Environmental Research Letters最新文献

Background. Fine particulate matter (PM_2.5) is a leading global health risk. Latin American cities exhibit some of the world's highest urban PM_2.5 levels, yet studies of neighborhood-level PM_2.5 exposure and associated disparities in the region are limited. Methods. We conducted a cross-sectional ecological analysis of 53 041 neighborhoods across 340 cities in eight Latin American countries, leveraging the Salud Urbana en America Latina study dataset. Annual PM_2.5 concentrations were derived from satellite data and linked to socioeconomic and urban characteristics. A multilevel model analyzed associations between neighborhood PM_2.5 levels and neighborhood- and city-level characteristics. Results. The median annual neighborhood PM_2.5 concentration was 18.49 µg m^-3. Of the 256 million residents, all lived in neighborhoods with ambient PM_2.5 concentrations that exceeded the 2021 World Health Organization guidelines (5 µg m^-3). Variability was greatest between cities (54.3% of total variance), but substantial within-city variation (26% of variance) was observed. Higher neighborhood PM_2.5 levels were associated with higher neighborhood educational attainment (mean difference [MD] comparing top to bottom tertile = 0.17 µg m^-3), higher neighborhood intersection density (MD comparing top to bottom tertile = 0.17 µg m^-3), and older cities (MD comparing top to bottom tertile = 1.45 µg m^-3). Lower neighborhood PM_2.5 levels were related to higher neighborhood population density (MD comparing top to bottom tertile = - 0.55 µg m^-3), more greenness (MD comparing top to bottom tertile = - 0.76 µg m^-3), and larger distance from city centers (MD comparing top to bottom tertile = - 0.86 µg m^-3). Conclusions. Neighborhoods with higher PM_2.5 concentrations tended to have higher educational attainment, more intersections, and be located in older cities, while lower concentrations were associated with denser populations, more green space, and greater distance from city centers. Our findings reveal important within-city heterogeneity in PM_2.5 and the factors associated with it, suggesting strategies to mitigate air pollution within cities.

As cities in sub-Saharan Africa become more crowded, noise pollution is also emerging as an important environmental concern, after air pollution. Yet, unlike air pollution, which is enjoying relatively more public attention, there is limited measurement data and policy efforts on environmental noise pollution. We followed a recent city-wide measurement approach used in Accra (Ghana) and characterized environmental noise patterns in Kigali, a contrasting city with very different topography and regulatory system than Accra to inform urban policy. We established 10 'fixed' (yearlong) and 120 'rotating' (weeklong) monitoring sites to capture both the temporal and spatial patterns in Kigali's sound environment. The measurement occurred between November 2022 and December 2023, and samples were collected at 1 min interval, resulting in 5155 014 (3580 site-days) and 1190 620 (827 site-days) site-minutes of valid data from the fixed and rotating sites, respectively. The 130 monitoring sites covered a variety of geographic and land-use factors across diverse neighborhoods and sources. We computed several noise metrics, including 1 h (LAeq_{1 h}), daily (LAeq_{24 h}), day-time (L _day), and night-time (L _night). Daily noise (LAeq_{24 h}) levels across the city ranged between 38 dBA and 85 dBA. Commercial, business, and industrial (CBI) and high-density residential (HD) communities experienced the highest noise levels, with some sites constantly above 70 dBA at day and 65 dBA at night. About 63% of our observed day-time values (up to ~72% in some areas) exceeded the Rwandan day-time standard (55 dBA) for residential areas, whereas 69% of the observed night-time values (up to 80% in some areas) exceeded the corresponding night-time standard (45 dBA). In Nyarugenge, the most urbanized district, as much as 75% of our site-days data exceeded day-time standard. However diurnal patterns throughout the city were similar, rising from ~5 am, peaking at about 8 am and plateauing until 6 pm before falling to their lowest at midnight. Overall, noise levels in the city did not vary much by day of the week, weekdays vs weekend, or dry vs wet seasons. Environmental noise in Kigali often exceeded both Rwandan standards and international guidelines, with residents in the city center district, CBI and HD areas at risk of higher exposure, and hence higher risk of adverse effects. Detailed assessment of the sources, at-risk population, and associated health effects may inform Rwandan's environmental policy efforts and city initiatives in the face of the ongoing urban growth and densification.

