Search

Indoor air pollution has gained importance in recent years due to its direct contact with humans and its greater potential for harm. In this context, the objective of the study was to compare indoor PM2.5 and PM10 concentrations with the World Health Organization (WHO) guideline. The methodology of the PRISMA 2020 statement was used, the bibliographic review was in 5 digital databases. The annual growth rate of scientific production was determined using the digital calculator Calcuvio, while data analysis was carried out with Microsoft Office Excel and VOSviewer. Approximately 80% of studies did not exceed the WHO guideline; the most studied indoor environment and pollution sources were homes and road traffic/cigarette smoke. The year and country with the highest scientific output were 2014 and the United States, respectively, with an annual growth rate in scientific production (1984–2024) of 13.49%. The most frequently appearing keyword was "particulate matter." The study concludes that corrective measures should be implemented to reduce particulate matter indoors, given that humans spend the majority of their lives in these environments. [ABSTRACT FROM AUTHOR]

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large‐scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community‐based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large‐scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri‐urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM‐enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%–100%). The final output, the OSM‐enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM‐derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open‐source geographic databases and apply it to spatial modeling by generating research‐specific landscape variables. As our occupancy results show, using context‐specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations. [ABSTRACT FROM AUTHOR]

In the Anthropocene, ecosystems are changing along with their capacity to support human well‐being. Monitoring ecosystem services (ESs) is required to assess the changing state of human–nature interactions. To standardize the monitoring of multiple facets of ESs, the Group on Earth Observations Biodiversity Observation Network (GEO BON) recently proposed the essential ecosystem service variables (EESVs), which are organized into six classes: Ecological Supply, Use, Demand, Anthropogenic Contribution, Instrumental Value, and Relational Value. We apply the EESV framework to three case studies in British Columbia, Canada, each targeting a single ES. Using trend and intervention analysis, we show how EESVs are changing and affected by policy. We discuss key challenges and solutions while providing guidance on how to quantify EESVs. Finally, we demonstrate the potential of EESVs to harmonize metrics across conceptual frameworks, monitor ES change, and provide decision support to assess progress under various international policy conventions. [ABSTRACT FROM AUTHOR]

Cuprous copper [Cu(I)] is an essential cofactor for enzymes that support many fundamental cellular functions including mitochondrial respiration and suppression of oxidative stress. Neurons are particularly reliant on mitochondrial production of ATP, with many neurodegenerative diseases, including Parkinson's disease, associated with diminished mitochondrial function. The gene MBLAC1 encodes a ribonuclease that targets pre-mRNA of replication-dependent histones, proteins recently found in yeast to reduce Cu(II) to Cu(I), and when mutated disrupt ATP production, elevates oxidative stress, and severely impacts cell growth. Whether this process supports neuronal and/or systemic physiology in higher eukaryotes is unknown. Previously, we identified swip-10, the putative Caenorhabditis elegans ortholog of MBLAC1, establishing a role for glial swip-10 in limiting dopamine (DA) neuron excitability and sustaining DA neuron viability. Here, we provide evidence from computational modeling that SWIP-10 protein structure mirrors that of MBLAC1 and locates a loss of function coding mutation at a site expected to disrupt histone RNA hydrolysis. Moreover, we find through genetic, biochemical, and pharmacological studies that deletion of swip-10 in worms negatively impacts systemic Cu(I) levels, leading to deficits in mitochondrial respiration and ATP production, increased oxidative stress, and neurodegeneration. These phenotypes can be offset in swip-10 mutants by the Cu(I) enhancing molecule elesclomol and through glial expression of wildtype swip-10. Together, these studies reveal a glial-expressed pathway that supports systemic mitochondrial function and neuronal health via regulation of Cu(I) homeostasis, a mechanism that may lend itself to therapeutic strategies to treat devastating neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The nematode Caenorhabditis elegans is well known for its ability to support forward genetic screens to identify molecules involved in neuronal viability and signaling. The proteins involved in C. elegans dopamine (DA) regulation are highly conserved across evolution, with prior work demonstrating that the model can serve as an efficient platform to identify novel genes involved in disease‐associated processes. To identify novel players in DA signaling, we took advantage of a recently developed library of pre‐sequenced mutant nematodes arising from the million mutation project (MMP) to identify strains that display the DA‐dependent swimming‐induced‐paralysis phenotype (Swip). Our screen identified novel mutations in the dopamine transporter encoding gene dat‐1, whose loss was previously used to identify the Swip phenotype, as well as multiple genes with previously unknown connections to DA signaling. Here, we present our isolation and characterization of one of these genes, bbs‐1, previously linked to the function of primary cilia in worms and higher organisms, including humans, and where loss‐of‐function mutations result in a human disorder known as Bardet–Biedl syndrome. Our studies of C. elegans BBS‐1 protein, as well as other proteins that are known to be assembled into a higher order complex (the BBSome) reveal that functional or structural disruption of this complex leads to exaggerated C. elegans DA signaling to produce Swip via a cell‐autonomous mechanism. We provide evidence that not only does the proper function of cilia in C. elegans DA neurons support normal swimming behavior, but also that bbs‐1 maintains normal levels of DAT‐1 trafficking or function via a RHO‐1 and SWIP‐13/MAPK‐15 dependent pathway where mutants may contribute to Swip independent of altered ciliary function. Together, these studies demonstrate novel contributors to DA neuron function in the worm and demonstrate the utility and efficiency of forward genetic screens using the MMP library. [ABSTRACT FROM AUTHOR]

