effect on mortality by all internal causes in the 2 d moving average exposure window. Additionally, we found consistently synergistic interactions between a 10 μg m^-3 increase in the 2 d moving average of PM_2.5 and extreme heat with interaction odds ratios of 1.013 (95% CI: 1.000, 1.026), 1.024 (95% CI: 1.002, 1.046), and 1.033 (95% CI: 0.991, 1.077) for deaths by all internal causes, circulatory causes, and respiratory causes, respectively, which represent 75%, 156%, and 214% increases in the coefficient estimates for PM_2.5 on those days. We also found evidence of interactions on the additive scale with corresponding relative excess risks due to interaction (RERIs) of 0.013 (95% CI: 0.003, 0.021), 0.020 (95% CI: 0.008, 0.031), and 0.017 (95% CI: -0.015, 0.036). Interactions with other PM_2.5 exposure windows were more pronounced. For extreme cold, our results were suggestive of an antagonistic relationship. These results suggest that ambient PM_2.5 interacts synergistically with exposure to extreme heat, yielding greater risks for mortality than only either exposure alone.