As global temperatures rise due to climate change, urban heat islands have emerged as an important public health concern, significantly exacerbating heat stress in urban populations. Meteorological data is critical for assessing heat stress, and localized microclimate data provide more precise measurements of heat hazards than traditional weather station data. Our study explored microclimate patterns in space and time in tropical cities with rapidly growing urban populations and warming climates. We established a microclimate monitoring network with sensors measuring air temperature and relative humidity throughout two large cities in Gujarat, India. We collected hourly microclimate data on temperature and humidity from April 2023 to May 2024 from paired indoor/outdoor sensors at 48 homes in Ahmedabad and 45 homes in Surat. We summarized dry bulb (T) and wet-bulb (T _w) temperatures at indoor and outdoor locations, compared temporal patterns across seasons and times of the day, and investigated relationships with urban land cover. Indoor and outdoor microclimates had different diurnal variations, with distinctive patterns during the monsoon compared to other seasons. Building volume had warming effects and vegetation had cooling effects on minimum T and T _w, particularly at outdoor locations. In contrast, building volume had cooling effects and vegetation had warming effects on maximum T and T _w, particularly at indoor locations. Temperatures were consistently cooler at locations with higher albedo, and relationships with water were weaker and more variable. A model comparison found significant differences in land cover effects for indoor versus outdoor locations. Given the increasing occurrence of heat waves and climate-related health threats in western India and other tropical areas, it will be essential to account for the different spatial and temporal patterns of indoor and outdoor microclimates to more precisely identify locations and timings of temperature extremes.

Fossil fuel energy infrastructure poses health risks for local communities, primarily due to the presence of air pollution emissions and other hazards. There is also evidence of racial/ethnic disparities in the siting of this infrastructure for select components. However, population counts and demographic composition near fossil fuel energy infrastructure have not been systematically characterized across all types, supply chain stages, and predominant fuel types. Here, we construct a dataset of 25 elements of fossil fuel energy infrastructure and characterize the populations living near this infrastructure (defined as within 800 m [∼0.5 mile] or 1.6 km [∼1 mile]). We estimated that 46.6 million people in the contiguous U.S., representing 14.1% of the population, live within 1.6 km of at least one piece of energy infrastructure, with racial/ethnic disparities observed across nearly all stages of the supply chain. End use infrastructure has the most people residing within 1.6 km, with 20.9 million people, followed by extraction (20.3 million), and storage (6.16 million). Storage infrastructure has an average of ∼2,900 people living within 1.6 km of each element; end use infrastructure has an average of 1,900 people residing within 1.6 km of each element; extraction infrastructure has an average of 17 people residing within 1.6 km of each element. Almost 90% of the population near end use, transportation, refining, and storage infrastructure are in urban areas. Our results represent a substantial population in the U.S. that is potentially exposed to hazards that are not well-characterized, with unknown cumulative impacts, and which constitute a major environmental justice issue.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM_2.5 observations exceeded 100 µg m^-3, affecting major cities such as New York City and Philadelphia, while many areas lacked PM_2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM_2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM_2.5 observations, with linear regression results of R² ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM_2.5 simulations by up to 40 µg m^-3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework's ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

The effects of a changing climate are already evident in Caribbean small island developing states (SIDS) like Puerto Rico, where heat episodes have become more frequent. Despite reports of increasing heat-related death rates, robust epidemiological evidence on the health impacts of high temperatures, as well as the effects of low temperatures, remains scarce, particularly outside of urban settlements in Caribbean SIDS. In this study, we conducted a case time-series study on municipality-level mortality and temperature in Puerto Rico from 2015-2023. We modeled the relationship between daily mortality count and mean temperature using a conditional quasi-Poisson regression, combined with a distributed lag non-linear model (dlnm) with a 21 d lag, adjusting for relative humidity, seasonality, and day of the week. We estimated the minimum mortality temperature (MMT)-the optimal temperature associated with the lowest mortality risk-and calculated the relative risk associated with extreme low and high temperature, defined as the 2.5th and 97.5th percentiles of daily temperature. Additionally, we estimated the municipality- and island-level excess mortality fractions attributable to both low and high temperatures, relative to MMT. Our findings indicate that exposure to non-optimum temperatures (both low and high temperatures) is significantly associated with increased mortality risk. Specifically, extreme low temperature was associated with a 1.23 (95% CI: 1.07-1.40) times risk of all-cause mortality, while extreme high temperature was associated with a 1.16 (95% CI: 1.05-1.27) times risk. We estimated that temperature-related mortality accounted for 3.88% of the total 280 568 deaths (95% eCI: 3.39%-4.29%), with low temperatures contributing 2.02% (95% eCI: 1.69%-2.32%) and high temperatures contributing 1.86% (95% eCI: 1.35%-2.35%). Furthermore, we found substantial spatial variability in temperature-related mortality burdens across municipalities. Our study identifies the vulnerable municipalities to temperature-related deaths in Puerto Rico, providing evidence to inform municipality-specific climate adaptation and mitigation strategies.

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.