In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and Seasonal Trend (BFAST) algorithms to monitor forest EVI changes (breaks and trends) in and around the Algonquin Provincial Park (Ontario, Canada) from 2003 to 2022. We found that relatively little change occurred in forest EVI pixels and that most of the change occurred in non-protected forest areas. Only 5.3% (12,348) of forest pixels experienced one or more EVI breaks and 27.8% showed detectable EVI trends. Most breaks were negative (11,969, 75.3%; positive breaks: 3935, 24.7%) with a median magnitude of change of −755.5 (median positive magnitude: 722.6). A peak of negative breaks (2487, 21%) occurred in the year 2013 while no clear peak was seen among positive breaks. Most breaks (negative and positive) and trends occurred in the eastern region of the study area. Boosted regression trees revealed that the most important predictors of the magnitude of change were forest age, summer droughts, and warm winters. These were among the most important variables that explained the magnitude of negative (R2 = 0.639) and positive breaks (R2 = 0.352). Forest composition and protection status were only marginally important. Future work should focus on assessing spatial clusters of EVI breaks and trends to understand local drivers of forest vegetation health and their potential relation to forest ecosystem services. [ABSTRACT FROM AUTHOR]

Agricultural and urban expansion has caused considerable degradation of ecosystems. In the case of Peruvian high Andean grasslands, it was reported that between 2000 and 2009, this ecosystem was reduced by 7%. The limited or no protection they receive is partly due to the fact that the benefits of ecosystem services are not widely known. This research aims to establish and predict the influence of high Andean grasslands on the annual occurrence of landslides. To do so, we identified occurrences of landslides, falls, huaycos, avalanches, and alluviums in high Andean grasslands. We also examined urban areas and agricultural zones of Peru for the period from 2003 to 2016. Subsequently, we extracted data on precipitation, temperature, slopes, soil types, and geographical variables. This data was used to train a machine learning model. The results show that 96% of landslides occurred in human-intervened areas, and only 4% in high Andean grasslands. Precipitation and slope thresholds for landslide occurrence are higher in high Andean grasslands compared to agricultural and urban areas. The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. They had coefficients of determination of R² = 0.80, 0.80, 0.66, and 0.64, respectively. Predictions show that if agricultural or urban areas are established in wet or dry puna grasslands, the average number of occurrences multiplies. The multiplier factors are 2.1 and 7.08, the number of deaths by 2.8 and 10.49, the number of houses destroyed by 2.4 and 7.51, and the number of roads destroyed by 2.2 and 7.37, respectively. The study demonstrates that conserving high Andean grasslands significantly reduces landslides compared to urban or agricultural areas. [ABSTRACT FROM AUTHOR]

Introduction Epidemiological studies of the health consequences of exposure to particulate matter (PM) with aerodynamic diameter [less than or equal to] 2.5 [micro]m ([PM.sub.2.5]) have been conducted mostly in high-income [...], Background: Studies on the extent to which long-term exposure to ambient particulate matter (PM) with aerodynamic diameter [less than or equal to]2.5 [micro]m ([PM.sub.2.5]) contributes to adult mortality in India are few, despite over 99% of Indians being exposed to levels that the World Health Organization (WHO) considers unsafe. Objective: We conducted a retrospective cohort study within the Million Death Study (MDS) to provide the first-ever quantification of national mortality from exposure to [PM.sub.2.5] in India from 1999 to 2014. Methods: We calculated relative risks (RRs) by linking a total of ten 3-y intervals of satellite-based estimated [PM.sub.2.5] exposure to deaths 3 to 5 y later in over 7,400 small villages or urban blocks covering a total population of 6.8 million. We applied using a model-based geostatistical model, adjusted for individual age, sex, and year of death; smoking prevalence, rural/urban residency, area-level female illiteracy, languages, and spatial clustering and unit-level variation. Results: [PM.sub.2.5] exposure levels increased from 1999 to 2014, particularly in central and eastern India. Among 212,573 deaths at ages 15-69 y, after spatial adjustment, we found a significant RR of 1.09 [95% credible interval (CI): 1.04, 1.14] for stroke deaths per 10-[micro]g/[m.sup.3] increase in [PM.sub.2.5] exposure, but no significant excess for deaths from chronic respiratory disease and ischemic heart disease (IHD), all nonaccidental causes, and total mortality (after excluding stroke). Spatial adjustment attenuated the RRs for chronic respiratory disease and IHD but raised those for stroke. The RRs were consistent in various sensitivity analyses with spatial adjustment, including stratifying by levels of solid fuel exposure, by sex, and by age group, addition of climatic variables, and in supplementary case-control analyses using injury deaths as controls. Discussion: Direct epidemiological measurements, despite inherent limitations, yielded associations between mortality and long-term [PM.sub.2.5] inconsistent with those reported in earlier models used by the WHO to derive estimates of [PM.sub.2.5] mortality in India. The modest RRs in our study are consistent with near or null mortality effects. They suggest suitable caution in estimating deaths from [PM.sub.2.5] exposure based on MDS results and even more caution in extrapolating model-based associations of risk derived mostly from high-income countries to India. https://doi.org/10.1289/EHP9538

Receiving a neurodegenerative disease (NDD) diagnosis, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, or amyotrophic lateral sclerosis, is devastating, particularly given the limited options for treatment. Advances in genetic technologies have allowed for efficient modeling of NDDs in animals and brought hope for new disease‐modifying medications. The complexity of the mammalian brain and the costs and time needed to identify and develop therapeutic leads limits progress. Modeling NDDs in invertebrates, such as the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans, offers orders of magnitude increases in speed of genetic analysis and manipulation, and can be pursued at substantially reduced cost, providing an important, platform complement and inform research with mammalian NDD models. In this review, we describe how our efforts to exploit C. elegans for the study of neural signaling and health led to the discovery of a paralytic phenotype (swimming‐induced paralysis) associated with altered dopamine signaling and, surprisingly, to the discovery of a novel gene and pathway whose dysfunction in glial cells triggers neurodegeneration. Research to date on swip‐10 and its putative mammalian ortholog MBLAC1, suggests that a tandem analysis will offer insights into NDD mechanisms and insights into novel, disease‐modifying therapeutics. [ABSTRACT FROM AUTHOR]

The burning of fireworks damages air quality by causing elevated concentrations of particulate matter (PM2.5 and PM10) in short periods of time. In this context, the research aimed to compare airborne concentrations of PM2.5 and PM10 from fireworks with the National Ambient Air Quality Standards (NAAQS) and the World Health Organization (WHO) guideline. The applied methodology involved the use of the PRISMA 2020 statement. The literature review was conducted on digital databases such as Scopus, ScienceDirect, Taylor & Francis, Wiley, and Ebsco. Annual growth in scientific production was calculated using a digital tool (Calcuvio), and data analysis was performed using Microsoft Office Excel and VOSviewer. The annual growth in production (1999 to 2022) was 18.74%. The highest scientific production per year was concentrated in 2019 and 2020, with China being the leading country. The festivities where sound pressure levels were predominantly measured were during the Spring Festival and Diwali. The most frequently mentioned keywords were "fireworks" and "PM2.5". In conclusion, the percentage of studies that exceeded the NAAQS for PM2.5 and PM10 was 2% and 15%, respectively, while for the WHO guideline, it was only 1% for PM10. [ABSTRACT FROM AUTHOR]

Author(s): Sze Hang Fu 1, Antonio Gasparrini 2,3, Peter S. Rodriguez 1, Prabhat Jha 1,* Introduction Most of the epidemiological studies that have examined the detrimental effects of ambient hot [...], Background Most of the epidemiological studies that have examined the detrimental effects of ambient hot and cold temperatures on human health have been conducted in high-income countries. In India, the limited evidence on temperature and health risks has focused mostly on the effects of heat waves and has mostly been from small scale studies. Here, we quantify heat and cold effects on mortality in India using a nationally representative study of the causes of death and daily temperature data for 2001-2013. Methods and findings We applied distributed-lag nonlinear models with case-crossover models to assess the effects of heat and cold on all medical causes of death for all ages from birth (n = 411,613) as well as on stroke (n = 19,753), ischaemic heart disease (IHD) (n = 40,003), and respiratory diseases (n = 23,595) among adults aged 30-69. We calculated the attributable risk fractions by mortality cause for extremely cold (0.4 to 13.8°C), moderately cold (13.8°C to cause-specific minimum mortality temperatures), moderately hot (cause-specific minimum mortality temperatures to 34.2°C), and extremely hot temperatures (34.2 to 39.7°C). We further calculated the temperature-attributable deaths using the United Nations' death estimates for India in 2015. Mortality from all medical causes, stroke, and respiratory diseases showed excess risks at moderately cold temperature and hot temperature. For all examined causes, moderately cold temperature was estimated to have higher attributable risks (6.3% [95% empirical confidence interval (eCI) 1.1 to 11.1] for all medical deaths, 27.2% [11.4 to 40.2] for stroke, 9.7% [3.7 to 15.3] for IHD, and 6.5% [3.5 to 9.2] for respiratory diseases) than extremely cold, moderately hot, and extremely hot temperatures. In 2015, 197,000 (121,000 to 259,000) deaths from stroke, IHD, and respiratory diseases at ages 30-69 years were attributable to moderately cold temperature, which was 12- and 42-fold higher than totals from extremely cold and extremely hot temperature, respectively. The main limitation of this study was the coarse spatial resolution of the temperature data, which may mask microclimate effects. Conclusions Public health interventions to mitigate temperature effects need to focus not only on extremely hot temperatures but also moderately cold temperatures. Future absolute totals of temperature-related deaths are likely to depend on the large absolute numbers of people exposed to both extremely hot and moderately cold temperatures. Similar large-scale and nationally representative studies are required in other low- and middle-income countries to better understand the impact of future temperature changes on cause-specific mortality.

In August 2018, the European Space Agency (ESA) launched the Aeolus satellite, whose Atmospheric LAser Doppler INstrument (ALADIN) is the first space-borne Doppler wind lidar to regularly measure vertical profiles of horizontal line-of-sight (HLOS) winds with global sampling. This mission is intended to assess improvement to numerical weather prediction provided by wind observations in regions poorly constrained by atmospheric mass, such as the tropics, but also, potentially, in polar regions such as the Arctic where direct wind observations are especially sparse. There remain gaps in the evaluation of the Aeolus products over the Arctic region, which is the focus of this contribution. Here, an assessment of the Aeolus Level-2B (L2B) wind product is carried out, progressing from specific locations in the Canadian North to the pan-Arctic. In particular, Aeolus data are compared to a limited sample of coincident ground-based Ka-band radar measurements at Iqaluit, Nunavut, to a larger set of coincident radiosonde measurements over the Canadian North, to Environment and Climate Change Canada (ECCC)'s short-range forecast, and to the reanalysis product, ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). Periods covered include the early phase of the first laser flight model (flight model A – FM-A; September to October 2018), the early phase of the second laser flight model (flight model B – FM-B; August to September 2019), and the middle phase of FM-B (December 2019 to January 2020). The adjusted r -squared between Aeolus and other local datasets is around 0.9 except for lower values for the comparison to the Ka-band radar, reflecting limited sampling opportunities with the radar data. This consistency is degraded by about 10 % for the Rayleigh winds in the summer due to solar background noise and other possible errors. Over the pan-Arctic, consistency, with correlation greater than 0.8, is found in the Mie channel from the planetary boundary layer to the lower stratosphere (near surface to 16 km a.g.l.) and in the Rayleigh channel from the troposphere to the stratosphere (2 to 25 km a.g.l.). In all three periods, Aeolus standard deviations are found to be 5 % to 40 % greater than those from ECCC-B and ERA5. We found that the L2B estimated error product for Aeolus is coherent with the differences between Aeolus and the other datasets and can be used as a guide for expected consistency. Our work shows that the high quality of the Aeolus dataset that has been demonstrated globally applies to the sparsely sampled Arctic region. It also demonstrates the lack of available independent wind measurements in the Canadian North, lending urgency to the need to augment the observing capacity in this region to ensure suitable calibration and validation of future space-borne Doppler wind lidar (DWL) missions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Studies on the extent to which long-term exposure to ambient particulate matter (PM) with aerodynamic diameter =2.5 µm (PM2.5) contributes to adult mortality in India are few, despite over 99% of Indians being exposed to levels that the World Health Organization (WHO) considers unsafe. OBJECTIVE: We conducted a retrospective cohort study within the Million Death Study (MDS) to provide the first-ever quantification of national mortality from exposure to PM2.5 in India from 1999 to 2014. METHODS: We calculated relative risks (RRs) by linking a total of ten 3-y intervals of satellite-based estimated PM2.5 exposure to deaths 3 to 5 y later in over 7,400 small villages or urban blocks covering a total population of 6:8 million. We applied using a model-based geostatistical model, adjusted for individual age, sex, and year of death; smoking prevalence, rural/urban residency, area-level female illiteracy, languages, and spatial clustering and unit-level variation. RESULTS: PM2.5 exposure levels increased from 1999 to 2014, particularly in central and eastern India. Among 212,573 deaths at ages 15–69 y, after spatial adjustment, we found a significant RR of 1.09 [95% credible interval (CI): 1.04, 1.14] for stroke deaths per 10µg/m3 increase in PM2.5 exposure, but no significant excess for deaths from chronic respiratory disease and ischemic heart disease (IHD), all nonaccidental causes, and total mortality (after excluding stroke). Spatial adjustment attenuated the RRs for chronic respiratory disease and IHD but raised those for stroke. The RRs were consistent in various sensitivity analyses with spatial adjustment, including stratifying by levels of solid fuel exposure, by sex, and by age group, addition of climatic variables, and in supplementary case–control analyses using injury deaths as controls. DISCUSSION: Direct epidemiological measurements, despite inherent limitations, yielded associations between mortality and long-term PM2.5 inconsistent with those reported in earlier models used by the WHO to derive estimates of PM2.5 mortality in India. The modest RRs in our study are consistent with near or null mortality effects. They suggest suitable caution in estimating deaths from PM2.5 exposure based on MDS results and even more caution in extrapolating model-based associations of risk derived mostly from high-income countries to India. [ABSTRACT FROM AUTHOR]